From 6aac5b298102da1fb50c69e748f8bafb1d55a020 Mon Sep 17 00:00:00 2001
From: jajupmochi <jajupmochi@gmail.com>
Date: Fri, 29 Jun 2018 17:41:16 +0200
Subject: [PATCH] 1. add Sylvester Equation Methods for the generalized random
 walk kernel. 2. correct an error in the common walk kernel. DON NOT use the
 old one. 3. improve the method to construct fully-labeled direct product
 graphs, much faster for sparse graphs.

---
 notebooks/run_commonwalkkernel.ipynb  | 2218 ++++++++++++++++++++++++++++++++-
 notebooks/run_randomwalkkernel.ipynb  | 1539 ++++++++++++++++++++++-
 notebooks/run_spkernel.ipynb          |  156 ++-
 pygraph/kernels/.#commonWalkKernel.py |    1 +
 pygraph/kernels/commonWalkKernel.py   |   71 +-
 pygraph/kernels/randomWalkKernel.py   |  246 +++-
 pygraph/kernels/spKernel.py           |  293 ++---
 pygraph/utils/utils.py                |   38 +-
 8 files changed, 4220 insertions(+), 342 deletions(-)
 create mode 120000 pygraph/kernels/.#commonWalkKernel.py

diff --git a/notebooks/run_commonwalkkernel.ipynb b/notebooks/run_commonwalkkernel.ipynb
index 0c6422d..3801a74 100644
--- a/notebooks/run_commonwalkkernel.ipynb
+++ b/notebooks/run_commonwalkkernel.ipynb
@@ -3,7 +3,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -14,20 +16,583 @@
       "\n",
       "--- This is a regression problem ---\n",
       "\n",
-      "1. Loading dataset from file...\n",
+      "\n",
+      "I. Loading dataset from file...\n",
       "\n",
       "2. Calculating gram matrices. This could take a while...\n",
+      "calculating kernels: 100%|█████████▉| 16830/16836.0 [00:41<00:00, 172.50it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 41.270946979522705 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:41<00:00, 407.94it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 1.0, 'compute_method': 'geo'} is: \n",
+      "ignored, as it contains elements that are not numbers.\n",
+      "calculating kernels:   0%|          | 0/16836.0 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "../pygraph/utils/model_selection_precomputed.py:114: RuntimeWarning: divide by zero encountered in double_scalars\n",
+      "  Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])\n",
+      "../pygraph/utils/model_selection_precomputed.py:114: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])\n",
+      "../pygraph/utils/model_selection_precomputed.py:114: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16831/16836.0 [00:41<00:00, 176.69it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 41.41118931770325 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:41<00:00, 406.56it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 0.31622776601683794, 'compute_method': 'geo'} is: \n",
+      "ignored, as it contains elements that are not numbers.\n",
+      "calculating kernels: 100%|█████████▉| 16819/16836.0 [00:42<00:00, 179.89it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 42.25164246559143 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:42<00:00, 398.47it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 0.1, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.91888215 0.66206897 ... 0.68055069 0.68110313 0.6838525 ]\n",
+      " [0.91888215 1.         0.52970854 ... 0.5444954  0.5449374  0.54713712]\n",
+      " [0.66206897 0.52970854 1.         ... 0.75002357 0.75063241 0.75366245]\n",
+      " ...\n",
+      " [0.68055069 0.5444954  0.75002357 ... 1.         0.99996588 0.99967726]\n",
+      " [0.68110313 0.5449374  0.75063241 ... 0.99996588 1.         0.99960911]\n",
+      " [0.6838525  0.54713712 0.75366245 ... 0.99967726 0.99960911 1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16831/16836.0 [00:41<00:00, 157.75it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 41.383506536483765 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:41<00:00, 406.83it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 0.03162277660168379, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.94130367 0.75842633 ... 0.82840756 0.82843178 0.82890317]\n",
+      " [0.94130367 1.         0.60094274 ... 0.6563927  0.6564119  0.65678541]\n",
+      " [0.75842633 0.60094274 1.         ... 0.88118037 0.88120614 0.88170756]\n",
+      " ...\n",
+      " [0.82840756 0.6563927  0.88118037 ... 1.         0.99999905 0.99997018]\n",
+      " [0.82843178 0.6564119  0.88120614 ... 0.99999905 1.         0.99996808]\n",
+      " [0.82890317 0.65678541 0.88170756 ... 0.99997018 0.99996808 1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16820/16836.0 [00:41<00:00, 186.55it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 41.26180672645569 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:41<00:00, 408.03it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 0.01, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.94654083 0.78708661 ... 0.87100417 0.87100501 0.87105897]\n",
+      " [0.94654083 1.         0.62259969 ... 0.68897998 0.68898065 0.68902333]\n",
+      " [0.78708661 0.62259969 1.         ... 0.91812692 0.91812781 0.91818469]\n",
+      " ...\n",
+      " [0.87100417 0.68897998 0.91812692 ... 1.         0.99999997 0.99999703]\n",
+      " [0.87100501 0.68898065 0.91812781 ... 0.99999997 1.         0.99999696]\n",
+      " [0.87105897 0.68902333 0.91818469 ... 0.99999703 0.99999696 1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16833/16836.0 [00:41<00:00, 181.49it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 41.750473737716675 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:41<00:00, 403.26it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 0.0031622776601683794, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.94802463 0.79594082 ... 0.88403517 0.8840352  0.88404082]\n",
+      " [0.94802463 1.         0.6293502  ... 0.69900638 0.6990064  0.69901085]\n",
+      " [0.79594082 0.6293502  1.         ... 0.92936404 0.92936407 0.92936998]\n",
+      " ...\n",
+      " [0.88403517 0.69900638 0.92936404 ... 1.         1.         0.9999997 ]\n",
+      " [0.8840352  0.6990064  0.92936407 ... 1.         1.         0.9999997 ]\n",
+      " [0.88404082 0.69901085 0.92936998 ... 0.9999997  0.9999997  1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16829/16836.0 [00:41<00:00, 173.42it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 42.02673888206482 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:42<00:00, 400.60it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 0.001, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.94847688 0.79871885 ... 0.88811083 0.88811083 0.8881114 ]\n",
+      " [0.94847688 1.         0.63147469 ... 0.70214884 0.70214884 0.70214929]\n",
+      " [0.79871885 0.63147469 1.         ... 0.9328725  0.9328725  0.9328731 ]\n",
+      " ...\n",
+      " [0.88811083 0.70214884 0.9328725  ... 1.         1.         0.99999997]\n",
+      " [0.88811083 0.70214884 0.9328725  ... 1.         1.         0.99999997]\n",
+      " [0.8881114  0.70214929 0.9328731  ... 0.99999997 0.99999997 1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16835/16836.0 [00:42<00:00, 160.66it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 42.378682374954224 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:42<00:00, 397.28it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 0.00031622776601683794, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.94861821 0.79959511 ... 0.88939511 0.88939511 0.88939517]\n",
+      " [0.94861821 1.         0.63214548 ... 0.70313974 0.70313974 0.70313978]\n",
+      " [0.79959511 0.63214548 1.         ... 0.93397746 0.93397746 0.93397752]\n",
+      " ...\n",
+      " [0.88939511 0.70313974 0.93397746 ... 1.         1.         1.        ]\n",
+      " [0.88939511 0.70313974 0.93397746 ... 1.         1.         1.        ]\n",
+      " [0.88939517 0.70313978 0.93397752 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16826/16836.0 [00:43<00:00, 181.75it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 43.28841590881348 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:43<00:00, 388.93it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 0.0001, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.94866273 0.79987199 ... 0.88980078 0.88980078 0.88980079]\n",
+      " [0.94866273 1.         0.6323575  ... 0.70345281 0.70345281 0.70345281]\n",
+      " [0.79987199 0.6323575  1.         ... 0.93432642 0.93432642 0.93432643]\n",
+      " ...\n",
+      " [0.88980078 0.70345281 0.93432642 ... 1.         1.         1.        ]\n",
+      " [0.88980078 0.70345281 0.93432642 ... 1.         1.         1.        ]\n",
+      " [0.88980079 0.70345281 0.93432643 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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AJ+s3BKoTAKTZPwvV3pT/VPQN8Vo8cW6w80dI/TdpT/KMcn+2Prl3ox4fEzyS0u/cR3qMNPaPjdF7T23p4BnF7RgIcpuzowB3Mf4BwJNE9EUA62Hki017vaAoGgghzkYFjqQ8AMD+l6TB3Jc9H/NBmD0IIhoK4LPMfMKrUBliCBGiAq6slckkT0RRGJnkV9kPIKJaokw36tsAlvgVKg2EECFhNpLW6jz8y+IUgEUA1gDYDmA5M28jokeJyEq/NQfADiLaCaAOgGumeYsMMYQIUSEna6kyyTPzQ7btFQBWBClTGgghQmJcgyjuTnxRNBCVRLir8W0s3TMb5ccY5VSWSRb7UNvNmQSz9nkR+fkcHu3Izq2wB0Gp7lYA6sVjnjvpTO321rHsCk6tw/TyBFgaSr0zTq/pVOUTcNp2Rp2ZGwBmV6wJVCcAGF/qn5B2deeFvsfs7a7FxNI3Ap17Qqn6K7em82Oer2tL5gZ3DS/RX8Eqe271vKO1MWPxmg5zweJItd5iQJu6VYveBMvPUeyh1kXRQAhxNmIQUj3FPZtTGgghQiQ5KYUQStZdjGImDYQQIZKLlBr2JarxzM8/jRfu/xG+hM/jl6dHZRbS3XTl85kVr+zrVuQnmLWHVNtnZarCpwH1BckHRjpX1uqoySaqPZIO9mGqVgu3+8aIvVrlxIerZg0aWoPni8msZu3lvuH+yWzSvA/7U8EWD1atPQL4J8+JUE5QIPYkg7/xHcly5f57hrWa5zA+3/0p/zVBAODuYc46/12A+khOSiGEJ7kGIYRQYhQ2UKo/SAMhRFhYbnMKIVxYCWOKWVE0EF2pMpQfY0wri+Kuxrfx+okpmehJK6oyjdyFdPNTxsUa1FGW9nwOdvYISYv9gqTFHnH5wqnztN6PxSsCEgCuq9SLgvSKyFzd6XwffvZ2e6fCA4ApZX/yPWZ8aaVvBGS+/IhIy+iSJs/X5UdgWtGPQViRkvkqzAhZK9JybeckrfJ0UwZ6kSGGEEJJrkEUUHGP1MRg8lGudiUNhIZxlccQu+UUvnrgGry5Yib+/LXHsXiIMenlN2eG4dHmOwEAmy/PppzLX0jXvqiNPfu0W5o41cQrVZyDfVihG7dgOVmz3fP5Y57JvrJ+c2aY63M68Qr54tzie8xhjbqt747i3uEH/A+0SbO6vu1p73iK/EWLFtYcDHReL20pI6ZiT9JYnMeKi/BztEcvZaAbiYMQQniSi5RCCCVmINUjodZCCBeDeohBRHsBnIaROjvFzLOIaCSA/wRwHoC9AO5g5uN9q6YQg89AuAZRiP7N9cw8w5az/0EA65h5MoB15s9CCAVm0nqEpT8GQLcBWGpuLwVwez+cQ4hBoYBL7/WLvjYQDOAPRLTJthRYHTO3mduHYKTXdiCihdYyYieOat7vE2IQYTauQeg8wtLXi5TXMvMBIjoHwFoi+ov9SWZmIlJmCmXmxQAWA8CUi8vV2USFGNQI6SK/i9Gn2jHzAfPfdgC/hbHC8GEiOhcAzH/b+1pJIQarQXsNgoiGEFG1tQ3gEwA+gLHc113mYXcBWNnXSgoxGFlzMQbrEKMOwG+JyCrnV8y8moiaACwnonsA7ANwR9+rKcQgxMZ1iGLW6waCmVsAXKLYfxTAjUHKqiTGDY3N2HK8AWWnGDUllbh56DYAwPITl+P0KedCLxdFc+PgXziVzTHZcjo7nTnO6nh51RRqVQ5J+5Rtv7kV+WpKvBeoeSXmPsfC7t3YBNfnrqt8O1CdAPWiQfk2x0f4HtMSPwfTo3qLzFhGufxOmuLneL7uYDK3Pg2lOwKdFwDqIupzb04Y35cjKePzGBnxn6sCAM0JVc7RNsU+dxJqLYRQYiDU6ws6pIEQIjTFH0kpDYQQIerpkQZCCKHALEMMIYQHGWIIIVwN2tucQoi+kyGGhtZkNVYvuwrP3vsT3DP3Czl5KNfPWoKaS2MAgKZ4trl9ruP6nDKern8js21PR96SVMdBrOl0psNX5Zy0p6bXzSFp8Ytz+OzQU1rl3FT1jutzJ3uC/wla1+WfAvj2IWd8j4lXHcexdLDz2z9Du1uHxDxfl+bc+rT3Yn7f9mRS2aX/VFW3udVtlq33n3ZuZVfwStgwwg2j1lHcM0WEGORY86GDiOYR0Q4i2k1EjjwsRDSeiF4joneJaCsRzfcrUxoIIcLCAPeQ1sMPEUUAPAXgkwCmAriTiKbmHfZPAJYz80wACwA87VeuNBBChKiAszmvALCbmVuYOQFgGYzkTTmnA2CNe2sA+K4dUBTXIIQ4WxXwLkY9APuiI60Arsw75mEYCZ7uBzAEwE1+hUoPQoiQWHMxNHsQtVYGNvOx0Kd4lTsBPM/MDQDmA/gPIvJsA6QHIURYGID+XYwOW2JolQMAxtl+bjD32d0DYB4AMPNbRFQBoBYeSZ2kByFEiJj1HhqaAEwmokYiisK4CLkq75j9MFMxENGFACoAHPEqVHoQQoSpQNcgmDlFRIsArIGx1vUSZt5GRI8C2MjMqwD8PYBnieib5pm/yOzd/EgDIURo9G5h6mLmlwG8nLfvIdt2M4Br8l/npSgaiHi6FGWnGBdFk5jf2Ix3YxMyWaTs2aXejKmzRuWzZ5ta2TlOeYw9U1SmHsN3OfbZM095rbKt4pUJCvCOkLTzykz1WpczM5afFp/sTQBwiUamqNrIEDTFawKdOz8zlGVi2U7P1+VnorKyQAVhZYzKV2tmkLIyTm2Oj9Qqb2r0aOA65JDZnEIITzJZSwjhTnoQQgg30oMQQriSBkIIoRQsUCoU0kAIESLuCbsG3oqigRgTPYnTH+/Cg23X4fUVl+H1RY8hconxm3upcygeaV4AAHhn1i8zr5lcfiinjC2JVGb7uY7rMttP1m9QnnN2xRrHvtaU87jVnRMz2/cN/9B5gAe/RW10k7143crUSeySL1Z5zPcYnbqt7/ZP9JIvyaeV+4/7/Eexf76APcmLvjSr63rcjEXYmUwAAOZV+SfUAYDj6QIEIksPQgjhhuQahBBCKUi6qJBIAyFEaEiGGEIID9KDEEK4kgZCCKHEAGRtTiGEG7mLIYRwV+QNhKScE0K4kh6EECGSIYYQwp3EQQghlBiATNbyV1XSg4saW9AaG47SmJHr8MZqIw/lxthEnOmsAACUUXYSzSXR3JWxX4k1ZLZbY8Mz22mX6XLjS515Hv/QNcSxb293NvdhnFt03k5GbcRZnp3OKtuAdw5JnYlX+apKor7HbEz4H7MrPgaXRPcEOrdbfs1dSfe8mwDwYXJUzs8TS/OXfPA3IlKl3L87aXy/DqWN/Jp1kcNa5Vmv6wsZYggh3EkDIYRwVeQNhO9tTiJaQkTtRPSBbd9IIlpLRLvMf0eY+4mIfkxEu4loKxFd2p+VF2IgI9Z/hEUnDuJ5mOv52TwIYB0zTwawzvwZAD4JYLL5WAjgp4WpphCDFJPeIyS+DQQzrweQfyXsNgBLze2lAG637f8FG94GMJyIzi1UZYUYdFjzEZLeRlLWMXObuX0IQJ25XQ/Anpet1dznQEQLraXMjx4t8ns9QvQT6tF7hKXPodbm4p+B2zhmXszMs5h51qhREvEtzkKD5BqEymFr6GD+227uPwDAvhhmg7lPCKFS5EOM3t7mXAXgLgA/MP9dadu/iIiWAbgSwEnbUMTVwWQ1Ei9dhH9f+AzuvepcM5P1LQCAtZf+HCenGUE0m+KJzGue67ghp4wnxr6Z2R7d8Epme29Kncl4deeFjn2qrNVTyv6U2T6c9nsnuTbH1QvVWnQzUnstpKubGdtOJwjq4xoxQLOi+3EmYN52+2dod02Fd52S5R05P/fmfW9LdCn3z64wvl9pNoLvTmnmaJhdoRfo5qnIb3P6NhBE9GsAcwDUElErgO/AaBiWE9E9APYBuMM8/GUA8wHsBhADcHc/1FmIQWPAR1Iy850uT92oOJYB3NfXSgkhioNEUgoRpoHegxBC9BMO9xamDmkghAiT9CCEECqE4r9IKRFKQoSpgHEQRDSPiHaYkyUfVDz/BBFtMR87ieiEX5nSgxAiLAWMkiSiCICnAMyFMcWhiYhWMXNz5nTM37Qdfz+AmX7lFkUDkeiJoDRmZIm6qrEFLfFzMlmk7NmldiXqMq+xZ40C3LNNbYjXQsWeKcqS5n2OffbMU+u7/QOM7LwyQQFAvOq4VjlemanWdweqEgAjE5SfWdH9vsdUlUTxbnewr1B+ZijLpLKDnq/Lz0TVm2xOVsaofGMjRiCwlXFqR7Jcq7xJ6AxcB4fCDTGuALCb2Uh7ZgYr3gag2eX4O2HENHkqigZidNlpnLq8G48enoPNKy/CD+/7PdqmGB/mqs4qPLz90wCAty57IfOa6vo/5pSxJR7PbC85Oiez/cS5G5TnnFj6hmPf/pTzkvKazo9ltu8dHixqfLpHBCQAHEvrfTua4uovNgDcOkQdKepFJ02cToTku92luKYi2Cg1Xt6u3B/ziYy0f74AMLtC7z+xXZJPKvefMd/q9kTMLFudmi7fyZ4CjNAL10CoJkpeqTqQiCYAaATwql+hRdFACHG2CnCbs5aINtp+XszMi3t52gUAVjCz7+QBaSCECEuwiVgdzDzL4/kgEyUXQDPiWe5iCBGiAk73bgIwmYgaiSgKoxFY5Tgf0RQAIwC8pVOoNBBChKlAtzmZOQVgEYA1ALYDWM7M24joUSK61XboAgDLzHlTvmSIIUSIChkoxcwvw5hRbd/3UN7PDwcpUxoIIcJU5JGU0kAIEZaQs0XpkAZCiJAMhLkY0kAIESJpIIQQ7qSBEEK4kgbCXyWlMHl8G9rj1Yh0A6NKKjFn2HYAwIeJUYiZk6TKqSzzmunR3CzHW2wTo9rj1ZntCKlDPSaUOt+6KttzWzI7KSzNzqzXXka5LHVvaYrrfTsOJt2zYyf5dKA6Ac6JTypu2aftPkyOcp1b4cb+GdptV8yDsdufyv0dXFDmO1PZoapEPdnuw7TxHTmUMr4340r1fqetqcBVyBXymhc6iqKBAICSYv9NCdEfivxrXzQNhBBnI8lJKYRwVewdZ2kghAiLBEoJITxJA+HvUGoYUr+fiH/50hLcf+l5+F1saCaL1OqZS/DXScYditysUdfnlPHYudm1OSN12e09SfX6l/ZMUZYv1zjvUowuacpst6eDDRibfFLO6WaDmli20/W5470Yw+5K+t/F8FsrEzDSxPllgsrndrdiRrl3hqgLo7nZoGI9wW8h7EuoX3Nx1MggdWGZkb8vpjfREdOi/r9HLxJJKYTwJg2EEMINafZWwiINhBBhkaX3hBCeirsDIQ2EEGGSi5RCCHfSQAghlGSylp4UlyDSbczQnDq+DQeTIzIzOO0zO+0z+uwzNgH3mZ57U0OV57TP0rREyLmMgH3W59ZExPG8F69ZmACQZnWMRj6vWaFbXO7te3Fb/s4uWd7he0xNSaVjxSs/+bMyLflxDvnyZ4HuSPuu+eJgzdbMN6HUWELPmu3ZmtQrL1Lai3UP80kDIYRQkUApIYQnChiJ+lErugZC8kKIs4ZM1tIzsrQTp6Yn8KMjc9Dy+4m45asr0DxxLABgdVcVHtpuLAz0p5nZ1b17znknp4ytiex40L669+Njco+zDC/Z5Ni3J+kcz6+NXZDZXljjvUR9vobSHZ7Pt2sOozcnal2f+1RV8HHwxFL/VcpPavxl252sCLzKtlsmKL+5FfnXHC6OVgQ6L5Cda+E4t7mG7c6kcS1iWnSIVnlnevp+DUICpYQQ7oq8B+G7NicRLSGidiL6wLbvYSI6QERbzMd823PfJqLdRLSDiG7ur4oLMRgUcPHefqGzeO/zAOYp9j/BzDPMx8sAQERTYSwOOs18zdNEFOzeoBBnCwbArPcIiW8DwczrARzTLO82GCsHx5l5D4DdAK7oQ/2EGNQGQw/CzSIi2moOQazol3oA9qwrreY+IUQegnGRUucRlt42ED8FcD6AGQDaADwetAAiWkhEG4lo48ljfV1gQIgBSHd4UcxDDBVmPszMaWbuAfAsssOIAwDG2Q5tMPepyljMzLOYeVbNSLmZIs5Og3KIQUTn2n78DADrDscqAAuIqJyIGgFMBqAORBBCZIOl/B4h8f3TTUS/BjAHQC0RtQL4DoB54BDkAAAG5klEQVQ5RDQDRtX3AvgKADDzNiJaDqAZQArAfcwcfFaNEGeJYg8c9m0gmPlOxe6fexz/XQDf7UulhDgrMACZiyGEcFXc7UOfbnMKIfqIeljroVUW0Twzgnk3ET3ocswdRNRMRNuI6Fd+ZRZFD6KCUhg7rgMd8aEoSQB1kUpcXb0bAHAkNQxdcSORh3359qnRwzll7LUlgDmWqEIPG21fhNRt4IRS5y99R9I58agjqU4yoqMu4r2wyvakXmaSI6lhrs+lWW/xHbsRkSrfY7YlunyPOZSuQZK9E73ks3+Gdm6L2mTOlZfsxW3ilZcyl6Dew+ZiPofSxiStCaV6n8vhdN9vzxfqGoQZsfwUgLkw4o+aiGgVMzfbjpkM4NsArmHm40TkvbITiqSBOFv1MIVdBRGmwt6huALAbmZuAQAiWgYjsrnZdsyXATzFzMcBgJnb/QqVIYYQITEySrHWA8ZdxI22x8K84nSimC8AcAERvUFEbxORao5VDulBCBEm/TDqDmae1cezlcKITZoDI4hxPRFNZ2Z1kg5ID0KIUAXoQfjRiWJuBbCKmZPmZMqdMBoMV9JACBEWZiMOQufhrwnAZCJqJKIojLQLq/KOeQlG7wFEVAtjyNHiVWhRDDGOpIai59Wx+MY9r+CNaZPwh64heGznXADAykuWYPN5Ruq23LRy1+WU8b26NzPbsdrNme09SXVqeXsqOcs9w1od+yqGvZfZbksFu2rtlSoO0E8XVxtx/wyP9wS/0Lk76Z+ubXaF/9L2YyPtOBNwpuGHafXfpIuj3ndWrNT0llgvAnStuxX5Ligz7l40lhplxjXLPr9MvaRCEIW6i8HMKSJaBGANgAiAJWZk86MANjLzKvO5TxBRM4A0gAeY+ahXuUXRQAhx1irgTE0zcdPLefsesm0zgG+ZDy1F0UCkOIKShBHbMGFcR07sgz0mwn4v/FjC+ItjxTu4xUgc6VEnVVXFN6hiJuzxEnuSZY7nvXjFLxj0ehBe8RQ7k4kANTIcStf4HpPmU77HjIhUYXsiWByG2+I1fnEN+fETVoLZQOdOq5PRWj0HK05if8o/BsQ4XnOFHTeyurcQwlOIuR50FEUDUVMaw+kpSTzdcR2OvDYWN/xdC96ZYPQa1nVVZa5H/HHmLzKvOTHqvZwy7JF/z9quTzw2ZoPynJHq9x379qecf43Xdk4CAKRRorxG4WWkx7UDAGhP610/2Bwf6frcvKrgKT/rIod9jzmlcW1jR7Icsyv8ozLtxpWeVu6P+fxHyV8OTzc1vZ1bhKR1zcHqOeheWyhE2vtin4tRFA2EEGcrzVuYoZEGQoiwMIC0NBBCCAWCdhBUaKSBECJM0kAIIVxJAyGEUGIEmawVCmkghAiRXIMQQriTBkIIocQM9BT3GKMoGojOdDlG7inD1XN2YWXjTDQnRmHDofEAgAfrXsGVY/YDAPalsq3tm6dzp7HPr8tGB15dvSuz3ZZWx9Vv6nbO5rx72IeOfTMq9me2j/YEi71vTozyfH5upV7M/9So+4S74y6zI73ozeb0j9CchE6c7Al2/laXCbHTot6zRyOluVGLvYlidMshaUVOWnMrdMseWuL/e/RV3O1DcTQQQpyt5BqEEMKdNBBCCCVZWUsI4Y6LvgdBXAQVJKIjADoBdIRdlz6oxcCuPyDvoRAmMPNonQNrKsbw1eO+oFXo6t2PbSpAVuvAiqIHwcyjiWhjGL+AQhno9QfkPXzkGEC6uG9jFEUDIcTZiQGWBkII4aYIhvheiqmBWBx2BfpooNcfkPfw0ZK7GPqYeeB8sAoDvf6AvIdQSA9CCOFKGgghhFrxx0FIAyFEWBhAOvgSgh8laSCECJP0IIQQatord4dGGgghwsIAS6CUEMKV9CCEEK7kGoQQQolZ7mIIIdyxJK0VQqhJoJQQws0AmKwVPGe6EKJwuEfvoYGI5hHRDiLaTUQPKp7/IhEdIaIt5uNLfmVKD0KIkDAALlAPgogiAJ4CMBdAK4AmIlrFzM15h/4nMy/SLVd6EEKEhbmQPYgrAOxm5hZmTgBYBuC2vlZRGgghQsTptNZDQz0A+9Jwrea+fJ8loq1EtIKIxvkVKkMMIUJyGsfXvMIrajUPryCijbafF/ciOc5/Afg1M8eJ6CsAlgK4wesF0kAIERJmnlfA4g4AsPcIGsx99vPZF3n9GYB/9StUhhhCDA5NACYTUSMRRQEsALDKfgARnWv78VYA2/0KlR6EEIMAM6eIaBGANQAiAJYw8zYiehTARmZeBeBrRHQrgBSAYwC+6FduUaysJYQoTjLEEEK4kgZCCOFKGgghhCtpIIQQrqSBEEK4kgZCCOFKGgghhCtpIIQQrv4/asw8uUKSuggAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16823/16836.0 [00:40<00:00, 166.83it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 41.07072997093201 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:41<00:00, 409.93it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 3.1622776601683795e-05, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.9486768  0.79995952 ... 0.88992902 0.88992902 0.88992902]\n",
+      " [0.9486768  1.         0.63242453 ... 0.70355178 0.70355178 0.70355178]\n",
+      " [0.79995952 0.63242453 1.         ... 0.93443673 0.93443673 0.93443673]\n",
+      " ...\n",
+      " [0.88992902 0.70355178 0.93443673 ... 1.         1.         1.        ]\n",
+      " [0.88992902 0.70355178 0.93443673 ... 1.         1.         1.        ]\n",
+      " [0.88992902 0.70355178 0.93443673 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16827/16836.0 [00:41<00:00, 173.16it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 41.14805221557617 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:41<00:00, 409.16it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 1e-05, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.94868124 0.7999872  ... 0.88996957 0.88996957 0.88996957]\n",
+      " [0.94868124 1.         0.63244573 ... 0.70358307 0.70358307 0.70358307]\n",
+      " [0.7999872  0.63244573 1.         ... 0.93447161 0.93447161 0.93447161]\n",
+      " ...\n",
+      " [0.88996957 0.70358307 0.93447161 ... 1.         1.         1.        ]\n",
+      " [0.88996957 0.70358307 0.93447161 ... 1.         1.         1.        ]\n",
+      " [0.88996957 0.70358307 0.93447161 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16827/16836.0 [00:40<00:00, 184.39it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 40.513415575027466 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:40<00:00, 415.57it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 3.162277660168379e-06, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.94868265 0.79999595 ... 0.88998239 0.88998239 0.88998239]\n",
+      " [0.94868265 1.         0.63245243 ... 0.70359297 0.70359297 0.70359297]\n",
+      " [0.79999595 0.63245243 1.         ... 0.93448263 0.93448263 0.93448263]\n",
+      " ...\n",
+      " [0.88998239 0.70359297 0.93448263 ... 1.         1.         1.        ]\n",
+      " [0.88998239 0.70359297 0.93448263 ... 1.         1.         1.        ]\n",
+      " [0.88998239 0.70359297 0.93448263 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16831/16836.0 [00:40<00:00, 175.18it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 40.359070777893066 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:40<00:00, 417.16it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 1e-06, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.94868309 0.79999872 ... 0.88998644 0.88998644 0.88998644]\n",
+      " [0.94868309 1.         0.63245455 ... 0.7035961  0.7035961  0.7035961 ]\n",
+      " [0.79999872 0.63245455 1.         ... 0.93448612 0.93448612 0.93448612]\n",
+      " ...\n",
+      " [0.88998644 0.7035961  0.93448612 ... 1.         1.         1.        ]\n",
+      " [0.88998644 0.7035961  0.93448612 ... 1.         1.         1.        ]\n",
+      " [0.88998644 0.7035961  0.93448612 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16827/16836.0 [00:41<00:00, 177.28it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 41.53633999824524 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:41<00:00, 405.33it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 3.162277660168379e-07, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.94868323 0.7999996  ... 0.88998773 0.88998773 0.88998773]\n",
+      " [0.94868323 1.         0.63245522 ... 0.70359709 0.70359709 0.70359709]\n",
+      " [0.7999996  0.63245522 1.         ... 0.93448722 0.93448722 0.93448722]\n",
+      " ...\n",
+      " [0.88998773 0.70359709 0.93448722 ... 1.         1.         1.        ]\n",
+      " [0.88998773 0.70359709 0.93448722 ... 1.         1.         1.        ]\n",
+      " [0.88998773 0.70359709 0.93448722 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16835/16836.0 [00:40<00:00, 183.55it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 40.112728118896484 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:40<00:00, 419.72it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 1e-07, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.94868328 0.79999987 ... 0.88998813 0.88998813 0.88998813]\n",
+      " [0.94868328 1.         0.63245543 ... 0.7035974  0.7035974  0.7035974 ]\n",
+      " [0.79999987 0.63245543 1.         ... 0.93448757 0.93448757 0.93448757]\n",
+      " ...\n",
+      " [0.88998813 0.7035974  0.93448757 ... 1.         1.         1.        ]\n",
+      " [0.88998813 0.7035974  0.93448757 ... 1.         1.         1.        ]\n",
+      " [0.88998813 0.7035974  0.93448757 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16822/16836.0 [00:41<00:00, 171.43it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 41.64281749725342 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:41<00:00, 404.30it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 3.162277660168379e-08, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.94868329 0.79999996 ... 0.88998826 0.88998826 0.88998826]\n",
+      " [0.94868329 1.         0.6324555  ... 0.7035975  0.7035975  0.7035975 ]\n",
+      " [0.79999996 0.6324555  1.         ... 0.93448768 0.93448768 0.93448768]\n",
+      " ...\n",
+      " [0.88998826 0.7035975  0.93448768 ... 1.         1.         1.        ]\n",
+      " [0.88998826 0.7035975  0.93448768 ... 1.         1.         1.        ]\n",
+      " [0.88998826 0.7035975  0.93448768 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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rODWMjJYnIKuuLDjj9PImr3wChdYf987MDTCzcnmsOgFMKAtPSLus6ezQY7a31jC57E+xzj2xzPsrt7zpI4Gv29PRPbhrVEm8Faycc3vPO3qx2VlZq7HDCeIqHRFtMaC1rV6L3kTPz+FcgyjuTnxRNBDGDFYWam2M8aQIqU5LWmuM8WE5KY0xnrJ3MYpZUTQQO9pH8KOFn+bn87/HPPk8Pz82JreQ7lsfeyq34pV73Yr8BLPukGr3rEyv8GnwviB5z+jClbUaq7sS1R5Ix7ug5LVauNtXT9oeqZy2UV6zBh0N8fPF5FazDnLXqPBkNmndwc5UvMWDvdYegfDkOaXSLeaHbR3x3/imjiGe++8Y2ZA5h/P57kyFrwkCcOvIXQX75sWsk12kNMZ4spyUxphAdg3CGOPJSTlnDYQxxovabc5IWlLlVB5UppdX8IXTV/LKB1Nz0ZPuqEr3Qrr5KeOa67yjLN35HNzcEZJZ7guSWe6Iy2eOnh7h3XQJioAEuHJotCjIoIjMZU2F7yPM9tbgVHgAU8tfCz1mQtnQ0AjIfPkRkVljS1YFvi4/AjMb/RhHNlIyX2UmQjYbaflik1eEZKGoKQP9ZBPGFLOiaCCMGaxsiGGM8WTXICIaP/QQzdcf5cu7Luf1xRfyx7//LguGOZNefn18JA9uuAWANRd3pZzLX0jXvaiNO/u0X5o4r4lXXnEO7mFF1LiFrCPVGwOfPxSYy6fLr4+P9H0uSrxCvjatDz1mX4S6vdpawZ2jCmMBgqTVu77708HxFPmLFs2r3h3rvEH2pJyYih2ZmJnbR0Yr+2BntJSBQayBMMZ4sjgIY4w/hZRFUhpjvNg1CGNMoAHdQIjIduAYTurslKrOEJHRwL8DpwPbgZtV9fCJVdOYgac/XIPojQHQVap6gStn/73AClWdAqzI/GyM8aAqkR5J6YsrJDcCT2e2nwZu6oNzGDMg9OLSe33iRBsIBV4QkbdEJDsVvlZV92S29+Kk1y4gIvNEZLWIrP7gYMSAAGMGEFXnGkSUR1JO9CLlFaq6S0ROBl4Ukf90P6mqKiKeqYQz6wouAJh63hDvdMPGDGhCurO4b3OeUO1UdVfm3/3Ab3BWGN4nIqcCZP7df6KVNGagGrDXIERkmIiMyG4DfwW8h7Pc122Zw24DlpxoJY0ZiLJxEAN1iFEL/EZEsuX8QlWXicgqYJGI3AHsAG4OK2ioKFdP2sDaw3WUH1WqS4Zy3fD1ACz64GKOHS1c6OWciu5x8M8c7coxWX+sazpzm3rHy3tNofbKIemesh02tyJfdUnwAjUvNfvPsXB7u3mi73NXDv1zrDqB96JB+da0nRR6TH3byZxbEW2RmawxPr+TVW0nB75ud0f3+tSVbYp1XoDaUu9zr2l3vi8HUs7nMaokfK4KwIZ2r5yjezz2+VDnOkQx63EDoar1wPke+w8C15xIpYwZLCwfhDHGk0Ki1xeisAbCmMQUfySlNRDGJKiz0xoIY4wHVRtiGGMC2BDDGONrwN7m7E0NHSNY9uylPHHn/+GOa2/tlofy1RkLqb6oGYBVbV2/zScbr+pWxuPj/pTbdqcjr+/wjoNY3lSYDt8r56Q7NX3UHJJZYXEOnxt+NFI5n6h60/e5I53xv2ErWsLXYrhp2PHQY9qqDnMoHe/87s/Q7YZhzYGvS2v3+uzvwfSdjT7fhU9VtWa2WjNlR/urfu3QlviVyGNDDGOMJyXZMOooinumiDEDnEZ8RCEis0Vkk4hsFZGCPCwiMkFEXhaRt0VknYjMCSvTGghjkqKgnRLpEUZESoHHgE8C04BbRGRa3mH/BCxS1QuBucDjYeVaA2FMgnpxNuclwFZVrVfVduBZnORN3U4HZC+MVQOhC4DYNQhjEtSLdzHGAe5ViRqAj+Udcz9Ogqe7gWHAJ8IKtR6EMQnJzsWI2IOoyWZgyzzmhRTv5RbgKVWtA+YA/yYigW2A9SCMSYoC0e9iNLoSQ3vZBYx3/VyX2ed2BzAbQFXfEJFKoIaApE7WgzAmQarRHhGsAqaIyCQRqcC5CLk075idZFIxiMjZQCVwIKhQ60EYk6ReugahqikRmQ8sB0qBhaq6XkQeBFar6lLgH4EnRORrmTPfrhrc/BRFA9GWLqP8qHJORQdzJm3g7eaJuSxS7uxSrzd7Z43K5842taRpvOcx7kxRuXqM2lKwz515KmiVbS9BmaAgOELSLSgz1csthZmxwtSHZG8COD9Cpqia0mGsaquOde78zFBZk8s3B74uPxNVNgtUHNmMUflGZzJIZTNOrWkbHam8aRUHY9ehu2i3MKNS1eeB5/P23efa3gBcHqfMomggjBmUbDanMSaQTdYyxvizHoQxxo/1IIwxvqyBMMZ4ykzWKmZF0UCcUnGEYx9v4d49V/LK4o/yyvyHKT2/E4DnmobzwIa5ALw54+e510wZsrdbGWvbU7ntJxuvzG0/Om6l5zlnVi4v2NeQKjxuWdPk3PZdo94vPCBA2KI2UZO9BN3KjJLYJV/z0EOhx0Sp26ut4Yle8nXoMc/9hzuDX+f+fMGd5CW6tHrX9XDmP+nmjnYAZleFJ9QBOJzuhThD60EYY3zZbU5jjB+xHoQxxlOcdFEJsQbCmMSIDTGMMQGsB2GM8RVy9yZp1kAYk5R4CWMSYQ2EMQmyuxjGGH9F3kBYyjljjC/rQRiTIBtiGGP82UXKcFUlnZwzqZ6G5lGUNTu5Dq8Z4eShXN08meNNlQCUS9ckmvMruq+M/VJzXW67oXlUbjut3veRJpQV5nl8oWVYwb7trV25D9u0PsrbyakpLSzPLcoq2xCcQzLKxKt8VSUVocesbg8/ZkvbKZxfsS3Wuf3ya27p8M+7CfB+x5huP08uy8/oHu6k0irP/Vs7nO/X3rSTX7O2dF+k8rKv6zHFbnMaY/zZEMMY488aCGOMryJvIEJvc4rIQhHZLyLvufaNFpEXRWRL5t+TMvtFRH4gIltFZJ2IXNSXlTemPxON/khKlDiIp8is5+dyL7BCVacAKzI/A3wSmJJ5zAN+2DvVNGaAUon2SEhoA6GqrwL5l8pvBJ7ObD8N3OTa/zN1/BkYJSKn9lZljRlwNOIjIT2NpKxV1T2Z7b1AbWZ7HOBO3NiQ2VdAROZllzI/eLDI7/UY00ekM9ojKSccap1Z/DN2G6eqC1R1hqrOGDPGIr7NINQPrkH09C7GPhE5VVX3ZIYQ+zP7dwHu1XLrMvsC7e4YQftz5/D9eT/mzktPzWSyvh6AFy/6KUemO0E0b7W1517zZOPV3cp45LTXc9tj617KbW9PeWcyXtZ0dsE+r6zVU8tfy23vS4e9k+7WtHkvVJsVNSN10EK6UTNju0UJgvp4hBigGRU7Oe4TiObH/Rm6XV4ZXKeOIY3dfu7J+17f3uK5f2al8/1KqxN8dzRiKvqZldEC3QL197sYPpYCt2W2bwOWuPbfmrmbMRM44hqKGGPyFfk1iNAehIj8EpgF1IhIA/AN4CFgkYjcAewAbs4c/jwwB9gKNAN/2wd1NmbA6PeRlKp6i89T13gcq8BdJ1opY0xxsEhKY5LU33sQxpg+osnewozCGghjkmQ9CGOMF6H4L1JahJIxSerF25wiMltENmUmS97r8fwjIrI289gsIh+ElVkUPYj2zlLKmp0sUZdOqqe+7eRcFil3dqkt7bW517izRoF/tqmVbTV4cWeKykrrjoJ97sxTr7aGBxi5BWWCAmirOhypnKDMVK+2xqoS4GSCCjOjYmfoMVUlFbzdGu8rlJ8ZKuvM8t2Br8vPRNWTbE7ZjFH5Tit14vyyGac2dQyJVN6ZNMWuQze9GCUpIqXAY8C1OFMcVonIUlXdkDud6tdcx98NXBhWblE0EGPLj3H04lYe3DeLNUvO4Tt3/Y49U50Pc2lTFfdv/DQAb3z0mdxrRoz7Q7cy1ra15bYXHpyV237k1JWe55xc9qeCfTtThVeMljd9JLd956h4ac7ODYiABDiUjvbtWNXm/cUGuGGYd6RokChp4qJESL7dWsbllfE6oW1D9nvubw6JjHR/vgAzK6P9J3br0COe+49n3urG9uZM2d6p6fId6eyFDnjvDTEuAbaqOnkRReRZnMmTG3yOvwUnpimQDTGMSVAvTtaKM1FyIjAJ+H1YoUXRgzBm0Ireg6gRkdWunxeo6oIennUusFhVQ2cXWQNhTFLizbNoVNUZAc/HmSg5l4gRzzbEMCZBvTjdexUwRUQmiUgFTiOwtOB8IlOBk4A3ohRqDYQxSeql25yqmgLmA8uBjcAiVV0vIg+KyA2uQ+cCz2bmTYWyIYYxCerNQClVfR5nRrV73315P98fp0xrIIxJUpFHUloDYUxCkk4nF4U1EMYkyRoIY4wf60EYY/xZAxFuqKT4yMTd7G8bQWkrjCkZyqyRGwF4v30MzZlJUkOkPPeacyu6Zzle65oYtb9tRG67VLzv5E4sK3zrXtme93R0TQpLa2HW6yBjfJa6z1rVFu3bsbvDPzt2hx6LVSconPjkxS/7tNv7HWN851b4cX+Gbhs95sG47Ux1/x2cVR46EbFAVYn3ZLv30853ZG/K+d6ML4v2O21Ixa5CIWsgjDGe7CKlMSaQNRDGGD+Wk9IY48uGGMYYbwmvmhVFUTQQe1MjSf1uMv/ydwu5+6LT+W3z8FwWqWUXLuQvZzp3KLpnjbqqWxkPn9q1Nmdpbdf2tg7v9S/dmaKyvlhdeJdibMmq3Pb+dLz+4KqQlHNRs0FNLt/s+9zhHnRRt3SE38UIWysTnDRxYZmg8vndrbhgSHCGqLMrumeDau6MfwthR7v3a86rcDJInV3u5O9rjjaPiekV4b/HUNZAGGO89Ies1tZAGJMkayCMMX4k4nAmKdZAGJMUW3rPGBOouDsQ1kAYkyS7SGmM8WcNRLiUllDa6szQPGfibnZ3nJSbweme2eme0eeesQn+Mz23p4Z7ntM9SzOrVAqzhLtnfa5rLy14PkjQLEyAtHrHaOQLmhW61ufefhC/5e/cOoY0hh5TXTK0YMWrMPmzMrPy4xzy5c8C3ZQOXdKhQHa2Zr6JZc4SetnZng0d0corLevBuoduNlnLGBPIGghjjBcLlDLGBJKYoeoftqJoIEaXNXH03Ha+d2AW9b+bzPVfXsyGyacBsKylivs2Out+vHZh1+renSe/2a2Mde1d40H36t7fPaX7cVmjSt4q2Leto3A8/2LzWbntedXBS9TnqyvbFPj8/ojD6DXtNb7Pfaoq/jh4cln4KuVHInxxt3ZUxl5l2y8TVNjcivxrDudVVMY6L3TNtSg4d2aJys0dzrWI6RXDIpV3vPPEr0HYEMMY48sCpYwx/oq8BxG6NqeILBSR/SLynmvf/SKyS0TWZh5zXM99XUS2isgmEbmurypuzEDQi4v39okoi/c+Bcz22P+Iql6QeTwPICLTcBYHnZ55zeMiEi94wJjBQgHVaI+EhDYQqvoqcChieTfirBzcpqrbgK3AJSdQP2MGNOmM9khKlB6En/kisi4zBMmGx40D3GmZGjL7jDF5snEQ/X2I4eWHwBnABcAe4LtxCxCReSKyWkRWHznUGyuQGNPPRB1eFPMQw4uq7lPVtKp2Ak/QNYzYBYx3HVqX2edVxgJVnaGqM6pH280UMzgNyB6EiJzq+vEzQPYOx1JgrogMEZFJwBTAO1LJGNMVLBX2SEjon24R+SUwC6gRkQbgG8AsEbkAp+rbgS8BqOp6EVkEbABSwF2qGn/anTGDRL+fi6Gqt3js/mnA8d8EvnkilTJmUFDA5mIYY/xYqHUElZLitPGNHGqvoqQdakuHctmIrQAcSI2kpc1J5OFevn1axb5uZWx3JYA51F6V2y4V78ssE8sKW+5NHYUTjxo7vJOMRFFbGrywysaOaJlJDqRG+j6X1miL77idVFoVesz69pbQY/amq+nQ4EQv+dyfoZvfoja5c+Ule/GbeBWk3Cdmb19mMZ+9aWeS1sSyaJ/LvnQv3H3rxTsUIjIb+D5QCvxEVR/yOOZm4H6c/ss7qvo3QWUWRQNhzGDVW9cgMhHLjwHX4sQfrRKRpaq6wXXMFODrwOWqelhEgpd+48QCpYwxJyLqHYxojcglwFZVrVfVduBZnMhmty+YFELaAAAFmElEQVQCj6nqYQBV3R9WqDUQxiTEiaTUSA+cu4irXY95ecVFiWI+CzhLRP4kIn/ODEkC2RDDmCRFv0jZqKozTvBsZTixSbNwghhfFZFzVdU7iw/WgzAmUTF6EGGiRDE3AEtVtSMzmXIzToPhqyh6EAdSw+n8/Wl89Y6XeG36WbzQMoyHN18LwJLzF7LmdCd1W/e0cld2K+Ofa1/PbTfXrMltb+vwTi3vTiWXdcfIhoJ9lSPfyW3vScW7ah2UKg6ip4sbXVLv+9zhTolVJ3BSxYWZWRm+tP1ppfs5HvM23ftp779J51UE31nJpqbPau5B/F32bkW+s8qduxeTypwy2yKWfUa595IKkan2ZhzEKmBKJoJ5F07ahfw7FM8BtwBPikgNzpDD/8uF9SCMSVRvzcVQ1RQwH1gObAQWZSKbHxSRGzKHLQcOisgG4GXgHlU9GFRuUfQgUlpCSbsT2zBxfGO32Ad3TIT7Xrg71gH8YyQOdHonVfWKb/CKmXDHS+xIxft1BcUvOKL1IILiKTZ3tMeokWNvujr0mLQeDT3mpNIqNrbHi8PwW7wmLK4hP34im2A21rnT3slosz2HbJzEzlR4DIhzfMQVdoL0YhxEJnHT83n77nNtK/APmUckRdFAGDMo2ere0VSXtXBsagePN17JgZdP4+r/Vs+bE51ew4qWqtz1iD9c+LPcaz4Y8063MtyRf0+4rk88fMpKz3OWjni3YN/OVOFf4xebzsxt3z4yXtr7UQHXDgD2p6NdP1jTNtr3udlV8TP61ZbuCz3maIRrG5s6hjCzMjwq02182THP/c0hf0nzl8OLmprezS9CMnvNIdtziHpt4YTT3kOiuR6iKIoGwphBq7jbB2sgjElSxFuYibEGwpikKJC2BsIY40GIHASVGGsgjEmSNRDGGF/WQBhjPClxJmslwhoIYxJk1yCMMf6sgQjXlB7C6G3lXDZrC0smXciG9jGs3DsBgHtrX+Jjp+wEYEeq65f5+rHus1Tn1HZFB142Yktue0/aO65+beuZBftuHVm4xs8FlTtz2wc748Xeb2gfE/j8tUOjxfxPq/CfT3PYZ3ZkkGizOcMjNM+kiSOd8c7f4DMhdnpF8OzR0rLuUYs9iWL0yyGZjZzMzq2IWvbwkvDfYyBV6CzuMUZRNBDGDFrF3T5YA2FMkuwahDHGnzUQxhhP/WBlLdEiaMFE5ADQBDQmXZcTUEP/rj/Ye+gNE1V1bJQDqytP0csm3Bap0GVbvv1WLyStja0oehCqOlZEVifxC+gt/b3+YO8hEUXwBzpIUTQQxgxKCqSL+zaGNRDGJEZBrYGIakHSFThB/b3+YO/hw2dDjGhUtX99sHn6e/3B3sOHrh/cxSiaBsKYQcl6EMYYX9ZAGGM8qUI6/hKCHyZrIIxJkvUgjDG+rIEwxnjr1dW9+4Q1EMYkRUEtUMoY48t6EMYYX3YNwhjjyW5zGmOCqCWtNcZ4UxtiGGN89IPJWvEXVTDG9B7tjPaIQERmi8gmEdkqIvd6PH+7iBwQkbWZx9+FlWk9CGMSooD2Ug9CREqBx4BrgQZglYgsVdUNeYf+u6rOj1qu9SCMSYpqb/YgLgG2qmq9qrYDzwI3nmgVrYEwJkGaTkd6RDAOeN/1c0NmX77Picg6EVksIuPDCrUhhjEJOcbh5S/p4pqIh1eKyGrXzwt6kD3rP4BfqmqbiHwJeBq4OugF1kAYkxBVnd2Lxe0C3D2Cusw+9/ncq0D/BPh2WKE2xDBmYFgFTBGRSSJSAcwFlroPEJFTXT/eAGwMK9R6EMYMAKqaEpH5wHKgFFioqutF5EFgtaouBf5eRG4AUsAh4Pawcoti6T1jTHGyIYYxxpc1EMYYX9ZAGGN8WQNhjPFlDYQxxpc1EMYYX9ZAGGN8WQNhjPH1/wH6+MH9ToEbBAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16820/16836.0 [00:42<00:00, 183.19it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 42.445392370224 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:42<00:00, 396.63it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 1e-08, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.9486833  0.79999999 ... 0.8899883  0.8899883  0.8899883 ]\n",
+      " [0.9486833  1.         0.63245552 ... 0.70359753 0.70359753 0.70359753]\n",
+      " [0.79999999 0.63245552 1.         ... 0.93448772 0.93448772 0.93448772]\n",
+      " ...\n",
+      " [0.8899883  0.70359753 0.93448772 ... 1.         1.         1.        ]\n",
+      " [0.8899883  0.70359753 0.93448772 ... 1.         1.         1.        ]\n",
+      " [0.8899883  0.70359753 0.93448772 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16825/16836.0 [00:41<00:00, 167.61it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 42.057525873184204 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:42<00:00, 400.31it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 3.1622776601683795e-09, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.9486833  0.8        ... 0.88998831 0.88998831 0.88998831]\n",
+      " [0.9486833  1.         0.63245553 ... 0.70359754 0.70359754 0.70359754]\n",
+      " [0.8        0.63245553 1.         ... 0.93448773 0.93448773 0.93448773]\n",
+      " ...\n",
+      " [0.88998831 0.70359754 0.93448773 ... 1.         1.         1.        ]\n",
+      " [0.88998831 0.70359754 0.93448773 ... 1.         1.         1.        ]\n",
+      " [0.88998831 0.70359754 0.93448773 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16834/16836.0 [00:40<00:00, 172.37it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 40.899293184280396 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:40<00:00, 411.65it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 1e-09, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.9486833  0.8        ... 0.88998832 0.88998832 0.88998832]\n",
+      " [0.9486833  1.         0.63245553 ... 0.70359754 0.70359754 0.70359754]\n",
+      " [0.8        0.63245553 1.         ... 0.93448773 0.93448773 0.93448773]\n",
+      " ...\n",
+      " [0.88998832 0.70359754 0.93448773 ... 1.         1.         1.        ]\n",
+      " [0.88998832 0.70359754 0.93448773 ... 1.         1.         1.        ]\n",
+      " [0.88998832 0.70359754 0.93448773 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16834/16836.0 [00:40<00:00, 169.51it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 40.95647478103638 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:40<00:00, 411.07it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 3.1622776601683795e-10, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.9486833  0.8        ... 0.88998832 0.88998832 0.88998832]\n",
+      " [0.9486833  1.         0.63245553 ... 0.70359754 0.70359754 0.70359754]\n",
+      " [0.8        0.63245553 1.         ... 0.93448773 0.93448773 0.93448773]\n",
+      " ...\n",
+      " [0.88998832 0.70359754 0.93448773 ... 1.         1.         1.        ]\n",
+      " [0.88998832 0.70359754 0.93448773 ... 1.         1.         1.        ]\n",
+      " [0.88998832 0.70359754 0.93448773 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16832/16836.0 [00:41<00:00, 189.37it/s]\n",
+      " --- kernel matrix of common walk kernel of size 183 built in 41.74810719490051 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:41<00:00, 403.28it/s]\n",
       "\n",
-      "calculating kernels:   1%|          | 87/16836.0 [00:00<00:37, 451.37it/s]"
+      "the gram matrix with parameters {'weight': 1e-10, 'compute_method': 'geo'} is: \n",
+      "[[1.         0.9486833  0.8        ... 0.88998832 0.88998832 0.88998832]\n",
+      " [0.9486833  1.         0.63245553 ... 0.70359754 0.70359754 0.70359754]\n",
+      " [0.8        0.63245553 1.         ... 0.93448773 0.93448773 0.93448773]\n",
+      " ...\n",
+      " [0.88998832 0.70359754 0.93448773 ... 1.         1.         1.        ]\n",
+      " [0.88998832 0.70359754 0.93448773 ... 1.         1.         1.        ]\n",
+      " [0.88998832 0.70359754 0.93448773 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels:   2%|▏         | 417/16836.0 [00:00<00:08, 2003.76it/s]"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "../pygraph/kernels/commonWalkKernel.py:170: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "../pygraph/kernels/commonWalkKernel.py:173: ComplexWarning: Casting complex values to real discards the imaginary part\n",
       "  D[i][i] = np.exp(beta * ew[i])\n",
-      "../pygraph/kernels/commonWalkKernel.py:81: ComplexWarning: Casting complex values to real discards the imaginary part\n",
+      "../pygraph/kernels/commonWalkKernel.py:84: ComplexWarning: Casting complex values to real discards the imaginary part\n",
       "  edge_label, weight)\n"
      ]
     },
@@ -35,23 +600,1578 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "calculating kernels:   5%|▍         | 795/16836.0 [00:01<00:42, 374.66it/s]"
+      "calculating kernels: 100%|██████████| 16836/16836.0 [01:20<00:00, 37.00it/s] \n",
+      " --- kernel matrix of common walk kernel of size 183 built in 80.14122176170349 seconds ---\n",
+      "\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 0, 'compute_method': 'exp'} is: \n",
+      "[[1.         0.9486833  0.8        ... 0.89053455 0.91333412 0.89884331]\n",
+      " [0.9486833  1.         0.63245553 ... 0.70402938 0.72205402 0.71059803]\n",
+      " [0.8        0.63245553 1.         ... 0.93506128 0.95900083 0.94378548]\n",
+      " ...\n",
+      " [0.89053455 0.70402938 0.93506128 ... 1.         0.97536827 0.96871046]\n",
+      " [0.91333412 0.72205402 0.95900083 ... 0.97536827 1.         1.01057143]\n",
+      " [0.89884331 0.71059803 0.94378548 ... 0.96871046 1.01057143 1.        ]]\n"
      ]
     },
     {
-     "ename": "LinAlgError",
-     "evalue": "singular matrix",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-bdaea04c845f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'task'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'task'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'classification'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNUM_TRIALS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0mdatafile_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dataset_y'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'dataset_y'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m         extra_params=(ds['extra_params'] if 'extra_params' in ds else None))\n\u001b[0m\u001b[1;32m     61\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/utils/model_selection_precomputed.py\u001b[0m in \u001b[0;36mmodel_selection_for_precomputed_kernel\u001b[0;34m(datafile, estimator, param_grid_precomputed, param_grid, model_type, NUM_TRIALS, datafile_y, extra_params)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gram matrix with parameters'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'is: '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0mKmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurrent_run_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams_out\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m         \u001b[0mKmatrix_diag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKmatrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiagonal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/commonWalkKernel.py\u001b[0m in \u001b[0;36mcommonwalkkernel\u001b[0;34m(node_label, edge_label, n, weight, compute_method, *args)\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m                 Kmatrix[i][j] = _untilnwalkkernel_exp(Gn[i], Gn[j], node_label,\n\u001b[0;32m---> 81\u001b[0;31m                                                       edge_label, weight)\n\u001b[0m\u001b[1;32m     82\u001b[0m                 \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m                 \u001b[0mpbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/commonWalkKernel.py\u001b[0m in \u001b[0;36m_untilnwalkkernel_exp\u001b[0;34m(G1, G2, node_label, edge_label, beta)\u001b[0m\n\u001b[1;32m    177\u001b[0m     \u001b[0;31m# print(ev)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[0;31m# print(np.linalg.inv(ev))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m     \u001b[0mexp_D\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mev\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mD\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mev\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mI\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    180\u001b[0m     \u001b[0;31m# print(exp_D)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m     \u001b[0;31m# print(np.exp(weight * A))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/numpy/matrixlib/defmatrix.py\u001b[0m in \u001b[0;36mgetI\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    936\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    937\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdual\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpinv\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0masmatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    939\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    940\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgetA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.local/lib/python3.5/site-packages/scipy/linalg/basic.py\u001b[0m in \u001b[0;36minv\u001b[0;34m(a, overwrite_a, check_finite)\u001b[0m\n\u001b[1;32m    974\u001b[0m         \u001b[0minv_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpiv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlwork\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlwork\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite_lu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    975\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 976\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"singular matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    977\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    978\u001b[0m         raise ValueError('illegal value in %d-th argument of internal '\n",
-      "\u001b[0;31mLinAlgError\u001b[0m: singular matrix"
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|██████████| 16836/16836.0 [01:20<00:00, 46.42it/s] \n",
+      " --- kernel matrix of common walk kernel of size 183 built in 80.26195192337036 seconds ---\n",
+      "\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 1, 'compute_method': 'exp'} is: \n",
+      "[[1.         0.67102578 0.12005371 ... 0.06773632 0.06961356 0.06865815]\n",
+      " [0.67102578 1.         0.11176548 ... 0.06305997 0.0648076  0.06391815]\n",
+      " [0.12005371 0.11176548 1.         ... 0.16703243 0.17166154 0.16930556]\n",
+      " ...\n",
+      " [0.06773632 0.06305997 0.16703243 ... 1.         0.99004011 0.92681459]\n",
+      " [0.06961356 0.0648076  0.17166154 ... 0.99004011 1.         1.00562138]\n",
+      " [0.06865815 0.06391815 0.16930556 ... 0.92681459 1.00562138 1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16835/16836.0 [01:18<00:00, 45.40it/s] \n",
+      " --- kernel matrix of common walk kernel of size 183 built in 78.97516989707947 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [01:18<00:00, 213.18it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 2, 'compute_method': 'exp'} is: \n",
+      "[[1.         0.45305678 0.01627996 ... 0.00450948 0.00461658 0.00425128]\n",
+      " [0.45305678 1.         0.0183758  ... 0.00509002 0.00521091 0.00479858]\n",
+      " [0.01627996 0.0183758  1.         ... 0.05900673 0.0604082  0.05562823]\n",
+      " ...\n",
+      " [0.00450948 0.00509002 0.05900673 ... 1.         0.98669324 0.91647986]\n",
+      " [0.00461658 0.00521091 0.0604082  ... 0.98669324 1.         0.9984286 ]\n",
+      " [0.00425128 0.00479858 0.05562823 ... 0.91647986 0.9984286  1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16832/16836.0 [01:19<00:00, 44.96it/s] \n",
+      " --- kernel matrix of common walk kernel of size 183 built in 79.53693795204163 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [01:19<00:00, 211.68it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 3, 'compute_method': 'exp'} is: \n",
+      "[[1.00000000e+00 3.05338116e-01 2.20333376e-03 ... 2.99440596e-04\n",
+      "  2.99654830e-04 2.57711521e-04]\n",
+      " [3.05338116e-01 1.00000000e+00 3.01114426e-03 ... 4.09224807e-04\n",
+      "  4.09517585e-04 3.52196557e-04]\n",
+      " [2.20333376e-03 3.01114426e-03 1.00000000e+00 ... 2.74202367e-02\n",
+      "  2.74398544e-02 2.35990409e-02]\n",
+      " ...\n",
+      " [2.99440596e-04 4.09224807e-04 2.74202367e-02 ... 1.00000000e+00\n",
+      "  9.82895783e-01 9.09770505e-01]\n",
+      " [2.99654830e-04 4.09517585e-04 2.74398544e-02 ... 9.82895783e-01\n",
+      "  1.00000000e+00 9.94131203e-01]\n",
+      " [2.57711521e-04 3.52196557e-04 2.35990409e-02 ... 9.09770505e-01\n",
+      "  9.94131203e-01 1.00000000e+00]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16834/16836.0 [01:17<00:00, 45.94it/s] \n",
+      " --- kernel matrix of common walk kernel of size 183 built in 77.77831172943115 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [01:17<00:00, 216.46it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 4, 'compute_method': 'exp'} is: \n",
+      "[[1.00000000e+00 2.05745775e-01 2.98188999e-04 ... 1.98781489e-05\n",
+      "  1.91349411e-05 1.55527486e-05]\n",
+      " [2.05745775e-01 1.00000000e+00 4.93286017e-04 ... 3.28838854e-05\n",
+      "  3.16544169e-05 2.57284925e-05]\n",
+      " [2.98188999e-04 4.93286017e-04 1.00000000e+00 ... 1.33484862e-02\n",
+      "  1.28494106e-02 1.04439126e-02]\n",
+      " ...\n",
+      " [1.98781489e-05 3.28838854e-05 1.33484862e-02 ... 1.00000000e+00\n",
+      "  9.78910000e-01 9.04199919e-01]\n",
+      " [1.91349411e-05 3.16544169e-05 1.28494106e-02 ... 9.78910000e-01\n",
+      "  1.00000000e+00 9.91866900e-01]\n",
+      " [1.55527486e-05 2.57284925e-05 1.04439126e-02 ... 9.04199919e-01\n",
+      "  9.91866900e-01 1.00000000e+00]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16832/16836.0 [01:19<00:00, 45.20it/s] \n",
+      " --- kernel matrix of common walk kernel of size 183 built in 79.69377207756042 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [01:19<00:00, 211.26it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 5, 'compute_method': 'exp'} is: \n",
+      "[[1.00000000e+00 1.38634474e-01 4.03554931e-05 ... 1.31946962e-06\n",
+      "  1.20565087e-06 9.37560037e-07]\n",
+      " [1.38634474e-01 1.00000000e+00 8.08079555e-05 ... 2.64210977e-06\n",
+      "  2.41419876e-06 1.87737292e-06]\n",
+      " [4.03554931e-05 8.08079555e-05 1.00000000e+00 ... 6.54028739e-03\n",
+      "  5.97611572e-03 4.64725522e-03]\n",
+      " ...\n",
+      " [1.31946962e-06 2.64210977e-06 6.54028739e-03 ... 1.00000000e+00\n",
+      "  9.74620904e-01 8.99013006e-01]\n",
+      " [1.20565087e-06 2.41419876e-06 5.97611572e-03 ... 9.74620904e-01\n",
+      "  1.00000000e+00 9.90736654e-01]\n",
+      " [9.37560037e-07 1.87737292e-06 4.64725522e-03 ... 8.99013006e-01\n",
+      "  9.90736654e-01 1.00000000e+00]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16835/16836.0 [01:18<00:00, 45.31it/s] \n",
+      " --- kernel matrix of common walk kernel of size 183 built in 78.61699271202087 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [01:18<00:00, 214.15it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 6, 'compute_method': 'exp'} is: \n",
+      "[[1.00000000e+00 9.34136689e-02 5.46152209e-06 ... 8.75783706e-08\n",
+      "  7.51531065e-08 5.65026535e-08]\n",
+      " [9.34136689e-02 1.00000000e+00 1.32375670e-05 ... 2.12271328e-07\n",
+      "  1.82155133e-07 1.36950405e-07]\n",
+      " [5.46152209e-06 1.32375670e-05 1.00000000e+00 ... 3.20717494e-03\n",
+      "  2.75215396e-03 2.06916266e-03]\n",
+      " ...\n",
+      " [8.75783706e-08 2.12271328e-07 3.20717494e-03 ... 1.00000000e+00\n",
+      "  9.70045141e-01 8.93944605e-01]\n",
+      " [7.51531065e-08 1.82155133e-07 2.75215396e-03 ... 9.70045141e-01\n",
+      "  1.00000000e+00 9.90212126e-01]\n",
+      " [5.65026535e-08 1.36950405e-07 2.06916266e-03 ... 8.93944605e-01\n",
+      "  9.90212126e-01 1.00000000e+00]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16831/16836.0 [01:20<00:00, 46.52it/s] \n",
+      " --- kernel matrix of common walk kernel of size 183 built in 80.3363790512085 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [01:20<00:00, 209.57it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 7, 'compute_method': 'exp'} is: \n",
+      "[[1.00000000e+00 6.29432978e-02 7.39136639e-07 ... 5.81262639e-09\n",
+      "  4.64518312e-09 3.40492292e-09]\n",
+      " [6.29432978e-02 1.00000000e+00 2.16851330e-06 ... 1.70533525e-08\n",
+      "  1.36282533e-08 9.98952052e-09]\n",
+      " [7.39136639e-07 2.16851330e-06 1.00000000e+00 ... 1.57281977e-03\n",
+      "  1.25692507e-03 9.21327075e-04]\n",
+      " ...\n",
+      " [5.81262639e-09 1.70533525e-08 1.57281977e-03 ... 1.00000000e+00\n",
+      "  9.65259655e-01 8.88906208e-01]\n",
+      " [4.64518312e-09 1.36282533e-08 1.25692507e-03 ... 9.65259655e-01\n",
+      "  1.00000000e+00 9.90005552e-01]\n",
+      " [3.40492292e-09 9.98952052e-09 9.21327075e-04 ... 8.88906208e-01\n",
+      "  9.90005552e-01 1.00000000e+00]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|██████████| 16836/16836.0 [01:19<00:00, 47.22it/s] \n",
+      " --- kernel matrix of common walk kernel of size 183 built in 79.02764654159546 seconds ---\n",
+      "\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 8, 'compute_method': 'exp'} is: \n",
+      "[[1.00000000e+00 4.24119795e-02 1.00031266e-07 ... 3.85768883e-10\n",
+      "  2.85251973e-10 2.05181214e-10]\n",
+      " [4.24119795e-02 1.00000000e+00 3.55235200e-07 ... 1.36995853e-09\n",
+      "  1.01299869e-09 7.28648072e-10]\n",
+      " [1.00031266e-07 3.55235200e-07 1.00000000e+00 ... 7.71296918e-04\n",
+      "  5.70325855e-04 4.10234326e-04]\n",
+      " ...\n",
+      " [3.85768883e-10 1.36995853e-09 7.71296918e-04 ... 1.00000000e+00\n",
+      "  9.60350080e-01 8.83869637e-01]\n",
+      " [2.85251973e-10 1.01299869e-09 5.70325855e-04 ... 9.60350080e-01\n",
+      "  1.00000000e+00 9.89959707e-01]\n",
+      " [2.05181214e-10 7.28648072e-10 4.10234326e-04 ... 8.83869637e-01\n",
+      "  9.89959707e-01 1.00000000e+00]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels: 100%|█████████▉| 16835/16836.0 [01:18<00:00, 44.24it/s] \n",
+      " --- kernel matrix of common walk kernel of size 183 built in 78.68765640258789 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [01:18<00:00, 213.96it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'weight': 9, 'compute_method': 'exp'} is: \n",
+      "[[1.00000000e+00 2.85777210e-02 1.35377598e-08 ... 2.56012737e-11\n",
+      "  1.74302676e-11 1.23641920e-11]\n",
+      " [2.85777210e-02 1.00000000e+00 5.81928858e-08 ... 1.10048636e-10\n",
+      "  7.49250680e-11 5.31482333e-11]\n",
+      " [1.35377598e-08 5.81928858e-08 1.00000000e+00 ... 3.78220260e-04\n",
+      "  2.57505951e-04 1.82662315e-04]\n",
+      " ...\n",
+      " [2.56012737e-11 1.10048636e-10 3.78220260e-04 ... 1.00000000e+00\n",
+      "  9.55387280e-01 8.78826335e-01]\n",
+      " [1.74302676e-11 7.49250680e-11 2.57505951e-04 ... 9.55387280e-01\n",
+      "  1.00000000e+00 9.89987895e-01]\n",
+      " [1.23641920e-11 5.31482333e-11 1.82662315e-04 ... 8.78826335e-01\n",
+      "  9.89987895e-01 1.00000000e+00]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "31 gram matrices are calculated, 2 of which are ignored.\n",
+      "\n",
+      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
+      "calculate performance:   0%|          | 2/35670 [00:00<49:14, 12.07it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
+      "  warnings.warn(\"Singular matrix in solving dual problem. Using \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                             \n",
+      "4. Getting final performance...\n",
+      "best_params_out:  [{'weight': 0.03162277660168379, 'compute_method': 'geo'}]\n",
+      "best_params_in:  [{'alpha': 1e-08}]\n",
+      "\n",
+      "best_val_perf:  14.68211047499795\n",
+      "best_val_std:  1.1491053376895624\n",
+      "final_performance:  [14.451338721830961]\n",
+      "final_confidence:  [4.576491037745378]\n",
+      "train_performance: [9.38126782468363]\n",
+      "train_std:  [0.4045984715626716]\n",
+      "\n",
+      "time to calculate gram matrix with different hyper-params: 54.55±18.30s\n",
+      "time to calculate best gram matrix: 41.38±nans\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:135: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  keepdims=keepdims)\n",
+      "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:127: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "params                                                                            train_perf          valid_perf           test_perf             gram_matrix_time\n",
+      "--------------------------------------------------------------------------------  ------------------  -------------------  ------------------  ------------------\n",
+      "{'alpha': '1.00e-10', 'weight': 0.1, 'compute_method': 'geo'}                     117.29±84.99        2833.30±3313.81      2402.40±3317.40                  42.25\n",
+      "{'alpha': '1.00e-10', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     9.67±1.61           26.43±11.85          24.02±13.37                      41.38\n",
+      "{'alpha': '1.00e-10', 'weight': 0.01, 'compute_method': 'geo'}                    9.76±0.46           15.11±1.21           16.44±6.81                       41.26\n",
+      "{'alpha': '1.00e-10', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   11.42±0.78          15.76±1.27           17.63±8.81                       41.75\n",
+      "{'alpha': '1.00e-10', 'weight': 0.001, 'compute_method': 'geo'}                   12.82±1.06          16.19±1.78           17.88±8.32                       42.03\n",
+      "{'alpha': '1.00e-10', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  13.52±0.41          15.99±0.74           14.45±3.83                       42.38\n",
+      "{'alpha': '1.00e-10', 'weight': 0.0001, 'compute_method': 'geo'}                  14.69±0.44          16.85±0.70           15.57±4.23                       43.29\n",
+      "{'alpha': '1.00e-10', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  14.90±0.41          16.84±0.64           15.19±4.31                       41.07\n",
+      "{'alpha': '1.00e-10', 'weight': 1e-05, 'compute_method': 'geo'}                   15.20±0.65          17.02±0.76           16.96±6.08                       41.15\n",
+      "{'alpha': '1.00e-10', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   17.78±0.25          19.44±0.65           17.29±3.10                       40.51\n",
+      "{'alpha': '1.00e-10', 'weight': 1e-06, 'compute_method': 'geo'}                   19.62±0.44          21.09±0.64           20.38±4.52                       40.36\n",
+      "{'alpha': '1.00e-10', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   19.81±0.81          21.24±1.04           22.29±7.38                       41.54\n",
+      "{'alpha': '1.00e-10', 'weight': 1e-07, 'compute_method': 'geo'}                   20.07±0.51          21.49±0.81           20.49±4.58                       40.11\n",
+      "{'alpha': '1.00e-10', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   19.98±0.60          21.84±1.03           21.87±5.93                       41.64\n",
+      "{'alpha': '1.00e-10', 'weight': 1e-08, 'compute_method': 'geo'}                   19.95±0.67          21.46±0.81           22.11±6.28                       42.45\n",
+      "{'alpha': '1.00e-10', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  20.26±0.41          21.68±0.72           20.44±3.47                       42.06\n",
+      "{'alpha': '1.00e-10', 'weight': 1e-09, 'compute_method': 'geo'}                   20.87±0.67          21.83±0.88           22.62±4.86                       40.9\n",
+      "{'alpha': '1.00e-10', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  24.00±0.37          24.73±0.44           24.99±3.33                       40.96\n",
+      "{'alpha': '1.00e-10', 'weight': 1e-10, 'compute_method': 'geo'}                   29.08±0.25          29.80±0.31           29.08±3.47                       41.75\n",
+      "{'alpha': '1.00e-10', 'weight': 0, 'compute_method': 'exp'}                       462.43±170.61       1239.40±513.35       1301.47±553.23                   80.14\n",
+      "{'alpha': '1.00e-10', 'weight': 1, 'compute_method': 'exp'}                       376.40±177.60       1054.21±535.65       1023.10±415.54                   80.26\n",
+      "{'alpha': '1.00e-10', 'weight': 2, 'compute_method': 'exp'}                       356.70±95.92        1070.16±362.33       1040.88±383.00                   78.98\n",
+      "{'alpha': '1.00e-10', 'weight': 3, 'compute_method': 'exp'}                       379.60±105.36       1125.43±379.82       1107.41±376.16                   79.54\n",
+      "{'alpha': '1.00e-10', 'weight': 4, 'compute_method': 'exp'}                       454.24±139.17       1543.10±459.10       1508.08±675.06                   77.78\n",
+      "{'alpha': '1.00e-10', 'weight': 5, 'compute_method': 'exp'}                       508.64±206.14       1851.90±1018.12      1856.65±980.62                   79.69\n",
+      "{'alpha': '1.00e-10', 'weight': 6, 'compute_method': 'exp'}                       439.07±143.44       1464.43±439.19       1618.72±761.04                   78.62\n",
+      "{'alpha': '1.00e-10', 'weight': 7, 'compute_method': 'exp'}                       398.68±128.82       1415.14±503.40       1663.69±915.55                   80.34\n",
+      "{'alpha': '1.00e-10', 'weight': 8, 'compute_method': 'exp'}                       386.76±222.42       1460.55±522.95       1751.47±2184.88                  79.03\n",
+      "{'alpha': '1.00e-10', 'weight': 9, 'compute_method': 'exp'}                       378.28±121.38       1391.97±490.23       1539.03±843.67                   78.69\n",
+      "{'alpha': '3.16e-10', 'weight': 0.1, 'compute_method': 'geo'}                     156.72±83.60        2768.33±3475.70      2971.60±4864.75                  42.25\n",
+      "{'alpha': '3.16e-10', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     9.37±1.06           21.80±10.56          19.68±9.33                       41.38\n",
+      "{'alpha': '3.16e-10', 'weight': 0.01, 'compute_method': 'geo'}                    10.39±0.49          15.68±1.24           17.04±7.27                       41.26\n",
+      "{'alpha': '3.16e-10', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   12.02±0.92          16.05±1.35           17.97±8.79                       41.75\n",
+      "{'alpha': '3.16e-10', 'weight': 0.001, 'compute_method': 'geo'}                   12.87±1.06          15.90±1.71           17.66±8.06                       42.03\n",
+      "{'alpha': '3.16e-10', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  13.99±0.40          16.25±0.69           14.70±3.76                       42.38\n",
+      "{'alpha': '3.16e-10', 'weight': 0.0001, 'compute_method': 'geo'}                  14.79±0.45          16.92±0.70           15.65±4.22                       43.29\n",
+      "{'alpha': '3.16e-10', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  15.00±0.40          16.84±0.63           15.29±4.26                       41.07\n",
+      "{'alpha': '3.16e-10', 'weight': 1e-05, 'compute_method': 'geo'}                   16.17±0.59          17.88±0.66           17.76±6.17                       41.15\n",
+      "{'alpha': '3.16e-10', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   19.03±0.28          20.69±0.71           18.50±3.42                       40.51\n",
+      "{'alpha': '3.16e-10', 'weight': 1e-06, 'compute_method': 'geo'}                   19.91±0.45          21.37±0.64           20.63±4.54                       40.36\n",
+      "{'alpha': '3.16e-10', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   19.84±0.81          21.27±1.04           22.32±7.36                       41.54\n",
+      "{'alpha': '3.16e-10', 'weight': 1e-07, 'compute_method': 'geo'}                   20.08±0.51          21.49±0.80           20.50±4.54                       40.11\n",
+      "{'alpha': '3.16e-10', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   20.00±0.60          21.80±0.98           21.82±5.67                       41.64\n",
+      "{'alpha': '3.16e-10', 'weight': 1e-08, 'compute_method': 'geo'}                   20.11±0.64          21.45±0.76           22.09±5.47                       42.45\n",
+      "{'alpha': '3.16e-10', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  21.07±0.37          22.17±0.62           21.19±2.87                       42.06\n",
+      "{'alpha': '3.16e-10', 'weight': 1e-09, 'compute_method': 'geo'}                   23.96±0.40          24.62±0.51           24.84±3.83                       40.9\n",
+      "{'alpha': '3.16e-10', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  28.98±0.37          29.70±0.45           30.06±3.50                       40.96\n",
+      "{'alpha': '3.16e-10', 'weight': 1e-10, 'compute_method': 'geo'}                   33.19±0.36          33.82±0.40           32.81±4.22                       41.75\n",
+      "{'alpha': '3.16e-10', 'weight': 0, 'compute_method': 'exp'}                       657.61±268.64       1793.96±921.36       1884.64±988.47                   80.14\n",
+      "{'alpha': '3.16e-10', 'weight': 1, 'compute_method': 'exp'}                       464.10±225.54       1300.23±650.67       1274.26±663.09                   80.26\n",
+      "{'alpha': '3.16e-10', 'weight': 2, 'compute_method': 'exp'}                       502.77±211.67       1535.25±802.57       1454.57±748.44                   78.98\n",
+      "{'alpha': '3.16e-10', 'weight': 3, 'compute_method': 'exp'}                       566.65±227.22       1668.27±678.59       1721.50±931.29                   79.54\n",
+      "{'alpha': '3.16e-10', 'weight': 4, 'compute_method': 'exp'}                       614.53±304.92       2124.78±1016.51      2052.85±1337.30                  77.78\n",
+      "{'alpha': '3.16e-10', 'weight': 5, 'compute_method': 'exp'}                       735.44±371.84       2569.36±1395.84      2694.79±1671.46                  79.69\n",
+      "{'alpha': '3.16e-10', 'weight': 6, 'compute_method': 'exp'}                       667.49±258.45       2148.65±815.36       2362.44±1290.15                  78.62\n",
+      "{'alpha': '3.16e-10', 'weight': 7, 'compute_method': 'exp'}                       595.38±316.70       2201.97±1313.12      2371.69±1470.68                  80.34\n",
+      "{'alpha': '3.16e-10', 'weight': 8, 'compute_method': 'exp'}                       469.06±166.26       1793.47±660.21       1953.81±1429.43                  79.03\n",
+      "{'alpha': '3.16e-10', 'weight': 9, 'compute_method': 'exp'}                       564.72±284.67       2070.89±1254.06      2249.61±1476.82                  78.69\n",
+      "{'alpha': '1.00e-09', 'weight': 0.1, 'compute_method': 'geo'}                     144.90±145.60       1930.26±3222.18      2808.78±7747.88                  42.25\n",
+      "{'alpha': '1.00e-09', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     9.40±0.93           16.98±3.46           16.85±8.45                       41.38\n",
+      "{'alpha': '1.00e-09', 'weight': 0.01, 'compute_method': 'geo'}                    10.79±0.52          15.84±1.22           17.20±7.42                       41.26\n",
+      "{'alpha': '1.00e-09', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   12.49±1.03          16.19±1.43           18.15±8.66                       41.75\n",
+      "{'alpha': '1.00e-09', 'weight': 0.001, 'compute_method': 'geo'}                   12.94±1.02          15.62±1.60           17.47±7.85                       42.03\n",
+      "{'alpha': '1.00e-09', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  14.46±0.39          16.62±0.66           15.03±3.74                       42.38\n",
+      "{'alpha': '1.00e-09', 'weight': 0.0001, 'compute_method': 'geo'}                  14.84±0.44          16.90±0.69           15.65±4.17                       43.29\n",
+      "{'alpha': '1.00e-09', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  15.37±0.39          17.05±0.62           15.64±4.17                       41.07\n",
+      "{'alpha': '1.00e-09', 'weight': 1e-05, 'compute_method': 'geo'}                   17.61±0.59          19.26±0.65           19.20±6.41                       41.15\n",
+      "{'alpha': '1.00e-09', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   19.74±0.32          21.40±0.74           19.16±3.61                       40.51\n",
+      "{'alpha': '1.00e-09', 'weight': 1e-06, 'compute_method': 'geo'}                   20.01±0.46          21.47±0.64           20.72±4.53                       40.36\n",
+      "{'alpha': '1.00e-09', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   19.86±0.82          21.27±1.04           22.32±7.28                       41.54\n",
+      "{'alpha': '1.00e-09', 'weight': 1e-07, 'compute_method': 'geo'}                   20.10±0.50          21.48±0.79           20.53±4.40                       40.11\n",
+      "{'alpha': '1.00e-09', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   20.16±0.58          21.78±0.87           21.77±4.95                       41.64\n",
+      "{'alpha': '1.00e-09', 'weight': 1e-08, 'compute_method': 'geo'}                   20.95±0.51          21.97±0.73           22.52±3.84                       42.45\n",
+      "{'alpha': '1.00e-09', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  24.05±0.32          24.78±0.45           24.19±2.85                       42.06\n",
+      "{'alpha': '1.00e-09', 'weight': 1e-09, 'compute_method': 'geo'}                   29.00±0.40          29.68±0.48           29.65±4.41                       40.9\n",
+      "{'alpha': '1.00e-09', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  33.08±0.40          33.73±0.47           34.01±3.75                       40.96\n",
+      "{'alpha': '1.00e-09', 'weight': 1e-10, 'compute_method': 'geo'}                   35.83±0.48          36.29±0.51           35.12±4.84                       41.75\n",
+      "{'alpha': '1.00e-09', 'weight': 0, 'compute_method': 'exp'}                       924.68±557.86       2521.15±1698.84      2677.73±1958.68                  80.14\n",
+      "{'alpha': '1.00e-09', 'weight': 1, 'compute_method': 'exp'}                       642.02±518.74       1801.60±1457.93      1764.50±1493.97                  80.26\n",
+      "{'alpha': '1.00e-09', 'weight': 2, 'compute_method': 'exp'}                       626.05±440.37       1878.84±1434.30      1765.19±1329.70                  78.98\n",
+      "{'alpha': '1.00e-09', 'weight': 3, 'compute_method': 'exp'}                       782.42±475.05       2280.57±1287.01      2334.72±1605.90                  79.54\n",
+      "{'alpha': '1.00e-09', 'weight': 4, 'compute_method': 'exp'}                       848.62±632.25       2911.03±2108.61      2908.14±2858.35                  77.78\n",
+      "{'alpha': '1.00e-09', 'weight': 5, 'compute_method': 'exp'}                       1042.82±813.36      3505.60±2904.97      3646.12±2726.36                  79.69\n",
+      "{'alpha': '1.00e-09', 'weight': 6, 'compute_method': 'exp'}                       905.84±493.76       2979.32±1898.84      3178.58±2196.81                  78.62\n",
+      "{'alpha': '1.00e-09', 'weight': 7, 'compute_method': 'exp'}                       845.99±628.16       3360.32±2893.77      3335.86±2783.78                  80.34\n",
+      "{'alpha': '1.00e-09', 'weight': 8, 'compute_method': 'exp'}                       585.05±346.86       2214.13±1384.51      2464.70±2207.91                  79.03\n",
+      "{'alpha': '1.00e-09', 'weight': 9, 'compute_method': 'exp'}                       775.96±561.07       2930.44±3001.60      3046.88±2555.80                  78.69\n",
+      "{'alpha': '3.16e-09', 'weight': 0.1, 'compute_method': 'geo'}                     109.46±102.50       1931.29±5396.91      1968.78±4949.17                  42.25\n",
+      "{'alpha': '3.16e-09', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     9.29±0.57           14.95±1.39           14.56±4.17                       41.38\n",
+      "{'alpha': '3.16e-09', 'weight': 0.01, 'compute_method': 'geo'}                    11.04±0.56          15.84±1.20           17.27±7.51                       41.26\n",
+      "{'alpha': '3.16e-09', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   12.73±1.09          16.09±1.46           18.07±8.51                       41.75\n",
+      "{'alpha': '3.16e-09', 'weight': 0.001, 'compute_method': 'geo'}                   13.16±0.97          15.54±1.47           17.52±7.78                       42.03\n",
+      "{'alpha': '3.16e-09', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  14.73±0.40          16.82±0.65           15.22±3.73                       42.38\n",
+      "{'alpha': '3.16e-09', 'weight': 0.0001, 'compute_method': 'geo'}                  14.95±0.42          16.90±0.65           15.67±4.10                       43.29\n",
+      "{'alpha': '3.16e-09', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  16.31±0.36          17.83±0.61           16.57±4.14                       41.07\n",
+      "{'alpha': '3.16e-09', 'weight': 1e-05, 'compute_method': 'geo'}                   18.84±0.64          20.47±0.73           20.55±6.63                       41.15\n",
+      "{'alpha': '3.16e-09', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   20.03±0.33          21.69±0.76           19.43±3.68                       40.51\n",
+      "{'alpha': '3.16e-09', 'weight': 1e-06, 'compute_method': 'geo'}                   20.05±0.46          21.50±0.64           20.74±4.47                       40.36\n",
+      "{'alpha': '3.16e-09', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   19.89±0.81          21.26±1.03           22.32±7.02                       41.54\n",
+      "{'alpha': '3.16e-09', 'weight': 1e-07, 'compute_method': 'geo'}                   20.25±0.50          21.54±0.75           20.70±4.03                       40.11\n",
+      "{'alpha': '3.16e-09', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   21.00±0.49          22.23±0.68           22.23±3.48                       41.64\n",
+      "{'alpha': '3.16e-09', 'weight': 1e-08, 'compute_method': 'geo'}                   23.99±0.31          24.76±0.39           25.01±2.88                       42.45\n",
+      "{'alpha': '3.16e-09', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  29.04±0.34          29.73±0.41           29.30±3.31                       42.06\n",
+      "{'alpha': '3.16e-09', 'weight': 1e-09, 'compute_method': 'geo'}                   33.08±0.50          33.70±0.57           33.83±5.10                       40.9\n",
+      "{'alpha': '3.16e-09', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  35.71±0.43          36.21±0.49           36.41±4.08                       40.96\n",
+      "{'alpha': '3.16e-09', 'weight': 1e-10, 'compute_method': 'geo'}                   37.33±0.57          37.68±0.61           36.46±5.19                       41.75\n",
+      "{'alpha': '3.16e-09', 'weight': 0, 'compute_method': 'exp'}                       1051.26±768.94      2836.20±2220.06      3030.60±2513.98                  80.14\n",
+      "{'alpha': '3.16e-09', 'weight': 1, 'compute_method': 'exp'}                       790.49±751.02       2237.89±2228.66      2202.83±2245.59                  80.26\n",
+      "{'alpha': '3.16e-09', 'weight': 2, 'compute_method': 'exp'}                       777.29±982.24       2467.16±3845.22      2274.00±3528.96                  78.98\n",
+      "{'alpha': '3.16e-09', 'weight': 3, 'compute_method': 'exp'}                       1165.24±1321.93     3231.47±3326.94      3320.43±3869.97                  79.54\n",
+      "{'alpha': '3.16e-09', 'weight': 4, 'compute_method': 'exp'}                       1132.73±973.27      3783.34±3305.70      3885.76±4497.81                  77.78\n",
+      "{'alpha': '3.16e-09', 'weight': 5, 'compute_method': 'exp'}                       1396.17±1582.88     4344.97±4449.40      4384.83±3434.47                  79.69\n",
+      "{'alpha': '3.16e-09', 'weight': 6, 'compute_method': 'exp'}                       1111.94±756.65      3771.58±3195.44      4185.85±4140.94                  78.62\n",
+      "{'alpha': '3.16e-09', 'weight': 7, 'compute_method': 'exp'}                       1219.25±1480.03     5246.83±7437.54      4937.59±6606.77                  80.34\n",
+      "{'alpha': '3.16e-09', 'weight': 8, 'compute_method': 'exp'}                       647.45±463.38       2444.51±1845.63      2729.58±2524.78                  79.03\n",
+      "{'alpha': '3.16e-09', 'weight': 9, 'compute_method': 'exp'}                       908.52±758.51       3309.52±3298.85      3503.21±3211.74                  78.69\n",
+      "{'alpha': '1.00e-08', 'weight': 0.1, 'compute_method': 'geo'}                     57.12±43.83         399.13±920.75        286.92±344.58                    42.25\n",
+      "{'alpha': '1.00e-08', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     9.38±0.40           14.68±1.15           14.45±4.58                       41.38\n",
+      "{'alpha': '1.00e-08', 'weight': 0.01, 'compute_method': 'geo'}                    11.45±0.62          15.97±1.22           17.57±7.52                       41.26\n",
+      "{'alpha': '1.00e-08', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   12.85±1.09          15.86±1.43           17.79±8.31                       41.75\n",
+      "{'alpha': '1.00e-08', 'weight': 0.001, 'compute_method': 'geo'}                   13.62±0.93          15.81±1.38           17.88±7.81                       42.03\n",
+      "{'alpha': '1.00e-08', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  14.84±0.40          16.87±0.63           15.29±3.72                       42.38\n",
+      "{'alpha': '1.00e-08', 'weight': 0.0001, 'compute_method': 'geo'}                  15.32±0.39          17.10±0.61           15.88±4.13                       43.29\n",
+      "{'alpha': '1.00e-08', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  17.72±0.36          19.17±0.63           17.95±4.31                       41.07\n",
+      "{'alpha': '1.00e-08', 'weight': 1e-05, 'compute_method': 'geo'}                   19.52±0.70          21.14±0.80           21.31±6.72                       41.15\n",
+      "{'alpha': '1.00e-08', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   20.14±0.34          21.78±0.76           19.54±3.67                       40.51\n",
+      "{'alpha': '1.00e-08', 'weight': 1e-06, 'compute_method': 'geo'}                   20.08±0.46          21.50±0.64           20.77±4.30                       40.36\n",
+      "{'alpha': '1.00e-08', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   20.04±0.79          21.30±0.98           22.36±6.30                       41.54\n",
+      "{'alpha': '1.00e-08', 'weight': 1e-07, 'compute_method': 'geo'}                   21.07±0.45          22.12±0.63           21.57±3.32                       40.11\n",
+      "{'alpha': '1.00e-08', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   24.04±0.31          24.85±0.40           25.08±2.54                       41.64\n",
+      "{'alpha': '1.00e-08', 'weight': 1e-08, 'compute_method': 'geo'}                   29.00±0.34          29.75±0.31           29.84±3.17                       42.45\n",
+      "{'alpha': '1.00e-08', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  33.14±0.38          33.74±0.49           33.41±3.92                       42.06\n",
+      "{'alpha': '1.00e-08', 'weight': 1e-09, 'compute_method': 'geo'}                   35.67±0.60          36.11±0.65           36.56±5.76                       40.9\n",
+      "{'alpha': '1.00e-08', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  37.21±0.48          37.60±0.53           37.74±4.34                       40.96\n",
+      "{'alpha': '1.00e-08', 'weight': 1e-10, 'compute_method': 'geo'}                   37.99±0.62          38.28±0.66           37.04±5.34                       41.75\n",
+      "{'alpha': '1.00e-08', 'weight': 0, 'compute_method': 'exp'}                       1151.12±1096.71     3059.50±2881.50      3259.60±3085.42                  80.14\n",
+      "{'alpha': '1.00e-08', 'weight': 1, 'compute_method': 'exp'}                       837.16±858.08       2368.46±2499.36      2339.33±2537.12                  80.26\n",
+      "{'alpha': '1.00e-08', 'weight': 2, 'compute_method': 'exp'}                       833.91±1205.45      2694.20±4834.71      2472.16±4432.29                  78.98\n",
+      "{'alpha': '1.00e-08', 'weight': 3, 'compute_method': 'exp'}                       2135.28±3822.97     5619.11±9476.16      5966.43±10830.75                 79.54\n",
+      "{'alpha': '1.00e-08', 'weight': 4, 'compute_method': 'exp'}                       1494.55±2016.65     4914.75±6909.46      5594.12±10176.34                 77.78\n",
+      "{'alpha': '1.00e-08', 'weight': 5, 'compute_method': 'exp'}                       2054.64±4258.25     5896.61±10614.55     5208.53±5171.13                  79.69\n",
+      "{'alpha': '1.00e-08', 'weight': 6, 'compute_method': 'exp'}                       1195.14±1001.96     4120.62±4231.88      4469.74±4841.57                  78.62\n",
+      "{'alpha': '1.00e-08', 'weight': 7, 'compute_method': 'exp'}                       1795.98±3444.61     8513.45±19007.28     7077.02±13975.34                 80.34\n",
+      "{'alpha': '1.00e-08', 'weight': 8, 'compute_method': 'exp'}                       647.05±462.47       2442.62±1840.04      2727.37±2519.52                  79.03\n",
+      "{'alpha': '1.00e-08', 'weight': 9, 'compute_method': 'exp'}                       1066.03±1182.84     3749.90±4135.07      3945.97±3987.35                  78.69\n",
+      "{'alpha': '3.16e-08', 'weight': 0.1, 'compute_method': 'geo'}                     58.48±148.97        161.00±314.03        251.80±726.40                    42.25\n",
+      "{'alpha': '3.16e-08', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     9.97±0.30           15.28±0.98           15.18±5.02                       41.38\n",
+      "{'alpha': '3.16e-08', 'weight': 0.01, 'compute_method': 'geo'}                    12.05±0.71          16.20±1.26           17.93±7.31                       41.26\n",
+      "{'alpha': '3.16e-08', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   12.95±1.06          15.60±1.36           17.44±8.10                       41.75\n",
+      "{'alpha': '3.16e-08', 'weight': 0.001, 'compute_method': 'geo'}                   14.09±0.92          16.16±1.34           18.26±7.85                       42.03\n",
+      "{'alpha': '3.16e-08', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  14.98±0.39          16.89±0.60           15.33±3.72                       42.38\n",
+      "{'alpha': '3.16e-08', 'weight': 0.0001, 'compute_method': 'geo'}                  16.27±0.37          17.88±0.60           16.64±4.40                       43.29\n",
+      "{'alpha': '3.16e-08', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  18.97±0.40          20.40±0.67           19.11±4.56                       41.07\n",
+      "{'alpha': '3.16e-08', 'weight': 1e-05, 'compute_method': 'geo'}                   19.80±0.72          21.40±0.83           21.61±6.68                       41.15\n",
+      "{'alpha': '3.16e-08', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   20.20±0.34          21.79±0.76           19.63±3.56                       40.51\n",
+      "{'alpha': '3.16e-08', 'weight': 1e-06, 'compute_method': 'geo'}                   20.23±0.44          21.54±0.62           20.87±3.82                       40.36\n",
+      "{'alpha': '3.16e-08', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   20.88±0.67          21.86±0.81           22.94±4.71                       41.54\n",
+      "{'alpha': '3.16e-08', 'weight': 1e-07, 'compute_method': 'geo'}                   24.06±0.34          24.82±0.43           24.72±2.63                       40.11\n",
+      "{'alpha': '3.16e-08', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   29.01±0.27          29.76±0.31           30.30±3.15                       41.64\n",
+      "{'alpha': '3.16e-08', 'weight': 1e-08, 'compute_method': 'geo'}                   33.08±0.36          33.73±0.34           33.81±3.40                       42.45\n",
+      "{'alpha': '3.16e-08', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  35.75±0.44          36.17±0.56           36.02±4.53                       42.06\n",
+      "{'alpha': '3.16e-08', 'weight': 1e-09, 'compute_method': 'geo'}                   37.13±0.69          37.42±0.74           38.12±6.16                       40.9\n",
+      "{'alpha': '3.16e-08', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  37.86±0.51          38.20±0.56           38.31±4.45                       40.96\n",
+      "{'alpha': '3.16e-08', 'weight': 1e-10, 'compute_method': 'geo'}                   38.22±0.64          38.49±0.67           37.25±5.39                       41.75\n",
+      "{'alpha': '3.16e-08', 'weight': 0, 'compute_method': 'exp'}                       1151.08±1096.62     3059.41±2881.28      3259.49±3085.19                  80.14\n",
+      "{'alpha': '3.16e-08', 'weight': 1, 'compute_method': 'exp'}                       837.08±857.84       2368.25±2498.78      2339.10±2536.47                  80.26\n",
+      "{'alpha': '3.16e-08', 'weight': 2, 'compute_method': 'exp'}                       834.13±1206.63      2695.27±4840.48      2473.15±4437.68                  78.98\n",
+      "{'alpha': '3.16e-08', 'weight': 3, 'compute_method': 'exp'}                       2220.09±3958.06     5856.56±9877.43      6228.52±11245.47                 79.54\n",
+      "{'alpha': '3.16e-08', 'weight': 4, 'compute_method': 'exp'}                       1903.97±3546.71     6790.26±15670.29     8162.35±22075.81                 77.78\n",
+      "{'alpha': '3.16e-08', 'weight': 5, 'compute_method': 'exp'}                       2069.39±4333.59     5932.58±10797.80     5221.73±5213.74                  79.69\n",
+      "{'alpha': '3.16e-08', 'weight': 6, 'compute_method': 'exp'}                       1194.05±997.78      4115.74±4213.89      4464.18±4823.31                  78.62\n",
+      "{'alpha': '3.16e-08', 'weight': 7, 'compute_method': 'exp'}                       1806.08±3478.07     8574.90±19230.55     7104.58±14057.90                 80.34\n",
+      "{'alpha': '3.16e-08', 'weight': 8, 'compute_method': 'exp'}                       646.10±460.23       2437.74±1824.62      2721.95±2505.94                  79.03\n",
+      "{'alpha': '3.16e-08', 'weight': 9, 'compute_method': 'exp'}                       1080.71±1167.73     3762.26±4102.96      4072.70±4323.88                  78.69\n",
+      "{'alpha': '1.00e-07', 'weight': 0.1, 'compute_method': 'geo'}                     13.11±7.50          36.70±27.74          43.42±39.27                      42.25\n",
+      "{'alpha': '1.00e-07', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     10.64±0.28          15.76±0.82           15.70±5.31                       41.38\n",
+      "{'alpha': '1.00e-07', 'weight': 0.01, 'compute_method': 'geo'}                    12.52±0.78          16.19±1.26           18.00±7.01                       41.26\n",
+      "{'alpha': '1.00e-07', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   13.18±1.01          15.55±1.26           17.24±7.94                       41.75\n",
+      "{'alpha': '1.00e-07', 'weight': 0.001, 'compute_method': 'geo'}                   14.37±0.91          16.34±1.30           18.44±7.86                       42.03\n",
+      "{'alpha': '1.00e-07', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  15.37±0.38          17.13±0.53           15.47±3.79                       42.38\n",
+      "{'alpha': '1.00e-07', 'weight': 0.0001, 'compute_method': 'geo'}                  17.71±0.38          19.24±0.63           17.95±4.73                       43.29\n",
+      "{'alpha': '1.00e-07', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  19.68±0.44          21.11±0.70           19.75±4.68                       41.07\n",
+      "{'alpha': '1.00e-07', 'weight': 1e-05, 'compute_method': 'geo'}                   19.92±0.73          21.48±0.84           21.70±6.46                       41.15\n",
+      "{'alpha': '1.00e-07', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   20.35±0.34          21.79±0.74           19.89±3.28                       40.51\n",
+      "{'alpha': '1.00e-07', 'weight': 1e-06, 'compute_method': 'geo'}                   21.05±0.38          22.10±0.54           21.60±3.04                       40.36\n",
+      "{'alpha': '1.00e-07', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   23.93±0.39          24.63±0.46           25.61±3.38                       41.54\n",
+      "{'alpha': '1.00e-07', 'weight': 1e-07, 'compute_method': 'geo'}                   29.03±0.28          29.76±0.38           29.85±2.36                       40.11\n",
+      "{'alpha': '1.00e-07', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   33.05±0.34          33.71±0.42           34.53±4.13                       41.64\n",
+      "{'alpha': '1.00e-07', 'weight': 1e-08, 'compute_method': 'geo'}                   35.71±0.41          36.18±0.45           36.26±3.80                       42.45\n",
+      "{'alpha': '1.00e-07', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  37.22±0.51          37.51±0.62           37.51±4.89                       42.06\n",
+      "{'alpha': '1.00e-07', 'weight': 1e-09, 'compute_method': 'geo'}                   37.75±0.74          37.99±0.79           38.79±6.32                       40.9\n",
+      "{'alpha': '1.00e-07', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.09±0.53          38.42±0.57           38.51±4.50                       40.96\n",
+      "{'alpha': '1.00e-07', 'weight': 1e-10, 'compute_method': 'geo'}                   38.29±0.65          38.56±0.68           37.32±5.40                       41.75\n",
+      "{'alpha': '1.00e-07', 'weight': 0, 'compute_method': 'exp'}                       1151.09±1096.51     3059.44±2881.11      3259.58±3085.23                  80.14\n",
+      "{'alpha': '1.00e-07', 'weight': 1, 'compute_method': 'exp'}                       836.93±857.40       2367.82±2497.69      2338.65±2535.26                  80.26\n",
+      "{'alpha': '1.00e-07', 'weight': 2, 'compute_method': 'exp'}                       834.74±1209.77      2698.14±4855.98      2475.81±4452.18                  78.98\n",
+      "{'alpha': '1.00e-07', 'weight': 3, 'compute_method': 'exp'}                       2230.49±3992.64     5885.56±9979.58      6260.94±11362.68                 79.54\n",
+      "{'alpha': '1.00e-07', 'weight': 4, 'compute_method': 'exp'}                       1928.71±3659.61     6908.95±16273.03     8324.67±22899.92                 77.78\n",
+      "{'alpha': '1.00e-07', 'weight': 5, 'compute_method': 'exp'}                       2064.17±4303.22     5919.78±10723.47     5218.98±5200.61                  79.69\n",
+      "{'alpha': '1.00e-07', 'weight': 6, 'compute_method': 'exp'}                       1190.67±985.60      4100.52±4160.85      4446.81±4768.42                  78.62\n",
+      "{'alpha': '1.00e-07', 'weight': 7, 'compute_method': 'exp'}                       1841.10±3595.32     8788.01±20013.28     7200.32±14342.81                 80.34\n",
+      "{'alpha': '1.00e-07', 'weight': 8, 'compute_method': 'exp'}                       643.51±454.33       2424.20±1783.54      2707.05±2470.29                  79.03\n",
+      "{'alpha': '1.00e-07', 'weight': 9, 'compute_method': 'exp'}                       1049.53±1059.06     3697.31±3963.41      3998.00±4194.47                  78.69\n",
+      "{'alpha': '3.16e-07', 'weight': 0.1, 'compute_method': 'geo'}                     10.77±3.61          24.42±9.08           31.13±26.78                      42.25\n",
+      "{'alpha': '3.16e-07', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     11.13±0.30          15.95±0.70           15.85±5.65                       41.38\n",
+      "{'alpha': '3.16e-07', 'weight': 0.01, 'compute_method': 'geo'}                    12.80±0.80          15.96±1.21           17.80±6.73                       41.26\n",
+      "{'alpha': '3.16e-07', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   13.66±0.96          15.84±1.19           17.36±7.90                       41.75\n",
+      "{'alpha': '3.16e-07', 'weight': 0.001, 'compute_method': 'geo'}                   14.58±0.89          16.43±1.23           18.52±7.87                       42.03\n",
+      "{'alpha': '3.16e-07', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  16.35±0.37          17.97±0.49           16.01±4.00                       42.38\n",
+      "{'alpha': '3.16e-07', 'weight': 0.0001, 'compute_method': 'geo'}                  18.98±0.42          20.48±0.69           19.16±4.89                       43.29\n",
+      "{'alpha': '3.16e-07', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  20.00±0.46          21.39±0.71           20.04±4.59                       41.07\n",
+      "{'alpha': '3.16e-07', 'weight': 1e-05, 'compute_method': 'geo'}                   20.11±0.71          21.54±0.83           21.74±5.80                       41.15\n",
+      "{'alpha': '3.16e-07', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   21.15±0.34          22.28±0.60           21.05±2.76                       40.51\n",
+      "{'alpha': '3.16e-07', 'weight': 1e-06, 'compute_method': 'geo'}                   24.05±0.27          24.82±0.35           24.64±2.99                       40.36\n",
+      "{'alpha': '3.16e-07', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   28.92±0.33          29.61±0.42           30.48±3.77                       41.54\n",
+      "{'alpha': '3.16e-07', 'weight': 1e-07, 'compute_method': 'geo'}                   33.11±0.28          33.75±0.43           33.75±2.74                       40.11\n",
+      "{'alpha': '3.16e-07', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   35.64±0.47          36.12±0.55           37.22±5.00                       41.64\n",
+      "{'alpha': '3.16e-07', 'weight': 1e-08, 'compute_method': 'geo'}                   37.21±0.46          37.57±0.53           37.63±4.10                       42.45\n",
+      "{'alpha': '3.16e-07', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  37.86±0.55          38.10±0.65           38.16±5.04                       42.06\n",
+      "{'alpha': '3.16e-07', 'weight': 1e-09, 'compute_method': 'geo'}                   37.98±0.77          38.19±0.81           39.03±6.38                       40.9\n",
+      "{'alpha': '3.16e-07', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.16±0.53          38.49±0.57           38.58±4.51                       40.96\n",
+      "{'alpha': '3.16e-07', 'weight': 1e-10, 'compute_method': 'geo'}                   38.32±0.65          38.59±0.68           37.35±5.41                       41.75\n",
+      "{'alpha': '3.16e-07', 'weight': 0, 'compute_method': 'exp'}                       1151.01±1095.65     3059.27±2879.52      3259.62±3084.45                  80.14\n",
+      "{'alpha': '3.16e-07', 'weight': 1, 'compute_method': 'exp'}                       836.41±855.87       2366.37±2493.94      2337.15±2531.10                  80.26\n",
+      "{'alpha': '3.16e-07', 'weight': 2, 'compute_method': 'exp'}                       836.75±1220.17      2707.58±4907.08      2484.58±4499.99                  78.98\n",
+      "{'alpha': '3.16e-07', 'weight': 3, 'compute_method': 'exp'}                       2265.28±4110.52     5982.51±10326.81     6369.77±11761.30                 79.54\n",
+      "{'alpha': '3.16e-07', 'weight': 4, 'compute_method': 'exp'}                       2019.39±4080.12     7342.98±18481.38     8918.43±25924.49                 77.78\n",
+      "{'alpha': '3.16e-07', 'weight': 5, 'compute_method': 'exp'}                       2047.49±4206.65     5878.91±10487.37     5210.17±5159.28                  79.69\n",
+      "{'alpha': '3.16e-07', 'weight': 6, 'compute_method': 'exp'}                       1180.84±950.80      4055.91±4008.60      4395.67±4608.58                  78.62\n",
+      "{'alpha': '3.16e-07', 'weight': 7, 'compute_method': 'exp'}                       1989.98±4116.35     9699.57±23519.16     7601.91±15492.05                 80.34\n",
+      "{'alpha': '3.16e-07', 'weight': 8, 'compute_method': 'exp'}                       638.16±443.77       2394.79±1704.70      2675.37±2403.67                  79.03\n",
+      "{'alpha': '3.16e-07', 'weight': 9, 'compute_method': 'exp'}                       997.39±916.90       3592.58±3810.51      3867.27±4022.95                  78.69\n",
+      "{'alpha': '1.00e-06', 'weight': 0.1, 'compute_method': 'geo'}                     9.45±1.03           18.35±5.59           20.82±11.45                      42.25\n",
+      "{'alpha': '1.00e-06', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     11.68±0.35          16.14±0.68           15.99±5.98                       41.38\n",
+      "{'alpha': '1.00e-06', 'weight': 0.01, 'compute_method': 'geo'}                    12.98±0.78          15.71±1.14           17.52±6.55                       41.26\n",
+      "{'alpha': '1.00e-06', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   14.15±0.94          16.21±1.15           17.55±7.93                       41.75\n",
+      "{'alpha': '1.00e-06', 'weight': 0.001, 'compute_method': 'geo'}                   15.03±0.86          16.70±1.14           18.81±7.95                       42.03\n",
+      "{'alpha': '1.00e-06', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  17.81±0.39          19.36±0.52           17.12±4.25                       42.38\n",
+      "{'alpha': '1.00e-06', 'weight': 0.0001, 'compute_method': 'geo'}                  19.71±0.45          21.18±0.72           19.88±4.84                       43.29\n",
+      "{'alpha': '1.00e-06', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  20.25±0.46          21.52±0.68           20.28±4.19                       41.07\n",
+      "{'alpha': '1.00e-06', 'weight': 1e-05, 'compute_method': 'geo'}                   20.97±0.61          22.11±0.74           22.31±4.32                       41.15\n",
+      "{'alpha': '1.00e-06', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   24.08±0.31          24.87±0.38           24.70±2.44                       40.51\n",
+      "{'alpha': '1.00e-06', 'weight': 1e-06, 'compute_method': 'geo'}                   29.03±0.29          29.77±0.38           29.96±3.46                       40.36\n",
+      "{'alpha': '1.00e-06', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   33.01±0.40          33.58±0.48           34.38±4.29                       41.54\n",
+      "{'alpha': '1.00e-06', 'weight': 1e-07, 'compute_method': 'geo'}                   35.75±0.35          36.21±0.49           36.04±3.55                       40.11\n",
+      "{'alpha': '1.00e-06', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   37.10±0.59          37.46±0.68           38.73±5.50                       41.64\n",
+      "{'alpha': '1.00e-06', 'weight': 1e-08, 'compute_method': 'geo'}                   37.86±0.50          38.18±0.58           38.22±4.23                       42.45\n",
+      "{'alpha': '1.00e-06', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  38.08±0.57          38.30±0.67           38.39±5.09                       42.06\n",
+      "{'alpha': '1.00e-06', 'weight': 1e-09, 'compute_method': 'geo'}                   38.05±0.77          38.25±0.81           39.11±6.40                       40.9\n",
+      "{'alpha': '1.00e-06', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.19±0.53          38.51±0.58           38.60±4.51                       40.96\n",
+      "{'alpha': '1.00e-06', 'weight': 1e-10, 'compute_method': 'geo'}                   38.33±0.65          38.59±0.68           37.35±5.41                       41.75\n",
+      "{'alpha': '1.00e-06', 'weight': 0, 'compute_method': 'exp'}                       1150.79±1093.04     3058.79±2874.75      3259.80±3082.23                  80.14\n",
+      "{'alpha': '1.00e-06', 'weight': 1, 'compute_method': 'exp'}                       834.89±851.46       2362.10±2483.20      2332.70±2519.15                  80.26\n",
+      "{'alpha': '1.00e-06', 'weight': 2, 'compute_method': 'exp'}                       843.51±1255.20      2739.20±5078.31      2513.94±4660.00                  78.98\n",
+      "{'alpha': '1.00e-06', 'weight': 3, 'compute_method': 'exp'}                       2406.55±4624.10     6376.65±11833.86     6810.17±13468.68                 79.54\n",
+      "{'alpha': '1.00e-06', 'weight': 4, 'compute_method': 'exp'}                       2631.24±7077.62     10249.66±33507.31    12908.69±46609.05                77.78\n",
+      "{'alpha': '1.00e-06', 'weight': 5, 'compute_method': 'exp'}                       2002.26±3935.38     5767.85±9822.92      5193.05±5064.37                  79.69\n",
+      "{'alpha': '1.00e-06', 'weight': 6, 'compute_method': 'exp'}                       1157.09±872.51      3945.48±3658.23      4266.16±4217.21                  78.62\n",
+      "{'alpha': '1.00e-06', 'weight': 7, 'compute_method': 'exp'}                       24810.73±127149.09  153317.00±801103.32  62395.27±303836.16               80.34\n",
+      "{'alpha': '1.00e-06', 'weight': 8, 'compute_method': 'exp'}                       640.91±455.87       2384.25±1702.38      2676.53±2424.19                  79.03\n",
+      "{'alpha': '1.00e-06', 'weight': 9, 'compute_method': 'exp'}                       946.81±842.65       3509.47±3817.56      3720.06±3869.12                  78.69\n",
+      "{'alpha': '3.16e-06', 'weight': 0.1, 'compute_method': 'geo'}                     9.37±0.48           15.87±1.48           17.71±5.58                       42.25\n",
+      "{'alpha': '3.16e-06', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     12.37±0.43          16.33±0.71           16.23±5.96                       41.38\n",
+      "{'alpha': '3.16e-06', 'weight': 0.01, 'compute_method': 'geo'}                    13.25±0.76          15.67±1.07           17.42±6.43                       41.26\n",
+      "{'alpha': '3.16e-06', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   14.54±0.93          16.46±1.10           17.64±7.96                       41.75\n",
+      "{'alpha': '3.16e-06', 'weight': 0.001, 'compute_method': 'geo'}                   16.03±0.83          17.54±1.05           19.79±8.03                       42.03\n",
+      "{'alpha': '3.16e-06', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  19.11±0.44          20.60±0.58           18.31±4.37                       42.38\n",
+      "{'alpha': '3.16e-06', 'weight': 0.0001, 'compute_method': 'geo'}                  20.15±0.46          21.50±0.69           20.37±4.48                       43.29\n",
+      "{'alpha': '3.16e-06', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  21.09±0.41          22.11±0.57           21.17±3.44                       41.07\n",
+      "{'alpha': '3.16e-06', 'weight': 1e-05, 'compute_method': 'geo'}                   24.03±0.36          24.86±0.46           25.14±3.04                       41.15\n",
+      "{'alpha': '3.16e-06', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   28.98±0.25          29.75±0.25           29.90±3.14                       40.51\n",
+      "{'alpha': '3.16e-06', 'weight': 1e-06, 'compute_method': 'geo'}                   33.09±0.35          33.77±0.44           34.27±4.14                       40.36\n",
+      "{'alpha': '3.16e-06', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   35.65±0.48          36.03±0.55           36.71±4.82                       41.54\n",
+      "{'alpha': '3.16e-06', 'weight': 1e-07, 'compute_method': 'geo'}                   37.26±0.44          37.58±0.56           37.30±4.12                       40.11\n",
+      "{'alpha': '3.16e-06', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   37.73±0.66          38.04±0.75           39.38±5.71                       41.64\n",
+      "{'alpha': '3.16e-06', 'weight': 1e-08, 'compute_method': 'geo'}                   38.09±0.51          38.39±0.59           38.43±4.28                       42.45\n",
+      "{'alpha': '3.16e-06', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  38.16±0.58          38.37±0.67           38.46±5.11                       42.06\n",
+      "{'alpha': '3.16e-06', 'weight': 1e-09, 'compute_method': 'geo'}                   38.07±0.77          38.27±0.81           39.13±6.40                       40.9\n",
+      "{'alpha': '3.16e-06', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.20±0.54          38.52±0.58           38.61±4.52                       40.96\n",
+      "{'alpha': '3.16e-06', 'weight': 1e-10, 'compute_method': 'geo'}                   38.33±0.65          38.60±0.68           37.36±5.41                       41.75\n",
+      "{'alpha': '3.16e-06', 'weight': 0, 'compute_method': 'exp'}                       1150.23±1085.31     3057.63±2860.89      3260.70±3076.36                  80.14\n",
+      "{'alpha': '3.16e-06', 'weight': 1, 'compute_method': 'exp'}                       830.74±840.22       2350.49±2456.43      2320.61±2488.98                  80.26\n",
+      "{'alpha': '3.16e-06', 'weight': 2, 'compute_method': 'exp'}                       869.66±1391.45      2860.06±5733.29      2625.95±5270.46                  78.98\n",
+      "{'alpha': '3.16e-06', 'weight': 3, 'compute_method': 'exp'}                       4914.37±17088.45    13397.72±47231.11    14477.14±51962.19                79.54\n",
+      "{'alpha': '3.16e-06', 'weight': 4, 'compute_method': 'exp'}                       2389.17±4948.82     8664.66±21401.44     10601.69±29523.42                77.78\n",
+      "{'alpha': '3.16e-06', 'weight': 5, 'compute_method': 'exp'}                       1928.79±3345.20     5580.88±8349.03      5272.60±5203.95                  79.69\n",
+      "{'alpha': '3.16e-06', 'weight': 6, 'compute_method': 'exp'}                       1123.88±786.30      3767.52±3215.82      4044.74±3590.03                  78.62\n",
+      "{'alpha': '3.16e-06', 'weight': 7, 'compute_method': 'exp'}                       2723.56±8680.05     13186.73±45993.72    11927.52±40160.06                80.34\n",
+      "{'alpha': '3.16e-06', 'weight': 8, 'compute_method': 'exp'}                       700.39±799.37       2803.46±4143.17      2844.50±3139.21                  79.03\n",
+      "{'alpha': '3.16e-06', 'weight': 9, 'compute_method': 'exp'}                       930.22±863.08       3563.81±4194.26      3633.75±3799.40                  78.69\n",
+      "{'alpha': '1.00e-05', 'weight': 0.1, 'compute_method': 'geo'}                     10.02±0.37          15.70±1.18           17.73±5.92                       42.25\n",
+      "{'alpha': '1.00e-05', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     12.93±0.49          16.26±0.72           16.29±5.67                       41.38\n",
+      "{'alpha': '1.00e-05', 'weight': 0.01, 'compute_method': 'geo'}                    13.74±0.75          15.94±1.01           17.69±6.25                       41.26\n",
+      "{'alpha': '1.00e-05', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   15.08±0.89          16.80±1.04           17.81±7.91                       41.75\n",
+      "{'alpha': '1.00e-05', 'weight': 0.001, 'compute_method': 'geo'}                   17.48±0.84          18.88±1.01           21.30±7.89                       42.03\n",
+      "{'alpha': '1.00e-05', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  19.97±0.47          21.34±0.60           19.23±4.15                       42.38\n",
+      "{'alpha': '1.00e-05', 'weight': 0.0001, 'compute_method': 'geo'}                  21.05±0.41          22.14±0.59           21.42±3.71                       43.29\n",
+      "{'alpha': '1.00e-05', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  24.07±0.32          24.79±0.40           24.37±3.06                       41.07\n",
+      "{'alpha': '1.00e-05', 'weight': 1e-05, 'compute_method': 'geo'}                   29.03±0.29          29.84±0.33           30.25±3.54                       41.15\n",
+      "{'alpha': '1.00e-05', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   33.06±0.35          33.77±0.32           33.42±4.24                       40.51\n",
+      "{'alpha': '1.00e-05', 'weight': 1e-06, 'compute_method': 'geo'}                   35.67±0.46          36.23±0.53           36.93±5.01                       40.36\n",
+      "{'alpha': '1.00e-05', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   37.15±0.56          37.42±0.63           38.02±5.16                       41.54\n",
+      "{'alpha': '1.00e-05', 'weight': 1e-07, 'compute_method': 'geo'}                   37.91±0.49          38.18±0.60           37.85±4.36                       40.11\n",
+      "{'alpha': '1.00e-05', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   37.95±0.69          38.24±0.78           39.61±5.78                       41.64\n",
+      "{'alpha': '1.00e-05', 'weight': 1e-08, 'compute_method': 'geo'}                   38.17±0.52          38.47±0.60           38.50±4.29                       42.45\n",
+      "{'alpha': '1.00e-05', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  38.18±0.58          38.39±0.67           38.49±5.11                       42.06\n",
+      "{'alpha': '1.00e-05', 'weight': 1e-09, 'compute_method': 'geo'}                   38.08±0.78          38.28±0.82           39.14±6.40                       40.9\n",
+      "{'alpha': '1.00e-05', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.20±0.54          38.52±0.58           38.61±4.52                       40.96\n",
+      "{'alpha': '1.00e-05', 'weight': 1e-10, 'compute_method': 'geo'}                   38.33±0.65          38.60±0.68           37.36±5.41                       41.75\n",
+      "{'alpha': '1.00e-05', 'weight': 0, 'compute_method': 'exp'}                       1149.76±1066.02     3057.30±2829.36      3267.11±3069.71                  80.14\n",
+      "{'alpha': '1.00e-05', 'weight': 1, 'compute_method': 'exp'}                       823.06±824.87       2328.73±2424.69      2297.86±2449.62                  80.26\n",
+      "{'alpha': '1.00e-05', 'weight': 2, 'compute_method': 'exp'}                       1053.78±2372.71     3691.73±10260.96     3393.95±9462.83                  78.98\n",
+      "{'alpha': '1.00e-05', 'weight': 3, 'compute_method': 'exp'}                       7505.15±29364.72    19984.12±78253.51    29561.60±130334.38               79.54\n",
+      "{'alpha': '1.00e-05', 'weight': 4, 'compute_method': 'exp'}                       1242.33±1310.76     4111.57±4512.74      4452.05±6473.14                  77.78\n",
+      "{'alpha': '1.00e-05', 'weight': 5, 'compute_method': 'exp'}                       1781.80±2555.19     5255.10±6412.56      5267.80±5495.86                  79.69\n",
+      "{'alpha': '1.00e-05', 'weight': 6, 'compute_method': 'exp'}                       1232.31±1159.16     3875.91±3719.50      4332.19±4930.28                  78.62\n",
+      "{'alpha': '1.00e-05', 'weight': 7, 'compute_method': 'exp'}                       1427.98±2656.34     6201.52±13439.26     5855.79±12205.54                 80.34\n",
+      "{'alpha': '1.00e-05', 'weight': 8, 'compute_method': 'exp'}                       589.86±388.30       2185.87±1331.67      2494.86±2738.85                  79.03\n",
+      "{'alpha': '1.00e-05', 'weight': 9, 'compute_method': 'exp'}                       1781.31±4117.62     5364.17±8780.34      7560.74±20819.05                 78.69\n",
+      "{'alpha': '3.16e-05', 'weight': 0.1, 'compute_method': 'geo'}                     10.89±0.39          15.85±1.18           18.12±6.43                       42.25\n",
+      "{'alpha': '3.16e-05', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     13.30±0.50          16.03±0.67           16.15±5.28                       41.38\n",
+      "{'alpha': '3.16e-05', 'weight': 0.01, 'compute_method': 'geo'}                    14.34±0.71          16.32±0.94           18.12±5.93                       41.26\n",
+      "{'alpha': '3.16e-05', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   16.19±0.82          17.67±0.98           18.56±7.55                       41.75\n",
+      "{'alpha': '3.16e-05', 'weight': 0.001, 'compute_method': 'geo'}                   18.86±0.89          20.12±1.04           22.39±6.98                       42.03\n",
+      "{'alpha': '3.16e-05', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  21.06±0.44          22.17±0.54           20.61±3.55                       42.38\n",
+      "{'alpha': '3.16e-05', 'weight': 0.0001, 'compute_method': 'geo'}                  24.06±0.32          24.84±0.42           24.51±3.19                       43.29\n",
+      "{'alpha': '3.16e-05', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  29.03±0.29          29.73±0.41           29.55±3.25                       41.07\n",
+      "{'alpha': '3.16e-05', 'weight': 1e-05, 'compute_method': 'geo'}                   33.09±0.40          33.81±0.40           34.30±4.16                       41.15\n",
+      "{'alpha': '3.16e-05', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   35.75±0.49          36.30±0.52           35.33±5.13                       40.51\n",
+      "{'alpha': '3.16e-05', 'weight': 1e-06, 'compute_method': 'geo'}                   37.14±0.58          37.61±0.65           38.37±5.61                       40.36\n",
+      "{'alpha': '3.16e-05', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   37.80±0.61          38.02±0.68           38.58±5.31                       41.54\n",
+      "{'alpha': '3.16e-05', 'weight': 1e-07, 'compute_method': 'geo'}                   38.15±0.51          38.39±0.62           38.05±4.44                       40.11\n",
+      "{'alpha': '3.16e-05', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   38.03±0.70          38.31±0.79           39.68±5.80                       41.64\n",
+      "{'alpha': '3.16e-05', 'weight': 1e-08, 'compute_method': 'geo'}                   38.19±0.52          38.49±0.60           38.53±4.29                       42.45\n",
+      "{'alpha': '3.16e-05', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  38.19±0.58          38.40±0.67           38.50±5.12                       42.06\n",
+      "{'alpha': '3.16e-05', 'weight': 1e-09, 'compute_method': 'geo'}                   38.08±0.78          38.28±0.82           39.14±6.41                       40.9\n",
+      "{'alpha': '3.16e-05', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.20±0.54          38.52±0.58           38.61±4.52                       40.96\n",
+      "{'alpha': '3.16e-05', 'weight': 1e-10, 'compute_method': 'geo'}                   38.33±0.65          38.60±0.68           37.36±5.41                       41.75\n",
+      "{'alpha': '3.16e-05', 'weight': 0, 'compute_method': 'exp'}                       1162.98±1059.96     3094.00±2864.78      3327.71±3177.48                  80.14\n",
+      "{'alpha': '3.16e-05', 'weight': 1, 'compute_method': 'exp'}                       831.70±877.60       2349.41±2582.80      2318.13±2595.66                  80.26\n",
+      "{'alpha': '3.16e-05', 'weight': 2, 'compute_method': 'exp'}                       877.07±1459.54      2919.07±6170.76      2685.13±5689.26                  78.98\n",
+      "{'alpha': '3.16e-05', 'weight': 3, 'compute_method': 'exp'}                       2963.80±6415.69     7894.13±16862.79     7789.92±16386.12                 79.54\n",
+      "{'alpha': '3.16e-05', 'weight': 4, 'compute_method': 'exp'}                       1055.58±901.96      3461.25±2750.00      3522.23±3679.76                  77.78\n",
+      "{'alpha': '3.16e-05', 'weight': 5, 'compute_method': 'exp'}                       1787.75±2853.50     5669.79±8327.27      5587.25±7506.01                  79.69\n",
+      "{'alpha': '3.16e-05', 'weight': 6, 'compute_method': 'exp'}                       1494.07±1934.49     5079.71±7839.30      4422.11±4507.75                  78.62\n",
+      "{'alpha': '3.16e-05', 'weight': 7, 'compute_method': 'exp'}                       986.56±950.03       3645.08±3556.81      3688.31±3941.40                  80.34\n",
+      "{'alpha': '3.16e-05', 'weight': 8, 'compute_method': 'exp'}                       819.43±1072.14      2891.11±3433.27      3686.78±6763.43                  79.03\n",
+      "{'alpha': '3.16e-05', 'weight': 9, 'compute_method': 'exp'}                       3859.38±11845.93    18237.51±64084.48    11732.08±35648.44                78.69\n",
+      "{'alpha': '1.00e-04', 'weight': 0.1, 'compute_method': 'geo'}                     11.73±0.48          16.03±1.16           18.43±6.72                       42.25\n",
+      "{'alpha': '1.00e-04', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     13.69±0.48          15.96±0.58           16.08±4.83                       41.38\n",
+      "{'alpha': '1.00e-04', 'weight': 0.01, 'compute_method': 'geo'}                    15.08±0.64          16.81±0.83           18.57±5.46                       41.26\n",
+      "{'alpha': '1.00e-04', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   17.87±0.75          19.12±0.92           19.77±6.52                       41.75\n",
+      "{'alpha': '1.00e-04', 'weight': 0.001, 'compute_method': 'geo'}                   20.42±0.78          21.45±0.90           22.94±4.65                       42.03\n",
+      "{'alpha': '1.00e-04', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  24.11±0.36          24.90±0.44           24.18±3.28                       42.38\n",
+      "{'alpha': '1.00e-04', 'weight': 0.0001, 'compute_method': 'geo'}                  29.05±0.31          29.78±0.35           29.32±3.46                       43.29\n",
+      "{'alpha': '1.00e-04', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  33.11±0.34          33.73±0.44           33.47±3.85                       41.07\n",
+      "{'alpha': '1.00e-04', 'weight': 1e-05, 'compute_method': 'geo'}                   35.69±0.48          36.24±0.50           36.75±4.80                       41.15\n",
+      "{'alpha': '1.00e-04', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   37.30±0.60          37.73±0.67           36.39±5.61                       40.51\n",
+      "{'alpha': '1.00e-04', 'weight': 1e-06, 'compute_method': 'geo'}                   37.77±0.65          38.20±0.71           38.99±5.86                       40.36\n",
+      "{'alpha': '1.00e-04', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   38.03±0.63          38.24±0.70           38.78±5.36                       41.54\n",
+      "{'alpha': '1.00e-04', 'weight': 1e-07, 'compute_method': 'geo'}                   38.22±0.52          38.46±0.62           38.11±4.47                       40.11\n",
+      "{'alpha': '1.00e-04', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   38.05±0.70          38.33±0.79           39.71±5.81                       41.64\n",
+      "{'alpha': '1.00e-04', 'weight': 1e-08, 'compute_method': 'geo'}                   38.20±0.52          38.50±0.60           38.53±4.30                       42.45\n",
+      "{'alpha': '1.00e-04', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  38.19±0.58          38.40±0.67           38.50±5.12                       42.06\n",
+      "{'alpha': '1.00e-04', 'weight': 1e-09, 'compute_method': 'geo'}                   38.08±0.78          38.28±0.82           39.14±6.41                       40.9\n",
+      "{'alpha': '1.00e-04', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.20±0.54          38.52±0.58           38.61±4.52                       40.96\n",
+      "{'alpha': '1.00e-04', 'weight': 1e-10, 'compute_method': 'geo'}                   38.33±0.65          38.60±0.68           37.36±5.41                       41.75\n",
+      "{'alpha': '1.00e-04', 'weight': 0, 'compute_method': 'exp'}                       8572.62±40746.15    23132.95±110278.40   23200.81±108322.09               80.14\n",
+      "{'alpha': '1.00e-04', 'weight': 1, 'compute_method': 'exp'}                       3472.81±12707.09    8573.20±31290.87     8316.09±28221.59                 80.26\n",
+      "{'alpha': '1.00e-04', 'weight': 2, 'compute_method': 'exp'}                       675.93±555.47       2003.04±1587.64      1839.16±1374.58                  78.98\n",
+      "{'alpha': '1.00e-04', 'weight': 3, 'compute_method': 'exp'}                       2654.55±6143.27     6014.68±12384.15     6156.38±12434.39                 79.54\n",
+      "{'alpha': '1.00e-04', 'weight': 4, 'compute_method': 'exp'}                       1427.88±2332.94     4351.55±6572.19      4935.44±9012.04                  77.78\n",
+      "{'alpha': '1.00e-04', 'weight': 5, 'compute_method': 'exp'}                       2190.98±3539.64     12861.22±36909.25    8937.00±18794.52                 79.69\n",
+      "{'alpha': '1.00e-04', 'weight': 6, 'compute_method': 'exp'}                       1384.84±1768.09     3645.47±2797.22      3956.85±3281.39                  78.62\n",
+      "{'alpha': '1.00e-04', 'weight': 7, 'compute_method': 'exp'}                       3129.11±7785.21     9617.48±22428.12     18991.35±73500.03                80.34\n",
+      "{'alpha': '1.00e-04', 'weight': 8, 'compute_method': 'exp'}                       782.80±984.36       2659.17±2512.14      3803.42±8414.70                  79.03\n",
+      "{'alpha': '1.00e-04', 'weight': 9, 'compute_method': 'exp'}                       1069.65±1268.99     4195.83±4650.56      3865.98±4264.75                  78.69\n",
+      "{'alpha': '3.16e-04', 'weight': 0.1, 'compute_method': 'geo'}                     12.71±0.59          16.31±1.11           18.57±6.55                       42.25\n",
+      "{'alpha': '3.16e-04', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     14.41±0.43          16.30±0.49           16.34±4.16                       41.38\n",
+      "{'alpha': '3.16e-04', 'weight': 0.01, 'compute_method': 'geo'}                    16.48±0.56          17.89±0.73           19.38±4.79                       41.26\n",
+      "{'alpha': '3.16e-04', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   20.03±0.61          21.01±0.75           20.91±4.27                       41.75\n",
+      "{'alpha': '3.16e-04', 'weight': 0.001, 'compute_method': 'geo'}                   23.86±0.41          24.67±0.43           24.99±3.05                       42.03\n",
+      "{'alpha': '3.16e-04', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  29.05±0.35          29.84±0.44           29.58±3.66                       42.38\n",
+      "{'alpha': '3.16e-04', 'weight': 0.0001, 'compute_method': 'geo'}                  33.16±0.37          33.79±0.36           32.97±4.01                       43.29\n",
+      "{'alpha': '3.16e-04', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  35.75±0.42          36.19±0.50           35.79±4.67                       41.07\n",
+      "{'alpha': '3.16e-04', 'weight': 1e-05, 'compute_method': 'geo'}                   37.17±0.57          37.59±0.61           38.11±5.24                       41.15\n",
+      "{'alpha': '3.16e-04', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   37.97±0.66          38.36±0.75           36.86±5.81                       40.51\n",
+      "{'alpha': '3.16e-04', 'weight': 1e-06, 'compute_method': 'geo'}                   37.99±0.68          38.41±0.74           39.21±5.95                       40.36\n",
+      "{'alpha': '3.16e-04', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   38.11±0.63          38.31±0.70           38.85±5.38                       41.54\n",
+      "{'alpha': '3.16e-04', 'weight': 1e-07, 'compute_method': 'geo'}                   38.25±0.52          38.48±0.63           38.13±4.48                       40.11\n",
+      "{'alpha': '3.16e-04', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   38.06±0.70          38.34±0.79           39.71±5.81                       41.64\n",
+      "{'alpha': '3.16e-04', 'weight': 1e-08, 'compute_method': 'geo'}                   38.20±0.52          38.50±0.60           38.54±4.30                       42.45\n",
+      "{'alpha': '3.16e-04', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  38.19±0.58          38.40±0.67           38.50±5.12                       42.06\n",
+      "{'alpha': '3.16e-04', 'weight': 1e-09, 'compute_method': 'geo'}                   38.08±0.78          38.28±0.82           39.14±6.41                       40.9\n",
+      "{'alpha': '3.16e-04', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.20±0.54          38.52±0.58           38.61±4.52                       40.96\n",
+      "{'alpha': '3.16e-04', 'weight': 1e-10, 'compute_method': 'geo'}                   38.33±0.65          38.60±0.68           37.36±5.41                       41.75\n",
+      "{'alpha': '3.16e-04', 'weight': 0, 'compute_method': 'exp'}                       1548.62±2477.51     4011.51±6408.28      4676.20±8162.77                  80.14\n",
+      "{'alpha': '3.16e-04', 'weight': 1, 'compute_method': 'exp'}                       2518.77±8002.84     6974.88±20545.14     7457.29±23472.89                 80.26\n",
+      "{'alpha': '3.16e-04', 'weight': 2, 'compute_method': 'exp'}                       1123.31±1948.71     2921.74±3995.57      2455.13±2750.74                  78.98\n",
+      "{'alpha': '3.16e-04', 'weight': 3, 'compute_method': 'exp'}                       2296.50±4769.47     5497.02±9572.81      6340.44±13672.99                 79.54\n",
+      "{'alpha': '3.16e-04', 'weight': 4, 'compute_method': 'exp'}                       8120.57±35235.08    19525.90±79121.56    25748.00±118314.25               77.78\n",
+      "{'alpha': '3.16e-04', 'weight': 5, 'compute_method': 'exp'}                       2316.73±6162.17     8211.51±21327.77     7087.18±16073.27                 79.69\n",
+      "{'alpha': '3.16e-04', 'weight': 6, 'compute_method': 'exp'}                       1121.80±838.60      3364.36±2370.65      4102.25±4924.27                  78.62\n",
+      "{'alpha': '3.16e-04', 'weight': 7, 'compute_method': 'exp'}                       989.41±1595.16      3782.18±7292.94      4108.41±8646.29                  80.34\n",
+      "{'alpha': '3.16e-04', 'weight': 8, 'compute_method': 'exp'}                       966.27±1111.75      3630.35±4089.68      4726.68±11553.06                 79.03\n",
+      "{'alpha': '3.16e-04', 'weight': 9, 'compute_method': 'exp'}                       993.09±1529.26      3620.73±5781.89      4759.28±11904.28                 78.69\n",
+      "{'alpha': '1.00e-03', 'weight': 0.1, 'compute_method': 'geo'}                     13.75±0.66          16.60±1.04           18.37±5.77                       42.25\n",
+      "{'alpha': '1.00e-03', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     15.71±0.36          17.19±0.40           16.97±3.02                       41.38\n",
+      "{'alpha': '1.00e-03', 'weight': 0.01, 'compute_method': 'geo'}                    19.05±0.47          20.09±0.65           21.12±3.83                       41.26\n",
+      "{'alpha': '1.00e-03', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   23.83±0.32          24.65±0.39           23.38±2.70                       41.75\n",
+      "{'alpha': '1.00e-03', 'weight': 0.001, 'compute_method': 'geo'}                   29.02±0.35          29.83±0.31           29.72±3.50                       42.03\n",
+      "{'alpha': '1.00e-03', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  33.10±0.40          33.83±0.46           33.70±4.14                       42.38\n",
+      "{'alpha': '1.00e-03', 'weight': 0.0001, 'compute_method': 'geo'}                  35.82±0.45          36.25±0.45           35.12±4.65                       43.29\n",
+      "{'alpha': '1.00e-03', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  37.26±0.52          37.58±0.59           37.08±5.20                       41.07\n",
+      "{'alpha': '1.00e-03', 'weight': 1e-05, 'compute_method': 'geo'}                   37.81±0.62          38.18±0.68           38.69±5.43                       41.15\n",
+      "{'alpha': '1.00e-03', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   38.22±0.68          38.58±0.77           37.02±5.87                       40.51\n",
+      "{'alpha': '1.00e-03', 'weight': 1e-06, 'compute_method': 'geo'}                   38.07±0.69          38.48±0.75           39.28±5.98                       40.36\n",
+      "{'alpha': '1.00e-03', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   38.13±0.63          38.33±0.71           38.87±5.39                       41.54\n",
+      "{'alpha': '1.00e-03', 'weight': 1e-07, 'compute_method': 'geo'}                   38.26±0.52          38.49±0.63           38.14±4.48                       40.11\n",
+      "{'alpha': '1.00e-03', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   38.06±0.70          38.34±0.79           39.72±5.81                       41.64\n",
+      "{'alpha': '1.00e-03', 'weight': 1e-08, 'compute_method': 'geo'}                   38.20±0.52          38.50±0.60           38.54±4.30                       42.45\n",
+      "{'alpha': '1.00e-03', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  38.19±0.58          38.40±0.67           38.50±5.12                       42.06\n",
+      "{'alpha': '1.00e-03', 'weight': 1e-09, 'compute_method': 'geo'}                   38.08±0.78          38.28±0.82           39.14±6.41                       40.9\n",
+      "{'alpha': '1.00e-03', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.20±0.54          38.52±0.58           38.61±4.52                       40.96\n",
+      "{'alpha': '1.00e-03', 'weight': 1e-10, 'compute_method': 'geo'}                   38.33±0.65          38.60±0.68           37.36±5.41                       41.75\n",
+      "{'alpha': '1.00e-03', 'weight': 0, 'compute_method': 'exp'}                       1202.12±1329.21     2981.06±3082.52      3197.81±3312.50                  80.14\n",
+      "{'alpha': '1.00e-03', 'weight': 1, 'compute_method': 'exp'}                       788.90±1037.30      2277.57±2595.68      2205.36±2765.29                  80.26\n",
+      "{'alpha': '1.00e-03', 'weight': 2, 'compute_method': 'exp'}                       1683.28±6176.35     3330.44±9227.65      4105.78±13954.53                 78.98\n",
+      "{'alpha': '1.00e-03', 'weight': 3, 'compute_method': 'exp'}                       1794.39±3683.68     4744.32±9786.62      5079.06±10237.46                 79.54\n",
+      "{'alpha': '1.00e-03', 'weight': 4, 'compute_method': 'exp'}                       1042.43±1663.94     2857.19±2497.75      3497.79±6413.50                  77.78\n",
+      "{'alpha': '1.00e-03', 'weight': 5, 'compute_method': 'exp'}                       1769.33±2368.98     5217.02±6426.05      5428.41±6370.06                  79.69\n",
+      "{'alpha': '1.00e-03', 'weight': 6, 'compute_method': 'exp'}                       2675.16±7269.13     10518.61±35602.69    15973.99±59677.59                78.62\n",
+      "{'alpha': '1.00e-03', 'weight': 7, 'compute_method': 'exp'}                       1108.32±1897.22     3036.79±3154.69      5108.35±13564.31                 80.34\n",
+      "{'alpha': '1.00e-03', 'weight': 8, 'compute_method': 'exp'}                       1057.96±1368.50     4445.86±6616.20      3677.90±4530.86                  79.03\n",
+      "{'alpha': '1.00e-03', 'weight': 9, 'compute_method': 'exp'}                       1758.02±3395.46     5667.55±10865.34     4406.49±6441.47                  78.69\n",
+      "{'alpha': '3.16e-03', 'weight': 0.1, 'compute_method': 'geo'}                     15.29±0.57          17.42±0.78           18.30±4.13                       42.25\n",
+      "{'alpha': '3.16e-03', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     18.37±0.23          19.39±0.31           18.67±2.45                       41.38\n",
+      "{'alpha': '3.16e-03', 'weight': 0.01, 'compute_method': 'geo'}                    23.42±0.35          24.20±0.47           24.99±3.31                       41.26\n",
+      "{'alpha': '3.16e-03', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   29.12±0.30          29.96±0.36           28.38±3.06                       41.75\n",
+      "{'alpha': '3.16e-03', 'weight': 0.001, 'compute_method': 'geo'}                   33.12±0.38          33.81±0.37           33.79±3.81                       42.03\n",
+      "{'alpha': '3.16e-03', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  35.72±0.46          36.27±0.49           36.14±4.55                       42.38\n",
+      "{'alpha': '3.16e-03', 'weight': 0.0001, 'compute_method': 'geo'}                  37.33±0.53          37.64±0.55           36.32±5.05                       43.29\n",
+      "{'alpha': '3.16e-03', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  37.92±0.57          38.17±0.65           37.64±5.42                       41.07\n",
+      "{'alpha': '3.16e-03', 'weight': 1e-05, 'compute_method': 'geo'}                   38.04±0.64          38.39±0.70           38.90±5.50                       41.15\n",
+      "{'alpha': '3.16e-03', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   38.29±0.69          38.65±0.78           37.08±5.90                       40.51\n",
+      "{'alpha': '3.16e-03', 'weight': 1e-06, 'compute_method': 'geo'}                   38.09±0.70          38.50±0.75           39.30±5.99                       40.36\n",
+      "{'alpha': '3.16e-03', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   38.14±0.63          38.34±0.71           38.88±5.39                       41.54\n",
+      "{'alpha': '3.16e-03', 'weight': 1e-07, 'compute_method': 'geo'}                   38.26±0.52          38.49±0.63           38.14±4.49                       40.11\n",
+      "{'alpha': '3.16e-03', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   38.06±0.70          38.34±0.79           39.71±5.81                       41.64\n",
+      "{'alpha': '3.16e-03', 'weight': 1e-08, 'compute_method': 'geo'}                   38.20±0.52          38.50±0.60           38.53±4.30                       42.45\n",
+      "{'alpha': '3.16e-03', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  38.19±0.58          38.40±0.67           38.50±5.12                       42.06\n",
+      "{'alpha': '3.16e-03', 'weight': 1e-09, 'compute_method': 'geo'}                   38.08±0.78          38.28±0.82           39.14±6.41                       40.9\n",
+      "{'alpha': '3.16e-03', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.20±0.54          38.52±0.58           38.61±4.52                       40.96\n",
+      "{'alpha': '3.16e-03', 'weight': 1e-10, 'compute_method': 'geo'}                   38.33±0.65          38.59±0.68           37.35±5.41                       41.75\n",
+      "{'alpha': '3.16e-03', 'weight': 0, 'compute_method': 'exp'}                       902.48±633.65       2179.84±1400.63      2352.18±1439.57                  80.14\n",
+      "{'alpha': '3.16e-03', 'weight': 1, 'compute_method': 'exp'}                       9577.03±46850.49    33834.58±171181.05   24277.59±118323.87               80.26\n",
+      "{'alpha': '3.16e-03', 'weight': 2, 'compute_method': 'exp'}                       821.45±779.72       2483.25±2373.39      2384.43±2122.88                  78.98\n",
+      "{'alpha': '3.16e-03', 'weight': 3, 'compute_method': 'exp'}                       2310.20±8999.39     7688.36±31224.54     5292.47±17302.38                 79.54\n",
+      "{'alpha': '3.16e-03', 'weight': 4, 'compute_method': 'exp'}                       2089.91±6497.61     5913.01±17472.42     4979.04±12400.27                 77.78\n",
+      "{'alpha': '3.16e-03', 'weight': 5, 'compute_method': 'exp'}                       1020.01±1310.02     3224.44±4896.96      4383.61±9568.31                  79.69\n",
+      "{'alpha': '3.16e-03', 'weight': 6, 'compute_method': 'exp'}                       1215.50±1538.42     3662.76±4207.14      3511.59±3300.12                  78.62\n",
+      "{'alpha': '3.16e-03', 'weight': 7, 'compute_method': 'exp'}                       807.70±954.75       2251.74±1572.52      3330.81±4967.66                  80.34\n",
+      "{'alpha': '3.16e-03', 'weight': 8, 'compute_method': 'exp'}                       1100.85±1385.84     3553.88±4293.35      3814.95±5254.55                  79.03\n",
+      "{'alpha': '3.16e-03', 'weight': 9, 'compute_method': 'exp'}                       1147.26±3252.77     5195.97±17450.57     4434.82±12386.40                 78.69\n",
+      "{'alpha': '1.00e-02', 'weight': 0.1, 'compute_method': 'geo'}                     18.52±0.35          19.85±0.43           19.54±2.62                       42.25\n",
+      "{'alpha': '1.00e-02', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     23.24±0.26          24.01±0.39           23.17±2.81                       41.38\n",
+      "{'alpha': '1.00e-02', 'weight': 0.01, 'compute_method': 'geo'}                    28.93±0.37          29.70±0.44           30.66±3.66                       41.26\n",
+      "{'alpha': '1.00e-02', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   33.26±0.34          33.98±0.37           32.76±3.78                       41.75\n",
+      "{'alpha': '1.00e-02', 'weight': 0.001, 'compute_method': 'geo'}                   35.72±0.43          36.21±0.45           36.33±4.43                       42.03\n",
+      "{'alpha': '1.00e-02', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  37.21±0.52          37.64±0.54           37.45±4.80                       42.38\n",
+      "{'alpha': '1.00e-02', 'weight': 0.0001, 'compute_method': 'geo'}                  37.99±0.57          38.24±0.61           36.84±5.22                       43.29\n",
+      "{'alpha': '1.00e-02', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  38.15±0.60          38.38±0.67           37.83±5.49                       41.07\n",
+      "{'alpha': '1.00e-02', 'weight': 1e-05, 'compute_method': 'geo'}                   38.11±0.65          38.45±0.71           38.97±5.53                       41.15\n",
+      "{'alpha': '1.00e-02', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   38.32±0.69          38.67±0.78           37.09±5.91                       40.51\n",
+      "{'alpha': '1.00e-02', 'weight': 1e-06, 'compute_method': 'geo'}                   38.09±0.70          38.50±0.75           39.29±6.01                       40.36\n",
+      "{'alpha': '1.00e-02', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   38.14±0.63          38.33±0.71           38.87±5.40                       41.54\n",
+      "{'alpha': '1.00e-02', 'weight': 1e-07, 'compute_method': 'geo'}                   38.25±0.52          38.48±0.63           38.13±4.50                       40.11\n",
+      "{'alpha': '1.00e-02', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   38.06±0.70          38.33±0.79           39.71±5.82                       41.64\n",
+      "{'alpha': '1.00e-02', 'weight': 1e-08, 'compute_method': 'geo'}                   38.20±0.52          38.49±0.60           38.53±4.31                       42.45\n",
+      "{'alpha': '1.00e-02', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  38.19±0.58          38.39±0.67           38.49±5.12                       42.06\n",
+      "{'alpha': '1.00e-02', 'weight': 1e-09, 'compute_method': 'geo'}                   38.08±0.78          38.27±0.81           39.13±6.42                       40.9\n",
+      "{'alpha': '1.00e-02', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.20±0.54          38.51±0.58           38.60±4.53                       40.96\n",
+      "{'alpha': '1.00e-02', 'weight': 1e-10, 'compute_method': 'geo'}                   38.33±0.65          38.59±0.68           37.34±5.42                       41.75\n",
+      "{'alpha': '1.00e-02', 'weight': 0, 'compute_method': 'exp'}                       1376.08±1564.01     3708.77±4644.97      3888.86±4520.44                  80.14\n",
+      "{'alpha': '1.00e-02', 'weight': 1, 'compute_method': 'exp'}                       1540.61±3189.82     3973.98±7766.44      4039.76±8337.80                  80.26\n",
+      "{'alpha': '1.00e-02', 'weight': 2, 'compute_method': 'exp'}                       544.72±370.42       1496.42±1307.91      1602.27±1423.89                  78.98\n",
+      "{'alpha': '1.00e-02', 'weight': 3, 'compute_method': 'exp'}                       1086.28±1042.98     2898.47±2970.49      3925.47±7565.77                  79.54\n",
+      "{'alpha': '1.00e-02', 'weight': 4, 'compute_method': 'exp'}                       1536.80±3635.95     4437.89±10907.26     4471.42±8916.82                  77.78\n",
+      "{'alpha': '1.00e-02', 'weight': 5, 'compute_method': 'exp'}                       1295.70±1372.56     3624.09±3596.74      3150.67±2785.22                  79.69\n",
+      "{'alpha': '1.00e-02', 'weight': 6, 'compute_method': 'exp'}                       1814.96±1967.76     7048.57±14207.87     6705.67±11365.37                 78.62\n",
+      "{'alpha': '1.00e-02', 'weight': 7, 'compute_method': 'exp'}                       639.23±854.32       2014.55±2021.20      2248.09±3445.71                  80.34\n",
+      "{'alpha': '1.00e-02', 'weight': 8, 'compute_method': 'exp'}                       696.70±732.72       2088.07±2899.70      1775.68±1149.85                  79.03\n",
+      "{'alpha': '1.00e-02', 'weight': 9, 'compute_method': 'exp'}                       838.08±1412.91      2136.58±3070.39      2299.41±2696.38                  78.69\n",
+      "{'alpha': '3.16e-02', 'weight': 0.1, 'compute_method': 'geo'}                     23.94±0.28          24.91±0.33           23.84±2.98                       42.25\n",
+      "{'alpha': '3.16e-02', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     29.11±0.30          29.89±0.41           29.34±2.90                       41.38\n",
+      "{'alpha': '3.16e-02', 'weight': 0.01, 'compute_method': 'geo'}                    33.05±0.42          33.71±0.47           34.97±4.20                       41.26\n",
+      "{'alpha': '3.16e-02', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   35.80±0.43          36.32±0.44           35.57±4.59                       41.75\n",
+      "{'alpha': '3.16e-02', 'weight': 0.001, 'compute_method': 'geo'}                   37.17±0.52          37.53±0.53           37.73±4.92                       42.03\n",
+      "{'alpha': '3.16e-02', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  37.85±0.56          38.22±0.57           37.98±4.92                       42.38\n",
+      "{'alpha': '3.16e-02', 'weight': 0.0001, 'compute_method': 'geo'}                  38.21±0.58          38.44±0.63           37.00±5.27                       43.29\n",
+      "{'alpha': '3.16e-02', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  38.22±0.60          38.43±0.67           37.87±5.51                       41.07\n",
+      "{'alpha': '3.16e-02', 'weight': 1e-05, 'compute_method': 'geo'}                   38.13±0.65          38.45±0.71           38.97±5.55                       41.15\n",
+      "{'alpha': '3.16e-02', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   38.32±0.69          38.66±0.78           37.07±5.94                       40.51\n",
+      "{'alpha': '3.16e-02', 'weight': 1e-06, 'compute_method': 'geo'}                   38.09±0.70          38.48±0.75           39.27±6.03                       40.36\n",
+      "{'alpha': '3.16e-02', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   38.13±0.63          38.31±0.70           38.86±5.43                       41.54\n",
+      "{'alpha': '3.16e-02', 'weight': 1e-07, 'compute_method': 'geo'}                   38.25±0.52          38.46±0.63           38.10±4.53                       40.11\n",
+      "{'alpha': '3.16e-02', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   38.05±0.70          38.31±0.79           39.68±5.83                       41.64\n",
+      "{'alpha': '3.16e-02', 'weight': 1e-08, 'compute_method': 'geo'}                   38.19±0.52          38.47±0.60           38.52±4.34                       42.45\n",
+      "{'alpha': '3.16e-02', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  38.18±0.58          38.37±0.68           38.48±5.14                       42.06\n",
+      "{'alpha': '3.16e-02', 'weight': 1e-09, 'compute_method': 'geo'}                   38.07±0.78          38.25±0.81           39.12±6.45                       40.9\n",
+      "{'alpha': '3.16e-02', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.19±0.53          38.49±0.57           38.58±4.56                       40.96\n",
+      "{'alpha': '3.16e-02', 'weight': 1e-10, 'compute_method': 'geo'}                   38.32±0.65          38.57±0.69           37.31±5.45                       41.75\n",
+      "{'alpha': '3.16e-02', 'weight': 0, 'compute_method': 'exp'}                       1077.72±1470.62     2756.52±3214.16      2652.65±3095.05                  80.14\n",
+      "{'alpha': '3.16e-02', 'weight': 1, 'compute_method': 'exp'}                       954.35±1292.64      2412.19±2933.62      2386.90±2467.51                  80.26\n",
+      "{'alpha': '3.16e-02', 'weight': 2, 'compute_method': 'exp'}                       1250.34±2309.68     3107.26±5177.48      2737.28±3477.12                  78.98\n",
+      "{'alpha': '3.16e-02', 'weight': 3, 'compute_method': 'exp'}                       766.16±755.10       1901.84±1976.87      1933.19±3053.70                  79.54\n",
+      "{'alpha': '3.16e-02', 'weight': 4, 'compute_method': 'exp'}                       1036.31±908.59      2842.13±2880.99      2836.57±2964.31                  77.78\n",
+      "{'alpha': '3.16e-02', 'weight': 5, 'compute_method': 'exp'}                       770.55±954.69       2098.04±3261.95      2300.00±3725.55                  79.69\n",
+      "{'alpha': '3.16e-02', 'weight': 6, 'compute_method': 'exp'}                       1289.81±2881.72     2651.24±4566.63      3216.33±5623.07                  78.62\n",
+      "{'alpha': '3.16e-02', 'weight': 7, 'compute_method': 'exp'}                       927.34±1621.26      3480.46±10919.36     3118.38±7697.78                  80.34\n",
+      "{'alpha': '3.16e-02', 'weight': 8, 'compute_method': 'exp'}                       356.54±221.01       1055.50±1080.76      962.82±779.24                    79.03\n",
+      "{'alpha': '3.16e-02', 'weight': 9, 'compute_method': 'exp'}                       780.67±1834.60      3189.23±9925.11      2481.58±7681.88                  78.69\n",
+      "{'alpha': '1.00e-01', 'weight': 0.1, 'compute_method': 'geo'}                     29.74±0.32          30.77±0.37           29.73±3.24                       42.25\n",
+      "{'alpha': '1.00e-01', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     33.34±0.34          33.99±0.40           33.71±3.66                       41.38\n",
+      "{'alpha': '1.00e-01', 'weight': 0.01, 'compute_method': 'geo'}                    35.51±0.49          35.98±0.49           37.40±4.81                       41.26\n",
+      "{'alpha': '1.00e-01', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   37.18±0.53          37.54±0.55           37.09±5.13                       41.75\n",
+      "{'alpha': '1.00e-01', 'weight': 0.001, 'compute_method': 'geo'}                   37.78±0.58          38.06±0.58           38.29±5.25                       42.03\n",
+      "{'alpha': '1.00e-01', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  38.07±0.57          38.39±0.59           38.09±5.00                       42.38\n",
+      "{'alpha': '1.00e-01', 'weight': 0.0001, 'compute_method': 'geo'}                  38.28±0.59          38.47±0.64           37.00±5.28                       43.29\n",
+      "{'alpha': '1.00e-01', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  38.23±0.61          38.41±0.68           37.82±5.51                       41.07\n",
+      "{'alpha': '1.00e-01', 'weight': 1e-05, 'compute_method': 'geo'}                   38.12±0.66          38.41±0.72           38.92±5.61                       41.15\n",
+      "{'alpha': '1.00e-01', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   38.31±0.69          38.61±0.78           37.03±6.00                       40.51\n",
+      "{'alpha': '1.00e-01', 'weight': 1e-06, 'compute_method': 'geo'}                   38.07±0.70          38.44±0.75           39.19±6.11                       40.36\n",
+      "{'alpha': '1.00e-01', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   38.12±0.63          38.26±0.70           38.82±5.51                       41.54\n",
+      "{'alpha': '1.00e-01', 'weight': 1e-07, 'compute_method': 'geo'}                   38.23±0.52          38.42±0.63           38.02±4.63                       40.11\n",
+      "{'alpha': '1.00e-01', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   38.03±0.70          38.27±0.78           39.61±5.88                       41.64\n",
+      "{'alpha': '1.00e-01', 'weight': 1e-08, 'compute_method': 'geo'}                   38.18±0.52          38.42±0.61           38.51±4.43                       42.45\n",
+      "{'alpha': '1.00e-01', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  38.17±0.58          38.33±0.68           38.45±5.17                       42.06\n",
+      "{'alpha': '1.00e-01', 'weight': 1e-09, 'compute_method': 'geo'}                   38.06±0.77          38.21±0.81           39.08±6.52                       40.9\n",
+      "{'alpha': '1.00e-01', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.17±0.54          38.45±0.57           38.52±4.64                       40.96\n",
+      "{'alpha': '1.00e-01', 'weight': 1e-10, 'compute_method': 'geo'}                   38.30±0.65          38.52±0.69           37.23±5.52                       41.75\n",
+      "{'alpha': '1.00e-01', 'weight': 0, 'compute_method': 'exp'}                       2409.79±8308.25     5173.83±16911.27     4465.08±12821.03                 80.14\n",
+      "{'alpha': '1.00e-01', 'weight': 1, 'compute_method': 'exp'}                       1818.01±4370.98     5152.96±16334.35     3936.04±9241.56                  80.26\n",
+      "{'alpha': '1.00e-01', 'weight': 2, 'compute_method': 'exp'}                       1092.86±1582.27     2723.78±5513.73      2222.98±3621.96                  78.98\n",
+      "{'alpha': '1.00e-01', 'weight': 3, 'compute_method': 'exp'}                       763.37±788.55       1537.64±1641.10      1404.78±1226.02                  79.54\n",
+      "{'alpha': '1.00e-01', 'weight': 4, 'compute_method': 'exp'}                       596.69±425.53       1155.23±815.70       1157.43±834.29                   77.78\n",
+      "{'alpha': '1.00e-01', 'weight': 5, 'compute_method': 'exp'}                       775.72±971.06       1476.80±2002.12      1789.40±2956.31                  79.69\n",
+      "{'alpha': '1.00e-01', 'weight': 6, 'compute_method': 'exp'}                       1481.61±4773.99     2443.34±7152.80      2685.89±8050.65                  78.62\n",
+      "{'alpha': '1.00e-01', 'weight': 7, 'compute_method': 'exp'}                       2073.72±7070.93     2950.36±9190.83      4671.59±15413.67                 80.34\n",
+      "{'alpha': '1.00e-01', 'weight': 8, 'compute_method': 'exp'}                       1238.41±2739.66     2211.63±5002.76      1887.11±3600.47                  79.03\n",
+      "{'alpha': '1.00e-01', 'weight': 9, 'compute_method': 'exp'}                       1298.19±3221.05     2029.06±4595.61      2123.04±4662.13                  78.69\n",
+      "{'alpha': '3.16e-01', 'weight': 0.1, 'compute_method': 'geo'}                     33.70±0.36          34.55±0.44           33.80±3.66                       42.25\n",
+      "{'alpha': '3.16e-01', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     35.65±0.43          36.10±0.45           36.00±4.49                       41.38\n",
+      "{'alpha': '3.16e-01', 'weight': 0.01, 'compute_method': 'geo'}                    36.87±0.55          37.17±0.54           38.66±5.29                       41.26\n",
+      "{'alpha': '3.16e-01', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   37.81±0.59          38.06±0.62           37.71±5.51                       41.75\n",
+      "{'alpha': '3.16e-01', 'weight': 0.001, 'compute_method': 'geo'}                   38.05±0.61          38.23±0.61           38.46±5.65                       42.03\n",
+      "{'alpha': '3.16e-01', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  38.20±0.58          38.44±0.61           38.00±5.11                       42.38\n",
+      "{'alpha': '3.16e-01', 'weight': 0.0001, 'compute_method': 'geo'}                  38.34±0.59          38.46±0.65           36.95±5.27                       43.29\n",
+      "{'alpha': '3.16e-01', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  38.28±0.61          38.38±0.68           37.72±5.49                       41.07\n",
+      "{'alpha': '3.16e-01', 'weight': 1e-05, 'compute_method': 'geo'}                   38.17±0.66          38.36±0.73           38.86±5.73                       41.15\n",
+      "{'alpha': '3.16e-01', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   38.36±0.70          38.58±0.79           36.99±6.17                       40.51\n",
+      "{'alpha': '3.16e-01', 'weight': 1e-06, 'compute_method': 'geo'}                   38.12±0.71          38.43±0.75           39.03±6.30                       40.36\n",
+      "{'alpha': '3.16e-01', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   38.17±0.64          38.24±0.70           38.84±5.71                       41.54\n",
+      "{'alpha': '3.16e-01', 'weight': 1e-07, 'compute_method': 'geo'}                   38.28±0.53          38.40±0.64           37.90±4.89                       40.11\n",
+      "{'alpha': '3.16e-01', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   38.08±0.71          38.25±0.77           39.53±5.97                       41.64\n",
+      "{'alpha': '3.16e-01', 'weight': 1e-08, 'compute_method': 'geo'}                   38.22±0.53          38.39±0.63           38.56±4.66                       42.45\n",
+      "{'alpha': '3.16e-01', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  38.78±3.01          38.79±2.57           39.06±6.86                       42.06\n",
+      "{'alpha': '3.16e-01', 'weight': 1e-09, 'compute_method': 'geo'}                   38.10±0.78          38.18±0.80           39.09±6.72                       40.9\n",
+      "{'alpha': '3.16e-01', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  38.21±0.54          38.43±0.58           38.45±4.84                       40.96\n",
+      "{'alpha': '3.16e-01', 'weight': 1e-10, 'compute_method': 'geo'}                   38.35±0.66          38.49±0.72           37.09±5.68                       41.75\n",
+      "{'alpha': '3.16e-01', 'weight': 0, 'compute_method': 'exp'}                       6702.14±16650.87    7683.12±18783.42     9148.98±21905.65                 80.14\n",
+      "{'alpha': '3.16e-01', 'weight': 1, 'compute_method': 'exp'}                       1378.86±2133.49     1866.89±2929.29      1788.56±2699.62                  80.26\n",
+      "{'alpha': '3.16e-01', 'weight': 2, 'compute_method': 'exp'}                       877.42±755.29       1064.09±876.15       1070.47±848.14                   78.98\n",
+      "{'alpha': '3.16e-01', 'weight': 3, 'compute_method': 'exp'}                       1080.38±1566.99     1374.13±2048.67      1226.61±1424.47                  79.54\n",
+      "{'alpha': '3.16e-01', 'weight': 4, 'compute_method': 'exp'}                       772.04±679.68       905.33±881.85        850.06±829.17                    77.78\n",
+      "{'alpha': '3.16e-01', 'weight': 5, 'compute_method': 'exp'}                       512.45±324.10       547.42±318.82        566.16±375.65                    79.69\n",
+      "{'alpha': '3.16e-01', 'weight': 6, 'compute_method': 'exp'}                       1574.38±4502.31     1818.47±5789.95      1445.28±4035.76                  78.62\n",
+      "{'alpha': '3.16e-01', 'weight': 7, 'compute_method': 'exp'}                       624.89±840.94       588.86±702.01        625.62±816.14                    80.34\n",
+      "{'alpha': '3.16e-01', 'weight': 8, 'compute_method': 'exp'}                       434.56±429.59       422.06±413.44        437.52±476.68                    79.03\n",
+      "{'alpha': '3.16e-01', 'weight': 9, 'compute_method': 'exp'}                       800.85±1088.57      802.82±1180.79       882.65±1600.09                   78.69\n",
+      "{'alpha': '1.00e+00', 'weight': 0.1, 'compute_method': 'geo'}                     35.86±0.50          36.43±0.59           35.93±4.18                       42.25\n",
+      "{'alpha': '1.00e+00', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     52.96±58.11         53.43±60.35          51.00±50.09                      41.38\n",
+      "{'alpha': '1.00e+00', 'weight': 0.01, 'compute_method': 'geo'}                    45.87±22.98         45.40±20.25          47.95±19.40                      41.26\n",
+      "{'alpha': '1.00e+00', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   43.30±9.78          43.35±9.58           42.95±12.16                      41.75\n",
+      "{'alpha': '1.00e+00', 'weight': 0.001, 'compute_method': 'geo'}                   80.68±113.01        80.56±110.41         79.53±110.70                     42.03\n",
+      "{'alpha': '1.00e+00', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  40.95±2.86          40.85±2.59           39.70±6.48                       42.38\n",
+      "{'alpha': '1.00e+00', 'weight': 0.0001, 'compute_method': 'geo'}                  54.37±38.88         53.71±37.49          52.36±35.62                      43.29\n",
+      "{'alpha': '1.00e+00', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  43.97±14.90         44.59±19.18          43.88±18.69                      41.07\n",
+      "{'alpha': '1.00e+00', 'weight': 1e-05, 'compute_method': 'geo'}                   43.87±10.49         43.78±10.62          44.18±13.73                      41.15\n",
+      "{'alpha': '1.00e+00', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   59.30±73.10         59.33±72.61          58.18±77.95                      40.51\n",
+      "{'alpha': '1.00e+00', 'weight': 1e-06, 'compute_method': 'geo'}                   251.78±892.54       226.87±804.45        228.14±794.39                    40.36\n",
+      "{'alpha': '1.00e+00', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   45.95±25.78         45.75±25.61          45.93±24.46                      41.54\n",
+      "{'alpha': '1.00e+00', 'weight': 1e-07, 'compute_method': 'geo'}                   55.54±51.14         54.02±45.30          56.71±57.55                      40.11\n",
+      "{'alpha': '1.00e+00', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   54.15±34.13         53.97±33.25          53.94±34.25                      41.64\n",
+      "{'alpha': '1.00e+00', 'weight': 1e-08, 'compute_method': 'geo'}                   42.55±8.40          42.18±7.59           43.36±10.50                      42.45\n",
+      "{'alpha': '1.00e+00', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  45.77±29.36         46.24±32.83          45.58±25.19                      42.06\n",
+      "{'alpha': '1.00e+00', 'weight': 1e-09, 'compute_method': 'geo'}                   45.25±17.75         45.39±18.63          46.08±19.94                      40.9\n",
+      "{'alpha': '1.00e+00', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  42.72±11.63         43.09±13.07          42.63±10.96                      40.96\n",
+      "{'alpha': '1.00e+00', 'weight': 1e-10, 'compute_method': 'geo'}                   46.90±30.48         45.81±26.21          44.77±26.52                      41.75\n",
+      "{'alpha': '1.00e+00', 'weight': 0, 'compute_method': 'exp'}                       995.87±1341.17      869.14±1247.83       823.77±1237.21                   80.14\n",
+      "{'alpha': '1.00e+00', 'weight': 1, 'compute_method': 'exp'}                       663.07±672.29       445.18±448.64        434.25±429.34                    80.26\n",
+      "{'alpha': '1.00e+00', 'weight': 2, 'compute_method': 'exp'}                       84.29±23.14         64.35±13.24          65.68±14.52                      78.98\n",
+      "{'alpha': '1.00e+00', 'weight': 3, 'compute_method': 'exp'}                       51.45±5.30          45.97±3.48           48.72±7.64                       79.54\n",
+      "{'alpha': '1.00e+00', 'weight': 4, 'compute_method': 'exp'}                       44.59±2.92          43.11±2.35           42.10±6.65                       77.78\n",
+      "{'alpha': '1.00e+00', 'weight': 5, 'compute_method': 'exp'}                       41.22±1.82          41.76±1.61           38.82±5.37                       79.69\n",
+      "{'alpha': '1.00e+00', 'weight': 6, 'compute_method': 'exp'}                       38.74±1.28          40.44±1.60           41.33±5.52                       78.62\n",
+      "{'alpha': '1.00e+00', 'weight': 7, 'compute_method': 'exp'}                       37.37±1.34          40.22±1.26           39.46±6.20                       80.34\n",
+      "{'alpha': '1.00e+00', 'weight': 8, 'compute_method': 'exp'}                       37.12±1.02          40.49±1.24           37.94±5.95                       79.03\n",
+      "{'alpha': '1.00e+00', 'weight': 9, 'compute_method': 'exp'}                       36.60±1.00          40.59±1.29           41.52±5.55                       78.69\n",
+      "{'alpha': '3.16e+00', 'weight': 0.1, 'compute_method': 'geo'}                     38.08±0.51          38.32±0.55           38.11±4.80                       42.25\n",
+      "{'alpha': '3.16e+00', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     39.36±0.61          39.39±0.60           38.85±5.82                       41.38\n",
+      "{'alpha': '3.16e+00', 'weight': 0.01, 'compute_method': 'geo'}                    39.96±0.63          39.98±0.64           41.62±6.13                       41.26\n",
+      "{'alpha': '3.16e+00', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   40.44±0.69          40.38±0.76           40.03±6.09                       41.75\n",
+      "{'alpha': '3.16e+00', 'weight': 0.001, 'compute_method': 'geo'}                   40.56±0.65          40.48±0.67           40.54±7.09                       42.03\n",
+      "{'alpha': '3.16e+00', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  40.61±0.59          40.58±0.68           39.50±5.41                       42.38\n",
+      "{'alpha': '3.16e+00', 'weight': 0.0001, 'compute_method': 'geo'}                  40.70±0.59          40.57±0.64           39.08±5.37                       43.29\n",
+      "{'alpha': '3.16e+00', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  40.65±0.61          40.47±0.68           39.38±5.42                       41.07\n",
+      "{'alpha': '3.16e+00', 'weight': 1e-05, 'compute_method': 'geo'}                   40.54±0.69          40.43±0.74           40.46±6.26                       41.15\n",
+      "{'alpha': '3.16e+00', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   40.69±0.74          40.59±0.86           39.16±6.86                       40.51\n",
+      "{'alpha': '3.16e+00', 'weight': 1e-06, 'compute_method': 'geo'}                   40.52±0.72          40.58±0.76           39.84±6.91                       40.36\n",
+      "{'alpha': '3.16e+00', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   40.51±0.72          40.41±0.75           41.44±6.71                       41.54\n",
+      "{'alpha': '3.16e+00', 'weight': 1e-07, 'compute_method': 'geo'}                   40.64±0.59          40.51±0.75           39.47±6.35                       40.11\n",
+      "{'alpha': '3.16e+00', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   40.48±0.73          40.44±0.77           41.03±6.29                       41.64\n",
+      "{'alpha': '3.16e+00', 'weight': 1e-08, 'compute_method': 'geo'}                   40.55±0.59          40.45±0.73           40.84±5.92                       42.45\n",
+      "{'alpha': '3.16e+00', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  40.57±0.58          40.40±0.72           40.38±4.85                       42.06\n",
+      "{'alpha': '3.16e+00', 'weight': 1e-09, 'compute_method': 'geo'}                   40.43±0.80          40.27±0.81           41.70±7.09                       40.9\n",
+      "{'alpha': '3.16e+00', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  40.57±0.54          40.53±0.63           40.60±5.34                       40.96\n",
+      "{'alpha': '3.16e+00', 'weight': 1e-10, 'compute_method': 'geo'}                   40.73±0.64          40.64±0.72           38.55±6.25                       41.75\n",
+      "{'alpha': '3.16e+00', 'weight': 0, 'compute_method': 'exp'}                       46.56±1.33          44.30±1.20           45.75±6.19                       80.14\n",
+      "{'alpha': '3.16e+00', 'weight': 1, 'compute_method': 'exp'}                       41.27±5.87          41.42±6.06           42.04±9.21                       80.26\n",
+      "{'alpha': '3.16e+00', 'weight': 2, 'compute_method': 'exp'}                       39.72±0.60          40.80±0.64           40.87±5.00                       78.98\n",
+      "{'alpha': '3.16e+00', 'weight': 3, 'compute_method': 'exp'}                       39.48±0.72          41.11±0.85           41.52±5.93                       79.54\n",
+      "{'alpha': '3.16e+00', 'weight': 4, 'compute_method': 'exp'}                       39.74±0.64          41.80±0.75           43.45±6.58                       77.78\n",
+      "{'alpha': '3.16e+00', 'weight': 5, 'compute_method': 'exp'}                       40.19±0.53          42.73±0.68           40.65±5.45                       79.69\n",
+      "{'alpha': '3.16e+00', 'weight': 6, 'compute_method': 'exp'}                       40.32±0.50          42.85±0.74           44.39±6.83                       78.62\n",
+      "{'alpha': '3.16e+00', 'weight': 7, 'compute_method': 'exp'}                       40.78±0.63          43.98±0.76           42.18±6.27                       80.34\n",
+      "{'alpha': '3.16e+00', 'weight': 8, 'compute_method': 'exp'}                       41.40±0.55          44.74±0.78           42.40±7.54                       79.03\n",
+      "{'alpha': '3.16e+00', 'weight': 9, 'compute_method': 'exp'}                       41.61±0.48          45.13±0.76           47.30±7.31                       78.69\n",
+      "{'alpha': '1.00e+01', 'weight': 0.1, 'compute_method': 'geo'}                     42.02±0.50          42.04±0.55           42.06±5.50                       42.25\n",
+      "{'alpha': '1.00e+01', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     43.40±0.63          43.25±0.67           41.86±6.20                       41.38\n",
+      "{'alpha': '1.00e+01', 'weight': 0.01, 'compute_method': 'geo'}                    43.73±0.68          43.61±0.70           45.03±6.72                       41.26\n",
+      "{'alpha': '1.00e+01', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   44.10±0.69          43.86±0.78           43.32±6.35                       41.75\n",
+      "{'alpha': '1.00e+01', 'weight': 0.001, 'compute_method': 'geo'}                   44.16±0.72          43.92±0.74           43.94±7.80                       42.03\n",
+      "{'alpha': '1.00e+01', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  44.28±0.60          44.10±0.75           42.79±5.78                       42.38\n",
+      "{'alpha': '1.00e+01', 'weight': 0.0001, 'compute_method': 'geo'}                  44.34±0.60          44.08±0.64           42.68±5.74                       43.29\n",
+      "{'alpha': '1.00e+01', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  44.33±0.61          44.00±0.67           42.80±5.88                       41.07\n",
+      "{'alpha': '1.00e+01', 'weight': 1e-05, 'compute_method': 'geo'}                   44.18±0.66          43.94±0.71           43.52±6.94                       41.15\n",
+      "{'alpha': '1.00e+01', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   44.29±0.74          44.02±0.92           42.73±7.80                       40.51\n",
+      "{'alpha': '1.00e+01', 'weight': 1e-06, 'compute_method': 'geo'}                   44.24±0.71          44.16±0.78           42.48±7.49                       40.36\n",
+      "{'alpha': '1.00e+01', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   44.07±0.76          43.90±0.81           45.34±7.46                       41.54\n",
+      "{'alpha': '1.00e+01', 'weight': 1e-07, 'compute_method': 'geo'}                   44.30±0.63          44.05±0.79           42.75±7.76                       40.11\n",
+      "{'alpha': '1.00e+01', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   44.11±0.70          43.95±0.72           43.84±7.01                       41.64\n",
+      "{'alpha': '1.00e+01', 'weight': 1e-08, 'compute_method': 'geo'}                   44.17±0.66          43.95±0.82           44.50±6.74                       42.45\n",
+      "{'alpha': '1.00e+01', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  44.23±0.54          43.95±0.67           43.73±4.57                       42.06\n",
+      "{'alpha': '1.00e+01', 'weight': 1e-09, 'compute_method': 'geo'}                   44.00±0.83          43.76±0.83           45.53±7.13                       40.9\n",
+      "{'alpha': '1.00e+01', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  44.19±0.55          43.99±0.66           44.41±5.94                       40.96\n",
+      "{'alpha': '1.00e+01', 'weight': 1e-10, 'compute_method': 'geo'}                   44.38±0.64          44.18±0.71           41.82±6.65                       41.75\n",
+      "{'alpha': '1.00e+01', 'weight': 0, 'compute_method': 'exp'}                       45.54±0.71          45.15±0.74           46.84±5.60                       80.14\n",
+      "{'alpha': '1.00e+01', 'weight': 1, 'compute_method': 'exp'}                       43.08±0.61          43.38±0.68           43.73±6.87                       80.26\n",
+      "{'alpha': '1.00e+01', 'weight': 2, 'compute_method': 'exp'}                       45.62±0.80          46.22±0.82           46.57±5.96                       78.98\n",
+      "{'alpha': '1.00e+01', 'weight': 3, 'compute_method': 'exp'}                       47.87±1.24          48.71±1.26           47.88±7.83                       79.54\n",
+      "{'alpha': '1.00e+01', 'weight': 4, 'compute_method': 'exp'}                       49.33±1.45          50.12±1.29           52.43±8.38                       77.78\n",
+      "{'alpha': '1.00e+01', 'weight': 5, 'compute_method': 'exp'}                       50.75±1.49          51.79±1.37           50.40±7.01                       79.69\n",
+      "{'alpha': '1.00e+01', 'weight': 6, 'compute_method': 'exp'}                       51.80±1.35          52.87±1.46           55.05±9.82                       78.62\n",
+      "{'alpha': '1.00e+01', 'weight': 7, 'compute_method': 'exp'}                       52.89±1.33          54.22±1.36           52.05±7.37                       80.34\n",
+      "{'alpha': '1.00e+01', 'weight': 8, 'compute_method': 'exp'}                       54.06±1.19          55.48±1.10           53.14±9.89                       79.03\n",
+      "{'alpha': '1.00e+01', 'weight': 9, 'compute_method': 'exp'}                       55.01±1.11          56.59±1.23           58.49±9.05                       78.69\n",
+      "{'alpha': '3.16e+01', 'weight': 0.1, 'compute_method': 'geo'}                     50.79±0.50          50.61±0.56           49.83±6.71                       42.25\n",
+      "{'alpha': '3.16e+01', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     51.49±0.61          51.11±0.73           48.94±6.61                       41.38\n",
+      "{'alpha': '3.16e+01', 'weight': 0.01, 'compute_method': 'geo'}                    51.45±0.69          51.16±0.73           51.92±7.85                       41.26\n",
+      "{'alpha': '3.16e+01', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   51.71±0.65          51.24±0.75           50.05±7.27                       41.75\n",
+      "{'alpha': '3.16e+01', 'weight': 0.001, 'compute_method': 'geo'}                   51.70±0.73          51.29±0.75           51.39±8.57                       42.03\n",
+      "{'alpha': '3.16e+01', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  51.84±0.59          51.49±0.74           50.21±6.68                       42.38\n",
+      "{'alpha': '3.16e+01', 'weight': 0.0001, 'compute_method': 'geo'}                  51.86±0.57          51.44±0.68           50.46±6.90                       43.29\n",
+      "{'alpha': '3.16e+01', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  51.88±0.58          51.42±0.61           50.95±6.97                       41.07\n",
+      "{'alpha': '3.16e+01', 'weight': 1e-05, 'compute_method': 'geo'}                   51.74±0.64          51.33±0.72           50.23±9.11                       41.15\n",
+      "{'alpha': '3.16e+01', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   51.80±0.74          51.35±0.92           50.22±8.80                       40.51\n",
+      "{'alpha': '3.16e+01', 'weight': 1e-06, 'compute_method': 'geo'}                   51.85±0.68          51.59±0.75           49.48±8.19                       40.36\n",
+      "{'alpha': '3.16e+01', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   51.57±0.77          51.26±0.84           53.00±8.76                       41.54\n",
+      "{'alpha': '3.16e+01', 'weight': 1e-07, 'compute_method': 'geo'}                   51.83±0.67          51.47±0.77           50.30±9.88                       40.11\n",
+      "{'alpha': '3.16e+01', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   51.69±0.70          51.36±0.68           50.13±7.72                       41.64\n",
+      "{'alpha': '3.16e+01', 'weight': 1e-08, 'compute_method': 'geo'}                   51.70±0.68          51.30±0.86           52.53±8.26                       42.45\n",
+      "{'alpha': '3.16e+01', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  51.79±0.47          51.36±0.55           51.46±5.10                       42.06\n",
+      "{'alpha': '3.16e+01', 'weight': 1e-09, 'compute_method': 'geo'}                   51.52±0.78          51.19±0.80           52.85±7.48                       40.9\n",
+      "{'alpha': '3.16e+01', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  51.69±0.55          51.33±0.64           52.61±7.47                       40.96\n",
+      "{'alpha': '3.16e+01', 'weight': 1e-10, 'compute_method': 'geo'}                   51.93±0.62          51.57±0.70           49.66±6.89                       41.75\n",
+      "{'alpha': '3.16e+01', 'weight': 0, 'compute_method': 'exp'}                       52.26±0.56          51.87±0.61           53.81±6.21                       80.14\n",
+      "{'alpha': '3.16e+01', 'weight': 1, 'compute_method': 'exp'}                       53.62±0.69          53.48±0.74           54.11±9.38                       80.26\n",
