diff --git a/.gitignore b/.gitignore
index 82dc1b3..c5f40fa 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,3 +3,4 @@
 *.tar.gz
 *.pth
 __pycache__
+fig-res
diff --git a/0_python/README.md b/0_python/README.md
index b25eef2..f3392f0 100644
--- a/0_python/README.md
+++ b/0_python/README.md
@@ -36,6 +36,11 @@ Python 是一门上手简单、功能强大、通用型的脚本编程语言。
 
 
 ## 参考资料
+### 视频教程
+* [《90分钟学会Python》](https://www.bilibili.com/video/BV1Uz4y167xY)
+* [《零基础入门学习Python》教学视频](https://www.bilibili.com/video/BV1c4411e77t)
+
+### 教程
 * [安装Python环境](../references_tips/InstallPython.md)
 * [IPython Notebooks to learn Python](https://github.com/rajathkmp/Python-Lectures)
 * [廖雪峰的Python教程](https://www.liaoxuefeng.com/wiki/1016959663602400)
diff --git a/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb b/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb
index 9f9d2a9..f175432 100644
--- a/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb
+++ b/1_numpy_matplotlib_scipy_sympy/1-numpy_tutorial.ipynb
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -127,7 +127,7 @@
        "array([1, 2, 3, 4])"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -227,7 +227,7 @@
        "(numpy.ndarray, numpy.ndarray)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -254,7 +254,7 @@
        "(4,)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -265,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -274,7 +274,7 @@
        "(4, 3, 2)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -292,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -301,7 +301,7 @@
        "24"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -319,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -328,7 +328,7 @@
        "(4, 3, 2)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -339,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -348,7 +348,7 @@
        "24"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -375,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -384,7 +384,7 @@
        "dtype('int64')"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -402,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -410,9 +410,9 @@
      "evalue": "invalid literal for int() with base 10: 'hello'",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-17-3eecc5e8509b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-14-3eecc5e8509b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"hello\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'hello'"
      ]
     }
@@ -430,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -440,7 +440,7 @@
        "       [3.+0.j, 4.+0.j]])"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -483,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -506,7 +506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -519,7 +519,7 @@
        "        6.00000000e-01,  7.00000000e-01,  8.00000000e-01,  9.00000000e-01])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -539,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -548,7 +548,7 @@
        "array([ 0. ,  2.5,  5. ,  7.5, 10. ])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -560,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -571,7 +571,7 @@
        "       7.25095809e+03, 2.20264658e+04])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -589,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -598,7 +598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -611,7 +611,7 @@
        "       [0, 1, 2, 3, 4]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -622,7 +622,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -635,7 +635,7 @@
        "       [4, 4, 4, 4, 4]])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -653,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -662,39 +662,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[[0.34933999, 0.78232989],\n",
-       "        [0.07449912, 0.57488499],\n",
-       "        [0.28079982, 0.65921106],\n",
-       "        [0.71455261, 0.88375022]],\n",
+       "array([[[0.57397454, 0.12434228],\n",
+       "        [0.74835474, 0.01034541],\n",
+       "        [0.91383579, 0.02807574],\n",
+       "        [0.14217509, 0.64698341]],\n",
        "\n",
-       "       [[0.00794753, 0.41466795],\n",
-       "        [0.21029866, 0.12968518],\n",
-       "        [0.98595403, 0.47316115],\n",
-       "        [0.50330171, 0.87038751]],\n",
+       "       [[0.65606545, 0.84787378],\n",
+       "        [0.31064031, 0.70205451],\n",
+       "        [0.30486756, 0.34702889],\n",
+       "        [0.47537986, 0.91154076]],\n",
        "\n",
-       "       [[0.10672402, 0.09192073],\n",
-       "        [0.48656172, 0.16710676],\n",
-       "        [0.46217936, 0.09035176],\n",
-       "        [0.19623019, 0.73555862]],\n",
+       "       [[0.32192343, 0.77700745],\n",
+       "        [0.80485914, 0.85919158],\n",
+       "        [0.29751565, 0.27228179],\n",
+       "        [0.57796668, 0.18255467]],\n",
        "\n",
-       "       [[0.75468369, 0.76685125],\n",
-       "        [0.68205367, 0.99455825],\n",
-       "        [0.23566499, 0.431837  ],\n",
-       "        [0.86997877, 0.52098775]],\n",
+       "       [[0.50020698, 0.58134695],\n",
+       "        [0.14200095, 0.97556272],\n",
+       "        [0.32948647, 0.35170435],\n",
+       "        [0.27768833, 0.75059373]],\n",
        "\n",
-       "       [[0.99353122, 0.72868516],\n",
-       "        [0.74724343, 0.63273805],\n",
-       "        [0.16946554, 0.06170885],\n",
-       "        [0.28687951, 0.6671094 ]]])"
+       "       [[0.23972627, 0.08461662],\n",
+       "        [0.1929383 , 0.80565903],\n",
+       "        [0.2627892 , 0.73361884],\n",
+       "        [0.18415944, 0.44976198]]])"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -706,19 +706,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[-0.47727318, -2.11212891, -0.10766674,  0.88444896],\n",
-       "       [-1.66157402,  1.80598739,  0.20359836, -1.1118912 ],\n",
-       "       [ 0.24731274,  0.0396289 , -0.54177391,  0.38118806],\n",
-       "       [-0.15762081, -1.05826785,  0.91565702,  0.79167261]])"
+       "array([[-1.74300737,  1.94689131,  0.18922227, -0.20440928],\n",
+       "       [ 1.31664152, -0.01176745, -0.43956951,  0.53571291],\n",
+       "       [ 0.02140654, -0.09635041, -1.84205831,  0.64951045],\n",
+       "       [ 0.35682903,  0.96657395, -0.50099255, -0.80044681]])"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -737,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -748,7 +748,7 @@
        "       [0, 0, 3]])"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -760,7 +760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -772,7 +772,7 @@
        "       [0, 0, 3, 0]])"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -791,7 +791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -802,7 +802,7 @@
        "       [0., 0., 0.]])"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -813,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -824,7 +824,7 @@
        "       [1., 1., 1.]])"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -856,7 +856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -882,7 +882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -893,7 +893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -902,7 +902,7 @@
        "(77431, 7)"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -913,12 +913,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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X8d0K9EvoT/Bhd+AXT0/HL5+ZmbGWR02NxLXPzsSMFZuz7me2TJyXsCb657uX4zjyyh0T5uCdHCh28s3uympc/p8vsGzjDtL2SzbQtss3fn2eYk4FvGDtNizPsfIik2Jwhxf4Xl2TA0uR8xYZCv51/9aRldQfQ+VlRSIVhXDjS19i0qJEsdqaGomJ89aFrq9em7Uaf3knvQzHnhKYL3IBC0VFir+Qy8Sbs9egYux4fLMlc9BpvnjwoyUAUi8XCufdPwnPTF2RsSaQ7zb36KdLrdtJKTFvzTby8esSpitsm9798ZUpycXGHXvx/LSV+NGjxZNdydeg3zkhe/9vhs79HyzGpTlQ7OSb9+auwxuz1+BX/5tF2r5YtMcAsGbLbvz5rXmQMhVN4bsQrdu6OyvXHSpLN+xAxdjx+JDg8nzcvR/iiLsm5rxPNvxXkMjBjYzrwvvpwg14/ot4lnqXVIwdjz+8+nWhuxGb/05e7qSdpKXISWsJNu3Yi3UOM6k+8dlyXPDgZwCAxyctxcWPfI5XZ32TVNr4WXV/9uR0/OWddKtRMc1j+YSFohLnmc8T/umzV9HSjuZznEepeZpyi7P3kOrD+9/Jy3HCXz7EJ17BPiaFjDGh+/eyTAhIKVExdjwOuP2d9LbhuxXkb6RR1xnLNkXTQI+87R089PGSGD1iCsnuymqn7rtPe3PsFGIWq0KtJcIeuZ8/NQ1/f28hvlq1Nfmc+NuN+uO72P/WtzO2+9qs1QHrzbcRBSk/09rLnjvajj1V2FNVvNpnfw4rK6JV4Xf/PRnXPjez0N0AADz8SW7mxI8WrMeXJeLpIZX3oSv2v/VtjPrju6G/vTBtZVap5Vd4mRrXbtmNyUs2kvYZP+sbAEEPJp/Pl25C/xsnRJ4LSgEWikocGdG31ZX5XkqJN75aYw1GjaIVS76wM21HPIPZqxMTyFKiy0tdxCS4hN03/7sykZokw7LK+R4nxZioo14EV4dVm3dhw/Y9uPW14tGKVlbX4I2v1uQlYcTarbvzVnds5bc7nbpbHnfvBxh405vO2juwVxsAQL8OzZy16RLbcNiluL6kFBbR2v/Zk9Nx0l8/BJAQDPe79W288VWEAs/JBWTi/0E3v4mT/pr/NNnUd8cqbwG5dVe04rC0PmRHLhIMVVbX4MKHp2Bmhmcw12nkf/DQFJxaIjHBNdQFS0yWb9yJ79w/Cdt2V0JKiWuenZlVavn3vGy9QtCV1TM9ATXM5fCfExdiT1UNpq/4NnafihUWikocW+KBXPL212tx+RNf4B8Tzam7o1iKfDKdR5w2axNTlmzCI1lq6jYbXva2a091KdlVAP9j6tivX06f7lYRaqDkm7+/uwCXP/EFJs7LfTr64+/9MHLdsd2V1bjp5a+wdXe0xeRhd07EGf/4JO17agwPAOxz4wRc8EDCVWTFptS9q6yuwUvTV0Vy5dVp27QBAGC/Hq1I2+fTSpoJX0khhKp4it4/v3aJv3D+PIPA/Jbn1r10w47kAvLtOan4ssXri1dZ5de1eXWW+0QL2eozpi1zvwid881WfDB/PX6dwT00zGIQxp6qanz3wc+werO7OXT15l2RXctyVag+0XaCXFkT731nPiYv2YS3Zq/FEw5c/tQ4x6ixcrYYSSnjz/vFCgtFJY7tRbd9T1XOqiVv2J7QHthimWocabRXb96FJ72JgdJmJk368o07UTF2PL5aRTPVZ5r3npqyHPv8dkLGeCidtTH8h8+7fxJ+n6VPdyYlV9hZ+BPji9Ps9Sbi3vNj//w+7pgwJ9I+UV969cuLZ7Eah5XeImPj9mguC0f8aSLueWtepH22xNCSPzVlOR6ftAx/f9ee1YjKjj10AXtvVQ0mLU53Cxk3YS6ufmYGjrzrfSd9KjXUOcmFAq1GsRgDwH8mLQ1drL4yMyFQzFq1JXlcP+tVoaFOUcXkPueTiy797qWvAABrvPu4u7IaW7K4Vy9MW4VPF23EIePclWI4ZNx7RtcyCq6N61J7Duav3ZYc83e+MRcVY8dn5Qqo3ubZxHUKhV17qyNbG/3kVeu27lY8k1I9/O/kxLz/f+8Fs9RFXQ8VCywUFSGR3M78DyGT5eCb38Tgm925kYRhm6TnrY2e6CBMy/r9hybjhhe/xJadlSQNY+Lyma/hu3MTGsvnptLqhWS6Hbe8Mht7q2uS2Z0ovDBtJQ7847v4wqD5q6mROXeTEiIx0aUCoDO/cd+fv84qjMTt86L1O3D/B/FqJ/kKgZoMk3CUOboI10NWDf/6bXvwvsGCtHzTTvzNe2FJKTNep7j4L8FiSvLnu+WZ3DlzcS0KNXbC5s5NnutLwlKUfbyfGlu4fONO/O7l2fiJl/o+rC9SSquiREppXTxFvT/VNRLn3T8pNKlD1KmpPJvrZJi/s7VeuKqzo+K7SW3zNP0n//UjDPvDWwAS76nL/5N+f4FEaYc3Z6e7UbpShpqgvhs3bMtdvIv6HAAJy/ovnpoOAPiXV/j+ySmJOkDZvsv9mEYb+lA1He/ed+bHmp8XrtuOUX98NzTG1rc86c9qpqRMxQoLRTlizjdbM2pbpZS49LHP05IBvByjPgJ1qsz0cK7ektAWVTlYLGSyKlDxNePUydbVpOy7C2QKLPWP9tsXv0z7bcvOytAASd/9xJQhr/cNr2cMrHUhNJ3xj0/ww4enkLdfumGndXERJSX6lCWbnCxKJSTenbMWvW94HXO+MQejRolbKRaZaPXmXWkpisOu2Ln3fYqLHsmc8e/CRz5H7xted9Q796z8didO/8cn2L6nKqfuLwDwxldr0PuG17Egg/JGz9r25cotVne8XCxc49K6SX0AQL2yMnLcpo3kPRGpFPdh7zlfgy6lXRh57NOl6HPD66E106Yu3YTeN7xOTnABJOIfpizZFFos1H+nUZNwZCPc9r7hdZwb0f2UQi4Fbv82LVbcpa55dibe8AQfPYb0owUb8JMQgSnXFoLeN7yO657LnAkyjlKWSg1BweCP+943vI6bXs5vbTDTM1cj460blm9KjAlb4qqwSzF9+beRvRQKDQtFOeKkv36E79xvnxQ37diLd+asw/f+PTntewpV1TXOrQkPfJjQ1k/O8CJassFerwYwx674TFq0MVKBWuoLITEnBzeesWJz0ry9k1iheZf28hzwuwn4c9gD7t2CF6anC4HD/vBW5ABJPzPTCxmEShe33heCgw0n/rv55a9wkOaysH1PlfW41zxLy5A0ce46nHf/JDySIbU6lbdmJ8ZRruojXfDAZ/jBQ5MzbvfW7DXJ2hBU9lRV496354fWgzhk3Hs44q6J+GrVFuv4X0qs7UJJixyFf3+0GBVjx1vnoSgaw8PunIiZKzbjYUdZ/2zH9rXcX2ZwT1Gztu3YU4VT/+9j/OzJacbtw+7TgrXb8PSUYGxA1GQkFz0yBefe92mkfcLIZmGtZt2yZbH0v5Owa8mf9+a4VSHxJ35M2+tffmPt0/Tl3ybndt9ab3uHPvEZLUYj2xo0Uz0vgF17q7FuW2KezXbOzqW4nalvfyAmnclHXZvnp6XSk6/8dmfOBDH1mduxpyr5zPqHsyeaSn3+z2fLctK/YsA0boQAzvznp0kvhVKBhaIcMjfECrBw3XYcfMe7WL9tD/6ape/9ciXFsBAC3+7Yi0PHvYevV8dP3Whj9uot+GJZQljyaxHZyGQFuODBz0gFaqMKfgltZnCfM/7xSdK8fdebNM2F/jDvrqzB30MecIpG2/SiCNv30U+WkvqXLfpl9RdLvqbwsUnLkn7mgf0caPD9+JhF67db03rW1Eg88/ly6+LWlWZeSmm8T5MWb8RHC8K1ZLsrq5Nj9LL/fJGsDUHlsU+X4q/vLrCm/x6vLgyLyFX7tvGJODDbI+pKb5OYf6IFmtssl77wuieC26u/6PYLA1M57t4PMfaFoCX5uudmoqq6Bifc+yHe81x67/tgEX7quaN9uXILDrvzvaQl5v156/H50vTzp15e23O7cfueZArewD7azdNjKYBwbfnrXyYEzpqaVJ2wMPx2bK+KReu3o7K6Bs98vjz0nXLmPz9Nzu2+Br9Ly0bG9vTrYHq/6DFFuyurcfTd7+PTRdHKPJz/wCSMuj1+PEw27K6sdmKNX7V5F/k9bJqPoyRKWLZxByn2ZfXmXTjszom4O0fWCPWZO+mvH2HkbYkyFL6l+LkiqB0Vhu1OZeNJY9vTZjUrpfgiForyzEMfL8E3W3bjra/XYBfRYmFizZbdiuYO+HDBeqzavAv/+iCYES5Tqs0wtocUjx3zt49x9r9065f5QXDtWxw22U5b/m2a+wb1sK6yRKlZeaprJK57bibma6b7het0y5r52LsrMy/SVm3elfXaOO7+cW/ryzNW4YEPE2MzqUmWwMcWk/zz01biN89/ifs/MGc5zIa1W3cnXe7+MXEhBvzuDWzOUPV8x54qXPnfaVi/bQ+27KzEgN+9gZtfCbpHVFbXYPysb5L+5Tb8+23TsAoUj0ufiv8IRX0Jz1yxOXKNi8T8k52lZNH67VjhKZN8gd+WXUmHkl2RGlu4Y08VNu3Yi3lrt+E3zycEpnET5mKCl+76L+/Mx8pvd+HzCO5jQMIC5SeRUS+9zX3ux49PxZVPTktqwr9YtinUKhyoV+Z9lz63pVzraqQMVaz4+PEsHy8wL4CFEHjgw8X4zfNf4n+WRSjVIqmfk2k+K9MsRYvX78CSDTsiFy+dqQjm2b4S9T7Z2F1ZjQG/ewN3vpkoWr1g7bZQixyFQzMkTdiyqxLTlieEB9NrdZM2r66w1I078q73cSHBrdt36ctHPUJVCX3nG5kLgatxfJlwbV2zHTfOGDQJumpbutCr7vMsMX67GGChqIDYXhY6T05ejlkrNwe+k0gtOFRBXNcMqS5FvsQ+8rZ3cOV/zS4gUfpmIo52IKyon6mVyuoanPXPT/GjR4OxFFKmYnVmLN8cuQ/ZsGj9dvzvi5XWawsoi8mQk6PIanur7K6Tlz72ecYXWVzivtevenoG/vh64mWSOkdpTTHtC7ybdqS7YoZeu4h9OnTce8maKS967o8btu+x3oNBN7+J8V9+g7+8Mx/rtyeek8cnBd0jZq7YjCufnEZ6efovxLDFZRiuYm2qayT5mCZSwq29T1JK/GPiwuRC6PR/fIJzM7gX54Jj//wBDv/TxMB3Ybf67a/X4l0vhbRa3+fVmcF4z/MfmITj7/0g8B3VVUZA5MTod9JfP8Ipf0+v92IrOLnSS0FfVS2xZVclzv7XJFzx32lp/fOFQgHa4irdZh/OpxaX0zKRcjW0pf29m+gB4Pdn6YYdiXnUclwgka00bNH61JTlqBg73hpfFjUVvBrftmTDjqyC1f1jP+sF6h9374dZvRNsFtWLH5mCs/75aaR3/l4Hgfg7NBf3D+avT3O3lJI+Z0opsXBd5lgk0tiXwDbi/ddjuPXzysTmnZWocTCfZ+L9EAu53/OXLLHwYTGDxUrBhCIhRHchxEQhxNdCiNlCiKu879sIId4WQizw/m9dqD7mhuivwddmrcYNL36J0/4vWMtDytTL5NpnZ2DphsSCY3zapJB+zA3b9yS3+9f7i3BOlhrYMOIIRdZaCdq73G9fjwmokRLTPGFogqXIoC2deLZkOveUz308BIBqy8z8zpx1GbWC+rigChQuLICqFomSmtwmpAgRfOlFcbdUX0b+p+3EVNASblzD/ulZk2xjFUi/Bm98tQZLI1g5dG56+SuMvucDa6KBTDFa/gI7k6Vo9uqtuOvNeQGBxPYCzxSgDyQWyE5iKkVCwfLYp0tRVV2Dyuoa/PjxqfjRY1OTffE31I/22eJNmL82twuRTOgWIDUgXn1WbQUn/a2EAPZ4AsDXq7emXV/f8klNxLN1VyVpXrEJRVICO71n0rYwXxzhWdi0Yy+Ouvt9/Ob5WcYxVC4EKqtrcMRdE/GLp6anLaz9a+Ff7+emrkizfupxY7bF+dtfr8Vx936Il2eswrptu3H03e/j969mDtA3CYq+J0Sm2F6qw4TNou+/g6tqasipzLP1lAGA7z4YjPO88OEpuCJEIbnA8oz+/tXZuOK/CZfVhz5egtH3fOgsNnXoLW+RttPfqW/OpsdaA4m5/B8TF2L0PR9Ykw0BMdcc2i1VlQR5qCWeVwppKaoCcK2UciCAgwBcKYQYCGAsgHellP0AvOv9XSuhTh43vJCe1QwIugts2L43Gcyps3xTcHGsa0LufGNuMijUJXEWz6FxG4ZmUjUzROj3mfrwbUgthjnfbA1daEZJtw2Ev6DfnL0mWRMijA/mr8euvdVk4eTxT4Ma6Uc+WUJy1/Jx5T43ce46jH0+czag1P6qAGOPCcrnhOune7/pZfM90qF0z4XwrR7HvyaXP/EFjvnz+7Hb/K9X+2uOIQMikBC8bPiPnvqc6dNajQTemRPtJf+fz5Zaf1+/bQ+G3vIW/jEx+yBeAYFHP1mKm1+Zjf98tsxYT8d15q9te6rcukSGNLbIG9N7qqrTsuhlbE6Yx3fCApR59N82fk7WbsoSQJOG5QCARvXLjW5XZM8yKXGf54774vRVgbNQlVlCiKQw9paSECiZbtzbs6pGYvH67fjV/2ZljJG1zWe+y/XcNduw1RNk9KQt+qWcOG8dht7yVmick5/1NM0F0kXGT61R/x1cU0O/D7qLeTbYDlkjJaq0gqUfL9iAa56dAQB45JOlyRg43w3P5tqXC0zjgqpY3ltdgy8898U1W3Zbn8xYiiRtF1+JUcvkIQAFFIqklN9IKad5n7cBmAOgK4DTATzmbfYYgDMK0sEcI6X5JUt9h+gV300DdOW3qQdcSlrMShRMD9k0outa3CA8fz99ElbnP5tQFObacNJfP8JRd7+f9j3FDQqwv/R+8p8v0l1rvB2uf+FLXPjwFFz9zHTS/RciPWPT71/9Oq2f1PSzkdDO8eJHPw+tpWBKN/7KzNUB90HS+SKhda0YO94aV0ChYux4VIwdb/w9SnFUyvvlnxOzj4dKxBSlX6iwR0dKiT+9MTfw3Ntws0gK/wwknsG/vBMtqcyGDPfAty6O/9IutFHYXVmddNPcttucCpxivYqCqtHV0x0DbhccO/dWJxvM9LylhCfz/Fkm6NciW2ve7srqZFp6KaXR7apMCNwxwXfPFZryJWgRVouTqt17RCm/UCaAKUvT47l8y+oKT9l43v2Tku6HNuVimBBQMXY87ng9rGi12QK7u7I62Zafrjws46UpTiVOYeZM+Ba8yUs2orw8fFmpz19UC6up/lqwcfug1ofg9x+aHJrd1U+gEqcsStoxIzzBpmekfj26QoH6mC0i1Hp0QdB7Iy+HdEJRxBQJISoA7AdgMoCOUkrf/2sNgI6GfS4TQkwVQkxdv95tmtlcMnlxYhJ7lJiKeP22PUZNm76WMS1uVEuKa22nEJlTR4e98FVUi5eUElXVNagYOx4ne7Eevl+uHsinF1BLfa9YiizyX5QHNZNJOir6fXjKS9f75uy1JM2qgLDG4vjY3FzSs8/RBgd1sjdlaVq/bU8gZa/NYqreS9+C99SU9JS6krrgczw7U6yh9b1Fwobte0gCyBtfrUmrbbXi25QAnKmFeWu34Z/vL8JPn7DHtaXay9yn3ZXVyeKOP39qerKgo59kRHWF2bY7XuKTKLgsDKjOxWHDR3Uti8IPHpocyYKq801YuvwMmFySWjaun4qNCvld9axL1WCxJCEQgnxfs3W3nb92G96dm5jrbC7BZUIEMuip7pl6im61ho06/lcqz5k+L/mnUVUjA8lYNu3YizZNGwAAKto2MfYvUXsrnfs/TC9aLVITZBpjn5+F4+/9EAvXbUelJ4yEZUWl4nJJ8ObsNeT2JhneD7rw+OpMe0p2IPM5RB2BUS3boceMcFDTa4Fq1VUTn+QiI4/NnZD0/giJFS9WCi4UCSGaAXgewNVSysBKQCZWMKFXXEr5gJRypJRyZPv27fPQUzq2BbTvVkUNiJsaoqny0V82JhcvdW6P+n5atXlXxuJ58y2BiZMXb8QBt78TWtjUxE7fp127jrprnb/A1YMZ1etii7uJgu7zft8Hi3DHhHQNH3XhJCyaQAoS0pjqVtUEWlMlK0d/N8JLgGpYUI9do7mlqNfJds38tPXrFMHav+9h3bjvg/QFhqlPNraGZF/U+XbHXjz4kf14QCImcN3W3Rh52zuh/u46lz/xRVptq0079gSsazZ8RUCm+jtROPru9zHE849/debqZEFHn2WKu4mepta1IAoA37k/WtrzTNhe7H6yFltNkjA+WrAhYzV625Xx3yMZi4ArrazaHD4nqBbqnaEWBEUq8thdWWMca6/MXE2O4zP9pLsoTZwbruSpUrJ7CpiFMX0eMbr+yeAiLzBPKX+UlZnPy5SFsEmDeoajBms72bC9QgREMqD90sc+tyq9xs/K3opqYp1B0VlVnX0amJdnBJWsJne8TFlCfVzPPrlIL53tHCmUNqhJUKKw2qKMoIQW/MOBt0S+KKhQJISoj4RA9F8p5Qve12uFEJ293zsDINhOi4tF62kCDzWmyAR14KvHsVkXwmKSjvzTRJyXRZYoX5Dx4xfCILthaH+b5iZVELJNYNnMG+MmzMX9IQtw6rlQF7gmXrGY97drQuLyjTtRMXY8Xp25OpmqV2fGis2BiXmNRUtNncCl4bMAcNtrqfo2tqfAL7Q7e3WqeKl+dHX/JRt2ZBAEMyMESLW+Zq3cEpqNR2fdtj3JRYQvTCxctw1jn58VaaFNnS58X3kqW3dVQUqJqUs3Ge9tJqtFdU2N0SKQixIVlMxVYZksTfh9XLV5F3bvDbbtF4qcv3Y72UrqMtvSjr1V1kVJkPBBsmtvNeatSbyXwuoRpfYW+NJLIb1lV6Xxni7buDPwnMXJJKZbfS7WsoiGtZ1YIGf2iLAKaZZ7GBCKLA+cSYNve0bLY7zv0+Y6pYktuyqtx5uylFZEOs7zaXo/VNdk72Naryy4LDWdo5qYxnppHc8/+rO93KCcnB4hWUOUsgBhzFy5JbnWkrC/v+Ngu74uY8OKgUJmnxMAHgIwR0p5j/LTKwAu9D5fCODlfPctW9Q5IVu3KwnzgPxQq+1gevbV2gbvzjELRXtCYo0yZRnKNM3XK8/8IlBfRHryA5NfOGAWeErJfzUMyrtzo6XGi65Ze3VWQoC64/U5QYFJu05fKYKATbCnXl71fq0IFBpOWfckaMqBhB90cDvf2vrAh4vJRfTUPplc2YQIFvg1JUrYtrsymakr83GDf9/62hw8/fmK0ALPmfa3Lep2V1aT2/S5+815eGXmapxz3yT0uv71wG97q2oCcRb6sXyqaxCqJADc1ytTsc2vNsWBjl/u4OnPV1gX+NRT8YvaxkUdp49PWkaqjwTY5w6qZl21krq4d6Y2qCKCup2tTk+58put17ZTUm99ueVY+k+Uy1RWRp87KdemvExY3atMruMunkZTG9WSbikybadbRtV3VhyLioTM6ZrgyidT1n/VAhklPfZPPHdknTh685oambW72uTFNIG6NlJIS9GhAH4A4BghxAzv38kAxgE4TgixAMBo7++SYrESyPbarOwC9rbsqjS6T6z6NrhYM1fmTn2uVy6SRdbywXRDsgVVQ26br9TA1bAA7jCoL3LXcUIA8JGlCGEYYbERVD9iEwEhQ6YW+Ku37A60rF+lPyjpX62KtxgvGHUM67JI1OxU+v5xq4pTT2PrrnBXOiEEdhDTyupLBb9a+9tfB90WTbEyNVImiwfarn+cIOpte6qMmsqHP1mCP7wWXqzyPcXdqaqmxiio6/3Va/3kCl3Y1i2oKqqCpUWjcBeoKMOUGpNgupf695RnzrZEFgBemG6O/QwmylAUBzGe9c+0BdXHYRlFY/LRgg0k97lvlMLmid9SP+qJWkxuvoE4jbSDZe5DYrPUF+Vl9jgs3yI5WXFXtwkBuhuynlihxqIA+njBBpz8149ix+aZ3rHVNTKZOS8us1cHvRleV5KpmBK6ZH403UlFekvq3KHHPVOhprgHEmsnF8lxbOiePfUNyTNslKpyupDZ5z6WUgop5VAp5XDv3+tSyo1SymOllP2klKOllNHKeRcBD3xI85+0vcB8/u+9hcZJW5+YbD7UPvXLy3CrYZETZxAvXLfdWivFX/zpfBvI/hM88KwVqUlR9SfXz9dkKfKTWRQCPQ4qDDXRwG3j56S9zCjmaGvdHuXzCmIGMiBzxi8f6qI2uChJfVaL623N4AISxswsakgEXPoMAz5tQWp01zEfJy2+wfBs6XE/938QPnfUSOCThYnFZjGmi33LUldDv87UGiDZvlT13U2lDXRMQvqOPVWBczGVQAASWewo/DE081j6XKcvFFVcLj7STt3mimpYoOp1r3ZHLGeQdhzl85xvthq7lCnhj48eD6M+33pqeaMApr29KfaRTBZxX5n6xbJvk2Nw6cadgTGntqAnMNLHnHouen2nsS/MwtffbI3tZmValO/cW42735ofvo/Xn69WbbFaLVULne6aFmeoS82j7wvH5Uek4bPOrr3VqBg7HkfdNTHttyjKrL6/nYALH5li/N1WyysuR+wTHrdfXSNzks2wkBQ80UJdZq3lpeqjF39T/bD1JAKmKfdzJVlD+2YNzdrJGFPO7qoa7IqR4lszZgT43JBcwqahUbn6mRmR++MMgub36LvfT2ZTAtIzRr1o0ej62KxJ6qLO6gan9Ok+fTFueX/rQfYmTNpE9aW0ccdeen0RIrZR7KooHxDdwhWGLjCsNixS1O3CslWltgv+rS569MKSFJo3tAeO+9iyacZVaupFqKMyV1NQ2OpFUQSL+Wu3B85l6tLsF1evGBQMeneuenpG4O9HP1mCirHj0+YO05i0JcOxHdtmdTe9Lz7TEvNk64JXT5sgTAkZ4hJMtJD6XFUtjYoTk9Ljq1VmpVh5mSCv6tXm1UVuuiUqxRtfBZ8Xhwka01BPQ1XSvJfh3ny9eitO+fvHOOtfnxrHxZCuLZOfL340uPgPplZXBVhbBtPg35OXhLuG5SIhjMq1z80AkBB0VeK8/0LrOXr8/tXZsXxN1PeDfiVMAv19HyxKKutqCywU5QCqS406gZo0NvrgDFqNgr/NWhmuTVQ1Sof1a0eyKNnQTcRxHsCAG5fluOqzuHOv2T0gG3bsqUJNjXSSVcZ4bZVftu+pCqR+zXTU7Xuq8Mzny4MaQ8tFp06yap8qq829SNPqES8T5XJW18g0AW/9tj3Y7w9vpSU7cJFO/tFPliY/Z3u3bd3RFySmY6W5EhK3o6IKgYs3pARk3dpkepT0zI4q1HGmP6em3XQFULZFb8sJ8Yw+QW2vRRCI5LITH7XuTNh1vuXVhLVfndt3V5oLP2/YFqX2VrjlxI+7Ct8n9VlP5GB2d6NdwWaaYP7E5GWGLWlcdEhF4G+T9VgIYL0hYUacJEmzV2+NpXi0zvXa4FizZXfy+u9V4kpsrqMqeu1DE6qlyGZ5UZ9pIYCT/5bIqrl4vTkhTu/2zZKf9Zo6pqtnd/eWViHCJ06yEJ0qy3vUJDC7UK6prN0aL8mLqkC1eYOoLpc2JePamK6EhYaFohxDXbfrWp5UA8E/awwvLCCV7ltHNUc/9PESWocsqObStVt2G4vEhfHs1BX4x8SFwYlAXxgqP6nTxd/eDRaAjOKHa2Lb7koMuvlN3P3WvKRPdy7IRn47/p4P8Jvnvwy8fNK8XIhZk+Kgv1CpL/aAK4rhtbWnqgZasiFMnLsO3+6sxMNagL+Ls1LdkEz3RC/eatpOF9JV9LSyRlc9YwtB9No/Jqi3XhdAVKgFf6nH0s/RtN997wetlXpsSlQaRPGDJ/rABLXTqe91l7FsURN92KzCEjLpbvuqUhRZ57B+7azHU09ZVdioU6xfrDTsN9ucYLoP5PIF2na22nOm/QLJGtLqD6X6rnpfvPP1Wtz08myEEWcu+sVT02O9B9T+ZspSdsGDn+HKJ6dhb1VNIGGGnnXUtz7p7b1myUyoslpRWNjuY6AUg+5yaHRNNEOJJ0vbBzSB54EM5RwofGtxC1wew+15s5Z4ynUsUaZyK2HoNb9MuKwll09YKMox1Do56gJfzQKmv2zUhSYlsxuQnkXHtEDT3dZMlhNVe7Zm6+5AYGgmfv2/WbjrzXmBiU93I1RdQmxalPeUTHqmVNOZ8AW8l2esjhUgSjW5W91QLL9t2VmZdKlSM1DpAuFyLbubCTVeKa6gRp2Xg7WJwreprqlJu8e2IpPZQrEI6Jm+1OukZuWxZQRboFVrN1tn9ec7fDv95WhCv6em627LrEW1mFI1nPqL3LTfPE2wUDWeVKFQRU/taxvvVEE/2EbqPOavpWeaoqAuKPTrovdn384tAABn7d816yQtQLAQqDo+ddnmrdnhAfA6jeqHLzOoyrT0xXSce6V8r91r9a+XlYyFVI8PvQ0besZYE89OTdW3Us9+t+aqHrgyQmClF0OaNp5VHaSUSQvjza8EhT7qtdX7YcI2lZh+sin1TM/phu17yYtwU1IRF7ExcfSRtrlYL4Aep+6i7Z5S46uCWYBpx3WhtC4ELBTlGJs5VUUtDqouznW3JvWvVo0bkNpO04wZtntLy4Rl8r3Vs97FQe2Snr5WjZmwZcpThbM3vsquUN2qzbtIBTszYZqAbFYF29yh3gO1aT2GQ71O6iJC747qRhB3ylLP0aYhp8yJe6pqzAlHciAV1cSY3NUX8QTiONtZqVnXlGMFMi9qffhy1ebQ9ly8X9RjubYmUo8LmJMQ2BYH97wdHrxtg6o00gPqbZeaGmOSLeRU7Uitd/VsZCo2pczarbtJbpu6MKtaVG3XzDR243otZPso6MIYeR5QtluoxWpShQmqlv2fitVU3UdXWOj1w/z1AlU4TrPCURVeMZSBZPd0m9VHhn9esmEH7nh9bsZ9bJjCD6K0F0cp0bRBufG3T7V4Heq5NG9UP/I+NijKRCBRf8+HuvYtNlgoyjHqxD/VkEAACErVtsWByd/bRmPLQ6dOuPphTZPYwC4tSMe1oU4eHxoy1AF2H2d1Qndiqo0xe5hcFnX+8Gp4xj/APsmoiTXsBVVTn/dUp176NkujDfX+LNDqLahWC5v/MuVYyzbuTFtsmbLgUS0T1sKNWc7T1HT26cdJfbHToiE31haJaZHMpcBJvZZ6n742ZFLTA+pVbAsqk0uJLQYmEOsA+kI7KCQon4n7u8bm6qi6Ri1eb56nPl6wwZhOXdVM6wVke7ZrkvxsG5+me0ddNPVo0yTwd7bP8FNTVgS/iNGeLUPoFotV1zbGjSi76Bnn3leLsRNjTtV5VN+MLOwo93SZoXgpEEwSQXUppips9L2/MM7NwS1Na4UplvWZiq4sjpOgIRhrZT7fK47uG/hbvT/Uwt8uRBP1GbadrloeI0rx7GKChaI8YkvXrAoGtgJ16vuFqnjRK2mrg3rTjtTDaRPGVOJqmeNMHnurzPsE3N1cLPJi7KMLbQGNinK+m2yFE4kH1rWCJlRXQj24MxAHQLwf+oJKFQRtL1GqVUa/dWo8hUqswn3aPnEWVOo+egptE9SU6Xu19Kmm/WzFelUes2SBU09drx0TzOrkls2aW4ravvritM0rtsdbdTVS0V3a1Oafm7pS+T7Yui39tRo8rO6VR8NbwNLxu5e+Sn7Wx7paKsEWEK16GaYtkrW6PSrDu7dKHdvS3zjJBdRzOah3m2CfHGcJq6IGKal9sLx8X1IW/9RxYXMlVBVUkxwX1UyPr6Ltpy6SbW5SqmJC95ZQj6Wmt6fGFOnu7qpheKMidOjnlCkuKxN6Jkj1vKhjXbWyphcCTrXRsJ7ZBXgNMZGBVUmI6PO+9flTfrKt3YoZForyCPW9adMmmfxhrcclHlifID83xAplSrtpQs0kQ62fY7MAqRq/uL702b5fbe/TbYFK3ObtqPWB9uvRitirFLqLClXjQ8XuNkOzaprG55qYWXSoZCsg2RjQKWhNDWQtU05Y16bFWVyr8wA1488dE4KuJjOJriPakUlb/fmtoJCrXosde1Lnb1PK2LSppphGvT01BkxdDFTXBFMv22ILVE1o3JiqbFEXodOWb06OmQc/WhK0Xlm6t1WJ0bLNnep80aCeeblgey5McyRZuNFORK8zlC13vxWuhNHRx0zwtxS2WMM/KQofdcztsGSHI88Jyob6pd1tcN+2JZ2woZ6+rnSlol7PJcrawJ40IbWPno5efd7VZCF/1RI0qaEKLt6BawKKEqqVK3Vg/f2v9qlF4/qB33QlGqV99V7Z1pa2epMmt8X046Zwkc23ELBQlE+Ik4dNY0pdaFLbU9Gzozz9ebgG9k1inZp0FMsJUfNtc1MY2Dm18MynO5CK7lseaC8wf5kbN8WR6NiKslEnrTgTld0dzWYpUj9HP67uVhlnitWDpVV3RPXFsdGSjS0O//simMlQ7bs6VNNTckcfyJ9b6uWoC/d5ljgVqlWvJoZQrY/bxcozEwzkp7kN65isaPoCwObqmf3r29z3SkUqoKZGtqEXtFTHTMA1yjIp7jZot3VvBlUAs2bzs42ZLFeeLoxw6qUY0bN14DdqLImK7vlnmn9t8XRqvRrb2Cd7ZigH09/lav0zW2kHk6eDjnpPbQ4mQWWQ+Tc1O5xVKFL20a1rZYGxn/pej8/TXRAp2NxUqYKKClWwaKKFPnzngUm0NtQxqLSop6On9mnV5p2k7dSMoUuJ6d2LDRaK8gjVnZguFGXboyDUl0PrJuYED/oLW+VbYgYtFWrAcVymK24lcdw8KrUJUb0n6gLcZg1yEkSvfra09/HCzPUa4h5XjzlQxyo11astVXScPtmy66jX6dbXLDFfxPuz1ZIhzRTIH2fM6dgys0nLQikO24npulX0tK/VhjmM6r6ro+41R1nU27K26cRZt1N7q7ZtKkwdBWr9JmrMgcoPHgoWy1TjQOprlqLJi1PnYhvHcYQiN1bs8Eb26dgs9PsoVBNd7qYus8QRK/fHLhTR++Vz40tfBv5WC95OX745+dkmOL8wzVxEXFWu2Vx7beNCjVVdqHy2Z59LobvtqbGuVOWSnvnWxJ4YBeptWBVPFgWVuh4yJawBgoqoud+k9mlqKcZte07//NZ80nbqdXJR/qUQsFCUR6gPqlUhl6X2PS6qhtN21JWbzQuvc++bZPwtDibtuw1dU/uLp6ZHPq5avNaWInMBsYo82YvE2oYykdKaIx/XqqlU2pijx8wpv9nqN6jYrGFxoLqDuPDVt7lJmRKk2F5sVH702NTkZ91qpArjh/a116pJYrlk6kiIO/uo6XzVRZPVUqR8ti32VY1ulOnRpFSwBQu3aZZSDuUzpogan2d1cyb2V1VS6JYi1S3JvsgzdIHYh55tm2TeKAM2600cTX96HE34SdqEYHWPuAqBAMoF1d9zqjJM/W3Vt+Yizjalgnr6eibUwHbKpbWVzbC5DwaPmzqwrvCKogTxoQo7VAtvnBp+OtQYJVtowetfppSQajxhfS0jp6rEHty1pbE99b1sLS+i9DdOrF4xwEJRHqHOe7YJstKSztdE66b1M2+UgZXK5GnLFqfXBjHhQpxTF5rvzFlr3E41l+vWjGB7tOOqk4yLWCHqREpdRFDTk7s4bsDHW3NncO2qR90ujv+z9VjU60RcaaoLAD1zk3qtdZ95Cqu08R3HEvXYpKWk7ezuG8TjKpvNUDTYtmNd+vjUwG9OBBLlAO/PS81vt2vlAlSaK1pXas0dW2YyKrYrGy8mzfyb+gzrCypqn0zoRZJV1MQ0ce+v6bz0RZ2eXdOEKszrChCTu1JapjsD9iQj5t9MZSRsRkK1f/r+geLE5iZipeSeZnm+1f7aBBD1sEf3b0/qgw3beagWWUrxV4C+hrAdVy1Sb40vt7VPfPeqIRJUwZz6rFdzSm4mE1TfYJtJW11o2xadqvBkc3dTOTxD1XMKVMHPdfCxrXgidXGubmVLBa5up09u6mJwriXboAq5fAPNYBO7kG2wvVSLj09aZtxOVQbpKXbV82rRKHvBnC7syNDP6e1RBTDSZvY2lM+2VMTqsRYSF2vW4yrtPW7R6Ko894V5IafOTfZrSzpUoAVbens1RksVWoB4MSd6/9Q/VYunbeyri2Q1C5yOOkdc/cwM43bUuDbdXVK1VgdqlJFas6NaRGxZUeNkhrS5Rv9Hue5xnz+Ty2rcBC53vpFKTkItpmxDVY5Y1wYxBrjr92vc9ls2ps376hj+7YvmZ0m9kbbxSMV2Fqf87ePkZ2oyCepVt2XsU5PllFuUzLZnTk0+Qy6S7GDtpv5UyYkWmIxQLUUOEi2ofsMfLaDFkejbxRnS1IxM974TvRijjgutjIkbLYscmwuj+rfNHB1sj2qxoUlFlUSzNfWy2FKYqk1s0zR86rWgZ1Sk9cmuJUt9tgogymebNpY8eogBwi7cXuP4a+8gxgOt2GS2plKXIdSaH9RLYcvoNdWSaEKFqvmmVo2/4cVU3Iat8PNjn5oFq2AfSJvhmmdnavspQpFykk6EeaUN23vJ9fKHao23oQrtao03m6eDDbWN/05eHvjtbcVTgdrfV2auTn7euMMsqFEXqy9MSykObGOYfD0tx6WO1S6tGpO2o8Zoqff0L+8ssGxJw3Yt1Fipz6ju1VQLmuUCqvG3trWBbT2gukuqyYbsrrfxEnKpqAJdHE+HYoCFojxCXVDodT1U1IepXwdzwOj2PTRNlm1yi5OlhWqCjZPxRyeO/24cs3/acdUFlDaXv/5lynWNWlvGRUaq3UrsQxzLWNpvxJfeCmWCfFhbqJMtMbRDkdv+dFHqhWBbTKtNuHDpt2nktuxKjQXb7Qm4r1j6ZEsMoaK24SJ+ScXuOkqbO5wkmiCmvo+RxMuKLZufCrX8QKzCngi+E9QaS7bEHwGLkuV81XnQNre7jm/9QBFc4rb8rOIa9JWl9hQV9Rz1eeUJxbJFHWdqAPyJf/kou84hqAi1xzfS2tPdnwYpRdupJRao7yKb5UTFfRg1rUHqu5zsWubAikJ+5ojbUYutPvyJWSGnDn0X65pCwEJRHqFaUV5VNEg6ag2NfZWU1DprttAWJXGCTG3kMd6YTEBLb/O1VrabucL8En1didm574NF2XQNAPCnN8x1MtQ+fbbIrK1S6zK4WKBQm1DTs+uaoUChWEsbeoE6ChPnmbW9quvoOK0eTwBi/1RscRW2Og8PfLg4+dn2QtSL7bqE6spiQw1uVs9Cvy6O39dkbDWG7DFQqc9k4Yk4alwI3DbUBeUqZR5Q3bOs6bQtqGPVnjKd1l5aMhYD6j2wWXZs93v26tSx4rj36dieW6o2XkVN7mI7jziucHHfAY3rp1JAP/6Z2cJJr2eU2m6klgo9sJ0DRZ4K9RnetZfqVeFWaLMp6wLtWX57bw6tXiT1PUy1FH2y0LwOceHSWGhYKCoxxitZRfSgahVbXv1cYtNOuob6rlAnZmuiBWXKsGk5dhI1IONnmYVbKuopTltOcxOiJn2hVrq2oS6U9GBUau0b+jSaasRmlVGPZbNYUM9RvU6N6pWbt7O0oQYZ5/PZVK/Fwb3bkrazoV539bpUalpl6uLFtfXKFrOjoi/CqAUOVfRzLhRq39UsUer9tgWK67VQgm3TXGCpi3DVLc6G2twrFiXhc1PN8W9qnI6L8IZCFaO84r/TIu9js7w0qm++3yq6wlQ9/y+o7yLlRrZpao5t/tSi8FMhZxMltve7ly3xS4HjkjYj989WKoJ6XKo79EfEMhwuEtaUvkjEQlFecTFggosS83ZUc7RrVL/rXEOdgLbuSk0eeuaqOIwe2JG0HTX7nA1JFCxitW35jboAUCdSPe222oStCG8c7K484Z91JigWP9uzqV4L2+KSen+oGalcY3vpURe1qobzqqdnGLejPpt/cRBbqEIVsvRFnWrBcP2cUbXRVBcdHdU6dJuSLa9Xu6ak/bu2Nsd9qAqWDywWG+rr5j+WxBVxoCY8cGE9p8aauU7PPtMiSHZvE37vvrG8h+euiWeNVtcUaup/PWW6evrquHjra3OGWFsmQhXqXXzra1oGVlu8rAp1fFP752J9Rh3StvGjst5xAfNShYWiPGJLG01FrcruelHrAif1FhQa1TcP0WHdW5HaoPr0U2c0m2ZVxVZUMw627gWFJ9qJfGLRIFGL5tqSJKja+HXEInk2NxJVHrHXtEkd1/YcXP9CKlDe5tqqCk+2lxnV8hSnqKYLbOdI1dTaEleoqLU2urRsZG4vj8ob9fT1DHYqcYUTE88rAfA2qFYUavs2a/dsYoyNKkz8/b2F5u3IsSOO3bUtrxs12Ufc5Aoq1BTDroXqHZaA9RE9wl3SbPOemobZRv+OzY1t2pJuqPNMY6JVynVMEdUVjPqupFr3qf0jJ27IY1HkTx0UdqdmWC5mWCjKIzZNCZXTh3dJfrY90IUy9bsWimyncTzRYvOIJTAwl9SL6cevEijkZrnfgRoaxFt/08uzjb+5iDWzZVRSUbv7hMWPXbVE2V7K6tiPG7yusidGEgsbVI2zCwrl4KUmaenexlx8820Hc6JrXMSfxMF1sgLbYlpN925PtEA7FtUdiIrJAqJDtcbHFThV9OyapQo1tq6hppBUBdpdFo+VOHMkNfvcl6s2k7bbvIs2Llwvk6jP8HRLzSYV2/iOM128P98ch+RCoKkFMhELRfnExYKqe+vUAsPWGjWTCBW65cmxUORiEeo4iJM6GVEX5K2amAPg73k75V5kO43/KMJEPhMt2HiHGAiq8vIMWhwWNRMW1arnAnKK+BKt3wDQX3ovTV+V/Hz68K456k3t4n9TaRYlKm6SC9AWq9RMpdShT12gPTVleeaNkF8lYT6fb9fvWxU9Q+zZ+3cL3c72vqG+i6j3h+oeu3ozzTOBnCGtQIt9a2mQGCovmzDmZiyVvlTEQlGJodag6WbxBX/mc7cvWNUSYcN18hEX7jXUJqiZkcjphonHpfrF5zLFtQ71ZeY6yxg1hfarliQW05ZtTn6m3ntb0hLXUOPzv16dfSa63Yq1oIfFYkPFFvSuohZiVev5lALfOijMGQdb/Y842BaawSK8ljaIY1WPJzRhe2epUN00qeTTTfPvE81uhq6JowCzrX1tcSVxFEzUy164lNw06O54Oe6Igush7aJYOFuKmLzz0EcpV7B9OjQ3btenPS3Ilv4QF592m6yZJ25IfRFTFwCu3WFcWLKGE+OwCuVCZCfVJ1uMgPqCzWetBNeWoqUbaUHANm5/PRV4Hyf1uY4tFqfYKcohrUDNJkXFNszUtYtNeKLOA00b1iNtZ8saqVLpOO6OqnhygesyFzaolnUqVIWfiloOQmfCV98Yf1MZ3KUlaTtqYhbX769XZqzKvBHiXb+4PKkUED5in/ZZt+ci5r041w3RYKGoxCAXESOOzR3EReOcb2iB9w8q9ViKBdfCyfNf0CZI1/NDM+LCo10zc+pTsmBF3i5/rnqq6T+fixzX95Fe2NQttSEINhuK/XXtOj25LaGHKggtsGSGpLo12ep3qXy2eFPmjSIcl4mO7U6pxcd14syDWy2Jc1RsLuQqSzfSrKmu4zZXE7Pq3l+g9U/f9s0KclwdFxl3Cw0LRY7JtaRMbZ+a5UfNOnV4v3bG7d6bS4sPman5IecS6pWmWoCowtOarbQJ0vXETE1r3aKR+QUzj5iOlXotqGuXBQ5M86p534XVg8oeoua3kvjMTV5CWxi6FubLiQvX2kpt0GJG4a/vLjD+piZw2WSxfK8jxgq5vrT5TEZS14grb95hK4RtgCrcukhCpVLXZGoXykkmAQtFtRSqS/b/vnAbe5RPHvt0KWk7qr8ydbFaKKhaGDUVrQ41Noz8UnEwF1Mn9HcVwdyWWcu1QYT6jDz6yVK3B3b9nuP3Zp1ip+UZmbosNUf86Y15xu2ocQauhxY1/TUTnTguXicMomV61SlUvcR8ujAWA6xDcAcLRSWGOslc+9xM43ZxtKKuNdO5hirEuAggjEPTPGY+cw05gQKvtJPYaizF4aMF2deNYFLkM74sX8RVAFDdpqm4fnUUajFdDFCL7uaTJg1orts6LsZFc6LbeF2G3U3dkVEoEkJ0EEKcKYS4UghxiRBilBCChSkDuZYrqHEBejpNE8Gin7TtGBqdW9IyLRUjVGFnk+NCl6VMZ0uR0jh8TcyGyNCgWklLiZ8e2SfWfq4NMa6VI3V5kdemqTkmtFC8OJ0WR6vjYt1QW+pD5RIXCm0XNf1qA0bhRghxtBDiTQDjAZwEoDOAgQBuBPClEOL3QogW2RxcCPGwEGKdEOIr5bs2Qoi3hRALvP/DSzbXUVxbc+qyRi7XlLJffKN6NCvXovXZZ0hzbWzq0Lyh2waJZJvIwJYgwwVqmmymbuNayeV6qttrSRLhukB4sVGbFJC8vMgPLtaFjXPo2dK2CAV9EzaLz8kAfiylPEBKeZmU8kYp5XVSytMADAMwHcBxWR7/UQAnat+NBfCulLIfgHe9v0uGXM8BrudL1aWk1NznCkVvontDKRfpzGvRU8ft1S8vjCE72+cn14/fvW/T0tkypUPcIVPKc31ttyLVprMb/yUtJTeTHU6eiRwOvI079mJLgeq/RcW4epBS/kpKGVouWkpZJaV8SUr5fDYHl1J+CEAPDDkdwGPe58cAnJHNMRg7ajCu7T1pcrdoVL/ueVKS6x6V2Mv7tGFdkp/z2XX3WuvCXPfKLH2Scq9QKa3xSMVFUdq6hmvXXmrqZSYzaumBUqe2C7DFwkYHqbBz7aa4dhsta2+hsbnPXSOE+FHI9z8SQlydwz51lFL66oU1AELTngghLhNCTBVCTF2/vngKCuY8JXcOl062rpuKZbZtWhhXpUJCdYsrZW1sPjmwd1un7X28sDAJCrItfldbhZZc07qEXDNcE3fIuHbVLFR9FqY0cR1/Wdd5l1gyhcmMTc3/PQCPh3z/HwCX5KY7QWRilRA67UspH5BSjpRSjmzfPvtqvqUCr5sKD7EcTUm7z+VToLPVVYpDsaRjPbRvNGEv55aiHLdfKGau2FzoLhSMpz8PdebIyN6q2joamFKgtseFMemUytrVJhTVk1Km2cSllHthL4qcLWuFEJ0BwPu/pETgXN93aiHJOMSxQtVF7Tb1nEst0YLa23ymMa+tab0/Wbgx80YKuRaiS2w4MgQ2x/TTr7QkMig2hnZrWegulARdW5VOtlOei+oepfKetwlFZUKINNe1sO8c8wqAC73PFwJ4OcfHYzxK2LCRV1ZvofnGlrI/ddwUrHFw/YIUrqu35olcj5ZdleZinkzdoopq7i4CqOUl6jq92xdffSOG8SkWD45M2ISiuwCMF0IcKYRo7v07CsBrAO52cXAhxFMAJgHoL4RY6cUwjQNwnBBiAYDR3t8lQylrQOJYfUr4dHNOKQtF+eTRT5cWugvFAQ8XJk9kmxSEKT5KqdhzXfQwqevc9ea8QneBhLFUsJTycSHEegB/ADAYiVf2bAA3SSknuDi4lPICw0/HumifiUYh1/ADOjXH3DXbCteBHFBq2ecKxRfLvnXa3qpvdzltL1/waGHyRacWHOjOFA6e6+oeLjLk5QNrPmUp5QQp5ZFSyrZSynbeZycCUW2lkH6To3q1yWr/GTECll0pfNrmuHBlIWBlWGFYs7U0Un/qsPbULUO6ciyKiW+ILsAMkwt4/NU9ykqkeostJfeNQgjjKlsIcYwQ4pTcdIuJRQmvqWrjenB7jvP+u+br1ey7X0hq4SNQUL7i8Wwk2/TxDMMwUSgrkVhfo/scgC8BvCqE2A1gGoD1ABoB6AdgOIB3APwx1x0sNQq5uC9EXRxXlrHaKBSVGovW7yh0F+o0/Ay4ha8nwzBMcVAqCVNsMUUvA3hZCNEPwKEAOgPYCuAJAJdJKUvTcb8WU4g1wNqte5y0UyrpGhkmV/AzwDAMwzCFw2YpAgBIKRcAWJCHvjBZUsoxCSXcdYZxwu7K0khZyjAMwzC1kRIJfWIolLJcMXnJpkJ3gWEYhmGYOkr3NqVTAJfJDSwUOaYQcT0+bG1hGIZhGIaJDq+hGBaKHPPh/MIVUOPnmWEYhmEYJjosFDEZhSIhxD5CiHeFEF95fw8VQtyY+66VJnurCxcXMDNGnSGGYRiGYZi6TqmV0WDcQ7EUPQjgegCVACClnAXg/Fx2qpQp5WQHDMMwDMMwdZFChj8wxQFFKGoipZyifcfiNMMwDMMwDFMrKI3yokwuoQhFG4QQfeCFrAghzgHwTU57xTAMwzAMwzB5Yhu7z9V5MtYpAnAlgAcADBBCrAKwBMD3ctqrEoatrwzDMAzDMKUFr98Yq1AkhCgHcIWUcrQQoimAMinltvx0rTThqvQMwzAMwzAMU1pYhSIpZbUQ4jDv8478dKm0YU0DwzAMUyiE4PcQwzBMHCjuc9OFEK8AeA5AUjCSUr6Qs16VMDX8MmIYhmEKBAtEDMMw8aAIRY0AbARwjPKdBMBCEcMwDMMwDMMwJU9GoUhKeXE+OlJb4DpFDMMwDMMwDFNaZBSKhBCPAOnZA6SUl+SkRyUOi0QMwzB2ygS7GjMJfnx4Lzz40ZJCdyNvDOnaEl+u2lLobjAMEwLFfe415XMjAGcCWJ2b7tQC+EXPMAxjhQUixqdR/fJCdyGvCK4QyjBFC8V97nn1byHEUwA+zlmPShxOyc0wDMMwNEQdkxLq2vkyTClRFmOffgA6uO5IbYFDihiGYRiGRl0TEWau2FzoLjAMY4ASU7QNQaewNQB+k7MelTivzmLPQoZhGIah8L8vVha6CwzDMABo7nPN89GR2sInCzcWugsMwzAMUxJUVtcUugsMwzAACO5zQoh3Kd8xDMMwDMNE4fwDuhe6CwzDMAAsliIhRCMATQC0E0K0Rsr1twWArnnoG8MwDMMwtZj12/cUugsMwzAA7O5zPwFwNYAuAL5ASijaCuD/ctsthmEYhmFqO5yNjWGYYsHoPiel/KuUsheA66SUvaWUvbx/w6SULBQxDMMwDMMUKb3bNS10F5wxpGvLQneBicGoXm0K3YVIZIwpklL+XQgxWAhxnhDih/6/XHdMCHGiEGKeEGKhEGJsro/HMAzDMEx+mfPNVqftdW3V2Gl7pUxZWe2xwjVuULeK/NYWfnhwz0J3IRKURAs3A/i79+9oAH8CcFouOyWEKAfwDwAnARgI4AIhxMBcHpNhGIZhmPyyZVel0/ZaN63vtL1SpnmjjAmGGSanlJWYeyyleOs5AI4FsEZKeTGAYQBybcccBWChlHKxlHIvgKcBnJ7jYzIMwzAMk0dcp+SOU0B9ePdWTvtQLAzq0qLQXXDGlCWbCt2FAI3r123LVS+ia2ZpiUQ0oWiXlLIGQJUQogWAdQBynUOzK4AVyt8roWW8E0JcJoSYKoSYun79+hx3h8mG2jQxMwzDMO4QRbBsKjFlNpm9VVwDKldIxJC+axE92zYhbVdqzxZFKJoqhGgF4EEkstBNAzApl52iIKV8QEo5Uko5sn379oXuDmMhjuaOoXFARetCd4FhmBxx45h9C92F2DSoR1leMHUNF+Pi6P685is09HVdaUlF1tEpErky75BSbpZS3gfgOAAXem50uWQVgtaobt53TAlSapqCUqJji0aBv284eUCBesIw+aUuWKAb1gEXHdfvh6YNo8fR1NZXVDG6eJ0ypHPWbfRp38xBT5hsaN2EFrtXaus/q1AkpZQAXlf+XiqlnJXzXgGfA+gnhOglhGgA4HwAr+ThuHln3861/8XO5A69xkf75g0L1BOGyS+lFsAbhwbltHPs0rJR5o2KiHNGdEt+dn0Xf3JE78j71FZnhsYNii/Rwun7dc28UQZaNi58Mo1i9IDp37F53o7VumkD0nalNk9T7JjThBAH5LwnClLKKgA/A/AmgDkAnpVSzs5nH/JFaQ2XeBTj5FEocp0htRj8811wWYyFjY1ieIkybimxd20shnZrVegu5ISRPXPn9tuwHs06UoxWFNcUY0ZuF32iPvtN61ga72KMc/Jjj+oTFTyFhiIUHQhgkhBikRBilhDiSyFEzq1FUsrXpZT7SCn7SClvz/XxCkUborRdylAf0wGd8qflKBTljt9Sugm7tiwUOzi2eNWW68LukSlqyS21Ql24F99SCLj3vOHG3w7t2y75Wbd2Z0uPNrQA8OMHdUz1wWkPioc4WvoWDtJ4XzCqh/G3cgf3mzpmqnOokS3GdOfFqIAWALq3aYxTh3YpdFdIUISiEwD0AXAMgFMBnOL9zzigGDXYrovfyWJ8UgtEvTK3wcdXj97HaXs6xTg+VXKpcS5G3pu7rtBdyDnFqByhpp91reSqIB63GDl6gDkYvpEi7B3dv4PT45aXiEY6GxqU094jcXRwLgq+2gTTfBaUza07WfGNs3yutKgCd0tPcVsqq8CMT5aUchkSSQ+O8T7vpOzH0CjGitPUuBTXi5cflFjl4zi4uN2H9Gmb/NxEcw9QJyoXC7T6xJdvoWhFDPasLUxfvrnQXYgNdSE3kJhAwbWFwQZ1cTWwQDGixfcWsaPeuo4tHFuFLb/p82UuyWX2vbP2p8XlxHlGXMSA1FgUofmMMWnfPBVrd81xuVUgxmG/Hq2ctpfP+Yd6Fzs0b1RSbv0Zn1ohxM0AfgPgeu+r+gCeyGWn6hL1i1AoonaJakWwTZAqtbWAnooLIfgnR/Yx/qa+b1yMrFy+v0ZVtDH+5tq46Po0msXIcOWCQmnbbAqQhsTF3/49W5G2s71A1cVgPqfO3VXVpO16ty9dy45r9lSmauSM3tdsDXI9pm1zlmqhUueYaTGVDUfsY7aGXXVsv1htUmhEdKuMM39v2rE3+k4aeyz1kVzo2ejnlbrJVY6LBLt4N1KbOExxN7Vx86kD43eGAUCz+JwJ4DQAOwBASrkaQPH5N5Qo+dR2UqHGvVBfZtQFrm27IrxMsaC+zOKyYO325GfqNbPF7+Tysjesn/3bsc55Zhbh+T754wNJ21E1xLbNvtm8W9mu+CYF167HpYx6v1s1CVqtTXfu/AOyrwtvE6o374y+4LfFjhSqXs55I1PX6dRh5liNQmnon56y3PKrg5giYhvq+8H1fHGMxe2TagGiTucdLNbUnx3dN/k511a4OpGchLDNXi81twQAIQSrwhziYgw3crC4VMnnYkNdkOfT5H6Wg7SgKu2a0VxAXEwqK7/dafxt/JffJD9T4yDaWvqey0n2jOHZ3wNq91zLEtRgbtfkM7vQqF4pS55tThjR02zxU6HeK9tmkxZvJG3nmmJPK9vXQeyE63T+Qnkt2fRs6k9HWiwv+3TMvjaNGqNFvaW2e9+5pVkIDljtHQ+florb8NkWV7pCeT/vqjRbVl1YeKnzoFrLzPUzfNuZg5Ofda+Zpy87KPn5glE0Qf/XJ/aP1Y84p/XIRfESSndSUv8X+ZQYG8oj86wQ4n4ArYQQPwbwDoAHc9utuoOLcTVmiNusHq7dUmzT1wM/HJn8XGOzFLnrDoD8umyouLi2i9btIB2X6t5YqEQYw7q3NP6mvvRsrmqFshSNGZp9AcI45PN81RSqzR24C1K1u9TFSz4FFepzS709B/dum3mjCFx1bN/MG+UZEfhMtRKat+vemqaIsA2L04dFV8TY2mvakKbk+s2JbrNGUkd+oaypUpoX+S4ysFLHkyrou17XqPG2Y08K3l81LfwxAzrChDqfX3FU8BkOCFOWiSXOO2FwV/O7d8JVh5Pa6NC8tGqjUaEkWrgbwP8APA9gHwA3SSn/nuuO1RVcvNhPcbxAI2cc0x7GIZYHzYTqmpDPxTk1zsk1Ll5StgDewEKEeCxb8HFu36nmxqm3x4X1oZRwPWptz7o6N+3TKXstPTWejnxP83pT3R7MdYKQcmJWS1sMTC5dwfJ5T10Pi6YxC6CqC/fLLXGg2VKMbqT7dGyGPu3D5wwXax5yE8qG1H2OivEc2BISubGM2X6L/lawXYt6SocP7GX2ArjwkIrIxy0FqMbVLwF8BOBD7zPjiCKcz3DjmHjBeqrJWMUmgKg1C1o3MWdLUyfSMUPyp6Wn1suJMUfH5vB+5qDLOIkWbIVS43SX6rZnQz0Pm2ZRj1UwUYShOLGgZnyk+rRTX44uYhNcPyNDu0VXwsTFtZa5UFlH1WzVeheu0tL7m9yyrx4dPYGAfk/VhXyZ4bMO+Rm2ueoRL/vFh1YkP193gjlrme25yOUt3rGnirRdHKuMPn/HOY8RPVtjmKHwsOtafTbURFZU4fG2MwZn3kjD1rLtsCbBUce1Epd6B3Rhr62S0TbKffzn9/bPaeIRl1Cyz10KYAqAswCcA+AzIcQlue4YUziaxnSVMe1XVW0RipQHy1qTQ3n+XMdV2IQCataXfFosgoJPsMV9lNgCap9sWq44Wsg/njmEtB3V9e93p5iF9G6taYHtNTbfzBLiIqJ2jpq623Z3DyWOfSr0oUTb8NrjzT741HFBxUVSmZ8fk3KPiVPA0hYHSV2fHDMgFRyuL2r0Nlo0CrdmUd1mmiv72+aRkxVPB9tpUD0JTP2OgtpGo3rx4kDjKMCoiTq27q5MHcfWh+hdSOu3LebURuum4ffBhWJw2cYdmTdCvLTocWoJ2s7JNvap87k9tCB3QmaLxsE1nXo91aNmKmQ7uGvLkqm5Rrn7vwKwn5TyIinlhQBGIJGim3FAMVqKqF3atzNNa22bmKjaBnWz5g2zf+mp79cfHdYr8FvTPNWyqBdXY2aZILsoL1XqZGmbcE0vNhsussqpfW/ruCBmsRAnsN21q4ytvcALMI9uTVQtuC2T47ED3BYEpS7Izx5hFlzUhUOc1N3dLMk9qM+66manz71UZXSceUvfQ/2bWr+KKpjq40LtLrXn1GMNtcRFxkmqc8PJ+xK3zN4V1fTutd0rKlKax2R7RciyeT3YmLx4E2k79fz7daBZZeLMdVYXdMt+Q6zW7tSePR0n9qG6MO7fo3Xg71tOGxS63c2nhn9filBmo40Atil/b/O+Y5xQfFIRdVKo56Cqtmlirq9VJVcn2MuONFt2qMxfu834m/pCzKWbSy4E4ngvMPMS4PqTUi9p19XBnVjNivD5odIig3YtDNdxd9asYKo/voNjUQW6PZaaQCN7tjb+pvKDgytI21HRL3trQ0xQq8Y0Ad51UWR6FsbUiejPjj6yzvQsU3r8Uxz3J30RZsrMNkK5v7r7XtyhXx5woYrXhgmbQBfH0kq1bFDdXuPE76RbELO7aDbLk80V0zbMqoiWf3WMH9THnNxEzSZqO1t9XeJz/MBOxn26aQlC4hQQ7qsIdIMsxa2p48d2T9WfqrXrbPJEqU2puilXcCGAyUKIW7xCrp8BmC+EuEYIcU1uu1f7yWd6SipxagDE3c70cO6jLcDVzaiaRRu7LSlD1f5SH/Y4i3N9n8Fdo1ejTrt8hsWGDdv7pbEygeczHXQ+j1VKvDJztdP2bM+m6uKVz2Bu27NEde2N012bkKr79B9sWGC5SPNswvUdyPTu8d1dTtAWfP201NiUU9G3UWMB1Z9aK1bhrq0aBwSjuDOCMIxj29jv2ormIhjXbcoEVelha7kv1SJi+F5P2hHnWVLPwh5vY/7VdiXUxbottlCNCdaPdMKgVFa4bNdhNuWpLtzv25n6nk+d42mWWlQq5NAHy/mqQ9AmfAbjAmmHLQUoq8tFAF5C6g69DGAJEgVcuYirgVGWrB1FD3GA92xLM+naJjeT1lF/N7h+5qgv2JMGmzVAKi7WjOSsf5ZjLVmf8rWmLrwa1LNojZTPBQvLsZxGy8bURXLtmLU3bs++2nxDRZu40VK93kVdmDjYXPqpVoo4d/viQ4NutKP3TbngDdGCxtXxRK1ZpS4w4gxHF0P4aKXgpL6Q0xfk/p9CBBc9ak22Zg3rkeYZ230zPZvfPbAnvn9gT2P/qJgObXMDVIujxoV6u9RrQz3DoKAX/O0SZRzHSVyh6xzpLn1K2zHfFWoiGVsbway15u0OsVjrVItVYAwSBYa4dLQUYjXhuuYVWWFKfOnripJShpKS+/e2f/noZCmSS//VXEOV+l24lpkCjnXNbJnlJRCHfh1o8rxtUnWNizou789frzZIwl6AMNUIdYKkvjjIGXUsm53jYPFCpXPLwtdlcDH2fzuGtsjpQVR6UKF23fYcUNtQLRFUy7Ie56NqXQdrLivqvDV6X3MdEhX1+XE97VPHhRpvo88jpgQKQphdwVo3rU96X1jddYz7BB/9uBm4zj+gR/KzmpWxhUUJRVWiuHDfzXZe0fuQrbvgoM5By0sXS/IHm0IgzrGvOS6V6e+4gebn6gcH9yRtF+wPbQy6dslOcx2N4YnjuoKI2oMrjgqmi1e7S3VT7NWuDglFQoiRQogXhRDThBCz/H/56FwpQ89Glj+piGqJyKdWnRyUb9BURkF1Twu4heXRAqIuoFQXsWuPM6d91VF9/PU79dMc1sOgL0po263btid+ZzziZPGKi+r/7iK2R12gmnzVbeixHtSEFHGumBvtJK2R0QPNSRKo/WijXAtqgc2GWpaxwKJE2zYQvE+d6x0rdgJtx7irujCjxyOk5icR6LtaOqFMCNKx4+jPEsH6wb/joC7c42Smi++2R9vOtSul2lycto8fFBQybE30Vyw7qgUkELsWoQ/qtrZyGOXE7egHpm0WZyykCZVZuyO6nTxstyfsnR9WlLeuuc/9F8AjAM4GcKryj7GQz2rr1JcFNf9+tj3/7oE9An/bFpCmDFL6Lur1tGWdstGmqTJpW/q0yxJvZIJ6zdRYKbULrZrUdyJIq24F1D7Zxs82JfVra8dZ4MjPiGUzcoC5AyFG7W9zB2l/1UQlPdsGrRQUwb+Ndj+o12J499a0DR1ArU1xppJuun9Hs899HIWNbZ/jLVrmg3qn4obSEi0YngVb76j1eExYa+JE1y1ldEVU3ec6t0hZM1T3SylBmmhsRWOpxLUUmQrlupgT8unKpBZHt8cyhX/W033rQfQmXK5loiQZsM2BrvWYVE8UF1ciThuuE+xQ59GwMXLFUX2xdNyYWO2VApSpdL2U8hUp5RIp5TL/X857VoLs2ptaTJNdRRyMJerz4iKzTWC7kO8W3n4SbteErziTqh5o7+I6qcr4QIa5PGrBbZs5N5ET+2RbbCzbuDP5+ULHGb3sLjVuJ1lViDm4tzkLETVV8qF9zW2onDeyW/LzuSO6BX6rrKpJfu7UIuhCQ7p1McfLPp2Irg5K+3HvRyfFNcgWw6G6uNkW+HGeVdsuQfef4AUdEch0F/xNfS7I/chySNuy8tnujzmGIYNQpHwO0w77UO4Jtd6brQ9x58cjDQLZtt201O+5hjosXvnZoco+5kV84Dfl+2GW9OE2qP07cVAq/lYG5o4UUQTbnyl1vXTixOfZFRapz3HHxX3fHxH4++1fHoEXrjgkvR/EDlMvFaW5X2qFmanzKFVwrk1QVsk3CyH+LYS4QAhxlv8v5z0rQQqVMcv1UakPo+oL3a5ZQnNar7ws7aHvqhVSpLj55CLRgtov1Y2iQ4vcxoqYXG0GdY33klLRr3VQGx3ehyiot2GgJRVoHGx9cv0sqRY0m4bc5o4XvNa0C6pmgjrvgGD809ffbE1+1uPzTF20vwCJgnkeXXbVI3W2ZPRS77dNWI5lYcmB4um9ueuSn9WYBtuoDYyeGJ2qrK4x/lZVY/7NdL8zdsE7aQFzzSrqcxpXk5xtXMX5B3Q3HrvahaVI+dxMy/zlIkY00F4MxVswJpS0e1p/dIu0inoJVU8MKrYzsqWtf3fO2ujHshxMPeede81Cka0NvWB0v47Nk3V+ju6fEszjPAm28iemYdy9Tao/urKvSQPVoyTYI9Xdj4WicC4GMBzAiUi5zp2Swz7VClyYE7s4Dux2bYlQC3vp/vgqenptCnpXqYUubZld1IVmnODWC0b1yLxRCAdUpDIRDuqSEoTGnjggsB09NsH8m6lQoZ6mtRexurRqtm9FzY5HxMUz4tpqb82SleVx9UWTin5Y0/2xulA5vhYBP3YHbdvqXFHnJlvQt4mrRpvj9Y7Z1xy/pC741e7pVeh7E5+lYKwHaZcAtsXz3G/MdddM7lS2gpDDu7dKRRQJPR4q9dlWpJOK6dbrpxvXfc7UO9frvbguTup5UpOC2N3nwq1INiHwJ0ekav/pbXe3ZldUn5Hw7IWZvovD1l2VmTfScFHKI+5YV+O5yZnfDBfrHM3jwKSYUIVKa3FZrT+qwORCcVBqUEbJAVLKkVLKC6WUF3v/Lsl5z2oxFx9akfxsL6JlcTchusLFQT2sza9X3e6o/maf8Y6aJYbymOkTwilDaXn6bZmgqDUqTNxx1pDoO2moi6E4wfWAtjjX2y8LX7zoqC9w27WIc52o++j1G0zYrlIuMwXtr2Sq0iG7Nij9s9Wn0OeBv52/X8a29S7EFarV+SjQvuPsR7rFOHAsw3F14gjmav2hEVrx10P6hLt1jejZOtAPNY5mZEWwjWAQtBl1LMSzeJn3sQkMgXgJpQ9tm5ktAOr52p6xHm2ahAp4UU7PJkyoC744C7Qrjza7YMUVstT4N2tGM/LzqHgwEEsMBPbP8LeP7TqriYds5zSQWGNHSpm8d7kIN4lz51RrS8cWDZ0riRta1mRxDqUL7f77Mk66eCmB//xolPH3doa5gC1F4XwqhBiY857UMtSJQC8ASrVSWINnY41VtwNcnVSaaYUPf+bgZaTGbVA1qzahaJNSk6Vd8+AkcJmiKYtDnBdgLo4V0BKq32uvSvUFoS489MkxjvaTuod9sUXTvsfB5vJDDWB2cSwVveYXJZGDq+Db3xrqkKjzVtxhe6SiLMln8hkV9TLpsVsm9PinYwaYLUqBY1l+M1lsqOQzw5MQwUQLJiraNQ3c14N6J6ziruIPVXfWY4n3QKV7mybJ/qfFtMV4fA7s1YaczZBKnNtKdQVTcZEchvru7t8pJTyp83ymvalzWpSEDWG8eMWhgb/7KVZs/b0UUEBbrns/B5bwwD4O12sSQKvGZiXIx785JvR7ForCOQjADCHEPC8d95eckjszQa2g+TdqGyE/JnGxODK99OOuY+rFtIIk0WOKHCQy2LA9lQJa91cmF041UFVN9K3X/m7RKFwzOEpxuYuCMPyhX5frjk+5FKnD56j+wXEQZ2Q1N5yTjs1V7eA+5kQGcRaX6kKhUwuzxSLOwssF+ks+znNHn1e0vw0Ha2nI2hUFtQaWrdq6y6DitLaJo1jvg+ruqF4jekV6M5ssRXNN6M+LmkDAVk8t7hzuv1cy7a66Nrf3ah3tZ7Gy0o8PnDIs5SHQl1hbzoT+jMVZ8P3qhP4BDb56beK+hYNFg6PfLP00AvOj0p6eujqg9LCsV1ROGBQsZq4+M6qy94JR3VHP0+pecbRSJsLRGlu9d3GWP7pVR00SYSpK//51R8X2S1CFSXIb2nmZzpMy70spjfOAgDmjb1xrailDEYpOBNAPwPFIxRNxSu4M7NiTCtaLqyGlpoakDlvb+DZNhLZ9RMw3wk+OSEySoy3+/DVSWjPsxCHQhONnPTDxa6iH0lMvnzY83C3Qlu0JluticpXRURNNqP3TEw3EURTZ4svUxVIm90FTGl89ixsF9bRsQtvQbq0it61zYK+UQGsTBIL70LLZqcR1n4uDPg9Qi1OrdLe6z9EG2uQlmyIfN857XQhzLEWf9tkXKrQlTaByrDJ/2hOERG97ePfWZLdAahHvAzS3Qx9bTJF6XuSadhZUV26qO14gk5owKyHjrh/1EhYU1EK76dnnaJiSZ2yxxOscYFHWqW5dQgiUlwksHTcGVysxfX4GPKrizEQuDRim69mpZSMnc2xbap1F7Vh+xr14iiHbccwNVhKVvbWJjLOMl367O4BjvM87KfvVRQLaFtXiqj9kWbpR6PvlU5gnp/U2pAUFUi5aLUPMuX86eyiA9IfYJlje+51hpD7l0n1HfUldqQlIw7qlkiuYqo3r52uqrZGJOEUl1YWHSUhzwaWH9YqUgSts8XH5kX1ymj3N5rJKfW4rFMH3pMGdjdupmlpdADRq9ZTvmzYILiyo9bviuHDqQsv1JyeShJjSHcc4AIkVm6Knws71/Ki2b0s7rqLORaoQTT0OENTak92piO+NAypaJ63pmcYV1a3P5L5l2l0gKLhUx1ygqfOFatWK52Eh0oQkH2qMpLV1w8XQlRCmGJBEGzRlomFYYI6SFVNHdzsPJmPJPBD85Ex+chJ1Hz3MwEouhSLDiMxmHvn5MYl6bTefOhDXHW9TeKbQr4dvHdMtnCaFUkBnHbPvNew+l44Q4mYAvwFwvfdVfQBP5LJTtQN1Yoq3iHO9iG/novKzAzcXWxOjvAVClJTc6nXSs6yphLlr+e5SLi91ubay/uVx5uxXrglailLo7oHq5R3cNeUOpKcVVRcOLYguhqbFRqP65bGeBXWfsScNsGxJbY92rLjPrWmxoXPCIEtSEILg94tjg3F7V4+mFUpNc58j7RPcyr82cV+Zp+dQ+C4k9sD78LH16MWjSPOPrpBSrR623YOKiNTnTNk0zxvZHVcc1Qe/yFCAV51zbILGDSdHe3bLykTAfSfb5Zm+f9z1nsntjmoVtmG6j+lW4eiWQZui0eRyp9OttS0TXWb0PqjjpUG9MvI9VsdFBTH7o603qmBhVWLHVMj179QcS8eNwcWH9rImZKDyqxNSglWnluEW+OAYiTfYOftcOGcCOA3ADgCQUq4GkJ1zbx1AHY9+8GnodpaHzPb4xRmrNtN38LjR3dbSJm3lc1q8jrdxmIaxLLnYCvrAHkDUpra11EpQM0/5k6BfgyHOZHeq4u+uL4SbKv7rTRqYXpbmY1Kz/ukvR5M1wyYsBpMzmPtEtUTkGvW8qJp5lYq25peoTRGhuhzmOmkApfkTNSuUOs6aZhmInIlsz97XnPrIwGfz5KbHNFAItJaD2xYnIFodto2J98oW90gVxlSOtsTPVdVINKhXhl+fOCBjLFjYs+Af85ShqTHawmApMrrPIbhojmRJ0BsKOVCmeImTh4SPNb1mjCklu+mW2BQCpnklSmwHdYjHq4WWHdZMgxHOUb0eB1mKcZvYGCOmT0KmWcrioJ/l2fuHu4Pr2/njX4hgTNTRlsy/ybZsYRCW/aprpDHuubZCEYr2ysRolQAghMhdWqhahDrQbPECcSegMgdaAK0nod+m5fY3aaEsT52awhRILSLCzt3/Tm9OrYmkY9J46QxR3Nj8uBf/he/6RXDm/l0zb6ShLl5aNbG4RxDbsAk4w7q3Mv52y6mpZJNxAi1te7h2MYmTgMMWA2GTsdRkJLnOBKaOadWSZ0N9Bi9UaunoqeRdJCtIWooijA/b4p+aIj5OKQK1bauiKXLLfvu07dRjd4+gcY8j+JuOS21pd2V1sA3LjqphXL8Ufzt/P8y/7aTQ3zL1qUmDegFrTtyYIlPfM1mKfn5MP3Rv0ziQBVUIYN/OSqYyIfDwRQeE7q+XovAxK8ksVp4Ii1p1TlSHjouETPqxoraZ5gES88Ubxcr3qxP6p61B0q3l5n74iph6ZWW47Ahz7HBc7jpnaPIZUXHpuha3peoaiR6GxBO1Fcos86wQ4n4ArYQQPwbwDoB/57ZbpY+TRbbVzYfWhCn9N0W7EEoM9zl9EUqZR6PMtdSgTXWheeqwLrjmuH0CZmgfcjFGw/cvzVhF2j9OfARgf5GUBa67uk9wO1MhUSGCGuQRPcMtdLqbHYURFa2DwbiRW3DDEf3MmbpM7ocA/QVuijnQsbWnunraguhNqNrsTG5SceYqyi56Io0mDeolM4DpxxzYhSb4ZSukx1kc2AT5ds0akPuknrO+wLddT0rRaOs9zOGDJkTwmfE/+9+UlYmMgqz+HPjvpmaN6pGzjNlcR33F3v5ajapMi859O7fAR78+xqig6t0+8Z4wWXf0DKc+qpBFxTbG+mheAMG40ugDw0XZCBNNHLgYApkKyga58ui+uPc7wwPfCRGMDbNZe+86Zxg+u/5YNKhXllFBMeOm4zDrluOt2+iCpOkZocYOUY4TVx7u2KJRTmN4ixFKooW7AfwPwPMA+gO4SUr5t2wOKoQ4VwgxWwhRI4QYqf12vRBioZcC/IRsjlNIqNaL4wea4wqoxMuBT8OUnjITlIk17GHz3dm+e2AP8sKbHFiqfC4vE/jFsf1CXUNsqaIpbW/dVUmaSPZW1TifcFo3VV/gNKuRin79OhmEattCp70Wu9bfq9/QqUUjY6IJa59om2VoI9WKngEwsJ1yMP1FSoWeApq6mE516ihiYgP9ml16WK/Q9nJJmOBtqhPURnF7tcYjGi6ZLU32acPixy+9e+2Rxhoefn/izL/6Qtrm0ulDndsyHcslUgbH002nDMQFo3rEcnP0UWMyAzFFlgtti3VpVL8cr/38MPzze/sHvo8jYAsA547ojvMP6I4Xf3qo136iv5R7mKmvpnk6LI7Jd6HS3Sptip3AsQzCE3WfKPjpwId2TXhrhF35KHfDFo9JoV6ZICWzARLvOtN7UKdVkwZGN9GomGR2fYzYsr366OEIgfYs9/SQPm2TiaNs3iW1CUqihTullG9LKX8lpbxOSvm2EOLOLI/7FYCzAHyoHWsggPMBDEIiFfg/hRDFEchAwDjo0rZLfaP7J9v287niqD7Gyu661eQnhqKk6e+D8CfwX98bYeyfvT0zJw7uhIGdW+CnR6Wbops2rIcld5yMK4/ui5tPHYTvHdgDU244lt64BdviwOWygbwIEalYqUy1T9RgZ1vrh/QJunr4jDSkw03rkhCBSde0ELH1oUWj+sk4uj7tzQsFam0oJ86hxFui3jtVG+nLcq/87FC8c80RDnpER5XRdW23eZ/gCedqXWxP2Z9+0OM8JVDrJuYiwbY2TT99Z2S6X/7ScWOw5I6TA7Fw1MvgPwN92jezxvc1aVgey31O17vcde4w434UIVu/1ocr1lBh2c5EFIFBPZf2zRvijrOGhCpNosogAiA//GdncFce3LVlmgLM9u419kkING5QjnFnD03W8erQohEeunAk/qEIXfY44nQGeZbSOO5ztgOo9zsXMfPUJgd7wpB1+EXon+lcWhOztsZxw7UdNxdUEBXSuhIyDFu/B1rWHjVSJrNGNimSmOJcQxkZx4V8l+4AGQEp5Rwp5byQn04H8LSUco+UcgmAhQBGZXOsYsc2SZgWyucf0AN/Oif8Jao3Z/IHTc8CE94HFwUcdVo1aYDXrzrcmDXGn8gb1S/H7WcOQQdiFXogg0XEgTtitvsE9kdCiz3lt8dmTIJxtSIU2YW7cI3f6cOjxzjZyLS4+s4BCTe5wV1bGq+TLW4qw9Ej79HPK/x4mFfk0mQNzCTQDu3WKmMRSXohUtp5qH3Vi+ua26YfK15GwPDvM1m+f33iAEy9cXTSIhyVqNp9/dyoCotMm53j1cq69LDesRIt6P1SrWqPXRL+yoviCPXvC0fiyR8fiPu+PyJonY3aUQKuLVG+26UQIpD9Sr3KN47ZN7BPnDGsxgZly7H7dgwoeaxzdMhPmYY11aqsZzazWxBN39PeL9mQK9sldRy0baYrZZQ2LL2L86zrUD1RyjPU8ctEQCAO+d1PoGCLeQ8UKq4jXnRGoUgI8VMhxJcA+gshZin/lgCYlaP+dAWwQvl7pfddWP8uE0JMFUJMXb9+fY66Ex+bdk79a7ilWOQPD64I/V5CBurY2B7TuuYPql5qXRuUzysRRbvr1ziyvsCUibR7G7NboQtBzcV2/ktGaN+ZxqPNxcnFffMXsTecnFhMXW1IM6y/r4ohdXScRad+HtmOC9OaTB/nvkYYCL9v5WXCankB7JpPcz/s+Mkq1IBrPd4yEGOQoUF/4V6vXJCDvoMB8GZLXhdLv8xtB/9uWK8ch/RphxMHdwrck7jY3iNRvY07ZNBs33rGYPzosF44un97Y/prWzZNMnnU+quEPc+ZuhIl5j649jAfw5RltnMruhKSii7UmU4nW0sMVWlCcTnLFXFiU7NFSpn2DFOar66RebWOFQM2S9GTAE4F8Ir3v/9vhJTy+5kaFkK8I4T4KuTf6S46LqV8QEo5Uko5sn17RwUEHWIb+M0UFzdqNXCbdiHOoG2jWYCoi6azR4RbHAr53AjDZx3bNQy4jGXbH0ETRvVAdHr78bTdeoHQMMqE2dfa1rZOUigi9jVKLac4C/yD+7TF0nFjMgb068/jnw1uTV1b5U4w1YlTmymuhYTcPmF8U+ta6diEprjpYX0tvhr8nqUeNvmJHhuW+hwroVxMa59+rFHE8gY0ZGRXpExDsV2zhvjdKQNRr7zMKKi6eN/EaSNXijV/DJkz5dl729yzNJ40uBM5nlkfF76V1xYTo7enF5A24fc+zYJt/CMecRfwwfgq83bZZoKMgkkhQEXvq/G8LKekjrs6bymSUm6RUi6VUl4gpVym/NtEaVhKOVpKOTjk38uW3VYB6K783c37ruSwPWSmvPQ2DlZy8cfxude59YzBwf2IO952xpBQ4aIUtAlRXRoyYZxjiIJFtoXwQo8d+BzshG0hn9xH67fpvpITN5C2yrzdFUf1Sbq+5XJuTn+RhB/NFo/g+lkYarEmq3S1JCZxfc38OSAtZT9S89vPju6b9puJI4kJJH5MiJHMFJ9nQj2VehkVFqkDmtYuNmEhzVJkOdIPDu6JBvXKMHpfs+tk2H1ItZ1qvVH9cjzxowMx+/eJHEajPLddff/01Mnm1tt7Qmym4qz+o6XXO7Odu2qJdOG6FGhbO0nbNYxK1PeJf01MirtMZ97Gcwf7xbH9QrMBhvcx+1nhtIiWdFeeKybhJJOC4oh92ifjrKNYhn3863lGHjwIdEE4an/VOSyREEXbgNAG5TiZLL+lhrtZwA2vADhfCNFQCNELQD8AUwrcp6zRx2Kc7Ga2F4LpN90NQ6W5pg2ivm7Ky0TS/70YBSFTjYbEb8Q2Yhwrzv7p7cXcMdBGqpFTs8i6lfk49t+TQ0MAAzolfPdNacBN7anj69cnDsATlx5oPeYIYhICG9TkDzaiPhbfOzBDymxiO4O6KK5rxJ10y8tQr5aXf89MHNirDX52dN/Q2MabThmIy4/sE8n10BQ3qAesmxau6jW/WKnRZEOv1j563444aXAia9rxEbKnmRZiE687yrwTYW7y5+8BnVpg/m0nobOhcj1gF8DUtsu99L9+0gE/nfNvtRgd/ZT09NIHhlibKG6RvzqhPx73Yqb8NNq24rANyhUByvH7Rsqgtb6bxS3Zx8UcHSao/PN7++OqY/slM3XqZEof7t+vMiGCiS+aNQw8Q00MtcKCwkqUJBvBczn/gO4kK+hF3jOant02eOywcQakErboqHuHKUcev2QUrvdcqM0KPzO+lfWcEd0tW7nBdM+pY1DN4igh0bG55pZLaK9GSuva89Oxx+Dta46kdahEKIhQJIQ4UwixEsDBAMYLId4EACnlbADPAvgawBsArpRSVptbKl5cu6v0bhfdh7pNswZ5C6TRHxwXheKy5dShwUWZy1TEB1uqaAsBDCH48VOtMlFQm4zjJuPqCiXdQSBwx1lD8fRlB8UuAkd1A8y0kCcdy7sCceow2dqzYStMHJcO2gvQnI41+MNvTkxo+/XscDplZQLXndA/Lf5HykRylrEnDYiV3UvnlKFd8MIVhxj7e/GhFbHbXrt1T+DveuVlSaE4s5tM6nfTelW3zFLdfP1TbGQpdhsFyrSnK+r0U9J/Vy0EdHdegSuP7ptMiX/16H2wdNwYq0BHjZ2Ng0R0y4ULS0eoANyqMX553D7Ga0k9dyGAkUrSnkP6tsPofRMChJTBLKZhNfqyZdzZQ7H4jjFp3+vvtrP274al48YEk+2EnKRJkWaaW/7zo5Ti7Luj7IJLHMtjz7ZNsXTcGBxmqXXnioMsawy9ztmEqw7HpOuDpQP0OcykfAgbcZ9dfyxOGtwJJw7upGyXvmWXVo2dKBKLiYIIRVLKF6WU3aSUDaWUHaWUJyi/3S6l7COl7C+lnFCI/rlAHY8uFgejFc2ILXOcMHyficAL2/tDz2STtk+R+JgGXBWV7y/TXG16RCj4lgndKqEXk8smuPn2Mwdn3shA1vdE0NqIImA2blBuneCBcOupr9XUf/Nf8j5/OH0QAODyI+nVxs1CQuL/bARUalFAukUyeh8oaVrjtm0jbqpbE0LQhEaqEibTQsh3YzIV3ozTZhLrxVaFcdcTawzPhAzXs2/7hJJuePfUPJdXPZiDY8mEVOQU35JpC+SP8sz5LpNqghCfe78zHAM6NU+zntqeQXWh3FGxzgb7RO9gZOVnxqZ19+XE/9RaY8Mj1NJRu+67ZieOmfuFzX9+NArvXWu3sBzStx0uOqQCZ3np5lXvHl/pdYBXamPfzi3SLMmqEFRTE6aENd+7Ti0b4V/fH4EmDerFeq5PHhK/TlmhcVNemAmlaYNy7NhbjUP7ZK9VsPncS8NnILcWm6g1SvKN3gWbJcHW3yYNyrFzb9BgKSExpGtLvDg9EfKmmuoFiMkKDP3tYnGTyTWuLJy+EHRuSP2YMMKu/7izh2JI12VpVjl9yx8eXGHM1BiVlFBUeEvn9N8dh7IyEetZOrxvvDnHP1Lc+I2zMtSLcU2Y9jKs71QN/3Un9EfLxvUjxQzEGSqmexpY0BLafePqwzFj+Wary5J9+IQH+GcK7D+wd1t88Kuj0KNNE0xeTAoztnLS4E5YsmGHdRu1S1U1Eh2aN8S6bXvMO2RAQgZHRYwsfzp3njMEv3xmJhpbarpEsTb1aJOwqumZEgHg5CGdcfKQzgCAnx3TF7/+36xQZcjRAzrgtvFzcNrwLpi6lHKvEtltN++sTO97Ll/roQq5xBdjhnbGKzNX5+zQrWOWCIjL4f1oMZS3nDYo+fl/lx+M9+etC8TkhVn09+nYDPPXbsfPj+mLD+cnMjPb5H9/LppxU1j1HXU7UpcBIBlnWIoUW0xRrcI3DcedSEwLsygLtpqazNscHtEUHHY+1PopucZ2ra2/ef9fdEhFQIMGAB/86mi8/ctgwU4pgQ4twh/8bBPUZBNQnK0wKhB0+/GHmu5Oluko3ds0wdJxYzJaiGy0adoAPz+2X04E7EmLN4Z+7wuFMvl34v8LiXEqiX2j3T/T1q2bNgh1TaBcDmpWy/TGw78+P4Mrik+ceEkb+S4p0KxhPfzyuH0iWfd1AeInR/RGu2bpixWS+1xYQLSFAZ1a4PxRPazPCKU5/ToP757ZOtezbdOMtVCo/Ov7I/DG1faiyOpzJRDM4hqHXOg96pWZx81vvViWjob3RhhUy/V5I7tj6bgxaBKSDa5P+2ZYOm5MmhWll1InUCA47qiL2lypjkwxQy4x9b3w6txwKto1xUWH9gp8F3YO/hi0CeYA8ANPkehbGls1aRBaO7Dw6sH8wkJRjoircbdlCOtlKHZqE5Ioeft1ISAOuiWrGDTtOrYFlnq7fL/rYd5LpH3zhuhnCIANb0vE0x5H3yUnhC2w9K8s7/4MbRu+j9dcbPR4kmQ/vI74z40QAov+eDJuOmWgsS1bgHkuFvUf/upoPPlje9IJaj/Ub08YpLjoauO3EbGuR5SzvTZCGnbj8SJeXlfTUrOGievRsF55mnb++pP3xdQb07WutoykfjHbYwaklEtxu6pXqLddI9P1MMmE6YHxyNvDa4vR0rn73GEZCwkDwWvTpGHmMZ45K6HfcPpXlx7eC4v+eHKkotW+jsFV5r3GSpxaJ8X6dMZ+XYNu+MZ5OvgD1V3W1vtD+yYUZz8+POXuno/lQxEuUZK4jtORUobEj/bHoj+e7NzludThq5EriLEZOiMrCP7zEdqjCjzhGofECeiWJF8TUcRzSijU+0GpHxQnSDe9P25WE43qUx/jzHeMHDRdNCKcW5KWIuVSlWdwYYtyG/34p8D+9N0BJKxwh0R0yTX10fdDn3rjaPz9gv3zOqZ9YYKyeM1EpnS1ro2N1xzXH786oT/O3K9rcj5s3ST+QqZN0waYcsOxuP7kfa13oE/7cMWYiq6wsBZe9eZ43cAXdg1n3HQc3rjKbs3JJb3bB5MN2cbaOSO64YEfjrS2pxe0vPiQhBbeNh5N2eHSG0//SggR2ZLqn6OfzOPQvm0xZmjnSG2onDcy3OKrp1WmzgNHEt3AUu2m41ulKto1TboJ+vXkcumuZxI0i8DznzxOwrbKlMk1tV308VgX4Jgih7h+mPRxnPT3t61tZaJOyPPTVkLKlLXDht7tRvXL8Ytj+yWD5fSEC0/9+EC8MnO1dRFQKC1Mtm4kVAtX26YNYrvqmThz/26YOG89+nfKXGdlYOcW+GbLLgDA704ZiN+++FVSQ3ztcfvg6AHR3BlbNq6PLbsq0xdHRNG3d7umWJwhJgAoHu2c6T77p59N3bzmWpafT8cekxwPPzy4Aje9PDvYl/iHio0QiXvhK2F0a1dabGKO+gAEM4zpZLJ0R33MXJ1H4wbluNKrw9TUszBcrLm2qFyvFeANW3TqacnDxuivThiAy5/4wto3eo0h4Ncn9Ee5EDhTiwULu05RLByuoLg8u2rf15jXt6Y3z+8i8tgBHfDAh4txSJ+ENeW/lx6UVXv1y8vwydhjQrMr2iyZpu9ju+kq/O6UgWjRuD5OHNQJDeqV4eUrD01LVBTrvRHzXhVDPLTr7MVp8XNR9i2Wl3aeYEtRHnA2poijOmqa2rDuXXPcPhjgLc51bUK/js1x7fH9i2LyAOharWwFJiC1gDt9eFdL4CKtLX2z04Z1wdJxY0hFVl+/6nBMv+l4r51ES8O8TFA/P7YfKfvdR78+Gi9deSiAYArt8L4Gv9ffhVGydYW2XxxDKRmncEoEbez/XbBf4G/V1VKUJdKWhtaYMZyzrZ5TXPRD3ey5A+rfJ+9DzDkrzm20zY9+ApMxQzvjoQvNmv8o7kX/+t7+ABI1VVzQsF45lo4bE0h3rHPJYb1w+vCU4NHdUhPHX5y3DXHL3K9HKwDp9aVU0oQi45YJQefWMwYns6Xt0zFhjaG4XacfN3+LJxHTEwMA/vHdxP2/8phgcWGnvXcwn504qBMO7N0WS8eNIRdwptC1VeM0D5JCvsvbNmuIP5w+ODnuh3Vv5cSCMdkQN5qkiNf6xwyIZoHLB1HGSBFf2oywpShHmDKQ6Vpkexs0Au+iGHNJkaxHYxGlQKntPP0HXiKlNR8RkgpY1bD591evNF8sC/xMdG/TJJlK3DSJGdc52kn6PtDnZcg2R/VVz5ZRvdpgyhJaVqyz9u+KF6Ylsgg2aVAPM246Lq24sY0DLckkWkRox+ePZw2JvE9UenmuSANCChzmC8o991Pf+wtZHX8hpWpWM72QTxzcCbecOhDnGlyJcoFAUNj1a/WE0a11E9x59hAcM8DsxtUoQxC1ygmDO+GxScuI/Yz+HObSldbUdjby15ihnTFm6Biv/bBjFgeFeo9IaV4Ax+1Sg3LfTTN/J7V8007r78W8cL9Uia+Ki+5SHDvhV9Y9KS1YKMoRvntK+g8uWpfaX4YsdSHfTb1xdFql5ERf3Q79jTv2Om3PRBSFEsXdTcqEsPD2L49ARWhiC194Sl0vfaIvIyZayDRJHdS7DT6LkO6Wegv/eGbIotvfl+g2oR/M1+yp2vAoRMnIROHHh/e2CkXtmzfEovUJdz/dlSQbN6FbTjUnZKBiswLERruBR+7THhOuOpxc8JY6P0R58R7gFReOotjw8WM7rjy6L/ZW1eD7B/XEs1NXYP7a7aHbXzCqBz5asAEDOjWHECIti9MPDuoZuQ9RiKqJ/84BPay/2+5GOy0+pHvr6PXZ4rwO8rl4cuZ8EXJfnLSdRSOnDeuCV2audmodiopr0eWOs4ai78dLcGjEUgHZ9CNTncVidgvrFJKCnUpoTFEWfUm2G2HbIr60GWH3uSLGFFOkM7BzZlcpn3bNGqb5rmcyVcfRBGaakLLFL+aW6eHzqzy3b94w0sKkX8fmdpcwCaQEpNxAiQcD3GgUkzJRWkxROLqWPds+RLk3Ls73CCVboosJfFi3xDM4vEfrrMdDlGtx3/dHAAB6ZwjAvygkpfi+nVukHct05FyM8V7tEtXhj7DUYAvj+Z8ejKcvS8RWNG1YDzeeMhCN6pdbi7yePKQzlo4bE5p4Zum4Mbj1jPgFk4uN35+WnsyDCjUFdNg+ucCkLDEdMmp5CUqb2TT27rVHZizSqeMviAtlKcrGNdFE++YNMfakAUUV2F/M6/Y4HgY+Yc+uzfqXiYN7t8XxAzuS5xVyMpIihYWiHCEQPrFkGpZxFmhxJxp/kJcJ4VwrFVYvwSVq6lrbs+5fG2qcRqbYBEowaqbfkttkGA2utS229vxgcZN7g/6tKWVopj4XzyvRMcmsdbl71Y6qaJP23YmDO2HurSfizQw1XtpELE7oPweDuybc6/Lp9pKJET3bhBZb9F3KwoLIC43rHtnaM5VuILWbzHZGH8e+8iab45oIi6sCgAGdm6fVYZl764l45KIDIrX//RxbCPu0b5aWNa/YsblaFUscsc7cW09MKz5aihlS7zlvGPp1cDNe1FuVTUr3RvXL8cAPRxo8Z8KP6yqFfCFg97kcYZrMXUwqmbLPUQ+hZo45d2Q3/PXdBdl1rABkOtcOzRvh+pMGJCt/G9uJeFwJYJgnSH53lNnVJRu3MN3N0cTBXkzLmfvRXNfCrtkzlx2Md+aszUmQv0q+pspMwkmuKm4nUrVH3Efra9j+n449JrR6ORAtviQTydg6r0sP/GAkXpy+ChVh9WmKjOtO6I9mDevhDOJzwKQT5+30/QN74NA+bfO6+G/aoB4uOawXrnp6RvK7OM8BJcV5ocj3kl4VIqIIFPXLBSqrcz2z29tvVL888v3XXxFzbz0xb27/Js7avxvO2t8el0tFPb+2EZVidRkWihyiTiSqFitQiTvCTFehBeOaBarsJyRXdViy2cc1/qTwkyP7ZNy2pbfgbNvUvlhOJueSCTeHpePGpG2za2918nOrxqnJ6JwR0SY76l2t8NyQyO2GNFzRrqmT4M5sGNa9FWau2OykrUyWDd+SefKQTk79raWk3zfTwqNpiGDahZCR0AX6ZevSqnEy9TRp/wJqZ5s1rIfrTuhfsOPbcDUfZmOI7NY6N2NICFEQa0hDYkFhG+o80cKzfus1e3LBsG4tMXPlltDfrjiqD9Zv24PvZWHFevLSAzGZmGgmjChFtj/5zTH4dmdl7GPF6YcL9EepUf1yUubXYke9ZuN/cRiueWYmjh/YKb99KEErnQ8LRTkkbGBEGSqmYDvqezGTtvykwZ3w4IeL8aPDelm3izMx5TqmiEKUfp86tDMqq2pw2nB70HfS595yF4Z1b4WKtk0xoFNz3HxqwkVxwe0noTzihXTtiZXLF4yLtk8a3MmZUNSQXNDWDanzl5FvnL61n3GtkBTa+eEXx/bDhu17CtyL3DCcGCvoik4tG2Fg5xa4/uQBGbf1C7+WQqC0izlHLXR7RL92uOe8YRm9ClxwZP8ORqGoVZMGuPc7w7Nq/5C+7XBIxKQGV43uh3lrt+GQPu1w34eLyft1aNEoLU65FIiTdr7UGNSlJd78Zf4LLrP7HJNGwJ8zwviwbapqo8N/8f+ivS3aNWuID399NABg1eZdpH2oHD+oE+6YMNdpm2FI6cbNQAiBswmWHMq1PWFQJzSqX443lDiPsKQNmV7qrieWWBmlIi/w4xfbdCmzRem2X5slG/w4liiWolzz48N74bVZ30Tax/eojR2W4+gmXnPcPm4aKhJ8S/zUG0dn5aIaRxCoX16G1686nNY+oscUFQon877qMiZE1q5LnT1FZikGm+/buQUmXncUAPO1LWSa8MhkDODO3MQfTo+ftKSuonodnTgovxYqF7BQlENcTyCm9nS/6CYNEm4FPQsYBxDVKkJh3FlDIgeM5wrbJO0qzjtX65J47pDBnfT4FhfmcpdDhm5NBS49rDf++Hp2Avxfzt8PD364GPv1aI3Zq7dat/31if0xqqINnpqyAkDuYgd+O2YgfjsmWorw4d1b46JDKnDp4XbrMROPdlnGsuVaVikGt+d84vp8R1a0wQtXHJKMNy1VimUcZPNeySwTZX6YaoM7Xb6RUibnqUP7mmv4FSuF93GqpTSuXx7rBfbL0eaq6D76w6wvWCvaNcW/fzgSd587LHoHHJGLSfX8UT1wvKZ5yPfkTcoq56hPLrS1L195aOSUsBQaNwj68x/ppVXuFqMmSi6IYuEqcyDFdm3VGLecNgjlZSLjsa84qi9GKpnkikknX14mcMtpg2Lfx2JZTNV2cn2di2lM5hJqVsUoRdf379E6Y0bYIkyQGMBsKcpvx/0sp1HcoZt7dd6G92hl3S7fbqz54vYzhuCAitbYp1MRZD0swRcCW4pyxKnDuuCet+dH3s8WsErRmtTzKkePHpiqhn7kPu3RPovg0dINmstdv+1ujm6O66R+TpYTv6kL+lx36eG9cMZ+XTOOM1vCAOp1s72Yu7ZqjFWbd9GTHeRgiBTy2AyTDfv3aI1ZK7egTRYFjPOFiwW6qbSAzmu/OAzTln+b9fFKhWJJvf27UweiX8dmOLp/h8wbe3x2/bH4+3sL8esMSVcuGNUjo4dACXiRpjGseys8d/khBe3DgM6JUg492xSHkjQKLBQ5RJ1HystEeJ2iiJPN45eMwtqtu9O+N2USCgvQfOySUZGOWXLkcf5OxXWZZ0vqLc60XTH59Wc6JSEESfC2CWku3sN9OzRLxsft36MVpi3fnH2jDJlsbuF/Lz0QK7/d6awvTHR+O2ZfnD+qO3qUQAp2F9N+B69kQqYaSz3bNkXPtsWbvts1xSESJTJKRs2K2rRhPYw9KXNSEb3OVSnh149zlb7bJVIm0vTv170VBndtWejuRIaFojwTdbIJq/YuZSJDTRimjHXZMKhrC4z/MlrAtskikIt0p/nM1pVPDVoxiEQUuewXx2Z2+aTg4trefOpA/OG1r3Fw77b458SFDnoVHaos27pJQkvdNMeFjkuFQyNmy6qLtG/eECcP6ZSz9Pn1y8swoFOLnLTtGhfzY4fmifflacPsWUfrGmFlAWob9UKSH5UK3Vo3iVSGI98IIUpSIAJYKMoL6uSdzbqPFM+SAx3P0K6tAEQLmjP5THd0lLrT12QO7dYqcr2Kod2yf1htC1/q4j7TvdKP8cvR+2Dyko2ktintRUKY/9zPkW921Ex3YfRu3wyPXlxYyyj1LK49vj96tGmCkwaXXoYenaYNyrFDqc/F5IbyMoF/fm9E6G8vXXko9lbV5LlHhcPFm65N0waYdcvxaJZnxUSxu6TfefZQHHTHu3jhisK6YWWifnlxX8e6xCWH9sLDnywpdDeyhoUih+hruvDH1fwQjxma+/oIJmxTSzHmnN+/R2u8c80R6BOxaODHvzk65xnsqNN01wzFFHUh4arR/QBkb5WJWC0p6+MVigtG9cDnS/MfB0AV7hrVL8cPDq7IbWfyTLHEIuSC135+GKYXcVxJbQ0cN+FqqLVoRIsrqkuYCpOruChlkA1TbjjWSQFfxg3nHdAND3+ypCjXi1EoXfthERNXezEygxvY6cO7AsiNCxplGGej3frX9/ZPtOFwzdS3Q/PIi7BurZugSRZaQb+WUasm2b9IMwX5llJMkZNjOF5QHzMgEZwbdp2beRmKsk2RXErcevqgnCleimek5o7BXVvWOiGWKQy1QXdATVKRKzq0aISWDt7DjBuK3fpJhS1FOcBfy/7h9MH4/auz0aWVG5exy4/sjYsPrUCjPAcIulibd64l+f5/ObofrjiqT17uQa5kIhfNBgQYR3NhPtPUHtGvHe46ZyhOzUEsQRHJsgF+cHBFzhf1teO1mB/Y5TA+LRrXR4N6pavTrQ3PSW1ZBNs4oFcb6+9CFO98byLn6fxL7HrosFCUQ47Ypz3evfaowHe2AZlpMAkh8i4QBY8fZduozoPuycXDn897UAxzSz4nONe3K1Pc17kjuzs+onfcAt+5KDVVXFHqL8JC8NkNx6Kymi9cHMrLRLLG0CF9Sq9AZKnd9WHdWmLmyi3BL2u/TJTRGjbz5uNRXULP8B1nDcnokRSX2mD9BFgoygm2wZHrcRN3QWaPKcotk284Fjsdakx7t2uKxRt2OGsvKq4mh4N7t8X/vliJAyrcTmJxuqcLuU3V4q2l807IC4UUEN655ghjZsp8EDb2G9cvR+ccZMUsdZpzLEtkzhvZDc9OXVnobmRNqa0f/3PpgVixKZguv9TOIReUWjzaBaN65Kztvu2b4aJDKnDhIRU5O0Y+YKEoz1gtRfnrRiQaeKkrW+TIh9hFRrrXfn5YxiripYZfiLdTy8K5HprGZL+OzfPajzgUSnNVyOe4b4fC3BebMubrP5yQx54wtZk7zx6KcWcNLXQ3sqasxN5VLRrVx6AuiaytR/dvj4nz1uPGMQPz2odc1J07Y3gXHNi79CyNxUhZmcAtpw0qdDeyhoUih1CsNDYtsouUxHGxHfmg3m1w45h9ce6I3LgbuUDNiX/N8fvgZ09OR5cCCRNlRWpHjmJFfPTiAwK1KvJxRqW2UDBRl13JwuIManNGOib3PP/Tg7F1dxWAxFjSh1Ndft4Kwf0/GIltuyvRNs9Jap6+7GDsc+MEp7Gnfzl/P3eNMbUCFopyQNQAxGLP7y6EyLpYYLOGCXerigyVw11wytAuOGVo4YrxHVwLNE9H9U9kbpu/dpvztv93+cGYsWJz2ve1ZelcTFkD80UdPGUmT4zoaQ92L0WaNyrdpVeDemV5F4gA1DpPEKY4KciTKYS4C8CpAPYCWATgYinlZu+36wH8CEA1gF9IKd8sRB/j4AtDHVqYJ4y6qjTt26E5Hr5oJA6qBQKDieaN6mHb7irnFg9XrcXJFtSrXVMc1b89fjl6H0e9AEZWtMHIitwvdHihnn/q6vzGFI5SHHM92yaUgyNyFPReG/Fv85n7dStoP5jaTaFyWr4NYLCUciiA+QCuBwAhxEAA5wMYBOBEAP8UQpRMda4G9crwl+8Mx7M/Odi4Tbh7SeL/Qi7i8vFeOWZAx6xqBBU9Rb4Ij5OEo355GR69eBSGhRSGPKp/+9jthlGvPDfTUSkumkqNP545BO2bN0zGHzJMvihF5YevN2tcwGyypUZZmcDMm4/HnWcPKXRXmFpMQd5gUsq3pJRV3p+fAfBF/9MBPC2l3COlXAJgIYBRhehjXM7Yryu6WGryhC3Q/K+iLi4fufgA/OO7+0fah8k9rhfhrt/52fTv9V8cjt+dkgiwdS1r5KJmUCEoZGxgLrGNm7NHdMPnvx1da+LCXHHZEdm5HTO1E185WhddbbOhZeP6OVOeMQxQHDFFlwB4xvvcFQkhyWel910aQojLAFwGAD165C7NoGvClgxxF6lHe3EfLsjV1HxghuJnTGkxsEsLDOzSIidtN21QjvMP6I5zRpS2e0RNLV3ndGjeEGu37inpopn55oaT98UNJ+9b6G7UakrRElwM3iEMw6STM6FICPEOgE4hP/1WSvmyt81vAVQB+G/U9qWUDwB4AABGjhzJU4tHRdvcJzKgMvfWE1GPNcdpNKhXhr1VNeTtXV1B31WjXllxLmqFEBh3dumn262t3HbGEPz48anYL8SVkmEYOr5QxJYihikuciYUSSlH234XQlwE4BQAx8qUv8kqAGre527ed7WGsPS0pwztggc/WuLE8tMopo9yXJef6b87zqipi9uX2s7rvzgMkxZtzPtxf3XiALRsXB+nDy8eN7VXfnYo5q/dnpO2/ZTiLpNE1GWaehkkS1EzzzDFhF+2gWUihikuCpV97kQAvwZwpJRSLZP8CoAnhRD3AOgCoB+AKQXoYl4Z1r0Vlo4bU9A+xJ2cWzdt4LYjJUzPdk3w1aqtKM+wauzboTmpyKbrF2azhvVwzfH9nbXnF5Vt1jB+Ud+h3VphaLdWjnoUpEG9soI8V728tPP9Ixa4ff6nB+P9eetz0SUnNPOEzM4FLCbMMLWBuHHEDMPklkLFFP0fgIYA3vYsJ59JKS+XUs4WQjwL4Gsk3OqulFJWF6iPDBOJRy8ehenLNweKnrqgWDXzN50yEAf1boNRHDcWwNcC9+kQzZV1RM82RV2TZWi3VvjbBfvh2AHuYhkZpi7iJyRhSxHDFBcFEYqklH0tv90O4PY8dodhnNCuWUMcN7Cj83aL9cXZuEE5Th8emgel5DhuYEdUVdPjvGx0atkIANC/Y24SUhSS02pJhkCm9lCs86MNX8/FMUUMU1wUQ/a5OoWv9X/8klFYtmmnfeM8wnNz8ZHMUFTYbtQJHvzhSGdtjejZGi9deSiGdG3prE2GYWoPfmwxz+0MU1ywUJQnzt6/G56fthLHeK4nR+zTvsA9CsK+zcVHPy/u6NA+bQvcEyYqwzlDG8PkhWJ1L7aRyj5X2H4wDBOEhaI80bt9Ir6gmeN4E1ewpaj4GNilBabeOBptOZlFGu2aNcRVo/sVuhsMwzCRKSuSQkVvXH04lm8sHo8Vhik0xblCZ/IOy0TFSbtmDQvdhaJk6o3WjP8MwzBFSyqmqKDdwIBOLTCgU+2LfWSYuBRnFUcm7/Ro06TQXWAYhmGYWk/nVolkLCcNCatvzzBMoWBLUZ6IWxw1X5SXlaBjNsMwDMOUGB2aN8KXtxxftO70DFNX4Scyz5RiUCjDMAzDMO5o3ih+0WuGYXIDu88xDMMwDMMwDFOnYaGIYRiGYRiGYZg6DQtFDMMwDMMwDMPUaVgoYhiGYRimpPBr/50xvGuBe8IwTG2BEy0wDMPE4O5zh+HD+esL3Q2GqZN0btkYS+44GYKzFzGOOX5gR7z19dpCd4MpACwU5YmTh3TG3W/Nx+lErdagLtEKql14cE/MXLklTtcYhonBOSO64ZwR3QrdDYaps7BAxOSC//vu/tixp6rQ3WAKAAtFeaJ3+2ZYOm4Madv3rj0S7Zs3jNT+708fHKdbDMMwDMMwjEeDemVoUK9BobvBFAAWioqQ3u2bFboLDMMwDMMwDFNn4EQLDMMwDMMwDMPUadhSxCS5+dSBOLBX20J3g2EYhmEYhmHyCgtFTJKLD+1V6C4wDMMwDMMwTN5h9zmGYRiGYRiGYeo0LBQxDMMwDMMwDFOnYaGIYRiGYRiGYZg6DQtFDMMwDMMwDMPUaVgoYhiGYRiGYRimTsNCEcMwDMMwDMMwdRoWihiGYRiGYRiGqdOwUMQwDMMwDMMwTJ1GSCkL3YesEUKsB7Cs0P3waAdgQ6E7wTAGeHwyxQqPTaaY4fHJFCs8NqPRU0rZPuyHWiEUFRNCiKlSypGF7gfDhMHjkylWeGwyxQyPT6ZY4bHpDnafYxiGYRiGYRimTsNCEcMwDMMwDMMwdRoWitzzQKE7wDAWeHwyxQqPTaaY4fHJFCs8Nh3BMUUMwzAMwzAMw9Rp2FLEMAzDMAzDMEydhoUihmEYhmEYhmHqNCwUERBCPCyEWCeE+Er5brgQ4jMhxAwhxFQhxCjveyGE+JsQYqEQYpYQYn9lnwuFEAu8fxcW4lyY2kXEsXmUEGKL9/0MIcRNyj4nCiHmeeN2bCHOhal9GMbnMCHEJCHEl0KIV4UQLZTfrvfG4DwhxAnK9zw+GadEGZtCiAohxC5l7rxP2WeEt/1C790vCnE+TO1CCNFdCDFRCPG1EGK2EOIq7/s2Qoi3vXXk20KI1t73vPZ0gZSS/2X4B+AIAPsD+Er57i0AJ3mfTwbwvvJ5AgAB4CAAk73v2wBY7P3f2vvcutDnxv9K+1/EsXkUgNdC2igHsAhAbwANAMwEMLDQ58b/Sv+fYXx+DuBI7/MlAG71Pg/0xl5DAL28MVnO45P/5eJfxLFZoW6ntTPFe9cL791/UqHPjf+V/j8AnQHs731uDmC+N0f+CcBY7/uxAO70PvPa08E/thQRkFJ+CGCT/jUAX8PZEsBq7/PpAB6XCT4D0EoI0RnACQDellJuklJ+C+BtACfmvvdMbSbi2DQxCsBCKeViKeVeAE8jMY4ZJisM43MfAB96n98GcLb3+XQAT0sp90gplwBYiMTY5PHJOCfi2AzFe7e3kFJ+JhMr0McBnOG4q0wdREr5jZRymvd5G4A5ALoiMfc95m32GFLjjdeeDmChKD5XA7hLCLECwN0Arve+7wpghbLdSu870/cM45qrET42AeBgIcRMIcQEIcQg7zsem0w+mY2UUHMugO7eZ547mUJjGpsA0EsIMV0I8YEQ4nDvu65IjEcfHpuMc4QQFQD2AzAZQEcp5TfeT2sAdPQ+8/zpABaK4vNTAL+UUnYH8EsADxW4PwzjYxqb0wD0lFIOA/B3AC8VpntMHecSAFcIIb5Awi1kb4H7wzA+prH5DYAeUsr9AFwD4Ek1Fo5hcoUQohmA5wFcLaXcqv7mWSe5ro5DWCiKz4UAXvA+P4eEiwcArEJQu9TN+870PcO4JnRsSim3Sim3e59fB1BfCNEOPDaZPCKlnCulPF5KOQLAU0jECwE8dzIFxjQ2PZfOjd7nL7zv90FiHHZTmuCxyThDCFEfCYHov1JK/52+1nOL890313nf8/zpABaK4rMawJHe52MALPA+vwLgh14mkIMAbPFMnW8COF4I0drLFnK89x3DuCZ0bAohOvmZkbyMdGUANiIRXNxPCNFLCNEAwPlIjGOGcY4QooP3fxmAGwH4mbxeAXC+EKKhEKIXgH5IBLHz+GTygmlsCiHaCyHKvc+9kRibi713+1YhxEHe3PpDAC8XpPNMrcIbTw8BmCOlvEf56RUkFJ/w/n9Z+Z7XnllSr9AdKAWEEE8hkbmrnRBiJYCbAfwYwF+FEPUA7AZwmbf560hkAVkIYCeAiwFASrlJCHErEi94APiDlFIP8mSYSEQcm+cA+KkQogrALgDne+b3KiHEz5CYKMsBPCylnJ3fM2FqI4bx2UwIcaW3yQsAHgEAKeVsIcSzAL4GUAXgSilltdcOj0/GKVHGJhKZ6v4ghKgEUAPgcuX9fQWARwE0RiL714S8nABT2zkUwA8AfCmEmOF9dwOAcQCeFUL8CMAyAOd5v/Ha0wEisSZiGIZhGIZhGIapm7D7HMMwDMMwDMMwdRoWihiGYRiGYRiGqdOwUMQwDMMwDMMwTJ2GhSKGYRiGYRiGYeo0LBQxDMMwDMMwDFOnYaGIYRiGYRiGYZg6DQtFDMMwTJ3GL8zJMAzD1F1YKGIYhmFKBiHEH4QQVyt/3y6EuEoI8SshxOdCiFlCiN8rv78khPhCCDFbCHGZ8v12IcSfhRAzARyc37NgGIZhig0WihiGYZhS4mEAPwQAIUQZgPMBrAHQD8AoAMMBjBBCHOFtf4mUcgSAkQB+IYRo633fFMBkKeUwKeXHeew/wzAMU4TUK3QHGIZhGIaKlHKpEGKjEGI/AB0BTAdwAIDjvc8A0AwJIelDJAShM73vu3vfbwRQDeD5fPadYRiGKV5YKGIYhmFKjX8DuAhAJyQsR8cCuENKeb+6kRDiKACjARwspdwphHgfQCPv591Syuo89ZdhGIYpcth9jmEYhik1XgRwIhIWoje9f5cIIZoBgBCiqxCiA4CWAL71BKIBAA4qVIcZhmGY4oYtRQzDMExJIaXcK4SYCGCzZ+15SwixL4BJQggA2A7g+wDeAHC5EGIOgHkAPitUnxmGYZjiRkgpC90HhmEYhiHjJViYBuBcKeWCQveHYRiGKX3YfY5hGIYpGYQQAwEsBPAuC0QMwzCMK9hSxDAMwzAMwzBMnYYtRQzDMAzDMAzD1GlYKGIYhmEYhmEYpk7DQhHDMAzDMAzDMHUaFooYhmEYhmEYhqnTsFDEMAzDMAzDMEyd5v8BgT8yOH4j7u4AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 1008x288 with 1 Axes>"
       ]
@@ -950,18 +950,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.81040687, 0.69606128, 0.42944323],\n",
-       "       [0.99033137, 0.60317056, 0.82435046],\n",
-       "       [0.70689463, 0.05604562, 0.53930095]])"
+       "array([[0.34743109, 0.34666094, 0.67796236],\n",
+       "       [0.37775535, 0.7452935 , 0.44639271],\n",
+       "       [0.7097024 , 0.54721637, 0.96400871]])"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -974,7 +974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -983,16 +983,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8.104068706347091755e-01 6.960612833436454761e-01 4.294432296218351208e-01\r\n",
-      "9.903313699879116028e-01 6.031705643128616456e-01 8.243504620080480683e-01\r\n",
-      "7.068946259685966460e-01 5.604562284444569720e-02 5.393009542524886957e-01\r\n"
+      "3.474310879390657414e-01 3.466609365910759966e-01 6.779623624489031775e-01\r\n",
+      "3.777553531256817587e-01 7.452935047749419395e-01 4.463927097637667707e-01\r\n",
+      "7.097023968559375007e-01 5.472163711854115542e-01 9.640087120207403437e-01\r\n"
      ]
     }
    ],
@@ -1002,16 +1002,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.81041 0.69606 0.42944\r\n",
-      "0.99033 0.60317 0.82435\r\n",
-      "0.70689 0.05605 0.53930\r\n"
+      "0.34743 0.34666 0.67796\r\n",
+      "0.37776 0.74529 0.44639\r\n",
+      "0.70970 0.54722 0.96401\r\n"
      ]
     }
    ],
@@ -1037,14 +1037,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "random-matrix.npy: data\r\n"
+      "random-matrix.npy: NumPy array, version 1.0, header length 118\r\n"
      ]
     }
    ],
@@ -1056,18 +1056,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.81040687, 0.69606128, 0.42944323],\n",
-       "       [0.99033137, 0.60317056, 0.82435046],\n",
-       "       [0.70689463, 0.05604562, 0.53930095]])"
+       "array([[0.34743109, 0.34666094, 0.67796236],\n",
+       "       [0.37775535, 0.7452935 , 0.44639271],\n",
+       "       [0.7097024 , 0.54721637, 0.96400871]])"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1085,7 +1085,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1106,7 +1106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -1115,7 +1115,7 @@
        "48"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1126,7 +1126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1135,7 +1135,7 @@
        "2"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1167,7 +1167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1176,7 +1176,7 @@
        "1"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1190,7 +1190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1219,7 +1219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1230,7 +1230,7 @@
        "       [5, 6]])"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1241,7 +1241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -1250,7 +1250,7 @@
        "array([3, 4])"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1268,7 +1268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1277,7 +1277,7 @@
        "array([3, 4])"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1288,7 +1288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1297,7 +1297,7 @@
        "array([2, 4, 6])"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1315,7 +1315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1346,7 +1346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1357,7 +1357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1368,7 +1368,7 @@
        "       [ 5, -1]])"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1393,7 +1393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -1402,7 +1402,7 @@
        "array([1, 2, 3, 4, 5])"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1414,7 +1414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1423,7 +1423,7 @@
        "array([2, 3])"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1441,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -1450,7 +1450,7 @@
        "array([ 1, -2, -3,  4,  5])"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1471,7 +1471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -1480,7 +1480,7 @@
        "array([ 1, -2, -3,  4,  5])"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1491,7 +1491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -1500,7 +1500,7 @@
        "array([ 1, -2, -3,  4,  5])"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1511,7 +1511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -1520,7 +1520,7 @@
        "array([ 1, -3,  5])"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1531,7 +1531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -1540,7 +1540,7 @@