+      "{'alpha': '3.16e+01', 'weight': 2, 'compute_method': 'exp'}                       58.58±0.69          58.58±0.68           59.14±8.31                       78.98\n",
+      "{'alpha': '3.16e+01', 'weight': 3, 'compute_method': 'exp'}                       62.84±0.84          62.97±0.86           60.95±9.98                       79.54\n",
+      "{'alpha': '3.16e+01', 'weight': 4, 'compute_method': 'exp'}                       65.57±0.76          65.70±0.77           68.93±9.52                       77.78\n",
+      "{'alpha': '3.16e+01', 'weight': 5, 'compute_method': 'exp'}                       68.52±0.75          68.82±0.89           67.68±8.75                       79.69\n",
+      "{'alpha': '3.16e+01', 'weight': 6, 'compute_method': 'exp'}                       70.66±1.06          70.97±1.13           73.47±12.44                      78.62\n",
+      "{'alpha': '3.16e+01', 'weight': 7, 'compute_method': 'exp'}                       73.09±0.81          73.44±1.06           71.31±8.76                       80.34\n",
+      "{'alpha': '3.16e+01', 'weight': 8, 'compute_method': 'exp'}                       74.95±1.07          75.39±1.02           74.84±10.40                      79.03\n",
+      "{'alpha': '3.16e+01', 'weight': 9, 'compute_method': 'exp'}                       76.49±0.86          77.05±0.98           79.03±10.57                      78.69\n",
+      "{'alpha': '1.00e+02', 'weight': 0.1, 'compute_method': 'geo'}                     72.80±0.50          72.45±0.56           70.28±8.13                       42.25\n",
+      "{'alpha': '1.00e+02', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     72.51±0.51          71.96±0.61           69.49±8.35                       41.38\n",
+      "{'alpha': '1.00e+02', 'weight': 0.01, 'compute_method': 'geo'}                    72.21±0.65          71.78±0.71           71.61±9.74                       41.26\n",
+      "{'alpha': '1.00e+02', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   72.36±0.55          71.75±0.65           69.67±9.95                       41.75\n",
+      "{'alpha': '1.00e+02', 'weight': 0.001, 'compute_method': 'geo'}                   72.25±0.67          71.72±0.69           71.96±9.76                       42.03\n",
+      "{'alpha': '1.00e+02', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  72.37±0.53          71.87±0.60           70.94±8.89                       42.38\n",
+      "{'alpha': '1.00e+02', 'weight': 0.0001, 'compute_method': 'geo'}                  72.35±0.50          71.82±0.66           71.55±8.92                       43.29\n",
+      "{'alpha': '1.00e+02', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  72.32±0.54          71.79±0.52           72.64±8.61                       41.07\n",
+      "{'alpha': '1.00e+02', 'weight': 1e-05, 'compute_method': 'geo'}                   72.32±0.65          71.79±0.76           70.11±11.87                      41.15\n",
+      "{'alpha': '1.00e+02', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   72.32±0.66          71.77±0.75           70.90±9.84                       40.51\n",
+      "{'alpha': '1.00e+02', 'weight': 1e-06, 'compute_method': 'geo'}                   72.40±0.62          71.93±0.73           70.26±8.62                       40.36\n",
+      "{'alpha': '1.00e+02', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   72.11±0.70          71.65±0.73           73.38±10.72                      41.54\n",
+      "{'alpha': '1.00e+02', 'weight': 1e-07, 'compute_method': 'geo'}                   72.32±0.74          71.85±0.78           71.43±11.42                      40.11\n",
+      "{'alpha': '1.00e+02', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   72.33±0.58          71.84±0.61           69.54±8.45                       41.64\n",
+      "{'alpha': '1.00e+02', 'weight': 1e-08, 'compute_method': 'geo'}                   72.17±0.65          71.63±0.74           73.75±10.71                      42.45\n",
+      "{'alpha': '1.00e+02', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  72.28±0.39          71.76±0.43           72.47±7.21                       42.06\n",
+      "{'alpha': '1.00e+02', 'weight': 1e-09, 'compute_method': 'geo'}                   72.12±0.64          71.69±0.62           72.69±9.09                       40.9\n",
+      "{'alpha': '1.00e+02', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  72.16±0.55          71.65±0.62           74.07±8.89                       40.96\n",
+      "{'alpha': '1.00e+02', 'weight': 1e-10, 'compute_method': 'geo'}                   72.41±0.52          71.90±0.61           71.06±7.35                       41.75\n",
+      "{'alpha': '1.00e+02', 'weight': 0, 'compute_method': 'exp'}                       72.29±0.49          71.78±0.48           73.57±7.89                       80.14\n",
+      "{'alpha': '1.00e+02', 'weight': 1, 'compute_method': 'exp'}                       78.83±0.85          78.49±0.91           79.71±10.80                      80.26\n",
+      "{'alpha': '1.00e+02', 'weight': 2, 'compute_method': 'exp'}                       85.09±0.79          84.88±0.74           85.62±9.49                       78.98\n",
+      "{'alpha': '1.00e+02', 'weight': 3, 'compute_method': 'exp'}                       90.02±0.93          89.82±0.91           87.67±10.96                      79.54\n",
+      "{'alpha': '1.00e+02', 'weight': 4, 'compute_method': 'exp'}                       93.07±0.83          92.91±0.82           95.68±9.32                       77.78\n",
+      "{'alpha': '1.00e+02', 'weight': 5, 'compute_method': 'exp'}                       96.27±0.78          96.16±0.90           95.82±8.52                       79.69\n",
+      "{'alpha': '1.00e+02', 'weight': 6, 'compute_method': 'exp'}                       98.38±1.23          98.35±1.31           101.01±11.99                     78.62\n",
+      "{'alpha': '1.00e+02', 'weight': 7, 'compute_method': 'exp'}                       100.91±0.81         100.84±1.00          98.74±8.70                       80.34\n",
+      "{'alpha': '1.00e+02', 'weight': 8, 'compute_method': 'exp'}                       102.52±1.07         102.50±1.01          103.00±10.39                     79.03\n",
+      "{'alpha': '1.00e+02', 'weight': 9, 'compute_method': 'exp'}                       103.95±0.90         104.00±0.96          106.38±10.24                     78.69\n",
+      "{'alpha': '3.16e+02', 'weight': 0.1, 'compute_method': 'geo'}                     104.34±0.69         103.98±0.75          101.01±8.66                      42.25\n",
+      "{'alpha': '3.16e+02', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     103.72±0.67         103.22±0.68          100.86±9.21                      41.38\n",
+      "{'alpha': '3.16e+02', 'weight': 0.01, 'compute_method': 'geo'}                    103.39±0.90         102.99±0.96          102.30±10.72                     41.26\n",
+      "{'alpha': '3.16e+02', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   103.50±0.83         102.97±0.89          100.54±11.30                     41.75\n",
+      "{'alpha': '3.16e+02', 'weight': 0.001, 'compute_method': 'geo'}                   103.30±0.84         102.83±0.87          103.11±10.40                     42.03\n",
+      "{'alpha': '3.16e+02', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  103.39±0.74         102.92±0.80          102.39±9.88                      42.38\n",
+      "{'alpha': '3.16e+02', 'weight': 0.0001, 'compute_method': 'geo'}                  103.33±0.74         102.87±0.81          103.12±9.78                      43.29\n",
+      "{'alpha': '3.16e+02', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  103.24±0.76         102.76±0.74          104.46±9.44                      41.07\n",
+      "{'alpha': '3.16e+02', 'weight': 1e-05, 'compute_method': 'geo'}                   103.42±0.96         102.95±1.02          101.20±12.64                     41.15\n",
+      "{'alpha': '3.16e+02', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   103.37±0.82         102.88±0.85          102.18±10.25                     40.51\n",
+      "{'alpha': '3.16e+02', 'weight': 1e-06, 'compute_method': 'geo'}                   103.44±0.72         102.97±0.83          101.76±8.63                      40.36\n",
+      "{'alpha': '3.16e+02', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   103.16±0.95         102.72±0.92          104.29±11.72                     41.54\n",
+      "{'alpha': '3.16e+02', 'weight': 1e-07, 'compute_method': 'geo'}                   103.31±0.98         102.88±1.01          103.06±11.56                     40.11\n",
+      "{'alpha': '3.16e+02', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   103.51±0.69         103.05±0.76          100.22±8.85                      41.64\n",
+      "{'alpha': '3.16e+02', 'weight': 1e-08, 'compute_method': 'geo'}                   103.12±0.92         102.62±0.93          105.25±11.74                     42.45\n",
+      "{'alpha': '3.16e+02', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  103.27±0.62         102.80±0.66          103.88±8.33                      42.06\n",
+      "{'alpha': '3.16e+02', 'weight': 1e-09, 'compute_method': 'geo'}                   103.24±0.82         102.82±0.77          103.33±10.15                     40.9\n",
+      "{'alpha': '3.16e+02', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  103.10±0.74         102.64±0.80          105.66±9.14                      40.96\n",
+      "{'alpha': '3.16e+02', 'weight': 1e-10, 'compute_method': 'geo'}                   103.38±0.62         102.92±0.67          102.76±7.63                      41.75\n",
+      "{'alpha': '3.16e+02', 'weight': 0, 'compute_method': 'exp'}                       103.11±0.67         102.64±0.65          104.12±8.66                      80.14\n",
+      "{'alpha': '3.16e+02', 'weight': 1, 'compute_method': 'exp'}                       109.47±1.04         109.11±1.10          110.92±10.81                     80.26\n",
+      "{'alpha': '3.16e+02', 'weight': 2, 'compute_method': 'exp'}                       113.93±0.90         113.66±0.87          114.76±9.64                      78.98\n",
+      "{'alpha': '3.16e+02', 'weight': 3, 'compute_method': 'exp'}                       117.22±1.08         116.89±1.07          115.12±11.21                     79.54\n",
+      "{'alpha': '3.16e+02', 'weight': 4, 'compute_method': 'exp'}                       119.01±0.92         118.74±0.90          120.64±9.45                      77.78\n",
+      "{'alpha': '3.16e+02', 'weight': 5, 'compute_method': 'exp'}                       120.95±0.83         120.66±0.91          121.02±8.22                      79.69\n",
+      "{'alpha': '3.16e+02', 'weight': 6, 'compute_method': 'exp'}                       122.06±1.21         121.85±1.29          124.64±10.84                     78.62\n",
+      "{'alpha': '3.16e+02', 'weight': 7, 'compute_method': 'exp'}                       123.74±0.84         123.47±0.97          121.52±8.54                      80.34\n",
+      "{'alpha': '3.16e+02', 'weight': 8, 'compute_method': 'exp'}                       124.42±1.07         124.18±1.05          125.49±10.42                     79.03\n",
+      "{'alpha': '3.16e+02', 'weight': 9, 'compute_method': 'exp'}                       125.12±1.02         124.91±1.07          127.84±10.41                     78.69\n",
+      "{'alpha': '1.00e+03', 'weight': 0.1, 'compute_method': 'geo'}                     126.57±0.87         126.24±0.93          122.96±8.76                      42.25\n",
+      "{'alpha': '1.00e+03', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     126.15±0.87         125.70±0.86          123.47±9.33                      41.38\n",
+      "{'alpha': '1.00e+03', 'weight': 0.01, 'compute_method': 'geo'}                    125.91±1.15         125.55±1.20          124.69±10.96                     41.26\n",
+      "{'alpha': '1.00e+03', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   126.04±1.10         125.58±1.14          123.07±11.56                     41.75\n",
+      "{'alpha': '1.00e+03', 'weight': 0.001, 'compute_method': 'geo'}                   125.79±1.05         125.37±1.08          125.66±10.50                     42.03\n",
+      "{'alpha': '1.00e+03', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  125.86±0.97         125.44±1.02          125.10±10.06                     42.38\n",
+      "{'alpha': '1.00e+03', 'weight': 0.0001, 'compute_method': 'geo'}                  125.78±0.98         125.38±1.01          125.85±9.94                      43.29\n",
+      "{'alpha': '1.00e+03', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  125.65±0.98         125.22±0.96          127.24±9.63                      41.07\n",
+      "{'alpha': '1.00e+03', 'weight': 1e-05, 'compute_method': 'geo'}                   125.95±1.23         125.53±1.26          123.79±12.67                     41.15\n",
+      "{'alpha': '1.00e+03', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   125.87±1.01         125.44±1.04          124.79±10.27                     40.51\n",
+      "{'alpha': '1.00e+03', 'weight': 1e-06, 'compute_method': 'geo'}                   125.93±0.87         125.49±0.96          124.49±8.54                      40.36\n",
+      "{'alpha': '1.00e+03', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   125.65±1.21         125.25±1.18          126.72±11.95                     41.54\n",
+      "{'alpha': '1.00e+03', 'weight': 1e-07, 'compute_method': 'geo'}                   125.77±1.18         125.38±1.20          125.79±11.39                     40.11\n",
+      "{'alpha': '1.00e+03', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   126.09±0.87         125.66±0.94          122.66±8.95                      41.64\n",
+      "{'alpha': '1.00e+03', 'weight': 1e-08, 'compute_method': 'geo'}                   125.54±1.19         125.10±1.18          127.92±11.91                     42.45\n",
+      "{'alpha': '1.00e+03', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  125.72±0.84         125.30±0.88          126.53±8.60                      42.06\n",
+      "{'alpha': '1.00e+03', 'weight': 1e-09, 'compute_method': 'geo'}                   125.77±1.06         125.39±1.00          125.69±10.41                     40.9\n",
+      "{'alpha': '1.00e+03', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  125.53±0.92         125.11±0.97          128.33±9.07                      40.96\n",
+      "{'alpha': '1.00e+03', 'weight': 1e-10, 'compute_method': 'geo'}                   125.84±0.77         125.42±0.80          125.52±7.66                      41.75\n",
+      "{'alpha': '1.00e+03', 'weight': 0, 'compute_method': 'exp'}                       125.62±0.87         125.20±0.84          126.51±8.81                      80.14\n",
+      "{'alpha': '1.00e+03', 'weight': 1, 'compute_method': 'exp'}                       128.77±1.18         128.41±1.22          130.53±10.63                     80.26\n",
+      "{'alpha': '1.00e+03', 'weight': 2, 'compute_method': 'exp'}                       130.82±1.02         130.52±1.01          131.86±9.69                      78.98\n",
+      "{'alpha': '1.00e+03', 'weight': 3, 'compute_method': 'exp'}                       132.40±1.22         131.99±1.23          130.49±11.33                     79.54\n",
+      "{'alpha': '1.00e+03', 'weight': 4, 'compute_method': 'exp'}                       133.00±1.04         132.66±1.03          134.07±9.69                      77.78\n",
+      "{'alpha': '1.00e+03', 'weight': 5, 'compute_method': 'exp'}                       133.82±0.90         133.43±0.96          134.14±8.18                      79.69\n",
+      "{'alpha': '1.00e+03', 'weight': 6, 'compute_method': 'exp'}                       134.13±1.19         133.81±1.25          136.67±10.31                     78.62\n",
+      "{'alpha': '1.00e+03', 'weight': 7, 'compute_method': 'exp'}                       135.10±0.92         134.73±1.02          132.89±8.56                      80.34\n",
+      "{'alpha': '1.00e+03', 'weight': 8, 'compute_method': 'exp'}                       135.12±1.16         134.77±1.15          136.49±10.49                     79.03\n",
+      "{'alpha': '1.00e+03', 'weight': 9, 'compute_method': 'exp'}                       135.30±1.16         134.96±1.21          138.20±10.73                     78.69\n",
+      "{'alpha': '3.16e+03', 'weight': 0.1, 'compute_method': 'geo'}                     136.42±0.96         136.10±1.02          132.71±8.78                      42.25\n",
+      "{'alpha': '3.16e+03', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     136.18±0.97         135.75±0.95          133.57±9.33                      41.38\n",
+      "{'alpha': '3.16e+03', 'weight': 0.01, 'compute_method': 'geo'}                    135.99±1.27         135.65±1.31          134.74±11.00                     41.26\n",
+      "{'alpha': '3.16e+03', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   136.15±1.23         135.71±1.26          133.19±11.60                     41.75\n",
+      "{'alpha': '3.16e+03', 'weight': 0.001, 'compute_method': 'geo'}                   135.87±1.15         135.48±1.18          135.77±10.51                     42.03\n",
+      "{'alpha': '3.16e+03', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  135.94±1.08         135.54±1.12          135.27±10.08                     42.38\n",
+      "{'alpha': '3.16e+03', 'weight': 0.0001, 'compute_method': 'geo'}                  135.85±1.08         135.48±1.11          136.02±9.96                      43.29\n",
+      "{'alpha': '3.16e+03', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  135.70±1.08         135.30±1.06          137.42±9.67                      41.07\n",
+      "{'alpha': '3.16e+03', 'weight': 1e-05, 'compute_method': 'geo'}                   136.05±1.35         135.66±1.38          133.93±12.63                     41.15\n",
+      "{'alpha': '3.16e+03', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   135.97±1.11         135.55±1.13          134.92±10.25                     40.51\n",
+      "{'alpha': '3.16e+03', 'weight': 1e-06, 'compute_method': 'geo'}                   136.01±0.94         135.60±1.02          134.67±8.49                      40.36\n",
+      "{'alpha': '3.16e+03', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   135.74±1.34         135.35±1.31          136.79±11.99                     41.54\n",
+      "{'alpha': '3.16e+03', 'weight': 1e-07, 'compute_method': 'geo'}                   135.85±1.27         135.48±1.29          135.96±11.29                     40.11\n",
+      "{'alpha': '3.16e+03', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   136.21±0.96         135.81±1.03          132.75±8.96                      41.64\n",
+      "{'alpha': '3.16e+03', 'weight': 1e-08, 'compute_method': 'geo'}                   135.60±1.31         135.17±1.30          138.06±11.92                     42.45\n",
+      "{'alpha': '3.16e+03', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  135.79±0.94         135.40±0.97          136.68±8.66                      42.06\n",
+      "{'alpha': '3.16e+03', 'weight': 1e-09, 'compute_method': 'geo'}                   135.87±1.17         135.51±1.12          135.75±10.46                     40.9\n",
+      "{'alpha': '3.16e+03', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  135.59±1.00         135.19±1.04          138.47±9.02                      40.96\n",
+      "{'alpha': '3.16e+03', 'weight': 1e-10, 'compute_method': 'geo'}                   135.91±0.85         135.52±0.87          135.70±7.66                      41.75\n",
+      "{'alpha': '3.16e+03', 'weight': 0, 'compute_method': 'exp'}                       135.76±0.96         135.36±0.93          136.59±8.83                      80.14\n",
+      "{'alpha': '3.16e+03', 'weight': 1, 'compute_method': 'exp'}                       136.86±1.24         136.49±1.28          138.74±10.54                     80.26\n",
+      "{'alpha': '3.16e+03', 'weight': 2, 'compute_method': 'exp'}                       137.65±1.10         137.33±1.08          138.77±9.72                      78.98\n",
+      "{'alpha': '3.16e+03', 'weight': 3, 'compute_method': 'exp'}                       138.39±1.29         137.96±1.31          136.56±11.39                     79.54\n",
+      "{'alpha': '3.16e+03', 'weight': 4, 'compute_method': 'exp'}                       138.44±1.11         138.07±1.10          139.29±9.81                      77.78\n",
+      "{'alpha': '3.16e+03', 'weight': 5, 'compute_method': 'exp'}                       138.75±0.94         138.33±0.99          139.17±8.18                      79.69\n",
+      "{'alpha': '3.16e+03', 'weight': 6, 'compute_method': 'exp'}                       138.71±1.18         138.35±1.24          141.23±10.12                     78.62\n",
+      "{'alpha': '3.16e+03', 'weight': 7, 'compute_method': 'exp'}                       139.37±0.97         138.96±1.06          137.17±8.59                      80.34\n",
+      "{'alpha': '3.16e+03', 'weight': 8, 'compute_method': 'exp'}                       139.11±1.21         138.72±1.20          140.59±10.53                     79.03\n",
+      "{'alpha': '3.16e+03', 'weight': 9, 'compute_method': 'exp'}                       139.07±1.24         138.68±1.28          142.03±10.88                     78.69\n",
+      "{'alpha': '1.00e+04', 'weight': 0.1, 'compute_method': 'geo'}                     139.93±0.99         139.61±1.05          136.19±8.78                      42.25\n",
+      "{'alpha': '1.00e+04', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     139.77±1.00         139.35±0.98          137.19±9.33                      41.38\n",
+      "{'alpha': '1.00e+04', 'weight': 0.01, 'compute_method': 'geo'}                    139.61±1.31         139.27±1.35          138.35±11.02                     41.26\n",
+      "{'alpha': '1.00e+04', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   139.77±1.27         139.34±1.31          136.82±11.61                     41.75\n",
+      "{'alpha': '1.00e+04', 'weight': 0.001, 'compute_method': 'geo'}                   139.49±1.19         139.11±1.21          139.40±10.51                     42.03\n",
+      "{'alpha': '1.00e+04', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  139.55±1.12         139.16±1.16          138.91±10.09                     42.38\n",
+      "{'alpha': '1.00e+04', 'weight': 0.0001, 'compute_method': 'geo'}                  139.47±1.12         139.10±1.14          139.67±9.97                      43.29\n",
+      "{'alpha': '1.00e+04', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  139.31±1.12         138.91±1.10          141.07±9.68                      41.07\n",
+      "{'alpha': '1.00e+04', 'weight': 1e-05, 'compute_method': 'geo'}                   139.68±1.40         139.29±1.42          137.56±12.61                     41.15\n",
+      "{'alpha': '1.00e+04', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   139.59±1.15         139.18±1.17          138.55±10.24                     40.51\n",
+      "{'alpha': '1.00e+04', 'weight': 1e-06, 'compute_method': 'geo'}                   139.63±0.96         139.22±1.04          138.31±8.47                      40.36\n",
+      "{'alpha': '1.00e+04', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   139.36±1.39         138.97±1.36          140.40±12.00                     41.54\n",
+      "{'alpha': '1.00e+04', 'weight': 1e-07, 'compute_method': 'geo'}                   139.46±1.30         139.10±1.33          139.60±11.25                     40.11\n",
+      "{'alpha': '1.00e+04', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   139.84±1.00         139.44±1.06          136.36±8.96                      41.64\n",
+      "{'alpha': '1.00e+04', 'weight': 1e-08, 'compute_method': 'geo'}                   139.21±1.35         138.79±1.34          141.70±11.92                     42.45\n",
+      "{'alpha': '1.00e+04', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  139.40±0.97         139.01±1.01          140.31±8.67                      42.06\n",
+      "{'alpha': '1.00e+04', 'weight': 1e-09, 'compute_method': 'geo'}                   139.49±1.21         139.13±1.16          139.35±10.47                     40.9\n",
+      "{'alpha': '1.00e+04', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  139.19±1.03         138.80±1.07          142.10±9.00                      40.96\n",
+      "{'alpha': '1.00e+04', 'weight': 1e-10, 'compute_method': 'geo'}                   139.52±0.88         139.14±0.89          139.34±7.65                      41.75\n",
+      "{'alpha': '1.00e+04', 'weight': 0, 'compute_method': 'exp'}                       139.40±0.99         139.01±0.97          140.22±8.83                      80.14\n",
+      "{'alpha': '1.00e+04', 'weight': 1, 'compute_method': 'exp'}                       139.68±1.26         139.31±1.30          141.60±10.51                     80.26\n",
+      "{'alpha': '1.00e+04', 'weight': 2, 'compute_method': 'exp'}                       140.00±1.12         139.67±1.11          141.15±9.74                      78.98\n",
+      "{'alpha': '1.00e+04', 'weight': 3, 'compute_method': 'exp'}                       140.44±1.32         139.99±1.34          138.63±11.41                     79.54\n",
+      "{'alpha': '1.00e+04', 'weight': 4, 'compute_method': 'exp'}                       140.28±1.14         139.90±1.13          141.07±9.86                      77.78\n",
+      "{'alpha': '1.00e+04', 'weight': 5, 'compute_method': 'exp'}                       140.41±0.95         139.98±1.00          140.86±8.18                      79.69\n",
+      "{'alpha': '1.00e+04', 'weight': 6, 'compute_method': 'exp'}                       140.25±1.19         139.88±1.24          142.76±10.07                     78.62\n",
+      "{'alpha': '1.00e+04', 'weight': 7, 'compute_method': 'exp'}                       140.80±0.98         140.38±1.07          138.60±8.60                      80.34\n",
+      "{'alpha': '1.00e+04', 'weight': 8, 'compute_method': 'exp'}                       140.44±1.23         140.04±1.22          141.96±10.54                     79.03\n",
+      "{'alpha': '1.00e+04', 'weight': 9, 'compute_method': 'exp'}                       140.32±1.27         139.92±1.31          143.31±10.93                     78.69\n",
+      "{'alpha': '3.16e+04', 'weight': 0.1, 'compute_method': 'geo'}                     141.09±1.00         140.77±1.06          137.34±8.78                      42.25\n",
+      "{'alpha': '3.16e+04', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     140.95±1.01         140.53±0.99          138.38±9.33                      41.38\n",
+      "{'alpha': '3.16e+04', 'weight': 0.01, 'compute_method': 'geo'}                    140.80±1.33         140.47±1.36          139.54±11.02                     41.26\n",
+      "{'alpha': '3.16e+04', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   140.97±1.29         140.54±1.32          138.02±11.61                     41.75\n",
+      "{'alpha': '3.16e+04', 'weight': 0.001, 'compute_method': 'geo'}                   140.68±1.20         140.30±1.23          140.59±10.51                     42.03\n",
+      "{'alpha': '3.16e+04', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  140.74±1.13         140.35±1.17          140.11±10.09                     42.38\n",
+      "{'alpha': '3.16e+04', 'weight': 0.0001, 'compute_method': 'geo'}                  140.66±1.13         140.29±1.15          140.87±9.97                      43.29\n",
+      "{'alpha': '3.16e+04', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  140.50±1.13         140.10±1.11          142.27±9.68                      41.07\n",
+      "{'alpha': '3.16e+04', 'weight': 1e-05, 'compute_method': 'geo'}                   140.87±1.41         140.48±1.44          138.76±12.61                     41.15\n",
+      "{'alpha': '3.16e+04', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   140.78±1.16         140.38±1.18          139.75±10.24                     40.51\n",
+      "{'alpha': '3.16e+04', 'weight': 1e-06, 'compute_method': 'geo'}                   140.82±0.97         140.42±1.05          139.51±8.47                      40.36\n",
+      "{'alpha': '3.16e+04', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   140.55±1.40         140.17±1.37          141.59±12.00                     41.54\n",
+      "{'alpha': '3.16e+04', 'weight': 1e-07, 'compute_method': 'geo'}                   140.65±1.31         140.29±1.34          140.80±11.24                     40.11\n",
+      "{'alpha': '3.16e+04', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   141.04±1.01         140.64±1.07          137.56±8.96                      41.64\n",
+      "{'alpha': '3.16e+04', 'weight': 1e-08, 'compute_method': 'geo'}                   140.40±1.37         139.98±1.36          142.90±11.92                     42.45\n",
+      "{'alpha': '3.16e+04', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  140.59±0.99         140.21±1.02          141.51±8.68                      42.06\n",
+      "{'alpha': '3.16e+04', 'weight': 1e-09, 'compute_method': 'geo'}                   140.69±1.22         140.33±1.17          140.54±10.47                     40.9\n",
+      "{'alpha': '3.16e+04', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  140.38±1.04         139.99±1.08          143.30±9.00                      40.96\n",
+      "{'alpha': '3.16e+04', 'weight': 1e-10, 'compute_method': 'geo'}                   140.71±0.88         140.33±0.90          140.54±7.65                      41.75\n",
+      "{'alpha': '3.16e+04', 'weight': 0, 'compute_method': 'exp'}                       140.60±1.00         140.21±0.98          141.41±8.83                      80.14\n",
+      "{'alpha': '3.16e+04', 'weight': 1, 'compute_method': 'exp'}                       140.60±1.26         140.23±1.31          142.53±10.50                     80.26\n",
+      "{'alpha': '3.16e+04', 'weight': 2, 'compute_method': 'exp'}                       140.76±1.13         140.43±1.12          141.92±9.74                      78.98\n",
+      "{'alpha': '3.16e+04', 'weight': 3, 'compute_method': 'exp'}                       141.10±1.33         140.65±1.35          139.30±11.42                     79.54\n",
+      "{'alpha': '3.16e+04', 'weight': 4, 'compute_method': 'exp'}                       140.88±1.15         140.50±1.14          141.64±9.87                      77.78\n",
+      "{'alpha': '3.16e+04', 'weight': 5, 'compute_method': 'exp'}                       140.95±0.96         140.52±1.00          141.41±8.19                      79.69\n",
+      "{'alpha': '3.16e+04', 'weight': 6, 'compute_method': 'exp'}                       140.74±1.19         140.37±1.24          143.26±10.05                     78.62\n",
+      "{'alpha': '3.16e+04', 'weight': 7, 'compute_method': 'exp'}                       141.26±0.99         140.84±1.07          139.06±8.61                      80.34\n",
+      "{'alpha': '3.16e+04', 'weight': 8, 'compute_method': 'exp'}                       140.87±1.23         140.47±1.23          142.40±10.55                     79.03\n",
+      "{'alpha': '3.16e+04', 'weight': 9, 'compute_method': 'exp'}                       140.73±1.27         140.32±1.32          143.73±10.95                     78.69\n",
+      "{'alpha': '1.00e+05', 'weight': 0.1, 'compute_method': 'geo'}                     141.46±1.00         141.14±1.06          137.71±8.78                      42.25\n",
+      "{'alpha': '1.00e+05', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     141.33±1.01         140.91±1.00          138.76±9.33                      41.38\n",
+      "{'alpha': '1.00e+05', 'weight': 0.01, 'compute_method': 'geo'}                    141.18±1.33         140.85±1.37          139.92±11.02                     41.26\n",
+      "{'alpha': '1.00e+05', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   141.35±1.29         140.92±1.33          138.40±11.62                     41.75\n",
+      "{'alpha': '1.00e+05', 'weight': 0.001, 'compute_method': 'geo'}                   141.07±1.21         140.68±1.23          140.98±10.51                     42.03\n",
+      "{'alpha': '1.00e+05', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  141.12±1.13         140.74±1.17          140.50±10.09                     42.38\n",
+      "{'alpha': '1.00e+05', 'weight': 0.0001, 'compute_method': 'geo'}                  141.04±1.14         140.67±1.16          141.25±9.97                      43.29\n",
+      "{'alpha': '1.00e+05', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  140.88±1.13         140.48±1.12          142.66±9.68                      41.07\n",
+      "{'alpha': '1.00e+05', 'weight': 1e-05, 'compute_method': 'geo'}                   141.25±1.42         140.87±1.44          139.14±12.61                     41.15\n",
+      "{'alpha': '1.00e+05', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   141.16±1.16         140.76±1.18          140.13±10.24                     40.51\n",
+      "{'alpha': '1.00e+05', 'weight': 1e-06, 'compute_method': 'geo'}                   141.21±0.98         140.80±1.05          139.90±8.46                      40.36\n",
+      "{'alpha': '1.00e+05', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   140.93±1.41         140.55±1.38          141.97±12.00                     41.54\n",
+      "{'alpha': '1.00e+05', 'weight': 1e-07, 'compute_method': 'geo'}                   141.03±1.32         140.68±1.34          141.19±11.24                     40.11\n",
+      "{'alpha': '1.00e+05', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   141.43±1.01         141.03±1.07          137.94±8.96                      41.64\n",
+      "{'alpha': '1.00e+05', 'weight': 1e-08, 'compute_method': 'geo'}                   140.78±1.37         140.36±1.36          143.28±11.92                     42.45\n",
+      "{'alpha': '1.00e+05', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  140.97±0.99         140.59±1.02          141.89±8.68                      42.06\n",
+      "{'alpha': '1.00e+05', 'weight': 1e-09, 'compute_method': 'geo'}                   141.07±1.22         140.71±1.17          140.92±10.47                     40.9\n",
+      "{'alpha': '1.00e+05', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  140.76±1.04         140.38±1.08          143.68±8.99                      40.96\n",
+      "{'alpha': '1.00e+05', 'weight': 1e-10, 'compute_method': 'geo'}                   141.09±0.89         140.71±0.90          140.93±7.65                      41.75\n",
+      "{'alpha': '1.00e+05', 'weight': 0, 'compute_method': 'exp'}                       140.98±1.00         140.59±0.98          141.79±8.83                      80.14\n",
+      "{'alpha': '1.00e+05', 'weight': 1, 'compute_method': 'exp'}                       140.89±1.27         140.53±1.31          142.83±10.50                     80.26\n",
+      "{'alpha': '1.00e+05', 'weight': 2, 'compute_method': 'exp'}                       141.01±1.14         140.68±1.13          142.17±9.74                      78.98\n",
+      "{'alpha': '1.00e+05', 'weight': 3, 'compute_method': 'exp'}                       141.31±1.33         140.86±1.35          139.52±11.42                     79.54\n",
+      "{'alpha': '1.00e+05', 'weight': 4, 'compute_method': 'exp'}                       141.07±1.15         140.69±1.14          141.82±9.87                      77.78\n",
+      "{'alpha': '1.00e+05', 'weight': 5, 'compute_method': 'exp'}                       141.12±0.96         140.69±1.01          141.59±8.19                      79.69\n",
+      "{'alpha': '1.00e+05', 'weight': 6, 'compute_method': 'exp'}                       140.90±1.19         140.53±1.24          143.42±10.04                     78.62\n",
+      "{'alpha': '1.00e+05', 'weight': 7, 'compute_method': 'exp'}                       141.41±0.99         140.98±1.08          139.21±8.61                      80.34\n",
+      "{'alpha': '1.00e+05', 'weight': 8, 'compute_method': 'exp'}                       141.01±1.24         140.60±1.23          142.54±10.55                     79.03\n",
+      "{'alpha': '1.00e+05', 'weight': 9, 'compute_method': 'exp'}                       140.86±1.28         140.45±1.32          143.86±10.96                     78.69\n",
+      "{'alpha': '3.16e+05', 'weight': 0.1, 'compute_method': 'geo'}                     141.58±1.00         141.26±1.07          137.82±8.78                      42.25\n",
+      "{'alpha': '3.16e+05', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     141.45±1.02         141.03±1.00          138.88±9.33                      41.38\n",
+      "{'alpha': '3.16e+05', 'weight': 0.01, 'compute_method': 'geo'}                    141.30±1.33         140.97±1.37          140.04±11.02                     41.26\n",
+      "{'alpha': '3.16e+05', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   141.47±1.29         141.04±1.33          138.52±11.62                     41.75\n",
+      "{'alpha': '3.16e+05', 'weight': 0.001, 'compute_method': 'geo'}                   141.19±1.21         140.81±1.23          141.10±10.51                     42.03\n",
+      "{'alpha': '3.16e+05', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  141.25±1.13         140.86±1.18          140.62±10.09                     42.38\n",
+      "{'alpha': '3.16e+05', 'weight': 0.0001, 'compute_method': 'geo'}                  141.16±1.14         140.79±1.16          141.37±9.97                      43.29\n",
+      "{'alpha': '3.16e+05', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  141.00±1.14         140.61±1.12          142.78±9.68                      41.07\n",
+      "{'alpha': '3.16e+05', 'weight': 1e-05, 'compute_method': 'geo'}                   141.38±1.42         140.99±1.44          139.26±12.61                     41.15\n",
+      "{'alpha': '3.16e+05', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   141.29±1.16         140.88±1.18          140.25±10.24                     40.51\n",
+      "{'alpha': '3.16e+05', 'weight': 1e-06, 'compute_method': 'geo'}                   141.33±0.98         140.92±1.05          140.02±8.46                      40.36\n",
+      "{'alpha': '3.16e+05', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   141.06±1.41         140.67±1.38          142.09±12.00                     41.54\n",
+      "{'alpha': '3.16e+05', 'weight': 1e-07, 'compute_method': 'geo'}                   141.16±1.32         140.80±1.34          141.31±11.24                     40.11\n",
+      "{'alpha': '3.16e+05', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   141.55±1.01         141.15±1.08          138.06±8.96                      41.64\n",
+      "{'alpha': '3.16e+05', 'weight': 1e-08, 'compute_method': 'geo'}                   140.90±1.37         140.48±1.36          143.40±11.92                     42.45\n",
+      "{'alpha': '3.16e+05', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  141.10±0.99         140.71±1.03          142.01±8.68                      42.06\n",
+      "{'alpha': '3.16e+05', 'weight': 1e-09, 'compute_method': 'geo'}                   141.19±1.23         140.83±1.18          141.04±10.47                     40.9\n",
+      "{'alpha': '3.16e+05', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  140.88±1.04         140.50±1.09          143.80±8.99                      40.96\n",
+      "{'alpha': '3.16e+05', 'weight': 1e-10, 'compute_method': 'geo'}                   141.21±0.89         140.83±0.90          141.05±7.65                      41.75\n",
+      "{'alpha': '3.16e+05', 'weight': 0, 'compute_method': 'exp'}                       141.11±1.01         140.72±0.98          141.92±8.83                      80.14\n",
+      "{'alpha': '3.16e+05', 'weight': 1, 'compute_method': 'exp'}                       140.99±1.27         140.62±1.31          142.93±10.50                     80.26\n",
+      "{'alpha': '3.16e+05', 'weight': 2, 'compute_method': 'exp'}                       141.08±1.14         140.75±1.13          142.25±9.74                      78.98\n",
+      "{'alpha': '3.16e+05', 'weight': 3, 'compute_method': 'exp'}                       141.38±1.33         140.93±1.35          139.59±11.42                     79.54\n",
+      "{'alpha': '3.16e+05', 'weight': 4, 'compute_method': 'exp'}                       141.13±1.15         140.75±1.14          141.88±9.88                      77.78\n",
+      "{'alpha': '3.16e+05', 'weight': 5, 'compute_method': 'exp'}                       141.18±0.96         140.74±1.01          141.64±8.19                      79.69\n",
+      "{'alpha': '3.16e+05', 'weight': 6, 'compute_method': 'exp'}                       140.95±1.19         140.58±1.24          143.47±10.04                     78.62\n",
+      "{'alpha': '3.16e+05', 'weight': 7, 'compute_method': 'exp'}                       141.45±0.99         141.03±1.08          139.25±8.61                      80.34\n",
+      "{'alpha': '3.16e+05', 'weight': 8, 'compute_method': 'exp'}                       141.05±1.24         140.64±1.23          142.58±10.55                     79.03\n",
+      "{'alpha': '3.16e+05', 'weight': 9, 'compute_method': 'exp'}                       140.90±1.28         140.49±1.32          143.90±10.96                     78.69\n",
+      "{'alpha': '1.00e+06', 'weight': 0.1, 'compute_method': 'geo'}                     141.62±1.00         141.30±1.07          137.86±8.78                      42.25\n",
+      "{'alpha': '1.00e+06', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     141.49±1.02         141.07±1.00          138.92±9.33                      41.38\n",
+      "{'alpha': '1.00e+06', 'weight': 0.01, 'compute_method': 'geo'}                    141.34±1.33         141.01±1.37          140.08±11.02                     41.26\n",
+      "{'alpha': '1.00e+06', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   141.51±1.29         141.08±1.33          138.56±11.62                     41.75\n",
+      "{'alpha': '1.00e+06', 'weight': 0.001, 'compute_method': 'geo'}                   141.22±1.21         140.84±1.23          141.14±10.51                     42.03\n",
+      "{'alpha': '1.00e+06', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  141.28±1.13         140.90±1.18          140.66±10.09                     42.38\n",
+      "{'alpha': '1.00e+06', 'weight': 0.0001, 'compute_method': 'geo'}                  141.20±1.14         140.83±1.16          141.41±9.97                      43.29\n",
+      "{'alpha': '1.00e+06', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  141.04±1.14         140.64±1.12          142.82±9.68                      41.07\n",
+      "{'alpha': '1.00e+06', 'weight': 1e-05, 'compute_method': 'geo'}                   141.41±1.42         141.03±1.44          139.30±12.61                     41.15\n",
+      "{'alpha': '1.00e+06', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   141.32±1.16         140.92±1.18          140.29±10.24                     40.51\n",
+      "{'alpha': '1.00e+06', 'weight': 1e-06, 'compute_method': 'geo'}                   141.37±0.98         140.96±1.05          140.06±8.46                      40.36\n",
+      "{'alpha': '1.00e+06', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   141.09±1.41         140.71±1.38          142.13±12.00                     41.54\n",
+      "{'alpha': '1.00e+06', 'weight': 1e-07, 'compute_method': 'geo'}                   141.19±1.32         140.84±1.34          141.35±11.24                     40.11\n",
+      "{'alpha': '1.00e+06', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   141.59±1.01         141.19±1.08          138.10±8.96                      41.64\n",
+      "{'alpha': '1.00e+06', 'weight': 1e-08, 'compute_method': 'geo'}                   140.94±1.37         140.52±1.36          143.44±11.92                     42.45\n",
+      "{'alpha': '1.00e+06', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  141.13±0.99         140.75±1.03          142.05±8.68                      42.06\n",
+      "{'alpha': '1.00e+06', 'weight': 1e-09, 'compute_method': 'geo'}                   141.23±1.23         140.87±1.18          141.08±10.47                     40.9\n",
+      "{'alpha': '1.00e+06', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  140.92±1.04         140.54±1.09          143.84±8.99                      40.96\n",
+      "{'alpha': '1.00e+06', 'weight': 1e-10, 'compute_method': 'geo'}                   141.25±0.89         140.87±0.90          141.09±7.65                      41.75\n",
+      "{'alpha': '1.00e+06', 'weight': 0, 'compute_method': 'exp'}                       141.14±1.01         140.76±0.98          141.95±8.83                      80.14\n",
+      "{'alpha': '1.00e+06', 'weight': 1, 'compute_method': 'exp'}                       141.02±1.27         140.65±1.31          142.96±10.50                     80.26\n",
+      "{'alpha': '1.00e+06', 'weight': 2, 'compute_method': 'exp'}                       141.11±1.14         140.78±1.13          142.27±9.74                      78.98\n",
+      "{'alpha': '1.00e+06', 'weight': 3, 'compute_method': 'exp'}                       141.40±1.33         140.95±1.35          139.61±11.42                     79.54\n",
+      "{'alpha': '1.00e+06', 'weight': 4, 'compute_method': 'exp'}                       141.15±1.15         140.76±1.14          141.90±9.88                      77.78\n",
+      "{'alpha': '1.00e+06', 'weight': 5, 'compute_method': 'exp'}                       141.19±0.96         140.76±1.01          141.66±8.19                      79.69\n",
+      "{'alpha': '1.00e+06', 'weight': 6, 'compute_method': 'exp'}                       140.97±1.19         140.59±1.24          143.48±10.04                     78.62\n",
+      "{'alpha': '1.00e+06', 'weight': 7, 'compute_method': 'exp'}                       141.47±0.99         141.04±1.08          139.27±8.61                      80.34\n",
+      "{'alpha': '1.00e+06', 'weight': 8, 'compute_method': 'exp'}                       141.07±1.24         140.66±1.23          142.60±10.55                     79.03\n",
+      "{'alpha': '1.00e+06', 'weight': 9, 'compute_method': 'exp'}                       140.91±1.28         140.50±1.32          143.91±10.96                     78.69\n",
+      "{'alpha': '3.16e+06', 'weight': 0.1, 'compute_method': 'geo'}                     141.63±1.00         141.31±1.07          137.87±8.78                      42.25\n",
+      "{'alpha': '3.16e+06', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     141.50±1.02         141.08±1.00          138.93±9.33                      41.38\n",
+      "{'alpha': '3.16e+06', 'weight': 0.01, 'compute_method': 'geo'}                    141.35±1.33         141.02±1.37          140.09±11.02                     41.26\n",
+      "{'alpha': '3.16e+06', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   141.52±1.29         141.09±1.33          138.57±11.62                     41.75\n",
+      "{'alpha': '3.16e+06', 'weight': 0.001, 'compute_method': 'geo'}                   141.24±1.21         140.86±1.23          141.15±10.51                     42.03\n",
+      "{'alpha': '3.16e+06', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  141.30±1.13         140.91±1.18          140.67±10.09                     42.38\n",
+      "{'alpha': '3.16e+06', 'weight': 0.0001, 'compute_method': 'geo'}                  141.21±1.14         140.84±1.16          141.42±9.97                      43.29\n",
+      "{'alpha': '3.16e+06', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  141.05±1.14         140.66±1.12          142.83±9.68                      41.07\n",
+      "{'alpha': '3.16e+06', 'weight': 1e-05, 'compute_method': 'geo'}                   141.43±1.42         141.04±1.44          139.31±12.61                     41.15\n",
+      "{'alpha': '3.16e+06', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   141.34±1.16         140.93±1.18          140.31±10.24                     40.51\n",
+      "{'alpha': '3.16e+06', 'weight': 1e-06, 'compute_method': 'geo'}                   141.38±0.98         140.97±1.05          140.07±8.46                      40.36\n",
+      "{'alpha': '3.16e+06', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   141.11±1.41         140.72±1.38          142.14±12.00                     41.54\n",
+      "{'alpha': '3.16e+06', 'weight': 1e-07, 'compute_method': 'geo'}                   141.21±1.32         140.85±1.34          141.36±11.24                     40.11\n",
+      "{'alpha': '3.16e+06', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   141.60±1.01         141.20±1.08          138.11±8.96                      41.64\n",
+      "{'alpha': '3.16e+06', 'weight': 1e-08, 'compute_method': 'geo'}                   140.95±1.37         140.53±1.36          143.45±11.92                     42.45\n",
+      "{'alpha': '3.16e+06', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  141.15±0.99         140.76±1.03          142.06±8.68                      42.06\n",
+      "{'alpha': '3.16e+06', 'weight': 1e-09, 'compute_method': 'geo'}                   141.24±1.23         140.88±1.18          141.10±10.47                     40.9\n",
+      "{'alpha': '3.16e+06', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  140.93±1.04         140.55±1.09          143.85±8.99                      40.96\n",
+      "{'alpha': '3.16e+06', 'weight': 1e-10, 'compute_method': 'geo'}                   141.27±0.89         140.88±0.90          141.10±7.65                      41.75\n",
+      "{'alpha': '3.16e+06', 'weight': 0, 'compute_method': 'exp'}                       141.16±1.01         140.77±0.98          141.97±8.83                      80.14\n",
+      "{'alpha': '3.16e+06', 'weight': 1, 'compute_method': 'exp'}                       141.03±1.27         140.66±1.31          142.97±10.50                     80.26\n",
+      "{'alpha': '3.16e+06', 'weight': 2, 'compute_method': 'exp'}                       141.11±1.14         140.79±1.13          142.28±9.74                      78.98\n",
+      "{'alpha': '3.16e+06', 'weight': 3, 'compute_method': 'exp'}                       141.41±1.33         140.96±1.35          139.61±11.42                     79.54\n",
+      "{'alpha': '3.16e+06', 'weight': 4, 'compute_method': 'exp'}                       141.16±1.15         140.77±1.14          141.90±9.88                      77.78\n",
+      "{'alpha': '3.16e+06', 'weight': 5, 'compute_method': 'exp'}                       141.20±0.96         140.76±1.01          141.66±8.19                      79.69\n",
+      "{'alpha': '3.16e+06', 'weight': 6, 'compute_method': 'exp'}                       140.97±1.19         140.60±1.24          143.49±10.04                     78.62\n",
+      "{'alpha': '3.16e+06', 'weight': 7, 'compute_method': 'exp'}                       141.47±0.99         141.05±1.08          139.27±8.61                      80.34\n",
+      "{'alpha': '3.16e+06', 'weight': 8, 'compute_method': 'exp'}                       141.07±1.24         140.66±1.23          142.60±10.55                     79.03\n",
+      "{'alpha': '3.16e+06', 'weight': 9, 'compute_method': 'exp'}                       140.91±1.28         140.50±1.32          143.91±10.96                     78.69\n",
+      "{'alpha': '1.00e+07', 'weight': 0.1, 'compute_method': 'geo'}                     141.63±1.00         141.31±1.07          137.87±8.78                      42.25\n",
+      "{'alpha': '1.00e+07', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     141.51±1.02         141.09±1.00          138.94±9.33                      41.38\n",
+      "{'alpha': '1.00e+07', 'weight': 0.01, 'compute_method': 'geo'}                    141.36±1.33         141.02±1.37          140.09±11.02                     41.26\n",
+      "{'alpha': '1.00e+07', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   141.52±1.29         141.10±1.33          138.58±11.62                     41.75\n",
+      "{'alpha': '1.00e+07', 'weight': 0.001, 'compute_method': 'geo'}                   141.24±1.21         140.86±1.23          141.15±10.51                     42.03\n",
+      "{'alpha': '1.00e+07', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  141.30±1.13         140.91±1.18          140.67±10.09                     42.38\n",
+      "{'alpha': '1.00e+07', 'weight': 0.0001, 'compute_method': 'geo'}                  141.21±1.14         140.85±1.16          141.43±9.97                      43.29\n",
+      "{'alpha': '1.00e+07', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  141.05±1.14         140.66±1.12          142.83±9.68                      41.07\n",
+      "{'alpha': '1.00e+07', 'weight': 1e-05, 'compute_method': 'geo'}                   141.43±1.42         141.04±1.44          139.32±12.61                     41.15\n",
+      "{'alpha': '1.00e+07', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   141.34±1.16         140.94±1.18          140.31±10.24                     40.51\n",
+      "{'alpha': '1.00e+07', 'weight': 1e-06, 'compute_method': 'geo'}                   141.38±0.98         140.97±1.05          140.08±8.46                      40.36\n",
+      "{'alpha': '1.00e+07', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   141.11±1.41         140.73±1.38          142.15±12.00                     41.54\n",
+      "{'alpha': '1.00e+07', 'weight': 1e-07, 'compute_method': 'geo'}                   141.21±1.32         140.85±1.34          141.36±11.24                     40.11\n",
+      "{'alpha': '1.00e+07', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   141.60±1.01         141.20±1.08          138.11±8.96                      41.64\n",
+      "{'alpha': '1.00e+07', 'weight': 1e-08, 'compute_method': 'geo'}                   140.96±1.37         140.54±1.36          143.46±11.92                     42.45\n",
+      "{'alpha': '1.00e+07', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  141.15±0.99         140.76±1.03          142.07±8.68                      42.06\n",
+      "{'alpha': '1.00e+07', 'weight': 1e-09, 'compute_method': 'geo'}                   141.25±1.23         140.89±1.18          141.10±10.47                     40.9\n",
+      "{'alpha': '1.00e+07', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  140.94±1.04         140.55±1.09          143.86±8.99                      40.96\n",
+      "{'alpha': '1.00e+07', 'weight': 1e-10, 'compute_method': 'geo'}                   141.27±0.89         140.89±0.90          141.11±7.65                      41.75\n",
+      "{'alpha': '1.00e+07', 'weight': 0, 'compute_method': 'exp'}                       141.16±1.01         140.77±0.98          141.97±8.83                      80.14\n",
+      "{'alpha': '1.00e+07', 'weight': 1, 'compute_method': 'exp'}                       141.03±1.27         140.66±1.31          142.97±10.50                     80.26\n",
+      "{'alpha': '1.00e+07', 'weight': 2, 'compute_method': 'exp'}                       141.12±1.14         140.79±1.13          142.28±9.74                      78.98\n",
+      "{'alpha': '1.00e+07', 'weight': 3, 'compute_method': 'exp'}                       141.41±1.33         140.96±1.35          139.62±11.42                     79.54\n",
+      "{'alpha': '1.00e+07', 'weight': 4, 'compute_method': 'exp'}                       141.16±1.15         140.77±1.14          141.91±9.88                      77.78\n",
+      "{'alpha': '1.00e+07', 'weight': 5, 'compute_method': 'exp'}                       141.20±0.96         140.76±1.01          141.67±8.19                      79.69\n",
+      "{'alpha': '1.00e+07', 'weight': 6, 'compute_method': 'exp'}                       140.97±1.19         140.60±1.24          143.49±10.04                     78.62\n",
+      "{'alpha': '1.00e+07', 'weight': 7, 'compute_method': 'exp'}                       141.47±0.99         141.05±1.08          139.27±8.61                      80.34\n",
+      "{'alpha': '1.00e+07', 'weight': 8, 'compute_method': 'exp'}                       141.07±1.24         140.66±1.23          142.60±10.55                     79.03\n",
+      "{'alpha': '1.00e+07', 'weight': 9, 'compute_method': 'exp'}                       140.91±1.28         140.51±1.32          143.92±10.96                     78.69\n",
+      "{'alpha': '3.16e+07', 'weight': 0.1, 'compute_method': 'geo'}                     141.63±1.00         141.31±1.07          137.88±8.78                      42.25\n",
+      "{'alpha': '3.16e+07', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     141.51±1.02         141.09±1.00          138.94±9.33                      41.38\n",
+      "{'alpha': '3.16e+07', 'weight': 0.01, 'compute_method': 'geo'}                    141.36±1.33         141.03±1.37          140.09±11.02                     41.26\n",
+      "{'alpha': '3.16e+07', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   141.53±1.29         141.10±1.33          138.58±11.62                     41.75\n",
+      "{'alpha': '3.16e+07', 'weight': 0.001, 'compute_method': 'geo'}                   141.24±1.21         140.86±1.23          141.15±10.51                     42.03\n",
+      "{'alpha': '3.16e+07', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  141.30±1.13         140.91±1.18          140.68±10.09                     42.38\n",
+      "{'alpha': '3.16e+07', 'weight': 0.0001, 'compute_method': 'geo'}                  141.22±1.14         140.85±1.16          141.43±9.97                      43.29\n",
+      "{'alpha': '3.16e+07', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  141.05±1.14         140.66±1.12          142.83±9.68                      41.07\n",
+      "{'alpha': '3.16e+07', 'weight': 1e-05, 'compute_method': 'geo'}                   141.43±1.42         141.05±1.44          139.32±12.61                     41.15\n",
+      "{'alpha': '3.16e+07', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   141.34±1.16         140.94±1.18          140.31±10.24                     40.51\n",
+      "{'alpha': '3.16e+07', 'weight': 1e-06, 'compute_method': 'geo'}                   141.38±0.98         140.98±1.05          140.08±8.46                      40.36\n",
+      "{'alpha': '3.16e+07', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   141.11±1.41         140.73±1.38          142.15±12.00                     41.54\n",
+      "{'alpha': '3.16e+07', 'weight': 1e-07, 'compute_method': 'geo'}                   141.21±1.32         140.85±1.34          141.36±11.24                     40.11\n",
+      "{'alpha': '3.16e+07', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   141.60±1.01         141.21±1.08          138.12±8.96                      41.64\n",
+      "{'alpha': '3.16e+07', 'weight': 1e-08, 'compute_method': 'geo'}                   140.96±1.37         140.54±1.36          143.46±11.92                     42.45\n",
+      "{'alpha': '3.16e+07', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  141.15±0.99         140.77±1.03          142.07±8.68                      42.06\n",
+      "{'alpha': '3.16e+07', 'weight': 1e-09, 'compute_method': 'geo'}                   141.25±1.23         140.89±1.18          141.10±10.47                     40.9\n",
+      "{'alpha': '3.16e+07', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  140.94±1.04         140.55±1.09          143.86±8.99                      40.96\n",
+      "{'alpha': '3.16e+07', 'weight': 1e-10, 'compute_method': 'geo'}                   141.27±0.89         140.89±0.90          141.11±7.65                      41.75\n",
+      "{'alpha': '3.16e+07', 'weight': 0, 'compute_method': 'exp'}                       141.16±1.01         140.77±0.98          141.97±8.83                      80.14\n",
+      "{'alpha': '3.16e+07', 'weight': 1, 'compute_method': 'exp'}                       141.03±1.27         140.66±1.31          142.97±10.50                     80.26\n",
+      "{'alpha': '3.16e+07', 'weight': 2, 'compute_method': 'exp'}                       141.12±1.14         140.79±1.13          142.29±9.74                      78.98\n",
+      "{'alpha': '3.16e+07', 'weight': 3, 'compute_method': 'exp'}                       141.41±1.33         140.96±1.35          139.62±11.42                     79.54\n",
+      "{'alpha': '3.16e+07', 'weight': 4, 'compute_method': 'exp'}                       141.16±1.15         140.77±1.14          141.91±9.88                      77.78\n",
+      "{'alpha': '3.16e+07', 'weight': 5, 'compute_method': 'exp'}                       141.20±0.96         140.76±1.01          141.67±8.19                      79.69\n",
+      "{'alpha': '3.16e+07', 'weight': 6, 'compute_method': 'exp'}                       140.97±1.19         140.60±1.24          143.49±10.04                     78.62\n",
+      "{'alpha': '3.16e+07', 'weight': 7, 'compute_method': 'exp'}                       141.47±0.99         141.05±1.08          139.28±8.61                      80.34\n",
+      "{'alpha': '3.16e+07', 'weight': 8, 'compute_method': 'exp'}                       141.07±1.24         140.66±1.23          142.60±10.55                     79.03\n",
+      "{'alpha': '3.16e+07', 'weight': 9, 'compute_method': 'exp'}                       140.91±1.28         140.51±1.32          143.92±10.96                     78.69\n",
+      "{'alpha': '1.00e+08', 'weight': 0.1, 'compute_method': 'geo'}                     141.63±1.00         141.31±1.07          137.88±8.78                      42.25\n",
+      "{'alpha': '1.00e+08', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     141.51±1.02         141.09±1.00          138.94±9.33                      41.38\n",
+      "{'alpha': '1.00e+08', 'weight': 0.01, 'compute_method': 'geo'}                    141.36±1.33         141.03±1.37          140.09±11.02                     41.26\n",
+      "{'alpha': '1.00e+08', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   141.53±1.29         141.10±1.33          138.58±11.62                     41.75\n",
+      "{'alpha': '1.00e+08', 'weight': 0.001, 'compute_method': 'geo'}                   141.24±1.21         140.86±1.23          141.15±10.51                     42.03\n",
+      "{'alpha': '1.00e+08', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  141.30±1.13         140.91±1.18          140.68±10.09                     42.38\n",
+      "{'alpha': '1.00e+08', 'weight': 0.0001, 'compute_method': 'geo'}                  141.22±1.14         140.85±1.16          141.43±9.97                      43.29\n",
+      "{'alpha': '1.00e+08', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  141.05±1.14         140.66±1.12          142.83±9.68                      41.07\n",
+      "{'alpha': '1.00e+08', 'weight': 1e-05, 'compute_method': 'geo'}                   141.43±1.42         141.05±1.44          139.32±12.61                     41.15\n",
+      "{'alpha': '1.00e+08', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   141.34±1.16         140.94±1.18          140.31±10.24                     40.51\n",
+      "{'alpha': '1.00e+08', 'weight': 1e-06, 'compute_method': 'geo'}                   141.38±0.98         140.98±1.05          140.08±8.46                      40.36\n",
+      "{'alpha': '1.00e+08', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   141.11±1.41         140.73±1.38          142.15±12.00                     41.54\n",
+      "{'alpha': '1.00e+08', 'weight': 1e-07, 'compute_method': 'geo'}                   141.21±1.32         140.85±1.34          141.37±11.24                     40.11\n",
+      "{'alpha': '1.00e+08', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   141.60±1.01         141.21±1.08          138.12±8.96                      41.64\n",
+      "{'alpha': '1.00e+08', 'weight': 1e-08, 'compute_method': 'geo'}                   140.96±1.37         140.54±1.36          143.46±11.92                     42.45\n",
+      "{'alpha': '1.00e+08', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  141.15±0.99         140.77±1.03          142.07±8.68                      42.06\n",
+      "{'alpha': '1.00e+08', 'weight': 1e-09, 'compute_method': 'geo'}                   141.25±1.23         140.89±1.18          141.10±10.47                     40.9\n",
+      "{'alpha': '1.00e+08', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  140.94±1.04         140.55±1.09          143.86±8.99                      40.96\n",
+      "{'alpha': '1.00e+08', 'weight': 1e-10, 'compute_method': 'geo'}                   141.27±0.89         140.89±0.90          141.11±7.65                      41.75\n",
+      "{'alpha': '1.00e+08', 'weight': 0, 'compute_method': 'exp'}                       141.16±1.01         140.77±0.98          141.97±8.83                      80.14\n",
+      "{'alpha': '1.00e+08', 'weight': 1, 'compute_method': 'exp'}                       141.03±1.27         140.66±1.31          142.97±10.50                     80.26\n",
+      "{'alpha': '1.00e+08', 'weight': 2, 'compute_method': 'exp'}                       141.12±1.14         140.79±1.13          142.29±9.74                      78.98\n",
+      "{'alpha': '1.00e+08', 'weight': 3, 'compute_method': 'exp'}                       141.41±1.33         140.96±1.35          139.62±11.42                     79.54\n",
+      "{'alpha': '1.00e+08', 'weight': 4, 'compute_method': 'exp'}                       141.16±1.15         140.77±1.14          141.91±9.88                      77.78\n",
+      "{'alpha': '1.00e+08', 'weight': 5, 'compute_method': 'exp'}                       141.20±0.96         140.76±1.01          141.67±8.19                      79.69\n",
+      "{'alpha': '1.00e+08', 'weight': 6, 'compute_method': 'exp'}                       140.97±1.19         140.60±1.24          143.49±10.04                     78.62\n",
+      "{'alpha': '1.00e+08', 'weight': 7, 'compute_method': 'exp'}                       141.47±0.99         141.05±1.08          139.28±8.61                      80.34\n",
+      "{'alpha': '1.00e+08', 'weight': 8, 'compute_method': 'exp'}                       141.07±1.24         140.66±1.23          142.60±10.55                     79.03\n",
+      "{'alpha': '1.00e+08', 'weight': 9, 'compute_method': 'exp'}                       140.91±1.28         140.51±1.32          143.92±10.96                     78.69\n",
+      "{'alpha': '3.16e+08', 'weight': 0.1, 'compute_method': 'geo'}                     141.63±1.00         141.31±1.07          137.88±8.78                      42.25\n",
+      "{'alpha': '3.16e+08', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     141.51±1.02         141.09±1.00          138.94±9.33                      41.38\n",
+      "{'alpha': '3.16e+08', 'weight': 0.01, 'compute_method': 'geo'}                    141.36±1.33         141.03±1.37          140.09±11.02                     41.26\n",
+      "{'alpha': '3.16e+08', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   141.53±1.29         141.10±1.33          138.58±11.62                     41.75\n",
+      "{'alpha': '3.16e+08', 'weight': 0.001, 'compute_method': 'geo'}                   141.24±1.21         140.86±1.23          141.15±10.51                     42.03\n",
+      "{'alpha': '3.16e+08', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  141.30±1.13         140.91±1.18          140.68±10.09                     42.38\n",
+      "{'alpha': '3.16e+08', 'weight': 0.0001, 'compute_method': 'geo'}                  141.22±1.14         140.85±1.16          141.43±9.97                      43.29\n",
+      "{'alpha': '3.16e+08', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  141.05±1.14         140.66±1.12          142.83±9.68                      41.07\n",
+      "{'alpha': '3.16e+08', 'weight': 1e-05, 'compute_method': 'geo'}                   141.43±1.42         141.05±1.44          139.32±12.61                     41.15\n",
+      "{'alpha': '3.16e+08', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   141.34±1.16         140.94±1.18          140.31±10.24                     40.51\n",
+      "{'alpha': '3.16e+08', 'weight': 1e-06, 'compute_method': 'geo'}                   141.38±0.98         140.98±1.05          140.08±8.46                      40.36\n",
+      "{'alpha': '3.16e+08', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   141.11±1.41         140.73±1.38          142.15±12.00                     41.54\n",
+      "{'alpha': '3.16e+08', 'weight': 1e-07, 'compute_method': 'geo'}                   141.21±1.32         140.85±1.34          141.37±11.24                     40.11\n",
+      "{'alpha': '3.16e+08', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   141.60±1.01         141.21±1.08          138.12±8.96                      41.64\n",
+      "{'alpha': '3.16e+08', 'weight': 1e-08, 'compute_method': 'geo'}                   140.96±1.37         140.54±1.36          143.46±11.92                     42.45\n",
+      "{'alpha': '3.16e+08', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  141.15±0.99         140.77±1.03          142.07±8.68                      42.06\n",
+      "{'alpha': '3.16e+08', 'weight': 1e-09, 'compute_method': 'geo'}                   141.25±1.23         140.89±1.18          141.10±10.47                     40.9\n",
+      "{'alpha': '3.16e+08', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  140.94±1.04         140.55±1.09          143.86±8.99                      40.96\n",
+      "{'alpha': '3.16e+08', 'weight': 1e-10, 'compute_method': 'geo'}                   141.27±0.89         140.89±0.90          141.11±7.65                      41.75\n",
+      "{'alpha': '3.16e+08', 'weight': 0, 'compute_method': 'exp'}                       141.16±1.01         140.77±0.98          141.97±8.83                      80.14\n",
+      "{'alpha': '3.16e+08', 'weight': 1, 'compute_method': 'exp'}                       141.03±1.27         140.66±1.31          142.97±10.50                     80.26\n",
+      "{'alpha': '3.16e+08', 'weight': 2, 'compute_method': 'exp'}                       141.12±1.14         140.79±1.13          142.29±9.74                      78.98\n",
+      "{'alpha': '3.16e+08', 'weight': 3, 'compute_method': 'exp'}                       141.41±1.33         140.96±1.35          139.62±11.42                     79.54\n",
+      "{'alpha': '3.16e+08', 'weight': 4, 'compute_method': 'exp'}                       141.16±1.15         140.77±1.14          141.91±9.88                      77.78\n",
+      "{'alpha': '3.16e+08', 'weight': 5, 'compute_method': 'exp'}                       141.20±0.96         140.76±1.01          141.67±8.19                      79.69\n",
+      "{'alpha': '3.16e+08', 'weight': 6, 'compute_method': 'exp'}                       140.97±1.19         140.60±1.24          143.49±10.04                     78.62\n",
+      "{'alpha': '3.16e+08', 'weight': 7, 'compute_method': 'exp'}                       141.47±0.99         141.05±1.08          139.28±8.61                      80.34\n",
+      "{'alpha': '3.16e+08', 'weight': 8, 'compute_method': 'exp'}                       141.07±1.24         140.66±1.23          142.60±10.55                     79.03\n",
+      "{'alpha': '3.16e+08', 'weight': 9, 'compute_method': 'exp'}                       140.91±1.28         140.51±1.32          143.92±10.96                     78.69\n",
+      "{'alpha': '1.00e+09', 'weight': 0.1, 'compute_method': 'geo'}                     141.63±1.00         141.31±1.07          137.88±8.78                      42.25\n",
+      "{'alpha': '1.00e+09', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     141.51±1.02         141.09±1.00          138.94±9.33                      41.38\n",
+      "{'alpha': '1.00e+09', 'weight': 0.01, 'compute_method': 'geo'}                    141.36±1.33         141.03±1.37          140.09±11.02                     41.26\n",
+      "{'alpha': '1.00e+09', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   141.53±1.29         141.10±1.33          138.58±11.62                     41.75\n",
+      "{'alpha': '1.00e+09', 'weight': 0.001, 'compute_method': 'geo'}                   141.24±1.21         140.86±1.23          141.15±10.51                     42.03\n",
+      "{'alpha': '1.00e+09', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  141.30±1.13         140.91±1.18          140.68±10.09                     42.38\n",
+      "{'alpha': '1.00e+09', 'weight': 0.0001, 'compute_method': 'geo'}                  141.22±1.14         140.85±1.16          141.43±9.97                      43.29\n",
+      "{'alpha': '1.00e+09', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  141.05±1.14         140.66±1.12          142.83±9.68                      41.07\n",
+      "{'alpha': '1.00e+09', 'weight': 1e-05, 'compute_method': 'geo'}                   141.43±1.42         141.05±1.44          139.32±12.61                     41.15\n",
+      "{'alpha': '1.00e+09', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   141.34±1.16         140.94±1.18          140.31±10.24                     40.51\n",
+      "{'alpha': '1.00e+09', 'weight': 1e-06, 'compute_method': 'geo'}                   141.38±0.98         140.98±1.05          140.08±8.46                      40.36\n",
+      "{'alpha': '1.00e+09', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   141.11±1.41         140.73±1.38          142.15±12.00                     41.54\n",
+      "{'alpha': '1.00e+09', 'weight': 1e-07, 'compute_method': 'geo'}                   141.21±1.32         140.85±1.34          141.37±11.24                     40.11\n",
+      "{'alpha': '1.00e+09', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   141.60±1.01         141.21±1.08          138.12±8.96                      41.64\n",
+      "{'alpha': '1.00e+09', 'weight': 1e-08, 'compute_method': 'geo'}                   140.96±1.37         140.54±1.36          143.46±11.92                     42.45\n",
+      "{'alpha': '1.00e+09', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  141.15±0.99         140.77±1.03          142.07±8.68                      42.06\n",
+      "{'alpha': '1.00e+09', 'weight': 1e-09, 'compute_method': 'geo'}                   141.25±1.23         140.89±1.18          141.10±10.47                     40.9\n",
+      "{'alpha': '1.00e+09', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  140.94±1.04         140.55±1.09          143.86±8.99                      40.96\n",
+      "{'alpha': '1.00e+09', 'weight': 1e-10, 'compute_method': 'geo'}                   141.27±0.89         140.89±0.90          141.11±7.65                      41.75\n",
+      "{'alpha': '1.00e+09', 'weight': 0, 'compute_method': 'exp'}                       141.16±1.01         140.77±0.98          141.97±8.83                      80.14\n",
+      "{'alpha': '1.00e+09', 'weight': 1, 'compute_method': 'exp'}                       141.03±1.27         140.66±1.31          142.97±10.50                     80.26\n",
+      "{'alpha': '1.00e+09', 'weight': 2, 'compute_method': 'exp'}                       141.12±1.14         140.79±1.13          142.29±9.74                      78.98\n",
+      "{'alpha': '1.00e+09', 'weight': 3, 'compute_method': 'exp'}                       141.41±1.33         140.96±1.35          139.62±11.42                     79.54\n",
+      "{'alpha': '1.00e+09', 'weight': 4, 'compute_method': 'exp'}                       141.16±1.15         140.77±1.14          141.91±9.88                      77.78\n",
+      "{'alpha': '1.00e+09', 'weight': 5, 'compute_method': 'exp'}                       141.20±0.96         140.76±1.01          141.67±8.19                      79.69\n",
+      "{'alpha': '1.00e+09', 'weight': 6, 'compute_method': 'exp'}                       140.97±1.19         140.60±1.24          143.49±10.04                     78.62\n",
+      "{'alpha': '1.00e+09', 'weight': 7, 'compute_method': 'exp'}                       141.47±0.99         141.05±1.08          139.28±8.61                      80.34\n",
+      "{'alpha': '1.00e+09', 'weight': 8, 'compute_method': 'exp'}                       141.07±1.24         140.66±1.23          142.60±10.55                     79.03\n",
+      "{'alpha': '1.00e+09', 'weight': 9, 'compute_method': 'exp'}                       140.91±1.28         140.51±1.32          143.92±10.96                     78.69\n",
+      "{'alpha': '3.16e+09', 'weight': 0.1, 'compute_method': 'geo'}                     141.63±1.00         141.31±1.07          137.88±8.78                      42.25\n",
+      "{'alpha': '3.16e+09', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     141.51±1.02         141.09±1.00          138.94±9.33                      41.38\n",
+      "{'alpha': '3.16e+09', 'weight': 0.01, 'compute_method': 'geo'}                    141.36±1.33         141.03±1.37          140.09±11.02                     41.26\n",
+      "{'alpha': '3.16e+09', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   141.53±1.29         141.10±1.33          138.58±11.62                     41.75\n",
+      "{'alpha': '3.16e+09', 'weight': 0.001, 'compute_method': 'geo'}                   141.24±1.21         140.86±1.23          141.15±10.51                     42.03\n",
+      "{'alpha': '3.16e+09', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  141.30±1.13         140.91±1.18          140.68±10.09                     42.38\n",
+      "{'alpha': '3.16e+09', 'weight': 0.0001, 'compute_method': 'geo'}                  141.22±1.14         140.85±1.16          141.43±9.97                      43.29\n",
+      "{'alpha': '3.16e+09', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  141.05±1.14         140.66±1.12          142.83±9.68                      41.07\n",
+      "{'alpha': '3.16e+09', 'weight': 1e-05, 'compute_method': 'geo'}                   141.43±1.42         141.05±1.44          139.32±12.61                     41.15\n",
+      "{'alpha': '3.16e+09', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   141.34±1.16         140.94±1.18          140.31±10.24                     40.51\n",
+      "{'alpha': '3.16e+09', 'weight': 1e-06, 'compute_method': 'geo'}                   141.38±0.98         140.98±1.05          140.08±8.46                      40.36\n",
+      "{'alpha': '3.16e+09', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   141.11±1.41         140.73±1.38          142.15±12.00                     41.54\n",
+      "{'alpha': '3.16e+09', 'weight': 1e-07, 'compute_method': 'geo'}                   141.21±1.32         140.85±1.34          141.37±11.24                     40.11\n",
+      "{'alpha': '3.16e+09', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   141.60±1.01         141.21±1.08          138.12±8.96                      41.64\n",
+      "{'alpha': '3.16e+09', 'weight': 1e-08, 'compute_method': 'geo'}                   140.96±1.37         140.54±1.36          143.46±11.92                     42.45\n",
+      "{'alpha': '3.16e+09', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  141.15±0.99         140.77±1.03          142.07±8.68                      42.06\n",
+      "{'alpha': '3.16e+09', 'weight': 1e-09, 'compute_method': 'geo'}                   141.25±1.23         140.89±1.18          141.10±10.47                     40.9\n",
+      "{'alpha': '3.16e+09', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  140.94±1.04         140.55±1.09          143.86±8.99                      40.96\n",
+      "{'alpha': '3.16e+09', 'weight': 1e-10, 'compute_method': 'geo'}                   141.27±0.89         140.89±0.90          141.11±7.65                      41.75\n",
+      "{'alpha': '3.16e+09', 'weight': 0, 'compute_method': 'exp'}                       141.16±1.01         140.77±0.98          141.97±8.83                      80.14\n",
+      "{'alpha': '3.16e+09', 'weight': 1, 'compute_method': 'exp'}                       141.03±1.27         140.66±1.31          142.97±10.50                     80.26\n",
+      "{'alpha': '3.16e+09', 'weight': 2, 'compute_method': 'exp'}                       141.12±1.14         140.79±1.13          142.29±9.74                      78.98\n",
+      "{'alpha': '3.16e+09', 'weight': 3, 'compute_method': 'exp'}                       141.41±1.33         140.96±1.35          139.62±11.42                     79.54\n",
+      "{'alpha': '3.16e+09', 'weight': 4, 'compute_method': 'exp'}                       141.16±1.15         140.77±1.14          141.91±9.88                      77.78\n",
+      "{'alpha': '3.16e+09', 'weight': 5, 'compute_method': 'exp'}                       141.20±0.96         140.76±1.01          141.67±8.19                      79.69\n",
+      "{'alpha': '3.16e+09', 'weight': 6, 'compute_method': 'exp'}                       140.97±1.19         140.60±1.24          143.49±10.04                     78.62\n",
+      "{'alpha': '3.16e+09', 'weight': 7, 'compute_method': 'exp'}                       141.47±0.99         141.05±1.08          139.28±8.61                      80.34\n",
+      "{'alpha': '3.16e+09', 'weight': 8, 'compute_method': 'exp'}                       141.07±1.24         140.66±1.23          142.60±10.55                     79.03\n",
+      "{'alpha': '3.16e+09', 'weight': 9, 'compute_method': 'exp'}                       140.91±1.28         140.51±1.32          143.92±10.96                     78.69\n",
+      "{'alpha': '1.00e+10', 'weight': 0.1, 'compute_method': 'geo'}                     141.63±1.00         141.31±1.07          137.88±8.78                      42.25\n",
+      "{'alpha': '1.00e+10', 'weight': 0.03162277660168379, 'compute_method': 'geo'}     141.51±1.02         141.09±1.00          138.94±9.33                      41.38\n",
+      "{'alpha': '1.00e+10', 'weight': 0.01, 'compute_method': 'geo'}                    141.36±1.33         141.03±1.37          140.09±11.02                     41.26\n",
+      "{'alpha': '1.00e+10', 'weight': 0.0031622776601683794, 'compute_method': 'geo'}   141.53±1.29         141.10±1.33          138.58±11.62                     41.75\n",
+      "{'alpha': '1.00e+10', 'weight': 0.001, 'compute_method': 'geo'}                   141.24±1.21         140.86±1.23          141.15±10.51                     42.03\n",
+      "{'alpha': '1.00e+10', 'weight': 0.00031622776601683794, 'compute_method': 'geo'}  141.30±1.13         140.91±1.18          140.68±10.09                     42.38\n",
+      "{'alpha': '1.00e+10', 'weight': 0.0001, 'compute_method': 'geo'}                  141.22±1.14         140.85±1.16          141.43±9.97                      43.29\n",
+      "{'alpha': '1.00e+10', 'weight': 3.1622776601683795e-05, 'compute_method': 'geo'}  141.05±1.14         140.66±1.12          142.83±9.68                      41.07\n",
+      "{'alpha': '1.00e+10', 'weight': 1e-05, 'compute_method': 'geo'}                   141.43±1.42         141.05±1.44          139.32±12.61                     41.15\n",
+      "{'alpha': '1.00e+10', 'weight': 3.162277660168379e-06, 'compute_method': 'geo'}   141.34±1.16         140.94±1.18          140.31±10.24                     40.51\n",
+      "{'alpha': '1.00e+10', 'weight': 1e-06, 'compute_method': 'geo'}                   141.38±0.98         140.98±1.05          140.08±8.46                      40.36\n",
+      "{'alpha': '1.00e+10', 'weight': 3.162277660168379e-07, 'compute_method': 'geo'}   141.11±1.41         140.73±1.38          142.15±12.00                     41.54\n",
+      "{'alpha': '1.00e+10', 'weight': 1e-07, 'compute_method': 'geo'}                   141.21±1.32         140.85±1.34          141.37±11.24                     40.11\n",
+      "{'alpha': '1.00e+10', 'weight': 3.162277660168379e-08, 'compute_method': 'geo'}   141.60±1.01         141.21±1.08          138.12±8.96                      41.64\n",
+      "{'alpha': '1.00e+10', 'weight': 1e-08, 'compute_method': 'geo'}                   140.96±1.37         140.54±1.36          143.46±11.92                     42.45\n",
+      "{'alpha': '1.00e+10', 'weight': 3.1622776601683795e-09, 'compute_method': 'geo'}  141.15±0.99         140.77±1.03          142.07±8.68                      42.06\n",
+      "{'alpha': '1.00e+10', 'weight': 1e-09, 'compute_method': 'geo'}                   141.25±1.23         140.89±1.18          141.10±10.47                     40.9\n",
+      "{'alpha': '1.00e+10', 'weight': 3.1622776601683795e-10, 'compute_method': 'geo'}  140.94±1.04         140.55±1.09          143.86±8.99                      40.96\n",
+      "{'alpha': '1.00e+10', 'weight': 1e-10, 'compute_method': 'geo'}                   141.27±0.89         140.89±0.90          141.11±7.65                      41.75\n",
+      "{'alpha': '1.00e+10', 'weight': 0, 'compute_method': 'exp'}                       141.16±1.01         140.77±0.98          141.97±8.83                      80.14\n",
+      "{'alpha': '1.00e+10', 'weight': 1, 'compute_method': 'exp'}                       141.03±1.27         140.66±1.31          142.97±10.50                     80.26\n",
+      "{'alpha': '1.00e+10', 'weight': 2, 'compute_method': 'exp'}                       141.12±1.14         140.79±1.13          142.29±9.74                      78.98\n",
+      "{'alpha': '1.00e+10', 'weight': 3, 'compute_method': 'exp'}                       141.41±1.33         140.96±1.35          139.62±11.42                     79.54\n",
+      "{'alpha': '1.00e+10', 'weight': 4, 'compute_method': 'exp'}                       141.16±1.15         140.77±1.14          141.91±9.88                      77.78\n",
+      "{'alpha': '1.00e+10', 'weight': 5, 'compute_method': 'exp'}                       141.20±0.96         140.76±1.01          141.67±8.19                      79.69\n",
+      "{'alpha': '1.00e+10', 'weight': 6, 'compute_method': 'exp'}                       140.97±1.19         140.60±1.24          143.49±10.04                     78.62\n",
+      "{'alpha': '1.00e+10', 'weight': 7, 'compute_method': 'exp'}                       141.47±0.99         141.05±1.08          139.28±8.61                      80.34\n",
+      "{'alpha': '1.00e+10', 'weight': 8, 'compute_method': 'exp'}                       141.07±1.24         140.66±1.23          142.60±10.55                     79.03\n",
+      "{'alpha': '1.00e+10', 'weight': 9, 'compute_method': 'exp'}                       140.91±1.28         140.51±1.32          143.92±10.96                     78.69\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "calculate performance: 100%|██████████| 35670/35670 [22:02<00:00, 26.96it/s]\n",
+      "\n"
      ]
     }
    ],
@@ -62,39 +2182,39 @@
     "import sys\n",
     "sys.path.insert(0, \"../\")\n",
     "from pygraph.utils.model_selection_precomputed import model_selection_for_precomputed_kernel\n",
-    "from pygraph.kernels.commonWalkKernel import commonwalkkernel\n",
+    "from pygraph.kernels.commonWalkKernel import commonwalkkernel, _commonwalkkernel_exp, _commonwalkkernel_geo\n",
+    "from pygraph.utils.utils import direct_product\n",
     "\n",
     "dslist = [   \n",
-    "    {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', 'task': 'regression'}, # node_labeled\n",
-    "#     {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge_labeled\n",
+    "    {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', 'task': 'regression'}, # node symb\n",
+    "#     {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n",
     "#     {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds',}, # unlabeled\n",
-    "#     {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, # fully_labeled\n",
-    "    {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds',},\n",
-    "\n",
+    "#     {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, # node/edge symb\n",
+    "#     {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds',}, # node/edge symb\n",
     "#     {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',\n",
-    "#         'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}},\n",
+    "#         'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}}, # node/edge symb\n",
     "#     {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', \n",
-    "#         'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt',},\n",
-    "#     {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'},\n",
-    "#     {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'},    \n",
-    "    {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n",
-    "#     {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'},\n",
-    "#     {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'},\n",
-    "#     {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'},\n",
-    "#     {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'},\n",
-    "#     {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'},\n",
+    "#         'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt',}, # contains single node graph, node symb\n",
+    "#     {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n",
+    "#     {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n",
+    "#     {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, # node symb/nsymb\n",
+    "#     {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n",
+    "#     {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n",
+    "#     {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb\n",
+    "#     {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb\n",
+    "#     {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n",
     "\n",
-    "#     {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'},\n",
-    "#     {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'},\n",
+    "#     {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n",
+    "#     {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n",
     "#     {'name': 'D&D', 'dataset': '../datasets/D&D/DD.mat',\n",
-    "#      'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}},\n",
-    "#     {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'},\n",
+    "#      'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}}, # node symb\n",
+    "#     {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n",
     "#     {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n",
-    "#         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}},\n",
+    "#         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n",
     "#     {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n",
-    "#         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}},\n",
+    "#         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n",
     "#     {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n",
-    "#         'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',},\n",
+    "#         'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n",
     "    \n",
     "#     # not working below\n",
     "#     {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n",
@@ -103,7 +2223,8 @@
     "#     {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n",
     "]\n",
     "estimator = commonwalkkernel\n",
-    "param_grid_precomputed = {}\n",
+    "param_grid_precomputed = [{'compute_method': ['geo'], 'weight': np.logspace(0, -10, num = 21, base = 10)},\n",
+    "                          {'compute_method': ['exp'], 'weight': range(0, 10)}]\n",
     "param_grid = [{'C': np.logspace(-10, 10, num = 41, base = 10)}, \n",
     "              {'alpha': np.logspace(-10, 10, num = 41, base = 10)}]\n",
     "\n",
@@ -115,7 +2236,16 @@
     "        (param_grid[1] if ('task' in ds and ds['task'] == 'regression') else param_grid[0]), \n",
     "        (ds['task'] if 'task' in ds else 'classification'), NUM_TRIALS=30,\n",
     "        datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n",
-    "        extra_params=(ds['extra_params'] if 'extra_params' in ds else None))\n",
+    "        extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n",
+    "        ds_name=ds['name'])\n",
+    "    \n",
+    "#     %lprun -f _commonwalkkernel_geo -f _commonwalkkernel_exp -f direct_product \\\n",
+    "#         model_selection_for_precomputed_kernel( \\\n",
+    "#             ds['dataset'], estimator, param_grid_precomputed, \\\n",
+    "#             (param_grid[1] if ('task' in ds and ds['task'] == 'regression') else param_grid[0]), \\\n",
+    "#             (ds['task'] if 'task' in ds else 'classification'), NUM_TRIALS=30, \\\n",
+    "#             datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), \\\n",
+    "#             extra_params=(ds['extra_params'] if 'extra_params' in ds else None))\n",
     "    print()"
    ]
   },
diff --git a/notebooks/run_randomwalkkernel.ipynb b/notebooks/run_randomwalkkernel.ipynb
index d83e62b..7999825 100644
--- a/notebooks/run_randomwalkkernel.ipynb
+++ b/notebooks/run_randomwalkkernel.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
@@ -16,34 +16,1533 @@
       "\n",
       "--- This is a regression problem ---\n",
       "\n",
-      "1. Loading dataset from file...\n",
+      "\n",
+      "I. Loading dataset from file...\n",
       "\n",
       "2. Calculating gram matrices. This could take a while...\n",
       "\n",
-      "gram matrix with parameters {'compute_method': 'sylvester'} is: \n",
-      "\r",
-      "calculating kernels:   0%|          | 0/16836.0 [00:00<?, ?it/s]"
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5175.74it/s]\n",
+      "calculating kernels:   9%|▉         | 1521/16836.0 [00:00<00:01, 15206.93it/s]"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "../pygraph/kernels/randomWalkKernel.py:81: UserWarning: The Sylvester equation (rather than generalized Sylvester equation) is used; only walks of length 1 is considered.\n",
-      "  'The Sylvester equation (rather than generalized Sylvester equation) is used; only walks of length 1 is considered.'\n"
+      "../pygraph/kernels/randomWalkKernel.py:99: UserWarning: The Sylvester equation (rather than generalized Sylvester equation) is used; edge label number has to smaller than 3.\n",
+      "  'The Sylvester equation (rather than generalized Sylvester equation) is used; edge label number has to smaller than 3.'\n"
      ]
     },
     {
-     "ename": "NameError",
-     "evalue": "name 'all_walks' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-b058c92f071d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'task'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'task'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'classification'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNUM_TRIALS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0mdatafile_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dataset_y'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'dataset_y'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m         extra_params=(ds['extra_params'] if 'extra_params' in ds else None))\n\u001b[0m\u001b[1;32m     61\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/utils/model_selection_precomputed.py\u001b[0m in \u001b[0;36mmodel_selection_for_precomputed_kernel\u001b[0;34m(datafile, estimator, param_grid_precomputed, param_grid, model_type, NUM_TRIALS, datafile_y, extra_params)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gram matrix with parameters'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'is: '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0mKmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurrent_run_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams_out\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m         \u001b[0mKmatrix_diag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKmatrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiagonal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/randomWalkKernel.py\u001b[0m in \u001b[0;36mrandomwalkkernel\u001b[0;34m(node_label, edge_label, h, compute_method, *args)\u001b[0m\n\u001b[1;32m     85\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m                 Kmatrix[i][j] = _randomwalkkernel_sylvester(\n\u001b[0;32m---> 87\u001b[0;31m                     \u001b[0mall_walks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m                     \u001b[0mall_walks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m                     \u001b[0mnode_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnode_label\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'all_walks' is not defined"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculating kernels:  98%|█████████▊| 16533/16836.0 [00:00<00:00, 17097.06it/s]\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9856514930725098 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1.0} is: \n",
+      "ignored, as it contains elements that are not numbers.\n",
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "\n",
+      "compute adjacency matrices:   0%|          | 0/183 [00:00<?, ?it/s]\u001b[A\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4995.43it/s]\u001b[A\n",
+      "calculating kernels:   0%|          | 0/16836.0 [00:00<?, ?it/s]\u001b[A\n",
+      "calculating kernels:  13%|█▎        | 2222/16836.0 [00:00<00:00, 22214.35it/s]\u001b[A"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "../pygraph/utils/model_selection_precomputed.py:114: RuntimeWarning: invalid value encountered in sqrt\n",
+      "  Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "calculating kernels:  24%|██▍       | 4106/16836.0 [00:00<00:00, 21080.87it/s]\u001b[A\n",
+      "calculating kernels:  34%|███▍      | 5740/16836.0 [00:00<00:00, 19389.07it/s]\u001b[A\n",
+      "calculating kernels:  45%|████▌     | 7609/16836.0 [00:00<00:00, 19173.26it/s]\u001b[A\n",
+      "calculating kernels:  56%|█████▌    | 9374/16836.0 [00:00<00:00, 18686.53it/s]\u001b[A\n",
+      "calculating kernels:  66%|██████▌   | 11103/16836.0 [00:00<00:00, 18241.56it/s]\u001b[A\n",
+      "calculating kernels:  76%|███████▋  | 12851/16836.0 [00:00<00:00, 18004.10it/s]\u001b[A\n",
+      "calculating kernels:  86%|████████▌ | 14481/16836.0 [00:00<00:00, 17290.20it/s]\u001b[A\n",
+      "calculating kernels:  96%|█████████▌| 16097/16836.0 [00:00<00:00, 16670.83it/s]\u001b[A\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 17479.54it/s]\u001b[A\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 1.003852128982544 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.31622776601683794} is: \n",
+      "ignored, as it contains elements that are not numbers.\n",
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:02<00:00, 8187.32it/s] \n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4749.74it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 18531.41it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9496591091156006 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.1} is: \n",
+      "[[1.         0.99734465 1.         ... 0.97126366 0.97126366 0.97892691]\n",
+      " [0.99734465 1.         0.99734465 ... 0.9857487  0.9857487  0.99113366]\n",
+      " [1.         0.99734465 1.         ... 0.97126366 0.97126366 0.97892691]\n",
+      " ...\n",
+      " [0.97126366 0.9857487  0.97126366 ... 1.         1.         0.99911683]\n",
+      " [0.97126366 0.9857487  0.97126366 ... 1.         1.         0.99911683]\n",
+      " [0.97892691 0.99113366 0.97892691 ... 0.99911683 0.99911683 1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 3745.92it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 17944.21it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9897515773773193 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.03162277660168379} is: \n",
+      "[[1.         0.99947611 1.         ... 0.9950873  0.9950873  0.9957061 ]\n",
+      " [0.99947611 1.         0.99947611 ... 0.99775202 0.99775202 0.99818019]\n",
+      " [1.         0.99947611 1.         ... 0.9950873  0.9950873  0.9957061 ]\n",
+      " ...\n",
+      " [0.9950873  0.99775202 0.9950873  ... 1.         1.         0.99993104]\n",
+      " [0.9950873  0.99775202 0.9950873  ... 1.         1.         0.99993104]\n",
+      " [0.9957061  0.99818019 0.9957061  ... 0.99993104 0.99993104 1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4863.38it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 18433.01it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9532828330993652 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.01} is: \n",
+      "[[1.         0.99985372 1.         ... 0.99871463 0.99871463 0.99877465]\n",
+      " [0.99985372 1.         0.99985372 ... 0.99943339 0.99943339 0.999475  ]\n",
+      " [1.         0.99985372 1.         ... 0.99871463 0.99871463 0.99877465]\n",
+      " ...\n",
+      " [0.99871463 0.99943339 0.99871463 ... 1.         1.         0.99999333]\n",
+      " [0.99871463 0.99943339 0.99871463 ... 1.         1.         0.99999333]\n",
+      " [0.99877465 0.999475   0.99877465 ... 0.99999333 0.99999333 1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4474.38it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 18289.56it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9632387161254883 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.0031622776601683794} is: \n",
+      "[[1.         0.99995537 1.         ... 0.99961757 0.99961757 0.99962354]\n",
+      " [0.99995537 1.         0.99995537 ... 0.99983399 0.99983399 0.99983813]\n",
+      " [1.         0.99995537 1.         ... 0.99961757 0.99961757 0.99962354]\n",
+      " ...\n",
+      " [0.99961757 0.99983399 0.99961757 ... 1.         1.         0.99999934]\n",
+      " [0.99961757 0.99983399 0.99961757 ... 1.         1.         0.99999934]\n",
+      " [0.99962354 0.99983813 0.99962354 ... 0.99999934 0.99999934 1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4938.09it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 18086.31it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9701957702636719 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.001} is: \n",
+      "[[1.         0.99998604 1.         ... 0.9998814  0.9998814  0.99988199]\n",
+      " [0.99998604 1.         0.99998604 ... 0.99994879 0.99994879 0.9999492 ]\n",
+      " [1.         0.99998604 1.         ... 0.9998814  0.9998814  0.99988199]\n",
+      " ...\n",
+      " [0.9998814  0.99994879 0.9998814  ... 1.         1.         0.99999993]\n",
+      " [0.9998814  0.99994879 0.9998814  ... 1.         1.         0.99999993]\n",
+      " [0.99988199 0.9999492  0.99988199 ... 0.99999993 0.99999993 1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 2612.21it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 17837.83it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 1.0160937309265137 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.00031622776601683794} is: \n",
+      "[[1.         0.9999956  1.         ... 0.99996272 0.99996272 0.99996278]\n",
+      " [0.9999956  1.         0.9999956  ... 0.99998393 0.99998393 0.99998398]\n",
+      " [1.         0.9999956  1.         ... 0.99996272 0.99996272 0.99996278]\n",
+      " ...\n",
+      " [0.99996272 0.99998393 0.99996272 ... 1.         1.         0.99999999]\n",
+      " [0.99996272 0.99998393 0.99996272 ... 1.         1.         0.99999999]\n",
+      " [0.99996278 0.99998398 0.99996278 ... 0.99999999 0.99999999 1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4781.64it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 18060.33it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9723460674285889 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 0.0001} is: \n",
+      "[[1.         0.99999861 1.         ... 0.99998824 0.99998824 0.99998824]\n",
+      " [0.99999861 1.         0.99999861 ... 0.99999493 0.99999493 0.99999494]\n",
+      " [1.         0.99999861 1.         ... 0.99998824 0.99998824 0.99998824]\n",
+      " ...\n",
+      " [0.99998824 0.99999493 0.99998824 ... 1.         1.         1.        ]\n",
+      " [0.99998824 0.99999493 0.99998824 ... 1.         1.         1.        ]\n",
+      " [0.99998824 0.99999494 0.99998824 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4415.56it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 18685.90it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9444630146026611 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 3.1622776601683795e-05} is: \n",
+      "[[1.         0.99999956 1.         ... 0.99999628 0.99999628 0.99999628]\n",
+      " [0.99999956 1.         0.99999956 ... 0.9999984  0.9999984  0.9999984 ]\n",
+      " [1.         0.99999956 1.         ... 0.99999628 0.99999628 0.99999628]\n",
+      " ...\n",
+      " [0.99999628 0.9999984  0.99999628 ... 1.         1.         1.        ]\n",
+      " [0.99999628 0.9999984  0.99999628 ... 1.         1.         1.        ]\n",
+      " [0.99999628 0.9999984  0.99999628 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4862.05it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 18726.23it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9388983249664307 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-05} is: \n",
+      "[[1.         0.99999986 1.         ... 0.99999882 0.99999882 0.99999882]\n",
+      " [0.99999986 1.         0.99999986 ... 0.99999949 0.99999949 0.99999949]\n",
+      " [1.         0.99999986 1.         ... 0.99999882 0.99999882 0.99999882]\n",
+      " ...\n",
+      " [0.99999882 0.99999949 0.99999882 ... 1.         1.         1.        ]\n",
+      " [0.99999882 0.99999949 0.99999882 ... 1.         1.         1.        ]\n",
+      " [0.99999882 0.99999949 0.99999882 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4739.82it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 17485.80it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 1.0035440921783447 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 3.162277660168379e-06} is: \n",
+      "[[1.         0.99999996 1.         ... 0.99999963 0.99999963 0.99999963]\n",
+      " [0.99999996 1.         0.99999996 ... 0.99999984 0.99999984 0.99999984]\n",
+      " [1.         0.99999996 1.         ... 0.99999963 0.99999963 0.99999963]\n",
+      " ...\n",
+      " [0.99999963 0.99999984 0.99999963 ... 1.         1.         1.        ]\n",
+      " [0.99999963 0.99999984 0.99999963 ... 1.         1.         1.        ]\n",
+      " [0.99999963 0.99999984 0.99999963 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4972.07it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 17745.19it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9879162311553955 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-06} is: \n",
+      "[[1.         0.99999999 1.         ... 0.99999988 0.99999988 0.99999988]\n",
+      " [0.99999999 1.         0.99999999 ... 0.99999995 0.99999995 0.99999995]\n",
+      " [1.         0.99999999 1.         ... 0.99999988 0.99999988 0.99999988]\n",
+      " ...\n",
+      " [0.99999988 0.99999995 0.99999988 ... 1.         1.         1.        ]\n",
+      " [0.99999988 0.99999995 0.99999988 ... 1.         1.         1.        ]\n",
+      " [0.99999988 0.99999995 0.99999988 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 5105.99it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 19013.06it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.923353910446167 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 3.162277660168379e-07} is: \n",
+      "[[1.         1.         1.         ... 0.99999996 0.99999996 0.99999996]\n",
+      " [1.         1.         1.         ... 0.99999998 0.99999998 0.99999998]\n",
+      " [1.         1.         1.         ... 0.99999996 0.99999996 0.99999996]\n",
+      " ...\n",
+      " [0.99999996 0.99999998 0.99999996 ... 1.         1.         1.        ]\n",
+      " [0.99999996 0.99999998 0.99999996 ... 1.         1.         1.        ]\n",
+      " [0.99999996 0.99999998 0.99999996 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4813.57it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 19004.32it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9257664680480957 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-07} is: \n",
+      "[[1.         1.         1.         ... 0.99999999 0.99999999 0.99999999]\n",
+      " [1.         1.         1.         ... 0.99999999 0.99999999 0.99999999]\n",
+      " [1.         1.         1.         ... 0.99999999 0.99999999 0.99999999]\n",
+      " ...\n",
+      " [0.99999999 0.99999999 0.99999999 ... 1.         1.         1.        ]\n",
+      " [0.99999999 0.99999999 0.99999999 ... 1.         1.         1.        ]\n",
+      " [0.99999999 0.99999999 0.99999999 ... 1.         1.         1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4655.14it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 18501.33it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.950998067855835 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 3.162277660168379e-08} is: \n",
+      "[[1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " ...\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4518.02it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 18584.48it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9485864639282227 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-08} is: \n",
+      "[[1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " ...\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4126.43it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 19034.81it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9307541847229004 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 3.1622776601683795e-09} is: \n",
+      "[[1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " ...\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 2655.58it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 18162.10it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9977066516876221 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-09} is: \n",
+      "[[1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " ...\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4636.44it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 17991.56it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.9774272441864014 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 3.1622776601683795e-10} is: \n",
+      "[[1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " ...\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 183/183 [00:00<00:00, 4503.55it/s]\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:00<00:00, 18738.20it/s]\n",
+      "\n",
+      " --- kernel matrix of random walk kernel of size 183 built in 0.941178560256958 seconds ---\n",
+      "\n",
+      "the gram matrix with parameters {'compute_method': 'sylvester', 'weight': 1e-10} is: \n",
+      "[[1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " ...\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]\n",
+      " [1. 1. 1. ... 1. 1. 1.]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "21 gram matrices are calculated, 2 of which are ignored.\n",
+      "\n",
+      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
+      "calculate performance:   0%|          | 2/23370 [00:00<20:28, 19.02it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
+      "  warnings.warn(\"Singular matrix in solving dual problem. Using \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                            \n",
+      "4. Getting final performance...\n",
+      "best_params_out:  [{'compute_method': 'sylvester', 'weight': 0.01}]\n",
+      "best_params_in:  [{'alpha': 1e-09}]\n",
+      "\n",
+      "best_val_perf:  32.12580224783455\n",
+      "best_val_std:  0.506792800236026\n",
+      "final_performance:  [33.347747238852]\n",
+      "final_confidence:  [3.723906391248989]\n",
+      "train_performance: [31.099533578717818]\n",
+      "train_std:  [0.45306322923936937]\n",
+      "\n",
+      "time to calculate gram matrix with different hyper-params: 0.96±0.03s\n",
+      "time to calculate best gram matrix: 0.95±nans\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:135: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  keepdims=keepdims)\n",
+      "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:127: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "params                                                                                  train_perf     valid_perf    test_perf        gram_matrix_time\n",
+      "--------------------------------------------------------------------------------------  -------------  ------------  -------------  ------------------\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 0.1}                     31.41±0.55     34.83±0.90    33.50±4.97                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 0.03162277660168379}     31.80±0.58     33.56±0.63    32.57±4.87                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 0.01}                    31.42±0.53     32.44±0.58    33.34±4.38                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 0.0031622776601683794}   31.93±0.44     33.04±0.60    32.35±5.14                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 0.001}                   32.01±0.67     32.95±0.78    33.88±5.74                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 0.00031622776601683794}  32.23±0.39     33.03±0.56    31.82±3.59                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 0.0001}                  32.50±0.36     33.11±0.49    31.80±3.32                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 3.1622776601683795e-05}  32.52±0.46     33.14±0.61    31.90±4.37                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 1e-05}                   32.31±0.48     32.95±0.58    33.94±4.28                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 3.162277660168379e-06}   32.88±0.48     33.39±0.61    33.25±4.22                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 1e-06}                   34.03±0.59     34.31±0.70    34.37±5.09                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 3.162277660168379e-07}   34.27±0.47     34.62±0.51    35.71±4.48                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 1e-07}                   34.19±0.47     34.52±0.61    36.51±3.90                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 3.162277660168379e-08}   34.52±0.55     34.82±0.63    33.98±5.69                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 1e-08}                   34.45±0.45     34.83±0.62    34.35±4.08                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 3.1622776601683795e-09}  34.42±0.41     34.70±0.54    34.57±3.76                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 1e-09}                   43.89±21.46    41.92±16.11   43.65±24.20                  1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 3.1622776601683795e-10}  35.75±0.42     35.94±0.47    34.34±3.86                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-10', 'weight': 1e-10}                   38.70±0.55     38.67±0.65    38.44±6.41                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 0.1}                     31.40±0.55     34.60±0.87    33.31±4.87                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 0.03162277660168379}     31.79±0.57     33.41±0.60    32.41±4.79                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 0.01}                    31.18±0.49     32.17±0.54    33.20±4.10                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 0.0031622776601683794}   31.94±0.44     33.01±0.62    32.37±5.15                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 0.001}                   32.04±0.67     32.95±0.78    33.87±5.75                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 0.00031622776601683794}  32.31±0.39     33.04±0.54    31.86±3.64                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 0.0001}                  32.52±0.36     33.13±0.49    31.82±3.32                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 3.1622776601683795e-05}  32.52±0.46     33.14±0.61    31.89±4.37                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 1e-05}                   32.40±0.48     33.00±0.57    33.94±4.29                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 3.162277660168379e-06}   33.53±0.47     33.98±0.62    33.95±4.22                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 1e-06}                   34.27±0.61     34.53±0.71    34.55±5.16                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 3.162277660168379e-07}   34.30±0.48     34.65±0.51    35.73±4.48                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 1e-07}                   34.19±0.47     34.52±0.61    36.52±3.90                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 3.162277660168379e-08}   34.51±0.56     34.81±0.63    33.93±5.62                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 1e-08}                   34.45±0.45     34.80±0.61    34.35±4.08                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 3.1622776601683795e-09}  45.04±28.98    44.63±24.93   45.05±31.56                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 1e-09}                   35.60±0.42     35.65±0.50    34.81±4.63                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 3.1622776601683795e-10}  38.78±0.38     38.86±0.42    37.26±4.76                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-10', 'weight': 1e-10}                   43.11±0.70     42.95±0.82    43.12±7.59                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 0.1}                     31.42±0.54     34.46±0.86    33.17±4.79                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 0.03162277660168379}     31.80±0.56     33.26±0.57    32.23±4.71                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 0.01}                    31.10±0.45     32.13±0.51    33.35±3.72                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 0.0031622776601683794}   31.98±0.45     32.99±0.64    32.45±5.19                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 0.001}                   32.00±0.67     32.86±0.77    33.78±5.74                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 0.00031622776601683794}  32.42±0.39     33.11±0.52    31.94±3.68                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 0.0001}                  32.53±0.36     33.13±0.49    31.83±3.33                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 3.1622776601683795e-05}  32.52±0.46     33.13±0.61    31.86±4.38                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 1e-05}                   32.77±0.48     33.31±0.55    34.17±4.40                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 3.162277660168379e-06}   34.03±0.47     34.44±0.63    34.49±4.24                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 1e-06}                   34.36±0.61     34.61±0.72    34.62±5.18                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 3.162277660168379e-07}   34.31±0.48     34.65±0.51    35.73±4.46                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 1e-07}                   34.19±0.47     34.50±0.61    36.52±3.89                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 3.162277660168379e-08}   34.51±0.56     34.78±0.62    33.80±5.41                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 1e-08}                   41.17±20.89    41.59±24.83   43.48±32.96                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 3.1622776601683795e-09}  35.61±0.36     35.65±0.43    35.08±3.50                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 1e-09}                   38.71±0.51     38.72±0.57    37.94±5.46                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 3.1622776601683795e-10}  43.26±0.47     43.17±0.53    41.51±5.83                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-09', 'weight': 1e-10}                   46.04±0.90     45.74±1.02    46.14±8.26                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 0.1}                     31.49±0.53     34.39±0.85    33.08±4.77                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 0.03162277660168379}     31.78±0.55     33.11±0.54    32.08±4.67                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 0.01}                    31.36±0.42     32.45±0.49    33.79±3.51                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 0.0031622776601683794}   32.11±0.47     33.06±0.66    32.61±5.26                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 0.001}                   31.97±0.67     32.76±0.76    33.69±5.73                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 0.00031622776601683794}  32.48±0.40     33.16±0.52    31.99±3.71                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 0.0001}                  32.53±0.36     33.13±0.49    31.82±3.34                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 3.1622776601683795e-05}  32.61±0.46     33.18±0.61    31.85±4.41                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 1e-05}                   33.44±0.49     33.90±0.56    34.71±4.59                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 3.162277660168379e-06}   34.27±0.48     34.65±0.64    34.74±4.24                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 1e-06}                   34.39±0.62     34.64±0.72    34.64±5.19                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 3.162277660168379e-07}   34.31±0.48     34.64±0.51    35.71±4.42                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 1e-07}                   34.19±0.47     34.47±0.60    36.53±3.89                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 3.162277660168379e-08}   116.46±392.96  95.65±281.70  127.13±446.23                0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 1e-08}                   35.59±0.46     35.73±0.51    35.48±4.66                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 3.1622776601683795e-09}  38.72±0.33     38.64±0.43    37.98±4.87                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 1e-09}                   43.18±0.57     43.12±0.64    42.55±6.20                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 3.1622776601683795e-10}  46.25±0.62     46.00±0.68    44.32±6.43                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-09', 'weight': 1e-10}                   47.28±1.01     46.92±1.13    47.41±8.52                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 0.1}                     31.60±0.54     34.36±0.84    33.05±4.85                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 0.03162277660168379}     31.70±0.53     32.90±0.51    31.94±4.67                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 0.01}                    31.60±0.41     32.70±0.48    34.08±3.43                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 0.0031622776601683794}   32.19±0.50     33.10±0.69    32.70±5.29                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 0.001}                   32.06±0.66     32.78±0.75    33.70±5.72                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 0.00031622776601683794}  32.51±0.40     33.17±0.52    32.01±3.71                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 0.0001}                  32.54±0.36     33.13±0.49    31.81±3.39                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 3.1622776601683795e-05}  32.99±0.46     33.49±0.60    32.11±4.50                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 1e-05}                   33.96±0.52     34.37±0.57    35.16±4.70                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 3.162277660168379e-06}   34.35±0.48     34.73±0.64    34.83±4.24                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 1e-06}                   34.39±0.62     34.63±0.72    34.63±5.18                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 3.162277660168379e-07}   34.30±0.48     34.61±0.51    35.65±4.30                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 1e-07}                   64.93±108.54   59.71±86.55   68.23±109.56                 0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 3.162277660168379e-08}   35.67±0.52     35.80±0.54    34.25±5.19                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 1e-08}                   38.63±0.49     38.70±0.54    38.36±5.88                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 3.1622776601683795e-09}  43.19±0.51     42.94±0.61    42.50±6.61                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 1e-09}                   46.14±0.68     45.97±0.76    45.54±6.74                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 3.1622776601683795e-10}  47.52±0.71     47.19±0.77    45.51±6.66                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-08', 'weight': 1e-10}                   47.71±1.05     47.33±1.17    47.85±8.60                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 0.1}                     31.76±0.56     34.33±0.81    33.05±4.94                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 0.03162277660168379}     31.53±0.50     32.62±0.48    31.73±4.60                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 0.01}                    31.68±0.41     32.75±0.48    34.15±3.39                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 0.0031622776601683794}   32.18±0.52     33.05±0.70    32.62±5.24                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 0.001}                   32.17±0.65     32.85±0.74    33.77±5.73                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 0.00031622776601683794}  32.51±0.40     33.17±0.52    32.00±3.70                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 0.0001}                  32.63±0.36     33.18±0.48    31.82±3.53                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 3.1622776601683795e-05}  33.66±0.47     34.10±0.61    32.71±4.59                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 1e-05}                   34.19±0.53     34.58±0.59    35.37±4.74                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 3.162277660168379e-06}   34.37±0.48     34.74±0.64    34.86±4.22                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 1e-06}                   34.39±0.62     34.61±0.71    34.62±5.14                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 3.162277660168379e-07}   39.02±8.07     39.64±8.64    41.63±13.30                  0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 1e-07}                   35.34±0.50     35.43±0.63    37.74±4.63                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 3.162277660168379e-08}   38.79±0.51     38.82±0.52    37.17±6.48                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 1e-08}                   43.12±0.65     43.05±0.70    42.48±7.30                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 3.1622776601683795e-09}  46.13±0.76     45.73±0.84    45.50±7.59                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 1e-09}                   47.39±0.76     47.16±0.84    46.79±6.97                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 3.1622776601683795e-10}  47.97±0.74     47.61±0.80    45.92±6.73                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-08', 'weight': 1e-10}                   47.86±1.07     47.46±1.19    47.99±8.63                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 0.1}                     31.86±0.57     34.20±0.75    32.99±4.98                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 0.03162277660168379}     31.27±0.47     32.32±0.46    31.42±4.39                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 0.01}                    31.76±0.40     32.75±0.48    34.16±3.40                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 0.0031622776601683794}   32.17±0.54     32.97±0.71    32.48±5.15                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 0.001}                   32.23±0.65     32.90±0.74    33.81±5.73                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 0.00031622776601683794}  32.53±0.40     33.16±0.52    31.99±3.66                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 0.0001}                  33.00±0.36     33.49±0.47    32.05±3.83                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 3.1622776601683795e-05}  34.17±0.49     34.57±0.63    33.20±4.63                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 1e-05}                   34.27±0.54     34.65±0.59    35.44±4.71                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 3.162277660168379e-06}   34.38±0.48     34.71±0.63    34.90±4.16                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 1e-06}                   42.36±17.72    44.50±25.64   42.64±20.41                  0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 3.162277660168379e-07}   35.50±0.45     35.63±0.48    36.51±4.74                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 1e-07}                   38.40±0.50     38.41±0.66    40.78±5.67                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 3.162277660168379e-08}   43.25±0.68     43.08±0.71    41.74±7.96                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 1e-08}                   46.12±0.83     45.91±0.86    45.18±8.12                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 3.1622776601683795e-09}  47.37±0.88     46.90±0.96    46.77±7.97                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 1e-09}                   47.82±0.79     47.57±0.87    47.23±7.05                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 3.1622776601683795e-10}  48.11±0.75     47.75±0.81    46.06±6.76                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-07', 'weight': 1e-10}                   47.90±1.07     47.50±1.19    48.04±8.64                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 0.1}                     31.86±0.56     33.95±0.68    32.86±4.98                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 0.03162277660168379}     31.22±0.47     32.32±0.46    31.22±4.20                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 0.01}                    31.90±0.39     32.81±0.49    34.23±3.44                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 0.0031622776601683794}   32.26±0.53     33.00±0.70    32.45±5.07                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 0.001}                   32.26±0.65     32.91±0.74    33.82±5.73                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 0.00031622776601683794}  32.62±0.40     33.21±0.52    32.01±3.61                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 0.0001}                  33.67±0.39     34.09±0.48    32.56±4.16                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 3.1622776601683795e-05}  34.41±0.50     34.78±0.64    33.42±4.63                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 1e-05}                   34.30±0.54     34.64±0.59    35.45±4.59                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 3.162277660168379e-06}   53.81±59.47    49.09±36.57   50.73±44.10                  1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 1e-06}                   35.52±0.63     35.56±0.66    35.50±5.17                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 3.162277660168379e-07}   38.57±0.46     38.62±0.54    39.74±6.25                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 1e-07}                   42.87±0.60     42.75±0.72    45.14±6.35                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 3.162277660168379e-08}   46.18±0.91     45.84±0.94    44.74±8.82                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 1e-08}                   47.40±0.93     47.11±0.95    46.32±8.44                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 3.1622776601683795e-09}  47.80±0.93     47.30±1.00    47.21±8.10                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 1e-09}                   47.97±0.80     47.71±0.88    47.37±7.08                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 3.1622776601683795e-10}  48.16±0.75     47.79±0.81    46.10±6.77                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-07', 'weight': 1e-10}                   47.92±1.07     47.52±1.19    48.05±8.65                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 0.1}                     31.81±0.56     33.63±0.64    32.70±4.97                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 0.03162277660168379}     31.49±0.47     32.65±0.48    31.29±4.17                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 0.01}                    31.98±0.39     32.80±0.50    34.16±3.45                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 0.0031622776601683794}   32.37±0.52     33.07±0.69    32.49±5.02                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 0.001}                   32.27±0.65     32.91±0.74    33.82±5.74                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 0.00031622776601683794}  33.00±0.39     33.51±0.51    32.28±3.65                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 0.0001}                  34.20±0.43     34.56±0.50    32.99±4.36                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 3.1622776601683795e-05}  34.49±0.50     34.83±0.63    33.51±4.57                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 1e-05}                   82.09±192.84   98.64±288.87  73.78±149.91                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 3.162277660168379e-06}   35.53±0.50     35.65±0.54    36.07±4.25                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 1e-06}                   38.60±0.57     38.60±0.56    38.54±5.83                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 3.162277660168379e-07}   42.98±0.69     42.85±0.78    44.56±7.53                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 1e-07}                   45.84±0.73     45.59±0.82    48.00±6.71                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 3.162277660168379e-08}   47.42±1.04     47.00±1.06    46.00±9.16                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 1e-08}                   47.84±0.97     47.52±0.98    46.72±8.54                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 3.1622776601683795e-09}  47.94±0.95     47.44±1.02    47.35±8.14                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 1e-09}                   48.01±0.80     47.75±0.89    47.41±7.09                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 3.1622776601683795e-10}  48.17±0.75     47.80±0.81    46.11±6.77                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-06', 'weight': 1e-10}                   47.92±1.07     47.52±1.19    48.06±8.65                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 0.1}                     31.72±0.54     33.33±0.63    32.57±4.94                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 0.03162277660168379}     31.70±0.47     32.85±0.50    31.38±4.13                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 0.01}                    32.01±0.38     32.73±0.50    33.98±3.48                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 0.0031622776601683794}   32.43±0.52     33.11±0.68    32.51±4.99                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 0.001}                   32.37±0.65     32.95±0.73    33.87±5.76                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 0.00031622776601683794}  33.68±0.40     34.10±0.50    32.85±3.82                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 0.0001}                  34.44±0.45     34.76±0.51    33.22±4.43                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 3.1622776601683795e-05}  60.66±88.26    60.99±81.91   54.73±69.13                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 1e-05}                   35.48±0.57     35.63±0.57    36.36±4.33                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 3.162277660168379e-06}   38.57±0.43     38.60±0.48    38.68±5.68                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 1e-06}                   43.10±0.64     42.99±0.70    42.90±6.67                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 3.162277660168379e-07}   45.88±0.89     45.59±0.99    47.71±8.18                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 1e-07}                   47.10±0.79     46.78±0.89    49.21±6.85                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 3.162277660168379e-08}   47.86±1.09     47.41±1.10    46.43±9.28                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 1e-08}                   47.99±0.98     47.66±0.99    46.85±8.58                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 3.1622776601683795e-09}  47.98±0.95     47.48±1.02    47.40±8.16                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 1e-09}                   48.03±0.80     47.77±0.89    47.43±7.09                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 3.1622776601683795e-10}  48.18±0.75     47.81±0.81    46.12±6.77                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-06', 'weight': 1e-10}                   47.92±1.07     47.52±1.19    48.06±8.65                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 0.1}                     31.56±0.52     32.98±0.62    32.36±4.80                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 0.03162277660168379}     31.86±0.46     32.95±0.51    31.47±4.06                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 0.01}                    32.12±0.39     32.74±0.49    33.88±3.56                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 0.0031622776601683794}   32.47±0.52     33.12±0.68    32.49±4.95                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 0.001}                   32.77±0.65     33.27±0.73    34.18±5.78                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 0.00031622776601683794}  34.20±0.42     34.55±0.51    33.32±3.89                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 0.0001}                  67.26±110.04   72.38±146.18  65.95±106.56                 0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 3.1622776601683795e-05}  35.63±0.57     35.77±0.63    34.77±4.16                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 1e-05}                   38.55±0.50     38.59±0.55    39.14±4.84                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 3.162277660168379e-06}   43.10±0.61     42.94±0.61    42.39±7.37                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 1e-06}                   46.09±0.78     45.86±0.88    45.75±7.22                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 3.162277660168379e-07}   47.11±0.99     46.73±1.09    49.02±8.43                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 1e-07}                   47.54±0.82     47.19±0.91    49.63±6.90                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 3.162277660168379e-08}   48.00±1.11     47.54±1.12    46.58±9.32                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 1e-08}                   48.03±0.99     47.70±0.99    46.89±8.59                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 3.1622776601683795e-09}  48.00±0.95     47.49±1.02    47.41±8.16                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 1e-09}                   48.03±0.80     47.77±0.89    47.43±7.09                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 3.1622776601683795e-10}  48.18±0.76     47.81±0.81    46.12±6.77                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-05', 'weight': 1e-10}                   47.92±1.07     47.52±1.19    48.06±8.65                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 0.1}                     31.30±0.49     32.57±0.60    32.01±4.50                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 0.03162277660168379}     32.09±0.44     33.08±0.51    31.65±3.98                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 0.01}                    32.23±0.40     32.79±0.49    33.86±3.63                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 0.0031622776601683794}   32.59±0.52     33.19±0.67    32.47±4.88                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 0.001}                   33.49±0.66     33.88±0.73    34.80±5.74                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 0.00031622776601683794}  83.17±114.23   98.84±168.27  75.51±90.41                  1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 0.0001}                  35.73±0.51     35.86±0.55    34.87±4.60                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 3.1622776601683795e-05}  38.70±0.45     38.72±0.51    37.46±4.75                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 1e-05}                   43.06±0.52     42.95±0.61    43.28±5.18                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 3.162277660168379e-06}   46.14±0.82     45.81±0.81    44.86±8.27                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 1e-06}                   47.36±0.86     47.06±0.97    46.95±7.46                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 3.162277660168379e-07}   47.53±1.03     47.13±1.12    49.48±8.52                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 1e-07}                   47.68±0.83     47.33±0.92    49.76±6.91                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 3.162277660168379e-08}   48.04±1.11     47.58±1.13    46.62±9.33                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 1e-08}                   48.05±0.99     47.72±0.99    46.90±8.59                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 3.1622776601683795e-09}  48.00±0.95     47.50±1.02    47.42±8.16                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 1e-09}                   48.03±0.80     47.77±0.89    47.43±7.09                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 3.1622776601683795e-10}  48.18±0.76     47.81±0.81    46.12±6.77                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-05', 'weight': 1e-10}                   47.92±1.07     47.52±1.19    48.06±8.65                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 0.1}                     31.23±0.47     32.36±0.56    31.74±4.19                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 0.03162277660168379}     32.27±0.43     33.15±0.50    31.82±3.91                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 0.01}                    32.31±0.40     32.82±0.48    33.82±3.68                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 0.0031622776601683794}   33.09±0.50     33.59±0.65    32.70±4.83                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 0.001}                   42.51±17.13    43.16±17.50   42.15±14.14                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 0.00031622776601683794}  35.62±0.41     35.72±0.47    34.81±3.49                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 0.0001}                  38.71±0.47     38.79±0.50    37.94±5.59                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 3.1622776601683795e-05}  43.24±0.48     43.09±0.60    41.33±5.86                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 1e-05}                   46.07±0.59     45.83±0.68    46.03±5.40                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 3.162277660168379e-06}   47.43±0.94     47.02±0.92    45.90±8.61                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 1e-06}                   47.80±0.89     47.48±1.00    47.36±7.54                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 3.162277660168379e-07}   47.67±1.04     47.26±1.14    49.63±8.54                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 1e-07}                   47.73±0.83     47.37±0.92    49.81±6.92                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 3.162277660168379e-08}   48.06±1.11     47.59±1.13    46.64±9.33                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 1e-08}                   48.05±0.99     47.72±0.99    46.91±8.59                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 3.1622776601683795e-09}  48.00±0.95     47.50±1.02    47.42±8.16                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 1e-09}                   48.03±0.80     47.77±0.89    47.43±7.09                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 3.1622776601683795e-10}  48.18±0.76     47.81±0.81    46.12±6.77                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-04', 'weight': 1e-10}                   47.92±1.07     47.52±1.19    48.06±8.65                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 0.1}                     31.38±0.45     32.39±0.52    31.59±4.03                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 0.03162277660168379}     32.41±0.43     33.18±0.49    32.01±3.84                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 0.01}                    32.55±0.41     32.98±0.48    33.85±3.82                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 0.0031622776601683794}   79.96±129.32   79.38±128.82  82.66±156.48                 0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 0.001}                   35.43±0.64     35.56±0.68    36.99±5.16                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 0.00031622776601683794}  38.71±0.35     38.71±0.41    37.84±3.69                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 0.0001}                  43.17±0.57     43.12±0.64    42.12±6.84                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 3.1622776601683795e-05}  46.28±0.63     45.99±0.77    43.89±6.62                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 1e-05}                   47.35±0.63     47.04±0.73    47.19±5.50                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 3.162277660168379e-06}   47.89±0.98     47.44±0.96    46.26±8.73                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 1e-06}                   47.94±0.90     47.62±1.01    47.50±7.57                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 3.162277660168379e-07}   47.72±1.05     47.30±1.14    49.68±8.55                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 1e-07}                   47.74±0.83     47.39±0.92    49.82±6.92                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 3.162277660168379e-08}   48.06±1.11     47.60±1.13    46.64±9.33                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 1e-08}                   48.05±0.99     47.72±0.99    46.91±8.60                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 3.1622776601683795e-09}  48.01±0.95     47.50±1.02    47.42±8.16                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 1e-09}                   48.03±0.80     47.77±0.89    47.43±7.09                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 3.1622776601683795e-10}  48.18±0.76     47.81±0.81    46.12±6.77                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-04', 'weight': 1e-10}                   47.92±1.07     47.52±1.19    48.06±8.65                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 0.1}                     31.81±0.44     32.71±0.51    31.69±4.13                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 0.03162277660168379}     32.62±0.43     33.25±0.47    32.41±3.73                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 0.01}                    35.51±5.39     35.52±5.20    36.47±7.43                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 0.0031622776601683794}   35.62±0.52     35.77±0.60    34.35±5.14                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 0.001}                   38.50±0.57     38.53±0.58    40.52±5.27                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 0.00031622776601683794}  43.21±0.38     43.04±0.47    42.10±4.42                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 0.0001}                  46.17±0.76     45.97±0.84    44.82±7.57                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 3.1622776601683795e-05}  47.57±0.72     47.21±0.86    44.97±6.93                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 1e-05}                   47.79±0.65     47.47±0.75    47.59±5.54                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 3.162277660168379e-06}   48.04±0.99     47.58±0.97    46.38±8.76                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 1e-06}                   47.99±0.90     47.66±1.01    47.54±7.58                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 3.162277660168379e-07}   47.73±1.05     47.31±1.14    49.70±8.55                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 1e-07}                   47.75±0.83     47.39±0.92    49.83±6.92                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 3.162277660168379e-08}   48.06±1.11     47.60±1.13    46.64±9.33                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 1e-08}                   48.05±0.99     47.72±0.99    46.91±8.60                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 3.1622776601683795e-09}  48.01±0.95     47.50±1.02    47.42±8.16                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 1e-09}                   48.03±0.80     47.77±0.89    47.43±7.09                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 3.1622776601683795e-10}  48.18±0.76     47.81±0.81    46.12±6.77                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-03', 'weight': 1e-10}                   47.92±1.07     47.52±1.19    48.06±8.65                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 0.1}                     32.71±0.43     33.45±0.52    32.35±4.33                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 0.03162277660168379}     33.40±0.42     33.85±0.44    33.62±3.65                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 0.01}                    35.49±0.46     35.62±0.53    35.72±5.00                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 0.0031622776601683794}   38.84±0.51     38.85±0.61    37.52±6.15                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 0.001}                   42.90±0.57     42.75±0.60    45.20±6.10                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 0.00031622776601683794}  46.21±0.49     45.89±0.59    44.89±5.00                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 0.0001}                  47.44±0.86     47.18±0.94    45.96±7.85                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 3.1622776601683795e-05}  48.02±0.75     47.64±0.90    45.34±7.04                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 1e-05}                   47.94±0.66     47.61±0.75    47.73±5.55                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 3.162277660168379e-06}   48.08±1.00     47.62±0.98    46.42±8.78                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 1e-06}                   48.01±0.90     47.67±1.01    47.56±7.58                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 3.162277660168379e-07}   47.74±1.05     47.32±1.14    49.70±8.56                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 1e-07}                   47.75±0.83     47.39±0.92    49.83±6.92                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 3.162277660168379e-08}   48.07±1.11     47.60±1.13    46.64±9.33                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 1e-08}                   48.05±0.99     47.72±0.99    46.91±8.60                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 3.1622776601683795e-09}  48.01±0.95     47.50±1.02    47.42±8.16                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 1e-09}                   48.03±0.80     47.77±0.89    47.43±7.09                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 3.1622776601683795e-10}  48.18±0.76     47.81±0.81    46.12±6.77                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-03', 'weight': 1e-10}                   47.92±1.07     47.52±1.19    48.06±8.65                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 0.1}                     34.14±0.44     34.63±0.54    33.64±4.70                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 0.03162277660168379}     42.75±10.04    41.76±8.43    42.52±11.67                  0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 0.01}                    38.85±0.50     38.88±0.58    38.29±5.91                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 0.0031622776601683794}   43.23±0.62     43.12±0.70    42.17±7.63                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 0.001}                   45.83±0.69     45.52±0.73    48.19±6.76                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 0.00031622776601683794}  47.48±0.56     47.09±0.65    46.06±5.26                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 0.0001}                  47.89±0.91     47.60±0.98    46.35±7.95                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 3.1622776601683795e-05}  48.17±0.76     47.77±0.91    45.47±7.08                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 1e-05}                   47.99±0.66     47.65±0.76    47.77±5.55                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 3.162277660168379e-06}   48.10±1.00     47.64±0.98    46.43±8.78                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 1e-06}                   48.01±0.90     47.68±1.02    47.56±7.58                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 3.162277660168379e-07}   47.74±1.05     47.32±1.14    49.70±8.56                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 1e-07}                   47.75±0.83     47.39±0.92    49.83±6.92                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 3.162277660168379e-08}   48.07±1.11     47.60±1.13    46.64±9.33                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 1e-08}                   48.06±0.99     47.72±0.99    46.91±8.59                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 3.1622776601683795e-09}  48.01±0.95     47.50±1.02    47.42±8.16                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 1e-09}                   48.03±0.80     47.77±0.89    47.43±7.09                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 3.1622776601683795e-10}  48.18±0.76     47.81±0.81    46.12±6.77                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-02', 'weight': 1e-10}                   47.92±1.07     47.52±1.19    48.06±8.65                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 0.1}                     37.10±0.49     37.37±0.60    36.61±5.48                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 0.03162277660168379}     39.36±0.41     39.43±0.39    40.19±4.75                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 0.01}                    43.22±0.60     43.09±0.69    41.66±6.73                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 0.0031622776601683794}   46.14±0.85     45.91±0.87    45.23±8.56                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 0.001}                   47.07±0.78     46.68±0.80    49.43±7.05                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 0.00031622776601683794}  47.93±0.58     47.50±0.68    46.47±5.35                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 0.0001}                  48.04±0.92     47.73±1.00    46.48±7.98                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 3.1622776601683795e-05}  48.22±0.77     47.82±0.91    45.51±7.09                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 1e-05}                   48.00±0.66     47.66±0.76    47.78±5.56                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 3.162277660168379e-06}   48.10±1.00     47.64±0.98    46.44±8.78                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 1e-06}                   48.01±0.90     47.68±1.02    47.57±7.58                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 3.162277660168379e-07}   47.74±1.05     47.32±1.14    49.70±8.55                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 1e-07}                   47.75±0.83     47.39±0.92    49.83±6.92                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 3.162277660168379e-08}   48.07±1.11     47.60±1.13    46.64±9.33                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 1e-08}                   48.06±0.99     47.72±0.99    46.91±8.59                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 3.1622776601683795e-09}  48.01±0.95     47.50±1.02    47.42±8.16                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 1e-09}                   48.03±0.80     47.77±0.89    47.43±7.09                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 3.1622776601683795e-10}  48.18±0.76     47.81±0.81    46.12±6.77                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-02', 'weight': 1e-10}                   47.92±1.07     47.52±1.19    48.06±8.65                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 0.1}                     62.98±65.03    62.59±64.71   60.17±54.45                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 0.03162277660168379}     43.22±0.51     43.16±0.51    43.56±6.24                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 0.01}                    46.23±0.73     45.92±0.83    44.00±7.20                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 0.0031622776601683794}   47.37±0.98     47.08±0.98    46.52±8.93                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 0.001}                   47.50±0.82     47.09±0.83    49.86±7.14                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 0.00031622776601683794}  48.07±0.59     47.64±0.69    46.60±5.38                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 0.0001}                  48.09±0.92     47.78±1.00    46.52±7.99                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 3.1622776601683795e-05}  48.23±0.77     47.83±0.91    45.52±7.09                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 1e-05}                   48.01±0.66     47.67±0.76    47.79±5.56                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 3.162277660168379e-06}   48.11±1.00     47.64±0.98    46.45±8.78                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 1e-06}                   48.01±0.90     47.68±1.02    47.57±7.58                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 3.162277660168379e-07}   47.74±1.05     47.32±1.14    49.70±8.55                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 1e-07}                   47.75±0.83     47.39±0.92    49.83±6.91                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 3.162277660168379e-08}   48.07±1.11     47.60±1.13    46.64±9.33                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 1e-08}                   48.06±0.99     47.72±0.99    46.91±8.59                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 3.1622776601683795e-09}  48.01±0.95     47.50±1.02    47.42±8.16                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 1e-09}                   48.03±0.80     47.77±0.89    47.44±7.08                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 3.1622776601683795e-10}  48.18±0.76     47.81±0.81    46.12±6.77                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e-01', 'weight': 1e-10}                   47.92±1.07     47.52±1.19    48.05±8.64                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 0.1}                     43.82±0.70     43.86±0.81    43.09±6.72                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 0.03162277660168379}     46.00±0.74     45.80±0.76    45.79±7.29                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 0.01}                    47.54±0.80     47.14±0.90    45.05±7.38                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 0.0031622776601683794}   47.80±1.03     47.49±1.03    46.96±9.05                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 0.001}                   47.64±0.83     47.22±0.85    50.00±7.16                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 0.00031622776601683794}  48.12±0.60     47.69±0.69    46.63±5.38                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 0.0001}                  48.10±0.92     47.79±1.00    46.53±7.99                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 3.1622776601683795e-05}  48.24±0.77     47.84±0.91    45.52±7.10                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 1e-05}                   48.01±0.66     47.67±0.76    47.80±5.56                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 3.162277660168379e-06}   48.11±1.00     47.65±0.98    46.46±8.79                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 1e-06}                   48.01±0.90     47.68±1.01    47.59±7.58                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 3.162277660168379e-07}   47.74±1.05     47.32±1.14    49.70±8.53                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 1e-07}                   47.75±0.83     47.40±0.92    49.83±6.90                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 3.162277660168379e-08}   48.07±1.11     47.60±1.13    46.63±9.31                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 1e-08}                   48.06±0.99     47.73±0.99    46.91±8.57                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 3.1622776601683795e-09}  48.01±0.95     47.50±1.02    47.41±8.15                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 1e-09}                   48.04±0.80     47.77±0.89    47.44±7.07                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 3.1622776601683795e-10}  48.18±0.76     47.81±0.81    46.12±6.76                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e-01', 'weight': 1e-10}                   47.92±1.07     47.53±1.19    48.05±8.63                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 0.1}                     45.84±0.77     45.68±0.88    44.69±7.23                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 0.03162277660168379}     47.30±0.88     47.02±0.91    46.83±7.70                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 0.01}                    48.01±0.83     47.58±0.94    45.48±7.43                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 0.0031622776601683794}   47.95±1.05     47.63±1.04    47.09±9.06                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 0.001}                   47.70±0.83     47.27±0.85    50.05±7.15                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 0.00031622776601683794}  48.14±0.60     47.71±0.69    46.64±5.36                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 0.0001}                  48.11±0.92     47.81±1.01    46.54±7.97                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 3.1622776601683795e-05}  48.25±0.77     47.85±0.91    45.52±7.11                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 1e-05}                   48.02±0.66     47.68±0.76    47.82±5.56                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 3.162277660168379e-06}   48.11±1.00     47.66±0.98    46.52±8.80                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 1e-06}                   48.02±0.90     47.69±1.01    47.66±7.56                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 3.162277660168379e-07}   47.75±1.05     47.33±1.14    49.70±8.47                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 1e-07}                   47.76±0.83     47.41±0.92    49.84±6.85                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 3.162277660168379e-08}   48.07±1.11     47.61±1.13    46.59±9.27                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 1e-08}                   48.06±0.99     47.74±0.99    46.93±8.53                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 3.1622776601683795e-09}  48.01±0.95     47.51±1.02    47.39±8.11                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 1e-09}                   48.04±0.80     47.78±0.89    47.45±7.04                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 3.1622776601683795e-10}  48.19±0.75     47.82±0.81    46.12±6.74                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+00', 'weight': 1e-10}                   47.93±1.07     47.54±1.19    48.03±8.61                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 0.1}                     47.19±0.82     46.88±0.92    45.80±7.58                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 0.03162277660168379}     47.85±0.94     47.54±0.96    47.33±7.69                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 0.01}                    48.23±0.84     47.80±0.95    45.86±7.43                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 0.0031622776601683794}   48.07±1.05     47.75±1.05    47.13±8.98                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 0.001}                   47.78±0.83     47.35±0.85    50.12±7.06                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 0.00031622776601683794}  48.22±0.60     47.79±0.70    46.65±5.33                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 0.0001}                  48.19±0.92     47.89±1.01    46.58±7.93                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 3.1622776601683795e-05}  48.32±0.77     47.92±0.91    45.56±7.16                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 1e-05}                   48.09±0.66     47.76±0.76    47.95±5.59                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 3.162277660168379e-06}   48.19±1.00     47.76±0.98    46.75±8.86                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 1e-06}                   48.09±0.90     47.76±1.01    47.92±7.55                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 3.162277660168379e-07}   47.82±1.05     47.41±1.14    49.74±8.31                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 1e-07}                   47.83±0.83     47.49±0.91    49.92±6.72                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 3.162277660168379e-08}   48.15±1.11     47.70±1.13    46.55±9.13                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 1e-08}                   48.14±0.99     47.81±0.99    47.01±8.40                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 3.1622776601683795e-09}  48.09±0.95     47.59±1.02    47.39±8.02                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 1e-09}                   48.12±0.80     47.86±0.88    47.53±6.96                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 3.1622776601683795e-10}  48.26±0.75     47.90±0.80    46.18±6.70                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+00', 'weight': 1e-10}                   48.00±1.07     47.62±1.19    48.03±8.52                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 0.1}                     48.43±0.84     48.06±0.90    47.08±7.98                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 0.03162277660168379}     48.66±0.94     48.33±0.96    48.27±7.35                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 0.01}                    48.93±0.83     48.49±0.95    47.12±7.44                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 0.0031622776601683794}   48.74±1.04     48.41±1.03    47.58±8.73                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 0.001}                   48.45±0.82     47.99±0.83    50.70±6.87                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 0.00031622776601683794}  48.88±0.59     48.46±0.71    47.11±5.45                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 0.0001}                  48.84±0.91     48.55±1.02    47.17±7.87                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 3.1622776601683795e-05}  48.97±0.76     48.56±0.91    46.14±7.41                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 1e-05}                   48.74±0.65     48.42±0.75    48.76±5.80                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 3.162277660168379e-06}   48.84±0.99     48.46±0.98    47.86±9.09                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 1e-06}                   48.74±0.89     48.41±0.98    49.10±7.69                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 3.162277660168379e-07}   48.48±1.03     48.07±1.12    50.27±7.95                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 1e-07}                   48.49±0.81     48.15±0.89    50.57±6.45                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 3.162277660168379e-08}   48.80±1.09     48.37±1.12    46.87±8.72                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 1e-08}                   48.79±0.97     48.46±0.97    47.70±8.09                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 3.1622776601683795e-09}  48.74±0.93     48.27±1.01    