        "array([ 1, -2, -3])"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1551,7 +1551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -1560,7 +1560,7 @@
        "array([4, 5])"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1578,7 +1578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1587,7 +1587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1596,7 +1596,7 @@
        "5"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1607,7 +1607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -1616,7 +1616,7 @@
        "array([3, 4, 5])"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1634,7 +1634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -1647,7 +1647,7 @@
        "       [40, 41, 42, 43, 44]])"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1660,7 +1660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -1671,7 +1671,7 @@
        "       [31, 32, 33]])"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1683,7 +1683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -1694,7 +1694,7 @@
        "       [40, 42, 44]])"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1720,7 +1720,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -1748,7 +1748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -1757,7 +1757,7 @@
        "array([11, 31, 24])"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1776,7 +1776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
@@ -1785,7 +1785,7 @@
        "array([0, 1, 2, 3, 4])"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1797,7 +1797,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -1806,7 +1806,7 @@
        "array([0, 2])"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1818,7 +1818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
@@ -1827,7 +1827,7 @@
        "array([0, 2])"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1847,7 +1847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -1857,7 +1857,7 @@
        "       6.5, 7. , 7.5, 8. , 8.5, 9. , 9.5])"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1869,7 +1869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
@@ -1880,7 +1880,7 @@
        "       False, False])"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1893,7 +1893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
@@ -1902,7 +1902,7 @@
        "array([5.5, 6. , 6.5, 7. ])"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1913,7 +1913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -1922,7 +1922,7 @@
        "array([3.5, 4. , 4.5, 5. , 5.5])"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1954,7 +1954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -1963,7 +1963,7 @@
        "(array([11, 12, 13, 14]),)"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1979,7 +1979,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
@@ -1988,7 +1988,7 @@
        "array([5.5, 6. , 6.5, 7. ])"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2013,7 +2013,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
@@ -2022,7 +2022,7 @@
        "array([ 0, 11, 22, 33, 44])"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2033,7 +2033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -2042,7 +2042,7 @@
        "array([10, 21, 32, 43])"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2081,7 +2081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2092,7 +2092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -2101,7 +2101,7 @@
        "array([0, 2, 4, 6, 8])"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2112,7 +2112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
@@ -2121,7 +2121,7 @@
        "array([2, 3, 4, 5, 6])"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2132,7 +2132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -2176,17 +2176,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.04434218, 0.59817098, 0.78569806],\n",
-       "       [0.03270677, 0.93918254, 0.01270568]])"
+       "array([[0.12684531, 0.88008175, 0.00646408],\n",
+       "       [0.56140088, 0.06651575, 0.79145154]])"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2199,7 +2199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -2208,7 +2208,7 @@
        "array([1., 4.])"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2227,7 +2227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -2236,7 +2236,7 @@
        "((2, 3), (2,))"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2247,18 +2247,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.21057582, 0.36170027],\n",
-       "       [0.77341514, 1.93822861],\n",
-       "       [0.88639611, 0.22543893]])"
+       "array([[0.35615349, 0.93812672, 0.08039952],\n",
+       "       [0.74926689, 0.25790647, 0.88963562]])"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.35615349, 1.49853379],\n",
+       "       [0.93812672, 0.51581293],\n",
+       "       [0.08039952, 1.77927125]])"
+      ]
+     },
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2269,7 +2290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [
     {
@@ -2277,9 +2298,9 @@
      "evalue": "operands could not be broadcast together with shapes (2,3) (2,) ",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-105-629678c55a83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mv1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-89-629678c55a83>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mv1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,3) (2,) "
      ]
     }
@@ -2304,20 +2325,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1.03315792, 0.6905855 , 0.84353203, 1.45096566, 0.87872966],\n",
-       "       [0.38571022, 0.35776393, 0.43959241, 0.58357586, 0.47922741],\n",
-       "       [0.92430843, 0.58422879, 0.78342705, 1.42096537, 0.66931662],\n",
-       "       [0.53165197, 0.29194347, 0.4070023 , 0.71839424, 0.36541866],\n",
-       "       [1.07740213, 0.81283748, 0.74676416, 1.1225275 , 1.16903802]])"
+       "array([[2.59833251, 1.8189686 , 1.32946437, 2.15441681, 1.55219543],\n",
+       "       [1.4561364 , 1.26875236, 0.97855704, 1.35013248, 1.05524471],\n",
+       "       [2.38061437, 1.70445667, 1.16297305, 2.27888345, 1.66499116],\n",
+       "       [1.08602725, 0.76015292, 0.46415646, 1.38753125, 1.00011024],\n",
+       "       [1.82122991, 1.34175794, 0.92375387, 1.74770416, 1.27559765]])"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2331,20 +2352,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1.44646503],\n",
-       "       [0.6220085 ],\n",
-       "       [1.17066732],\n",
-       "       [0.68703402],\n",
-       "       [1.71817279]])"
+       "array([[2.0139906 ],\n",
+       "       [1.41657535],\n",
+       "       [2.09784627],\n",
+       "       [1.2752073 ],\n",
+       "       [1.6253844 ]])"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2355,16 +2376,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[2.28303296]])"
+       "array([[2.08466462]])"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2382,7 +2403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2392,16 +2413,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "matrix([[0.73710128, 0.81515879, 0.7439564 , 0.16244929, 0.70382519]])"
+       "matrix([[0.45282687, 0.64874757, 0.70028245, 0.91412865, 0.36429705]])"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2412,20 +2433,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 95,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "matrix([[1.03315792, 0.6905855 , 0.84353203, 1.45096566, 0.87872966],\n",
-       "        [0.38571022, 0.35776393, 0.43959241, 0.58357586, 0.47922741],\n",
-       "        [0.92430843, 0.58422879, 0.78342705, 1.42096537, 0.66931662],\n",
-       "        [0.53165197, 0.29194347, 0.4070023 , 0.71839424, 0.36541866],\n",
-       "        [1.07740213, 0.81283748, 0.74676416, 1.1225275 , 1.16903802]])"
+       "matrix([[2.59833251, 1.8189686 , 1.32946437, 2.15441681, 1.55219543],\n",
+       "        [1.4561364 , 1.26875236, 0.97855704, 1.35013248, 1.05524471],\n",
+       "        [2.38061437, 1.70445667, 1.16297305, 2.27888345, 1.66499116],\n",
+       "        [1.08602725, 0.76015292, 0.46415646, 1.38753125, 1.00011024],\n",
+       "        [1.82122991, 1.34175794, 0.92375387, 1.74770416, 1.27559765]])"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2436,20 +2457,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "matrix([[1.44646503],\n",
-       "        [0.6220085 ],\n",
-       "        [1.17066732],\n",
-       "        [0.68703402],\n",
-       "        [1.71817279]])"
+       "matrix([[2.0139906 ],\n",
+       "        [1.41657535],\n",
+       "        [2.09784627],\n",
+       "        [1.2752073 ],\n",
+       "        [1.6253844 ]])"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2460,16 +2481,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "matrix([[2.28303296]])"
+       "matrix([[2.08466462]])"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2488,7 +2509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2497,7 +2518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
@@ -2506,7 +2527,7 @@
        "((5, 5), (6, 1))"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2517,7 +2538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
@@ -2525,10 +2546,10 @@
      "evalue": "shapes (5,5) and (6,1) not aligned: 5 (dim 1) != 6 (dim 0)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-119-e8f88679fe45>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/anaconda3/envs/dl/lib/python3.7/site-packages/numpy/matrixlib/defmatrix.py\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\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    217\u001b[0m             \u001b[0;31m# This promotes 1-D vectors to row vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masmatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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[0m\u001b[1;32m    219\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__rmul__'\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    220\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-100-e8f88679fe45>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mM\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/matrixlib/defmatrix.py\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    218\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\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    219\u001b[0m             \u001b[0;31m# This promotes 1-D vectors to row vectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masmatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\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[0m\u001b[1;32m    221\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misscalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__rmul__'\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    222\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: shapes (5,5) and (6,1) not aligned: 5 (dim 1) != 6 (dim 0)"
      ]
@@ -2554,7 +2575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
@@ -2564,7 +2585,7 @@
        "       [ 1.5, -0.5]])"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2583,7 +2604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
@@ -2592,7 +2613,7 @@
        "-2.0000000000000004"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2613,7 +2634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 103,
    "metadata": {},
    "outputs": [
     {
@@ -2622,7 +2643,7 @@
        "(77431, 7)"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2644,7 +2665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 104,
    "metadata": {},
    "outputs": [
     {
@@ -2660,7 +2681,7 @@
        "6.197109684751585"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2673,16 +2694,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 105,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.5275611748380306"
+       "0.4931528475182218"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2708,7 +2729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 106,
    "metadata": {},
    "outputs": [
     {
@@ -2717,7 +2738,7 @@
        "(8.282271621340573, 68.59602320966341)"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2735,7 +2756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 107,
    "metadata": {},
    "outputs": [
     {
@@ -2744,7 +2765,7 @@
        "-25.8"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2756,7 +2777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 108,
    "metadata": {},
    "outputs": [
     {
@@ -2765,7 +2786,7 @@
        "28.3"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2784,7 +2805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [
     {
@@ -2793,7 +2814,7 @@
        "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2805,7 +2826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [
     {
@@ -2814,7 +2835,7 @@
        "45"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2826,7 +2847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
@@ -2835,7 +2856,7 @@
        "3628800"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2847,7 +2868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
@@ -2856,7 +2877,7 @@
        "array([ 0,  1,  3,  6, 10, 15, 21, 28, 36, 45])"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2868,7 +2889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [
     {
@@ -2878,7 +2899,7 @@
        "         40320,  362880, 3628800])"
       ]
      },
-     "execution_count": 134,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2890,16 +2911,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9739601910770402"
+       "1.4446600641166332"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2927,7 +2948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
@@ -2955,7 +2976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
@@ -2964,7 +2985,7 @@
        "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.])"
       ]
      },
-     "execution_count": 137,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2975,7 +2996,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
@@ -2993,7 +3014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
@@ -3020,12 +3041,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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AH06zpuXY1v5U1VlVdVP38gvAPtOsyeCfgSQLwEOAL864lOV4M/DXwC0zrmMS9gWuBf65m7p6Z5LdZl3UuKrqSuANwPeAq4Drq+qs2Va1bHtX1VXd8x8Ce8+ymAl7HnDGNDs0+Kcsye7AvwAvraqfzLqecSR5InBNVV0w61omZBXwUOBtVfUQ4GfM11TCrXTz309h9AvtN4DdkjxrtlVNTo3OQV8R56En+RtG08Dvn2a/Bv8UJfk1RqH//qr68KzrWYaDgScn2Qx8EHhMkvfNtqRluQK4oqq2/AV2GqNfBPPq94D/qKprq+p/gQ8Dj5hxTct1dZJ7AnQ/r5lxPcuW5DnAE4Fn1pQvqDL4pyRJGM0hf7Oq3jjrepajql5VVftU1QKjg4afrqq5HVFW1Q+B7yd5QNf0WOAbMyxpub4HPCzJHbt/d49ljg9Wd04HjuieHwF8bIa1LFuSQxlNlT65qn4+7f4N/uk5GHg2o9Hxhd3jCbMuSr/0IuD9SS4CDgReO9tyxtf95XIa8BXg64z+n8/NcgdJTgU+DzwgyRVJjgTWA49Lcimjv2jWz7LG22I7+/NW4E7A2V0WvH2qNblkgyS1xRG/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH4JSFKLL0JLsirJteOuPNqt9nn0oteHrIRVTLUyGPzSyM+A/ZPs2r1+HHDlMra3J3D0jj4kzYLBL/3KRkYrjgIcDpy65Y1uPfiPduunfyHJAV37cd166+cmuSzJi7uvrAfu112c8/qubfdFa/6/v7uqVpo6g1/6lQ8CT+9uWnIAt1499TXAV7v1018NvGfRew8E/gA4CDi2W5NpHfDdqjqwql7Rfe4hwEuB/YD7MrqaW5o6g1/qVNVFwAKj0f7Grd5+JPDe7nOfBu6WZI/uvQ1VdWNVXcdo8bDtLRn8paq6oqpuAS7s+pKmbtWsC5B2MqczWsv+EOBuPb9z46LnN7P9/1d9PycNyhG/dGunAK+pqq9v1f5Z4JkwOkMHuG4H91O4gdEiXNJOxxGHtEhVXQGcsI23jgNO6Vbv/Dm/WiJ4e9v5UZJ/626wfQawYdK1SuNydU5JaoxTPZLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNeb/AGb7TuHgiT13AAAAAElFTkSuQmCC\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -3065,19 +3086,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.27954547, 0.39434877, 0.4540566 ],\n",
-       "       [0.98942566, 0.36000135, 0.33399721],\n",
-       "       [0.88427852, 0.87776476, 0.80368109],\n",
-       "       [0.54534545, 0.12886051, 0.29466367]])"
+       "array([[0.85882078, 0.0838741 , 0.4529751 ],\n",
+       "       [0.32355282, 0.23641565, 0.37693805],\n",
+       "       [0.06769945, 0.30438005, 0.9780961 ],\n",