47.81±7.83                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 1e-09}                   48.77±0.79     48.52±0.86    48.19±6.89                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 3.1622776601683795e-10}  48.91±0.74     48.57±0.77    46.81±6.69                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+01', 'weight': 1e-10}                   48.66±1.05     48.30±1.17    48.44±8.34                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 0.1}                     53.35±0.80     52.93±0.79    52.43±8.97                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 0.03162277660168379}     53.40±0.83     52.97±0.85    53.28±7.27                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 0.01}                    53.58±0.76     53.10±0.87    53.13±7.92                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 0.0031622776601683794}   53.45±0.93     53.01±0.95    51.77±8.31                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 0.001}                   53.17±0.73     52.60±0.74    55.06±6.76                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 0.00031622776601683794}  53.56±0.53     53.11±0.68    51.31±6.74                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 0.0001}                  53.53±0.82     53.15±0.97    51.72±8.10                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 3.1622776601683795e-05}  53.64±0.70     53.15±0.83    50.73±8.47                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 1e-05}                   53.42±0.60     53.02±0.71    53.73±6.77                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 3.162277660168379e-06}   53.49±0.91     53.14±0.90    53.62±9.85                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 1e-06}                   53.40±0.81     52.98±0.88    54.84±8.89                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 3.162277660168379e-07}   53.20±0.91     52.75±1.00    54.42±8.02                   0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 1e-07}                   53.20±0.72     52.80±0.78    55.04±6.54                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 3.162277660168379e-08}   53.51±0.97     53.07±0.99    50.79±8.05                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 1e-08}                   53.47±0.87     53.06±0.88    52.43±7.88                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 3.1622776601683795e-09}  53.45±0.83     52.96±0.91    51.91±7.89                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 1e-09}                   53.45±0.70     53.12±0.74    52.78±7.70                   1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 3.1622776601683795e-10}  53.59±0.67     53.18±0.68    51.44±7.31                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+01', 'weight': 1e-10}                   53.37±0.93     52.98±1.04    52.53±8.19                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 0.1}                     72.02±0.75     71.55±0.71    71.90±10.25                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 0.03162277660168379}     71.98±0.60     71.42±0.68    72.08±8.91                   0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 0.01}                    72.01±0.63     71.50±0.66    73.43±8.83                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 0.0031622776601683794}   72.08±0.69     71.52±0.75    69.80±8.44                   0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 0.001}                   71.83±0.57     71.19±0.60    72.89±7.54                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 0.00031622776601683794}  72.15±0.51     71.64±0.61    69.48±9.19                   1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 0.0001}                  72.10±0.65     71.56±0.77    70.30±8.98                   0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 3.1622776601683795e-05}  72.18±0.65     71.59±0.73    69.57±10.12                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 1e-05}                   71.96±0.54     71.43±0.65    72.67±8.25                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 3.162277660168379e-06}   71.95±0.78     71.49±0.81    73.62±10.80                  1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 1e-06}                   71.86±0.72     71.33±0.74    74.58±11.06                  0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 3.162277660168379e-07}   71.87±0.68     71.37±0.77    71.89±10.30                  0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 1e-07}                   71.84±0.54     71.31±0.58    72.99±8.01                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 3.162277660168379e-08}   72.16±0.67     71.66±0.63    68.56±8.26                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 1e-08}                   72.05±0.67     71.51±0.72    71.05±8.84                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 3.1622776601683795e-09}  72.08±0.63     71.53±0.73    69.81±8.94                   0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 1e-09}                   72.03±0.61     71.52±0.64    71.18±10.17                  1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 3.1622776601683795e-10}  72.14±0.57     71.61±0.58    70.16±8.93                   0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+02', 'weight': 1e-10}                   72.03±0.67     71.56±0.73    70.38±8.49                   0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 0.1}                     102.52±0.91    102.11±0.88   103.03±10.58                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 0.03162277660168379}     102.48±0.75    101.97±0.80   102.76±10.02                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 0.01}                    102.35±0.75    101.91±0.72   104.90±9.13                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 0.0031622776601683794}   102.67±0.73    102.17±0.77   100.18±8.81                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 0.001}                   102.43±0.66    101.90±0.70   102.88±8.19                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 0.00031622776601683794}  102.69±0.77    102.24±0.80   100.13±10.14                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 0.0001}                  102.62±0.76    102.10±0.82   101.02±9.46                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 3.1622776601683795e-05}  102.67±0.83    102.13±0.90   100.60±10.53                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 1e-05}                   102.43±0.73    101.94±0.81   103.42±8.88                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 3.162277660168379e-06}   102.31±0.93    101.86±0.98   104.92±11.05                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 1e-06}                   102.23±0.94    101.74±0.92   105.73±11.85                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 3.162277660168379e-07}   102.49±0.86    102.05±0.89   101.83±11.75                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 1e-07}                   102.41±0.71    101.93±0.68   103.08±9.08                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 3.162277660168379e-08}   102.78±0.65    102.33±0.61   98.91±8.59                   0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 1e-08}                   102.56±0.78    102.06±0.83   101.70±9.69                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 3.1622776601683795e-09}  102.67±0.74    102.18±0.82   100.17±9.66                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 1e-09}                   102.54±0.87    102.04±0.91   101.77±11.52                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 3.1622776601683795e-10}  102.63±0.76    102.13±0.75   101.00±9.73                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+02', 'weight': 1e-10}                   102.63±0.71    102.20±0.74   100.61±8.89                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 0.1}                     125.27±1.09    124.91±1.07   126.05±10.52                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 0.03162277660168379}     125.27±1.00    124.83±1.02   125.65±10.28                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 0.01}                    125.05±0.93    124.67±0.89   128.03±9.13                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 0.0031622776601683794}   125.54±0.90    125.09±0.93   123.01±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 0.001}                   125.28±0.83    124.83±0.87   125.51±8.33                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 0.00031622776601683794}  125.53±1.01    125.13±1.02   123.08±10.27                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 0.0001}                  125.44±0.95    124.98±1.00   123.96±9.52                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 3.1622776601683795e-05}  125.47±1.04    125.00±1.09   123.67±10.47                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 1e-05}                   125.21±0.92    124.77±0.99   126.31±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 3.162277660168379e-06}   125.03±1.14    124.62±1.18   127.99±11.00                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 1e-06}                   124.94±1.20    124.50±1.15   128.75±11.93                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 3.162277660168379e-07}   125.35±1.12    124.97±1.13   124.50±12.00                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 1e-07}                   125.25±0.94    124.83±0.89   125.74±9.36                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 3.162277660168379e-08}   125.66±0.81    125.26±0.79   121.77±8.63                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 1e-08}                   125.38±0.99    124.93±1.03   124.60±9.90                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 3.1622776601683795e-09}  125.53±0.94    125.10±1.00   123.01±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 1e-09}                   125.35±1.14    124.90±1.17   124.68±11.76                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 3.1622776601683795e-10}  125.44±0.99    124.99±0.97   124.00±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+03', 'weight': 1e-10}                   125.50±0.89    125.12±0.92   123.37±9.00                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 0.1}                     135.61±1.18    135.27±1.16   136.48±10.46                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 0.03162277660168379}     135.64±1.11    135.22±1.13   136.06±10.33                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 0.01}                    135.38±1.01    135.02±0.97   138.50±9.10                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 0.0031622776601683794}   135.94±0.99    135.52±1.02   133.41±8.92                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 0.001}                   135.67±0.92    135.25±0.95   135.83±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 0.00031622776601683794}  135.92±1.12    135.54±1.12   133.52±10.27                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 0.0001}                  135.83±1.05    135.39±1.09   134.39±9.51                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 3.1622776601683795e-05}  135.85±1.13    135.40±1.18   134.14±10.42                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 1e-05}                   135.57±1.02    135.16±1.08   136.72±8.98                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 3.162277660168379e-06}   135.37±1.24    134.98±1.28   138.45±10.95                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 1e-06}                   135.27±1.32    134.86±1.27   139.18±11.91                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 3.162277660168379e-07}   135.75±1.25    135.39±1.25   134.84±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 1e-07}                   135.64±1.05    135.24±1.00   136.07±9.42                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 3.162277660168379e-08}   136.07±0.89    135.69±0.88   132.18±8.62                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 1e-08}                   135.75±1.10    135.34±1.13   135.02±9.93                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 3.1622776601683795e-09}  135.93±1.04    135.53±1.09   133.41±9.80                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 1e-09}                   135.73±1.26    135.30±1.29   135.11±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 3.1622776601683795e-10}  135.82±1.09    135.39±1.07   134.45±9.88                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+03', 'weight': 1e-10}                   135.90±0.98    135.54±1.01   133.75±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 0.1}                     139.33±1.21    139.00±1.19   140.23±10.43                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 0.03162277660168379}     139.37±1.16    138.97±1.17   139.81±10.34                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 0.01}                    139.10±1.05    138.75±1.00   142.27±9.09                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 0.0031622776601683794}   139.69±1.02    139.27±1.05   137.16±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 0.001}                   139.42±0.95    139.01±0.98   139.56±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 0.00031622776601683794}  139.66±1.16    139.30±1.16   137.28±10.27                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 0.0001}                  139.57±1.08    139.14±1.12   138.15±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 3.1622776601683795e-05}  139.59±1.17    139.15±1.21   137.92±10.39                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 1e-05}                   139.31±1.05    138.90±1.11   140.47±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 3.162277660168379e-06}   139.09±1.28    138.71±1.32   142.21±10.93                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 1e-06}                   139.00±1.36    138.59±1.31   142.93±11.90                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 3.162277660168379e-07}   139.50±1.29    139.14±1.30   138.57±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 1e-07}                   139.38±1.08    138.99±1.03   139.80±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 3.162277660168379e-08}   139.83±0.93    139.45±0.92   135.94±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 1e-08}                   139.50±1.14    139.09±1.17   138.77±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 3.1622776601683795e-09}  139.68±1.08    139.29±1.13   137.16±9.80                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 1e-09}                   139.47±1.31    139.05±1.34   138.86±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 3.1622776601683795e-10}  139.56±1.13    139.13±1.11   138.22±9.88                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+04', 'weight': 1e-10}                   139.65±1.02    139.30±1.04   137.49±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 0.1}                     140.56±1.23    140.23±1.20   141.47±10.42                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 0.03162277660168379}     140.61±1.17    140.20±1.18   141.05±10.35                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 0.01}                    140.33±1.06    139.98±1.01   143.51±9.08                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 0.0031622776601683794}   140.92±1.03    140.51±1.06   138.40±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 0.001}                   140.65±0.96    140.25±0.99   140.79±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 0.00031622776601683794}  140.90±1.17    140.54±1.17   138.53±10.26                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 0.0001}                  140.81±1.09    140.38±1.13   139.39±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 3.1622776601683795e-05}  140.82±1.18    140.39±1.23   139.16±10.38                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 1e-05}                   140.54±1.06    140.14±1.12   141.70±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 3.162277660168379e-06}   140.32±1.29    139.94±1.33   143.45±10.92                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 1e-06}                   140.23±1.37    139.83±1.33   144.17±11.89                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 3.162277660168379e-07}   140.74±1.31    140.38±1.31   139.81±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 1e-07}                   140.62±1.10    140.23±1.05   141.03±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 3.162277660168379e-08}   141.06±0.94    140.69±0.93   137.18±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 1e-08}                   140.73±1.15    140.33±1.18   140.01±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 3.1622776601683795e-09}  140.92±1.09    140.53±1.14   138.40±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 1e-09}                   140.70±1.32    140.29±1.35   140.10±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 3.1622776601683795e-10}  140.80±1.14    140.37±1.12   139.46±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+04', 'weight': 1e-10}                   140.89±1.03    140.54±1.05   138.72±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 0.1}                     140.96±1.23    140.63±1.21   141.87±10.42                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 0.03162277660168379}     141.00±1.17    140.60±1.19   141.45±10.35                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 0.01}                    140.73±1.06    140.38±1.02   143.91±9.08                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 0.0031622776601683794}   141.32±1.04    140.91±1.06   138.80±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 0.001}                   141.05±0.96    140.65±0.99   141.18±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 0.00031622776601683794}  141.30±1.17    140.94±1.18   138.92±10.26                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 0.0001}                  141.20±1.10    140.77±1.13   139.79±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 3.1622776601683795e-05}  141.22±1.18    140.78±1.23   139.56±10.38                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 1e-05}                   140.94±1.06    140.54±1.13   142.10±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 3.162277660168379e-06}   140.72±1.30    140.34±1.33   143.85±10.92                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 1e-06}                   140.62±1.38    140.22±1.33   144.57±11.89                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 3.162277660168379e-07}   141.13±1.31    140.78±1.32   140.20±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 1e-07}                   141.01±1.10    140.63±1.05   141.43±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 3.162277660168379e-08}   141.46±0.94    141.09±0.93   137.57±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 1e-08}                   141.13±1.15    140.72±1.19   140.41±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 3.1622776601683795e-09}  141.31±1.10    140.93±1.14   138.80±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 1e-09}                   141.10±1.33    140.68±1.35   140.50±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 3.1622776601683795e-10}  141.19±1.14    140.77±1.12   139.86±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+05', 'weight': 1e-10}                   141.28±1.03    140.94±1.06   139.12±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 0.1}                     141.08±1.23    140.75±1.21   141.99±10.42                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 0.03162277660168379}     141.13±1.18    140.73±1.19   141.57±10.35                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 0.01}                    140.85±1.06    140.51±1.02   144.04±9.08                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 0.0031622776601683794}   141.45±1.04    141.04±1.07   138.92±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 0.001}                   141.18±0.96    140.77±0.99   141.31±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 0.00031622776601683794}  141.42±1.17    141.06±1.18   139.05±10.26                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 0.0001}                  141.33±1.10    140.90±1.14   139.91±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 3.1622776601683795e-05}  141.35±1.18    140.91±1.23   139.69±10.38                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 1e-05}                   141.07±1.07    140.66±1.13   142.23±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 3.162277660168379e-06}   140.84±1.30    140.46±1.33   143.98±10.92                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 1e-06}                   140.75±1.38    140.35±1.33   144.70±11.89                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 3.162277660168379e-07}   141.26±1.32    140.91±1.32   140.33±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 1e-07}                   141.14±1.10    140.75±1.05   141.55±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 3.162277660168379e-08}   141.59±0.94    141.22±0.94   137.70±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 1e-08}                   141.25±1.15    140.85±1.19   140.54±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 3.1622776601683795e-09}  141.44±1.10    141.05±1.14   138.92±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 1e-09}                   141.23±1.33    140.81±1.36   140.62±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 3.1622776601683795e-10}  141.32±1.14    140.90±1.13   139.99±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+05', 'weight': 1e-10}                   141.41±1.03    141.06±1.06   139.25±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 0.1}                     141.12±1.23    140.79±1.21   142.03±10.42                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 0.03162277660168379}     141.17±1.18    140.77±1.19   141.61±10.35                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 0.01}                    140.89±1.06    140.55±1.02   144.08±9.08                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 0.0031622776601683794}   141.49±1.04    141.08±1.07   138.96±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 0.001}                   141.22±0.96    140.81±0.99   141.35±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 0.00031622776601683794}  141.46±1.17    141.10±1.18   139.09±10.26                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 0.0001}                  141.37±1.10    140.94±1.14   139.95±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 3.1622776601683795e-05}  141.39±1.18    140.95±1.23   139.73±10.38                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 1e-05}                   141.11±1.07    140.70±1.13   142.27±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 3.162277660168379e-06}   140.88±1.30    140.50±1.33   144.02±10.92                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 1e-06}                   140.79±1.38    140.39±1.33   144.74±11.89                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 3.162277660168379e-07}   141.30±1.32    140.95±1.32   140.37±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 1e-07}                   141.18±1.10    140.79±1.05   141.59±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 3.162277660168379e-08}   141.63±0.94    141.26±0.94   137.74±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 1e-08}                   141.29±1.16    140.89±1.19   140.58±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 3.1622776601683795e-09}  141.48±1.10    141.09±1.14   138.96±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 1e-09}                   141.27±1.33    140.85±1.36   140.66±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 3.1622776601683795e-10}  141.36±1.15    140.94±1.13   140.03±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+06', 'weight': 1e-10}                   141.45±1.03    141.10±1.06   139.29±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 0.1}                     141.13±1.23    140.81±1.21   142.05±10.42                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 0.03162277660168379}     141.18±1.18    140.78±1.19   141.63±10.35                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 0.01}                    140.90±1.06    140.56±1.02   144.09±9.08                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 0.0031622776601683794}   141.50±1.04    141.09±1.07   138.98±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 0.001}                   141.23±0.96    140.83±0.99   141.36±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 0.00031622776601683794}  141.47±1.17    141.11±1.18   139.10±10.26                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 0.0001}                  141.38±1.10    140.95±1.14   139.97±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 3.1622776601683795e-05}  141.40±1.18    140.96±1.23   139.74±10.38                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 1e-05}                   141.12±1.07    140.72±1.13   142.28±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 3.162277660168379e-06}   140.90±1.30    140.52±1.33   144.03±10.92                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 1e-06}                   140.80±1.38    140.40±1.33   144.75±11.89                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 3.162277660168379e-07}   141.31±1.32    140.96±1.32   140.38±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 1e-07}                   141.19±1.10    140.81±1.05   141.60±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 3.162277660168379e-08}   141.64±0.94    141.27±0.94   137.75±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 1e-08}                   141.31±1.16    140.90±1.19   140.59±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 3.1622776601683795e-09}  141.49±1.10    141.11±1.14   138.97±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 1e-09}                   141.28±1.33    140.86±1.36   140.68±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 3.1622776601683795e-10}  141.37±1.15    140.95±1.13   140.04±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+06', 'weight': 1e-10}                   141.46±1.03    141.11±1.06   139.30±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 0.1}                     141.14±1.23    140.81±1.21   142.05±10.42                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 0.03162277660168379}     141.19±1.18    140.78±1.19   141.63±10.35                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 0.01}                    140.91±1.06    140.56±1.02   144.10±9.08                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 0.0031622776601683794}   141.50±1.04    141.09±1.07   138.98±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 0.001}                   141.23±0.96    140.83±0.99   141.36±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 0.00031622776601683794}  141.48±1.17    141.12±1.18   139.11±10.26                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 0.0001}                  141.39±1.10    140.96±1.14   139.97±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 3.1622776601683795e-05}  141.40±1.18    140.97±1.23   139.74±10.38                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 1e-05}                   141.12±1.07    140.72±1.13   142.28±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 3.162277660168379e-06}   140.90±1.30    140.52±1.33   144.03±10.92                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 1e-06}                   140.80±1.38    140.40±1.33   144.76±11.89                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 3.162277660168379e-07}   141.32±1.32    140.97±1.32   140.38±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 1e-07}                   141.20±1.10    140.81±1.05   141.61±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 3.162277660168379e-08}   141.65±0.94    141.27±0.94   137.76±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 1e-08}                   141.31±1.16    140.91±1.19   140.59±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 3.1622776601683795e-09}  141.50±1.10    141.11±1.14   138.98±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 1e-09}                   141.28±1.33    140.87±1.36   140.68±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 3.1622776601683795e-10}  141.37±1.15    140.95±1.13   140.04±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+07', 'weight': 1e-10}                   141.47±1.03    141.12±1.06   139.30±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 0.1}                     141.14±1.23    140.81±1.21   142.05±10.42                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 0.03162277660168379}     141.19±1.18    140.78±1.19   141.63±10.35                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 0.01}                    140.91±1.06    140.56±1.02   144.10±9.08                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 0.0031622776601683794}   141.50±1.04    141.10±1.07   138.98±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 0.001}                   141.24±0.96    140.83±0.99   141.36±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 0.00031622776601683794}  141.48±1.17    141.12±1.18   139.11±10.26                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 0.0001}                  141.39±1.10    140.96±1.14   139.97±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 3.1622776601683795e-05}  141.40±1.18    140.97±1.23   139.75±10.38                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 1e-05}                   141.12±1.07    140.72±1.13   142.29±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 3.162277660168379e-06}   140.90±1.30    140.52±1.33   144.03±10.92                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 1e-06}                   140.81±1.38    140.41±1.33   144.76±11.89                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 3.162277660168379e-07}   141.32±1.32    140.97±1.32   140.39±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 1e-07}                   141.20±1.10    140.81±1.05   141.61±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 3.162277660168379e-08}   141.65±0.94    141.27±0.94   137.76±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 1e-08}                   141.31±1.16    140.91±1.19   140.59±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 3.1622776601683795e-09}  141.50±1.10    141.11±1.14   138.98±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 1e-09}                   141.28±1.33    140.87±1.36   140.68±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 3.1622776601683795e-10}  141.38±1.15    140.95±1.13   140.04±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+07', 'weight': 1e-10}                   141.47±1.03    141.12±1.06   139.30±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 0.1}                     141.14±1.23    140.81±1.21   142.05±10.42                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 0.03162277660168379}     141.19±1.18    140.78±1.19   141.63±10.35                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 0.01}                    140.91±1.06    140.56±1.02   144.10±9.08                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 0.0031622776601683794}   141.51±1.04    141.10±1.07   138.98±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 0.001}                   141.24±0.96    140.83±0.99   141.36±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 0.00031622776601683794}  141.48±1.17    141.12±1.18   139.11±10.26                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 0.0001}                  141.39±1.10    140.96±1.14   139.97±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 3.1622776601683795e-05}  141.41±1.18    140.97±1.23   139.75±10.38                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 1e-05}                   141.12±1.07    140.72±1.13   142.29±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 3.162277660168379e-06}   140.90±1.30    140.52±1.33   144.03±10.92                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 1e-06}                   140.81±1.38    140.41±1.33   144.76±11.89                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 3.162277660168379e-07}   141.32±1.32    140.97±1.32   140.39±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 1e-07}                   141.20±1.10    140.81±1.05   141.61±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 3.162277660168379e-08}   141.65±0.94    141.28±0.94   137.76±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 1e-08}                   141.31±1.16    140.91±1.19   140.59±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 3.1622776601683795e-09}  141.50±1.10    141.11±1.14   138.98±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 1e-09}                   141.28±1.33    140.87±1.36   140.68±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 3.1622776601683795e-10}  141.38±1.15    140.95±1.13   140.04±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+08', 'weight': 1e-10}                   141.47±1.03    141.12±1.06   139.30±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 0.1}                     141.14±1.23    140.81±1.21   142.05±10.42                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 0.03162277660168379}     141.19±1.18    140.78±1.19   141.63±10.35                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 0.01}                    140.91±1.06    140.56±1.02   144.10±9.08                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 0.0031622776601683794}   141.51±1.04    141.10±1.07   138.98±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 0.001}                   141.24±0.96    140.83±0.99   141.36±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 0.00031622776601683794}  141.48±1.17    141.12±1.18   139.11±10.26                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 0.0001}                  141.39±1.10    140.96±1.14   139.97±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 3.1622776601683795e-05}  141.41±1.18    140.97±1.23   139.75±10.38                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 1e-05}                   141.12±1.07    140.72±1.13   142.29±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 3.162277660168379e-06}   140.90±1.30    140.52±1.33   144.03±10.92                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 1e-06}                   140.81±1.38    140.41±1.33   144.76±11.89                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 3.162277660168379e-07}   141.32±1.32    140.97±1.32   140.39±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 1e-07}                   141.20±1.10    140.81±1.05   141.61±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 3.162277660168379e-08}   141.65±0.94    141.28±0.94   137.76±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 1e-08}                   141.31±1.16    140.91±1.19   140.59±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 3.1622776601683795e-09}  141.50±1.10    141.11±1.14   138.98±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 1e-09}                   141.28±1.33    140.87±1.36   140.68±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 3.1622776601683795e-10}  141.38±1.15    140.95±1.13   140.04±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+08', 'weight': 1e-10}                   141.47±1.03    141.12±1.06   139.30±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 0.1}                     141.14±1.23    140.81±1.21   142.05±10.42                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 0.03162277660168379}     141.19±1.18    140.78±1.19   141.63±10.35                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 0.01}                    140.91±1.06    140.56±1.02   144.10±9.08                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 0.0031622776601683794}   141.51±1.04    141.10±1.07   138.98±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 0.001}                   141.24±0.96    140.83±0.99   141.36±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 0.00031622776601683794}  141.48±1.17    141.12±1.18   139.11±10.26                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 0.0001}                  141.39±1.10    140.96±1.14   139.97±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 3.1622776601683795e-05}  141.41±1.18    140.97±1.23   139.75±10.38                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 1e-05}                   141.12±1.07    140.72±1.13   142.29±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 3.162277660168379e-06}   140.90±1.30    140.52±1.33   144.03±10.92                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 1e-06}                   140.81±1.38    140.41±1.33   144.76±11.89                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 3.162277660168379e-07}   141.32±1.32    140.97±1.32   140.39±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 1e-07}                   141.20±1.10    140.81±1.05   141.61±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 3.162277660168379e-08}   141.65±0.94    141.28±0.94   137.76±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 1e-08}                   141.31±1.16    140.91±1.19   140.59±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 3.1622776601683795e-09}  141.50±1.10    141.11±1.14   138.98±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 1e-09}                   141.28±1.33    140.87±1.36   140.68±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 3.1622776601683795e-10}  141.38±1.15    140.95±1.13   140.04±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+09', 'weight': 1e-10}                   141.47±1.03    141.12±1.06   139.30±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 0.1}                     141.14±1.23    140.81±1.21   142.05±10.42                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 0.03162277660168379}     141.19±1.18    140.78±1.19   141.63±10.35                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 0.01}                    140.91±1.06    140.56±1.02   144.10±9.08                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 0.0031622776601683794}   141.51±1.04    141.10±1.07   138.98±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 0.001}                   141.24±0.96    140.83±0.99   141.36±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 0.00031622776601683794}  141.48±1.17    141.12±1.18   139.11±10.26                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 0.0001}                  141.39±1.10    140.96±1.14   139.97±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 3.1622776601683795e-05}  141.41±1.18    140.97±1.23   139.75±10.38                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 1e-05}                   141.12±1.07    140.72±1.13   142.29±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 3.162277660168379e-06}   140.90±1.30    140.52±1.33   144.03±10.92                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 1e-06}                   140.81±1.38    140.41±1.33   144.76±11.89                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 3.162277660168379e-07}   141.32±1.32    140.97±1.32   140.39±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 1e-07}                   141.20±1.10    140.81±1.05   141.61±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 3.162277660168379e-08}   141.65±0.94    141.28±0.94   137.76±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 1e-08}                   141.31±1.16    140.91±1.19   140.59±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 3.1622776601683795e-09}  141.50±1.10    141.11±1.14   138.98±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 1e-09}                   141.28±1.33    140.87±1.36   140.68±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 3.1622776601683795e-10}  141.38±1.15    140.95±1.13   140.04±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '3.16e+09', 'weight': 1e-10}                   141.47±1.03    141.12±1.06   139.30±9.01                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 0.1}                     141.14±1.23    140.81±1.21   142.05±10.42                 0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 0.03162277660168379}     141.19±1.18    140.78±1.19   141.63±10.35                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 0.01}                    140.91±1.06    140.56±1.02   144.10±9.08                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 0.0031622776601683794}   141.51±1.04    141.10±1.07   138.98±8.91                  0.96\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 0.001}                   141.24±0.96    140.83±0.99   141.36±8.34                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 0.00031622776601683794}  141.48±1.17    141.12±1.18   139.11±10.26                 1.02\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 0.0001}                  141.39±1.10    140.96±1.14   139.97±9.50                  0.97\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 3.1622776601683795e-05}  141.41±1.18    140.97±1.23   139.75±10.38                 0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 1e-05}                   141.12±1.07    140.72±1.13   142.29±8.97                  0.94\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 3.162277660168379e-06}   140.90±1.30    140.52±1.33   144.03±10.92                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 1e-06}                   140.81±1.38    140.41±1.33   144.76±11.89                 0.99\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 3.162277660168379e-07}   141.32±1.32    140.97±1.32   140.39±12.02                 0.92\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 1e-07}                   141.20±1.10    140.81±1.05   141.61±9.44                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 3.162277660168379e-08}   141.65±0.94    141.28±0.94   137.76±8.61                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 1e-08}                   141.31±1.16    140.91±1.19   140.59±9.94                  0.95\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 3.1622776601683795e-09}  141.50±1.10    141.11±1.14   138.98±9.79                  0.93\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 1e-09}                   141.28±1.33    140.87±1.36   140.68±11.79                 1\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 3.1622776601683795e-10}  141.38±1.15    140.95±1.13   140.04±9.87                  0.98\n",
+      "{'compute_method': 'sylvester', 'alpha': '1.00e+10', 'weight': 1e-10}                   141.47±1.03    141.12±1.06   139.30±9.01                  0.94\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculate performance: 100%|██████████| 23370/23370 [08:04<00:00, 48.26it/s]\n",
+      "\n",
+      "\n",
+      "COIL-DEL\n",
+      "\n",
+      "--- This is a classification problem ---\n",
+      "\n",
+      "\n",
+      "I. Loading dataset from file...\n",
+      "\n",
+      "2. Calculating gram matrices. This could take a while...\n",
+      "\n",
+      " None edge weight specified. Set all weight to 1.\n",
+      "\n",
+      "compute adjacency matrices: 100%|██████████| 3900/3900 [00:01<00:00, 3658.26it/s]\n",
+      "calculating kernels:  24%|██▍       | 1863175/7606950.0 [14:19<28:06, 3406.31it/s]  "
      ]
     }
    ],
@@ -95,7 +1594,8 @@
     "#     {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n",
     "]\n",
     "estimator = randomwalkkernel\n",
-    "param_grid_precomputed = {'compute_method': ['sylvester']}\n",
+    "param_grid_precomputed = {'compute_method': ['sylvester'], \n",
+    "                          'weight': np.logspace(0, -10, num = 21, base = 10)}\n",
     "param_grid = [{'C': np.logspace(-10, 10, num = 41, base = 10)}, \n",
     "              {'alpha': np.logspace(-10, 10, num = 41, base = 10)}]\n",
     "\n",
@@ -107,7 +1607,8 @@
     "        (param_grid[1] if ('task' in ds and ds['task'] == 'regression') else param_grid[0]), \n",
     "        (ds['task'] if 'task' in ds else 'classification'), NUM_TRIALS=30,\n",
     "        datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n",
-    "        extra_params=(ds['extra_params'] if 'extra_params' in ds else None))\n",
+    "        extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n",
+    "        ds_name=ds['name'])\n",
     "    print()"
    ]
   },
diff --git a/notebooks/run_spkernel.ipynb b/notebooks/run_spkernel.ipynb
index e1abf8b..86d73bb 100644
--- a/notebooks/run_spkernel.ipynb
+++ b/notebooks/run_spkernel.ipynb
@@ -12,9 +12,9 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "ENZYMES\n",
+      "Acyclic\n",
       "\n",
-      "--- This is a classification problem ---\n",
+      "--- This is a regression problem ---\n",
       "\n",
       "\n",
       "I. Loading dataset from file...\n",
@@ -23,21 +23,130 @@
       "\n",
       " None edge weight specified. Set all weight to 1.\n",
       "\n",
-      "getting sp graphs: 100%|██████████| 600/600 [00:01<00:00, 387.62it/s]\n",
-      "calculating kernels:   0%|          | 116/180300.0 [00:36<13:08:20,  3.81it/s]"
+      "getting sp graphs: 100%|██████████| 183/183 [00:00<00:00, 2750.49it/s]\n",
+      "calculating kernels: 100%|█████████▉| 16808/16836.0 [00:11<00:00, 607.39it/s] \n",
+      " --- shortest path kernel matrix of size 183 built in 11.701499700546265 seconds ---\n",
+      "calculating kernels: 100%|██████████| 16836/16836.0 [00:11<00:00, 1447.26it/s]\n",
+      "\n",
+      "the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}} is: \n",
+      "[[1.         0.47140452 0.33333333 ... 0.30151134 0.30512858 0.27852425]\n",
+      " [0.47140452 1.         0.         ... 0.14213381 0.11986583 0.17232809]\n",
+      " [0.33333333 0.         1.         ... 0.36851387 0.37293493 0.34815531]\n",
+      " ...\n",
+      " [0.30151134 0.14213381 0.36851387 ... 1.         0.96429344 0.95175317]\n",
+      " [0.30512858 0.11986583 0.37293493 ... 0.96429344 1.         0.96671243]\n",
+      " [0.27852425 0.17232809 0.34815531 ... 0.95175317 0.96671243 1.        ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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zuosFjGKCEJogdNBa4D3qgYLarKI/KGiZdVZ8BY6iHZgUztnEVU3HV8hYYHUl7YLGa+UKy25pUh+SgnVt4wSuawByI7ghirV2Hi5o5/t+m/Kkovw39zP74f1Ex6uc/um38/Kf+l/xnphh8ZXbKP3ENFMPjpG5GndR0t+S8R9f9kneceqlOHeXqZ3OTdTS8QXaNw9ht1Iu/QTovoE7bzJ4+yxXzg1hdiTbXnCFhU6Rg4Nz3Hd8D4WhHtW/KdKvS/pDgvJFRWYL6id7KMcgdQ0mv8/A7EiSaobZNMhGYuiYUEmwphwyV2M3JamvSQZSMDX/5uaH+fvHnsetuyeZ65XpRTbBYzXkvi7u50q4TUXiiTwGUc192sKcIikIjBBSH+ono3U3ASCsmzjNDCPMuPS9DmYfMgfMniAcT7DnTbQFaiLEPO8iMoE2NKmvkYmgcGCV9vkqqpoipIaeiazGZH0DYWqknVG618fqaeKKQKR5fENLMNYmVCPWmKFmZb9BXFXYTYkWkJQ0ZiCIhlPMpoFMQKYCZWm0kQ94bUA8mGKVI7JZHxkLBh7T9IckRqhJfYGzopEpeVBZQOVCQlw2UFYeb5h/gYHVyQOjQoMWoGzInDU3zMjfy0SsvwoN3i3LNKeqWE1JUlI4ywalyTze4TTz+9sdl0R1jdnNn8uTEFl+jScDoGYP7HYeaI5LAiPWPPJn/2nDM/3uw77+rx++aUNj5Id2H7suFsQNoSCKtXH9ooNvpPG7l1kMi5w7N7LuVnz2XX++7m4EPfdpbkQwbNBfixf/4Y//xdPcj98/+n4AfuXED9BsFjBMRZZKRoeaKC1YfXCIaCTFaBt5QMtX/PbLPkiiDe5r72J/YZZbvEu86bE7iSKL/+XAfXxg8nnP6hIAtHsutVLA/HyVHeOLRKnJ/KPDGKEgGkoRqUDGgqyUYXQM1GDMT958/7O6LK5MqBh9LoUDlM2Q95x8PklgU2t06HQ9TCvjh/Yco272+KvztxHGFlHPRtoZWcumMNwj7NtkkYGQGtuPse2ULJOEfRtz0l1XFkYIaUGjBhJ0aGCUY7LI4BUHTjEfllgMClSckDG/hSkz+plF0YyJMhNTZnz65H50ZCC9FGFosp5FY7TF8vn6ursgCymuFxOs+FQftehsV8hEkDRS7HJEsuCxbd8cMysVKsU+vpXgmglSaFqRy6DX4+xSg2DFR0SSQ4cnubA8QNi3UWlubaAEXikkWPVwKxFK5b694yRkmSSOLOzjPv0tGe6cgbJB2bl1OH5zvsxFkFhIoVlYLCNNjV50UH4GlgIlEIZGpwJhagw7o1bpESW5EZ5mEtdOOPaat254IO86XNC/8+F9GxojP7L74W8eF+NaIZsBZrPPyffs582/8He8409+eN2teKq7UZniaW6E2dMMTivclZjfuO3p7seTrodzdxmvms8otSlNtzqCv6BoJIrknJnbeQJkKvnnW/fwyRMH0H2T1UM+f3zsZbzp6OcYMVv86aXvfFaXgLZD4gvEIHT7JewCrDwwjrLAl2A3NU7TxOpqMktgBYLEF8gzDu8Kn+my3Lu0nwMHL5NkBjU34LNLQ+wYXGFXeYmSGbIUFSmYEYk2+FJrG+m9dQwDSmEem7B6Gi0qCD8P8HUnBAiHNMtnPsuB6hlFZ6vEzMMO2E2BedZGS4E2TLQBn+kdglKCjg0WvJTTyQjlWkC9EDB5ZgR7MODIlhkqD7jIVJMULUSaz+bh2QaNBU3qmoQNgTYtZM/Dt6A4naEMI6dDZy20sPA0TOoRjL5k2fFJdjRZ0R7dtodOJHOtIfRAjLlqwnifXmKjH6zgZeAsa5BPsh0Og8ua/pCdL+QocvrWkGB5UD2vsHo5K6UMAWvU5WV3GKMn0aZm8OAiheNuTknnvZPCdG5JZc7aPezmTNjqLSAue9hNQVrQdL+GeKO6wVmMG8KCKAxM6Bevvog3nJ7kt951J8MPxetuRfgDzXV345bf+tmnuRHh9jpxxcRupYi3LD7N/Xjjj3+Md5x6Ke5dZayexow0K/sMilO5qVycSUh9g35DUr4YEw5YrO6XRNsjxkdXmLowiNUyct5bwdYXX2HpgxPPcAlkmvP6hbmccpNxbkInRUHtTMLSYYvq+YygIbF6mtQVVM9HtLc7LL1APdNlkRprKs9xMEJBfyzFXjQQKqc1AVJfoyop9rSFPyuwevl+meXRfqubm8xhQ+Cs5h1aWbkCMaLcPbC7KlcIa/2zX/+yfNqA1p58MKS1FNnL80uMWGCEAmXk1kc0nDH0BUFczoOnmZNfQ0uBcvLBqWwwgzX54pz2tAJF6kjCmkAm4DYVKwcM+mMpRk9iBGLdJYjLGiPKlVhc03m+gwHugsBp6TyXwgIjzs18ofR6Loay8hGbrcniLyjaOySVCwqZ5G01Yk1rl4GyIBzKkJGgflzQH87ZGvRarsvaRKIl69cMRsW6yxVXNGZPcOqtv7jhmX7n4YL+7Q8d2tAY+fE9D3zruhje7i16/A1vpnwRBu9d4Oc//lF+4+xr8K2EyfkBKve4aAMe/rW388nAwRUJv3XxNbxs6Ax73Vk+sXIEz0h4eHGcgh0zf/c4wXiG2Zbc/RO/S6JhKi1zyO7QUZqzSY1Hw63stBcZM1e5u3uQQ94V/nruRewtLmDJjCv9Gv3M4v4v3ZQn1DiKj776j1jMCrgiIcZgUAbMZCV2mi3mMw9fJlxK6lSNgAmjy0e7BzngXmE5KzJmrhLqPIj1ydYRfqj6JT7bPcDFfoNUSySaut1jyG5zLhjCMxKWoiK7Cou8+76XIFKRz+h9E1lMANCp5J3f9t9pK5dQWygtKRl9OpnHoNnGEhkrWRFLpLgioSBillWBS/EgiTawREY3c2mYHcasVdrKZcDoYqC5t7eXM70hjLVMsLrVI1IW/czClBm91OH4wig/sfsBKkYfpQWuzOXyZcRiWsZA4csIVyYoLQm1xQfmbuWm0jxDdofdzjyJNpiMG9y3shNTZtgyo2SFdBIXhUCi2e4v8/6Tt5DFBjqROJWQX7v548wkVZaSEpbIsETGqN1kISlTMXLTyBIpUmhskSJRfGTxKHcOP8BnWgdoJy77inOsJj4rSYGSGfK5qT0AvHb742x3l3isO0HBjFiKilStAGctINHNHAasHqe7w7QTl35qMeD2KJgx737hf9/wQN5xuKh//e+PbGiM/NTe+66Lgrgh7BsdGhSPLjPwWIf+jhpVGXDHlpO5RRBLorqgdjrmk4HDHX7EsNHlcG2GitFnPqlSsfo8vDhOw+8RJBaprxFJPrt/oT9BrCW3uxG/Of+dJAhCbbGUlJiwlnmwv5Pb/PO4IqFkRTzWGuNDF4/QSlwmO7U8yJYIRCo4Fo0Taotj4VYMFHf1DvCZ9kE+1DnCsNEnUBaH7QWUlhyPhzjsTvEn0y/nVmeaz3UPMJdWebS/jTsqj9HTNqd6w7QSl4vtAVqJy4NL27hvZSeWULQSD0tmrCY+ZtvA7Ep0IjG6Er3s4BcinELME9EYgXI4E46SIfn7peczGTfoKI8/nvkuBo02VRkwaPRoKxeLjE8vHuDb/DO4IiHTkqoRcCIco5N5dJTHXFrhfDDIVLfGw3PjnFod4mJvgMtBnpQ22a3TTlyk0FzsD/LZlX10lEsz8znW20on8/i7qefzeG8cQ2iqMuBcNMxiWuL8YoOd3iJLSZFHgm34MsKXMYv9AsemxnlicYTZfgWFoB27XGzXuRQMkAYm1pSNM2tiWRnNzKebuZztDDIflbncrzMd17hncQ+dzOVSOMDZ/jBLSYkngjESbXJhdYAPLt7Khc4AUiimwho1K6AVu1wJqvRaHkoJ2qnHQlImUrmfcbY1yHRYJVA2s1GFS70B2qlLmJks931mViucXhpivl+6tn6v+ZatxbgmaEvTDVxae4vIVLPN7PPp2X1krqZUC/IsuoqJKxJOxgH7bZ9PX7qJe1Zyjb/LXaRgxyz3ffbVFihdzM8Z1xVbrFVOxCM8HmuG7A5VCa/wmqRK4oqMUJsE2qEk+8z0Koz5TapeyEJQ4sVDF3FWJP5sTpkVZEQz8zkfDnEq2kI3c7m1cIlt9hInkgaWyLinv5ObrDYjZosv9PawEJQ4lTRopR7nwmEcmXAq2oKBpmxGxJnJauCxEJRIlGTcb+adNbU50xyklzrICNxFgdE0sVsSuyXorvpE/dwiWUzL+UyYFumkDjWzx1xSoWRGfDHYzaloC3NpicWszAPBLvqpxV2dwzzU3c79qzt4oLcTgJWsQKwNFtMyEk2cGaSZJFrj/nuJzdnWIHUnoJ9atFo+mRYoLVhKSkTKYiXOE8eUFkTK5L72bh7pbyfRBsc7Y0SBxWPdcU53hllZy3s/H345M1kKzaXVGpdadbqxQxhbeTKRzIOpRijotTxW0wKLcYmiFeEZ8fpgLpgx3cyhr2z6yl6zXgTnwyHC2GI2KDO1WuVsc5Bz7UFOdEbpJC6pNpCWIljxWYl9ZqIqj6+McLYzSMPrEqYWS1GR5aiALVO6mcNkq0aaGVQKfaTQRNm1hvQEaoPb9cIN4WKUquP65ve/Du97LmJOjHPxD6r84wvewff9v79M9WxGXJQM/tMc0TsyDtdm+PSlm3ji9vdwJe3yhrN3cm5ymMIZm9TXlC5Cd6tg4PEMq5ux9MaAH931MDNRlbvP3USl1Gdprszz9k6SaoN25GLInLe6/KUxzL7A7EFvR8bQfYLVmwTaAn9a0H9pl/0j82wtrFA0Is71Bnnw8V0YpYTv3nuSB+e3csvQFJ8+tR/LSXnToXv4yNwRXto4zxOdUQ6WZvnU7D5GC21ascfkA+OkvkY7+fVlKLFXJNFQhtWSJEWN0ReYgSAtrvnxSe4HI9b88qNdBqtdilaMY6b0U4up5SrPH5/i2NwYexsLSKHpJg62kTHsdjixOsy/nXiYIHMoGSFX4hqfuryPXfUllvpFLCPjwqNjaFPnFKmVW2TazGW1lkzSksLoS9w9LUypqBcCksygaEecmR3CdRNGK20kmpIdcmxqnNu2TTLhr9JMPCyheGQ5/w+bbaVVHvjCPmSyVkdialQpRfSNtUCjwJ+RmF0ItuTxFHt3G4Cg5WG4uSuRpRKdSUw3IY1NDDO/r2nfZHi0yS2DV7hvZjv7G/OUrJDV2EdpwcPHdiEygTcrc9e0EWKYih2NZVqRy+zpIajGuIU4Z0SaDrKQonom1qqJ1RKkviYtai79H2/esCuw7VBJ/8oHN/aXp2/c9/lvXRYjW6sxMPbvgW6fnY1lAPpbMuy2gdnThNvrvHzon6kYfa4MVbmSdhk3ixypTjO5WCMYNxCJIBiVmAH0hg0KwI76CqNWk4bZob3NpWyFnHTyP496XnWKILO52BugZve5UB8miyThiAY3o70jzzFQa0VO2xsr1JyAmhVQNEK2+QbHB7awtb7KDm+RZs3jecXLXBobYMDNMyZf3LjANmeJdupSN3uMFtqUrIgtXotzg8OQSrAVCI2yFKErEV5K7MmctlQC56QLQZ5LICNB5uXKIrNhqNql4fWQQuEaKUUzolVwGXVbrNR8Xli7xFRYx/FXyJAUjYhFr0hJhgSZQydzqRh9Rsu5XL4ZU7X6nKsOQyx5supJW4CpkU5G5htoS6MyjW2mFOwEz0woWDGmyKhXerhmimOkHK1eIVImF4oDFMyY2bCMZ+SximG/gymfPL8mdXJGwhwI0ZGZB2UFaC9DmTbaBDMQxBXNULlLN3KIXAvPi4Hc+ohiE9+NiawMw1CYUhE5JuOlJktRgZsaC8TKYDEsUrQilJZoJ69lyRyJSAWWnTJS6ZApiSUV2s1wvATHSslMQeobOG5CmAlSz0DGkFQU2r72yTa7MYz4q+KGUBB2LQ8F93bXcJZ8fnH8vbx19g7+48s+yZ8sfy+D04q4YrLXnWU+qfId9bO84eydHKlO83ujDzPmNPnj//FKIFcqWz4Hy4cM0oLBt5dnmE2qJNqg4XTZ581SMCJeVDzPC90ZTsQ1Tjtb2OPMcTcHcu4+kYjApL8tpvHPuRnfHxJsLa6Sasn53iBlKyRRBgdHZhn3m6wmBbb7yzzS3coLBibxZUzd7OLLiJ32AoNmGwMN9dykfnn5BB9zDqFrRppLAAAgAElEQVRSiWybKDevKDTKCSoVSDtDxQaWn+Au5h0vrgjsjiYpCPojGuVoGl6uiHqJQ6oN+qlF3QuYDSsAHO+MYckMZy2AOB+VGfbadJQL5AG3DMnR6hUAhuxcTmln6K6ByCTa0uhCCkqgEgmFDLI8rwMgTE0sw8IUio5yGC81ubg6AMCx5jhVu0/ZjZgOKpyeHuYlu86TCsWe0iK+jDndzRW2EUqUpUkjE53m5xapAF/l1Z8p2E2IKzDg9uhGDradYkpFqiQVL2RF+fh2ninpWSkFK6afWsTK5Mz8IK/YcYbHV0fX79k6TIU2cwX8JBa6RYTQGMU8OJkqSZZJDCvDtRPCnp3vL6wpBuPaFIRGbK5J+bXi/ult3M82ANyVmMySfGLlCBWrzy53kXOTw0wu1hhzmvxi/QLvaEtkCnFdY3UzzCCviAQ41hpHCs2ppSHiMZPCGjf1oc5BjnUm+M+jd+ELjblikjYSrNWc9sqc3HQwQ03mClZjj5IVcXJ5iOFil1bk8oLBy1wJqoSZxdbCKo8sjnNwYI7FsEhSM3j38Rfy+sNfpJ26ZEh2uYu5/NEIKsrrRWQk0CKvhMwiAxEYeep0V5BUDTJHYERf7nwyzn8jMuileSftJxaOmXJ5qcYLt15ioV/iSrPKlKgyUW0yG5TJtGQl8HCtlFQZJFrSjH3izGBveYGiEdHNXB7vbEHFRl5mEAsya+3aiUSGEl1N0JmBGQhcK6XZ87CNDNOKWe751JyA5nIRazhjenGESjnAsVIW2nVUkHe58+0Gs1aZF9YvAbkFIQIBFtiXbeIBhZb5dXWSF4slpTybUWSwHBaI0y/7/+2+S6Ly2ThVkjQzSI3cOkmUZKFXJE1M7jq7HyE0WwZaXFqq47sxZAJEfg2hIMskq4FHq+XjFSIq5R7tjo+UmiQ2cb2YJDOga+Wl877C6Eky+9oH+41uQdwQMYjC4IT+toEf58p/tYmOV8l2huvU5lv/0zvXKc+bKgvPoDIBzLbkzE89nQL991vv4RMrRxh1WsxHZVqJy8HyLK3UYyEscd+jezCqMb4f0Vkq4JQj3nHre9hptfGFoKU055MaH1x5PrEyOVCcIVHmM2hJYJ2aHHHanO8NUrd7jNht/uKxFzNY79ANHSp+P3/1Qq7M1tk2tsQf73nvM2hTVyjmMw8pFHNphYP2Ar89+yr6mcVOf4nlpMAWp0Xd7HE5qlMx+8xFFdqpg9ISS2bYMsUSCs+I6WUOEo0pMxyZ0kw87p3auX7vk8TAcxO21VZZ6fvUvQClBW+euIuFrERBRiTapCoDMgQd5RGq3KrqKYd/XLgZ34xRCGyZK57t/jJnukOYUlGx+jmTYQZcCga478RunEqIaSp2DuSu5JVWhTt3fglfxlgiwxUxiTZJtIErE1yR8NHlm+kmDqmW3FSaRwrNUlxkMSwihc7p0OIyl3s1RrwO5triF1JoikZEP7P40BNHuW3XJR6+PIFSknIpj5u8fs8XcUXK+XAQX8ac6w0y4a3y0PJWinZEP7XwzARb5rGOILVpuF0Ol6aZiyo0E5+t3goKwW8f+YcNxwomDlX0L7z/RRsaI7904FPfujEII8joP79GeMKidHSZlaUSUV0w/EC0Tnm+5+PfQe/mvGpwue+vU5mQrzvwJAV6Mg44XJtZpz8fa43hGjmF+aGLR7h15AqTnRoiFRiGotv0EbYiTUxudyM+1J3gC53d/GzjHkJtkSoDW6YsJSVmwzJhZrEQlGh4Xc63GwihqTkBW7w2y3ERS2asxAU6qYtfiJifqTIytspyu4AQsJgUcQoxvdjmWDTOgNHlVDTKYXeKu3oHUFryg6XHABi0F5jJfC516nhmQuSazPYr9DOL7xg6xQH3Cn+9+GISZTDdq1CyIyZXa4yW29hGxoXlAY4Mz1C3AwCaiQdAsOwztnWZZuAhRD6I5rol0kzimHkV5BPRGKtpgYtBg3FvlSGrzZi1yojR4p5wH+P2ClUjYLFfoBvWGau0yJSkHTuUrDwoWSqETFSbDLsdLgUDtBM3pykbGVFkshgUqLl5zsLF/iCPr4yypdjitY1HMUTCSlLgVH+UhtVlNihz8cogTiHmVUOPc6yzlVbsMt2qYBoKx0yxjZRTC8O4oylhZq7HN6TQeEaCWLI5Ux0k6VtIO6MbONh2ngsylzocX93CSwfP000cJnWdVEmSzGBmtUK1GFBeUxbdyMaWee7FdFhlplthOqgw5reuqd/n/6x1Y1sQN4SCyFwDmWrSsYhu4FKqBXk6dcV8GuX5VCrz8YtDBKN5wVZc18+gQHfty035fmZxcnWEjulS9UKGnA6NoS4fPDeI6yQkoYkQGteLeTzWbLFWGbI7nIhHeJW/xFtXRujHFq+YOE3ZjGjHHquBh9KCTAtubeQLZ13u1dlTWuCR5TG+d8sTdDOH7upNiNCgHbgoJUhjk0oloNXyGSx3n0abPpm0tN+d4UTSYNDocCIao250aQYe0/0KmZLMdUosOgW+UNhDxQyQaILUIs7y+EOaSepOwHy/hO/EXGzXCXybmhPQzyzmgxIowexSJXcj+gatksnwYIt+nNci9OLcbVlNfDqpw2riM2S1mUsqXIiGqJk9VtIiV+I8L0IKTTd2cMyUMLbWE4qUFlxardEpOAz7HaZblXWaUseS0Isx/R7dwFmnS5UW3NfejWfESKFZiX0qaznhlpes06S91KaTuBSc/LhESaTQuHZCrAxSbZBmBlU7r9SdDcrIVNANHIxVE2UbJI4itRWLcZ6/YArFI82JtTJyc73cu1Lok2YG7dghTo287DszebQzQZhaNLwuQWrTz66tmhNu/BWlbggXoyzq+iVbf5Lu0S3oNy2S/fnQOrV56XcK65SnUDyNyjQDqFzI6Uz5SwtPo0BfefK1nJscxp20iYYyjI7EXcrTiIvTirAmcZp6vVpQpnDbzzzCP13ZSRRavP7gF/mLB17KXa/8I4YNyRsufB+P//PuZ9CS3pwksyEayjA7eSVn+bwkKUBS0TjLgsxdqzZ0NFbnyeAbxC94Jm368ORWvnvvSS73aoz5TU6ujvBvxx/mxf5ZMgTNzKcgIx7u7+CelT088uDufMk5odcpQaMvSEsKq5WXQitPYXQlytZoT1E4bxE28jYoR2P0JWZXkLkaYy1Il+3vsmMwN5trTsB0t8KO8gpVq8/dF/eyd3iR/eU5Pnj37fl53LwUWjkaI5CIFDI/T1VWjkLGElVMseestSXxNHJN1qSo8Xa30FpgGRlDxS5bCi0ud2s4RsqZ2SEGax1aPY/X7nqcZuJx9xduRhsaezWfgZ+8x2YgSCprzIiRt00bGuVqrLWKU+XkRVoyzleGsne36fcchND8m4PH+OC9tyGUQJVSSCT+pElS0WRuPlasliQpK246eplTl0fydSxKuVK8/Ia3bNgVGDtY1f/hfS/d0Bj51UMf+6rnFULcAfwR+RILf661/p1/8f1W4C+B6toxb9Faf/wrnfOGsCB0xScZH6D25klWQp/lQwaDxzLaNw/x+0ffxVtn7+CNP/4x/uBzdyASwfKtmi2fy6nM5UMGZmDwa1vvYT6psmvfIq88+Vo+vf8f+f3hnbxNvRwRGaT1lG5FIIsJvT0GhbM2nV151FrGeWcetDu8dvvjBCqfQX/4lof42bN3AvAftv4Pfnl8HN2zkKFEuQrlKHp7Uwwng1TAQIYOLIIXxwgBXCwQNjRZQSFSgUgEyajC6griRsortp8n1ZKVuEBqGVSskNfsO44vY0qVkGbiccfoCd557nbeye2MVVos9IrU3D6vH7+Pcb/JI0MROlwLbFoakQqSwRSkJjY1wk9zN6KagpKoOA/AKgvMvsCI8hLzcEuKiCTJcAIabh6dp5/mM2I3cdhWyhfG6WU22xsrX352Vq4EtKnJbIVIJHJbDzVZyGnKYophKVQmIDDxZwTdbXnCU9zIyMYSxJyLbabra1FYRsZqlEeY+6nFeKPJXLNEHNh84Inn8ZJd57G29IiaLkk5z9MgA1GLSZYcdC1GK4E0FQxoBPlosC/6dG9K8CatL+dbmOA7Ca6d4FkpU/0a2tYY5Qi97KAtTdRQeY2JrRBZroBUOSVRBraXkJoa28mZk2vF1ytL8ikryb+SfA3YB4UQH9Fan3jKYb8KvE9r/fa1VeY/Dmz/Sue9IRTEs+FJ5gK+zGiY/4KpKABpIWcr/iXD8fvDO/nF+gXexsuRgURk+SKniaFB6nytxUjklXh+/mBPdkbYXVjks1N72DuwyI7CMnOtEkHLg608K+sgysk685CWwJ6zSCZUXjMQCZwVCEblupWi7DUGIpbPyoqMFtqEa6bqQq/ISXOEMLYImy6ZFvQ6LssUOdcYZpe7iIoNSL7cyWQs0JbKlUYiMGccklpGEq4lHfUlMgGr/WT8Ji+Yyrw1UzeRyEDSS236icViq0i93MOWGWFm0opc9lQXacY+j66O5UopfrqZnGX5c9KmxrlsE9cU2s+QoST1cxZCpiJXtI7EAFwrZWamjl/pc3J1hGqlh2eltEOHghNjWRmppch6eZeNQwsykdPDkCd0ZRIMnb9m+VoRGBlZItF9k7QA1qKZF3x5uZISliZO1/68xkqZ6VYQmSBt2xixQOmcpZD9/JwiXSuaywQzzTJZalAp91hdKWJ7yTX18XzBmK+bi7G+kjyAEOLJleSfqiA0UF57XwFmvtpJbwgFEVdh9iUFnL/eSeORDm/56w/wm9tfAzpfz8G5u4zI4BO/+rt8oT/BFmuVnxt7HTvqK3x7+cttfKw1Rj+zcCdt3qZeztt4ORdf/eecT7qcShqMGS0aRsKV1ON8MkRPra0MrQUH3Gneu/wibi1cwt8ZI9EoBP2OC4ngvzz+Wo7d8TYirZjLDCwUBakYNvJznE4ytpmaj/bGOepcoSIzPhts50o8wG53jr3WAk3lMWx0+WD7Fl5TepTLaY2VrMi+whyOTCjJkBGryelwC6GyuFKocaQwxR8+/nKkl/P9hpXhugmfmD5AEFt84RV/SFPJ9Y7WVA47zWB937EolyfUBnUZM5P5HA8nkEJTNXoEyiHWJre5F8kQlGTCTFoi1BZnoxGCMZuKETBitWhmPvNJhW32Es3M58NzR/mrV72dDIlNHrQLtcmEGfCFWyfYbi1xf38X+5xZtpmrTKUV3r98Gy8oX8RAsc+ZoSojziaDnI2GWRgqM2S3udgfZIe3iCUyDDQT9jL/7fx351ZGUTIflPnwt/8pc2keO0gwyLRkzGwyl5UZkD0yBAWRYAmVu2bK5VPtwxzyrnAy3ELF6JMhCDKHhtWhJPv8/fytALzple9lyOjwRDRG1cgDvKGyKMiIDImBomT0eTTYxmrqY4kMxmHMWeXnr6nnX9OitQ0hxJee8vnP1hZ+fhJfdSV54NeBTwkh/negALziq130hlAQIhGEDc3AEymI3M9+/q5JHjy+i2azgFcVlC4rEg03O9OciEf40V0PM2o1mU2qHGuNU7FCXCPh5OoI0VCGiAxkIDmfdNllFXHEEifiGo5ocSoeJdYmBZnnQ3Qyl5WsiCdjToejTPYHOFic5hNzh7D9GHyIY4P5TFGXMGEoitKhqyI+1y+yxWyx25SsqJhv8yY5FddIjC5HnGkSbXLQniPUBlvMDnNZgXF7hRjJTJIH+XwZ48iEUFucDrfgy4hO5jJst0m0iWEonGJIxQvJlKToRpTsiDg1aCqJLRSxlhSkItEJk6nHfjvmZGxzizPFiAGhjvGlQd2IOBZKvs07sx4gS7SkqTzqRkCoDXwZMR3XSLRBog0yJM3MJ9OShtlhKqnjipSq06ejPCSKqhlioPFFysqaBeTLhBd55xk3+8xnNvvsVfYW5thuLWKvDf5QGwwabR7JttHPbLpr7R40OzSz3M1oZy5RatLpuVRLfUp2COTrgJ6KtlCQEZZI6Wmb6aRO1QkItbV2ftYp5HF7BV9G7HLmKRshEoVCMp3UCLVNK3bZXV7CFhmuSDjsTqG05O7OQUbtJoMyfx6TcYObjD7DVouFpMRsWKFu9675b/Q0XMtvlr4ONOedwLu01r8nhLgd+CshxCGttbraD24MBaEgG4kxonx16EGzQ8Pp4c6bpHvj/D8eIs1UWuZ2NyLQS3ymdYCG2SHRRr7yUOJSsiI6povRkaT1FJFJTiUNHLHEuFnkb9tbOVB+lAlrmfct38adA19cnyUyLfGMhCtRjfmwRNms45gp8aKPFhrM/PorImYuq3DYnuN4PMKFaIie7bDTXCDSMG46NM0OiZZ0lMtD3e28snCOT3f2M2GtMJdW2G4v5hWlaWm93Lhhd1mJCzScLqN2M5+VACkU/dkiAEHdIW3btB2fiS0rGFJzNmlQliFn4hF22fPc272Jbc4Srpjio+2j/PzA/TSVpiol81neD/5pdQ8v909zPNrCclZkl73AdFJjTkaMmC0yBJNRg1O9YZqxT9GMGPPylbiG7A7ngiEcmbIa+tzX2003dbilOElBRlyKGxx2p/jI0lFmKjW+p/gECngi2kJBxrx/8haGd7W4HDeomT1udS9xNh7heGsLs70yNbfPLbUpzoXDLMZFWonH/uIcq60Cet5hWfr4BxIWs7xs/aHONrY4LSJlss+b4fOreykMRKykeZl73ezSyTxsZ5r3z9zKtw+e43R3mCG3gy9jxpxVLvQHMaVivlVCa8HZwjDSUTzen6BudjnRHqFbcPJitLTAfFTGEhnzSZnLvRqzvTIrrn/NlOXXOZNyIyvJ/zRwB4DW+j4hhAs0gIWrnfSGIGGVpxj5pMXCrQ7eY1N8vrWXTzx0hMHbZ8lSSXEqX+zlkN3hl2ZfzCc6R7j73E3c29xDK/U4tTTEwfIsD82N008t3CWR+4sKxowWJ+Iav7uyi1+qn+d0UuFsNEIn+f/Ze/Mgy677vu9zzrnbu29fuvt193RPYzYsA4AgSJAESYk0ySJF0bJslSJRlmXJdqKUU67KYpctV2UrZSnb5biSshJZUqTIcWyzFDq0ZGrjTokgCAIDAgPMhsEMenrvfr289b67nXPyx31oUS4rAiTFQmKdqlevu+fN7fv63XvO7/x+39/nG/Av+48z5wz5hc33cWWyxtXBMkt+n7+w+DTDvETDiyAv9p1oQVXG3MnmaciIK8kyD3n7PBRs8+5gh1+eLHMna/LT/Yu8lCwTipz/df87+fHOV/mHh9/BA/4uR7pCVU75u3c+zt10nrqaMtY+dXfKMA+4XNnhcmmLa+NlBrpUtIPnIdYz2NcTfa4hrMVs7TU53KkTiIz1rEMgUq7HZ/hI9WVS67Cet/nx1tP8xuQsX4ou8GvRCpt5jWfjVd5VX+fv732UK5M1bk8X+PLoQQKZsZm12MhaHOkKoUooqYy3NzZZDAaEKsWXOXennZkAy+NgXMEVmicqr7GTNdjOmsw5I7azJp+cf4aWM+ZKvMLnJxfwhOZIV1irH/MrvccY5CXaaszXo4t8c3QORxrev3CXC9UeABvTJqMsoONNUMIgpcFZjpDzMbsnNfbyBrtZg4Y7JTFOIWAyPu+sr7OfFTJzV2iO8wpVNeVmushCacTHqi9RdlLujjsAPN0/zzAv9CElPy06a9Na0fae1rg5XaTtF9uM7aSBsYKSStnPauwnxXa+XYo4Vzn6g137f3TOWs8CF4UQ9wkhPOCTwK/8a6/ZoCDNI4R4EAiA3v/TQd8SEQS5ZP/d0PmWZfroCj/Q/t/41soZtl6dZ/F8j3GjS2XTMjKWvzb3FRoSfrn6KDU35oHSLumywyAv8Y7uFvP+iF9Vi8hKRqYsHZXhiwEP1V7ki9M6Hy5pHvFeYSNp88nmN7meLPLnl7/Jea/oemw6E742vETNmbLs93lKP4RMJXopYU4meN4O63mb826PnbxKS40xwAdK92hIh7Z6hUc9hcHlI63rfGb4OJ9sPsN61qGtxgQy5QdXnuPdwTrPJyusBCfcieboeGM2khYL7oDLle2ibwM46x8iUomKJLqkERNFpEM6S4Uop60mlGXCZtbmoWCL56f38UiwSSgTvhKtcd47oCpTukozMZaySHlmdI7vbl1lL69zkNVY9Y5oqAmByDjv9ooWeOOjhOXGqEvVSVj2+wwo0fWH+CKnpDLqpZi6mrKZtTjrHVKWCdtZi/eV7vALR+/jXKlH1xmw7OzTNyGpVdw5afMDa99ipItekI9VruHLjOf1WV44OcNcMAZgwS+iw0FWoulMcFzN9LiEjBSd+3ucdw/IrGInbVJVcZH78I54enyRd5Xv0NchgcyYV6MChOOOOYrLPDs9x0laTP6JcThf7pEZRUUlKGlRMmfRGxCIDF/mzHtD9qhTcRLqakpkPKSw3OcXGpvEKGJtOE5D1sI3N0kUPIg/mgji9yLJCyF+EnjOWvsrwF8Hfk4I8Z9S7HB+zP4+Ooe3xgQhLVZZkrosTG2E5rhfwRlJjBWEB4Y8ENzOmsTW5SOlPod7NW74C5RVQlklbE8b3Bs16cyPqWwbJhcVSMtWXuJmusiKe8TtpMsj3ivMqzLrUZterXyasBqaoFBdNjP6WYn9qMa5xR7+UcFuHLUUt7MmPV3jhckqF0v7jHXAO8O7rOcux7rCOfeQK/Ea97t32dGab47OcaO/wKp3xPVoCY1kLThkN21wz2tyL+nwymSeO4MOu16NQOWsBYc8N1gruk77C9ABZyTx+oKp4+GOJNqXHLpVpGu4liwRGZ+NpM2yf8ILoxXmnGFxMyYLDHS52Hd7BxzpCjfiJa71F3GFZjeusxvVuFSvcX+4z0gHaAQ7WZPDrMrdUZtR6hM4xYRwkFQYpgEP1PfZmDTZOmxwuzXPJPcZBCGLXp9rk2XmnSG3RgtIYdkQbRa9PsZKro2XGIxCXhot05tWGNYDqmrK1fEZtqM6R5OQ3EjujZp4SlN2U8apT9WJC/aFEThjwcFRjRfiVW5PF8isInEdduLG6Xt6Wa4w0KUiEVraYT+vY6xkd1Tli84D3DqYpxrGbHpN2sEEYwV1L2YSe0xHAdfaSwxKJZ7prbFUGVBWKdtxg6qTMMp9yiplql2uHK0ihP22hrA3l4MA/kibtWaahl/71372X37b19eB972ZY74lJgihLLaa076W4/Zjnp5cZKnTx+/mbD51hk5mCI5zXoxXOcyqfHUoefulwuHsPZU7APyNb3wSkQv+xatz1JqS8m0PHcCdbJ7UOvzS0bsYZQEbSZv1qM0/u+/L/OxgiaYz4bFgA1fkvHRrhbudNpP9Mm4z5u/f+S5Uy2AmhT3e9WSZc94BF0v7RMZDI3h+usZYB8TG5bazwNPH5/ha/wIllfGfzH+R/9F+mIaK+NH21ymLnM28VuQ8EHxm4234Tk6UeIxmHg2vHr2PPJekkYfNJJsHLXRTo32JqWqSkkEmEqzAjF020g51NeWB0g5KWC6V9wHYSDucZCHfOLyPjyzc4Mrxu5jzRkTa4z8++wUi4/NQe5fIuHw9usgFf4+GihiZgFCmfGXvIlJY4swh04obgwWGcUCUuFgryK1EJ4qHy9sMdImWKpJ0l8I9XoxWuVzfJZQpv779EJeaB6yWTjgf9pDnLT8y9xSxcRmZEn1d5uPNl/ifBh8GINWKnY023ZVjHGkQwnKtv1iULl1D2jWEpZTb0wUcaZh3RkhhWAr67KYNFoMBY+2TG0nopNxJFjhKKzhS88Pnn2M3rfN9C1foOgOemlxiJ27w6rDDKAtIYo9yfUpJZfTSKk/Ov0YoCwCNk2tcYfC9HF9mZFYxST2MLRrlpumbV1EWOYi3xC7/9xxviQnCGsGl1X2mtSV0qcwHyjfZbjf46tYFkm5O9mpRuz7n9fhA+SaB0Pzn4z/L2xubvDvY4TOjy6hGilKGwM/wn2mciqAmxqcsE36o/Qz/sv84n2x+k16tzM8Olvjx+g6/NK4zNAFr7jHN7hAhLNNKhhCwtHpE9NkuQluM55AYl1eTLutxm4ulA3azKgrDvDfk0dIGAHPzIx7wdumbkM28TsOd8t5gh69OV6iqKXtZA4CuGlJyM/rTgHE/JKgkpKnDUnuAtgJVHzOKfeYrY/a+uYpKLdN5F/+kgOROygKUZZCXiLTH88kqy0Gfq4NlDquVIjSOa3y8+zKu0Hyi+QJHeYVjXeFTB+/mxxa+xlcn93OYV2g6E15NurhCc9br0VZjpLDsHDbQkYMs5diGIMkVgZfRm5RJcwe04LMHj5LkDudrh5Rkym5c5221LT67+TDnm4d8R/cOy/4JL43O4EjNV+9e4ExwQi+tMueNeLJ8my+OLrN50MKceJyEmnInYpJ4DCYlkthlvj3E2fPwjwpI7OBRaF6MuDpc5rl4lXYwYZCUWKsece14kcutXXpxhdDJWC71mWiPRWfAp+6+gyeX1vmFjffTCiYYK5kLxhxNQlylMcce45HLq/UO56pHvHB8hrKbsDVqEDg5NT9mnBaTT7c8RBvBcBQWuSF+l9/yGx5vdan1W2KCcFzNK7eX6JYkjbt9XohXudZf5PLcHt989X6wMO1Ilp0TnokuEFuHYRIQaY/raZMXRiuEYcK4H5LFDqr8OyKo18e1ZJn3Vm9zPVnkRlxEDr80rvMDlQE/P+jS1yGZVgRuznxnyCTxOB6VoVOUYY2yXPD32cxaPFzeZqwD7vN73Innqcr4tGQ5MgG9vMqKe0xfh7yrfJcvTc9SlgltOWGPYoJYUik1PybVCr8zQhtBLYwpORnDtNj/u44uJqzuTHnYMORlgXHsqVvMqn+MK3JCVWg3Pth+heO8TGYcHq1tn5bR7iYLuCIns4o/O/c8t5KlWYY/4KK/R1kUfEslDI/7O9T8mLgxRrUMUeJR9lIcVVSMBIUN3TTwOBP2qTrxKTi2VC44lx87c4P9pEZFJUTa53zYwyCoVqYYBI9WNpl3hoxMCVdoatUIr1l4jJS9tPCoCCw6lOgAwMwAACAASURBVLRLEXuNIopSU4Fbzoi0x0rpBEcYOv6YeX9M2xuTNxRz3piaEyOFpapi8KGiEjqVCR+uX6flTtiOG7ytusl20qQVTgmcjJNqFcfTrJT7LHhDkrJDSWVEucfZynGx2peKFvuWF3EYVFDSMk1d6uGUwMl5M8MiyP8A25J/m+MtEd/kmWLlvh7l3ZR0rsyad0jTj3j6pYuneLXaaylfGF/mfn+Hi/4+Shpem7S5lSzxtxZ/k9FhGaQtnKJmTst5aDFWMNIBDRXxMxsfYGJ86mrKY8EGgcj4+UGXv1Lf47vK98gyRZw57N1rk6ZOgTB7XQFZMvTyKl1nwO1pATj51niVikq4OS0AJIHMyGZsxKvRCo+X1vm7tz/GsnPCetrhQFcJZMb3Vu6woz36cQklDeOpT6YVx8OQg3GFXCuOJyGDcYmDcaVwj5qh3IUGdyyQvkb4moEuFc1KVrHgDvhG/z5W/SMMgi8fXGI7aVKVMRoxK6uWuTK5j4v+HlU5LaoXaZu+Cek6RXLumXiJceqjjeB4WCbNHHIjidMilHakKUhLRtBwIranDaqq8B3dT2qEKuGp3rmiUW7cLRrXtM9BWiWKfd5XfqUAziYLzDlDLgT7KGk57FcYxz739ttYK1DCkmROIfmWBWpf5sWCoimas1pexLxXbDO0lYxyHyUMiSnOWWIZ5QGZVeRG8k92n2Q3rnMxPOCl0RkS4xT8TO1gLQhpkMIUXEvtUnYSOsGE1Dj4MmeqXUoqw5G6ACRriZSG/qRUMCLe5HirMynfEhGEVIZR7FNa8Aj3Uxqy6MArz0+YHJSRuSRuuzxc2sIgmVMFj7DpTbno7xEKi19LyLMC5iHzoGgUAh4KtjnWFbSVvLu9znnvoKA7i5y12Sp/oCfMqzKelzNXmZDPQunAyxikZV7/fBoqmpn0Riy4A2TZsOYdklqFwjLnDOnrkHPeAbHnklnF2zo7tFTEY8EGGsGy0+fFtEZXjan5MVHmoZSh7KeUvIz58vi0M3IY+7TLEVHawqpCLyLToqnK6oJy3XSKvb8vitWr4xdVjTPeMY+3Nqk7xSRQVVMyx6GXV9mMWwDE1qWiYhLjImfqwIac0tdlan7MKPFwHE21lFDzY1ylybTCVwX78vXeg5obn36WLW/CnDOi6iWcL/c4yUI67niWs5G0qxP28jqR8eg4I2oiQVuJsUVXqJSGUpjgKY22At/NKTkZIp3BYEpA4nI+OOBW1C1uVO2RGIdAZpSdIpKSwtJ0I+pOhEEQypS6F7NWOeIgrhIZ73fdeJ7UCAHWSHKjqDtTdkXRWu9KzST3cIQpOk5n+oWyl3KYlouoatY2/2bGH2UV4/+t8ZaYIJwDSfN/rnDwuGTv44If+cZfwb0e0rqp+dv/3ad56h0X+fKvPs7/sfceqm7CzqTOxnPL3G0t8AUewjl2+Ll/72d4Mkh4ObX8/Mp3MueNuDHq8qmj91CSKSWV8fJwCSksVwfLvHRrhWZ3SKYV/32m8Lycq+/65/xqFHC9u8wP1V7kVlbnU633UHYK4MirycKpsOkkDzlOyzxvV1kMhix6fTazFpH2+cb4AhWV8FO7H6I/DejF38txHJ5mrJPcYbE65K+v/iblf014pa3kY+EBrihWo32d8B/wSZpBxP2VfW6P51kMBnxP4wUm1uPF6CyJdXgtarPjNNiKGnzFPEDNmXJrtMB7W3fp5YV3RDIDvfyrVx5hc6XJQVQl0Yqal1B2E6LcYzEsJt9/f+m32JtvsJs2OOsf0lJjVp0TOirjC9E5VtwjsvMOP7vzncTanbVrFyv4gjukF5X5Fis8Wt8+Xd0T47B7c55fkO8nzh3W6sdcnaxwZ9jhT69eO31vf7n9FH3js57N0ZuVkj8tLDd3FmjWIv7a+S9zrAvx2I1+F9/JCVTGnDfmtWGb1dIxvsyJjctu2jj163jx9grBgxlXd5e45nQpeRmBk/Ojq08TGZ/fVJqPzV3nRrR4em0mxuHmyTyL5eLvIoVlN6rRqk/42/f9Gr82eBsbkyZzwZiV4ITffpPX/p8kKd/A0L7AO46ZnBNUXvZxv/MIjkpMW5LMKn7j+kOwlnCpcsDVwTJr1SM2pqvoRGJLmryTcc4d8plx0afx21vn+J61l7lQ7vGO8jq34kW2kibvaq7TdCYEzYy7nXbBgXCLlXeuMuFXo4BPhDHL6jqJhbJIkcJwd9zhB7vPUpVTjr0K+1kdX2Y8UNql6/YBuBUvcc4/4HP9h3m0vElVFfkFVxWrz5lKn2EWsBz2eXpnjTl/zDv9MYk1hDIjtZLH/T0WlM+tDEZGsZ7N8bi/yaPNbTanTXyR0/AKxNpG1mIj7fDO8DW2syYdZ4wrclaCY856h8TW5aFwh+tRkWsAWHAH7Gd15psjetMKgVN0IAphOV85ZKJ9Wu6EXlrhCf+AY/eIvu9TlSlLStPTglezGh8L73JsFE9Nz/Nk6y7AjASVkVmHc/4+71+osxYccm2yTFXFvC3c4Fa8SO2+Pr7KCZyMldIJFZUwLAV8tPoSDwZtltwTPj95kMeCDc67B6y5PVoyZqPZ4f7qPv0sJLYeS+4JrtC03YLqZRBcCnYJuhmr3iHAKY0KYCdrsna2R9VJaFUiSm6Gr3JyI1lxjyjLBH+heO19pR5VGdPxK1RUwp/q3iaz6pRLEYcui16fvgl5JNziicprxMZlzhm+qev+T5iUb3BYBWqrhwyXqG4p9vplOhGMVwRPD89jpw4r9/VwpWaj32CYBDgTCvp0JnFPFKEQfH10gXlvRBK7RMbjS5sXCc+l3Ju22Y+rfLB+g68NL9HPSkz2y0wrGfOdIeOtBnmuuN5dZlld5zHf53NRhbvpPJuTJsYKfvXwUZ5s3OXlyRIllTHVLveH++xndc54R9yYLDLSAePc4+XJGS6E+xz0ajx+boNre4vIluV4WtT5RwcVNqpNbmUOt9Muy84JB7rKY/4OPZ3wUnKWQGT08iqJp7gz7pDOcHeT3KNVnvC544fZGDWpnIlZjztMdQFmXS0d8/VREcFcKu3hSMPLkzP4MqPiJBylFVKtOByWUcqgtaTkZzT8JnuTGp1SUcF4KW2yl9c57x2wnrXYyTWxdTnIa7ySdkmMy1ba5LVJm24wLNynvCkSy2FerO63oi4td8JuWieziteiDuNJQBR7hEFKJ6gzznw2Bg2+XHmIR0sb7GRNWmrMC/EqmVUsuX02MTzfXyFQObmVfO7wIS7XdtmJG5ykJTypMQhi47Kf1DjMKvgyxxV6Jg8fcpKX2T2p8a7OPU4mJQ7SKnPNAgf4zeg8dTU9jRyGWcAj1W1uDhZOf6cn84K7KQxR7rLv17gQHrCb1pnkPpcr22xFLeDqm7r2/zjzC29kvCUmCGdq0Q/M494N6D1W/GyyJKi9ZniwvMvJwyHPvnSerXqPS+0eB1GVyX0aAo2IHIwLA2P5q52vcj3t8hcvPwPApXYPieVyZZua0+LzJw9Tc6bsRzXcZowQMEk8/NaUwMv4odqLJBY+F1X4aJjxi3nGJxe/iSc0Q1NQpN5e2SCzzmktfKBLHOY13lFdL048hP20xiAPubSyX4BWOkeETnraZNTsDqm6MQ2Z8rFwA4WgIjPGRrCpFX+uvMuhSXki2EEC72/doeMMWXWPWc86dJ0B318f8kJrBVdowjBlrANcUSQtV4JjWmrCVtqirqZcLm0xMiWqcsp5f58rh2d4fHnr1NrOIKg6CfXGPu4swbfiDFhxBiRWcdYvpPpVqYAeN1KPJWdKT3v8inw7AOdKvYKJKTR1NeGbo/OcK/UY6YBHwi1ckbPo9nmpvshq7QRHGhaDAZmvmAvGvDN8jb4OaakxNRlzzr132jUbWYeL1R7b0wZP1Nd5W7BBT9eYd4fcirpIYQllyqp/RD8LeSgsOnzLMmFifAKR0lJjllsDptrlfOeIQGU4wpBXJN9ZuUlmFYd5hY9Xr/L16CJ1FfHh+ZsAvDhcKSol7giDYCdp8GC4y4fKN/lKdIndtBBpPVzaelPXfYGc+5MJ4vcdOhDkZZdkoYCWLMwN4Nc98kDweGmdn3rhT+EOFFPtsjup8d7510j/yQLD+3ymZ1O0r7kzU1l+PDzkb/zWD/D9j1/hvnKRzf/1vYfxnZyGF7Hs9zm32OPv3/kullaPOB6VMTPr9ltZnbJIuZvO84t5xo/VDrj89A+TpQ7vXN3g4eoONycL3Bl0qHoJgcr5YOcWAF/rX+Tx2gZfObjIX1p5ith63LrXRQiImzPI69RjuT1gMAwx9T7rWYNn/w3KzM9NK6w4x1yJ11hze/z8rSdJYo+lTp/DUZlaGPP43NZpeXE7brAb1QidlP2oyhNzGzw7XSsqOGnAXGmRBX9IYlzujtvsH9TpnVQxucRGDqqW8sDSPodRmbX6Mf2kxLPxKjenSwUm3x9yqbSHxHCY11hyT7gSV7g6PsO9cYtEO5TdlNBJOYiqfLR7g6/vrnGt1CXVirnShNXyMVcOVxh8q8O31iqYTLK3XON87ZBvbJ+lpIr+iIVgRMcvtkSu0BxnZS6UDnjuaJXeuMw31td4z9o6HX/MxqRZOGJhmeYuUcPj9nCOzEpS4yCxLPhDMqvYnjZ47cYio3M+h3dbBceilCMdyy+XHseRhpf6Szx7fBZjBS0/4vntMzQqUzrhhO1JHUcaMq0I3ZTjNORW1OUwLVNWBei27k6B59/4hW/f+mXOtwRyru537Xvb38/xh+5j7wOaxlUXb2RpvDJh9ycy/vLFp/npf/lxsopFZuAfS/KSxZkKwt3i/B//qy+QG8W14y7/++V/zF+9/UPsDapMRwFemJL2wqLxSoN/JElbhuprkrhToMpkCu/7My8Wno2TJp9c/CZ/99rHuPbkP+VQT/j06BL/4OqHqYQJnqOZJB7WCiYnRaOPCjQ6lcWW58g5RdN5vRlCv1oYsIhIFVAXAe978jrvrd9hyT2hl9cYmYCvHN7Pw/UdXhnPU3YKSe9Prf4rMmtpzdgT+zrh+aTLlwYP8qWNS3hOTsnLTsts09TFGEGauORjl8rchPFJiBumOI5h2g9AC5xqkYPIolkZ0TGYqQNG8OQjt/lw6wYaSWJcBrrQK0TGY5z7MzWpzz9fLzqQHaVJ80JH0utXqIRJAbE9qKAqGbXKlJKXsXdYR0qL42qy1EGPXNxGzA8/+BxKFDL7L/cu8dH5GzNtRc5XTu7nO5pFHuABf5e+DvkvrnwvjVpUcDSlQQlLbiTGCDxHI6XBWkGcFsrP1ysu05MSlc4EISxJ4pKOPT50+eZpN/Cfa1/hv7n1pxlPfeZqYzIjOTyp4vmFcY4Qlij2CbyMJ5fWOUzKDNOA0ElJjcOvf+AfvmHkXPOBefuhX/j+N3SP/F/v++l/d6nWYLHaFN4PElRcoMCMr0gS6DoDMEWZTxiBM4Ws9juOV05sSY2DJ3OmqcuCKjLD0aAEmYAQ7OulqLTorTATgdAFGVvlgICyk3B33MFYgSeKC/hQT+ioMldGa2RTlzFQnjk5AYhYYZ3CGYtcIjyDSgTat1hTsBILTZOdTVACZ1Jg3najOhtBm6qKCWRGZHwGaVCUJZ2Ejj9mM2ryYlrjSX/KyKSMjGVOORzlFTYmLdLEIU0cksxBa4njGKJhQKlabGdEoMnz31mlHEcjxw6mpNGpwvHzItadKowvEJkEU/hO3InnCVVaeH0mdS6E+/gy49njs/gyp+OOORmGKGWQsrC+M2GCzhSjcYlSmCDDHMfRRLGHksVnkE9cTFAQoEQuyBOHa6NFqk6CFJaVch8pDHfiORa9Ab1pha/aS+RG8thykewUM/1IPPTBUuhClEUniqyUIaVFKUOauEilUQrSxAHHMJ16SGnQmQIL+3GVflxi2wr6jZDhJEAIS5w7RImHPvGJwuK4QkA68NFVSdWJee5ghcG4RKs2QZs3X5H4ky3GGxjGd7HLcxx8fwyxw/gsdK4a8kDxlx/6Gv/L+gdZfe8Wr95axCrB6DzUbs1Wy3mBDgQPVXY4zKp8ZOUWf+nun+E/Wv0KrMJ/9fL3kKYKHFvYqy0ljFrFhWE8B6MKqCvAVLv8YPdZfvXwUYamxDtXN/j06BJXRmv83MpTPNFbxndyJol3uiI1VvqkuUOeS8K5hFGvQnphWgi2Ygd1rEg7GnxdMBLDnCRwkeWMT3Rf4px3wF5eOEdnVvHhhVvExiUxDrdHc3RLIzazNv/i+BzvqKwzMgHrcYf/sP1bqAXD4bSAnkphcZWmPw1YXhlwPC1gK9G0RLVdTBYlvzB8MbUchMUJMowpMG3hQlENsFaQpg7fMfcqq94hu1mz2PpUX2MrbXGYVfiLS0+jkexndVbmTki1InSLCsDrDlfD2KfkZYwOKgg/Z6k5IM4d9FQRtiK0lriuRjQs1go+0r7BnXiejjviX2y8nTPBCY+EW4xMwJOdoiciMh7/9PBJfmTuKX6j/iCBk6NrsuhnyVxCN2OcetS8BINAzWwDo8wjzh1GBCTaxZ05Y41NgHUsl+u75FXJuVKPskxZbA6Jc6doGPNSpkFCpiUlN8cCuhpR9lKO0zJvn9uithizn1SpOClX3sR1/yc5iDc40rpg/z11al+A1vWI9/2jp/nF7vsQmeLT995O8qUOKobP/s3/gReSM5Rlwt+a+z7WOsesVk44SUtkxmE3rlFzEl5+6gJ/88wZTKJ44bv+IfvasJnXqMqYOZlwO2tyPVkmMS4X/H16eZWGKizqq3LKk427GCt4uLrDP7j6YbKpyxO9ZZ59/Jc40RE9Y9FW4AlDKCCUBW1qQUmejhssOQPmZM7nozV2syZnvGOWnRPk7IK9nXT5jvAOI+PSm5nTBCIlkBldZ8BO1uRCUJzX/f4u//WN72E8Cbje7jKMfZS03B13SLXicw9/igzNwBQeTcfGYUUZesYyMi5Huswj3gkjKwiF5dg43M06TIzPnBqykzcJZcJ7g2228hILqijlDYzLRt7EE5rUKs57BzRUxNALWHJPiIzPF04e4qcufmqGd8txBfSNw1nH8q2kcO4+OldhxT3iopOxqSU/Wf7T/IXuN3BFzkX3CIXl5ux8zgcHNNSEH1h9njNeUXpsGI8Phrf4b7c+weG0wmAa4Mt3888e+sdkCI51QDhz8F5QGXfzkLNOhAFcQAlBai2ZhX90/H4+UX+Bm8kSUCQxe3mVh/xtajLm5w4+QNlJ+HsX/0/aMuFaOk/XGdDTVaoyxkOjEYXgS055ITlDbFxi63I53GbFPeJn3uS1/1afIN4SKg2ZQ9IUlI4NSEFmFeVOhDOSjCIfqyDoG3q6TFuNiY3Lg919lsoDciupugmvTTvE2mUrahQS64kLuSSxBSauLFJup10GxqWna5zzDnCFZjNrnSokT7KQY13h5UlxAd2cLFAJE9xShu/knOiIpgqZk4KuAl/AyAr2taEqLJk1RXlSl9nUPpf9HQ7SKl2nTyAy2nKKh6btjImMQ09XcUWOwuAJTWxc1tMOsXXZSlucZGU2szb1Uky1MqUVTKiXYnynMOmVwhLZDBdFSzosqpCyyPGFQ0tCbB0Mkqp0UFg8IWjIHG0lF7091tw+j/mbtNWYyIrfNTkMrY8nCklzIDM08pSfaKwkEEUVoCU1ZZGzoBw60qOrNJk1uCJnyRkRW5dAaDIsXaV5vL7JQ94+y2pAZiUSuOgezTQURVS4nTQoy4SySCnLhNg6hVGN0rTKEbWZHiEQlqpMqYocX2g0zDiWv3tEVqAEvKt8tyB6uT3eFbxGQ03oOgPUjFsJMO+NKIscjeC8e0RXJadaiqpMKYsMhcEVxXt8fbzON30z43UdxBt5/HGNt8QEAeANQFiLczjmlfF8IettaJJ+ABaykijoSWmHK5M1VsvHrJaOyYziam+R3EoOoiqvnbSwvkHGEmeg2NMKhWBP14ueiLzNC5NV9vIG63GbUKZcmaxxfbrMcVZmP6tTUoXg586gg+doyqWUSeLRM/Z0kgBoSY/bWZvIOIys4F5erCYvxSsc6wpHuswTldfYyxuzhJvhyJR5eXqGbV3nSFfo5TWCGY/SE5pjXeE4rzDIS2RWcZKX8aSmUYpxpKHqJbRKEcMs4DAqMzCWY1PkRE7MlDOOz708xwBLKkJi6Jucni6RWosEqnJ6ynsMZ3qBYx0wMC47OiS2Dn1dAF6qcorCFs1sVpFZh+28Sd+E5FbSN5JQQGQ1A5NSES53c4+zTjS7gadFNcVYKsLl4dImPV0ilDkSy+2sSd94jExBmFJYak5MZh36ZsakNAHjvLgB28GEsfY5Nh4jo9jTFXrGZ2Rdjo2DsZJjoxgZxcgK9rSibzwmpmj2W3MPWZ4Jmrozyf7IlBiZ0qnxzcQ6xFZRlZrYCjazNiMTMDIeKZKertE3XqEcNQEHWe30OG9qWMitfEOPP67x1pggLEwXCjMZ3QxZKZ3QqUxw+goRaIxbRBkpikeCTR4o7VJRCRUVU3NjFipjJJZOaUy9VOy3TWDQgcWlAMw+4u1xoKusOMenPIeLpQPGOmDRG3DWP6TjjfFlIYLyZUbVS5gkXuHqPHPSAk4nicTmp6vLGeWyoFLud4+46O/RUmPaasJm1uKyt0NsHbLZyttxRkUD1ex4fR0SzojJxb8PaToRTSei44zoTcokuVPY/k0qJLlD6KSn2v/MQk8Xq9mrWU6GpC1LHBuP8+4JDelwzo1pSY+W9HgxXmXFGRBbhbaCskiZWA9XGKoyRc/278e6wmbWPv2YApnRUBGxcZkYH0cYeibkbh4yMRYNDExKWyY8l3RxgVXnhLOOJraKyGYF/BbJni4zMD7LM9fzXl7lRrzEZtYinK3aE+MzNAEZiqNpyN6wyjjzmWqPOZUSW0Uvr9HTVfbyeuHklXXQCCbWQVtBIDQehqo0bOeF+Ota2mVoffZ07fT9xNZlmBVcj8j4uBgmRs5+R5VeXiNFFb07eY3MKqpqykle5iQL2U0bbzqKeD0H8ScRxO8zrAOVLcu0o1DHhdR347CJ7qZgwZ1A0hDMyYhnp+fYzRq8OpnjIK2RGcUgCWh5Ew6nFbQt/BaYdUSXpWFsEl5Kuzzm7zAyAWNddPdtpcX++1a0QC+vcpyWqcqY+8P9wjhW5VgrTh+eMEyswRfO6STRlhELKmNLZ0RWcCtr09dlPAw300UeCra5li7RkAnZTEh0dbLCyBTVitSqU4nynBrScsYMdPlUhJVZB0eZU4t7JQ1lN6E3rTCa+kggsgolCrHY0syCfmpTHnRhW1fY1zk7uUNkM45NyoIz4MVkmWMdMjA+27MIZ2IdIuMWJG0srtB0nQFVOcWblRxHJkAJUwBTcg+FYU5NiawisoJASCbW4aLbIxCSIxOyowtBVmYtDRWxnnbIrCKUxXasp6soLMveCS1VaCCGpviMyjJBUbSYN8MpLT9ikAXEVhDbokFLW3m6TZhzhsSzrYoSdvY3NwxMEQ2tOid4QtPXIQrLXl4nnb0+mClkY+tiEETWoT8D6EDRyg/giZzYuqcTQkll1J3oD3Tt//96ghBCrAshXhJCvPA6s18I0RJCfF4IcXv23Pz9jmNVET0kDYGph6yWTrB3yzBy6HRGeEOLzGFHVzlIazwY7PDsy+f57J2HOUlLPDG3MTsfyzs6m5T2JPgGMZewoHyeSZrcTea5m7UIZcI7w7uzvarhTjzPkj9gzTsks5Ku2yc2LgdZjQ92bjE5KRHtVnCVnoXSgi/HNZ5Pq7yQJDzm+4RScTOdoyosT08ucNHbwxWGV+MFPn34BFtpi+28xpX47GnEobCn2P1XkwX2szo7eZN5NWKkAwyCzaiJRjCcBGzuN9kd1jg4rnH7aI5J6hWlPAEj47GTlzgyPl9PWnSV5nqmeCWz/Nb4AbZ1hQNd4V7u8vV4mdeSea5GKzw/XeML48s8Nb4EFDeAKzTbebPYmuiQu+kcO7Pvj/IKryVzdJ0BqXXYn1bRSNazBj1dpm88nk+rtGTOs/FZbmU+vzF4lGfjs2RIvjpd4Zd672I3a/LM5AI7eZ2GnLI9Y2lsp00GusyLwxXupUXicjtroRGn0dKdQZtBUiK2ir4plJdF/qZwQ3vdFTy2LrFVuMIgsWzmDb4yepDfji7xhf5lrk5X+fzgMs+N7pt5gyicGULgSFfYzmt8YXyZ20m3oJAbl6O8wk5etM/H1uXVuEtsXDru6JQq9mbG/xdyEH8UVYw/Za09/LbvfwL4orX27wghfmL2/d96swfNawaqxc2UhYK8BOecAS+6xQqjqhmrrRPOhH22ogbvbr5Gc0Yf1h4oX6NnTlhLzoCJ5/Owd4QB1nOXsQ6Y94ZUZczN6SKpVSwGxZ70jHfEYV47PRfrGNLcIZSKkTYsqwIYu6AyBsZQlyVWnRNCqbhc2qIhU5aUIlQJH2leJ7OKeTUm+7b0WUNGpCiOqFBRMYHICGVCKBM67ghvlgBb8w7R+e9Ou7lK4ylNriSBEIQyI7OyWEHViJGxVEVGVRoeLW3QkDFKTmlJDfTYzxozk5y46NFQMWWR0rclAqE57x6cJu1eXz2BogTo9k9XzqqbEBv3tFsSimSwgtPGpXeU11lyTwiEZsk9OX1d3YmYU6PTGz+2zinq/221zRnDoli/ApGRGUmSOVgraAZTGjJnjyISeJ36/LpJjis0LppgdrxAGLqqUFQ+UbrLflZsEbr+gMHM+MYTmlQXk4QrcrrOiKYzmVU6aqevgaInpcqUjjsqhGO6iDK+PWn5Rof9d7CK8b0UBqHMnv/s73sSCfQfhOYrGWnTJ5AZD13ewN30GU4C4jko7xn2dYk/V71KIFM+eukGH1m4QShTYu3yajTPUmnIxqRFMq8xuUBEiluZ5oJTtFB/dXq2KAXqCrFxOecVZOTzwQEKy6JXuFr99uB+Qpnw3GANFWhUNSPP5Wm1IpQ5l1zByAq+Om2fRhIA3x3uk1lJT+d8JpVLIwAAIABJREFUqHyDnazBh8K7HOgKc2rEXl7nO2u3cIVmJ2sWWHMrCWXCftbghfgsXaePtpJVr6AkWytQrib0U6QqFIKr1ROa4ZQjXZQYM6uYk0mxH9chXQXPJV3eG/S431Vccj1ayudhT7CVNvm+6ss84O9w2d/mPaU7HOly0b2KJZQZR7pCYly2kyZ9XVR3Yuuy5J6c0rPmgjEjU2Ina1ITCeXZDZJRdLemKJ4INnmnlxIZh2U1LlD6pXW6zoCRCaiKnPNekQu6E82xlTapqyldd4DCMNYBI1MiyRz6xxWmictCaYRLUaa8l84xMf5pxHA9XsbDEBmfkXGLz8L4LDk5Ulj2dJ26E3He32fOGfH28B6uKFrDT5KQeb9wXXcxXPL2WHGPuDFdIrYuDRUhhWEna6CEoev0Z01obXbTOrH13vTN8lYHxvxhJwhLYeV1RQjx47OfLVhrd2df7wEL/6b/KIT4cSHEc0KI5/J4gjsSTDtFQNN0Jrx60EEHlmY1wplCXipWyn1doquGPLu/yrcGqwCslk/o+sWKdbF6gDOSOJ6GasZZp8jy7+ucOTWcJewOf9eeMZDZ6Yp3zj/gTFCsdI/XNtCpRE9cPC9nQUlcIRgZ97Ry8IR/wH2uYWCm1GWJe3mRB5hTDnu6RqR9etqjLBNGJqDrDBiaEqHIZ/v734GtFPTpAjpblgmR8QlFsQ3JU4c0d9C5ItOKKC8uxjllcQWnqy/AihMRW0NDRuxoRWQyEpuhraWnE0KZsp5XONIV9vI623mTtprgCk1VFlqNtioStqFKCURGS40JRHY6SZRlQmIUDTUhnG2VJBaNoCO90/PZzGscmpSqzOgbj6MknMFqQwKRUZWC2LrU1ZSySk/R8q9HJr7MaMgI381xZ96XmVEEQp7a8rXVmKqcFpGb28cXmqqMqcqMltTMyYTUWiazSshJVmagyxznxfv3hCaUhVvZRHsoLEpYDnT1VJ/x+nZQYVmYkbci4xPKlMVgSN2Znr7mjQ5r3/o5iD/sFuP91tptIcQ88HkhxM1v/0drrRW/h+XxzFfwZwHC+RWbNA3dZ3JEbrk7neOx5W2uvvQA+/sNvDKU9mE9a/GId8BXp+d4fH6Tt1c2+NZ4lW/1znCpWbSDf+toueiDiFy8PZfPTs7wHaV7nHF8/tV4mbZ6hSvxGk8fn2NufsTIBBznFfo6JNI+n+s/zDj3IISvHFyErJBPj3oVno4bpzdJSx5xN2vx9OQCl0tbfHe4z7V0ymWvxBenirIoJhBDkUn/7egSS+4Jz0XnWHAH3MrmuZ0scJIVCcl41u+wkbZZ9Y5mF19CT9fQIxd8TZS46FSSu5Jp7jJKfJ5J2pRFyp10/hTtv57O8aHwFs9O7+M/a95mV0dUhaCnUwIBN8ZdfqjxLDtZk4nxWXZPuBKv4YmcnjPAFTnX4zPciBaZapc9VWc3KxKZq94hTw0vUnNiYu3yW+MH0FYSlbaK881rxLbHjWgRV+T8UO1VFA6/Fi0wr0acJCGxdTnWZa4ly6TssZG1+eZgjWEacJiW+Z7Oi/TyGutxm8S4JMald1zD9nxMqDmeC7mdu7ySLvDbg/s5E5yQWUVkfL508iBBq4iAyjKhLBNSq3iHv836pMVRpcJRVj7tAK2omFtxYQNw96iN09HcTrq01JjI+Bgr+ezBo9xf3ScOXI7zSqG5qRUT9K3xAvdGTc5WT8jedOOV+APJs/9tjj/U2Vlrt2fPB8BnKByG94UQiwCz59/T1uv1YbwCpTZYc8lDxburd3ju3ip5aLnvTI/SfpHIbKiIe3kBLPn8zQf5zO7bmfPGXG7vFd6IRvGJpWvU7kjcckq2kvCYv8XNtMmtTDPnjHjUU/z56l0qbsIDXhHouDLnnHfAWPs8Wt6k5iTspzX+0spTuEcO/rqPLOUszWzpbiaLXM86HOkKn6i9wGP+zmnk8MWp4sMlzcNexovRWTamRYISYCtts+AO2EpbLDknxWonc+5FLfbSGqM84HJpi4EOkcLwUnQGhUFNJN6WR3Qc4vQ80oOQw6jQCLyeMCtC6sIV6qK3x2ZexxWaz09L3MtDRkYTWcXzaYfcSL40ucTVaIWvDS7yXHQfNTkltQ5VOS322GpKoh2OkjL9rFSs7Nrj2vQMF8LiI90e1/FFjrEFPTyzDod5la4aM849Bjrk06P7eCYp4wnN89M1do7rXI1WuD4uwva2nLKbNgo4bFaUlH9rcInb03kyq+hnJXyZ4bg5JjCogcPxtNBkjEyJpaBP3YmKtneZ0PIKybiiiIRej3xuZh2OpiEbaZsbJ12uDpZ5ebTEy5NlolmCM/RT1vstBrrEetbh6eF5bsRLXKwU7/c4rxSIf6/w0rgeFYSys9UTzExh+ebvIfGGHn9c4w8cQQghyoC01o5mX38U+EkKu68fBf7O7PmXf79jWQH+SdHA5ESa/ayBNYKsnZPkBe8hcwQrasxL6TybOsH1c9rBhFCm9OIKF1sHjPKAsfbJyhR8wUxSl5pMjcms5HF/E4PLjtaUVEbfhKy4x1yNVog9l4pKqKqYC+E+g7wgF+WhRfsFDHdO5sQ25gF/t+ALqAGuMCwp9X+z9+Yxth35fd+nqs5+99vr6379NvKRjzs15MyQM9olS7IiyYYs2JKROLADxwlsB0ESw4iRxI5hKHICWLFkG4ET2Yaj2IaEkRwttsayKMmSRpwhh0MOh8vj2/u91/vd7z33bFWVP+r2JUeSNaQygYhgCrgAu/n69Ol7T1X96vf7fj8/jnWFLzxqomBqSurSlTG/qe3s4FfCfcYmoiZz3kjdOXlFTTn2GpxP+nS8FF9WroqhYnfECAO6aoquGYwvULWSSgtkvSTyK0qt2FQz+mh2gh6JyHm6dsexGUXBR+LbRKKkJioCIehKTalGPFA/IZIlq/4UX2o63ow1b4zUhpooiRYCoo3QcSsSWbDqjQllibESKQwdL6UVZqz6E7aCAW2Vkoici+ERidA83bjHhj9iTY1ZkanLb4SG1daUSJaci/s8ECw4Eyqj5c851+jTDVIuRCekOiS3jiq95o3xfU2RVGhpif2SNTVnzXMW9kQW+L5mRU3ZDge01QxfVDSlS75GXumOBsmUT9be4VZzBSUsZ8IRUlhaXkpLzUn8EhUaVr0J296A81F/cRRtOqalSklUTm58up77XZVR5Maj6c3Z8D8oUerDL7X+f3PE2AB+Vghxep1/Zq39JSHES8BPCSH+E+AO8Ce/0oWEhvIjU3b+p4piJeawbPIDj32Bn/rCsxy+tkEiofNWyS9MH+OJ6C7/dvI4f+nxXweg600pO4qf+OInSGo508HD+C0Lt2qEueCF9AJPhveZmIgfPfxGvr37Jp+bXOK/XP8V7lYthjrhj7c/T2kVf2//Wym04ui4yUM7h1y9swmhwRog8/jl9AKPhXtIDArLG8UW17MNEpXzrbW3eHOhqPux/nkiWfJfdW/y1w6f5HtWb3Gv8ng27PNy3uUHO5+lQPIP7n8LdS9nXDq78PG8zv+tn+JcY8DerEWoKn5VPuw6j8XOnagDg1lUZ9I84DPzi2x5g+WEuFZsuq7Wcs6nR0/wVG2Xp6N7vJq7xFokSh6KDvihxn0yW1Faw9XSKQmvBC7BOrMB//zoOTLtMcgTYq9kd96lMIqGly+t0aM8IhAVQ52w4/eWUuyX8m32i9biyNGA8D63yzUeCg74upX7/OXu59jTir5OKK3km2pv8z/2vo+jWZ1WmPEGZ3ikdcBcB0yqkH5RI52GMPIh0cReyev5GSY6JjUBiXST9o38rMPYqyl9XXdJxQXD4tHoPp9cuc61YpMfWH2ZB/weL84vAvDi+AEA9gdNHt48IrM+t8s1Ho3v44uKN2bbdP3ZEvDbr2rUVZO7WZfrk1WGWUygNP1W7YPNIOvyEB/m8QdeIKy1N4Gnfo/v91g0CH3f1/IttV+t0X8SVn/1Lv9272Em85Dvf+oVfvEXniMYWk6e8Hk0useP3f82jtIGsV/yidWbJDLnn77+cda6Ew732ohMEfYE2aol7LuwvrQen59e4L8582l+dvwR3hpuuI5X/pyP1W7yl9780zy1usdwHuErzUcu7XJ/2kII8I8VOgLVV+yXHV6bneOj9Vu8lp3j0eg+t+crfHvnTV6YPUKqQwyC3XmXb2pf5a8dPskPb3yRv917mu+sf4mX8y6vZ2f5qdvP8EMXXiavPO4O2879mEe0IydLPl0cwNGWg2OPqmYp89ixJLole7sryKni+qUNXtXnSGRBr6zxja13+NTxMzzVvMc3NN/hdrHKp0bP4AvNmQU/c2IifvjkaerK9da8X3RIZMG/q2Ja3pzzwQn9POFwWqebzJmVAdMiJPJKpmXIvZGDp0xnEb9Qe5IrjUM+lX6U0igeTI64n7e5FB9zK1/jXNjjS9kOu3mX3PicFDX+i93v4anmPS6GR/zi6ClOijrXjtZYa04ZZDEfW9/lrdEmvVlCPSxI/AJxECENVJ7l7TtnuLpyhi9NtjicN/CEIfZK1qIpc+1zUjjk3WowZVgmtP2U0iq+MN7hf975OX5i8HF+avYxLiQ9rs/WuDPukPglxSjkKusAfLJ7g/2ixaiM+eLxGdZqMwK1TeIVHMya3Ihd89+7gzbzWcj66phrw7UPPI++hpx7H0NoAd/dp/GjNWZPnuHPX/x57hVd/skrz8N6RTj0aN9w3aH/l/M/y9vlKi/PLnE+POFScMSfeeKz/PSNr2Nze8A4jdCHLXTNkJ6RPBgd8FhwwB+pXefHT76BH+x8dtGsNuUT0R4vzM/zP1/5FF2Vcpz9MWZVwBsHZ7i42iPr+EwPAoSFYlVzNujzbHKTg6rNH6m9yRvFFldqB5RW8aear3GsAyKhuV9zkcT3rN7ib/ee5q+uXOPFzOOy36Owih955FNsqikvDi9S83P6WY31xCXwGn7Ow+1DRqXT9Te8nDcXQjJT18i5j516dM8PSLOQi+ExDTXnuGryXP06n5s9wDd23uHR6D4/P/w6/kT7ZRJZEglNuujZ8X8NnuOvr/8210unRnxoIUNfTya05ZzMemzEE+aVTz+N6SZz1qIpBrGgJjnV4T3V5krjkKkOea5xnUBojqsm3956g588fJ5PdG6w4/c45w34NfEwG/6QtAr4qzv/irvlCoHQ/PmV3+TtYo13hmscjeo0kpyjvM6Fet8BcyqfM8mYq3VN0FPIuWTl3MCJ3WTFO94mG+GYqQ55IDrms6OLfLx5k8OyRdeb8URyl76u82h4n7dnm/z89BFKo/iWztvcK7p8c+cqL9greMJwK1ql25zxeHOPs0GfUJb4sWaQJ6yEM87GA0ZVjCcMj9b3OSia3E8cAKfUkrON4Qd67i0ffh3Eh2KBsJ4lfbVLtGFpXpvw2myH3z64yDMP3uGVVx/An1rSRWetfz56llEVcyftMq4i1rwx4yqilczpjWsY45x7ohIg4CH/iMwqfnnyCN/WfJPb5Spvplv8xyuf4dfnO9RkzqvZOZ6OdulnCWfrQ2TXkniuCqEbxsFeQs22NyASJVeCfWbW5+nwHmtqzLqa8nbRoSZzfiN1qsQr4T73Ko/vrH+JFzOP5yLFQEuuBMf808FzfF/rFTqB6/+ReCWxKllfdLaeVgGFVksITtmtEKHGDytKCUGtoKg88rnPjt8jtSFXwj3X2LjxBrNFPf6Ptz/PS/NLPBo5JuWWNyCzPt/WfJOX84Q1NaMl57yUdznn9YmE0wpg3e6r6wLq0A3SJQB2bgLOxO6svSdaPBLvUZM5EkNpPZ6I7lJYxfesvsamN+K17Byl9djx+6yoKZ/s3qCnnQV8Tc0ZmgAlDGdqYzaSCdEicmp6c0woqNVyQlkhahVFaKi1HdU7kiUNmfFY/T7GSlb9CV1vyte3r5Es+oKcvjdNlVGieL51g0/GN7jmry0VrYdli06QEquSWiOjFWZ0fCeQUp5xdPDm/rLq0QrntLw5DZUxVSGtMKMeFNS8Yvn5vf/xNar1+xtWUKwYgpEBKWl6GZc7x+xOOq5RjC8IR+7Duhgec91u8Fhjn643Q2HRSKZZ6GhFhYcIHSlKVjA0MVvehB2/z265woqaopHUREVDzVmRM2TgFHjGCsZlRH+e0AgyZvMAG2moBNJzHZcassAXhshqSiu5W65QohYKRsdKuFesMDbRMudw2e8x0JKOSrhWlTxfv8aKzDELWrLEdQCbVgGZ9gmkJls4CzPtI+PFcSPQWFNhLQ4xF7lcxKYaLaTjKZn1lv6Ou+UKLTVjRabOr4B1FmpV0JUFa8oZyB7yj5aSZCUsDeUqE3WvINcec+2jlGG+yPZ7UhMuKM+nPo2umjr/iqgWjItiYTXPaMg5JYquSl27gOCYRFgMEAnNmprQ8HNy7ZKSozLCk4a277QqvtAEUUmZexgj6MYpqQlZ8yaO5eGl7jNdQGrbKiWz7v2LhDOINbw5bTVjYgIu+SeL61ZEsqRX1hwFW2maQcaaN8EXFW0vpbCKW3KNVW9KS82Wqs2uN2W/aNEO50xLd/yqqQ+mgwAw5msLxFceyqAmkvmqJDqBrjfjZ/afIvQrdEPjp4Iqcm/kYdli1Z/wz3Y/6pqZdOGB6JhX4h2OyzqtVkr5ZkR5xmAC2FBTDnSNg6q1aDVXcCE64W7V5KBsc4BrrLLtufBwOxlSLWrT2ysj7hxvuiNQ4ibpxAT0TI1IlPhC80R0F4CDqsWmN1rqHGoyX+YcCqu4EhxzrSr5WOjz44MNVuSMWBXkxmOmA2qqoO4VrAVTDvMmkSoZFTGr0RRTSdCCXPlUMx+VuI7gulKsqBk9XVsYqJxN+1Sl6IuK/XKDdW/CzDi+w9AkzEyIwrDpjZjZ4N2/xU+RQvKbWYeOnzLPXceqmud2cYBhmdDws6U1GlyHroOqtZw8bZnyenGWh8N97hVdIlmy4/d4Mz/D9XSDF9WUtkq5EhwvjVWBrBjkCe1gTjoPlqImgKaXOazdQih1ktaQuEW9rdKl5qEh5xRKoYQhwEmjZ1ax4/e4X3V4Y36WLPQZ6dpS3NVYMDB8qWlGOdMydC38dMzVcouWSln3XbWktB4ap3p1Kk/naO2GM9dL9T1itfczrP3aEeN9DZFLwofGdH4aio5zzD195j6ff+EKKrGUiWDlzYxfGj3Jd7W+yNv5FmdqYxp+zo1sjQeiY+7tdwlrBaNRQsKif2Uu+NT4I5wN+lwIjvnbN/4of2rnZfaLNm31rpLyO5NdXiua5JXHb+9dYHJUp7M5ZjROkIVjSOaRz7V8kxVvypfmZ1n1JnxxtrO8xjc2r/Jqdn6pc3gj3eYHO5/lp24/w4888in+6eA5nq9f48cHG/zlzh1ezOCd8TpSWPfAC8tkHhL6FZ1kziR3E+S2XoFMEfQUxYZA5BLRi+ChgkZ9zovzB7gcHPB2vsWmN+KXB4/x8dZNBlWNf73/GH/0zBtk1meoE+4ULrHmC81HY9fw5o18myvhHn1d59d1nfaiJPnKcIdM++yPm1RGstUcM8ojYr8k9koCqZkVPneKNXbzLk/XdtFW8NrsHJfiY36r9yCv+Q4X/14h2gt3LvNnn/kN7ldtXpyf55zfp6frHM6b3Bx0GRUR/ZnTeLSDObuTDucaTtk678UExx7RR2f81uwhzvhDTqoGHW/GzIS8nW9xbb7BE8k9+rpGqkNaXspR0WQ7HPDiyUVuxGs8Wnf6l5GOSU3IcVHnuKhzMq3RSiQjnXBYthhUCa045dp8g3Nhn9SE9Kt3F+Orow0aQUYvq+FJ8wdqo/e1I8b7GDawzPoxo4c9koOS88EJvzh/HHllSjn3ke+EjC+E/In2y8xswGPhff5l8TRb8Yhvbb7J9XyT89snzIrAZcK/eIZitUIUku9pvEaBJLM+f+78b/Hx6DZ3AueS3FRjtlTBnSpgU0050xizFk7ZbXRo+BmmNeT1o4uOR1Er+YbkBqnx8JOKhszYCRwyrS0XQh1RcbVc59HGfQIMBZIfuvAym2rK97VeYWXR4fvFDJ6LFM92dxmWCZEqSbySqiXZjoZMdUhlFKMy4nzS5+6NNXRsEYHGloKqrWl4rivUM9FtfKEpg2PaMuWb2m9zITjBBJLOzoy2mrHj9dnx+oA7cvV0fWlq2vYHNGTGJW/EoY45683pyoCnWq5CE6kSTxou1noMyxhjJW0/JTceB7MGLTXjSpyzshAkRY2SB/xjpt2IjucERReCEySG1IZcWHmAnnbCqSfD+5z3NL+RRTSDOWdbIxfix65btxSGUFVciHt8vtoBz1KsOLLUN9bfXvwdkjVvzETH7Pg9cuO7v0nN3+2oHrjo8Y3mNt/R/hJ3ixWkMJwN+mTGpxuk1FXOl7wzBMoJrh6O9pYd0Tue82CE0tm6jZV0vSkPtw45yhusRDM8adgIJx/82f//a5nzqznUVHD5J0pufr9P80bAj7zzXeQvrBJl8IN/4df4J9knEaXkhemjvD3boOnl3PncWa6vbfCL4eOYXPEL3/7j7/IqP/r9fPuFGwyKmN2qw17Z4aRq0FJzXsl3uJOv8rO7TxH7Jc0wY5jFNMOM//rcp3k2nHK19GjLgttlm58Mnmc/bfEfbL6+ZEgOdY1eVXeCJJlToLhduIY21/INZiZkRU35B/e/hbzyeHF4kU7gHqxYFbwzXufZ7i5/a/3138W4XFWOb5ka5+V4NOhRfkxxc7rKM51dekUdg+C5xnVK6xGJirFdhMUmXjgOYWiSpROxLd2Z/PTrnzt5epFH0IzKiJpX8GzzNkdlcxlO/5WVVzkxBUPj8hQP+ppDbZgYn0texcwaPtPcBiCiXLAaBQr3xHe8mQvDTcJQJ1wJDrhRxuyPm/zc4CMYBI/V7vM5obmRrfNXz3yaY12jq1KuFes84B/TkCUz6xibq09MuDlf407a5T/cfHG5MGfSJxIlRkraKqXrTWnLlLZ0EaIUhi3PMTR3Zx2uxRu8PtlGCUusSsZlxP+w/YtEwvJQtE9TZa6RscyQwiwEZRPnG1Fzmgul6aY35Ae6LwGubNyQGW2Z8Xc+4LP/tSPG+xiyAqQgHAjyDug0xF9wKE/5lMU7zSV3clzEVInDzJvKnXtPeZVDnfDI5uGSVXna5HVQJtzNOuxEA96ZrRN6FcN5RKEVShrSMqAmCnJruFZs8p3JLi/pJp9o3WA3WuFScPRlDEklcCQi49OjjkHS03UGpQttj70GdS/n7rBNzc+XFYnceEhhGZbJe/B17mGeWZaO0eGCLjU0Hs/Wb7EZjPlIcpuDsMXMON7l3arL2Dq/QLDojakX7lBtJf2qzmHppNen7wM4zFlNFjS9OeHink47c011xERHpLakKz1KWxFKSERMV2b4FIQioCk9VtR06aQ89Z7UZM7MOkJ3U815M3Xn+Mx61GS+ZEpq5AJhp2gpZ0UfmoraAsay5VUkwiO1mhJnHpsGESv+jEeDQ26WXXxREQiNwak7S6vQC9s7OGPVu2wInzPxmIO8RTdIaXlzRlVMKCuihV0oEJrL/jF3q/YylxPJguPF++YYnXaZAF2Rc3omXhq3VuT8Az33lj9cGfX7GR+KBQIcixKbEIxgOIzwFxzKUz7lrK2X3MlBGmNDA4FBjj1k7niVb+dnuJGtc67Wp1/UeKu3zpXaAYks6Jc1Wv6cG+kaN0arpHnAdJgQrk4YzWKUMhzoFoks2fYGKASvzs7xDY2rNFTGQdVe0I3crnLqRryeb1BXmWMhUqe0iuuTNc4nfcZlRCvO6Gc1Eq9EYpnpgJO0RqRKjo0F3CIx0CldGXBSWpDVckJnVrHiTVnxpmyq8ZIR2dOObflkvOsmCK7K05Bz7lcdFIaGmnNS1elr13BYLvwJnjBMqpBYuebElZVLBqaxDho8MpZIwVkvJLMVU5MvfhruVBVrqsQXLmpxC5nLG5xWT1YWZCjHl1SMFzSmU6ZkfSFZ3i9ahLJiaCTnvRSFY2Y6UpamLnz2dcFQJ4SyXCzU8TIxeVw1lwnH3gJ829N1AqGRGCYmXuLvY1Us7wcc66JvEyYLk9XYxPjCMDYRkSiXuYajsumaPhsHrO1XdRoyY2tRuTqumuz4PSbmg9u9v5onDCHEdwF/F1DA/2Gt/ZHf49/8SeBvLH71a9baP/37XfNDsUAI61iUslqwKSON8X1kBTvxgEE9YTrsLLmTxgrGNEBYTGSwQi55lf4iZK18xUbdWZZDWbIaTBlXEavBlP2gySQLieo52gg8T1MLC54IDiis5Eg3qMuSy/HhEiqbmoBIFCjxLkOytB6JdHboZFHiyoxPHnl0vJTEKxjnEevJhFg5NHtNOZZk4pW/i3E50CmgOKt8fBwLwgC/OXuY88EJYxlyu1hl2x+womasehOUMKTa7eL+AqUWiZIVb0qvrDuPgcw54w9YWXT53k+bPNQ6orKKQFZ4i93XF9o1wNGnUVlFai1nPdBYVmRMS2qulg6cUhOFk1IDa94EbQWFdBbwz80e4JFoj0TlTg6P292nZcjcD6isoq5yVv0pEx1xbBKulnXW1NjBd4TPoS7QwrEnb+Vr7GdNuh1XAVlT4wXgt8ZIOENXW6Xsl202/RGZlTTkfBllaAQneZ2ml3E/a9Py5xTGI5SOl6kR7BdtyliSmpBAOcp4YRWD8hTwW2Cs4KSss+GPWFGWz2ZNjqsG2rou4R9oWLBfpTKnEEIBfx/4I8A94CUhxM9Za998z7+5DPy3wCettYOFC/v3HR8Kr6nxBKo/I+9a6vfsl3Eo38un/F3cSSOW7Mnfyas8ZVU2pNsx+kXtd7Emi8Ij8DR57mGs4PVik5o0PB3uMTX5Albi7OClVf9ehuS7HEmPkY6prMKXFcfzOu1oziBP8KXTTYSqYjIPXcfo34Nxecq3vKvrXC1XKBYcy1M7M7gqxNvFGb4420FhKRaTu7RqiXH3RcXl4GDpehzpGpkJGOoaNb/w01+UAAAgAElEQVTgcN5kWrly4qwKkMIuQ/7KKCSgBGwpSyQ8FIK5LbhXuYVwZCz3qzYKu9RAKOEweqX12F4YuABSEy7CfW/JlCyNWixu7h4Uhsv+MWtqTs8k9E1Bah33QWJRGC4mvXeZlij3WSw2gNOx6k2XjMnTcfp5NfyMhspcL89FmdYxKN0+2VAZPRNTWA9fVMxMSGpCYrXgUCyu6z5LRU+/S9065Yl80PFVdHN+DLhurb1prS2Af4GDN713/Hng71trB+5326/otP5wLBAKTCuhahhEBaurEyct9uFcPKA8iUFa1oMx3WjGM6t3CfquD6ZqlphmRSQMxkoeifZ45c45al7OmdqYTX9IbnxWwymr3piP1m/xzatXsVawtTKiFWbU4oL12hRtJRsqJBGWu1rybHKTXzt5mJ/Ze5rc+K4FIE7zMNERQ5NwwT/hgn9CKEsuBMdEsuSbW2/x0fiW62JlBQ1/EV1on1gWhH7FdjRkVTnQ6zul5U6l2K+mnPHqpMYjEiVjE9GWhlcm5/jJ/ef4lfGj/FLvCX5m8Cx7ZRtgKVRymHpvoffIyExAZv3l5DmFvGTGJ9eeQ9YXCbvTDmkV0FBuIV31J7T9lJoUHOuAa5XPW4VBY7lbGe7rOjVR0TMhN4t1Musx0jVmJkRbwf2yy5oac1Q2l6xJcKKkvbLDG8ebTMuQw8ztumeCETMdMDMht6sOB7rGcdXkZlWnZ2KuliFKWHLj0SvqS6ZlTRSUi4mtsBgr3+2hsUjUaitpymyJgrs63EBbyc3JCrcnKxxldaZluEToSWF4bX6efMGWvJGt018ko7V1PUpPuZ0Sw52qg17+Xn+pA/kgw9r393ofYxu4+56v7y2+997xEPCQEOK3hBAvLo4kv+/4UCwQXm4pOiH1m4rhI3wZZu69+LnfiZUTcbVEy/1OHN0piu5qtuVC7GDIb4we5rhq8PLoAlXlUPbjIsRThmkR8p3JEVdLza/Pz/OgJ+nrOo+39pbt8H4vRNxnZpeXmLiZCTkX9LhXrHCjWOdcY8C0CNmIx0yrgEBqDvMmnWTOVIe/J8LuFF/3qK/57uSQRLpjwNPtezyR3OVC0uNyfMi31t7iG5tX6S1yFacP9f3cVW0y6/Pi9EG6asqaGi9pR9v+gEke8nDjkLVwylZtxFo0ZVTFy+soYehpl4s41g2n0TCWTQXPh5oDnbAmc56LbzDVEfcKl/OYmJipjpiZkBvpGodly+HjvBETE9P1pktkXN3LXVIRQcPLmJiYq9kWe2WHj0Z3ecJPaS5oWqnxmOqQL/a3lsg6cIvOftHisGySmoCxibg1X0NbuUTZD03C0CQkIictfVITEHslnSglkJpOmC7pXTfna4tjarVcvLrelFvpCqVVyMUmdFS4Y9XjQY/M+rw1d/f9OyOXrzROvRjvM4JYPSWwLV7/6Ve4/O851YDLwDcDPwT870KI9lf6gT/0YZRAVJYqAeNZAmUQOZQ1QUvNl12rnQw3JlAV/khSxBIZaKwWy7Z2a2rC7qxDo+VQbpnxmSwy9N3ASbObfkaRBqjWFCUsk3lE6FX4wjVciUTJiSnY8fr8s+nHaXg5ufF4MDrkXtFlVMVkxndJPYQjMVXN5YMWLsqfp67MURlTaEWmfSJVMsmdziE1HkPcQuTT40DXiUTJ1GTUZURuS0pTcn28Ri905Klbs5Vl7T+zPoVW9HWd3bxLy5szrJIvOy6U1luSmE8z+nnpsZ+16GU1cu3RDuf4UtMvkqVicmRCbpTrTHSEtnKRdEwpyTjWjiJ4rJvcm7soQWGQwjLVIWeDHpMy5F7eYdWf0NN1ZibkqGySDyP2VloURrEeOd3A7qzLzWjRxsAqvim+y83Kc1UaHbHuTTicNxmkMdflKnebK7RVyr1ihaOsQc1zydYzfp1+kXBQtRZY/XcrK76o6I9qsA0HkwYnqkYrylDC9cswVnKQNXkj32a/aNNQGVfTTWpezrBI8IXBWEFuPPpFjf2yw6He46hs0i9qRLJk7ysD3L98WBwM5f2Nk6/Q3fs+sPOer88uvvfecQ/4rLW2BG4JId7BLRgv/fsu+qGIIGTlYqhwAT2eTyKqGFQGt7MVOlGKylySCRzJqKxbZKAxhcKfCg6qFgBv5ttsJ0OGZczRrM69/N0PbT9rcq/o8NZwA1tKJllIVnmUpWKcLZSL5Rp3yy4An88uUPMKVsMpaeVzXDVI9buZ6tQEXJuucyt3Nt/TjPq1+QZ3y+7Ssg1QLJyUo8Lt1KMycszK9zRbSU3I7dKpHXNbEgqfmXULX2sBbolUSSKLZUQw0jVy41MYj1QHzLXPcVEnNQEnRX1JnD4t9UWipNSKhp85lL9fEKiKcelKvpMqcuG8qS134cz69HSdzEpCIbm/QNVFonTHAx0sSqVu4etXddLK3cvp5DwtowIoaQikpjKKQeUSgFMdMdMhEx2RSEW0aEVYWo+xiZhVAdncvfeRLJktmh3384RBETOrQkY6YZg7RkSqQ6Y64qRsLLuInSYEA89RwZUwy2Tk2MSMi2iRQ3GLa6wKfKHpzRPmerEhWMmwiBdJa02qAw6zBtMq/LLP8v2Or+IR4yXgshDiohAiAH4QB2967/iXuOgBIcQq7shx8/e76IcjgvCc8zJbhehYEjwwo3gnpEpcmeyFk3Xm2xUP1I4ZlAlX2kcczTfQRuAnJWVb8VhwxJ52fRLeGmzyXWfe5C1vkydrdymthxSGK7VDzocnsAp3j7qs16cIYam0ZKWWcqhzPhLeJQ9cku6Cf8wL+gp30w6b8YSHw33uyhUGVY2uN6WhMs4EQy4EzvyzI3oc6ybP1W/QVVN+VT5MIDUNLyeQFZn2WY2m3NYrnE/6PBr0GBrX5s0A570BbWnQKEpTcmxzznp1an7BoIg5E7kE4lSHTHTMXtHhqeQOvcpRuhsqQwrLhj+mpVJIWDotmzKjWEQQ9cgtLqf3FEjNdjykX9Q4Gw5Qwiwb6xaL5jVbauKiLWO4HBy4EqNusrloFbAeTPCFZqRjLocHbMSXWA8nS5DN+eDE0bA9SyA1BbASTFn1ppzkdc4FPTrejK6acqgNIOmqKQ01Z01N2IgnpF3faUh0wmPhfSYm5ny9T125PqUXwyOu19bZ8fvLBOZp/xFfVPhBRctLEcLiSbc4BLJixZvio3mwcUJhPVb9CZveiHfEJoksON8YsB5NWPcn5NbDIDjjDymtZDscYKzgUny8LO1+oPFVqnNaayshxF8CPo0rc/4ja+0bQoi/Cbxsrf25xf/7DiHEm4AG/sqC3/LvHcJ+CLSezdqW/UTyvfS++2Em5wReBo27hvioYPc/q7i41uf2Z3Yo1jTeWCFzkKVAFRAdW3QoePrPvM7tSZdhGvPnHvxt/tH158kKH2txTsT9OjYwiELiTSRlR9N8y2O+abECVAHnP3mXJzv3uTFd5eu7N/iJq8/z6x/7h7xWNLlbrvBj73wLrTgjkJrjWQ1PGcazCF2pZaZZT3zUTKJrBqQlOPawCspuhYwrJ+zKFFj43o99gWfrt1jxplzNtvCF5pXJOQJZcX3sopKaX/Bzl3+JFzPNJT8jEYrXioBXs/O8MdvmzcEmkVfSDVOGRYwnzZLjYIxkuN+kszVi0KsTN9zC4HmayUGD+saUslToSiGVwfc12TxA54rvf+oVvrv9GhMTc1C20EhOygaHRZNHkn263pR7RZfXxmeRwpJpj3SRZ7k56HKp02eYx9y5uY5qlDx45ghrBfdHLtJrJXPSPGDYq9NZnfA3Hvn5ZX/O//7tP8YfO/dFHon2KK3ipelF/lT3s7RlwbVyhZkJ+euvfR+X148JZEVaBSRewaSI6EQpwzwmUhWzKmCShwhhmeUB7Tjj3n6XB3aOmJfueDacxPzQI59nWoVcio+5HBzwv+19M7cGK2w0nP080x6edEcoT2iqhV5kLZpyIe4tO42Nqpgf/8i/+PxXOAosR3jprN36m3/xfc2R2//RX3vf1/1qjg9FBCGMBa0JZgYdSZIjqEKBjhRlKnmgecIdcw5RCYSBcCiYb1hIF4q53JWtYq/k/rzFJ5Jr/COeJxtGyLgirC/Q8hZUKgmGAh1KVGFRmUBoB8XtRCl35x0K47HqjcmzgNJang/nfKp/ieksQghLO85IAudR6C2OPcrXVIUHoUYdK4wvsLGlqllEBSJcOP20IOgpdGy5OV1lMxiz4k05H5y4pGBW5+n2PXphjZafMShiXsz0gichmJiKZ0PFS3PFm4NNRvOIvo1JY8eQqAUFh+MG7WSOtgKRuJKuUBbP00R+xfH9NviGovCoJxmDQZ0qV9gYdKaglNxJu7we7dBWKTtBbwnoaaiMl8bneay+j7aSq/01It/Zw7WRrNemVJXiYNZgszbhXqMkTnL684RunLJan3Hv2B37qkqCgazw+bejx1jxZ1xng8udY874Q97KtngwPORO2uXvFY4A9t9t/Br/cPAMUVByMG0wnYcYI/B9jRSWvXETX2k8ZUj8krx8t3UhgAo0/TQm8p0j1hjJjdka4zLiS8Mt/vNzA+6OOwhhCZTmZF5j/6SF8jT1JMdThuE4oV7L+ET3Jr9y9DCzIqAdfTAV5enz+DUl5fsZQiC6HapQUNUs8zVJfGwRxtJZndDwMqxnoVFSRhLjeQgjML6laLk3+FJyQh55aOOkttutEdoKPGloxRlpN8RogY41cy/ANDTzdZ+ibbAShIGH64eEouLWfJVzfp+t1SFdFTIxBc/Ub/PmyibdhTEn0z6VkTSb7sFIQgdxSXOfdFOiaiVKGco8xtQd7CUINLnynSsz0DzT2eUjyW021ZixdGfYR5oHPBTtL5NsZyLFJT9joMWiYXBJakqejna5v9rhrfEmnjAkXoFBUBlJ2KnwpGGYxST1nJ32ECkN3cQlI4fNGKUsrdocJQ1JPUdJQ+hXpIFGa8kTzT2eiO6iFzLux+N7RLIgkTlhq+RycMCxbnK+5RJHkaqchNsrKDtOvh6oiiguWKmlrCcTal7Ba0dbdNtTAqXRVjDxDO1kzrP1W5iFlfq4aLDj99j2ByQy54nmHmeCocPxVR7P167xG/UHaYVzBlGCEm53D6RmVgW0gvlitzc0w2zpmJwWIVFc0IxyYq9cfv+Jxv2lkvSC1+N8q09aBayFU5p+RmUksV/SCpw/I/FLutGMVX/CE509ZlVIzXPR2S9/0Gf/Dz+A/33Hh2OBAOxgBGziTwX+FLy5e+cm03iZnLRzDzWVBCOJjiwyFwQT9+96ZY39eYuDiUtKHc3qzCYRytdoI6nGAfgGMVP4E0keG8IBVDUXQchCcG26TjuYM6sCbpernEwchHRiLBMTMc5cuNoIco5mddSCywiQZo7PoAuJN/CotEAHBlkI5NynlGBNRTXzEbnEloJeUecgdE7D2wsr9v256z9xa+aclJVRJKuKiamWictjm3O7XKVf1BhkMZFXIYVbtBKvIC0Dssotlukk5CiqMxzWKCoPX2mUsmSTEN+vEEA2D/CDirJS5HNXLj0smk6dieagai3LqYOqhsTSV3X2yvbSY5JWAcVCsnwwaXB55ZheVmM+CzgyAl9pCu3uyVrh/C95wGzkOKCZDRYwH8G9tM3t+hpNOUcjOC4aXAyPiFRJgZN3742biJZ1DY2VQRvJsAzwlUPEhapCG8m4CJe7dORV5LlH0NTki3vJ5gH9qsakivCEZmhipmXIpAip+7nr7L54zaoAX2oqI5mUEbezVSqjqBaw3vcyLN7/+FoE8RWH9SRYQ5kIrATjgZWCrOvh+Tk1L6dKLLJeYvIQ41usBB1byppAFrAVjlwGP6xRkzmdaE6POlFUUo9yxmFC0sxIdYIOJTKXVLErq/qZQEeWM5HDoHdrMza9Ec0k41DnrCmP29kqStplybUVZtT8nPECdGutoNSKypcUM29xrwrbLbFTj6BWuHxIUiF6EVVbYxALeItl2x/gC816NGE7HCzAJAVTHfJaEfBs6DpknSYuZyZkf950WfjKwxMhufaIVEla+qwmMwKpySvXbzKuFXSSOb7SDIc1hDKUpUccFghhMUbgeQapFv+94EtueiN2/B5DnXAhOKGn6rw0vUjXm5LIgkEWU/cLCuPyMHU/Jw5KJkXEWjxl2I6IfFfNkcJQj3KO+s1ldKdCTakVt7NViJxa8an2Pdpqxl7ZoaHmGAQvTh6k7aV8YuU+vz7bYKWWkmuPYRpjrCDwNLHvSsiBcseNUFX40gFtKytJS59GLeMkTdxnWSmk0syqkLn2mZR1xq2ItAwcxFZ7TMqQSRqSB4pGlKOE+50kcC7s8Uvjx5mUIds1saxUfbCH/6s5k77640OxQIjKkH/8QWbbAi5Pmd+uIayg+1bFn7j8qisvtSokUDs3ZjpI8I8WUtlNF0l0vRnftP42n6ld5pX5Rf7M2d/m+uoG//r+ozSCnJ2tPvcOOqxujTjxG2AFs5oAYSnXNFZLvrf9Krtll3/Tf5wfaI35yNo9Xsk36VV1/sLKv+PmdHWJqW+Fc47ndeKgJFCac42BK+1VPidxvpwUe7srdM8PKCqPOChJ88DBXryK5xrXeSzco6drrKgZbxdn2A4HPBffYM1z4qaJjnk1O89Lc8XT0S63S9f1+i+27/JcfIMf3f8OfKmZlCFbfs7utMO55gApLNf7q5xpTLjcPKbfSPCk6wdy53CFBzaPqazEWEEclChhUdKd1Ssj+Uj9DmvemBvFOitqumQ6POAfETWcoU0j+HT8GHnl8WDzhNw4juZTq/d57WQbtTjWnKu5+/Gl5gvXzvPIpb3FJCw42xpRaMVD8QFvpNtshwM63oxznmNY9nSdp+u73C86vD7a4keqb+NvbL7ArbmT09+WXWqL49VGNOGtwQZPdvfIjeeIXYlHYTxqquCFO5fp1lMsboHvzxNCv+Lr6neYmIhBVeNKcMzl1vESqwcscwyr0YzCKHylebB5zE7Q4/HWHv3SRVVb4SE//UEf/q8tEF95WLF4edZl0j036VWm6XozXh6dJ7jvU+5YqkohPOMSjQGY0CI07OZdHo3u0fJSfrX3MGeTIQ9Ex6SFT1EplLTYRT8J6RvM1Adl4VStVrp+ELvFKruTDq92d2h4GS+MHmF31kVtGJf1NvGXEaCktFRKcjh36rpJHi4jjTQPkFNFmoXkc58ychWDRn1OZSSldWKg30mouub3l92y9ooO9/IObw42ub/acSKdeZPn4hs8EwaklU/dN6RV4DQIMxde9+cJo7HbKQ+CBpMiWiLWlaeXu+h4FlGLc3xl0KVP7JfLTlmzYo1XxufxpObB5AhjJS2VLjtyt9UMiaUwilkVkFYBJ/MaVU2SFj6TeQdPOYHRRjxhWMQE+z7Ts05/IoTFWkGuFRMdc23ivEOnnoc1b8Ir0/PsRH1HjSpDXji6zJ9d/Q1uT7usRq7xD7iSbWUlvtLkxiM3ilHpaFjGSiYyZN6PGQhL4GmXoFwcPU51DwBvFhuu4e+8TqEV6/GE2C+p+znGuhxPzS8wVvK52QN0PedOhT8Awv6raNb6/2p8KBaIoqWYng1o3oT2p33O/53X+ZXwcSYXQ/7PGx+j+s0uyQx+9E//Y97Inbz87wbfwnp7ymo8c4kpb85PHn8CieULLz3IF9ZzTKH4zLf/rwyN5Fq5SnSlZEXNeCPfYrdYZVTFnAv7jHRMx5vxWnqeZ5Nb1M9mroW80Pzi7mMUucfJ/Ov5N4//C1JbMjJugpwamiIh6GnBmrJ8Nl+hITM21YzPzC9y/dIGF8PjpdNvRc14cf4Az0S3l7CXJ+NdlDBcCE5oyDk97R7Oka7xVHKHf3PwCKN5xFvjTQaLJi0/uv8dpJXPzzz4y6SmYLpozts3ig1lSK1laDwOqgZPh0OGBtoShgZe3zlDZn22vcGSpbmxqOG3peHYeDRExd6iIU4gNFveiL5OmNmANTWhtIp/PX6KH7v4KQB8XGE9t7ChPF7ZjmjLjJvlKpf8Ex7xfXarOT8cfxffu/IqbZnyaDBhaOBYx2gk3c2pY1dm21wJ90lkznZ7wHlvzA8ffCeNIONMe8ynhs/yTx78aSRwrAUlkoao2PJC7lQFZ5WPWZjgSzSR8OjrnH/ceoavr191/Ewraag52kou+H1qouIfD57nZr7O39r+V0jgXhWzpuYc6ISGLKiJCo0gs4otpXmtaFJaj/PBCTWZs+MN+esf9OH/kEcQHwolpTBQNAXh2CyVp6Jd4M1dCcwq8GeWsYlYUVOMlawtFofTcZC3KI1iUMRYBSZTkEuGxrkmmzJjr+qQWY/UhLTUfLEIVKwvWqbl1uN+2eF25sL4+1mbwHtXDVmiSYRPKFjwChR7VcyBVgTCTcqaKBy7UEdseQOmOqSh5g6/LjN6usbl4ABf6CXsxeAgqUdVg7vlyuLnnRqyV9WJFtZwTxgir6LQCl9q6n5OagoSGVAXPi0Z4AuDQqAtHFQNlDCEwvlOCmtRWFbUlE1vyJY3oaumJDInEAaDILOOND2x3tKB2dN1B+tdSKZLq5bkqIaQ+EBXhbSlhy8gs5oATWreBduWVpMIR8S+5J+w5U0oFhqchiwIcJGAWYBuCqscts8qMitpeq4ykVUeLW/OxFh8IalJQ1dWNKSgtHp5XxpLvugc1teuwtDyHA172xuwtUDtKWEWZHT34IWyJBICs7ivrpREoqItKxIBkbDURIVa5I9O8fngupt/4LEMn7/C6w9pfCgiCABv5gRL3iTnMGtgtUCHoGcBjYWMIbM+1/MNBmVC3Xf6+1kZMi99xlXI/VmLQisQFpG6kNEgKKzknWKTSBTcLVfYzVe4Eu/xSn6ORBWUVrnyZrrCque6M011xH7aJA5cLV0Ky8houlLQlh7HuqIhNRODC0lN7FgUxTqpCdkJeviicr1DqyZXwj2HpheGt/MtyuB4WUI8hb34wjUHyo3Pbt6lMJ5jJoYpaezEQFIYPBEyKUPSKmBqSzCQyIDUFGwoiVlMvHU15fPZeS77I/Z0iy0mbreVGbfLVS54I1bUjImJCDAksmJiPYYmcmAcUTlAjTVoXPm4X9VZVxO0cFTnRPrUl/uMpis97laGs96c3MJL80s05JwtNSYSko81bgEssfcHusaadMzHtpqRyJyDokkW+2gENZmTW4UUlvXQRTmZ8RmagNZiMuvF35taTVdKUlsuPnuYGfd7Cuv4nUOTsKlSCitJrUff1pdkbbdYGAprmRhFQ2pAOtm3dfkqidscMmtIFwvEKZCm/APst+JrEcT7GBbyFYHUFl1z9ecgLvFmwvktPJDaWXovhsdciHqEXkWkKkKvohs73mMjyEn8EoQD4drAvfs1aXggOGSka6x5Y7bDAUpYtqMhEsu5oMeGP6LpOUluYTwHofUKSq3Q2p1tJRCLgMzqxY7lMvMawYaa05UFO36PLX9AIhwwtVfW2PGd6+90R970RrRlusTEGd5lPvhCu5Z43pzaopnwsHBmL4Mg086u3fBzAqnfnRyLSGJkNKHwaEjB0MRs+wO60mNTTWlIQ0NU3CjXlkee093TXzzkPma5K85sQLrgXZ5i5JOFbDnAeSkmpuBQOy1ILAIHllGWE+0jgTVvzObi+CKFYKQTurJiZJwxbltNCYRhYiImOqav64SyWnpNTjtm94sau2mHaRmSqJy2LCitZWggEnJ57LtTucleLqMTt0G0pWFmnePzWMcEwjA0McXS1CbpFXVK61FaaMh3e3X3TMzQBGgLEyPpGxfvJDJ3blGdcFQ1lgvNB3nu3/frD2l8KCIIq6B9XZM3FY3P93hzsEE+iuBsCaMAf2bJuu7M+DMnzzKpQuaVT93LmVc+uycdPnH+FncGHSotneKyEshCMDTh/8PeuwdJluX1fZ9zzn3lzXdWZVV1VVd3T7+m59Ezs8PMvmAX0K6AXbBAJqwQ4QgLhRz6R7ZwSEFYjrCDPywrsOU/bIccUsiWAMk2CERYLIthd3ksy7Lsc3ZmZ6dnunu6p7vr0fXMyufN+zrn+I9zK2fAwO6sMRphTkRFV2dVZ2bfvPfcc36/7/fzpbAFn50+yoebX+eF+SO8ONnkan2fr402+K6lW3zi+AmWwxnbSYfNaMC5mqtL7Ceu8Oh5huE8YmA8Mptw1gt5vShZV5bXiiZ9NeFGZbK6na1xkLd4pn6f2/kaH2zf4ouzS3y4+QpplVvxqZMn+M7OaxXpyJGPThmIe2WbnazLsHQGIVn5BupBTmkksecMWw+mXQazmIFRTGzBqpI8KKec8xq8micoAU8GE35peombasKdos9V/4AcyYqa8IXkMs/W3N18r+wsJqhT7cOpsauvxhzrBsfGMR+OyhZ1maOEYZDHDAysKrWYJPoq5KtZyLdHBQ91seA8+OKYnjQ8Gu3yQrbCOc91Nj6fnl+wLTf948o3Y9krO6TKpylTjk3MpAxp+Sk1VbCV9tiqtejIOTPrU9iM1AYVfEezXXr4wuALB9nVCBJt6VchQqHQHGqHytstui4BXWbMte8+d10jli4VfWTEwql7Ggx0rJ2zdqhdAnxiAta80e/jfn5z49/u9uGbGe+MFQQwW5VYAcXmEu9a3kHkkmDfo746wwqBP7VMdI2r9X0+vPQqW8cdXh8uo4ThPefuEciSM60xT6/touYC09Do1ZyLngOmng+PeCU7y/Voi7/YfYXzwRGPNvcZlHU+vPQq3966zWo04XxwxEnh+IfP9x8wz32SccRynLCpDGdUwN3CUZy3tOT58JglmdGRCdeDI3yh+a7mq6x5I9a8Eb9y9BRng8Hi7rXmjXhP+y4XgiPW/RM2vJMF13FS/fxMMKLnzwhkyao/Zl767I+bVZegwfakQzdMONMas6oMfSkw1rIsA17NEx4LnHvxRlEntT59lbPhnRAKvQi56XkuYexQt5iZkDU1Y01NuegNCIRmzRsRCO0uVOvTV8545QvNleCAvltDxiAAACAASURBVBrjSU1PwshomlLRkwH7OuPpYMpXc8PQeOwXbUedkq6g+OnxY2x4Q4bG6Reejx7QU1OW1JStYom8on9FomBJziisYkkmtP2U0kruTXu0vJRVNaUtCy56CWc96MicdaVRwrLulbSlpiPholeyJDNiUXIjO8OL2SY3ixU0gjv5isP4q4QlmdDxXQJZU+b4mGoS0VwL9unIhKbMnWbFG7NaBe605JwLwREauSBpv63x5yuIbzyEhbzt4vVE6QJImhtj5pMO6TxAxBVWzhsTyYK9os1zZ7c4E414mLY5mDe53DwiUJr9eZOy6YxSNlUMjeSxICcSW9wrl4hlRm4VD/JlVoIxhXFQ0rPBgJbnkOYNldFTM740v4AxglrT9cwPjaUnC5YULMmAuc25UQQ0RcHjfkpq4S/EN9kq29RFTirnPN3a5vHI2fJPJ4GTso4JJEMTV5F4xjkKRUlqAsdwrNyIbZUwSGp04rmTeJcey/EMKSyDeUxi7WKb0ZQFSsCtYsZVv45iylr9NdpSEflz2jJg3c4XqyljJZvegI5M2C2b9FSCxrE1EhNyoJvVlmhIav1F9sVr+SqBcJqKm0WNSJRATiQMsRDMrGGoY94fTfiB1ktsKsPAWPoKvq1+j6Gpoa0kRxLjCpO38jWSioL1VMMJpXbKLk05Z2xDbg5XSEuPC+0Bg6JOX1l2tcdL+TI9NWViIq74x7yW96vELo+OLBdCxSVlaao5HVwSV0d6tFRKRyVuJSB8tuZdnmrtMDShA+iKlNQqfie5TCwzLgUHpNZnp+jyRLjLOX/A52ZXSExA20tovk2qNcCCBPwOHe+ICcJ4sPxyweBRn95vH3NzuMr0bhuxmaIzRXRsmZ11QS//x74DuLy4t8Gg6+6U28MO1zu73D1eIg5zB5PxXD6nQfBqHvDx8TP8zd7v8enkAneqQude2uKp1g6/dXCVZ3tb3Jys8ni8y9XaHtt5D2MFefZmJd7lYnisq4StsuQxXxJgaErDb6drdGTCl+aP4AvNs7V7fGJ0nQ+0bvHLw3fxQ52vsFUs4YuSX334BN1NFxA7KBs01ZzjosGVYA8tSr4yu0BhlYuxj8EY14UYpjW0kQRS8/pgmdE4XrQyV9SU+2WNJ4MJNwo3OVzyG/z0eMXlQtiQK/4hx6ZG20v4hZN3cyk6IJYZD4suj0U7HOsGShhmJqQp50x0jb43RltZdS887uZ9empGYkK2Zl2GvZgL3gm3CzcpnvXm3C0bXPJPSKzgxfQ8W960yi41+ELzG+MnuFrbI5IFL6Qri8Lsuu98HffLZW5na1ULsSTRIblWdKM5bT/lzniZu8uB60oZnz3bprAeezKlJdMFEt/YhBz3GW2VblJa84Y8KJZ4JdugLjPeyPqs+OOqOya4P1/iXtjHF3sc6iYTXaOwHgPtQ04FMA65U/SZ6BonZbxoiR+Wrbd34r89YMy/lfGOmCBUDvvP+Sy/rMkfWeFs4x48CXufP4M5lzPdFEQDGOgGP7b+Kb6QXOZkucZ7uvd4ebLBlugw0yFPre7yxrjnHJRxibcb8mJ2lmfDLf720uf5tdkFLgUHjHSdzx89wkfWvk5hFde7u7S9Oe/v3eVGsu4MXmrOJI8opz4i0iTzGse6jkFy24Rc8k/4ct7gM9NrPFV7wHdE++xqxd/p3uZTc7dleLr+gHv5Mj/c+TJfml+krWY8LFb5yJlX6KgZh2WL/aLFUeno01+dX3DhtrVdF4pTd3vjf/bwg4i4JG5kJJOQrFScaU6cCKpqZX4lPc+Gf8IvTS+5YJf6a/z0eIUfbR3woJzSFJKJNfTklH9ycpm/u/4JXs7OkpiQR6OH7JWOPLbhD1jz5tzL+7ycnEVhCGVJ158RiZLzwRGfHV91q6ww4YuzS7wkSi6GB6Rqzp35Cpv+Mf9s8H6eiHf4D5sHZLbkk/MePTXlf9n+AP/+ma8uMPyn0XsvjM/xAueoqYL/YOmLi27Tw7zNufCYo2GDg5MlbjdXePL8LvUKKvuZ0VXORwOyKnfj14dP8OHOKxyUTSaqtii4Phkc87fvfYi/sf47vDg9R8dzaVpn/CEjHVNYxZ3BMo8t7zPRkbOWV1ud3zq+yrXmPnGUs5+3eWPe593NuzTVnEFe5+5kiavtA85Hg7d97r/TuxjviAkCQJQgc4uOFC0/dcYoLQjiHESIP7UL+Oipmm8r7eFL7dyKWHpBQhIH7NWWEAKKruaZcJs1BUPjOgjNypH44dVXF4Sj6/E2kSw4LJv0vClfn53lido2/doZdvszylLRXEq5HpzQlB5DU9KRHjDlg43X6MiUpgzYpOChTmhKqIuSZ6JtfnH0bcSy4PFomyWZsOJNSK2/WNqfhtrEMmNiHG2qr8bsll3HQcTQXR9hrWCzM+QgauBJw5XWIXuBE0GFQnLFH9GTHjfVhL7KaUvFuneyKFxOTcqqdKuhd3feoK9y3hPdI7OK1Hpc8I7xhSEWmtRK6mHu0Pmly59Y908Wbb0Pt1+hLjM+yXX+cusFUutx2U9RCIbmiJ5SDBp3uegf8VphOe8J3hPuEUvFtfY+H6i9zsBEdGRGT2p6yi3PHxZdlr0Je2WbJ8IdLgSHzEzIhjfk8tohu3GLOCh4d/ceZz2IxX36asyKmpJaj7PeHDrw/mifmbHUpbs7p9bSVyHfvXSLusj5UPsGa96QY+0Cjx6PdgjQfH75Is+17/OB+HX6UvD+6D6RgMnKS6x5I9a9EalVPBo+5Ikqn+M97bucqw3oebNFkPPbGn8+QXzjYQXo2FLGEmGsK9B5mkRZgqCk1I5wHYmCZtVKnBYhYTwglAUPkxZeJbnthglqKpGdkiJVpFaR2pyOlESyYE1pLgUHfGXwbr6/+yJ3s1UXnVYJp8CJZSamxmo4ZnpyefE+J1aQ6NK1yfyUSZWspeQcicQXkqYQnPcSAiF4MXPdgUhoJtW+e2ZChjpm03Ny6tNQmzP+CSNdZ90/wVRahdT6tGTKyXEDoSxSGobDOrV6zqAZM8kjhga0tezqNmtqyp2iT2pPiHwnzmoK+fsZl1ZzK1ljVr/B7WJ58RoSgxKGdeW0EmMTcTtb5aSos+xPF12OppqzW3RdBkdR45V8vQK67hAJzUA3qMsJN+YbNOWcK/4IX4Tsa4E0lnvTJW62VjgoW1wK9mnLEfeKHneyVbbnXQZhnafiLfZ0m72i7TIqhObhuMXkJCYJNXd6fUzXsqUb3Mg2GHgjUusTywfczla56B8xNBEtky3i+5qi4Ouzdc4Hh3xxdpEzgUtz76kpYx1RlzmH8wY3/VUuh3vE4oCbxRKRKLg1X2MSuJboxNTYLTq0qs/nXrrMftZiGoRVUvitP/Xr5//L8Y6YIBDgjwVGWVRqGJcRSe5TxhalJcK4OkVd5OyVTQ61czGedqq1lS6dqagx1z4msGAkCOjJnFi6vEtHnrYc6wb9YMJx2cAXJaOySeF5ZMZn1R/R8FyUfGZ8/NiBT2thTiwsgRCEYk5DBJz1CjQzelIvpL2H2uU59KQTRZ3xhyTGY907QWEJhF6kbJ/2+mVVpKy/hTV56g/IraLWzPA8TS+ek5ce3XiOJ50btCMht4Z1JjSl4ap/QCg0bRlwxT9kYg2r0l9YxSWSs+EJmVULRJrL+CyIROkUlVbQUQnr/tDJxv3Rm9kXWPpVYE8oSza8E6QwLMkMX0BHjgmF4vHaDhe9ETMj0dLSUwWxEDzd2SYShdvKnMq7VcKKP3bBSP4EhaGnXAwBTOjIhEaUUTQV3UZC00sJhUdHpqz7J3RkQoGiWW2BmqJEyXn1GQhCWyKF4sn6LhvekEvRwcJarrBEMqcucrphwlro6hF1KVivYg5O6dbucdfu7Mg5qS1oVzGCPW/2LWVjvNO3GO+INmd14yYcuW/2UreUlIVwCLQQVAbHxvEZ3sj6rEYTGipjVoYMkhrDwi3P95MmtmYwuULMJbsVsBRcv39f13g1XSfRAQPdYGIiEh0sEqL2izbHuetn350u4XmGKHKg14HxSN+C6Eut5U7RZ7cC2Q5MTiTgXtllX/u/T4Z7mud4GlE3NDUOy98PrT2tkJ9+H4li8e8iv8SXLtnaV3oR5jI0zv9QICks5EgOTUxqS45NjdMSa2E1hdX4wk08TVngC02zSgYbm4jUekyMT2Glq+xX0Ni0yoMoqkCZw7JJanxCWTI2EYkJya1kZtx7GBntQmesoi7NghbuC3e6SUyltVBMjHssMcHiK5IFAXoxSc5sQF565Lm7n/lCU+CCiAZVtODMhEys54C5VjGzHol1x+f0NU7l1HWZUZcZQSW1L6zHzAZuVVr9PbenkmrLftFaHIui0rKk1onpUuMzyOuuA/MtRO/9udT6mxg6BH8KJhDUXzngcN7g8LhJ6/ETkq936dwyIOBe3udTh48zL32mRcBhrcFqbUzkl3x26yLJcQxGUL/nYXyQBbycbvJiKvmdkyu8u32PL0wu8srwDD92/tf5uYP38EP9FzgqmmylPX751nVWuhNyrfjK0Vn2D9rYQiKnHqZVcrdYrvrkc15Kz7HqjXgjW2G/6PDzwy6xzHl1ukZpJJcaR1yN9piYiP/95L18qHWDusqZmXAhqf7Y0TOUVuIJw8OkRd3PybTHJAvJCo9CKxqRWz0c7nQWJKjhsM79/SWUp3l58wxLakpTpnwp32BFuT38Z6eP0vYS/snJZd7deYNbyRpnwxMKq/iJ/g3+/tFz/KXWi9zOV4hkzpKcMTYRB9XE9LGTZ6mpnJkO+cTscTrBnKO0TlIEXGo7SO+d0TJ/tfsFfGHYKtsO3y80r2ervCe+w/85fhdfG2+wHM74vs7XeDGrsZt2mNRqfKD2kImx/PL0SS4GB0y1o2knOuS/uflRNpojLjUOGZc1+sGE8+0B/fqUWw9X+Io8xz+Qhg82X+N6tMVWscRV/4Ab2Rmerd3jtXwVX5S8pmOOyhahLJjqiFAWbJU9PlC7z+fTDS74R7ycbrKd91DC0AsTvjw8x+O1HX52/AiRKNkMjvmOxi06MuFAN6nLjGdr99gpu/zU7rfzgaXXWY+GdL0Zr2erb+/Et/x5m/ObGSqHyfvnrP4PJcljazzR+RrvXr7Pv/6t90JsmZyTLL1aUljFf3Xul/nE5DqxymjKlImJKI3i87PzbJw75uFRm3RZYny3bZHC8oHaLf5CfJP/fu97+Gjva/hCk5iQH139LDezdb67+SoAW5tdDucNjsZ1nt3Y5vCkiU4VpqZBWGYm5Eqwx1DH/EDjZV7KNsiMh0bwt5Y+y72ywY90vsRvzq4SyYIfae7wD46e4SdWfo8vZzE9maMwPF+7i0bgSU1d5kzKkKvtA/bnLVAlT63u8DBt0/SdCeWTL1wH3yxIUEIZLq0dcpTEpNYpBe8Vy1zwj/hCcpmeN+XDza/zCyfv5u+uf4K+ypnVb5BVK4e/f/Qc/+Xya2yXJc9Hu+zpkN2yy5oasSRmCy3EzrzDMK/RClJafkrdy1gLx9ycrtILEmI/5/PzS0x1xHPxXfqeU10+XXvAvxk+y/V4m+84c4tNb8zvzi9wLXzIb9rHeE+0y+0iZmZCvrd+g5EJeXm0zkHSpBWmPLuyxWZ0wkHepLSSzHi88MY5xFGAaZUs16Z8f/tFtoolfndylY3whE/NHmfdH/K/Hb+f72zdZLfo0qtYn8e6wUeaL/Pzw+fpqSmfTi5wNdjnxfQ8LTlnXDoq2As7Z3lmY4e9os3ztTecYUxofubw29mIhjwSHvK6jtnJOjwe7/Kdy7f43ePL7M2atMOUR9v7b/vcf6dvMd4RE4SVYA9CTGAJhhnDosZe2sR0SrwjH2/uCFO+0LyYnmM763I2PCGpfPhFhfwaJjVMrlCAN3fLso5yJ/zL2Tq9YMZe2eZh2ubxpYf89uxRelWWY2p9DpImkedYkgaBKSVes0DnCi9ysuOOzOlIFyi75o3Y9V0ozVbpNPm7RZcH2RLL/pTUljRUyuuFoK9m9JW3iO/zhSaUmpY3p6ZySqtoBXOGecywiDlO64yLiECWNFan5LlHuz7H90uKwkXnKWmdM9GbcKF63mdrb6Cr9K9L0QEvZ2d5T3SP28UyS2pKbiR/qfUi22XJ2aq7kdoc5Q3oyZzUSgokLS9lyZ9xWKVItTyXBjYqa6xFY7pewp7X5L21OxRWsarmTKzHkkzY0y0+0LxFJHNeTjfJK1HRksy43tymKRXPhZpDfcK+DuirOdfbu+yGHfrBhNcmq5yPBmxGA6Y6IpY56ytDRk0HDW76GRMTccE/pNOc4aOJZIGxku/tvExHJnTUbLEFAJiYgPc1brtjFByhhECK+wx0g41wiBSGzaUhG9GQK+EeuVWseRMioXlf+w5r3nARHXA2OGZJTXk46/Boa5+lcEbdy7hYO/wWTv5v+bL5UxnviAkCS0WXdkrKnVmbZpAhpEWdAqmlC1cZ2ZjtpMNj8UMmOmKqQ4Z5TFEohAAxV5jQojKBLMVij3+sXQDvQdHiYdIiMT5HZYOJjohkQUOlZFohhEu3llhs4iHaGV5YYoxkt+yyoqbEslyYmhITEEoXDpsYl/P4MGtXAa9OT5Faj7acU1gHpXkl22DDP2FURITS8SQDWRJKRVZ6mFCQaQ9VwXGLQtGIU5R0SJJa6FKmx7OIvbJNbhVLarawOg90g01vQFwlfWXVBAjOyn07X+H5aHfR3djLYWIiInHCoa6TW8WwiN8shHqJc7xWhKVBXiesOJQ7ZacKC9bMbMCSnDM2EcZKNuSM2wiUMBzoJue9MReDAwbaCYuOtM/Q1PCFYS9rMdc+x0W9ok+VjMqYSBaMyhqHowZlrthcPWGunfFtr+ywV7YXhd3TdPM1b8TMBC5m0NSYmIgl6y22URMr6AvBUMcc6wZHRQMpLEfTOrrjgoGWvCmR0AxNwJ10hROvzplKyPUgX+ZccEQsc74+WeckjWkGKY0q4f3tnvvv5PENJwghxD8HfgA4sNY+WT3WA/4VcAG4B/yVKk5cAP8j8FEgAX7UWvvCN3oNK6FsGIwnEVJwtX3ASR7DzKOsW4KhWw0sexM6KmHUqrGdd2krV0HOtaIWFc6S3fRQe+GiBpFbj8JKLgUHPMiWOBccc7Xd4nPJFbrejCvhHlv5EpnxaQUZQlhqYYFBoFo5ReK7D1FZYplxbGLGVlMXOQe6yURHNFRKJFzRb8M/4VJ8SNebcbOosZN3uRrs8aWsx1X/gEgUXAt3acqUupeTGW9Rh5iVLjfSE4ZOOCdQJYHU3C5XODlpEDcyJz0XllpQUK9lrHmjSkMRoRHslR1mJqQjEx4WXR6NHpJaj5ZMq0nMVe33dEhqc/Zy+GAEn0w0vjD01YzCSjp+Uk2+rjNUVzmFldRU4Sa6MmQ5cjyOmQmZiIixiRjoBsZK7mYrpNanV3UqltSULd3gk8PrqO5LXPGPCYXjNygsLS9lJ+nQ8edEquSkqKORDDIX5tNvTzkcNdg+6hD7uWNk+kcMdcygbPBotOuSvkzocH3VZF1YRUclzGzAb0+vcTYYoIThRd3gmXCX58MR+0UbjdOZzLXPhn/CXtnhhvXoyISeN+PR6CE9NSU1LqR3wz/hVnqGy/VD9r0WvWDGsv/2gnOE/bOxxfhp4B8B/+Itj/094DestT8phPh71d//c+AjuKy/K8B7gH9c/fnHDutboj3F+JEa7dcTOv6c37h9DdnJMYXEux0w77kP5Ua6wao/5l/eeTdnWmOe6WxztXVAoDR70yar/RHD11dJ10t0TfDu6A2GpsZO0eWpeIuOmvFovM/lcI/XszXqImfNHyEx1P2MS40jOmGXppdxbX2fV95Yh7kiXp3x/miHxAoGOmJoanRUwve3X6Quco51nQv+EV9JL3A53KfvjZmYGrF0E8k5b1A5DJ1W4KI34rnWPbcqqpDrvWBGU6WMyhq+1IyLiI3aEKkMZeZQ8n5QYoxACYuvDKuVXTrALJ5/Tc3YLZs8Fu2wV3a44B0jqxXP2EQsyRm7ZRflDZiYiE8mmu+JC24VboXRlppHwkMG2mkgEh04NWMVPXilvU9hFb95dI21SoPQEhk9lTAxAT0/panmFNbjXr5MS6U8Heyxq2MGeUxHJkyM7wxW3oSiCsqNuzmxzMnMCm1v7lZ1vs/Z4Jiaf5U4yskKj+XIkadiWXAl3HOWeevALqk/oC8TCumWngrLxARI4XQep36KSBTs6Tqfm6/Q85zOI1Knz+Ow/rtlF1+UPFnbIhKus6KkYdM/piPntD2HGYhrObHKFpPh2xrvcKn1N2xzWms/A/xBDekPAj9Tff8zwA+95fF/Yd34PNARQpz5hu/CCIqmJRzrhbmm3Zqh5wqbKld/qIhS7cpQdKl3xGrNhb82lAtmKbVkmoboyCIyCUagEfSUS3DeLTqkJnBSWpXgC80r2cYiwj0pA2Y6ZG/WQgrj2JOegdCVmrfLGj6WSJSsVcalwjpOZas6IQNRMjY1jnWDdW/EqKyxoiZEokRiue47x9++rnFQtBatPF9ojBUcFW4rNMhjktK10Hxf49cKQr90sXGeO1F11b5LrXLbnirhqbCSnkoo7Glb0F0cURWDNzYRa2pET+Zc8E644A8XBq9lpbhbNlycvZWMyhqhLBdbFHBbvcIqYs9h2AI0YdWrjkRJYj2Guk5fjRcXXIEgEiWPNx9SFwWxKPGFqx894rnk7MO86RKqiqg6Hg5Sc7pN9JShHuXkxtGuTrd5Qx0jq2wMf0GmcifSrGpHFlZxLjhip3T27jVvREtkPBs9AEBbQVDVhE6L2E05py4KfHSlFSnfzBd9y97AIBb1sLc9/oy6OVettQ+r7/eA0/7OBvBWvel29dhD/sCo4sv/JoDqdlw8XV3iTzxW/THGSIRnkXGOVR5lJFhSUwKhaao5vzK/TuzlrARjpjqiV0sIvQApLNtZh2K1gELSlMWioJhbxSX/EI1gYiLOB4fVhVPQkXPOxGN6/ozl2hRfGC60B+xvdxGFy3I4tfjOrE9TOHBpR86JhEZiaUrDoTdanFiFlbS9OR3pcGlKWKRwtuCz3pwV3713YwVSWiSWjeCErNIYTMqIs+EJvzW/ik4VSaDJ5j5SOUZEzS/oSENqYWI9/IpMJSsIjBKGDX9ALDTrakIgDBg4sD5LYkZqJYe6Tl/NiIRmZOa0ZY1NNWVLJYzKeDGBRaIgE87p6mouLn5uUjkwm+TVsQm44E35igmdGrUSWYETf92arZA2PFLr0VcpsTJsa0OiQwqjnKEqGi/S2jPjSFtJ4TPPffJcQRd6yilZh9oF5yTV1mIXiRIWKnReVG1jUqt5I1vhffXb7JVt4qpuUZcZeeXjmBYhNVUjtT4dOedeuURd5Ay0C0E+Bcyc/jwSJUfa5aL6QmO+FaLUO7zN+f9aKGVduOfbnuOstf/UWvuctfY5VW8gSyeYEsYJU3xPIwONzhRWOaiMwpIan/2ig680HX+OwvL1yTrGCnfCZtVMbkEmkt2K9KMRRLIgtR67RZedokdhPZ4Ojhjq+iJV+zB3BavMeAyzGhgBhoVIZ2T8xbI1tT6v5WeYmIACycA4IdHQxGgEMxtwPjiqOJg+PpbPpk4I1ZMBmfGZVHfjqQ5dQnV1UYIjNSth0JmLw9PafVzGiIrWLDg0HgWCoYmYVLCXfd1gaKIFM/G0M3FKPIqE41kUyIqoJAkFvJTXeKOY8ojfYFA2GOna4piMdLzYCo3KmMz4VZGyy9DETExAUknPh8ajJefslW06ytUphtUyf5jH5LgVw8RYCmu5XSxxUDTxpeakjGl56eLYJNqt+JIsYD4LKJIATxiGJmBoatwr+qTWZ2hiDk3Ig2Jp8bOBjkiMx1blshyXERsVMmBiaihh2CqWGOkaQx1zNK8zK4MF+OUUBXg3W6n+XzFDE7NXdhbQmGkZ8jB1BdZB+TaBMfbNOsQ3+vq3Nb7VFcS+EOKMtfZhtYU4qB7fATbf8ntnq8f++Dcxhwsfn7P14ZiyFvLJB9coP9ujOYPn/tpL/MbsSeRc8tnZVe4kfSSWuy9t8Hpn1SHpcsVPfec/f5N4Pf5unj6z7yLjrc9O3uV+tkysXFX/qGjy6b0rSGFphSnTPKQVpvzH65/h+fCAl/Mum96IL6Xn+LXwOsdpnQ/0X2dkfMY2XNy1FJa+Glc6hB5LasqN9GylyYefPXgvgyxmtTZhOXB24q6f8MJwk6fbO/z40ov/D0p2XTpC9siEHJs6V/xjDp5ucj/pcb21y37ewhOaZxv3nS1blBVg1iyIUB2VLJbI9/I+9TBnbKIF++BjJ88ihaHlpQyLmI6f8Eh4SGp9tlTCx6cN/tPufQ70DQ61xBeGdaUYmJJJRc0urOU76rcWd3opDKZibPo4hmXfG7OVL6ECy6P+MffLFrMi4NdGTxGrnMeiXVLrs1+0+Tv9T3OvbHDBm/JCtsb1YI9YQGLhnFdDXv0MO3mX7XmXf2/pRbc6UDMI9imsR10m9GXGpWCfJZmxVK0Qciu56I851AGDos6nk6s8yJYAiKXjkf5nS58nEopLl539vS7casggkcLwaPSQunTye42kJVM2vDFrjRvQcC3UpsxZU5off7tX0p+BIuUfNj4G/DXgJ6s/f+ktj/8nQoifwxUnR2/ZivyRQxhQ0wxvFmMUjE7qNAtHst5Pm9AsELOQW7MVtqZdcq1cVmcusVOFAA50k5OyzkkR80h/wLz0mRd+xRVQvDZbpaZcQMzdyZILej3qkHamaCOYZAF7Kx0G/jF7ZZtNb8Rr83U+1HuVO+kK54IjHpRdgmoPeli2Fm01cEvhLVnwanKGTHushmNS7bE/bTAvfXRD0PBy5llAqn3uzZc4Mjk96REpONQlSsBW6TvrceHuWgrLRzsv8XK0yfVoi9T6HJYt+t6YWd5nV8fOTyBKZjaoWq9h1WGp8XJyliVvyu1slXV/SGp8aipnNICS/wAAIABJREFUZ95hyZ/hSc1Uhwx0vao5xIx0jQN9gxVVJxSOIA2grdvTKwpiedo2dZJvhaVA4IuSu2WPjppRWI8bybqDzlQT6VI0I5ROHzIxEfezZZoqZVWFSKasqhpb+RLPhnssqxqZLRiZlGvhLhf8Q7aCJecSzVe55B86EDDO6TsyfgXl8fGFqaTcEdI4gM1GNGQn63I2OFlsnca5E0ml1vE2L3hTDnXAjm4Ti8wVJoVBVpJHhfu+qOojTVmgZIYvzILS/bbGv+sThBDiZ4HvApaFENvAT+Amhp8XQvwN4D7wV6pf/79wLc7XcW3Ov/7NvAkrQQ4m6KhL7cRg5wqVuWDew6SOzRVlt0QJy2DmipFCC6wwCC2RuaMfv5EsMynDRYDJ4ahBshG4QmIe80hni1cna0zykLTw0ImH6hlG0zqep3mYdxiGIZeCAzKr2M9aXIiOiFXOw8JNDpOKITkzIWveiMQExDLnqGgQq9z18rM6TT/lJIvpxXMGSQ0akGmPzHg8HLeIVMHQeBS25KwXkljLurTsITg2dSY6IrX+m3te5XBoPg4Hdydf4YXx+QXHsSmde7OvxuyVHQrr0ffGKAzHZYOTok6zanXOqvblYd7EIBjmNZb96WILAXCoJaFwNYnMzkispqgKhnu6oC5zdsozDg2npsxssBAsHZcNLgUHHOsGNeW2dccmprAebT/luKi/+RnlTabK5Vk84tVIrLPjz4xknzl9FbKrNTfSDToqYc0fcq/okZiQnbLDUMeLFVNHJW5bVflLFK6wrcSb1PDYy0hMsJggHqbtBcPydrZGXTxgq+wtfDCp9rmXLxPLnJacL2TyiQ1ZUyPuFs0FT3P4zV51b72+/l2fIKy1P/JH/OhDf8jvWuBvve13YcE2Y2QJ854Ev3AJ3qWLSDuolXDoagtx6Fpdc98dWetbtO9YD2drJ5wU7o4+LUJ6rVnV9ZA0vIxpGdL0MiKvpNAKWXMR8FJamrXMxdvLnHtFj/PhAavhmMz4dL0ZUx2RVxi4UzJ1U87pe66TshKMXetMtSl8l8xd8wpmRUAvntMLEqcn8FwgrCfdXSiUkNqSsx5EwifAsOkN0VaSWp+6zLidrbEZHFeMyDabvlPyedKRpiem5tD0VnKsG2gEa9VznBKil/0pa/6I1Ph8YvY4rcB1XTqee1+JDmgo51Q8zBsV8NWS2Rkrqs7IzDnvefiMSayqtkPZYgUFUAhDncytWPI1rofb+EIvjpHE0AtmC6FVW83pBy6EZ2J8XslLYmncRKCc0Qpch+GNrE8sc36g9SL3imXWvVMRlPs6DcI51g3WxJACj+aCpm0orIcShsz4nBQxS/7MnQ9+So4jdg103a08jI+vygVda1TGTEVEEJTkVnFS1llSU856c15LzlSuU74lN+c7fbwzlJQCynYN4zlbd62TYr0AlVs24hE3izWkhZ4/40x9jCcNX7nTxfpg607d15HOMrzij/nK0SbnmycEUrPmj9zysjYkVjkb4ZCaKnh1tIrtiAUtuhWm9NSUdaXZLd024hQ9t5e1ea75BpeCAzRycVEYJOueU9dFoqCnpjwsOlysHbLsjXkw7zHNQ/qR67Mr5SzS660xj9SPuexrYlFjajI0FmMLznqWibGLXMp1NeE11vny7CJP1rY51o1FkO7l+ID1aqLSODT9salzwTsitT4zE9L1Z6xXwcA9NWUiIjrBnJaf0vLmFFZRVznnwuOFPqCpUtaVW0kk9s3uxsi42sojngIczOb0/9+S6aIOccE7YaJrzKxbXTVlyrqacGhi3pgtcbVx4MxR3pRlb8xR2WJd5QyMoineDLHpK4/CatY9wSPhIRMTLVLJfKFpyRRflIu7/QXvhNQEXPAH1WeiKzy+436OyhorgTt/AELp/D1rKsMHNqt/t+aNFpqRFTUhCV0u6Zo3wuDqLG7lBheD0/IbbHxLOoi3/0/+NMc7Y4KwILRxyseO40Aq5bYentS0ugnz/Q6Z8SmtYpTWXA3Cs67LULj94IZ/wl7R5pGW+6BT7ay5ugqpLYxiRI2DrME4jVzytVKkuY+vNOe8Ew61cG076S4AX2gux/ts573FMv8UKKsxHJRt6jJHV7H0vtAcFQ1CWZAbReQ5VaaxgnllBx5lEcOixr429GSKAZZkjbnN2SsNa0oACT2ZooTlqGhyPjwikq54dhqka6xkUMmFNYJAOJv1zIQsqemid59UisJTnsNRWqfuZUx1WF0kkqPK7p4Jn1FZY2BKtIUCwXnPW0wSPjnbuqAn3aRwK18jECWTKqUqFlml6GzT98Y0lbuID0288EUMijqBLJl4rksyKOvcLZ15K1UzrgX7hMLjyOTEQjAyltvzVcZljQ91bjCzAU2RcqCb3M1XFpqEuszYKnoLeOySmi1s2YX1GJcRK8F4gQaYa3c+DatU7pvpGR4PH3Kgm25isCFDo3g9XSWWbxYud/Muscy4Eh3xO/PlxfZGvV1rpv3/QZvzT2LI0qLrPuHQokMwWlA2HANirn169QSVCubapx9OKbTChgYZapAWmbqLc02N3Iflz/GlZpRF7BdtAFaCCceF40mM84gkc5OCFBYhXNjusirY1w0OqrbYUdkiqS7qo6LBWEfMTMDMuDCZw7LFnWyVw7JZ3cUF5yoEurGSppchhaXtzxeTw7CIq4BcycT4TIxlZiwlmn3tLqSGDCtgiSAWlv28xaB0zIOTss6xbtCSKe1KQnyaeDXW7v+7Xzi59d28z6o/rFYFriXclHOSImAtHC9CeGuqINEBiQ4W25KJURyakN2yyW6ZMdB6Ec6zWzaZGEskCo6KJrt5l5kJGOqYvbLtuiFpj3vV6y9V1GmDZGvSwRMudKeo2s/GOon4vaLPVtmjL0uUEEyMYl+74zQo6twe9fn6/OxiQjh9nf2ixUjXmJmQrbRHgSKxIRMTVS3YiEgU3J/28IVmrv1K4q6Q2KqYGVRCLUlqfBeIVG0ddtJORdx2n/1xFScQCue/eSPrV/GEtbd/8v8ZFUr9iQ7jC2Smma8IagcW9XSGf+Cqyw0v5/6tNXxl8aTm/rTH2caQvaM1dKygrrGdgtT4/HZ6ja4349ffuMqF5QFXOoecD47YKnq8nqxQUzmhKLnW3sdaweGsjgBaUUaoSn49ucj3xne5la/xah6w7p+wk3X40uA8/9H67zkcnJWu91+1Ot9Xf52ZCbmf91n3T/jd8RUuxwdIYRgVEdsjN0GdqbkciaafUvMKOn7CRa8kFAH3y5KbhQY8NtSUL2Yxh3qVnaLLlWCPx+KHfGl8nrBdILF8afoIUdN5T/rKEZdWqtyKusy5EhzwWr5KT8347PgqH26/wm7Rpe9NOCybXGofcXO6ylo0ZpDXKazkSnt/wUwYlTGryqAo2NMFiVU84im2dcFuFvFdNUNiAj6VbPL++m1yqxZbjImJaMqU5xtv4AvNr51c54nGDt8V3+RB2aUR5LS8tDJ/FSgMDZXxnbWH3C0D1lTGTw2/jR9svchZz8FnYmH4SO9rDFoN7qQrNGXKoW6y5o34aOclmjJlUClXVfsGG2q0OLd6wsm/D3STa+19ttIeq6GDDZ2UTvrdr2hYH+68wsyemryct6UjE35w6aukpioUI9j0j1lRE76cBzwTPeAD8escmxpravaHnN1/9BD8GShS/mkMod1R8idQNAVp6iNiCMeWTHsE/QSx32RWhgRKc5g2KJsG61vQAqvdfvlsMGBQNri66my3w9xZsCNRLpSJNVXwYNaltJK89Aj9EmkF89Jn0z9mYJRDsHtzvpI2uFLbJ5QlGrmAtr4VC/egcD31ULp9cMtLOchbdL2Epp/hSUNU0Z9CWTLXPoF0EfUza2hJj74qAM3IWI5NyLpycuxYOPJRz5vyROOhA9yqBj1vyka19z/dNuhKl6GEYWICgkou3FDuOU5zQTvVc/eChK6XEMqSWRlWXEmnpVDC6Rxi6VOXebXMVPQk+N646twEXAr23Qqgig50EX560QkB6PjJ4jU7KsGXmraXUJhT7YZHXLkgg0qLeDE8YF1ZYhGCzCmsqVZcEWeCEasq51BDgGZo4oX8uaiYn7wlAc+J5OTiGLhoQxdpaBDkxiOq5P1jHS08IwB15bohp1sKX5RIK5nZECkMG3LOsQk5rlYO6lu51f8JThBCiO/DmSUV8L9aa3/yj/i9Hwb+NfC8tfbLf9xzviMmCFm6o1Q0IV3RyIcRwoCXulXDU+u7fPnwMi8fnHGOzVGMmkuMtshc4CUuabmjErbzLo+19gB46WSDf7P3DJ1wzkkaczBt0K6lbB91nDpRC+ZRgDXCbTMuefzu/BLbeZdDHfC16VkejfdZ9qfsF21+/eRxPGF+n/tyf96k6Wf0oymZUaTaZ2faph2mjLKI6SxiW3XYFW0C5U7kWe6zN2vyudYGS2rqUqpFzk7Z4W6+wntrdzjULSJRcKhbbOc9t6XRLXaLDrHM3d1NzfjV8dMATHVIaZyHw5MOSbc169ILEz7JdYaF81SEsuTOaJnYz9nzXFrXcjTjN4+uEXvORp6UAd9Rd/DVnfIMdZkhcQnpkSj4VLLJpWCfD9U0P773LuY6oO5lTCrwSk3m3Jys0glcLcBYwW7eZaRr3Hq4AkAvTGj5KXvzJg0/42JwwNfnm1yOHIvhZhFWCsn2IprwtekZaqrgpWiZz0yuseqPuZcucZA1uVw/JJIFu1mH7doSGrHoWHT9hNT4fPLBNS71jlxKuPbphTO3Qi1rDE1MXeYY4HPJlYWbtOfNeD1ZYTUcLya246LObtTlWviQ1PrcTM9wOdxnYiKc8+CbHH+CKkkhhAL+Z+Av4iwOXxJCfMxae+MP/F4T+DHgC9/M874jJogylowv1ujd0DQ+PiX776bc99co64pPvfoY7S9GrAwNP/xffJE35n30GcHnmo8QeK6DEfklv3zwNIdzV2PYf3kV61usb/mXH/nHTEyN35tdxu9r2mrO7d4KT9Z3+PjBU5yNh3Q8d4f7p7sf5H29u7wxW+Jj8l3cn/Z44WCTk3HMZv+Ef3Tl5+hJzdA47qOqFIOO2Vijo2Z8ZnqNZ1rbLPsTAlHy8fpTXGvu81htd1E8u5/3aau3WKVNjcOyWYX7Kn518hTb8y4zHbAWjTlIm9wc9DnfPiE3HidpjU/UnkBi+Z8e+UWaQhJLn4nJGRjoSbhZ1Bj2Yr44u8Rfbr3AK/k6G94JYxPxV7tf4PPzS7y3doedKg9jzRtRFyUT4+TTsvKonJqh1r0TbuVrHBVN3l+/jUbw43vv4h+ufZXEuALe1Bak1jLQPnLJkhifXzh5nqfrD/ho/T6HWhA/kfO9zZdZVXNSK7lfdjm/WA15XPCP+Ie734dafolrwUNyq+irGS+ZkNVwzNdGG3wxuMT3tF7muXBKQ4Qc6IRlVePEpMRCUVhX+ZtZw8RIp/UwIe978jY7RY9vr90hrYq2B7pJW2b0VMpvzq7yqfJR/nr7a9wsatRFwVmvxO/KxXMqBL5wf/9Xkyu8t3aXC41jcuRCvfm2xp/cCuLdwOvW2rsAlVjxB4Ebf+D3/mvgv4VvTvT5jihSWgnBxFDEEjlJmOUBwgjyjsFmClla8pagreYM8piTPK4CbQuUtAxnNWIvZ5qGJFkFDrUgcoFGIjFMy5DL4T6p9ZznQdfISo+ml/Jg3mNYxqTaVdnXItfPzrSbP5Uy5NoV1BILHeli4vtqTmr8ShuhFsXLqQ4JRMlQx1xr7jPV4QKSapA8yHoLp+VpaxGgrVw4zWm1/3RIYYl8NxkGsqTh52Slx2ihAnRE7dRaVpUksU5ZecE7IRQlqfUWWwgHajULR+apiCi1/sJfot5SWj/lXYJzqgKVf8NjroNF4bLAJY03hWRmfTaVoSlzun6CEpbUWurScDYYMDQxBoiEcWnbJiaphF2R0LT9OdqKRbcotYpBWSdWOZ1gvjg+I6M5MXMKYGRScmsJhc/MGtLqgi5w8B+AJTnjnH9M/JZt0FDHjEy4UF26bpStnJuG1Fr2tWFkLCNjmVjjJOfWuUeHpsZBBSOa2bd/vxXmm/v6JsYfZZR887WEeBbYtNb+yjf7/t4RKwihYbLh0dwtKVdaLMeHHPWahLcjspWMouFTO7AYK/i29n2OiibHaZ2aV+Arn0A58tFGe8Q0DxlFxrVBcfvUjpfybOM+W0WP88ERoyimp2Zcarmw3etNZxfR1u2hj7IGF2uH1P2ctPSQ0hL7Lu15VXkkVrOuEpKKWQhwLXBLy6S2vdjubPrH/GLyPO9tvu5EQmrKXtnmmfqDBXa9LnKGJqbvTRYX8SPhIQpDZjxWggnbSQdjBZFyFKfcKC63jpiVAT7QU05Etqpq7Os5bamAnNtFm4vhAZf9FCl2WJIZuUzYKts8F99ltcoCmYiIlsgIhaaJ23fLqqYx+7/Ze9NYy7LzPO/Za8/7zMOdx5q6hu6qJntgi83ZlJukREm2YTMyFAtS/iQObAeIAAsQpCCyE0FAIgFBAsOWEcdJ7ASRqMRQFGrgqIFTN1nd1dXVNVfduvO95575nH32uFZ+rFOHUhSL3QRlt41ewP1zb93h1Fl7Dd/3vs+r9IJbFhFDEbHqtKf6g5yCpXfMxy3QwMiJVcqaFdKSBrahWLJ7nHMOKRg6N6SfB/yl4B6moaXbF5yjaW6IzghxyClbEet2h4qI6UuXgpERS4u9qMog9Xh/tc2yOaQiTEKZUxH61FASOmW8KqzZju+YOUOpWLNCHqRlImWTop25R7lmepREMls0F6w+JWEACSVhUBIut9OcgpFREgaJUsQK6kKwbrcBvchLKZgzv4dszrd+gmgahvEn6wW/ppT6tbf6zYZhCOBXgZ96y7+Rd8gCoYQOxkl9gS8VRVtPPGWAYSqMTJOvvWkhsG6NSXOTgp1gGRLLTnBERi4FrpUhEoPc0RPksVa/IGKCae7EktMjVSa+SLT89nGPe8ouqDoTDQKxEg6yEnkmpj8LBIJcaZaBjZzZhm1DIlDTYlau9QBKkEq9iKTTXVxOVXuPHZWPx+PFqSQmDKWPMBT5tPgXZg65FLqwKk2UMoilqT+PRtpDjm/oh9nGxDOkDhA2J5gYeEaObUCiNJJuzhownOLeB9LT/Ig/caCUSmhvBTmpIWfAlccGphyDYeYxUim5UgSGRurHKsUGIsAzdGCuZ+TkKFByZu7q5LY+GRn5DMVnG9nU6anFUvGfKHb20oBx5iBQxNLGMSSh1GauCiYnMqcuFEMpKBj6BGEbBq4hGJLjGcb0/1nQyT3qpuZiDKXPWFkUyOhnPkPbn54gdKyBrTItzTYEgmQqN7cIpu9fTwYk0/c1lG/zcXp7LcwTpdRzf87Xv5tRsgQ8BXxFg99YBH7LMIwf/fMKle+IKwYKvI7CUCBGCXsjjZtPS4p8bGMorbAE2Iqa3BgtUXRiLCMnkSYHwxKZNBkkLiejAtJVGKnASAWRshAotpImVTNkL61zY7xCpGwOogqpMnl9tMpuUmOY6YBagcI2cg2xtTMKgZZH96TFSKVUhIMJzAnNldAVdoeO9GhlZe7FCzNr8NngmFZWniZj6yP6tfE6x3mJXl6YXTE8kVI1x0RK28AfFx37uY8jcuYLIwpWQtGOmQ+GJNLiZFIgVtCR+ueOVMyc6XIidedh1ZrQywv0pKQzrcw7U+pUOy8ilUFDTJDovz9Vgp70phkZUzOWSGfgl8DQxcrhn/h6pBSeIYhVOmNc9qSgKqApHCJpT9PNdFfkGX+L/TzQ9Cskh3lBf13aM9jL40U9UhbB9IoxzFyEoThTbBFKZ3bdG0qbgzxhrCx6Ui8+PakZGQChzKf8D8WaNWDR6lERMTaKsojIlTG9YplkUnMvQmVOr5IWsZK0ZYGe9Amn1vie9BlOF/lB7jHM/enr/B6uGN8/u/crwDnDME4ZhuEAP442T+pHTKm+UqqplNpUSm0C3wD+3MUB3ikLhIC0CKlvEK2WuFQ7wohMrNCgudQn9zTVOhAxl4NdPlK9w52DeXaHVWpuyNn6CZtBmyuNfT60ch8zFNhzE0Q9Zs0KiZXJZW+HXBl8wL/PS9U36GcBT5d3CUTCk8U9rgQ7vK+yxWn3iIYzomKOeWnxJq1ekcHARymDDUshgDdSPTl3csF5u8+GNWHZmvBeJ2PTafHp0us87+rFey+usuG0SJRJKy9RFSGn/RZn7Bab9onmXObFaWKTxZPuLk95O7xYustThV0+XLzFg26dBycN9scVbp/Msz2oUbEnLBYGLJgWgWGwk0liJfl6pOP2IqV4kBVZs9vUTZNLjj6S2wbcixcoi4jDvMyNZJFI2tSndYZNa8SiNWTBHNGTAVtJk54MKE1PXyt2l5KIMJH4IqGT27yZelMreMbNJOSiE/CVyTL3Msm8NWROxJSEyW4W848PPsZYutxKFpAYXLZDHV6kXN6MV9hPa6y7bTwjJcGklRcoiZRR6vKg1+AbrU1yJdiwHATomtKUt1E3TXayio5ZNCAwbJqmz7Jp0jRN/pfeC4ylyxfGF7kaL/NatK6j/dBgmVhanGQlTSETgjdTk3b+nZOHXhxczRYxFCUR6RMhur0q3mKx4E+N75NQSimVAX8H+D3gJvDrSqkbhmH8A8MwfvTt/2F6vCOuGAD1mzG9My7+q4/4w60zmCNBvJCR3K/TPFbENYNWVub/2HlO38e9FM/KeNht0GsXEecUr+2sAiAykI8KiAy+9qw+df3WyXv48flv8s/aH+D2cIEnKwf89s5TfGL1Jp8/ukDJiWmFBT64oIVNLw/P8LWDTYpBzHDkM4hcXo0L2EbGhhXyrXiRc3aLL4SnmbMG3I6WsY2cm+ESo8zhPaVdDpIKp/0W/+Lo/Xy6eY2CSLierPLV9llGdY+aNSadRuC9PD7DitPljckq98M5hqlLmDks+Kc5XetwOC5hCsm5Roth4nHtZIUwsbm64uGQs2pN2M9sPuClvJpo0tIZu8v/2HmRTvEBb05WuOTvMZYuLwT3+Ve9Z/hQ6c4MMFsy9Wnj29KlPPV2VM0xZ5xj7iaLDHOfw6zCTlTn+eJDAG4PFxANxSUroiUNIqBhKn59VOEzxT4vxyZfH5/lrrXIC8E9qkLyycZ1DrMKm84JPenyudE55qwhJRHx0eA2A+XyxdYlwqLDpnNCYMTcTRtUnQnrhS6D1KOVlPjsaJE1u42JxDNyQmzeSFw27R7XU53KvZMzPRnp09MPl18jVSZ/qXCLvaxMwYn54uASdWs0QwYOc49WXqAnc8pGTDhN0zINxfF0Ic+V4G5a4dpkg6IZ6ZOYFXKYVd72vP9+CqWUUp9DO6r/5Of+i3/Nv/3oW/mZ74wFQsFg00VkQKPKXGXEXs3F6lhk9YzM085OE8mV+j6xtNiy67imPlrbCzmWkJQK+s0aBR7KUihLTXfplP1KTcNi/RbCUAQi4UzthKO4zEqhz5lCi1dZY9M74Xa4yGm/xQ1/kc44wA9ifEd3GubMCRJ4wT3EMwQXHI27eC54oP8WI6OfByzYmjb9MJ7jxdr9mdHnvHvANXuNmjXGRFKeFrYuevszPb8IFLtxjUluM+8OeThosFgY4pgZ7ajAnD/CFJLhpEZVRITSJlZQESkHeYpnWLzoDQmVwZPBHqftE0piwmmrT6hM/q/Be6eJ5gkrYqwLd8pi024xlP7MR5Eqi3Ze5LK7y1g52lsxLVACVJ0JobTZJ8E2FJ6hKBoOF5wjXo5N3ufavOm2uOTqYN/AUFxx97iVLM5EZ8/4W+RKcJhVeD1eoSASytaEC+4BY+lSntYKdkeaZv1CfYvdqMbz3vYUHhzMjGnrVpf9rERJROQYzImYRTNmKB9Ts3LeTBu0DG0y20qbPBnszQROk9ymMm15lw0d/NubSsgTZXHGOWIs3dkCd8nb45vjM1NClcc59+0H57xr1noLw5AQNQxKO5K87GGJAdWFIeFRDRRETQNrAoGIeX/5Hl8fnEWgeE91l9d6q+y1Fnnv3C5r1R5bXf09FDPcbWfW7/9E8QbfjtZYtPpsGw1+Z+8SH1q8T9GMuTlapJsGXKnscWO8Qt0eM8w9ktxkcFxEBBnD4yLt00X2sholMWHd6tKWAb/bv8KzhS2e93bYycr8zfI9Pjs8xZw5oJWVWHfbrNltrkXrlETEblKn6Wq61EgGvBkuU7YiAlMbjQCa9pCmPZyq/zJ+/ZvvY7eU4vkJk7FDr+pRDyZYpuTBFJX3ykSfZMbS5Sit8OnyNV6LNviJ0jG3UsU5WwfplkTO64MVPrh0h+vRGncxqJsjtpImD9Q8C3Z/RoJ6M1zGN9NZIbdkRizYPX63e5nqFL77G93nqdkhS3aP0rTtO28N+fr4LG+6LX6qfExfZvzOeJlFq88vP/oUL83f5Ha0xIZ7QtUMeXOywuvDlal3RfKTza9yK17m1mQJV2QsOT22D+swtLlTm+fC8hGhtLgRr/AHvSdY9zUAJgwe8eX+BT5WuaVpW1P/iScSnnZO+E8f/nX+s9XP85ud5ymYMRVrQiASdmWdQCS8fqz5yraR8yPFN/hGtIGJ5NcPnuNi+XCGwbsbzvNC+QHLdpf7YZMH/SZXGvvE0gZee+vz/t8T7P2/kZG7IFJFWrRp+iPGiYPIDEQhQ1k2Vqh0IU+ErHkdDqMSsbSoOhMqZd1rX/CGDAsuQ7eEaUuSmuSCe8CqpXf9VFmsWEcsOT2eqB2zMk3nOh2c0LRHCENSMiMOkgqXg13m/DFHxTKWlWO4GWt2G29Kn96wcux8xJPBHst2lyXTwTYGmFhsOi0aIgR3jzeiNdat7gzq4omUTlbUR+w8oGLqUJq6NZrRmxetPu1cJ4+XRYRZSvGDmEYh5FgaeHbGeqGLVAanbZ2TWRITFs0RW1mNFbvLminZsUbEKmPDMrANl1wobMOk6Y5ZswYk7v5M81CeZns8BsyajmZsRMqaXQFsI6OZulVGAAAgAElEQVRhjniyuEfVDJHK4OnCNqahOOcc4hk6kHdOxNy1Frnk7tGXGRXhc845Ys5M2Ch2OO/tM8x9Fq0+C+YI28iIlUU/86lZGvpyztVtY9PQNutiecJIGpSLEzYLHU7ZkrHa56RYYtXpEEmbC84hO0GdC84hA+XSEJMp6VtQFw4Xy4fYRs4pvzWjaz3GA3pGylxxzJlCiwvuARVh8KSzT47BE+VjNr0259xDLeUGLnl7FIyEdV+LvOr2mLPvniD+YoYyofxQkhQF/rF2+nXu1LFshecniLHWuksleDk8Q6pMXttZ5UGxoY1WdkbVCtkKGywEQ7aTJWRuoIKcDUvHu92IlzXgRAZIJVj3u1wfrnIm0L6NUGon49PB9hRDn7Fe6LBdrBJGDsu1PueslBTNa9jPtRPwGW8bz8i5k1qUBHwuXMAxdLDOVjrHdlznK8Z51uwOKaYmD+UBAskF55BIWQykR4o54xsMpXYmjvIKG84JZ5eO6UwC5oMh9lSuLQzFgj/kom1rZoKpxV220aYuoCO1kev3J3VecA85yg3qZspQmnyy+jpfnWyybnc4zks0zBFPO4ek07ZrTzqct9ssmgPa0zTyxzyHngz4aHAbgP2kxg8VHmkRlCHIUUQqoyRMXgju4Rk5vzNe5pxzxLOuw26m28km+uq3ak3oSItNq8ta5dvsZFXmzCGvTE7zrLfFD/gPiZTJmpXyqY2b7M3rrNAN/4ShzHnOzdmwXiVVUBIGFeFREtfYtAJAkmERqQwbk75MZu3ty96uxt/ZOmnrgtPFMwzuLb5B3Rrxfq9HKNWUxSn5jxt/SEFIStPX+Ly3z4Lp8q3Y4kcrV4nKNgUjYcFM3vbcf/cE8RaGkUFaNCjuZxi5pDUpYK2EqAcFwk5AYINItCDFFSl3hvO8b+MRBSthL6xwPKizFTYYpB57/QqymEFoISLBTlbhgtOlIBLaeZFEmdwYLXMmaM2i5Ua5S44Oib0dLfEwbLJk9/j2yRr+VK0ZZRY7uWDRzFkyHUKVkirFH0xWWLa7rJgjetJh3hxydbKJcCVPOPpY+tjyXDdD3oyXCHOXULncT/2Z8UsTpCz200Xq1ojjVDMv99MaShnU/ZCClZDkFsKQ2CKnl/hsZxMCAzxDIAyDupC0coM5E4ZSi7MCYSKkIjAMKha8FvtccA9oiJgNa8BOXmR/amp7LNZ6lJU1+VpZCOSM52Ci2M5qOuEs92nlBgWhsA0BSjJnOuxmMVUhCQzFotVnzkzYzRJWrSIfrtxh2errwqIyiJSJg2Qra5BMdQ/mlMPxOPBmJ7O5PVigF/ssF/pTxDwc5TEP0jJ1M6STCepiwmEe4BrhbNEIlSJSkroweN5/QMFIcY0cz5C0pH4f9jPN8bg+WuEHa29ylEuqwuAJ2yFUCa9Ea6zYXZbNIRLBVlYjtU/wDL3xHGUVLXRzvit+9c+OdxeI7z4MqRcJIwfx2h0O7j2NWY8xU6hesynu5WS+wWcPn+V+q0kc2vyNK1c5iMrc3ltAhhZf757FPbAwIwMnUAT7BlkAv9F+H08UDvmNR8+wWelwv9ugPwwQZxR/8OAspeKEMHJplMYc3JqnfKrHaOxxvbJE/9Um2WaEYUA+MfkHhU/zTGWHp/ydGQvxt9vaLOWbKe04oBsH7HcqNCsj3tvY4yQpEGYOH6jfxxUp98IFvvToHJuNMxwMytQLIQ1vrBF5bsiN1iKT2CbuaRk1lqJQndAsjrl2vEwuBUUv5tW7GzgHNr/kfxJL5Lyv9JB+HnDe2+crg4s8W9DUpX+6+yEuVI7YGjV4uroLwH5U5UvqIpdLu5x2jvn93mU6ScCl0gF3xvP0koBx6tDwxlTsiLoz5uFYu1Z3hlWKToItcu4czBM8mbDqdOhPw2ue8bf4xwcv8cnGda64e/zyo0+xUdQAnw9X7vATpTY/c/Ai5/wj7kfz7E2qnC8e8Up3Y2bu+sbWJldW98iUybniMbeHC9x4uEypFvJsY5t/8uaHuLO5OJs/WjxlIZXBqt9ld6KjBTQy0JrljP7K8pf5+wcf5QPlu1TNMdfCDXbjGg+GDRwz587RHPvjCl8qXMQSOR+u3OFOtMiNwRKJtFjwhrgi4zAq4ZkZo9Rlu1/VjmBDUfMmwH//9ib/uwvEdx/KhM5lRe1OSvb8RZ66/Ihx6rD7cIXhpkSaJpVHGedLR3xq/g1eH63SS318M+UDZ+4D8PLOBnYzZ9z3MY8cRhsKI4fnyw/ZtFssnOnzW6338JnNV7k+XOFvzX2VVa+LxOADhTscZhX+mfggrpkRRg7r5S6vbhYRQpGNbYJ6yH+4+A0uOUe0phLdraQ5k2m/199iKHXoyuvNNTyR8nfrL/P3tj/Nz659jnZe5ILT4hvmiJ9+9o9o5wV+q/vMLDV7Yjv0U4+l8oAFf8h+o4IpJI7IuddustuqUa+OUMrguFPm4ul9RqsuP9J4bVaHqIuMq/E8P177Jj3p88XBk/y1pVf5kH+P2+V5LaFGMvR9XvD2KQmTTp5j1q5RFSEFIyUqWiSY/G7/Cq7IaKcFOkmBJ4rHdNICi96AshXNqv2fKF2nJ7V8GmA/D/iphT/mMKtwK1nkpfmbnPf2MVEsW31+5uBFfmXpKg/TEfveDoWa3tElBu2kSGEaefhCbYuiGdHNCnykeYde5NMeFvjcgyf5odM3+Ej5FmURTYN6uuxlZUxDcphV+XjpxgwSA7CX1njS3ee3x0tcKexw2d1jzZRE0qFujXgi0PWOQezxVHWfv1y5MQs/etF7xOedJyiLCXVzRKhcrRsxR7wcnuF8+WjG2DwbHPE7b2viv/OvGO8IoZQS4LUE0hZYg4he5DOMXZK5DJEaiBwyVzDvDNmKmhxMKtMcSv2xPaxjWZI4tlCJQJkKkYKZGJhog9J20uRUoa373BOdhtRKSlp7EK2xnTSJMgvDUARegiWkzgW1c4SXkecC28gIpTXT3K/YXXIlpgh3Hb6yn1Y5jjW+bT83ebq8y07aoGGOCAyl1ZxZdQpP12ayohlrToIdYQlJnFsk0iSXWlpdCSZ4foJj5niOLliGqUOU6XBZz8ipi4y+NFm3prATJXjCP6SbFehIj+PplQHgQ/4Bj7IAz7CwDThntzUzYxaflxKYCRUrpGaHVO0Qc5pALqfIvVRa1N2QBXPCObuNaehQoaqIGUqfTUd3KIZTpeHjv/OcfzQL5zlthRzmZRxDMm8PqNohq05XS62nHElXpAQiYbEwYLXeo1oMGWS+TtIyxyyaA1p5gc0pOeuCc0DBSJgzhxSMhFRZVM2Qdl5g0eqz6Zxw3jYZK8kz7j7v8bapmyOqZsipcpuyFbFoDqacCYlpwLrd5oJzyKI15NxU4NaYunHX3TYbfpvzwSGLVv//Z3Z/t8n/Fj/+LY13xgnCAnsE4aJDKcpo+idce7SKXY5JDVAHNlHN0J773GM16PFqe4WFYMi5UosDu8zpRkQrLBD5CeNOjaSZIyLBBVfvXjXrO1XmQUVr8OecIfPWgH7u07SGbFY6rPldml6FJa/P4UqZ3VYNlQtsO+ec3dZ5CMrQJjAz4plgizlzyFB6rFhdbsQrPFfe4oxzTCcPOOUe4xj5TD9xwWnxjckGV9w9nizskSoNqCmaMaYhOeWfkCqTeW9IJk0azojf2rpMlunfG8YOlpAEdoJhKC45QxKlZjREYSiqYkKCwBP6xFCdBsosmqOpa1Exli6tvMtJbuNO4a62AXNmxFAqLnr7U74BVMwJdWvE0PL/FAmqbEdESuAZklzp3y1QXHCO6E11DhvuCYtWn1VrQqgM7kfz7Hs7eMaIJatIS/YZyu/AcivmmCuVPRbs/gzysmj3SOSTSGWQS4Etci65B9iGnDkoUyU4bYUafCtycgWeASWZUBE5fWny1ckZPh7c4UEq6MiAwlSt+biDYxmP/TtSA3CVia207yRB4Cg5y44tiZR154RIOdNFLJ6Bcd7OeJdJ+VaGguEpiTQhano8U92hUIrIDwKccowyQKSanvS+wn0+WX0dQD8oQotnAGrehLVqD3NiIAopspJSnU6yZ70t9tMal919PlG9TqRs3l+4S64ELwT3+QH/AXUnpGjqjMZUmZwpn5APbRjpk4WJNiXtZBVKIqGTByyaA2xDsmENOGtHnHMP+WhwhzWrR1VMeGV0mkvO0TQOzqInHdbtjm6TTnfsg6SCxKCf+fohsTTtyRI5TWtEr10kHrgMJx6DbsBg5GMJiVIGPQmhMtjKinhGzvV4hUBkVEWiZct2h7rIOW/3qYicwMj4v0dPsWb1Ocod2jLgaAq9HcrH0Xy6IPwobnIvXGCYe7PgGO3y1CSow0mJR1mNV+IVhsrCM3JaMmDZVHwtPDeF2oQsmCM609e+N6lSMFKuJQ1eTyKuOB5jZfMwnuPGaIlr43ViaZGjmZ8nmQ4AetStsXXYoDcINJtUZLRyn50p0etuOkdPCl6JNhhKk/08YKgM5kxJf7po7CU1biTzfCk8T08GvBydYidtcJhWOM5KPBg22I1qbGU1bENymJdp5T57aZ034xUO8zJvxkvcSRZoTWMJb4eL7ER1ro3XZnSxtzP+fY3e+74OQ4I1MSgcJJhxzpvDJQ2UTQzSYx9fgdeTPJoGmNyP5tgo6f7z7ZHODX7MfhyFLrKoMA49TPTEmTMH3E0WuTeZxxUpr49W+VTtOl8cPqmp1d4RuRLcHzQZ+B7b/Spz3ohv7G1gVyOy2EIpg1tpk3N2m6oZTg1INt+ONCHpcaz8dtrgIKlSMiM+UrjFSVLkVjKHaUjmTJ0P0c6L/FHkcT+ap2JONA4v9xhnLgdxRTMqxlo0dRIXqTWHRIlNNZhgmzlpbpLkJnFu0sp9SiJhTujdqzHtpshpRkY7L1I3Q7bS+myHO+0c05cuc6bWCWhQjZhyJyV30yZHaYWSGTEy9WJ5kpXpZAWkMiiaMYEZU7RjNqwuLRnMXJkFI+FaUmTOGpIrwZuTFewpm8JBcr54hGvo09hQ2nw1knzAE1yPOvSmeRUvdzdZcbuzaLxEmZxrtHhk1TEMhSMyric1LjjdabfD47R9wlDp2oBAURUxPelwmOuksrbMuejt8yiZ43n/ASWRcNk5YShN7qZzJMrkfOWYOWfIObvNVlqlIcbUzYgzzhEFI2HZmhCpDp3coyJivpmVOR8cMso9hCFpvF3s/b/l68NbGe+IBcKM4dRnBxx+sILXldy7d4rKyx6NnsT/qUMeqUWSisnXO6f/DDUKdHjO3/7IFzRtShl8zdW0Kc/OuBsv8Gq+wfX+MpaQXM032Asr/Hf9j7NzXKdcCjHFRaSCT6/f4KXSdb5cvMRzwUN8M6Fhj7kxXOIHGzenJqO5P0WAAm0Y2h5dpGJOeLm/iW+mVOwJv9j+Ue4ez3GnN8dSYUDJjnFExtGkTNmZ8LNLv/dnCFWPCVMPvDlGuce60+Yzcy/zhf6TPFd8SKQctqImT/iHDHN/ysrUFOZh/p3A3jvJIrG0uTpYpyQm3I8XmLc1T3KUe1zvL3O5ss9hXKZsRSw5fYShU7aP0xL/+dxXWJgmXg2lzbKZ8CDTwbUf8XU773EmRMHQ/2astHszVC4lEWn59HCFWFmsVb7NVtbgle4GEoN5WwcNPYznuB51+E+qe7we3OesJfiFtMCLwV2estUMff+8t01nxeMLw6f4ROk6N+IV1qw+JSMlNUxsJBKDdUsXnm1DIqWYgWG2kia3Jsu004J+ndLBMzL6uc/frb+KVIrL0zalDtHR6VnCkFr0BvSkpU+RQkc4nvf2ecFtYxuCUOZ4xvdwIH93gfjuQ5og9lukxQrN6wnHPQd7rJjMC7qdCuZEMFnJsETOUUcj6UUKmaswI4ERGgQi4Y3OElIZKGUgpWB/v87xfJlJ7nAwLvPBhQe81l2lPZ4G33QdnNqQk55O9L47muei1+CKv00vD3gwajJfH1KyYu5H85zxjmfmqm5WYMXpspfUsI2c7UmdgpkwSDwO0zLrpQ7H4yJz5RHH/SILwZA4t+jGAQ+6dVYr/akpSJu/bqdFztktejLgUdLkOCkzzl1q1hhPpjTsMRKhdykPboQr3B3OU1/Uu1bVHDPMfdbsNjtpg1BqKvdV1jlIdeitQBFKh1haHIcl9t0qk9xmL6wS1BKNfZcmtsjZyooIRpyyfG4kGR2piVntvMiDzMFB8sZkbRbx15kqP21ytpImHw1u83qs5dP9zGcnq5Iok6ozoZ0UyZWgZEZsT2r00oDXg/tccTy6eUjR0oldu6JHVRg8yByuTjZZtPucco+5kyxwkpW4nixNU856XIsbeCLFNjLt4RARiTJnKPpUWbgiY83r6mTy3KWvAl7rrfLh4i08I+XqZJPTzvH0teRE0mYnbdDKSlN2RYIndPZpIGJMFN+K60TKZs3qvG2797tU67c4DAWUCogckoqF8nNy18KMFJXihLYbYI4FjsgpBDprYmj7IEDaCmytn18u9pHKYBw72FZOUJkw7wwY5R41b0IsLea8EZkUJLlJN5hW7b0UISRLXp9lu8t+WqNujljwhtiGzs5o2sNZlP3joqdnJDQt/YDOu0Mq5oSTpEBgJdSdkIob0Y18SkGMZ2b4pobR9BOPshNRN0MKRqbZEtNaRklELNvdaTEwo26OaJgj7rFAIOKpeMlkxdVXrDW7rVkN0wmbKou6OZrxGnwzpWkN6bgFmvaQUDqEuU4zn5tmhVRt7UlYdnVcXzcL2LR0kK7OypSUDEVkjqeZlDECOOsdsmmf4Bk5DjnJFCe9aPUZKFcTsQ1JzQpn1yuAghWzYA+omGOOnRINe8xZS9DNQ2qmplQ3zDELpkAqxbIZE02lza9l66zYWrq+bnUQU2Teaed4GmKcM57WQyJl4uQ6lHc/rWm8nrRpTuldJSPifOmITWuEDaTeDtVpsfEwq8yk5SUxwTFyjeybvs6CkdDOC5yxu+To/BJ4+0rKd08Qb2EYEvJmCWsEvbMWqJTJnDZole2U9FSP8PUaJTtio9Zlq1tDWQqroesDzraDZyT8SPMaXx+cpV/0sc2cm91FHk7mWHAGPFP7Dq7v0bDG/naDQjOk4CScdEv4Qcx/1Pgqnx9fpG5q+2/THfHl1hOsFXr85vZ7+cz6Vfbi6sxcdZRVuDZY4+nyDme8Y0Lp8CPNa/xh/wk2vRNusMT75rc5josA9FOPcOLQGQfM+SPuJvPTFC9t/trJyrSyMs97O3zE3yEQJke55Ceu/zTnai1aSYndsMrT1d2ZVfzNaIVOVuQwKeMKLdjpJAWuFHd5lDX5G42XOcwqXAl2MJGs2Cn/9e0f4pn5HW4NF/TOaGbEcp5+6s10DlfjRXaSxqw6n2NwwTniBfeQ/6n3LKfdYxJl8t/sf5KKPaFsRQhDg3bW3TZfbF2ibE34yeZXGUuXVyanMZF8Y2uTC8tH3DQWuVLZQxiKl7ub/EJaoGjpgvLPN2/xWqz47PAUtpHhGDm/eu8H6fQLnF1s8dL8Tc66h3xh9CRfa5+m6kywRM4z5W3+uHOWF2vaWVsxNWti0dIGtF98/dP8/JXP8c93XwSg5mo/yefHZwH4F7sv8B+sfIvdpM7HSm/yzennP7f/JKcrJyy4Q6QyuD+a47naI/5m5Vv8o5MPc3OwyDO1HZr2kLdFtQaM7yUR/N/geIcsEIrcs7AiRe4b4Oh2kttReFZKR/mI1GCYegRWQuCkDEsZKrZQmUHSeBzOmuKbCcuFPt04oFoZc8pvMWcNuRctsD2pseAOccycxbUO49ghTG2aNf25nnR5j7fNa9E6p+1HALw0fxNhSFa9LqtOm/Pe/jTSTmc3UoaSiJi3BlpbkekHNcxdLlYOudlfZLPYoWxNsIRknGmzz7w74ozdYtnKEGi4SUdqGXFdCB5kFp7MAcGPrb/Okt1jzW6zVZyjao5Zt7Q68YJ7QOKYRL4O+j3MqngFTae6Gy+ykzZ40t3jMK9QN0c45KyU+qx5XTa8DrbI6KYFKtZkelrSAbeXnUOecQ8ZS8HilPzmGham4fJj5ddYNhW3UxezeY1cGazbHXKM6S6eEhYdLrgH3IqXOecezvI0r6zu8UJNW7wX7D45ghW3y4vBXXp5QMMc81qseI/rsmrdx8SgJyUfW77LXq3Kh2t3eJ+neRTVYkjFnMwMV+ecQ6jDi8FdnWwmklkb1EHyl0/dopMV+WDzPk17yHFaJpI2Hw3ukWOwO1/n08Xb3E4rVMWET5Su6zb0ksW8PWDTaWnDn9vjueAB65bPh8p3OO23WLT6nLFbb2/i/zsQvfeOWCCUASLJyV0wJyAsiRVq8IswFKOBj6dAYjBIPBwzx5iYMzCtEhoX30kLCEOxPdJSW9/OsI2cXh7QSoqzuLeCrYVQ/bGP8BSmkOTKYCud44yt6wyHuTkLWrkfzXE52KUgYg2ZnXISUmVOMyoEh2kFT6RsRbrVFU+pz+1xQMFKkK4xs0hXnYl2joqUwNAQ3KM8IZwW1B5lJjtZfabYu+jtczNaZsXuUhYT9tMaDXPEnDXUOzxyJoKKTJuGGLOX1UiVxXbcYNNpcZhWZrmiZ4otjpMSa16HfhZMxVoRcho1lytBYEDT9DliQk/qIN0TmTCUJqsWBIbOrbjgHJAqk4qIiZVJpLQSc9M50UXdibZQ/4D/kKF0yJRJ0dREqoKIaWVlApHwlK3YFT0WTMFnh6dYte7TNAuMZERVwMdKNzn2S5xkZSoi5XbaYM3q8Yz/EM/IyDEoTfkS1SljNFd6wdLsSJ+yFXGSFVmye9iGjkBIpaZNSaV4rvAAE1g0R7Slj0NORcQ8GzykLKIZZ6IqQhbNMQ8zuOxMOG2fYKKYM79HotQ7eLwjFgiEgTmIyd0CXlshI0vrHgo66FalgqSsRTjdyCdK7GmFB4xMPxieSLk1WaKTBLhmxiSzGUTurNPQT32azph+6jNKXAxDEUc2eSAIU52w1cpKbNraCmwj6aQFVp0OS06fofSoSgdT6B0yMUyq5phkynAY5d7U8GXPZODDzKXoJkwym0LhO5kJ28MarplpVJrKKRo2uZFQFYqhzLXDM/e0RdzUCdxn3aNZDeJxzP3V0QYr1S49ZWrSEYLSVH5cEhM2nIyDpMJYulMrua4DDDKfTAlGuaft53GB2LZnYTNh7hAqiFXK3JSYnaqcwDAYonHziISerJBM0fT9qQ06ECnj3CEwYspmhCuyKVpfm8DOFY/pZgVcoTkToXR0G1UmVIWBVArbyDAxZozL3WzE3XgR28h4LnjA/tQN25Y+nbw4A/0umjrJ3UEv7N407UsqHe68YA+mEQCWBiBLm0Hmc5LnCOAwrZI6J7RyTQ6zjZy+dGllZXpGwLw11Ma1vIxtZKxZIXdTnUVaFSGC6G1P/XeLlG9hKAPyiqeZEJkiqIfkbgWRwpw/5rA/jxnDZqAx4xKDVx5WUX4OgUSlmkbdtEdUrAlfe3ia1WaPgpuw5rQZ5B4Xi4eYhqRmjSlZETd6S8w3BjT8kElm41uahVAXETtIQmVx1j/mK93ztCZF3t98yEeD2zN7dmFaFFyxO3hGqoNzREgsbdxCqiv7SYHATlgKBrhCT7iyFbFe6rLpt1kzJSlwkCf6AUJRNyNCaTFvDRlIjzlzyJf6l3gU1rlc3qeVlJAYvKe4zZrXYcMaECkxI0C3ZUBDhAyUS5i7rLttVqwejpFTFTrsd84ZEkuLQCT0M59VV1+fQulqXUHusW759GXEfp6TKx0k3JdaLxEYcoqZnzBnjomUSWEK5NX27AF30wYD6bHk9Fi0+qxZqXZlDhf4SPOOlk/bPQoiJlEmgaG7Fcumzg/pSUlVwG42YtUqUjHH3I8XeHW0zl+tX2XZ6lMVGQVDp4zNmWPqQp8kNqwJpqER9YHKMQ3oyZT70RyXgn1GuUcn0xGGgRmzarlYmPqklbualaF063lOhJxzDzXtXCTkyqBhjlk2E3pSzEA8BSFpCud7mPzfr6foL2a8IxYIkSis4wGFfZ+oLkjul2nuSbx2xt2TJqqR4Ox7/MbNZ8hCCwQU9wXScjR/0oLfvvg0B6Fugc7Vhhz2Sth2zn97/yXizKLbLyCExLJz4omNygXWocNhVSeEG4ngs4Ziu9bkam+Nc6UW32qv89eWX+UP1BMEIuG/2v1hHJEzylzakwADfQVKpSB+fArplLHsDNvOCUcuxqHH7WKOUchwvBRzegz9drZG8/JQC5vygIfx3CwLY5S7HE3KjDOHBX/I31v6PP9D8nGWnB6n3GO+MTzLXlKjZo35pcNPULYms+LkMHOp2BG3e/MkuclJr8jZxRYHgzJFLybJLDYqHa4+XGd5vkerX2SuMsK3nwAgTG3C2EE88YdccPd5M1rhYTzHKbfF3ckCnbTAp+qvI5Wglwdcky6drDBzTQ4zl1HqUnUm7I6qbB/WKZYnfGrjJrcH2pXZi3wWCwMS+SSPujXONVo8721zdbJJ5O3xq/d+kI8t3+VjpZvcjRepmGN+snzCzWSbf7j3w7TzIr9499P8ldXXZ8K5S8E+t8NFGvaY35EOnkhnbE9H6DzXTujzu5OLPLV0wOG4zOnKCZ24wAX3gIH0eN7d43oyzx/Hi/ze0SVcK+NcUcNiXJESSmeqg4j5Qlbi2mCVX1j5f0gR7Gcur2Q1YPutT/x/B8xa74gFQlkGau+QpLxIaS8jrln4xwlxzSLsBFhdi6SmyBMT58AmKyiskfZwGJnO9BylLg9357B9/RAmoUNmSxwrYzj2UEcuxkrIpOODNMCWuG2D3BWIVF9Tbu0vcL50hGdm7E2qtEYFUmWSSUEoHU4mxRmwZTDxqAW6OBanFr1OEdtPUS2X2LNJggz6NkKC0zZJXEkaWwg/ZdL2wVI8mMwxcnSi9kFU5lTQZpD53Owu0g19oolDWLepriRU7ck0PCelaoVc7y8zTl1KTkQ/9Zh3R7TiImU7Ypi5RPPb0UwAACAASURBVJlFzZtw3G2wH5QZdgPSkkmSWMwVRhgnDv2SR5aYtPpFbVAzJZPEZjJ22EtqbNotqmZIIBKG0mOQ+TwcNOiUiwylx63REgvugMBM2IuqOrfCUByPi6wXugR2AkObkTQ07CX2KdVC2sMCvp1q7N7I45FVp7PisWj3KRgJnX6BvVqVY7+EbWTcjxe4mWxz0dEk8hvhCoPQ49Z4kU2/zTDVqtROWmDTa3NrvEDRShimLsPEw7dSjodF5ksj+oMCUW6T5CZbgwa9ice36qem6suQnbTB7XCBfuzh5Rk7Zg3fTBlkWunqmhpO1EpKtKIi34w2ZwSwR3Hze5j837fH6C9kvCMWCCNXICVuXzKpmbg9SEsW0jYwYgGrE4x7PioVGAq8E0G4rLBCA6enORKZEriFhDi0+StXrvLZG+8lH1vIoqBamtAWATIXiNDEGhkkixIzRvs2Msh8qJVDemlApgTPV7Z0S8494D0r2/zLk/fTn3gzfoNdzam7Iff7DZQycIJp+E6QY/YtcqEgyMkshZgICtUJUuqFyGlZJI2cR2Gdhj3mtHNMvaYdhb8+fh/nq8fcE00ojrWAK23w8wtfYT+zSBC82Njjl7OP86XjcyxVB0SZfhszKfDNlK1Rnc1Kh4odcbc0T+CkhG5OraiLpHcO5pHlDMNQrC102T2pEqcWlpmQJCYyNdmd1NhxGizaPT5dfo1vTM7w8eqbLLir3I/mWXL6+GbK6/0Vqs6EQeoh0LkVO6ZOwHqhvsWd2jzl4oReomEvzza2+dyDJxklOgzINCWGofjC8ClOuce8lq1zdrHFh2t3OMnKPBc84NXROv9w74d5urzLzzbu8jMHz7BYGXIQltkdV0lzk7tqDt9KaccXZ1fGsh2RTPNbK37ExcohR8MiaW6yXu6yO6ySZSb7cZU4t7jaX+MzC6+wNWxQcmNOl044mFT44sETxKlFLZjgmhmdySolN+alhTf5P4+ewREZnpm9bez9u0KptzgMqciev0hcEQzOSAypZgyIpy5vz9gQbiX6U8yHpKJIKnqB+P+yIh5zIo7CMiUnIriUctAt0zzf4rhdJvAT+lfALqQIO4fY5u+c+TKRcvj9k0s87W3zA5tb9PKA29ESf2vuq7jihT/Dbyg7MTVvwoI/JJUmnbmAziTAt1N8K+XWoyUa69rCXPdDTsIC3vNjbDP/1/Il3utvsVPWysBeHjCWLr/WfZb3F+4ylD5/MF7gv1z8Ej/d/CN+s/ccFUvDYgMzZieq80Jji05a4P6gyVMb+7yvtsX9+hwlSxOpvy3WafojSnbMJLcJ7ISmNyKRFtTAMuSMM7GV1tlKmzzrbTFWDj9YukFJRCyYCde8Ji87Z4ikzfurbV3glA4vLd3Smo2oxoXlIzYLHTZ8HXP4T978ED90+gaDzMcWOZOGjSMyPlG6zp1kgRW7y0vzN3mf95CKSNnPA/5q/SrtvMiNcIWfOXiGX1m6ys+JjHlnwF5cY8np0899ataYe+ECFwv7Ond0qhwd5h6lesSXW0/wRKPFojfEN3Unq+sGfKb+MmPlsJXM8SFvj/3FN4ilTc0a45Qzvto/R90Z60Q3ZdJNAza9Npe9HdYWOwykdrmu2F3+5fcw99/J4x2xQKQFQeeShz1SrP9eRunndrleWiOat+i0G6hXKngh/P2//jl6eUA3K/C/332W+fKIhjemHen25mvDdcaZwzdfOY+9PCaJbP7Vh/8RAK28wGFW5Yx9zGvROncnC9TOhYS5Q47gjKcltst2lyfLB7TyMk13xC98+8cwhOR3Kxf53y79zwB0pMOcmRBNkWlVkWGjsW93M20OmjMnXI+XuN1Y4rngwUyiK5B8dfwEHy7ewkTxIK3TzovMmQMa1oh1W+sbqmbIWDo86e7xt6/+BJ6T8kfFs+wPyjQKIQ8nc2yN6vzzs7/BUCp60qEqEnb8MgvmiDlT8aDpUDAyVi2QNYVrWKTk/JKQ/HDlNYbSw0TRy4NpxoQukvakg2fk3EoWCKXLsqVNUSUjmv5flmjl8IfDC7xUvg5oZqUzjeTbsBw+O1rkeW+bUFqcsiVDqTFxdzYX+Uj5FqF0ueQeMDc1Xt2IVzjJSqTK4uwUWHs7bSCQLFt9fvHupxmEHouVIT8nMn5p4XVuJBPuOnOUxIRI2TztnPBNu8ML3j6hMlgwBSYGOQobk1wJPlG6zh+FT7DpnHDoVWaO2jlzyP/afZFY2toH4qQ8yvTDmyiLgojZtFtEyuYwq/IedxfbkLwWrTPKPSrmhKp4m3bvd81ab20YEqwQnJHEHmUMYg8jEdhDg2ji4Ofg9hX7aZVRroNTAEaxyyh2STKTk1KRfuIxTD2UqTSyLTc4zEpan5DMcZBWSZXJ3ckClpC8Plhhzde7++1QI8xsI2c/qjJvD9ge16iWQ0YTF8/KSKeJSp6R08odImXRkwGH5LOuxp1kQbMmrAHD3OeN4TK2yCiJSDscMViye7TzIlURYhsZBRHTmfIyD5WFbWTsJg3SqZfg3HyLw1GJijvBqCji3NL9e2+EAJqmSUWkpAqqYoJnSPZzi/2sxli6BMYjdvIiVRGRKpMPl26xkzbYtFscZtVZdoiNZChtTWMyx5yxW+xlVYbSpywijvMSkbJZtPo45CzYA55zR/SnAbo6K1P9v+y9aZBk2Xme95xz7p5rZe1VvU33dE/P9AwwBGYAYSEJGIAJWpRkygrKlulNtCVLpuU15DVEy7QiHHRItn/YkinZkkOyRJFBL5BEElwAEiSxDgeYpQe9TK/VtVdl5Xrzbucc/zi3coYShZlBwMExgzeioquyMm9mZ908y/e97/OyU+Wc9Y/p64jr+SZTu8NzoWZfu1ZvW2Zc8o/xheFulXA1OOGsN+SVYp1zXp9fnlyj20w56w04NjFdWfHPn3mZG9M1dtM2K8GI68WMa0HMstpnYOCM8jF4PBvusKpiJIITM2NsLQpIrabjpdwuVvkD8R2mNuCZxh4DE3DeKzHApxdfJZE5z4eCO5XLMt1QiiS5xbLyiEWAoWBitmjLiF/NfP5Q81WO64Dh1W9DB/H7Qqm3cVgB/sxQxQKZluRa4Q8kVoCpBOGxRRg4KlvcHi/T9HPSYUwe+QRBRacx4zBrsj3s0AgLghNJ2RaYyL37N/INfmt8nq4/Y6dYcEAWb8wL2Tk8YegFjr3wzYGrgp8UMTfTNSqrmBU+2ShEt13oa0sW7Okmh1WbSLrW6EAn3JxtcDY45teHT7ARDebL7f1Zi1veGtea2xxWLboqrePdJJn0CYTmsLZSR9Ih0naLDgdZi36ecL7ZJ5AVk1nISZS4jNA05r7s4UtdQ2NdOO3YCqbWZ1WUvFQskRmfLwyvsKxGvJY7uG6/avJMtMVvjq/QbU3ZqzoMdDJv5Q10wv1yGYL9uchsbGJ8UXG3WGEr6/HPdV9ysXzZIk0RUooZqdHzrMzMeigMA5Pwa4MrHDVbnPe+zt3SdZmmNqBlM6bWY6/q1quT0gFyheGLxxfpqBnvi+/R100aoiCRBRfiYx5Nu2znC9wOlllW+6yoBpmdoITgSLv09SPt2pwDA6lxnpSH1QINmfOgWKq9Lh53ygUGJiESe/M81p1ygQO9zd2a7eBzzDkvYWQyUuuK0h0ZcGJmvJA+wXLrFRZlTmYVw29nu/D/9xWEEOJ/A34AOLDWPl3f9l8B/xZwqi39z+vYL4QQ/xnwI4AG/py19rNv54XkbYksAU8SeRVVw+JPhavkSNC1IWsxTIlVgYoq4tjtI6WwSGHxlPsemFvBS5yIaCMckhuPlsrIfXchLkZTlsIJK8GYVAeEdUErkHpOR5LSgIXQq0hkSSI0LZmRKR9tJakJMUh63oRIlJyJTuh4rvKfyBxPGFZDF37b9VKOdZMFb8qy5+zOBme0Goq4hrFYQlk5grVRNFXOVrWAMQIlDEoZjBU0vKL+/0kkBm0ty1JS2pxEKnpqwp7tcD7qs6Im9L0hXZlilGSrXGQzPMGvVz79qjkfHJQwbPp9J13HIfLGJiYSJQpLrMp5utZB3uJAp5RARyo6KKBAWlubpXzOxSecCfqUFnoqRWK54J1wqJ1tf1FNGJuIUijWvIETHQWzOhW8oiVnaAR3smXGZUSpFevBkJZ0Cs/MTjjnNblTTggF7GjtgDfGAg71v1PFnPNOyKw/V4z21MQxQrJV1jzXPXlxcp6VYMzASM56A0rrMj6vl0Utr3fMi2NREghLR6VsVV0UlsKqbws5924vUr4dA/vfAj79O9z+31trn62/TgeHp3Cpwtfqx/zPQgj1Ozz2tx3Cgj+1+KkBY5iVLtHbCsAIdChQhRsgjBX0i8Z8IKiMZDRzFfTQqyiNREeABlEItHUsydx4SOESo3cyt1UZ5jHGSg6KFsdlg0iVGAQGZx+fVT5KWGSoSUsfYwXZm+TISjh+g65vK6yTcg+rxCU9mZDYK5loxxXQyDnmfqwdV/EU9R7Wq4eyViZKYerKuCXxCnzfDVrWCgLPQV49qWmJikgIfCHoG6dYPNIOolJaj7wOv8msT4lTM14N9hwPoX7OJ6IdN4CpdO4IjURZMzhDjJW/Le27r5ukJuTxxiFLKiYRgtKaecp2anz6NZuirJO7W1JQWhctsF212fDGLKspA5PUIqfMrcpEiSddXcBRrmIioXkq2Zknkg21gwOfUT6rKuROOeGS35wnrreEZFm5Aa6vfRJZMrIh/crZuLtyRl83eTZ8xEebNzmrJvRkwQdbd1jwppz3PI5NzNT6KOFCm58NPB73PZ4MJFf8iHXlalfPBEec9QZcDQ7ZUG+oZd/WYQFr397X79LxlisIa+0XhBAX3ub5/gjwU9baHLgnhHgd+ADwpW/5HBJ0IKhigW4GLCV9jljCBBC3MnQYUrQE68EAU3sOvlQ9hhSWTpxRGsmF5jFB/YE6FCuIhQKrJZvegKkNuBrvkJqQs8ExvtDsFl0utI5ZDCZo6/iNy8GEK9EumfE5Fx6TdgN2RlcQypL4JauqRAOl0ZzznM0X5RiGrlhZzFkBi2rC9fwMy9GES9EhPW9CQ+a05Iwb+QZnazJVaRXHopzTnhoydwpEv8mwZlq+fLKJFJZAagZlQOyXrEZjKivZ8EJKq0mtpiUFU2voSMFl/5g9mdGQOWe8GYl8SEtUjK3Ha/k6G/4AY2UtSS6JhHvvlr0RiclZlrmD0KqUlppxwTuhIXOmJmTDG7rZVZacmIzCWlpS0ZMWaSrOB5ZXC5eVmSYPuBrs0ZERPem0HEoYDnXMRS/lCf+AsfUxCCLpVinvaz/kcrBHS5SsqSE9WXEzXaNfNoi9kgVvynuDIwweR7oiFLBbOcblw2rCpkqo0JzxFKXV+ELR1xNerL0j29rRx7aq7tzD0pCCB8USy96Y3FZc9UsOtUQCDypBQ6S0pFNn5hbWVcCz0QN2KjdYRaIk8WZv82PyxvF7uQbxo0KIfxV4AfiPrLUnwCbw5Tfd51F927c+BMRHFdN1D393xNagizeR6NCSncQsH1uqCA7KNl88ukjDcx/+vPDo18lPD6cL3DhYJQpKvFRQHoWgLHu6zXbZ4wsnV3iuc58vTS6zmzko7fX+OlVXMa5CGl7BvdEi0VrJft5mUCbcHi1jjEDnikkRcLdK6lWM5H65NA+41QheyzbZ8Ad87uRJesGUzfCE7TqA9yvDx/ho9zZTE1IoVyTNjU/Pm8wNVLtllyVvQmY97s2W6RcJgzzm9cYKC1HKzsgpK32lGech3zxZxVeaB1Xhug9S8qBShELTN3CjWKYtM355cA26cDtf5XxwRL9q8r74Pn/n+MN8X/eVeRs18/v4aHaQPCwXuRTsE4lyDonJTMBW2WMr66E6rzE1ITt5l0QoFqTPvp4xNhKQfCVrc8EfsFO1+PzwKltJj5Z8iT2dcCY+Ya/qcjXYpW8UX8vO01MTznkn+KIiEprf6D8OPXhf7NrMpylYF6JjjvMneT1d5St+vy5IeuxozbIUPKzcduNeOcEXjtV5Wje6U65w1u+zXS7wTLTFTrUAomK77NGVKS1b0JIZh1WLvjHcKZ3hzxcDlmVFCRxrV6RuScG+LviN6TW+t3FjzuIYmrdcLP/jl/27fovx7Q4QfxX4cdwi6ceBvwz8yXdyAiHEnwL+FIDfWiBd9fBTS7HZYbOzx41uh3hfoi7nzFYCwhNLR834+PItJjrktWiVJHLW78pI1uIx0XpFYRRf7yxg6xXEopzSDVMaizn7ZYcPNO7wqjzLRIdc6+2yHEw4IwwSy7m4z7ngiKOyyVPJDqWVHEyalHFJO8g576UooG8UT4W7ZPWWwheaTzVec4lNPbds76opPTXhqGjywfbdOVdBCcMzySM2/RO60nEWjnWTNX9IYRWB0FwMDtmrOqQm4Kzf529tfxhfaTrBjJM8IVB6nnJ+Rrmlf2pLzngVjyqPDVVxNTjkbtnjk93rfDjadwnfoiINFDeKVb63fdO5EmtWwinTUgnrIDUyd7RpWbAmBlzw+7TkjMvhHptqCAoexYuU1jAwM7rSoyEMA2N4PhzyStmgJTM+3rnB1WCPC55LvHo0W+ATresAtKTm+egBEotBMDUhU+vx4YW7fDi5TVcWBGjOezN+3gTcmK4yq3yebOzwwch1K470jEgACDZVMkfqa2vIbTWPw+vJA/7++GkuBgeMTVTnXhS0woyLXoovXIrY+eCIdRWQiBPGVrChFDta0xKWs56ktDC1hnNeUg8OJanxCUT1zt2cv8vbh7dzfFtUa2vtvrVWW2sN8Ndx2wiAbeDsm+56pr7tdzrHT1prn7PWPqfiBsHEYCXIXJOWAWomsAqnPjRuGwJwXDYYVAlV4ZGXHrPSY1b4eEKTac+JfQBrBGiBRpBZf77/HOiEoY7Jjcdh1iQ3HidlwknlouUAQumKlYXxkNIgpa1rE1DWrz+rawe6fgsHJiatbeCns3JfO1DMftmpg3B9SuvR1w3HkqiPoO7Fn/5b1gNPaTznllTVvAAbqorQczWV3CgMBrfxgdK6PMnCWodsFxUjHTE1loGJSK1iWrdRDyvXspyagNSElDjDV2q8eYiwLwyRKOevyz3HG3PK6fJcAaU1ZNbU7417nK4/9CN76gh1jxubCI1AWxiYcM6QbMuMqH6uU+u5LzRKuO1H0yuIvdLVd6xAIlDCnccXkgrXmtTWoGo+ZDl/TW9cewpLZv3aTRqihEAhSOr6kC8UJc4uXlpXOPaFQCIJhUck3P+7QBHVDA1fGPxTJv47OH5PUq2FEOvW2tMgwh8EXq2//wzwd4UQfwXYAC4DX33rE7qCpPEEVkmMFa5zEdr5740v8GvHIIDyXEU/9iuqeuT2pKHSygXneAYroFFX30/tu1Gd6ZB4BYlXzilIC35KZvyaa+iq+xJXFFTKoITBr5/bF4YWDlyaWWcrVlhaspwXIdsyo6tSloIJPW/KWd85UQOhSXU4v5/CInHOSHDFT1nj/E29z59WAYnvchu0kYSqIlYFw7KNRFLaCgMo4T5ogRB0ZYWxKWMV05CCtsnpSU1kNTd0Qs+b4AvNpn8yT6yW4rT74IbBrFYjttSEqMbABeaNwSI3PlP7xqzpC4EvHPxmS8OyzGmpGYtyRoVHSwpWw7p7U3cIltXMUbiNpLCKzCo6KqVVOycjoSmsnRuv2n6GwrKqJCfGdTJAsK8rznhu4MhtBRYSGaCs+2AbmdNTE9a82rovXWjP1IQMDCTCcC9f5un4EUd6RkcqJM4ufqhjxqaipwq0hbH1UGQsS7hbOiZlInIScfKWl/o/cby7FxBvvYIQQvw9XJHxCSHEIyHEjwA/IYR4RQjxMvBx4D8AsNZeB34aeA34BeDfsdbqf8qp3/QkkHcl2gcTSNYaI0xgkaUgDEvqCR0prOMXxo6m7ElDwy/ohNm8q9ENUtRMIpVFBRq/rgL1vInTP6gxV+MdDILNeEBLZSz6UxY8x5t0F6+b+VbDEVnhk2fOxquEm7Eyq1DC1m2w0zQqtwxtSCcSaskMiWFQJnRqlqNBMrUBHS9lUU7rzoaYp3lPTUggNKb+s5yG5Y7zkOEsYlKFjIqQUR6RG4/KSEo0GsvUWCROh+AjiIQLezmNn2vJ0s22CI6qtptFjc9AN9wsip2viiJRUVjpDFommq8oMusxtc6QVSA5KRPGRtI3HiUuwNetYpRTaQrqwdmQ2YrUugxNgIFJ6BtFV7oMjqkNGNd8h7GJalaGx9R6lJa58WpURrWrUpBZS2o8ImEZG5dy3tcRJZrUluS2JBQ+qS3q5PGAvm6yV3XdwKAbjHXM2Pik1oU4H+sm43rlMDaS1FqmNmBgYqZGMjQ+YxOQWYcEGOiEwioGJmH8DqG18HtgBWGt/Zd+h5v/129x/78E/KV3+kKivmG2KJG55iRPUKlAhxapJUqCDiEQFSdVgrGCauaRhx6zyqc0Lr5OCkthPKyyCECXsk5FUox1TM+bMDIR+1WH46LJVAcQwriK5lV0gJVgRCRcJoO1Aqnctqewdp5Ibaybrd0sZxhYj8Rqd7HohMgrOaradP2Uvm7SVtlc0nxQtNkLOmzUvXklXDzgaWvxVF1Zek7DIWqNxymxO/ZKCuPVs7BHanIMcFhvqfrGDYoBp8try9R6hLZCW0koS47r7c/YRLXWIXDLeSwDE3PRHyGNWzlJYSjf1PEYG9fiW/BTtz3AMDaWcT3jtmxJaRPGRhHJwj0WRWbdgLldunTwjtQcajVH0wMkMp/rCU5hL0o4bUrsub/PWEdoLKeP2qlipwQVilZNk/KFRCIZmhkdGXOgp+wWXa6Fj9irOhzqNgrDojeptwfWRSAKjY/TUIytj7LFHKOnhMXH4KPnq7WWmjE14Txa8B0dFniXezHeFclaQsN0VRGMLSZQKGGozuTIUlDkPlXsbN0Sw9V4l1BWrK4PeHJln240w1rBTPvEqmSQx5ioFkvNPAYmYlmmXAu3SY1b2hsr8aRmPRzSUTNWg1EtbHJpVCeVE/Fsz7ouUUtZssrNZJGwtETJknIX64NqgaHxueC56Lr3h9tO0ITlqWibJX/CU+E2vqhY9kZsVwtshie05IzUhHN9gcOiJY5WhJ7f3xcV0zxAmzf2t5WVNFSBJzX9Wr5cWElXmlq0I2lJh1l7OjhmVYWcVYaWVGx4gomO+APxXda8ARf8I3xR1XRoj8wqltWUQx38tlrKoKZSOZpzc65vGJiQvonwBbTkKeKtFqlZyXuDIx7zFENT0JPgSc21cIdIlPPEK19otssFMuOzUy6w7I0IMKQmrGdvVy+6d9KjnyW0VIaPIrVOIRmJioGJ6OucO+UiqdH0tWZickprONBTVpRTqrZkQSRK1rwBstaxpMZnaHzup7W83XosyNhtkYTlTrHCsWnUsYUBO9UCY1NL7qs2t7J19qoOff1GXeltX/vm7X39bh3vCqm10KBySzjURK894rW7Z5GRRlgIXkno3nGxfJ85fJa7J4tkhc/Hzr/OUd7g1v4yVenxfx09izgKkJXALyG4l1A14BdHz3Am6PMzO+9nNR5znDXYHbf4ly+9wE/dfT9LzSmVkXSCjJdun+XC+UN2T9ps9obc++Y60fqUIvcYE/HX+h/lA427bHonvFYs0JIzvjB+gtIqpLBMq5D70x7Hs4TVZMJHFl/n66Oz3Jiu8aHOHbpqyvXZGb589BjX25s8nC6wHo+IVcFR3qTlZ9wcrJKWPv1hA2sEflCx0nFo/UnhPCFp6fO5B5eZ9WP+Zuf9dLzURQvqgGU15aV8k5aaoa3kz93/BB9fvMWr0w2ebuygEYSy5KcHz/Oh5m0W5ZRfm1xFCcO54Ih7+QqjKqJfNtiMBnWR0DCsYkZVxINJj6sdJ8P+xYdX+dDT7hynMuqz3oi/cvwcf7D9DUKh+bP3/hhPtvdYDwY8H9/lL298nn84XWfNG/Kbs0tsFws8Ge1wY7Yxx9P9xZd/gE89doO2l7Hqj7iTLdNPY1ZaE57s7PH5wytoK+l4KQ2Zz4vQL1qfs36fvz9+GqDG/wfsFl0iWfKfLN7mxw7fzw+2X6QlS/aqLtfTTb5mHiOUFQ/HC8TqHKVVfNYqvrdxg51qgaOqxcNikbveBF9UDHXC3XzFcSPKmIYquDVd4ZXwLHDvnV3838EuhhDi08D/iKsb/w1r7X/7j/3+PwT+TaDCqaD/pLX2wbc657tigNARhGPLdM0jXlmguZgyOWwQv++YwYMu/lRRNuE/Xv0qPyvfz27a5ks7F3hi6YBPPnaLz95+kg9cesCt7jKTNMTebTB5osQ/9Hg6fkQic75n+XW+r/UKX5td5Fe8q+wWHT60cZ9PdF7jb+9+iAvNY6InS1pezgeWHjDTPuOLIYc7XfAMufb5g51vsFd16qp1xYqasB4MeT6+y14tvjluNnlYLPKRxi1uF2v8xNnP8A8mT/KR+I7bu4Y+d+Jl/tnuq9yOV9nLO7S9jLaX0VIZ56M+qQnm6pGOl/JXX/gYKtBEcUGee7QaGb1myomwfLR5k9SEDEzCyEQsytTJlUlZ8wb8yMav0xAF54NDNr0BAFtVj14dEzcyEWeCPpeCA7arBT7UuM2mN+JX0yts5wskXk5ufFaCESvBiCcbu2xlPXyhudQ7YrvsoXzXFThVrX6kccsh+ctF/r0zv4QvtIMFi5I/v/sx3tPYoqtSPpHc4rq3woNimeOywdnohMz4/Jfv+Tn6VZOjqklmPZ5KdviF2ZMMRw32x02uLB7yfa1XuF2s8qBYoqmc9PtqsMt2uTBP/FrzhvR1k2vhI1qy4McO389fXL7OP0o7bGc92nKGLzXr3pCmyviCuYSxgpbM+HB8lx3dQmLmgN9QlkSioF81OR8csdE54QujJ9ibtXiqvUevrmO9k+M7VV+oFcv/E/ApnP7oa0KIz1hrX3vT3b4OPGetTYUQfwb4CeCPf6vzvisGCFnCZEOyiAK+WQAAIABJREFU8vUZJvKRokI1Ksav9VC4JdbCLc0vDq4RKlexXG+POM4aHGcN1heHvLSziTGCchKgAot/6P5rp3vtURXxP+x+iqVgytGswb+9+av8jd3v5uv+eT62dJOjssX9YY+lZMpvbj3Ge9Z3ODpq0Vyakk5COp2UgU64GuzPi3k3ijUy6/Hr6RWWvTGZcSG0W+kCJ2XCDy9+kR/b+X6ea9/nTrnIJf8YJSw/tPxVMuuznS+w4rsQl1EVsZ+3OcyatIMZW5MFPGnwpeaxM4ccjJustcdURnI8TVhvjQm9CmMll/1jQgG5hUMd80z4iK2qx69Nr7JftvnDnRe5W2dwBEJz2T9kTzZ5LpxwpN0KIRIl14IdRjbkUDdITcCVeI+dYsH5VGTJQdGeh/Z01IyHsx6fbtyitNRbGretGJuYsm6n/oPBd/FM8ohnoi1CoTkTnvDHWrdIhM++dkXhf6Zxg46aslsusB4O+D92Psj3r1znPfEWx7pJICret/mIURkxyiOutXe5Uy7zTLjDM+EO4DpLY+Oz4Q3nWaOuK1MyMAml9fie5g1+arzADzR20dEJ3yxc8fnUA3N14YBnWo94Pr7Pjm5xwRvSkYKGzOnK2RyAfDXcJRKaX548xbPNhwzjGF9oLgRH7+zC/87avT8AvG6tvQsghPgpnLJ5PkBYaz//pvt/Gfjhtzrpu2KAEBZMAKKyCG0oK0UYFVQ6ctRqA1iYVs7TkFUeVzoHjjNYsyDKwsNaEDOF8cGbCqxnyY1P5Jcuw7IKGMmKQiskrui3nXVJ6sLWrPDJQ4+qdEVIO/MQHYvyNdYK9qou5+oQ2lNpdG4c/2HVG9YfCsesDFVFQ1SURs21BBpBZtxyWArDsIzp1PJcZzgzzCqfyCuZFgGB0pTSIe/acYYvnRcl8ivnSZkm8xXNskwJxGlR0qMrU8K6pXt6nBbbrhdrnPP65NbQk5Lf0k2momTNG87dm0elyx41uLDesvayYCHVodv3FzGH2lGpJTNAkAjN1IRIaVjzhvPW8qFusewf8d7kAQpB3xTs6ISyLjXuVx0y4zsmhHGekYFOCERFv2rycLxAUZOgUh0QCM2ebs6TxPpVs04d8+Z8iK5M66Jxg8grueANuVOsMjYV656rqwx0MneRPpp2udrYY6fqIDEkwtnX79fhy2ve0NWKdINF5drEt2erzHRQryrfWZHSKSnf9gixJIR44U0//6S19iff9PMmsPWmnx8BH/wW5/sR4Off6knfFQOEUY4HUTU8ov6UOBSkWUjZMnhTCUJQRYKrzT22sgUSv+SkSGj6OdMy5P6RC+GdpCFlaFBDD+tZVOaq+hKnK3i67dSHW8ECvzm9grGS97a2eGV8BoMgDkpCVbG8MMYTBhFX5LmPLhWTmmS9rTusqRFjExEIzUmZsBYOMch5TkbTy1kPh3x59hgXkmMeFQ6Nf6rjGOqYM0GfWJUMq5hEFhTGQwlLK8iQWDpR5robSrM3aZHmAcYKtJGUlaI/c92c09CY1HrsaOe6vFOs0FbOh9FU+XwVdSoau+Af8Y3sPFI8YKATng132NONedejq6bzZO3SKjfgyYqZ9qmscp0kBL1wOu9A7OuYkYlo13LlRpBzv1ya064W/SmHJuSl9DyZCXhfuENDlFwvezwTHLkgXZvQEhkLYcpB2aaj3GN73oSLnSPujxZ5NO7iScNe1OGZxh53ygVuF2s8Gz5iq+qCcKTrrpyxp9sMdAOF4bhub7bljLEVvJSGfDrJueXf4pV8ncz6PN97wLCKuej3eTE7w+2qdOdSKU8Fe0TCEAl4UE2JRMWXqsd5vnmXB8USq96Qtnrn2HvefgHyyFr73Dt/gn/yEEL8MPAc8L1vdd93xQAhDGRLIF816E5MOxpirYDjFmXTYhWownJSJpyNThiXEYVRc7VdEhWUWhEEmiowCONI18K3pNrpDzbDE740uMSlxiGL0ZSdrMtyNGE7X6DhuU5A5FVURjLJQqqmRHqWYhKABevZOtHbMK4RcXtVh9z4DKuEi8EhUxnSVC6y71SX8fp0mY8t3GS/DtZpqbp7YXxGZUQoK/o2IZTafQCNouXn88EhkBWDcYwxtTZiFiCVJvTdYKNrvULfOuDMTrkwd2bey5dZ9wfOjq4mc1HWK9lZ2rWj8Vg3eT4c8sXZCu+LHtKQOVulq+aPiojdrEPLd6CZqkbz94uEwng0vYID3WKgk7ktfK9ytZhfGT3FtWSbRBa01MzpKWzIo3yBnjdh34tdxqXMGRvFUMd8Y3CGJ1r7zjVrfEqjGFUxicrp5w0Gs4iqUpyEzhMzMAEDk9SW7QEawXbZoxVmb3hMjM+iN2FqQq6nm/jSKR/7VZNb/i2u+A2+OHPO01EV40nNoY4ZmZiicBbuqQnYqjqsqAmHSPZ0h2U1ojSK/bJLbnzGJp7rV97Rtf+dK1K+LRWzEOKTwH8BfG9tqvyWx7tigJAlnP2llOOnY7w05PhAEb8U0zqwJH9il4fRKlXikrPuTRcdxfi1DWxYD79a8Ge/+1fmtKlfa10iCUuKSrHkj9kuF7g7WyZWJaVx24fXR0scTxN6iXMYAvxr577EWf+Yr6aX+J7mDf6f+H0MyoT9rMW1zi5P1S3MsYkZ1OEtTzce4QvN1AQsqgk3s3ViVdLxUr48usSD0QKfs1dZCFKOS9c+PSya9IKUv7D5j2qRj1MQZtZjWrdit6uF+cX91fYl7kyXeaa1Tb9qMK1Cvqv5gNIqLvj9uciptG4701WuXrLijxjqhKeibUY6qjUJHo+KHqMqYjMccFQ22S879LwJL2bnKKzHUMf8+4vOc/eoBuWuqZyBqdugMq9n0piOzBmqcB5zl1mfAE3PcwPSI+NWT1eDE3aqmLvjRa4ke9wqVmmrjLGOuF0u8+/2vs73NG9wwZvwS9PH+VjyOkvKWdfPeCFXw11e6D3GTt7lh3pOnHveK4nEnsvdqIuuXZly0UtRYsrA1MIxYUiNz9fMY6x7Q8ba1Uheydf54iziX28foK3hG/EdElGxqCyKLTSCNZXSVSmLMqclBdpaunKfnoTWwle56MGhrmhIwYKM+JF3cuFb+53UQXwNuCyEeAw3MPyLwJ948x2EEN8F/C/Ap621B2/npO8KHYSVgLVUscCfGZSnwThxFICaSowPLc8t4WKvROgaJuMb8CyRqJjUuZezaTh/31vSZU560tALpjRVTifIXAFQaSKvRElD7JVznUSnTrPypGFYRgyy2NUBZFZbo0siWdJWGT1vwqI3IRAuRfo00q1TL/1PJdKxKgllRbNerTRVTvSmErZGOFaBSimtVzMYAnxcTWNURgyrmHEVMdUBYxMxNhENUdWeCU1UC6zaIicSJYtqQihLAjQNWdAQhUutqmsVUjh/x+lzyzoOL5EFUY3x8OsC3qnM3JnT3PcDk6CErUVKhka9vG/JgnZtNU9q3YFjVmiCOjbA1CrPpA7OMdbVR/w3vR/GOnWoh2JkHPMj107NmUgnDpNYGsLVkBpS0JKFk3zXidstWdbMjMq9/ypDI+ep4I7n4bwbWa3edNdNUa+43ng92lpOqwzqNOrQOtamthb5u+jFsNZWwI8CnwW+Cfy0tfa6EOK/FkL84fpu/x3QBH5GCPENIcRn3uq874oVhDDgnaSUrQZlIql2ExZ2DbJyQS7Ws2Qrhs9vXWY6jJG+Id6V6FBiPYvx4E62zCsnG652ICyxX4Ff8X/uv59hEbE/bBGHBUpapllAngWYfsBJq+WKmwI+qzThask303WOqiavDDZYjcdsW8HF+JC/fuC2bDPttgeRKvGkodCKkzyhFeTcPV4kCQsS31G0i2HIvWiJRivDV5p2lHM0afCqt86VaJdAaEYmZrfo0lIZUhjuzpbZy9qMiojHW0c82djh1cGGG7SEZlw252Kuv3nyIQCUMBwXTWbap+unbM0WXBJ1f4kvL13kcNZkIUyZlCG9MOXF7TOcXRxwNGlwtjsgUqULBSpDjmYNLj1+QCJzbudr9HWDs36fm9k6h0WLT3avM9IRDVnwuekVxiZyAJ4qpjJqLqeeaZ+XD9ZZbk55fe1VXplscmt/mVEe8Vj7GE8Y7o4XeaJzwDPBLi/OLlBGW/ydRx/k0UqP5xp32Su7XAgOeT7cpitTXhye5X6xzN8++TCfXnwVX2henJzng607PCiWaMmM0nokMudevsxEhzRVzv10kYfjBb5gLnGpc8z+rMXzvQeMqphvxHfIrMdHIskvpC3ulBE/338P2gquNA7msYenXSCJ4Wsm5ivji/z5FdcY6BvFjq5+h6v7LY7voA6iBjf93D9221940/effKfnfFcMELIymGZEfGAZnxfIDGbLgsaum+GWrx1yeH0ZcACZtJ+QntGISiBzgTCQyIKPLt/h64Oz/NFr32BrtsDOxO2HH28fYa3gJI1RsmI2jmh0ZkzGPl6gEdJgjeT7lt9oGX9/62W+1j/PDy7+FoNu4mZgL2clcPmWmfGZaZ9700U8abjW3WOqA7wlzf1BDxUanlg74CYr9NpTOmFGO8iYlCGdRBIoTVtlXPYP8YWhjCXHJual2Xn+6MJvcT3frGPpPC74h/yZcydc8I4ZmJhRJ+JqcMhrxSp385V5t2LJmzDUMdpK3tPe5sFskSeX9nmu84Cb/ipr4YhQVLwwOMezm9tsRgN0VzLTPsZK2t6MWMXOyCZzLngTGuLh3M/yVLhLaZ2f5L3BHgb4peoJElmw6g0Z+65IelS1GOuIjuc6PZcaDpjzyYXX2Jl2eLq7Q9t7o6C3XL+nF4MDujLnj2++wA80bzqXaHDEng55pVhhq1zkh1a/xndH2+TGJ5E5O+WCw8TpBsvemMOqxfm63fh0/Ihj7Vy868GAWJ3DWMGl5JBnWtsM65pDUhePfyFt8ekk52cnIR9s32XDP0EJwwvTi/RqTODUeuyXHc4HR/zppS/woErYq7osqsncDfq2j99P9357h44ko4sNGvuaxVcLjv/TlKHXwyrJ5LBN45WI3qHlk594lVEV019LeOHhOfygLtRpyevTZSalMzL97EsfwAYWoQU/+qmfIhCa241VDoo268GQ64sbxKrk9c4SZxtOclsZxTfTdR6LDxmVEV9ML2Os4Mdv/gCjacT6woifuPwzNEQdulvLpI+bzbl3QmG5na8x7MYseeO5jPrp9g4L/tRRra1kqBMSmSMxbFXduSGqqJe3N/J1dgtH4F7yx/y1w4+xNVrgfKfPpAxJy4DLnUNKK/lvNn/OGbOspbSum9CSBQMTcj9cZqwjvjt5ncfDPRbVhNJ6PBVvs1d2uBzusV0usOmf1D4Qx5E8BcUe6mCeMr7mDR3V2vhs+ifs6IQvppf5NzovMzYOojI2ltSq2gHpVji+0FwNd/lQNGBfGz7XeJJPda6zpkb4wnC/scBl/xhtBX3dpKtSHhU9bpYd1tSEQ93grDfiN/I1bqar3B8vsrP2Kh9ObvN8KDjQ2wyM5LznkduKvjGsqwBfKI6061j4WMbWo7SKlsy4Gu4wNq5bcahjFpVlkZI7ZcTPTkL+heaIl4sDFJYNZbnqO0ZEKNzfZ2If0RQhvzBb4L2By/toiIoN9e1Aa9/dXox3xQBhJOhQUCaK8Ahiv6KfGPKeQHoW68FsVXAhOuKgbGMQKM+w1nEzz0kaczY+4YHtkWsPYQSqnVONAlbUmEiUyNCw4Q+cByCOOSxaXGwdsxqMmOhwrkdoyYxnWtt0VEovTHlw3EMI58VYrNO1IzQtNcHULdQ1b4yPmcNW7pdLbHon3C+X+UjvDmeCPg2Z44uKsY7ZLzs8Ee3Qkq7aHomSQOl5G1RbSUu5zsGaN+TvnjyPEJa0ChgXoatrSM3hrInEmbHGta8hkSUSy3kvxRd7dGXBshQk4oBGjUz7e6PHeD6+R2EVi96EvarL5WCP1IR05Yx2XSfZ1h03cKiKSJSsqDHSM4xrlsVJ2eBmGROJCiiIBBg0PSkZGE1b5Pyh5qt0pCA1lq6sOZpyxrbucME74bJ/zP2yS7+e6feqDh9vvUZXzjiuadqFlXx2/ymGeUQrdMrOp4OSO5XmbrnIWW/AC0XAVb/kTrlAIk7mIF3fGkBwTgZ81io+HN/llWLdbVeyM4xMjGKLliz4+f57+GD7Li8XB7wniLhXTsgsfDk7z6KasKZGFEgO9QJX/SMu+zM+l15gv+pwMTgk89+hUAre9Xbvd8UAoUqXMNTaKRHastdvQ2jwJh7VoSs8xgeWlydnyY3Hq/01Hls6RhtHfBoOE34rOkdlJHv9NqZVYY9DVCG4nm/yTLTFq7Oz7BVtQlnxlcMLfGjlHt/onyFveMy0z65w25GlsMmN4SqfWLnBi9tnWOlMyCqPQGmuFytc8o/pyoqpkWRW8fXZBRa8KVeCPQ50i9SEfGl0ifNRn6fibXaLjtsCeIaul3Kz3OCkSribryJr8Y0Shsz4HJvmPNbuZrpGrApuiTVWW2MCpVkOJzT9nEJ7hLXg61HlVgwt6dA1DVFxqGOUyDjULW4XDT4cPeBmuciGN6RRF+wchXlMJDSvWZeh0ZIz7lcurMYgSUQ+d5uetim1dQ7ZhiochFe4AmVLOvv1qvJ4rVS0RU5Llnw5O8+1YAdfGK74Ad/TuUVDlHS9EzKruF8tsCidHT4zPr6o+Mr0cb6v9QoBzsh1aBJCryLSFRdbRyx4Ux5Udl4sLa1EYTjUrqQ4tgJtBRLN2EjG1mdZps5boVs8G+7QEpbbVUlRqNpyH7gUc/8EhZ2TqSYmIzM+WgnHBbWGzAQUdaL6aUH5WDdZq6Xs7+T4DrY5/z853hUDhBUOWmt8iVWCRpIzSj2X3J1oQIKFhpfTIGe9MWI/beHLuiDZyGkGOaVW7rGjAOtbjHUJVcY6LP1B2WIlGLHRHJLIgoafE6uSpXDCTPvkxnPdhVrO3W3OKI0kzQMaQcGaN6QlNZkVlDXbYckbz1cHXemeay0c0fFcKM6wjPFjt3QvahhKJ3a/i2Q5bz2eErGd5bii4bmOSCILIlVxNGvQ9jMqIxmXbtBciccsqxk96ViQAEMjWFYz5zyshVORYM6FyKzibHBc28t1nchVB/gIJwzKrI8UhgBNpl2lf0WNGdTMRWdtLul5U854FZm1tGSIbysHzxU1K8EEKBzzYkMZUltwK1vjw9EDlADfGjKb0quLs1vlIot1u7K0yrVQTciyTLncPGBLLbA76xC03d9nQyl8jokEKFEhcQzJDaUorXteIw3KutXNTrWAxHVlSqAh3N90TaVI4ErjACUMG8qSWZiYjKaMeDZ6SE8WdKWkxJLIXTaU4qXC51q4zYHn8k421LeRrKV/f4B4y8N4kBxoZsse8a4zvDRedzp5fENj24KAo7zJ7eEyS/GE3Zsr2EijmhWd9pRZ5bNz0qHTmFE98MiXDDpxFaBfHl/jtdEai2HKHh0aqmCiQx6Nu6RVwFI0xZeaGycrfHztNpWVvDQ6y1Iy5ebOKvokZBblHOoWYxOxVS5yWLVcmEtwyGHV5jPD93EpOuAfHryHy80DoM316SYvH65zkic81d7lnlxmxR+52D8vZckfc4iD8Z6UCbFy7dMvDi8xKBKOZwnnWydk2mP3qENlJNpIxmnItHB06z2dMDAO9npsYg6rNleDfX49fZzSenz++ArjlZe4NVtjPRiyX7b5aPMW//vhR/hQ5w53shV63pSn4y2maPq6yd18hSeiXZQw3C+WGFYJaXjE69kq21mXP7L4dXaKBV5PV/AXJH1juFlqUhNwbFztoaxhKj+9+xxX2gf86cUv8LXsLNdH6/xScIVz/vFc2HQp2GdRphxWLVpyxs/tXCNf93h/co/Dqs3lcI8L0TGxKvmV3Sv85vAyhfVIkluc8xKulwXnPcuDSrAsK3a041oc6pipDVA4y/ZR1eKgaFG2vLm4a2qCuWz+8XCfF6YXueof8eXsPJnxeTZ6yPvDgANdMq7pWee9kH2d838PPsS/0vsSz3oHTI0kte8UWmt/fwXxdg5hwASCYGywngQ0RdcSHgswgqIjCIaWrp+y0RySVT50C8LYzYqjsTMvdZsplVaUHdf6lDNJZnzWgwGThpt1m17OdtbFqzSRV3G+2acwHtMqYL0xorSKQFYshRO2px2CsCRNFKWWtKSrvC97o/lr3yoW8YXmXHhMICqeaO0DrqvS86csN6Yshi6le8mbkBufc2Hf5V7Us/rCm1yA2krWI1fAa3o5K9GYURmhPE3sl2gjyQNFN5rR9HNasqArKzIriERZKxOdTqCvfa629h1mLXDAnETmdGXKZjRgzRtw4jV4ItolqpOtIllyMTyokXtOoj4R0fz7zWjggoJlzmo4mjMbG6KiFE7b4DJDLIX1eLLtPtwNadj0TyiMR1vO3tjPV+25jkEK66C9nSNW/BFtmTEQrkYTypJRFZGXHr1gSkPmLCuPkXFtzabwaAgX4tMSjiE5NtUcSb/mD3hYLHI26rPmDRibiKeCPbaqzry2lMicnjdlXTnRm1aCniw40CUrqkFqCvxaH9KRipVgxLKsODReTRT7NloSvz9AvPUhLBQNSTjSmEAR+jnjxBAdKISyTjAlHEx2LRpxlDeJGgWh76TRUloCWdEOckZFiI4sNjCgnRNvWY7Ijc923qWjZrS83EFSQ5ey1FAFXv3H7XgzAqVZ8cd40hD6FWVcEvsVAZpWTVpuBRljE3FcNQmEZlM5HmEWOTNWR6U0VUagNjkTn5DIgo6aUtYdkLCWXQdCE5ly7n3IjVveGyscIs8fc1cs0UycwGtaBbSinKVo6hK2hAsORliWZV77QUouBQdQOBn6hueI2addjAPd4rHwkLbMWPdP6KkJAZpIpgxMzIBkzshsyxlBUM0pT5l1M64vqnkbE5ybU1LM06cOdJNLwT658bkc7tESkg01ZjUa01OT+X5+zRuy4c0YGI9IFEhhWA3HXAgOacmMFW9c540ETKuQhWRGz5tywT8kFgGpnREJTYWgJQXHWnDWczSpnioIhZ6Lue56k3lA0Zo3JBKGFTWZKyQz67PsjQiFx5oaIYWlKyVja0hNQSIDtDXMbEEiAi6H+5RQE7MM0bcjO3yXDxDvDiWlgGBqsFIgC01WeshCYAKwldM5nFKth2XMqIzQWqKtQGtJWTin4azyKSo3wgstEJVAIx1JumoQysrFwdeKy0nh/h2UMdMqYKZdW9ITBoOg1AohLELU20VEbSN2RUWfN/iRI+P0/P2qOU9+muiIxCsYVs4ObJBoJP3K2amNlW62rZ2lp/v13Pg1c1KRWw9P6Jpq7ezfSlgKo8i0y+Qooe5mSCJR1QlgDsDrXosLAc6sT2Z9GjJnWLMUwWk6HJrPgWMLq+a07lOFpUHODWlOVSgpjYfCgWoLaylrJeG0fg5tJeM6YUvXztBQVqTWMTClcMnimXUkqqhWfbrB0Zsb7XT9+1BVhKpyNCvrY95ErgL3GiJh5iRrbakLiZK0LoBGoiASletGCSiQc4Wkc9zW58LBk8v6OXyhfhst22AorMLHuWTVtwN2sDiz1tv5+l063hUrCITrYlSRBGudDHoiHW7Ls/gTF9470SHHeYNAVhSDkCpRKN+BVNIqYJIHhH6FP5SUHeMcnRgeFEvs521iVSCFpaEKQunyNKZVQFxH7u2mbbLEJ618FwrjF+wPWxTDEN1K0Uim1udQtzmo2gTCpXZn1mer6LHqDbk3W6IXTMmNT79qsDdt4wkzb6Mm0hmxTuPsTmlIR2UTv8avHRQt+kWDQRFjEFRWMRglJH45D+/1labhFzV52hXoHHa/waY3YrtcIDUh92bLPBHuslMnmw90wvvi+2znXc4ExzwsljBIzvrH89cz0A3aNXT3WDvVZiA0O8WCA7v4x0xtyHHZwBcSrCG3kBqPgYnnA8he1eF2ugLA89EO96sF9jKnpRgbV2MqrKKvozpfIqRAcWeyzGY4oCtTDnWbRTV1WyMvpT87w0npxEkTs0VHBhyLEg+fvOZSTK1B8/+2dy6xcV3nHf+dc59z7zw4w6FIkSIly5IVy1bjGGnspkkbtECRLtpssmgWRQt00U3XRbvoprt201W7CNA2RTcNEBSFgQYOkCZBijZQrBQxkjiWLDMWJZIaksN5z9y5j3O6OKOxa4iWFKgmFZ8fcAEOeXFxZzjzzTnf4//PGGiXkTaB/G5Ro1dEHOZl1rwO3SLmVj7iblFjQZptoUTRymoM9R32izqJ8onkLmfdWQu/TkFDWYZ0ijE3ktO8HG5TkUbif/xzrAZsDuIhySKJzCGvBET+hH6scccCxy+Y1gRBxyhae+XCeF3GOUGYEfoZWeHQDIf40nhj7FUVqppDYcahLzlmHqOVVXkq2GdSGA2Hlbg/d/ZWWtCojTjtd2kFVZ6NdjlMI0I/o6hIYj9lUU6MwKkoWHL68w9UhQkrrumxSKpmLLvhDik7Vd4uNblc3qUym9twUEbDwB3Oy2IVmbDs9Wa6Ckb6bTerM1Y+p70uW6M65TihEY4YZCFEcKG6j9KSVafAQZJoxbJjtimRMLqPb2dLfKqyyXP+npHhlxMS7bKd17kc7bDoDNnwD2YmPkYQZUFOCEXGmts337w6YNEZsu61Tbtx6Jl+CKHYCevzb+uGlEQiI5I5odC8ldU45x/wUnWTy+E2y05A5h0QOjmNmZZCZbYVqsnpbBswJRYpn6zf4pPRJivOCE/krDop38wr7KcVKsGUc2GbF4I7VGVIR03whWaqc047Pq0iZcM1bmsOCYkc4wjBorzL5vQUZ/0DHDSLzpBQ5Cw5fRrSzFa8pkqc9Q8oi4CPeQekWrLqOLSKKTXpEAkfhaJTjKk7EZ+Jb1BoGM2MfWvy0ZKUwInfYpyIACEziHdT+hsB4+dDuncalHoCdwT12ojOi1C8EXF9uExSuNzq1VEjl6QQJCMfhh5XLm7jiYLXB+vIF7arfj7VAAAK50lEQVTIlMNOt8rr47MseyZ735pWmRQeP2hvMEqNEOxBWGacecR+yp8/9XW6KuJCtMdvxG9yfbxCY/UdKm7CYRrzw+kZPGFUnO6pGd9MVmh6A1bcLvvK6DK+OTnNWtDhdmImGe+mVYZOwG5aY1gEXO8tc6nW4ouN11iUE1adAYuOpl0IbuV1nvfbtIqd+X7+anieTzc2aXoD3kmabARt1v023x89zetpdSa8ajxBu0XEDWDDO2RQlKg4E/aLe9sL0y35jzu/yq83b7A7MmI5N5LT83xCKMwSfqX8xmyasccZd0Km4WJ4QCBcrqU+a3LCx4Jdvjq4iBR6XpUIhXH/fn1ylsvhNqteh1ikXJu6hMJnmAV8f/w0ABuzluireZVL4Q4OmnYR86XaNTbcEj/LYd0d01WS1/tn2E/K/NbyG1wJb+MJxXcSj2vjS9Qcs7p7Ibw1t8NLcViSMNb+fJjt+niZ1VqHbw+fpekO+V5+gUw5VOpmOvTq4Dx/3Pwur07qXPQmTLUpZf5b91c45fe5GLRItcON5DSfiW/wuZLiK/3z7KR1zvhtnvFbj/bG1xrUye61PhEBAsAdpCgvoLxT0P0EBF3w+5okcxFbJZwp9LOQ9iRCCvA6LnnpXr4B7k5rbCcLJLnHm1sr+KWMIjfiJntZha2R8VqcKpNXUNpUPxypyQvJQRrz9d7HuRLdYTet8Z3xMxykMUoLru2t84mlOyTKI8FjoEJ+mqwCZv8+Vj5vJ6eIZMr14TJSaHLlcHPQ5HZnge2oRi1IWAgmuEJR8RP2phUA2qrEQIVcTUxwKbTk7fQUe1mVceGzFnQ4V2rzH3uXuFLfIVcOr/af5/naDg3XJD3vTZl2VUSifapywn+PLtLJIw7TmJdqm7yTvCve8tnFm/xX+wKXqi1+PFjlQrw/19Y4KMpmKrZsvDQ2swpvjk9z3t/jPydNxirghXCL9qzV/OXSJl1VAsx0Zzsvo5CUnYSro6d5e9xko9Thd2v/w0+mq2z1FrhUbbERtEm0z/XxCpeiu7wUtLk2bfC01+HvDn6Nz1ZvcMWf8FZW46LX4y/W/p2ryTn+tfUi6yuH/DDZ4HfKP2ap8iNu5wtc8Q/YyUtzO7yGSNjMGnSLyATJvEovK/Hd/iWejXbZmjb45fImrWyB866ZyvzTU9/mVh7xcf+Ab43PkWmX54Jtfr/xvbk/pwe8HG5TaPhK/zx/WN1jr/gZ0YNN7O/PyY4PJyRJ6YDIFVkskKkGqfF7pvcBwO+aHya5R3cY4UiF1xP4PWGaqUrGb2FnaOTX6PrkmUu1MsYTBQfTMrujKqlySQqP2EvxHIUqBJPUM113QrM1qs+crAJ20wViJ6WfhvSGJapuMt9SdPKYTmas+preAEcodpMqY+Vza2CmKKfKpZuUmIwChonpQBxmRjKvncSm4UmFZNphP6+yn1cYqQBHKLami+wkC1wfLBuzXKEYpSaLPyp8BlnAYRazNW3MzXXH2qwixsonlBnjmc/F5mCR3XSB1rTKYR5zJzFbl7ujCgfTMp0kojWtzk2EE+Wxn5YZKN8MZilz74DJaUyXqEjTTHU9OQ2YPEJfhbNmL5dUu0YaT2Zs9ppsTeok2qOV1wi8nMM0ppPH9IqSUawqQjxhcjIFgp/2V9jO6nN9iZGSZEhW3B6+zOmrEsMipK0CYpEbcyHlzBu8xsqjq/z545EKuJM2iJ2Uu5MKh3nMpPC5lTbpFSX2i5yeMq7dd/MFdoqAVl4zfRNFhYVZKXO/8GkVnjHy0S47aX0uqT/WBYfq0ac5hdYPdRwXJyJAAIhpBgJkpiCX5BGkFUFeSLJYk9Y0i+EI38uZZi55pEkXFMrTOCPJRumQyDPJSl3J8YOMzqGRWmv4I+rhhHPxIe5M93GSmopFLZqYioiSLIVDEuXxXHmb036XUeETuSmN6ojWtDK3yzvl9am6CU1vYCoVMqXhjwllxtlKB4Wgeq9c2uzjuzmxm1L2zF7blQpXqvlo8rrXpuGM5vv8036X1bDLxfIe50v7ZNr0PcTulJo3YS3uImfZ9VhOiaXRfzBzIT1GKqDmjjnl93mmtkfDHdHwRzTcEathl7prpktjd0rFT2j4IyLHWA16omDJH850FRTrXnveSLTgjNkI2rRnK4YLQYsUSSyn+KLAn1kWOiiWXJOn+KXFHTZKZpthfqcJZE7NmdBwTP+H+VAXrLuHRELzYv22aUFHsyDHxNJ86NtFmXBWxag5E2KRk8yqLvd8RI3uptEDjYT5fy3IMRv+AZmWc/VpKRTLbo+6OyKWgkiYLtNFZ0gscs77+9Td0ayZyjioRSInkvcqIAVn/DaRcOZBIhKPrgcxN/B90HFMnIgthtCQ1yNkCkVJ4kQZWezhDzShnzEURoQ2dlPWaj2mhUu/vID2NTjatGkjWIt681KlEBq/lLEWdMj0u/J056I26axlWGNk5hbLY6TQrIcdltw+d8YNni/doeZN2M4WKJSk7KZzf82BKs0/NIljymfmjTYlU6ZEuOz1OazFvNVd4kyly6lwSDxzbiq0YDkYsCATFuWEgfL/j3dn6O+xI+qMHZMgfGuyPH+tRnlAqlxWgxYKwbprgkU2k5U7LMrGzl6aZfXZ8JAr4W0qzoQlt0+ifG5OTQ7kfGmfsjOl6Q1pOOZQSA7zMiuO8cTsYhrD1hyTYF1wxqw4o9m3dsiinDLSLkvOhLFySWYCsHfzGhcD0wdxIWiZBKq/Sz2ccCFqseT2WXDGFLGcJQyNYAukNL0BT3v7LDkKSUJT+ryW17k1bSKFmjujLzuKntKsuD0ioYncCT3lsOSomWBMh4E28yZVmfCjYJ2GO6LmjLgU3aXqJCgkdRkiEewUOZGcmlZr74AVt8uqM2asHSri3T6HsdbUpDPPOSRac1CMaDrxo73xnwBnLaFPQBZVCLEPjICfYxzuxNDkyb5/sM/hcXBWa730MCfWwhX96Y0/eKiLvvrWX//gcYnWPgonYgWhtV4SQlw7jhfgcfGk3z/Y53AsnIAv6A/iRAQIi+UjiQaKk13GsAHCYjk2NGgbIB6WLz/4lBPNk37/YJ/Dh4/dYjwc77MRe+J40u8f7HP40HkCqhgnJkBYLB9J7ArCYrEciQ0QFovlvmgNxaM5gn/Y2ABhsRwndgVhsViOxAYIi8Vyfx6ru/f/CzZAWCzHhQZtG6UsFsuR2BWExWI5EpuDsFgs98WWOS0WywehrWitxWK5P8crJ/cw2ABhsRwXT8Cw1okRrbVYPpJo9XDHQyCE+LwQ4roQ4qYQ4s/u8/dACPHV2d+vCiHOPeiaNkBYLMeEBrTSD3U8CCGEA/wt8NvAZeBLQojL7zvtj4CO1voC8DfAXz3oujZAWCzHhdaPcwXxKeCm1npTa50C/wJ84X3nfAH4p9nPXwN+U4gP1uq3OQiL5RjRj6/MuQbcfs/jO8BLR52jtc6FED1gkQ9QAbcBwmI5JgZ0vvFN/bXmQ54eCiGuvefxlz8M9SwbICyWY0Jr/fnHeLltYP09j8/Mfne/c+4IIVygBrQ/6KI2B2Gx/GLwGnBRCPGUEMIHfg945X3nvALcc+r5IvAt/QDnLLuCsFh+AZjlFP4E+AbgAP+gtf6JEOIvgWta61eAvwf+WQhxEzjEBJEP5ERY71kslpOJ3WJYLJYjsQHCYrEciQ0QFovlSGyAsFgsR2IDhMViORIbICwWy5HYAGGxWI7EBgiLxXIk/wvZVkDdROfUQgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "1 gram matrices are calculated, 0 of which are ignored.\n",
+      "\n",
+      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
+      "calculate performance:   0%|          | 2/1230 [00:00<01:26, 14.18it/s]"
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-1-90cefd800f5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0mdatafile_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dataset_y'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'dataset_y'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0mextra_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'extra_params'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'extra_params'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         ds_name=ds['name'])\n\u001b[0m\u001b[1;32m     62\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/utils/model_selection_precomputed.py\u001b[0m in \u001b[0;36mmodel_selection_for_precomputed_kernel\u001b[0;34m(datafile, estimator, param_grid_precomputed, param_grid, model_type, NUM_TRIALS, datafile_y, extra_params, ds_name)\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[0mnb_gm_ignore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;31m# the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_out\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam_list_precomputed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m             \u001b[0mrtn_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams_out\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m             \u001b[0mKmatrix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrtn_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m             \u001b[0mcurrent_run_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrtn_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/spKernel.py\u001b[0m in \u001b[0;36mspkernel\u001b[0;34m(node_label, edge_weight, node_kernels, *args)\u001b[0m\n\u001b[1;32m     93\u001b[0m                     \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m                         \u001b[0;32mfor\u001b[0m \u001b[0me1\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 95\u001b[0;31m                             \u001b[0;32mfor\u001b[0m \u001b[0me2\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     96\u001b[0m                                 \u001b[0;32mif\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0me2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cost'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m                                     \u001b[0mkn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnode_kernels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mix'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
+      "  warnings.warn(\"Singular matrix in solving dual problem. Using \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                                          \n",
+      "4. Getting final performance...\n",
+      "best_params_out:  [{'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}]\n",
+      "best_params_in:  [{'alpha': 0.01}]\n",
+      "\n",
+      "best_val_perf:  10.66283832911368\n",
+      "best_val_std:  0.5408278153570373\n",
+      "final_performance:  [10.315559722243599]\n",
+      "final_confidence:  [2.384096453432681]\n",
+      "train_performance: [7.431503564719363]\n",
+      "train_std:  [0.22208257392321618]\n",
+      "\n",
+      "time to calculate gram matrix with different hyper-params: 11.70±nans\n",
+      "time to calculate best gram matrix: 11.70±nans\n",
+      "\n",
+      "params                                                                                                                                                                                                                                                                                            train_perf    valid_perf            test_perf              gram_matrix_time\n",
+      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  ------------  --------------------  -------------------  ------------------\n",
+      "{'alpha': '1.00e-10', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     324939.95±1779702.52  162506.31±890024.17                11.7\n",
+      "{'alpha': '3.16e-10', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     103389.05±566218.31   51709.44±283164.71                 11.7\n",
+      "{'alpha': '1.00e-09', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     32773.42±179440.54    16394.79±89738.41                  11.7\n",
+      "{'alpha': '3.16e-09', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     10371.32±56739.21     5191.57±28375.88                   11.7\n",
+      "{'alpha': '1.00e-08', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     3287.91±17941.77      1649.18±8973.41                    11.7\n",
+      "{'alpha': '3.16e-08', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     1047.95±5673.01       528.99±2837.84                     11.7\n",
+      "{'alpha': '1.00e-07', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     339.61±1793.28        174.75±897.61                      11.7\n",
+      "{'alpha': '3.16e-07', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     115.62±566.41         62.73±284.06                       11.7\n",
+      "{'alpha': '1.00e-06', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.81     44.78±178.46          27.30±90.09                        11.7\n",
+      "{'alpha': '3.16e-06', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.81     22.37±55.86           16.10±28.87                        11.7\n",
+      "{'alpha': '1.00e-05', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.59±0.78     15.26±17.31           12.54±9.86                         11.7\n",
+      "{'alpha': '3.16e-05', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.57±0.72     12.94±5.71            11.36±4.52                         11.7\n",
+      "{'alpha': '1.00e-04', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.52±0.58     12.03±2.70            10.88±3.23                         11.7\n",
+      "{'alpha': '3.16e-04', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.43±0.41     11.49±1.53            10.55±2.70                         11.7\n",
+      "{'alpha': '1.00e-03', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.33±0.31     11.06±0.82            10.26±2.40                         11.7\n",
+      "{'alpha': '3.16e-03', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.28±0.27     10.76±0.60            10.15±2.28                         11.7\n",
+      "{'alpha': '1.00e-02', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.43±0.22     10.66±0.54            10.32±2.38                         11.7\n",
+      "{'alpha': '3.16e-02', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  8.11±0.20     10.86±0.47            10.89±2.61                         11.7\n",
+      "{'alpha': '1.00e-01', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  9.76±0.21     11.90±0.42            12.20±2.84                         11.7\n",
+      "{'alpha': '3.16e-01', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  12.88±0.23    14.53±0.39            14.79±2.97                         11.7\n",
+      "{'alpha': '1.00e+00', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  17.64±0.24    18.83±0.32            19.02±3.17                         11.7\n",
+      "{'alpha': '3.16e+00', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  32.86±17.99   33.24±17.71           33.25±17.11                        11.7\n",
+      "{'alpha': '1.00e+01', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  34.19±0.37    34.39±0.43            34.96±5.17                         11.7\n",
+      "{'alpha': '3.16e+01', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  49.15±0.43    48.90±0.56            49.48±7.14                         11.7\n",
+      "{'alpha': '1.00e+02', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  76.17±0.53    75.76±0.64            76.22±8.54                         11.7\n",
+      "{'alpha': '3.16e+02', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  108.04±0.74   107.66±0.79           108.09±8.81                        11.7\n",
+      "{'alpha': '1.00e+03', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  128.28±0.90   127.91±0.93           128.37±8.78                        11.7\n",
+      "{'alpha': '3.16e+03', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  136.82±0.97   136.44±0.99           136.92±8.75                        11.7\n",
+      "{'alpha': '1.00e+04', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  139.80±0.99   139.43±1.01           139.91±8.74                        11.7\n",
+      "{'alpha': '3.16e+04', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  140.78±1.00   140.40±1.02           140.89±8.73                        11.7\n",
+      "{'alpha': '1.00e+05', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.09±1.00   140.72±1.02           141.20±8.73                        11.7\n",
+      "{'alpha': '3.16e+05', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.19±1.00   140.81±1.02           141.30±8.73                        11.7\n",
+      "{'alpha': '1.00e+06', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.22±1.00   140.85±1.02           141.33±8.73                        11.7\n",
+      "{'alpha': '3.16e+06', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.23±1.00   140.86±1.02           141.34±8.73                        11.7\n",
+      "{'alpha': '1.00e+07', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.23±1.00   140.86±1.02           141.35±8.73                        11.7\n",
+      "{'alpha': '3.16e+07', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.24±1.00   140.86±1.02           141.35±8.73                        11.7\n",
+      "{'alpha': '1.00e+08', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.24±1.00   140.86±1.02           141.35±8.73                        11.7\n",
+      "{'alpha': '3.16e+08', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.24±1.00   140.86±1.02           141.35±8.73                        11.7\n",
+      "{'alpha': '1.00e+09', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.24±1.00   140.86±1.02           141.35±8.73                        11.7\n",
+      "{'alpha': '3.16e+09', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.24±1.00   140.86±1.02           141.35±8.73                        11.7\n",
+      "{'alpha': '1.00e+10', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.24±1.00   140.86±1.02           141.35±8.73                        11.7\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculate performance: 100%|██████████| 1230/1230 [00:40<00:00, 30.18it/s]\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:135: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  keepdims=keepdims)\n",
+      "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:127: RuntimeWarning: invalid value encountered in double_scalars\n",
+      "  ret = ret.dtype.type(ret / rcount)\n"
      ]
     }
    ],
@@ -51,7 +160,7 @@
     "from sklearn.metrics.pairwise import rbf_kernel\n",
     "\n",
     "dslist = [   \n",
-    "#     {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', 'task': 'regression'}, # node symb\n",
+    "    {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', 'task': 'regression'}, # node symb\n",
     "#     {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n",
     "#     {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds',}, # unlabeled\n",
     "#     {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, # node/edge symb\n",
@@ -62,7 +171,7 @@
     "#         'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt',}, # contains single node graph, node symb\n",
     "#     {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n",
     "#     {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n",
-    "    {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, # node symb/nsymb\n",
+    "#     {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, # node symb/nsymb\n",
     "#     {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n",
     "#     {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n",
     "#     {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb\n",
@@ -103,6 +212,14 @@
     "        datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n",
     "        extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n",
     "        ds_name=ds['name'])\n",
+    "    \n",
+    "#     %lprun -f spkernel \\\n",
+    "#         model_selection_for_precomputed_kernel( \\\n",
+    "#             ds['dataset'], estimator, param_grid_precomputed, \\\n",
+    "#             (param_grid[1] if ('task' in ds and ds['task'] == 'regression') else param_grid[0]), \\\n",
+    "#             (ds['task'] if 'task' in ds else 'classification'), NUM_TRIALS=30, \\\n",
+    "#             datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None), \\\n",
+    "#             extra_params=(ds['extra_params'] if 'extra_params' in ds else None))\n",
     "    print()"
    ]
   },
@@ -672,13 +789,6 @@
     "print('\\n Mean performance on test set: %3f' % test_mean)\n",
     "print('With standard deviation: %3f' % test_std)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/pygraph/kernels/.#commonWalkKernel.py b/pygraph/kernels/.#commonWalkKernel.py
new file mode 120000
index 0000000..99c68c9
--- /dev/null
+++ b/pygraph/kernels/.#commonWalkKernel.py
@@ -0,0 +1 @@
+ljia@ljia-Precision-7520.4716:1530265749
\ No newline at end of file
diff --git a/pygraph/kernels/commonWalkKernel.py b/pygraph/kernels/commonWalkKernel.py
index f247377..669ea5e 100644
--- a/pygraph/kernels/commonWalkKernel.py
+++ b/pygraph/kernels/commonWalkKernel.py
@@ -24,7 +24,7 @@ def commonwalkkernel(*args,
                      edge_label='bond_type',
                      n=None,
                      weight=1,
-                     compute_method='exp'):
+                     compute_method=None):
     """Calculate common walk graph kernels up to depth d between graphs.
     Parameters
     ----------
@@ -40,10 +40,11 @@ def commonwalkkernel(*args,
     n : integer
         Longest length of walks.
     weight: integer
-        Weight coefficient of different lengths of walks.
+        Weight coefficient of different lengths of walks, which represents beta in 'exp' method and gamma in 'geo'.
     compute_method : string
         Method used to compute walk kernel. The Following choices are available:
-        'direct' : direct product graph method, as shown in reference [1]. The time complexity is O(n^6) for unlabeled graphs with n vertices.
+        'exp' : exponential serial method applied on the direct product graph, as shown in reference [1]. The time complexity is O(n^6) for graphs with n vertices.
+        'geo' : geometric serial method applied on the direct product graph, as shown in reference [1]. The time complexity is O(n^6) for graphs with n vertices.
         'brute' : brute force, simply search for all walks and compare them.
 