+       "       [0.46162058, 0.42681981, 0.71106984]])"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3091,16 +3112,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 121,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9894256638866764"
+       "0.978096099540799"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3112,16 +3133,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 122,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.98942566, 0.87776476, 0.80368109])"
+       "array([0.85882078, 0.42681981, 0.9780961 ])"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3139,7 +3160,7 @@
     {
      "data": {
       "text/plain": [
-       "array([ 0.7859115 ,  0.77223336,  0.50666828,  0.55104521])"
+       "array([0.85882078, 0.37693805, 0.9780961 , 0.71106984])"
       ]
      },
      "execution_count": 123,
@@ -3175,17 +3196,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.77814945 0.11415817 0.62327907]\n",
-      " [0.78832786 0.9410027  0.3151632 ]\n",
-      " [0.0368383  0.24516094 0.74711683]\n",
-      " [0.81727139 0.76195462 0.19487213]]\n"
+      "[[0.58458652 0.95489874 0.76873658]\n",
+      " [0.79144906 0.35559767 0.96031963]\n",
+      " [0.55942317 0.78723157 0.3650356 ]\n",
+      " [0.04685468 0.43444695 0.33839966]]\n"
      ]
     }
    ],
@@ -3198,7 +3219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 125,
    "metadata": {},
    "outputs": [
     {
@@ -3216,18 +3237,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.77814945, 0.11415817, 0.62327907, 0.78832786, 0.9410027 ,\n",
-       "        0.3151632 , 0.0368383 , 0.24516094, 0.74711683, 0.81727139,\n",
-       "        0.76195462, 0.19487213]])"
+       "array([[0.58458652, 0.95489874, 0.76873658, 0.79144906, 0.35559767,\n",
+       "        0.96031963, 0.55942317, 0.78723157, 0.3650356 , 0.04685468,\n",
+       "        0.43444695, 0.33839966]])"
       ]
      },
-     "execution_count": 148,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3239,25 +3260,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 127,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.77814945]\n",
-      " [0.11415817]\n",
-      " [0.62327907]\n",
-      " [0.78832786]\n",
-      " [0.9410027 ]\n",
-      " [0.3151632 ]\n",
-      " [0.0368383 ]\n",
-      " [0.24516094]\n",
-      " [0.74711683]\n",
-      " [0.81727139]\n",
-      " [0.76195462]\n",
-      " [0.19487213]]\n",
+      "[[0.58458652]\n",
+      " [0.95489874]\n",
+      " [0.76873658]\n",
+      " [0.79144906]\n",
+      " [0.35559767]\n",
+      " [0.96031963]\n",
+      " [0.55942317]\n",
+      " [0.78723157]\n",
+      " [0.3650356 ]\n",
+      " [0.04685468]\n",
+      " [0.43444695]\n",
+      " [0.33839966]]\n",
       "(12, 1)\n"
      ]
     }
@@ -3270,18 +3291,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 128,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([[5.        , 5.        , 5.        , 5.        , 5.        ,\n",
-       "        0.3151632 , 0.0368383 , 0.24516094, 0.74711683, 0.81727139,\n",
-       "        0.76195462, 0.19487213]])"
+       "        0.96031963, 0.55942317, 0.78723157, 0.3650356 , 0.04685468,\n",
+       "        0.43444695, 0.33839966]])"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3294,19 +3315,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 129,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([[5.        , 5.        , 5.        ],\n",
-       "       [5.        , 5.        , 0.3151632 ],\n",
-       "       [0.0368383 , 0.24516094, 0.74711683],\n",
-       "       [0.81727139, 0.76195462, 0.19487213]])"
+       "       [5.        , 5.        , 0.96031963],\n",
+       "       [0.55942317, 0.78723157, 0.3650356 ],\n",
+       "       [0.04685468, 0.43444695, 0.33839966]])"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3324,18 +3345,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 130,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([5.        , 5.        , 5.        , 5.        , 5.        ,\n",
-       "       0.3151632 , 0.0368383 , 0.24516094, 0.74711683, 0.81727139,\n",
-       "       0.76195462, 0.19487213])"
+       "       0.96031963, 0.55942317, 0.78723157, 0.3650356 , 0.04685468,\n",
+       "       0.43444695, 0.33839966])"
       ]
      },
-     "execution_count": 152,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3348,7 +3369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 131,
    "metadata": {},
    "outputs": [
     {
@@ -3365,23 +3386,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 132,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.15246604 0.88310479 0.72114104 0.13062197 0.34755784 0.23403431\n",
-      " 0.92416609 0.02256229 0.5800577  0.74899696 0.92496278 0.98122918\n",
-      " 0.48526406 0.61784995 0.74526605 0.36587567 0.53935338 0.48663655\n",
-      " 0.46254169 0.60397314 0.20181377 0.4031567  0.91153399 0.550312\n",
-      " 0.05484349 0.73570383 0.52350869 0.29877126 0.64086659 0.74656651\n",
-      " 0.4431057  0.26241441 0.8531424  0.44721405 0.25633508 0.68545314\n",
-      " 0.90912925 0.91285705 0.95180396 0.74203516 0.57374628 0.02461786\n",
-      " 0.65634028 0.3354893  0.89448353 0.42890204 0.09926527 0.93041583\n",
-      " 0.65132991 0.40817373 0.39369988 0.38511924 0.59963332 0.36952358\n",
-      " 0.22752502 0.56710332 0.95845757 0.97355957 0.43180755 0.20890057]\n"
+      "[0.88616566 0.11474399 0.49426839 0.86496944 0.44553257 0.01731081\n",
+      " 0.26391484 0.81714822 0.9077824  0.45350327 0.34418481 0.30680307\n",
+      " 0.22397584 0.96490185 0.25766897 0.1628303  0.35022665 0.87266285\n",
+      " 0.14436895 0.2987234  0.04567582 0.62524215 0.03006832 0.15222984\n",
+      " 0.86554462 0.30036796 0.66637188 0.51245662 0.46296801 0.53384373\n",
+      " 0.90012971 0.00319531 0.48428543 0.24703543 0.53384405 0.48024175\n",
+      " 0.17175873 0.1834814  0.43739033 0.64565657 0.49266811 0.72123815\n",
+      " 0.57728476 0.76663343 0.68360823 0.34881945 0.64329004 0.79011718\n",
+      " 0.7055079  0.32594224 0.48795517 0.43684614 0.32047664 0.63067622\n",
+      " 0.24496431 0.25019593 0.57181523 0.38889906 0.53574819 0.02653888]\n"
      ]
     }
    ],
@@ -3393,18 +3414,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([10.        , 10.        , 10.        , 10.        , 10.        ,\n",
-       "        0.3151632 ,  0.0368383 ,  0.24516094,  0.74711683,  0.81727139,\n",
-       "        0.76195462,  0.19487213])"
+       "        0.96031963,  0.55942317,  0.78723157,  0.3650356 ,  0.04685468,\n",
+       "        0.43444695,  0.33839966])"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3417,19 +3438,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 134,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "array([[5.        , 5.        , 5.        ],\n",
-       "       [5.        , 5.        , 0.3151632 ],\n",
-       "       [0.0368383 , 0.24516094, 0.74711683],\n",
-       "       [0.81727139, 0.76195462, 0.19487213]])"
+       "       [5.        , 5.        , 0.96031963],\n",
+       "       [0.55942317, 0.78723157, 0.3650356 ],\n",
+       "       [0.04685468, 0.43444695, 0.33839966]])"
       ]
      },
-     "execution_count": 156,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3454,7 +3475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 135,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3463,7 +3484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [
     {
@@ -3482,7 +3503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 137,
    "metadata": {},
    "outputs": [
     {
@@ -3504,7 +3525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 138,
    "metadata": {},
    "outputs": [
     {
@@ -3525,7 +3546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 139,
    "metadata": {},
    "outputs": [
     {
@@ -3534,7 +3555,7 @@
        "(3, 1)"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3546,7 +3567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
@@ -3555,7 +3576,7 @@
        "(1, 3)"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3574,7 +3595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [
     {
@@ -3604,7 +3625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [
     {
@@ -3625,7 +3646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [
     {
@@ -3675,7 +3696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 144,
    "metadata": {},
    "outputs": [
     {
@@ -3694,7 +3715,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 145,
    "metadata": {},
    "outputs": [
     {
@@ -3703,7 +3724,7 @@
        "array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4])"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3715,7 +3736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [
     {
@@ -3725,7 +3746,7 @@
        "       [3, 4, 3, 4, 3, 4]])"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3737,7 +3758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 147,
    "metadata": {},
    "outputs": [
     {
@@ -3747,7 +3768,7 @@
        "       [3, 4, 3, 4, 3, 4]])"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3791,7 +3812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 149,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3800,7 +3821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [
     {
@@ -3811,7 +3832,7 @@
        "       [5, 6]])"
       ]
      },
-     "execution_count": 186,
+     "execution_count": 150,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3822,7 +3843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 151,
    "metadata": {},
    "outputs": [
     {
@@ -3832,7 +3853,7 @@
        "       [3, 4, 6]])"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3850,7 +3871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 152,
    "metadata": {},
    "outputs": [
     {
@@ -3861,7 +3882,7 @@
        "       [5, 6]])"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 152,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3872,7 +3893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 153,
    "metadata": {},
    "outputs": [
     {
@@ -3882,7 +3903,7 @@
        "       [3, 4, 6]])"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3907,7 +3928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [
     {
@@ -3917,7 +3938,7 @@
        "       [3, 4]])"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 154,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3930,7 +3951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 155,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3940,7 +3961,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 156,
    "metadata": {},
    "outputs": [
     {
@@ -3950,7 +3971,7 @@
        "       [ 3,  4]])"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 156,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3964,7 +3985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 157,
    "metadata": {},
    "outputs": [
     {
@@ -3974,7 +3995,7 @@
        "       [ 3,  4]])"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 157,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3992,7 +4013,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 158,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4001,7 +4022,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 159,
    "metadata": {},
    "outputs": [
     {
@@ -4011,7 +4032,7 @@
        "       [ 3,  4]])"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 159,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4025,7 +4046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 160,
    "metadata": {},
    "outputs": [
     {
@@ -4035,7 +4056,7 @@
        "       [ 3,  4]])"
       ]
      },
-     "execution_count": 196,
+     "execution_count": 160,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4062,7 +4083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 161,
    "metadata": {},
    "outputs": [
     {
@@ -4085,7 +4106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [
     {
@@ -4121,7 +4142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 163,
    "metadata": {},
    "outputs": [
     {
@@ -4150,7 +4171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [
     {
@@ -4160,7 +4181,7 @@
        "       [ 9, 16]])"
       ]
      },
-     "execution_count": 200,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4186,7 +4207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 165,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4202,7 +4223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 166,
    "metadata": {
     "scrolled": true
    },
@@ -4212,10 +4233,10 @@
      "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;31m------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-202-b49266106206>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mTheta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\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[0m",
-      "\u001b[0;32m<ipython-input-201-cb840dbb09da>\u001b[0m in \u001b[0;36mTheta\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0m阶跃函数的普遍版本\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \"\"\"\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\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      6\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-166-b49266106206>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mTheta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\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[0m",
+      "\u001b[0;32m<ipython-input-165-cb840dbb09da>\u001b[0m in \u001b[0;36mTheta\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0m阶跃函数的普遍版本\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \"\"\"\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\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      6\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
      ]
     }
@@ -4235,7 +4256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 167,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4244,7 +4265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [
     {
@@ -4253,7 +4274,7 @@
        "array([0, 0, 0, 1, 1, 1, 1])"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4271,7 +4292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4284,7 +4305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 170,
    "metadata": {},
    "outputs": [
     {
@@ -4293,7 +4314,7 @@
        "array([0, 0, 0, 1, 1, 1, 1])"
       ]
      },
-     "execution_count": 206,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4304,7 +4325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 171,
    "metadata": {},
    "outputs": [
     {
@@ -4320,7 +4341,7 @@
        "array([0, 0, 0, 1, 1, 1, 1])"
       ]
      },
-     "execution_count": 207,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4334,7 +4355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 172,
    "metadata": {},
    "outputs": [
     {
@@ -4343,7 +4364,7 @@
        "(0, 1)"
       ]
      },
-     "execution_count": 208,
+     "execution_count": 172,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4369,7 +4390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 173,
    "metadata": {},
    "outputs": [
     {
@@ -4379,7 +4400,7 @@
        "       [3, 4]])"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4391,7 +4412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 174,
    "metadata": {},
    "outputs": [
     {
@@ -4400,7 +4421,7 @@
        "True"
       ]
      },
-     "execution_count": 210,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4431,7 +4452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 176,
    "metadata": {},
    "outputs": [
     {
@@ -4465,7 +4486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 177,
    "metadata": {},
    "outputs": [
     {
@@ -4474,7 +4495,7 @@
        "dtype('int64')"
       ]
      },
-     "execution_count": 212,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4485,7 +4506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 178,
    "metadata": {},
    "outputs": [
     {
@@ -4495,7 +4516,7 @@
        "       [3., 4.]])"