     Return
@@ -66,6 +67,8 @@ def commonwalkkernel(*args,
     if not ds_attrs['edge_labeled']:
         for G in Gn:
             nx.set_edge_attributes(G, '0', 'bond_type')
+    if not ds_attrs['is_directed']:
+        Gn = [G.to_directed() for G in Gn]
 
     start_time = time.time()
 
@@ -77,7 +80,7 @@ def commonwalkkernel(*args,
             file=sys.stdout)
         for i in range(0, len(Gn)):
             for j in range(i, len(Gn)):
-                Kmatrix[i][j] = _untilnwalkkernel_exp(Gn[i], Gn[j], node_label,
+                Kmatrix[i][j] = _commonwalkkernel_exp(Gn[i], Gn[j], node_label,
                                                       edge_label, weight)
                 Kmatrix[j][i] = Kmatrix[i][j]
                 pbar.update(1)
@@ -90,7 +93,7 @@ def commonwalkkernel(*args,
             file=sys.stdout)
         for i in range(0, len(Gn)):
             for j in range(i, len(Gn)):
-                Kmatrix[i][j] = _untilnwalkkernel_geo(Gn[i], Gn[j], node_label,
+                Kmatrix[i][j] = _commonwalkkernel_geo(Gn[i], Gn[j], node_label,
                                                       edge_label, weight)
                 Kmatrix[j][i] = Kmatrix[i][j]
                 pbar.update(1)
@@ -106,7 +109,7 @@ def commonwalkkernel(*args,
 
         for i in range(0, len(Gn)):
             for j in range(i, len(Gn)):
-                Kmatrix[i][j] = _untilnwalkkernel_brute(
+                Kmatrix[i][j] = _commonwalkkernel_brute(
                     all_walks[i],
                     all_walks[j],
                     node_label=node_label,
@@ -122,7 +125,7 @@ def commonwalkkernel(*args,
     return Kmatrix, run_time
 
 
-def _untilnwalkkernel_exp(G1, G2, node_label, edge_label, beta):
+def _commonwalkkernel_exp(G1, G2, node_label, edge_label, beta):
     """Calculate walk graph kernels up to n between 2 graphs using exponential series.
 
     Parameters
@@ -168,7 +171,7 @@ def _untilnwalkkernel_exp(G1, G2, node_label, edge_label, beta):
     D = np.zeros((len(ew), len(ew)))
     for i in range(len(ew)):
         D[i][i] = np.exp(beta * ew[i])
-    # print('D: ', D)
+        # print('D: ', D)
     # print('hshs: ', T.I * D * T)
 
     # print(np.exp(-2))
@@ -176,16 +179,16 @@ def _untilnwalkkernel_exp(G1, G2, node_label, edge_label, beta):
     # print(np.exp(weight * D))
     # print(ev)
     # print(np.linalg.inv(ev))
-    exp_D = ev * D * ev.I
+    exp_D = ev * D * ev.T
     # print(exp_D)
     # print(np.exp(weight * A))
     # print('-------')
 
-    return np.sum(exp_D.diagonal())
+    return exp_D.sum()
 
 
-def _untilnwalkkernel_geo(G1, G2, node_label, edge_label, gamma):
-    """Calculate walk graph kernels up to n between 2 graphs using geometric series.
+def _commonwalkkernel_geo(G1, G2, node_label, edge_label, gamma):
+    """Calculate common walk graph kernels up to n between 2 graphs using geometric series.
 
     Parameters
     ----------
@@ -207,46 +210,14 @@ def _untilnwalkkernel_geo(G1, G2, node_label, edge_label, gamma):
     # get tensor product / direct product
     gp = direct_product(G1, G2, node_label, edge_label)
     A = nx.adjacency_matrix(gp).todense()
-    # print(A)
-
-    # from matplotlib import pyplot as plt
-    # nx.draw_networkx(G1)
-    # plt.show()
-    # nx.draw_networkx(G2)
-    # plt.show()
-    # nx.draw_networkx(gp)
-    # plt.show()
-    # print(G1.nodes(data=True))
-    # print(G2.nodes(data=True))
-    # print(gp.nodes(data=True))
-    # print(gp.edges(data=True))
-
-    ew, ev = np.linalg.eig(A)
-    # print('ew: ', ew)
-    # print(ev)
-    # T = np.matrix(ev)
-    # print('T: ', T)
-    # T = ev.I
-    D = np.zeros((len(ew), len(ew)))
-    for i in range(len(ew)):
-        D[i][i] = np.exp(beta * ew[i])
-    # print('D: ', D)
-    # print('hshs: ', T.I * D * T)
-
-    # print(np.exp(-2))
-    # print(D)
-    # print(np.exp(weight * D))
-    # print(ev)
-    # print(np.linalg.inv(ev))
-    exp_D = ev * D * ev.I
-    # print(exp_D)
-    # print(np.exp(weight * A))
-    # print('-------')
-
-    return np.sum(exp_D.diagonal())
+    mat = np.identity(len(A)) - gamma * A
+    try:
+        return mat.I.sum()
+    except np.linalg.LinAlgError:
+        return np.nan
 
 
-def _untilnwalkkernel_brute(walks1,
+def _commonwalkkernel_brute(walks1,
                             walks2,
                             node_label='atom',
                             edge_label='bond_type',
diff --git a/pygraph/kernels/randomWalkKernel.py b/pygraph/kernels/randomWalkKernel.py
index 5139e2f..ac5c521 100644
--- a/pygraph/kernels/randomWalkKernel.py
+++ b/pygraph/kernels/randomWalkKernel.py
@@ -19,7 +19,11 @@ from pygraph.utils.graphdataset import get_dataset_attributes
 def randomwalkkernel(*args,
                      node_label='atom',
                      edge_label='bond_type',
+                     edge_weight=None,
                      h=10,
+                     p=None,
+                     q=None,
+                     weight=None,
                      compute_method=''):
     """Calculate random walk graph kernels.
     Parameters
@@ -33,7 +37,7 @@ def randomwalkkernel(*args,
         node attribute used as label. The default node label is atom.
     edge_label : string
         edge attribute used as label. The default edge label is bond_type.
-    n : integer
+    h : integer
         Longest length of walks.
     method : string
         Method used to compute the random walk kernel. Available methods are 'sylvester', 'conjugate', 'fp', 'spectral' and 'kron'.
@@ -46,7 +50,25 @@ def randomwalkkernel(*args,
     compute_method = compute_method.lower()
     h = int(h)
     Gn = args[0] if len(args) == 1 else [args[0], args[1]]
-    Kmatrix = np.zeros((len(Gn), len(Gn)))
+
+    eweight = None
+    if edge_weight == None:
+        print('\n None edge weight specified. Set all weight to 1.\n')
+    else:
+        try:
+            some_weight = list(
+                nx.get_edge_attributes(Gn[0], edge_weight).values())[0]
+            if isinstance(some_weight, float) or isinstance(some_weight, int):
+                eweight = edge_weight
+            else:
+                print(
+                    '\n Edge weight with name %s is not float or integer. Set all weight to 1.\n'
+                    % edge_weight)
+        except:
+            print(
+                '\n Edge weight with name "%s" is not found in the edge attributes. Set all weight to 1.\n'
+                % edge_weight)
+
     ds_attrs = get_dataset_attributes(
         Gn,
         attr_names=['node_labeled', 'edge_labeled', 'is_directed'],
@@ -71,76 +93,224 @@ def randomwalkkernel(*args,
     #         labeled=labeled) for i in range(0, len(Gn))
     # ]
 
-    pbar = tqdm(
-        total=(1 + len(Gn)) * len(Gn) / 2,
-        desc='calculating kernels',
-        file=sys.stdout)
     if compute_method == 'sylvester':
         import warnings
         warnings.warn(
-            'The Sylvester equation (rather than generalized Sylvester equation) is used; only walks of length 1 is considered.'
+            'The Sylvester equation (rather than generalized Sylvester equation) is used; edge label number has to smaller than 3.'
         )
-        from control import dlyap
+        Kmatrix = _randomwalkkernel_sylvester(Gn, weight, p, q, node_label,
+                                              edge_label, eweight)
+
+    elif compute_method == 'conjugate':
         for i in range(0, len(Gn)):
             for j in range(i, len(Gn)):
-                Kmatrix[i][j] = _randomwalkkernel_sylvester(
-                    all_walks[i],
-                    all_walks[j],
-                    node_label=node_label,
-                    edge_label=edge_label)
+                Kmatrix[i][j] = _randomwalkkernel_conjugate(
+                    Gn[i], Gn[j], node_label, edge_label)
                 Kmatrix[j][i] = Kmatrix[i][j]
                 pbar.update(1)
 
-    elif compute_method == 'conjugate':
-        pass
     elif compute_method == 'fp':
-        pass
+        for i in range(0, len(Gn)):
+            for j in range(i, len(Gn)):
+                Kmatrix[i][j] = _randomwalkkernel_fp(Gn[i], Gn[j], node_label,
+                                                     edge_label)
+                Kmatrix[j][i] = Kmatrix[i][j]
+                pbar.update(1)
+
     elif compute_method == 'spectral':
-        pass
+        for i in range(0, len(Gn)):
+            for j in range(i, len(Gn)):
+                Kmatrix[i][j] = _randomwalkkernel_spectral(
+                    Gn[i], Gn[j], node_label, edge_label)
+                Kmatrix[j][i] = Kmatrix[i][j]
+                pbar.update(1)
     elif compute_method == 'kron':
-        pass
+        for i in range(0, len(Gn)):
+            for j in range(i, len(Gn)):
+                Kmatrix[i][j] = _randomwalkkernel_kron(Gn[i], Gn[j],
+                                                       node_label, edge_label)
+                Kmatrix[j][i] = Kmatrix[i][j]
+                pbar.update(1)
     else:
         raise Exception(
             'compute method name incorrect. Available methods: "sylvester", "conjugate", "fp", "spectral" and "kron".'
         )
 
-    for i in range(0, len(Gn)):
-        for j in range(i, len(Gn)):
-            Kmatrix[i][j] = _randomwalkkernel_do(
-                all_walks[i],
-                all_walks[j],
-                node_label=node_label,
-                edge_label=edge_label,
-                labeled=labeled)
-            Kmatrix[j][i] = Kmatrix[i][j]
+    # for i in range(0, len(Gn)):
+    #     for j in range(i, len(Gn)):
+    #         Kmatrix[i][j] = _randomwalkkernel_do(
+    #             all_walks[i],
+    #             all_walks[j],
+    #             node_label=node_label,
+    #             edge_label=edge_label,
+    #             labeled=labeled)
+    #         Kmatrix[j][i] = Kmatrix[i][j]
 
     run_time = time.time() - start_time
     print(
-        "\n --- kernel matrix of walk kernel up to %d of size %d built in %s seconds ---"
-        % (n, len(Gn), run_time))
+        "\n --- kernel matrix of random walk kernel of size %d built in %s seconds ---"
+        % (len(Gn), run_time))
 
     return Kmatrix, run_time
 
 
-def _randomwalkkernel_sylvester(walks1,
-                                walks2,
-                                node_label='atom',
-                                edge_label='bond_type'):
+def _randomwalkkernel_sylvester(Gn, lmda, p, q, node_label, edge_label,
+                                eweight):
     """Calculate walk graph kernels up to n between 2 graphs using Sylvester method.
 
     Parameters
     ----------
-    walks1, walks2 : list
-        List of walks in 2 graphs, where for unlabeled graphs, each walk is represented by a list of nodes; while for labeled graphs, each walk is represented by a string consists of labels of nodes and edges on that walk.
+    G1, G2 : NetworkX graph
+        Graphs between which the kernel is calculated.
     node_label : string
-        node attribute used as label. The default node label is atom.
+        node attribute used as label.
     edge_label : string
-        edge attribute used as label. The default edge label is bond_type.
+        edge attribute used as label.
+
+    Return
+    ------
+    kernel : float
+        Kernel between 2 graphs.
+    """
+    from control import dlyap
+    Kmatrix = np.zeros((len(Gn), len(Gn)))
+
+    if q == None:
+        # don't normalize adjacency matrices if q is a uniform vector.
+        A_list = [
+            nx.adjacency_matrix(G, eweight).todense() for G in tqdm(
+                Gn, desc='compute adjacency matrices', file=sys.stdout)
+        ]
+        if p == None:
+            pbar = tqdm(
+                total=(1 + len(Gn)) * len(Gn) / 2,
+                desc='calculating kernels',
+                file=sys.stdout)
+            for i in range(0, len(Gn)):
+                for j in range(i, len(Gn)):
+                    A = lmda * A_list[j]
+                    Q = A_list[i]
+                    # use uniform distribution if there is no prior knowledge.
+                    nb_pd = len(A_list[i]) * len(A_list[j])
+                    pd_uni = 1 / nb_pd
+                    C = np.full((len(A_list[j]), len(A_list[i])), pd_uni)
+                    try:
+                        X = dlyap(A, Q, C)
+                        X = np.reshape(X, (-1, 1), order='F')
+                        # use uniform distribution if there is no prior knowledge.
+                        q_direct = np.full((1, nb_pd), pd_uni)
+                        Kmatrix[i][j] = np.dot(q_direct, X)
+                    except TypeError:
+                        # print('sth wrong.')
+                        Kmatrix[i][j] = np.nan
+
+                    Kmatrix[j][i] = Kmatrix[i][j]
+                    pbar.update(1)
+    # A_list = []
+    # for G in tqdm(Gn, desc='compute adjacency matrices', file=sys.stdout):
+    #     A_tilde = nx.adjacency_matrix(G, weight=None).todense()
+    #     # normalized adjacency matrices
+    #     #          A_list.append(A_tilde / A_tilde.sum(axis=0))
+    #     A_list.append(A_tilde)
+
+    return Kmatrix
+
+
+def _randomwalkkernel_conjugate(G1, G2, node_label, edge_label):
+    """Calculate walk graph kernels up to n between 2 graphs using conjugate method.
+
+    Parameters
+    ----------
+    G1, G2 : NetworkX graph
+        Graphs between which the kernel is calculated.
+    node_label : string
+        node attribute used as label.
+    edge_label : string
+        edge attribute used as label.
+
+    Return
+    ------
+    kernel : float
+        Kernel between 2 graphs.
+    """
+
+    dpg = nx.tensor_product(G1, G2)  # direct product graph
+    import matplotlib.pyplot as plt
+    nx.draw_networkx(G1)
+    plt.show()
+    nx.draw_networkx(G2)
+    plt.show()
+    nx.draw_networkx(dpg)
+    plt.show()
+    X = dlyap(A, Q, C)
+
+    return kernel
+
+
+def _randomwalkkernel_fp(G1, G2, node_label, edge_label):
+    """Calculate walk graph kernels up to n between 2 graphs using Fixed-Point method.
+
+    Parameters
+    ----------
+    G1, G2 : NetworkX graph
+        Graphs between which the kernel is calculated.
+    node_label : string
+        node attribute used as label.
+    edge_label : string
+        edge attribute used as label.
+
+    Return
+    ------
+    kernel : float
+        Kernel between 2 graphs.
+    """
+
+    dpg = nx.tensor_product(G1, G2)  # direct product graph
+    X = dlyap(A, Q, C)
+
+    return kernel
+
+
+def _randomwalkkernel_spectral(G1, G2, node_label, edge_label):
+    """Calculate walk graph kernels up to n between 2 graphs using spectral decomposition method.
+
+    Parameters
+    ----------
+    G1, G2 : NetworkX graph
+        Graphs between which the kernel is calculated.
+    node_label : string
+        node attribute used as label.
+    edge_label : string
+        edge attribute used as label.
+
+    Return
+    ------
+    kernel : float
+        Kernel between 2 graphs.
+    """
+
+    dpg = nx.tensor_product(G1, G2)  # direct product graph
+    X = dlyap(A, Q, C)
+
+    return kernel
+
+
+def _randomwalkkernel_kron(G1, G2, node_label, edge_label):
+    """Calculate walk graph kernels up to n between 2 graphs using nearest Kronecker product approximation method.
+
+    Parameters
+    ----------
+    G1, G2 : NetworkX graph
+        Graphs between which the kernel is calculated.
+    node_label : string
+        node attribute used as label.
+    edge_label : string
+        edge attribute used as label.
 
     Return
     ------
     kernel : float
-        Treelet Kernel between 2 graphs.
+        Kernel between 2 graphs.
     """
 
     dpg = nx.tensor_product(G1, G2)  # direct product graph
diff --git a/pygraph/kernels/spKernel.py b/pygraph/kernels/spKernel.py
index 4c5e956..28356b6 100644
--- a/pygraph/kernels/spKernel.py
+++ b/pygraph/kernels/spKernel.py
@@ -8,6 +8,7 @@ import pathlib
 sys.path.insert(0, "../")
 from tqdm import tqdm
 import time
+from itertools import combinations_with_replacement, product
 
 import networkx as nx
 import numpy as np
@@ -39,8 +40,6 @@ def spkernel(*args, node_label='atom', edge_weight=None, node_kernels=None):
     # pre-process
     Gn = args[0] if len(args) == 1 else [args[0], args[1]]
 
-    Gn = [nx.to_directed(G) for G in Gn]
-
     weight = None
     if edge_weight == None:
         print('\n None edge weight specified. Set all weight to 1.\n')
@@ -89,174 +88,158 @@ def spkernel(*args, node_label='atom', edge_weight=None, node_kernels=None):
         # node symb and non-synb labeled
         if ds_attrs['node_attr_dim'] > 0:
             if ds_attrs['is_directed']:
-                for i in range(0, len(Gn)):
-                    for j in range(i, len(Gn)):
-                        for e1 in Gn[i].edges(data=True):
-                            for e2 in Gn[j].edges(data=True):
-                                if e1[2]['cost'] == e2[2]['cost']:
-                                    kn = node_kernels['mix']
-                                    try:
-                                        n11, n12, n21, n22 = Gn[i].nodes[e1[
-                                            0]], Gn[i].nodes[e1[1]], Gn[
-                                                j].nodes[e2[0]], Gn[j].nodes[
-                                                    e2[1]]
-                                        kn1 = kn(n11[node_label], n21[
-                                            node_label], [n11['attributes']],
-                                                 [n21['attributes']]) * kn(
-                                                     n12[node_label],
-                                                     n22[node_label],
-                                                     [n12['attributes']],
-                                                     [n22['attributes']])
-                                        Kmatrix[i][j] += kn1
-                                    except KeyError:  # missing labels or attributes
-                                        pass
-                        Kmatrix[j][i] = Kmatrix[i][j]
-                        pbar.update(1)
+                for i, j in combinations_with_replacement(
+                        range(0, len(Gn)), 2):
+                    for e1, e2 in product(
+                            Gn[i].edges(data=True), Gn[j].edges(data=True)):
+                        if e1[2]['cost'] == e2[2]['cost']:
+                            kn = node_kernels['mix']
+                            try:
+                                n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[
+                                    i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[
+                                        j].nodes[e2[1]]
+                                kn1 = kn(n11[node_label], n21[node_label], [
+                                    n11['attributes']
+                                ], [n21['attributes']]) * kn(
+                                    n12[node_label], n22[node_label],
+                                    [n12['attributes']], [n22['attributes']])
+                                Kmatrix[i][j] += kn1
+                            except KeyError:  # missing labels or attributes
+                                pass
+                    Kmatrix[j][i] = Kmatrix[i][j]
+                    pbar.update(1)
 
             else:
-                for i in range(0, len(Gn)):
-                    for j in range(i, len(Gn)):
-                        for e1 in Gn[i].edges(data=True):
-                            for e2 in Gn[j].edges(data=True):
-                                if e1[2]['cost'] == e2[2]['cost']:
-                                    kn = node_kernels['mix']
-                                    try:
-                                        # each edge walk is counted twice, starting from both its extreme nodes.
-                                        n11, n12, n21, n22 = Gn[i].nodes[e1[
-                                            0]], Gn[i].nodes[e1[1]], Gn[
-                                                j].nodes[e2[0]], Gn[j].nodes[
-                                                    e2[1]]
-                                        kn1 = kn(n11[node_label], n21[
-                                            node_label], [n11['attributes']],
-                                                 [n21['attributes']]) * kn(
-                                                     n12[node_label],
-                                                     n22[node_label],
-                                                     [n12['attributes']],
-                                                     [n22['attributes']])
-                                        kn2 = kn(n11[node_label], n22[
-                                            node_label], [n11['attributes']],
-                                                 [n22['attributes']]) * kn(
-                                                     n12[node_label],
-                                                     n21[node_label],
-                                                     [n12['attributes']],
-                                                     [n21['attributes']])
-                                        Kmatrix[i][j] += kn1 + kn2
-                                    except KeyError:  # missing labels or attributes
-                                        pass
-                        Kmatrix[j][i] = Kmatrix[i][j]
-                        pbar.update(1)
+                for i, j in combinations_with_replacement(
+                        range(0, len(Gn)), 2):
+                    for e1, e2 in product(
+                            Gn[i].edges(data=True), Gn[j].edges(data=True)):
+                        if e1[2]['cost'] == e2[2]['cost']:
+                            kn = node_kernels['mix']
+                            try:
+                                # each edge walk is counted twice, starting from both its extreme nodes.
+                                n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[
+                                    i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[
+                                        j].nodes[e2[1]]
+                                kn1 = kn(n11[node_label], n21[node_label], [
+                                    n11['attributes']
+                                ], [n21['attributes']]) * kn(
+                                    n12[node_label], n22[node_label],
+                                    [n12['attributes']], [n22['attributes']])
+                                kn2 = kn(n11[node_label], n22[node_label], [
+                                    n11['attributes']
+                                ], [n22['attributes']]) * kn(
+                                    n12[node_label], n21[node_label],
+                                    [n12['attributes']], [n21['attributes']])
+                                Kmatrix[i][j] += kn1 + kn2
+                            except KeyError:  # missing labels or attributes
+                                pass
+                    Kmatrix[j][i] = Kmatrix[i][j]
+                    pbar.update(1)
         # node symb labeled
         else:
             if ds_attrs['is_directed']:
-                for i in range(0, len(Gn)):
-                    for j in range(i, len(Gn)):
-                        for e1 in Gn[i].edges(data=True):
-                            for e2 in Gn[j].edges(data=True):
-                                if e1[2]['cost'] == e2[2]['cost']:
-                                    kn = node_kernels['symb']
-                                    try:
-                                        n11, n12, n21, n22 = Gn[i].nodes[e1[
-                                            0]], Gn[i].nodes[e1[1]], Gn[
-                                                j].nodes[e2[0]], Gn[j].nodes[
-                                                    e2[1]]
-                                        kn1 = kn(n11[node_label],
-                                                 n21[node_label]) * kn(
-                                                     n12[node_label],
-                                                     n22[node_label])
-                                        Kmatrix[i][j] += kn1
-                                    except KeyError:  # missing labels
-                                        pass
-                        Kmatrix[j][i] = Kmatrix[i][j]
-                        pbar.update(1)
+                for i, j in combinations_with_replacement(
+                        range(0, len(Gn)), 2):
+                    for e1, e2 in product(
+                            Gn[i].edges(data=True), Gn[j].edges(data=True)):
+                        if e1[2]['cost'] == e2[2]['cost']:
+                            kn = node_kernels['symb']
+                            try:
+                                n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[
+                                    i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[
+                                        j].nodes[e2[1]]
+                                kn1 = kn(n11[node_label],
+                                         n21[node_label]) * kn(
+                                             n12[node_label], n22[node_label])
+                                Kmatrix[i][j] += kn1
+                            except KeyError:  # missing labels
+                                pass
+                    Kmatrix[j][i] = Kmatrix[i][j]
+                    pbar.update(1)
 
             else:
-                for i in range(0, len(Gn)):
-                    for j in range(i, len(Gn)):
-                        for e1 in Gn[i].edges(data=True):
-                            for e2 in Gn[j].edges(data=True):
-                                if e1[2]['cost'] == e2[2]['cost']:
-                                    kn = node_kernels['symb']
-                                    try:
-                                        # each edge walk is counted twice, starting from both its extreme nodes.
-                                        n11, n12, n21, n22 = Gn[i].nodes[e1[
-                                            0]], Gn[i].nodes[e1[1]], Gn[
-                                                j].nodes[e2[0]], Gn[j].nodes[
-                                                    e2[1]]
-                                        kn1 = kn(n11[node_label],
-                                                 n21[node_label]) * kn(
-                                                     n12[node_label],
-                                                     n22[node_label])
-                                        kn2 = kn(n11[node_label],
-                                                 n22[node_label]) * kn(
-                                                     n12[node_label],
-                                                     n21[node_label])
-                                        Kmatrix[i][j] += kn1 + kn2
-                                    except KeyError:  # missing labels
-                                        pass
-                        Kmatrix[j][i] = Kmatrix[i][j]
-                        pbar.update(1)
+                for i, j in combinations_with_replacement(
+                        range(0, len(Gn)), 2):
+                    for e1, e2 in product(
+                            Gn[i].edges(data=True), Gn[j].edges(data=True)):
+                        if e1[2]['cost'] == e2[2]['cost']:
+                            kn = node_kernels['symb']
+                            try:
+                                # each edge walk is counted twice, starting from both its extreme nodes.
+                                n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[
+                                    i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[
+                                        j].nodes[e2[1]]
+                                kn1 = kn(n11[node_label],
+                                         n21[node_label]) * kn(
+                                             n12[node_label], n22[node_label])
+                                kn2 = kn(n11[node_label],
+                                         n22[node_label]) * kn(
+                                             n12[node_label], n21[node_label])
+                                Kmatrix[i][j] += kn1 + kn2
+                            except KeyError:  # missing labels
+                                pass
+                    Kmatrix[j][i] = Kmatrix[i][j]
+                    pbar.update(1)
     else:
         # node non-synb labeled
         if ds_attrs['node_attr_dim'] > 0:
             if ds_attrs['is_directed']:
-                for i in range(0, len(Gn)):
-                    for j in range(i, len(Gn)):
-                        for e1 in Gn[i].edges(data=True):
-                            for e2 in Gn[j].edges(data=True):
-                                if e1[2]['cost'] == e2[2]['cost']:
-                                    kn = node_kernels['nsymb']
-                                    try:
-                                        # each edge walk is counted twice, starting from both its extreme nodes.
-                                        n11, n12, n21, n22 = Gn[i].nodes[e1[
-                                            0]], Gn[i].nodes[e1[1]], Gn[
-                                                j].nodes[e2[0]], Gn[j].nodes[
-                                                    e2[1]]
-                                        kn1 = kn([n11['attributes']],
-                                                 [n21['attributes']]) * kn(
-                                                     [n12['attributes']],
-                                                     [n22['attributes']])
-                                        Kmatrix[i][j] += kn1
-                                    except KeyError:  # missing attributes
-                                        pass
-                        Kmatrix[j][i] = Kmatrix[i][j]
-                        pbar.update(1)
+                for i, j in combinations_with_replacement(
+                        range(0, len(Gn)), 2):
+                    for e1, e2 in product(
+                            Gn[i].edges(data=True), Gn[j].edges(data=True)):
+                        if e1[2]['cost'] == e2[2]['cost']:
+                            kn = node_kernels['nsymb']
+                            try:
+                                # each edge walk is counted twice, starting from both its extreme nodes.
+                                n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[
+                                    i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[
+                                        j].nodes[e2[1]]
+                                kn1 = kn([n11['attributes']],
+                                         [n21['attributes']]) * kn(
+                                             [n12['attributes']],
+                                             [n22['attributes']])
+                                Kmatrix[i][j] += kn1
+                            except KeyError:  # missing attributes
+                                pass
+                    Kmatrix[j][i] = Kmatrix[i][j]
+                    pbar.update(1)
             else:
-                for i in range(0, len(Gn)):
-                    for j in range(i, len(Gn)):
-                        for e1 in Gn[i].edges(data=True):
-                            for e2 in Gn[j].edges(data=True):
-                                if e1[2]['cost'] == e2[2]['cost']:
-                                    kn = node_kernels['nsymb']
-                                    try:
-                                        # each edge walk is counted twice, starting from both its extreme nodes.
-                                        n11, n12, n21, n22 = Gn[i].nodes[e1[
-                                            0]], Gn[i].nodes[e1[1]], Gn[
-                                                j].nodes[e2[0]], Gn[j].nodes[
-                                                    e2[1]]
-                                        kn1 = kn([n11['attributes']],
-                                                 [n21['attributes']]) * kn(
-                                                     [n12['attributes']],
-                                                     [n22['attributes']])
-                                        kn2 = kn([n11['attributes']],
-                                                 [n22['attributes']]) * kn(
-                                                     [n12['attributes']],
-                                                     [n21['attributes']])
-                                        Kmatrix[i][j] += kn1 + kn2
-                                    except KeyError:  # missing attributes
-                                        pass
-                        Kmatrix[j][i] = Kmatrix[i][j]
-                        pbar.update(1)
+                for i, j in combinations_with_replacement(
+                        range(0, len(Gn)), 2):
+                    for e1, e2 in product(
+                            Gn[i].edges(data=True), Gn[j].edges(data=True)):
+                        if e1[2]['cost'] == e2[2]['cost']:
+                            kn = node_kernels['nsymb']
+                            try:
+                                # each edge walk is counted twice, starting from both its extreme nodes.
+                                n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[
+                                    i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[
+                                        j].nodes[e2[1]]
+                                kn1 = kn([n11['attributes']],
+                                         [n21['attributes']]) * kn(
+                                             [n12['attributes']],
+                                             [n22['attributes']])
+                                kn2 = kn([n11['attributes']],
+                                         [n22['attributes']]) * kn(
+                                             [n12['attributes']],
+                                             [n21['attributes']])
+                                Kmatrix[i][j] += kn1 + kn2
+                            except KeyError:  # missing attributes
+                                pass
+                    Kmatrix[j][i] = Kmatrix[i][j]
+                    pbar.update(1)
 
         # node unlabeled
         else:
-            for i in range(0, len(Gn)):
-                for j in range(i, len(Gn)):
-                    for e1 in Gn[i].edges(data=True):
-                        for e2 in Gn[j].edges(data=True):
-                            if e1[2]['cost'] == e2[2]['cost']:
-                                Kmatrix[i][j] += 1
-                    Kmatrix[j][i] = Kmatrix[i][j]
-                    pbar.update(1)
+            for i, j in combinations_with_replacement(range(0, len(Gn)), 2):
+                for e1, e2 in product(
+                        Gn[i].edges(data=True), Gn[j].edges(data=True)):
+                    if e1[2]['cost'] == e2[2]['cost']:
+                        Kmatrix[i][j] += 1
+                Kmatrix[j][i] = Kmatrix[i][j]
+                pbar.update(1)
 
     run_time = time.time() - start_time
     print(
diff --git a/pygraph/utils/utils.py b/pygraph/utils/utils.py
index 6bf4eb6..563ed36 100644
--- a/pygraph/utils/utils.py
+++ b/pygraph/utils/utils.py
@@ -119,7 +119,7 @@ def untotterTransformation(G, node_label, edge_label):
 
 
 def direct_product(G1, G2, node_label, edge_label):
-    """Return the direct/tensor product of G1 and G2.
+    """Return the direct/tensor product of directed graphs G1 and G2.
 
     Parameters
     ----------
@@ -137,7 +137,7 @@ def direct_product(G1, G2, node_label, edge_label):
 
     Notes
     -----
-    This method differs from networkx.tensor_product in that this method only adds nodes and edges in G1 and G2 that have the same labels to direct product graph.
+    This method differs from networkx.tensor_product in that this method only adds nodes and edges in G1 and G2 that have the same labels to the direct product graph.
 
     References
     ----------
@@ -147,25 +147,37 @@ def direct_product(G1, G2, node_label, edge_label):
     from itertools import product
 
     # G = G.to_directed()
-    gt = nx.Graph()
+    gt = nx.DiGraph()
     # add nodes
     for u, v in product(G1, G2):
         if G1.nodes[u][node_label] == G2.nodes[v][node_label]:
             gt.add_node((u, v))
             gt.nodes[(u, v)].update({node_label: G1.nodes[u][node_label]})
-    # add edges
-    for u, v in product(gt, gt):
-        if (u[0], v[0]) in G1.edges and (
-                u[1], v[1]
-        ) in G2.edges and G1.edges[u[0],
-                                   v[0]][edge_label] == G2.edges[u[1],
-                                                                 v[1]][edge_label]:
-            gt.add_edge((u[0], u[1]), (v[0], v[1]))
-            gt.edges[(u[0], u[1]), (v[0], v[1])].update({
+    # add edges, faster for sparse graphs (no so many edges), which is the most case for now.
+    for (u1, v1), (u2, v2) in product(G1.edges, G2.edges):
+        if (u1, u2) in gt and (
+                v1, v2
+        ) in gt and G1.edges[u1, v1][edge_label] == G2.edges[u2,
+                                                             v2][edge_label]:
+            gt.add_edge((u1, u2), (v1, v2))
+            gt.edges[(u1, u2), (v1, v2)].update({
                 edge_label:
-                G1.edges[u[0], v[0]][edge_label]
+                G1.edges[u1, v1][edge_label]
             })
 
+    # # add edges, faster for dense graphs (a lot of edges, complete graph would be super).
+    # for u, v in product(gt, gt):
+    #     if (u[0], v[0]) in G1.edges and (
+    #             u[1], v[1]
+    #     ) in G2.edges and G1.edges[u[0],
+    #                                v[0]][edge_label] == G2.edges[u[1],
+    #                                                              v[1]][edge_label]:
+    #         gt.add_edge((u[0], u[1]), (v[0], v[1]))
+    #         gt.edges[(u[0], u[1]), (v[0], v[1])].update({
+    #             edge_label:
+    #             G1.edges[u[0], v[0]][edge_label]
+    #         })
+
     # relabel nodes using consecutive integers for convenience of kernel calculation.
     # gt = nx.convert_node_labels_to_integers(
     #     gt, first_label=0, label_attribute='label_orignal')