       ]
      },
-     "execution_count": 213,
+     "execution_count": 178,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4508,7 +4529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 179,
    "metadata": {},
    "outputs": [
     {
@@ -4517,7 +4538,7 @@
        "dtype('float64')"
       ]
      },
-     "execution_count": 214,
+     "execution_count": 179,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4528,7 +4549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 180,
    "metadata": {},
    "outputs": [
     {
@@ -4538,7 +4559,7 @@
        "       [ True,  True]])"
       ]
      },
-     "execution_count": 215,
+     "execution_count": 180,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4583,7 +4604,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
diff --git a/1_numpy_matplotlib_scipy_sympy/README.md b/1_numpy_matplotlib_scipy_sympy/README.md
index 52164f7..dfb3af6 100644
--- a/1_numpy_matplotlib_scipy_sympy/README.md
+++ b/1_numpy_matplotlib_scipy_sympy/README.md
@@ -1,4 +1,4 @@
-# numpy, matplotlib, scipy等常用库
+# Numpy, Matplotlib, Scipy等常用库
 
 ## 内容
 * [numpy教程](1-numpy_tutorial.ipynb)
diff --git a/1_numpy_matplotlib_scipy_sympy/random-matrix.csv b/1_numpy_matplotlib_scipy_sympy/random-matrix.csv
index 5e031a7..295c447 100644
--- a/1_numpy_matplotlib_scipy_sympy/random-matrix.csv
+++ b/1_numpy_matplotlib_scipy_sympy/random-matrix.csv
@@ -1,3 +1,3 @@
-0.81041 0.69606 0.42944
-0.99033 0.60317 0.82435
-0.70689 0.05605 0.53930
+0.34743 0.34666 0.67796
+0.37776 0.74529 0.44639
+0.70970 0.54722 0.96401
diff --git a/1_numpy_matplotlib_scipy_sympy/random-matrix.npy b/1_numpy_matplotlib_scipy_sympy/random-matrix.npy
index 1df9863..36d736e 100644
Binary files a/1_numpy_matplotlib_scipy_sympy/random-matrix.npy and b/1_numpy_matplotlib_scipy_sympy/random-matrix.npy differ
diff --git a/2_knn/README.md b/2_knn/README.md
new file mode 100644
index 0000000..464fccd
--- /dev/null
+++ b/2_knn/README.md
@@ -0,0 +1,14 @@
+# kNN 分类算法
+
+K最近邻(k-Nearest Neighbor，kNN)分类算法，是一个理论上比较成熟的方法，也是最简单的机器学习算法之一。该方法的思路是：***如果一个样本在特征空间中的k个最相似（即特征空间中最邻近）的样本中的大多数属于某一个类别，则该样本也属于这个类别\***。
+
+kNN算法不仅可以用于分类，还可以用于回归。通过找出一个样本的`k`个最近邻居，将这些邻居的属性的平均值赋给该样本，就可以得到该样本的属性。更有用的方法是将不同距离的邻居对该样本产生的影响给予不同的权值(weight)，如权值与距离成正比（组合函数）。
+
+![knn](images/knn.png)
+
+
+
+## 内容
+
+* [knn_classification]([knn_classification.ipynb])
+
diff --git a/2_knn/knn_classification.ipynb b/2_knn/knn_classification.ipynb
index ae6ea5d..f646626 100644
--- a/2_knn/knn_classification.ipynb
+++ b/2_knn/knn_classification.ipynb
@@ -322,7 +322,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -479,7 +481,9 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# split train / test data\n",
@@ -570,7 +574,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/3_kmeans/1-k-means.ipynb b/3_kmeans/1-k-means.ipynb
index 3046e03..993f8a5 100644
--- a/3_kmeans/1-k-means.ipynb
+++ b/3_kmeans/1-k-means.ipynb
@@ -249,19 +249,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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DAdM0E27BndihzUaury9SKlSwiYiIFEA+OwjxTk1zRzOD0UGiv0t+Hmsqtx2y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\n",
       "text/plain": [
-       "<Figure size 1080x648 with 2 Axes>"
+       "<matplotlib.figure.Figure at 0x7f9a42914e80>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -287,15 +285,22 @@
     "\n",
     "# 画出数据\n",
     "plt.figure(figsize=(15, 9))\n",
-    "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
-    "plt.colorbar()\n",
+    "\n",
+    "marksamples = ['or', 'ob', 'og', 'ok', '^r', '^b', '<g']  # 样本图形标记\n",
+    "for i in range(len(X)):\n",
+    "    markindex = y[i]\n",
+    "    plt.plot(X[i, 0], X[i, 1], marksamples[markindex], markersize=10)\n",
+    "       \n",
+    "plt.savefig(\"fig-res/k-means_data.pdf\")\n",
     "plt.show()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# k-means\n",
@@ -384,21 +389,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "最初的中心= [[ 4.55381657  3.11045927]\n",
-      " [ 2.5733598   0.05921843]\n",
-      " [-0.1580079  -0.42688107]]\n",
-      "the SSE of 1th iteration is 63997.694980\n",
-      "the SSE of 2th iteration is 20276.698293\n",
-      "the SSE of 3th iteration is 4446.593824\n",
-      "the SSE of 4th iteration is 3500.485900\n",
-      "the SSE of 5th iteration is 3502.239035\n"
+      "最初的中心= [[-0.47103831  0.23204994]\n",
+      " [ 7.49671415 -0.1382643 ]\n",
+      " [ 4.96044485  5.6815007 ]]\n",
+      "the SSE of 1th iteration is 4705.401208\n",
+      "the SSE of 2th iteration is 3502.239035\n"
      ]
     }
    ],
@@ -410,36 +412,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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9IHlP2nIlFWBUZ7vIOCqKKwCX3EEFMOPCGex4bEdE1Uzzbc3hdRm2QO/MXm5p\nvoXm25rZvHYz636wTguEDCAjhAKwBNid7kFoNKnArroY//vxsIFA4XuMn31X9fkTxsWTvC1SW+u6\nYaqfcYE2l3zqEjZ9fxMlG0tQjyojKvpjwCRDlWOf2N0m/kiqGcvriqPm9bfYxnEqdI/r9t+3LoKT\nGaRdfaSUOg34H+CbwG0iUuPQRquPNEMCr8/LWVefxfHZoTO5kcZi89rNcRWEiaVtrG0uXXApHb0d\nxlKxH0rzStm6ZmtQm3hyBwVd11RZOamyqqdU85Omn0Qco45GToyszH2klHoMQyCcBCzXQkEz1Km9\nuZYnJj0R0WYQzwQcS9tobRLJGRRtog7qsxPDvfUzhAXU1eW42z/qDtX5XWuHW9W0ZJB1QkEpdRXw\nLyJyq1KqCkMozHJoJ42Njf73VVVVVFVVDdo4NZpkkgmlIUMn9La322g9uzWsnbV7cTo/2j2EGaff\nxLClFBFWxnNB04LYkgBaJBDpPBx2Gi0tLbS0tPjfr169Ousimi8GapRSn8Go6zFWKfVTEbkxtGFT\nU9Ngj02jSQluUbeDKRBCg+k8LR6oIObgrkjlLa2JOixgbDIwE8r+XEb56eUU5wTuO1JwWaQkd4ne\n71DNexS6YF69enXcfaRdfWShlLoUrT7SZCnZtBJ1c5H1/NEMMusB/gQ57+dwct7JfOr8T/G9pu8F\n3U+k9N7WziJZthG3Og2x7hSGczrtrHVJ1WiymViCzTIJR5fXETDq2ChOefIUcjbnwKeh/9p+3v3M\nu6z/23ouXXBp0P3E4iEVT8BYpLYDjUbW9ZnjI2N2CpHQOwVNJpPKlWgqdiBh4+3EqM1gJaNz8hJ6\nFuqmBhvCB9MuMpCKbHqnkF02BY0m6xmoztsNN134mjuNWs17u/ZyEichuUJXX1fMQmPhdQtZ/5X1\ndI/rNgy+RwlkQ3VJWUFO8P0Mtl0kWtbYSOj6zPGhhYJGM0BSlYHT0Zhb1sZVK64ydP+5BFb45mQX\nzYDq9XlZ8O0FdM/q9p+T80QO/SPNLHRWIZ7QnUJ/+P1Em6gzxc6SbsN+tqHVRxrNAEmVKsXRmNtC\nQL1jf20RRS3iqErZBHwa57rPZjrt0tHBgWzRcHomk/84mWkfmMZBDma8MX6ooA3NGk0aSFUGTkdj\nbh+BCd1F1RNJbeVodP0YjHpqlHGtcRgV2h4H9ahi/O/HU/uh2rDI5mjpKJx2OW9+/E3Wt6/PCmP8\ncEarjzSvCwhrAAAgAElEQVSaJDAQnbcbTrpwT6eH7uOm6sdF1RNJbeWo6iqACTKBvc/uNZaJCvgM\nSIFw5aErwzKzxuLz72Zn8S9DHeIaNJmB3iloNBmK0w5kw482BNwzrZKZcbhqLrxuYXBW1OOQ92Qe\nEyZOCNRVqDK/ex6e2PEE5ReVU3ltJfWL61navNQ1aM2Om8sqdkWGdgvNSPROQaPJYJx2IHajaeGH\nCpH9wqG+QzEZUB949AF6L+w1XE8FUNB7YS8H/3wwsIOw2RUOjTzEoeOH8G3xsXPSTvJ35UNtSKcO\nk7vTLsdf6tNCl8PMSLRQ0GiyjIGoqvZ27YVyArsBk0mTJ5H7l1xjEncrr/k8HPUcjUllFerxMzZ3\nLLtG76KjoMN/jnYLzUy0UNBohhFu7rMVEyv42bd/RsO9DWzo2UDnyM7gE60Yho/B6KdHc+TyI1F9\n/kOFV1gAmnYLzUi0S6pGM4SIFhsQi/usWwSwFe1cu7cWT6FHl9DMArIudXasaKGg0UQn1niJaCkj\nnPqxah9Yqa61EMgOtFDQaIYxA83xY99lFOYWovoUB44eYP+b+5lUPImK4gq9K8gykpr7SClVCnwX\nKAH+AHxXRE6Y3/1GRK4eyGA1Gk1yGUgOJqf4g1QkuMuU1BcadyLFKazBCKRfBHwA2KqUsv7kpqR4\nXBqNJk5iSWftRqSiOcki21KMD1ciCYVTReS/RORFEVkE/AjYppSqwPBD0Gg0cRJLiohEGUjdgcGo\nOTAYgkczcCK5pI5QSuWLyFEAEVmnlNoPPAWMGZTRaTRDiFSXhRxINtBUZXq1k6oU45rk4mpoVkot\nA/4sIltDPp8G3C0iMwdhfNY1taFZk/VkcrGXwSiak8j9e717aGhYy969/ZSU5NDcPI/ycq29jhXt\nfaTRZDCx1DVOJwOpbhZr//EIHq93DzNn3k9b22oM5cRhKioa2bhxkRYMMaIrr2k0GUyqVTQD9exJ\nRabX0P7jUW81NKy1CQSAMbS1raah4R7WrWtM2TiHO1ooaDSDRCrLQqbaXpEs4hE8e/f2E26+HMO+\nff1JH5cmgE6drdEMEqkqxgND07OnpCQHOBzy6WGKi/W0lUqi2hSUUkXAvwHFIvIvSqlzgAtF5CeD\nMUBzDNqmoNFEINPtFYkQzaagjdDRSZVNYS3wP8BK8/3fgV8AgyYUNBpNZAbDpXSwKS+fwsaNi2ho\nuId9+/opLs6huTkgEEIFRmurNkIng1h2Ci+IyCeUUrtEZJr52YsiMnVQRojeKWg00RgMl9JMor5+\nNQ8/vIJgm8Nh6uq0EdpOqnYKh830FmJepBI4mMD4NMOIPV4vaxsa6N+7l5ySEuY1NzOlfOhNTpnC\nQALXshFthE4dsQiF24AngAql1HPAqcC1KR2VJqvZ4/Vy/8yZrG5rMzf20NjayqKNG7VgSCGpdinN\nJAJG6OCdgjZCD5yI6iOlVA5GVdX/BT6EUXb7b1a21MFCq4+yi9X19ax4+OGQf1e4p66OxnXDY9LS\npJZEA9uGm3E66eojEelXSv2HaUt4dUCj0wwb+vfuddjYQ/8+neNmuJLsyTiSETrSGLRxOjqxqI82\nKaU+B/wq2ct1pdRpwE+BIqAf+G8R+UEyr6FJDZFsBjklJQ4be8gpzl5PGE3ipGoyLi+fEpdRWUdI\nx4iIRDyAQxgT9nGgy3zfFe28WA5gEjDVfO0B/gac5dBONJmDr71dlldUSDeIgHSDLK+oEF97e0zf\na4YXdXVNAt1i/jmYR7fU1TUN6jiqqu4KGYNxVFffNajjGEzMuTOueTmqVUZExopIjoiMFJFC831h\nkgTSfhF50XzdDbyGUelNk8GsbWjwG5HBWHetbmtjbYMRPTulvJxFGzcaNoTqau6pq9NG5mFMpngK\n6Qjp2IiqPlJKXeL0uYhsS+ZAlFJlwFRgZzL71SQfu81gD0Z0Yz/w8jPPsMfrZUp5OVPKy12Nytpd\ndXiRKZ5Czc3zaG1tDDNONzcvGnYG6EjEYlP4mu11PvBJ4E/AZckahFLKAzwOLDF3DGE0NTX5X1dV\nVVFVVZWsy2csmTp5WjaDFzHC2u/H/Bc7cIDGmTMj7gq0u+rwI1MmYzfjNDBkDNAtLS20tLQMrJN4\n9U1AKfDLeM+L0F8e8CSGQHBrkxwFWxaRyXp5X3u73FJaKrPMcdkVtN0gTXV1ruc21dXFfY4m+2lv\n90ldXZNUV98ldXVN0t7uk/Z2n1RULLfZG7qlomK5tLf7BnVsmWLzSAUkYFNIJHX2W8DZAxNFQawB\ndovI95PYZ9bjpre/p6Eh7b7+U8rLOWnaNCZ2dMTtejrY7qqZutsabjh5CtXXr84Ib6BMsXlkCrHY\nFO7HTHGBkWp7KvDnZFxcKXUxUAe8rJTaZV7n6yLyZDL6z2YGY/KMNmFG+r6gqwtnTXFk19PBdFfV\nqqrMJlMm40yxeWQM0bYSwE22ow64ON7tyEAPhqH6KNVqloG6lTbV1clukOU2FVI3yHyPJ6KKazDV\nYlpVldlkitomU9RYqYAE1EexTMhhun6nz1J5DEehkOrJM9qEGe17a3y7QZpAVoLM8nhk+9atMd1b\nU12d3FVdLU11dSmzk9xVVSUhM44IyF3V1Sm5niY+Ep2MLftEVVXAPpGMsYTaPIYCiQiFWGwKNwGh\n+v55Dp9p4iCa6sbv69/QQP++feQUF7MoifrwaG6l0dRX1vjWmuMbUVzMHQsX8swDD/B0Y2NE/X0k\nd9VkoiOr008k76JUp6pwu7bb5zqq2cRNWgA3AL8F3sfIkmodW4BN8UqfgRwMoZ2Cr71dVtTUyJz8\nfFkF4kuTZ5G1E/A5qICWV1TIzTNmyCqQu8ydgM9hp9BUVyd3VVVJU12dbN+6NeO8pTLZg2s4kAq1\njJvKqaZmRUzX3rp1+5BVFTlBMtVHwBSgCtgBXGo7PgrkxXuhgRxDRSg4TlIOE+5gjmWVg1vpbpCb\nCgqCxrkM5JbSUvG1tzvexyyPJyP194OlqtKEkwqbgVuqivz8OUETu9u1y8quyQg7xmCRVKGQScdQ\nEQquenpTMMwuKvKvvAdj8vK1t8vciRPD/sOaHARFN8iKmhrX+1gZ8t5n9jNn7Fj//YTuLlJxj4Nx\nDU1spCLXkNtkD6uCJna3a48bN9fx86KiuUm1UWQKKREKGPUUXgC6MZLi9ZGkhHgxD3KICAU3w+fX\nzJV4OtQcsUzwoQZap/uwCxInldTiyZPlltLSlN6jVhdlFqnYKbS3+yQ/f36Q+geWC/iksvJrfgO0\n247A7XNYNSTVSakSCn8EzgB2AbnAfOBb8V5oIMdQEQpuO4Wrwe/FY+nwdw+S6sVpIr0iLy9uz6Td\nIPNtOx+n81dF6DMZaBfUzCJVrp61tUvNSfwugSYBn0C3eDyzbNfaLXl5N4Vd28mmAMvMPpIjuDKJ\nlAkF8+dLts92xXuhgRxDRSg4TcDzQa50WFkvB1lSWTlo42qqq5OvVVbKLI9HNjqNJ0oMw3zzvCaQ\nuW47jQi7j2SgXVAzj1S4ejoJG49nvsDukF/9bikruybs2vYxTZw4O0QgGMdQSaediFCIxSW1Ryk1\nEnhRKXU38A+InnJbE47lxnnjZZdxts/HCKARowh2cLC/8X5OR8egjatx3TpW19fzSGsrY4AzgXuA\nE8BrZWXca4sCdnKXvXnhQn69YIGRigPnSOfQONVku4f2FBZqF9QMIxWunk6urG+8UcjOnaHZd86m\nvPw8Nm9e7f8k1B21sHAK69dPCDlvGEczQ0w7hSkY2VELMeawe4Ez4pU+AzkYIjsFi9CV9nKXlfV1\no0YNqj7cbaU9N0YDuLXjWFJZKfNt3kiDYVOwkvSF2mYWT56c0meoDdvJwy0oLZZgtVjsF047jMmT\nF0tp6S1JV3FlCqRCfWT0y2jgQ/F2nqxjqAkFkWBXyWvKylx18IOpD3fTya9KYCJ3cgVNpXuoPe6i\nyVRVrQJZWlubtGuEog3byaO93WdOzpatYJWUlt4Sc1xBLPaLSDEObiquVERPO917qq6REqEAzMIo\nk+k1308Fnoj3QgM5hqJQsONrb5f5+fmO8QuDoQ/3r/AvuCBshb/MHEemG27TYU/Qhu3kYRiPl4UZ\ngEtKrnScyCdOnC11dU2ydet2/4RaU7NCamuXSnV14LV9oo3XRXYwciKl+hqJCIVYbApNGIV1WszZ\n+UWllE4xmUSmlJdz0hVX8O3168nBMNgsAiaQen14aCbR14CrR4+moKCA3K4uKk6cCGqfyjTXkcYY\nLf11OlJaDHYa8EwkWUVyduzYAzxEsGWtmbffno1TJtW33/4wDz98Hb/4xXfo7f0P7MV71qy5nAUL\nfk1b2zewp8I491xFPNlQly27j7Y2D3A3xn/lvKSn9m5oWJsR6cODiCY1gFbz5y7bZy/FK30GcjDE\ndwoi6VNFhK52fTjETNh2C+mKvHZ6LnZ9/tLaWlk8efKgPr/hvlNI5ip34kTnoLJRo2Y77hQMV9R4\nopZ3y2mnfVby8+eYKipfxPFGiodIpmdSKgL87JAi9dFPgDnASxhOKfcD/xXvhQZyDAehIJKelAyh\napcmnGMMrM/TlaMpdDxLa2vDhMUtpaWyoqZm0J5fum0Kg6HvjsRAgtOssV9wwRIpK7tGCguvs03W\ngb4uvniBQ1zBcrOd84Q6duw1psCw4hi2m+cE+hg9er7U1i51NWbX1KxwEUarkhrDkOr04akSCgXA\nNzGiml8AvgHkx3uhgRzDRSikikgeMqGTrlMsgQ9k5ogRMnfiRPnqjBmytLY2ordNMj1y3GwFs4uK\nMmKVnq7cSplQAyDaKjeSN5Ex9t1hk3UgkMx4fcopV0pt7VKpqVkhRUVzQwSH04S6UZSaIbDS/H63\ngLNdwpp4nZ6lsaNw+tO7RmbM+Krrc45XUGeiTSHSRPyQ+XNQaye4jCUpD2g4Em01u33rVpkxerRc\njRF0dhVGdLJdIHwR/BlTV5nvfS4r42Svnt12Ck45m4ThE6iWCQVqIo0h0mQXOM8tj9FsCUQq3yWw\nWzyeWTJt2mIzatkKUguNWt4tSs0JETKLBWojCi/n+1jlMjaj7eTJixPygHIilbUcki0UdgPFwF+A\nk4FT7Ee8FxrIMVyFQiwr7mhtIum93Xz75+bm+gXDIsJtDMtAlrqszpOtZ3cTMitqajJip5AuUq2L\njoXgSdAnsEry8+f4PX/cBEZl5e3me+d7CHzeLbBUQncTHs98qaxc4vc+qq1dKkVFs2XUqJkuE/nV\nEQVo+LP0CSwVpepDBIyltnIWwJkgqENJRChE8j76L2ATcDrwJ0DZ7dPm55oUEUt94VjahHrIWAV1\n2jZs4LbnnuOcjg7uJNjn4z/7+rixrIzzysv56/PP8+tjx0J8QmCu7b3d2ybZHjluxYYAGl99Nfje\nKyr83w11MqGusBVZvGzZKp5++iBHjtzP0aNjeOKJw+TnLwLeCRnfGNraDvPKK381x+5a5dv82YhR\nRr4J+19od/f9TJzYxLp1jWzb9hybNrXR3f0R4E2cPJUMU2gjgbwBh8nN/TJvvDGeq69exmuvvQzM\nBsZixOgeB76HyDvAtzF88j6M4RM4xd9vaC3pRGtOJ8uDK2lEkxrAf8YraZJ9MER3Cm6rfF97u2tA\nWzyrcquflRiG4u0E5zRaibMNQWxqGDc1zdwEx5TM57V969ZhWyshE2wKFpHSWYd+ZngGbRS4SZxs\nCnl5cyQ390oJ2A7c6yds3brdzHlkne+m8rH6sozPqwQWmZ99UcLjI6zvJGK/ydgpZJVNIZOOoSgU\n3NQiVgWzaOmrRURur6wUITiKtwkjkZ5b8j27vaAJHIvs2NVLNSUlzrUVGBybQqr7zWYypa6wmyor\nJ+dqCej/jcnugguWSMAA3CSwROAagVsFZpif+2wTtbvACXc9tZ9ntbtRINS11DJmN0UQJE0h/c4L\n6iNZNoWs9D7KhGMoCgW3FbW1Q2iKMFmLGJPkLI9HdhO8+t+Nkfq6Zvx4V9dS670P5BbCbQaLJ0/2\nC6fQ/rtB6kAWTJ0a3c6RxBX8cI8JyGQi7RSUul4KCmqlrOwaf/Sx4RkUKkRC+zBsFOPHXyM5OaHG\nY0O371wwxydwtRnfYO0QLAGwUmCmBHYBd0l0u4ZxzZKSK6WoaLYUFc2VmpoVUb2PYk2ZkYlxCrFE\nNGtSgJvufUxnJ2OAeYRqQYN15msbGvhOdzd3AI+YbfZgBJX8sreXu99917H/Q2af/Ria2+uAfy8p\nYW5vLx6g8MMfZsSYMfz75z/PQ2+/zRgMTao/YyqGT/Kj555L47p1jvdmZV1NJjp6OHNpbp7Htm3L\n6Ogowvir6gcOACsRmUBPzw34fN9iwYKfsGbNbNav/w7d3aG2hBMh76cAzXzkI40UFnaxfn0Dho2i\n2/yuh3HjDtPZGdrPBPLyOjnrrNP5y1/s9qVG8+c1GLkCMMfaS+ScvoeZPPnrtLT8V0x6/tCssF7v\nHurrV9PW1sMrr7xGd/d3gLNJNMp6MBjG+WHTi5WWwc5h4PC4cRzG+LO3JuNVwI1lZWEG5LMx/rys\nP6e1BISIZaoL7f9lpVhhtlsBrMvLY9XPfsav9u+neccO8r1evrF+PR82BQLmWBoxhMF55jVDJ+M9\nXi+r6+tprK5mdX09e7zexB+OA27PS6fFzgyUGg3cifGXdSdGDk0w/ho/AjxKW9tqHnjgGTZsuAOP\nZxGBv9DDGLGx4b/h4uIcrr/+Uyj1KjAZqMAo/thISck4SkuXhfTzdXp7P8zLLx/CSEq/J6i/wF/z\nYYyl136gIaSPmzFCsm4EGhA5ktAz8Xr3MHPm/Tz88ApaW79Dd/cjGMu2PVjpLJTqpaLCGo9x/YqK\nRpqb5yV0zaQQ79YiHQdDUH0UzaYQTXduqVOabKodu9HY56D2mW+qm9xUMHYVjb3fUPVTqNrG0X6R\nny9La2uTmhpb2xQyE2f10W4xbAUrzZ9LgtQiliolEJAWHnVsVUorKLhJnG0Chm7fSJq3UmCFwC0u\nbXcLzBK4XQw312VSVDRXamuXyvTpC6WoaLaMHz9HcnIuE7sdxBjT7oR0/O5qtYC9orr6ruyJU8ik\nYygKBRF33XssOnlrkrTr/JcSCDJbAXIzRqnP2aNGyYqaGllw/vlBBmmfJUwcai//FcPD6CmQXSD/\ntNkf5uTny4qaGv+4IqXcjmfijhZzka7oYU1knP38QyOV5/sn1/Z2n9TWLpWJE42JuKDgSgkYmAOx\nDu3tvgjpJoyJtbDgu2b1NBE3o3Rh4cywoLa8vJtk69btQfcR6VqJ6Pjd7AX2OIxUxzBooZCFDCQl\nhL2ozZUlJXLjyJGOaa+tvEA3FRSEJbqz14JuqquTl0BWn440fB55/KfIk08iP3sImXdDrsz8YI68\n5LBSt7ygQo+vEW4cd7tXvRMYXGJNxxBL4ZtwLyDnydnjmSVbt2436yYEewkVFNzoD0izj8UtUR7c\nJUq9JJ846yKZML7G7CuSF5R75LV1H0bwW3hpTliZ5J2CYQQvLb0l5R5jWihkGcmcCN1W6zXmrsDu\nYWT/fpbH47/ed+9eLo135khXl/OvoqsL+fcVyI8nBs5vqqtzjam4xrYTiXavsXoXpaLS2XCrnhar\n66Rbu/DCN6HpJpy8i0QKC+eYAsQuEEQCnkbG9xdcEBAOxi7AefV+5mm3yzvrN8r5Z14gkycvFnf3\n0qsdx1NZucQh2Z6lbgqcn5t7RUKTt9PzC1V9aaHgPOFfCfwV+Dtwh0ubZD+rjCCZbpauRWbMPuc4\nL7fka5WVIiLy4x9/Q554YqzE8mt74qGAYLirulqWXHBBmP1iOcgS2/0sra31q7Ys1ZX9XmMpkrN9\n61aZ5fH4g/F2D0CIWoQKq92moLy9snLICohYfeMD7YIDv0477bMO5++WsrJrpLraaecQmMiNn/Zk\nc26qpo1SUbFcZsy4WZyK78Brcv1lXxVpeUGuv+wWKSmZL9OnL3RJTeGccsN9nKtsr+fJjBk3J/ys\nw20nwQInE9VHaXVJVUrlAD8EpgP7gBeUUutF5K/pHNdg0b93L+9geBhZLqLzSMzN0qnIzGvAKxgl\nQnpwdrwbU1FBW9sbHDx4PzfffCima82qh3v/Ai/fAz1jx3JycTHX7dwZdB83Az/DcKOdvXAhP7ni\nCgIlTwz/j0W2e+0pLHQcX8/YsYDh3fSTq67ike7uoD5ubmtjbUNDmAtsLIV5wHDttVJlWC69j3R3\nM6a1lcOtrWFpQ4YCsaZjMNq9g5EtP+AcvXfvfIfzz6a8/Dw2b15tet002orH2H/jYzAy5Fi/7bW2\nvjF/3g/cQFvbdzj33DWUlh6ko+PbGH9Zh8jJaeWkMXu4s+7zANwx5zKeWtbJpEkHqKnpZP16q61V\nrqoHj2cR3d33+8dTUdHIqaeegc/n5OjsM8fbT2npCB54YGVQKorCwh6U6uXgwcKoaSksF9Xq6kYO\nHFgd9ZlnAumOU/gk8LqI7AFQSv0cqMXYOaSVWCeVgfT/v6+/zpsYUtH612kApLAw7v7mNTez6Je/\n5P6jR/0V1L4D/NTs+zXgyxgJrULjHh58qJFlyw7Edb0vNkDdr4CnnmLixz/ON/LzOf3oUb6I4QX+\n1VGjODBhAh+aMIF7b7qJn5rjwrz+aoysMnmmS2mvUjRg5FUKehbKSLm1tqGB+02BYO/jHpzdY6Pl\nhLKwxz+sJXx6Wt3Wxj0OQiebccub5PW+gte7xz/BGe1+TOhTEfmQ4/mWb72VE6mh4R42bGijs7OC\n4LxBXwRuxfjLdxZQlhvroUMFfPVLp7Dxt0/jyT+ZUaMUR45MpCDvA5xfcQYAU884g8s/vpbut/bx\noQ99gCsrX2b/O/m07aviUM8EsxrbzTzwwD3s29dPcXEOzc2LaGhYS2tr+H2UlfVQXg7FxXk0N68E\nYObM+0OEXAOwAJhAa2sjGzcuihjHkAm5qmIm3q1FMg/gc8ADtvf1wA8c2iV3TxWFger6Y/GiWV5R\n4ZpiYkVNTULjtlQ0czG8jkL73o0RMW333unp6ZGGhg9KIr/C5dcaWVSDXFFHj5aF06fLLaWlQTmW\nnFRDc/Lz/c/mrqqqsHQdPpw9o+zHSgd1WzxqOXvbaHmghgrOum7D9dJuWzCqjznVFfDJ6NHBqSPc\n0jkY2VJXSaDgjaFPtyKE8/PdMpsaEch1dU1y7Ngxabz9Tvn1t+4VaXkh6vHg178hZ5d9SiZOrJXa\n2qVBdZyd6zpEvo/orqXR1UDpylVFtqmP4qGpqcn/uqqqiqqqqpRdy65SgPhWjLGsUq3+78Z5jVRw\nKLIax9rF9LS18cb+/ZQWFXHyGWdw7W238etXXuFf29r8qho7ZwPnlZezevNm/2cvvvgi06a9FfF6\nbnzss1D+eMjG/8gRbmxr46cdHf7PR2A8h3cwVuP95nHgpJNYs2ABOSUl9BQWMoFA3CkEB6e51WB+\nyePh/pDMqPFEP89rbqaxtZXVbW3uOTuHWICctZK/7LIb8fnOxvgNGSt5e33g8vIpXH55MU88ER41\nfPnlJ+HxBK+8Q1fKXu8edu3qB38e3sPArYwc+T4f+9h53HffrQB85CPBqh1TMYjHcwfNzfczcuRI\nmr7zLX792GM0PbKWr19bx8gRI8Lu69jx4/yf++7nV1s/y8HDzwKHefLJr/D00z/gyJEm4FHgBOvX\nL2LDhju45JKL/TuaSPfhpm4LRD1HVwPZd0+RrjVQWlpaaGlpGVgn8UqRZB5AJfCk7f2dOBibGeSd\nQixGTzdiWaVa/Te57BQiGZoddzE2o+v2rVtlaW2tXGpzT43U97Zt22TjxhxJ5Ff4h98jzzo8p2tH\njAh678M5x9Iigl1mI9VYdgyQ83hk+9atCf0OQp+p5do73+MZNm6xseTdGcgKN/IKO9CPke10ltir\npeXlzZFHHnk8rM/2tja5fcEtjjuEuf/yOYGXHa63TJyysYbGKSR2H7HtFNIFCewU0i0UcoE3MJSN\nI4EXgbMd2iX/aUVgIF5BsQgUq38f4VHH0SYh17ERUD25JbJz6nvXrl3y+OOjJZFf4c/WIjscxuKk\nunJziW2yvV5aWxsxOM0evLa0ttaox5zkmIfhFCAXqxdSohG3sQRv1dSsEBFxFAyW+6td9fP6623S\n8KWvOgqFeVd9QaDP4XpzHe/T45kV071Ecy0tLb1FampWpK1WdiSyTigYY+ZK4G/A68CdLm2S/awi\nMpBJJRaBYu/fhxH5Gxol7EYk11PBiF62ru8zJ92VGLYEp77/unu3fKkuTxL59d1xLbI4RPAsI7xu\nQ7d5f5HGHSo4B/r7GU6Te6KkWs8dS5qHnJxrZfr0hWaQWqjL5u6Qegm7paCgSn60bJVIywvy+rrf\nyFUX3iSvr/uNSMsL8sMl/yrwJ4frOcUpGNlUJ06cG3MtZUswWpXlqqvvktrapWaMRPrrWjiRlUIh\npkEOslAQSXxSiZTTKBkFYqLtFK52Xpq5TrhNdXXScDquAWtux8GDRuSzJXjuApk5YoQ/dYb989lF\nRfLVGTOcjeougjOhZ6DTaMdNMvLuRIp6djZo+2zvl0lwXID9e7tQMeIZTvL8l7z24GPyk9vvkbJJ\nMwRelbJJd8qaO/5ddj/4qJxSOD3keovFqNMQue5CohN5JpbgtKOFQoYQKlBiTXIXa99hem8Mm4JV\nO9ltwnTyirqrqkrewIhUjufXcs8K5I2Qa3x61KighHvWLmjuxIny2dNOky867CqW4i443Z7PQGw+\nmoERKgDCo5u7wzyYjCCx68VISGdPNrdMjMCy4NoFgZ2EPTLamHzPKFkmdTO/IieNeSyo7Umex6R+\n5g1SUXKzlJTUyKhR15k7hCViVFe73jbG2CqpxUIm1MqOhBYKGUqyV7ZWZO9yjFQSt4JMNydhJzvF\n/Pz8MDdRayJeUVMj3SDfnYj84qHYfiWPrMuRK4rzwib4L4J8LidHdpvjCCveYwoBu8vp3HHj/ILz\nls2sL+8AABgOSURBVNJSf9TzKgzjs5Ng0DuFwcdKYpefH+yKatgBIk+wxsR5lwRHRlvuqZYNwX6+\noUoyEuVZfRuT7ymF9wu02doG7BOwRE4pvF+mTVscptI59dS5Zn8rzf6TM5HrnYIWClFxW40ne2Vr\n341cU1YWNAHbbQlXE/DwsRLk2SfSpbW1/piJH000dgwHDzr/Kg4eRO66PUeuvvRsuXLECLkaI+ld\nk+0aqzDsF7OLiiIalq3315SVyV1VVe47idrasHvfvnXrsPISSjcBNZDTCts5z5F9gjUmTre8RLMk\nNPWDtSv4wAfm2yb3yJ5MAbVTt4wZc5lj25qaFWbKCed8SolM5O3tvjABNBg5jWIlEaGQNXEK2YBb\njII699yk+7/bq5s1VlezwOfzV2qbglFApwG4j0AcaTNGBHCj+X4MUNjVxYKNG2morKT57bdpuwfu\n+xWc+ChM+ywUTIT334YtG+DkP0F9ez/9vEYzAY/yebZr5GDEQiDCmAPBUdJjMOprWff/ZeAbPh9n\n+3wcBv4VI5ZhjHk0A3NbW4P62OP18usFC/had7e/GtxLHg93rFkzpFJRpBN7SoeSkhy6uztpa/sG\nOEbWWFEowX/dY8f2UF+/2p8WYty4l+jsvAb4hHnOdRi/4XwCldCC02H84x/3M2PGEt577wa6u6eY\nnwfiGZS6BZEujNj4RWY/X0VkrMM4x3DoUAHr1zc6puEwCtssivv5FBb2cPz4P80xGFXnEi3KkzHE\nK0XScZAlOwU3tYa1Gnda2SYjQ6d13e0Y6qS5IDNDdgX+3YmLyiV07D0Y7qbzzZ9fsfUX1oft9SoM\nt1jXrK0lJTInP1+uBseCPytCPptbVBTTM9aqo+TgZBw2VEaW6ic8EV6wh5CxUjbSYwc+MxLV2e0J\nRtK7kpIrpaZmhVlv2VIpBfoPXtUHai2XlV1jFtfZLcEqqd0CnzB3JkvEKPBzu8Aqqa1dGnSfiRjY\nI7unDmzXkQpIYKeQ9gk/pkFmiVCIpCZy8mZKVupsX3u73FJaGqRCckuhscr2er4tbbZbUJzP9r6J\ncBWQJSTsdgWr4prTvS2trZVu3FNKzA0VEiEpP7SRObW4u5Fa7qLOqbTtE6yR2iKyK6r13lIzGdcN\nn+Dd6ilUV98lU6d+WcJtFD6BG82+gsdaXPyVAat1YnGztcaXCSQiFLT6KIm4pWLIKS52LGa/ur4+\n4XQadqaUl3PStGk02VJLfBHCEsx9HWOD2wi8DEw+7zy/ymVKeTmLNm7knoYG2jZsoKKzMyiFmaX6\naQCWmJ/twUiX9hIwCyP3ZQOwpqsrqL/+ffvIKS5mUXMzaxYsCKohHfqsDtlef33yZG67776ge430\njDUDxy2lQ35+O0eP9gAK+DI5Ofuprj6d//7vr1NePoVLLrnY37q6utGxj0BaCOv9CYqLjXQVCxfO\n4Be/+A69vf+B9Rebl/dVKir6ePvtBgJZT+cBEygs7GLnziMYilJ7eozjwI8wFKXBifz27bubpUub\nWL/+uwk/nzfeeJ/wvMZTQu4tQxPdxUq8UiQdBxmyU4g10V2sK/9krnqd+vKBVOfny0oMr58VBDx7\nFkVQubipaGaMHClXFhS4eheFVnKL1LePcC+pZSBXlpTEVIY03t2Vr71dVtTUyNyJE2X2xIlJrR89\nlHBbCc+Y8dUwNZGbb39sq+ngiGK3Os85OcFlNGGZP4LY+RozzNfOrqJFRXMTfjbt7b6wZ2AlErTH\nWujgtWEiFGKdjOIJekumftytr5tnzJDpo0bJvJAJeA7IwunTY5547aU953s8Uj1ihOP1rsjLc+2z\nqa5Obq+slFkej1+wrDLHsgx3F1S3vmIN/PO1t4flVYrnesMJtyhnN5WQk+7cqQ+l5ordpuDxzA/K\nPeTs7+8sXKyUEk6TvhGb4O6tVFQ0O+ZSpKH3ZMRbWC60gQC8goLP+iOcdZqLYSQUUmHgTGY5Tqe+\nrCRzkewLbkFjvvZ2+eqMGVJjtvOFnFvj/B8pi6dNi2ls8z0e+dLUqXJNWZksSXGVM7ff3aoB/v6G\nKk5G2HiDtKw+Kiu/ZsYybBTLSGzVarbjvFNwd3d13ylcZVu9hxuEZ8y4OUxgTZ68WGprl7oKiWiR\n2ZWVX0vZ72KgaKGQQlJl4Exmjp7QvqzANNc6ARjqnlCf//kejyy54AK5bMwYWe5yrlPSOzchGa0U\nZ6qJlC9KG6hjw00lVFZ2TcSVcTxJ98InXufAuJqaFaZ306KwST8n51IJGKwt76PlAldHUTu5q3+i\nZXvNFE8jJxIRCllsDRlcLAOnnUgGzj1eL6vr62msrmZ1fT17vF7HdpYBevXmzTSuWzcgX/vQvgq6\nuoKMumFjB9ZAWEWz+7u7UTt3ctHhw7RhGI/3hJxbhmHWO2z7bCGwb//+oHve4/Vy8KmnuBPD7LcC\nw9P8HRIrO5oIbr+7ftJvoPZ691Bfv5rq6kbq641SlplIc/M8KipCf+ON+HzfYObM+13HHWvpT6ve\nQF3dPRQV3Yjh938HoX9lFRWNKNVLR8f3gOVAE3AjUE9JyWts2fJNKip+gvGXdh/wU0aPfo/a2jK2\nbl1JV1eB43jwT4VjzJoSa6PeA5ww4xvmOd571hKvFEnHQQbsFOJR9SRTLTQQ7Ebd0GymizHiGm4I\nWT37MIzS9YQbgX3m6+vNHYaPQNK7ZSCfDznHnkYjdEcxmKqbTLUppKsaV6JE0qu7rZYTSQMR/Fx8\nAqskP3+O1NSscFBlBVJnWPaCSDEIibiUJrpLygTQ6qPUEquqJ1MCrKx0ELsxitwE5RXC8EAKTY+x\nHPcYh6sxguLuApmFWQrTFBBXuJxzw/jxEvLfJALy+ZwcxwI5qcLvfVRUJLOLijLC+yjT8+Y4kYht\nIRHBF2liD6iAnOMmIvWdSPBZtglvO1ooZAiZEGBl7VZ2Y0Q5O03Yl40cGVQRrcn86WaDmD1qlPzn\nfffJTXnByfDmYEQ+h7b3gVyRm+t47WXofEWZnmHTiURX/gNNz21nxoybxbA3XC+BTKiB+s+x1Eu2\n10YIjb52mvCTfQ+DRSJCQQevpYBMCLCy15k+A2eN6ElK8T0M/f49QBtEDCw7/9prOfDCC/xHb2+Q\nDeIBoN7hnB8D3+vr8+dkskKMFmFoiickEKg3lCgpcX7SmRz41Nw8j9ZW97xBVl6gN954nwMHOpg0\n6QwqKgpobp6XlHrEXu8etm/vBx4hOGjtOgxr1aKY6iWvW9fof79t23PMmXMtb7/dR06Oh/Ly8P/T\n0HMijc+eNypZ9z2oxCtF0nGQZTuFZLuaxpMbyWo/96STpAnDbjDLZadQE6LaaTI/9xEeWLZ48uSI\nGV9vGD8+TG9vVVvzEbA9NIEsSdPuKdPIVrWE26o5cD/h7qCJ3ldoTIG795C1g1klEyfOjiv+wNgp\nBBfdSSTTaSb+PtHqo8whGa6m8QoXp/azwLFe83yPx5+HyK7usVRJPoIDy6wU1pHsJW4usWFtQ84b\nzmSrWsLCPmkbRujd4hY4Fq+txDk53xxHlVsggvkGvxoplgk5UlrveMebiTYiLRSGGPEYrH3t7XJN\nWZnf+Osz26+0TfhBq/XKSkchct3Ikf50GPZ+rBX9QL2w7F5MA7EpJCO7rGZguAd13e44ccdrK3Ge\nZN3qMlhtV8Q1IQcKACU2XrtQjJS8L10kIhS0TSHD2OP1srahgf69e3l5925/fQGLMYT791t1HH7q\n8zEGeA1Db3828Ib5/mwCdRQOA/dUVDgmrSvu7qZp/XpXe4hbojun+IrQtj1jxyJKsaarK+J5sTwj\np7oVizZu1DUVBpGGhrU22wLmz9UYcQPhthKv9xW83j0x69id4wO+yOjRizhyxKqr8BpGPMMHgRvM\n1xbh8RChGHadXsfxRrPteL17uPTSb9LRUYRhiTtE4L8t0M/YsT0R+8k44pUi6TgYJjuFSCvrSDsF\n+47C56AqmquUv3ZB6Ao9dMWdzHrSqSJTXH6HO+75h26V8BQThq0hHnfUiROdK6TV1i6VurommTr1\nS5KXF5owL5B+IlZPpERtCkY+qGUh17fSdlvvjQR+6VILotVH2U2kHD2RJmi78bcJZ6PyNWVlYfYN\nN1WQPxdSElJvpIJMcPnVRA7qqqxcYpbFDA90ixT0FR64Fjzp2oVKtPQTsRp5rfrTRUWzpahorj9I\nLhpuQstwk7XXeEifXUELhSzHbbKbW1QUcYK2CxPXPEcOE2a2rrizddxDjWjeNu47iZVxpN02IpqL\niuaGGeLd+h83Lryt09jjzZQaipsNwclGkS67QiJCQdsUMgi3+IaKGTPCfPnttoeewkKWlZbyvY4O\n1xgDpxiJ/r17nUuhJCEnkX18OSUlzEvQfuDEvOZmGltbg20KFRUsam5OSv+a2LDyFTU03MO+ff0U\nF+fQ3LzIbzNwi8OAEbS1/SsNDfeE+f6H2xGmAM2cc05jWFu3/q+6qiJiTIFRo/n+oFiL1tZGNm5c\nFFdMQWVlEU884XR/oXaMzI49CSNeKZKOg2GyU4inZkNou8WTJ8vS2lpZUlkZlvXUzSaQqhX3YOR+\nSmZ2WU1qiJZy2mn1HI9bZ6JxAclyHW1v98nkyYuDrl9c/JWYIqQHC7T6KPuJZbKLNpnHOmGmavKO\n15VWu5YOXeJNohfvRB8pkM5NPZTM9CJO18+k2BMtFIYJkQytiUZAJ3PFHashOFOyyWpSS7Im+mRd\nLxODzFKFFgrDBLeV+IqamoyYZGPdKWiD8fBhMFfP0Sb9TExHYScZRnCLrBIKwN0YkR4vAr8ECiO0\nTfihDEXcVtihaSvSNcnGugPQrqWaZNPe7jNdRe0uoRKmHsokFY+dZAusRIRCOr2PngbuFJF+pdS3\ngX81D00U3KKK1yxYkDJvomSML9T7KBOyyWqyG3tW0tzc/ezYcZSenocIzqC6CJgQ5AEUa9bTwcB+\nDz7fK/h8P8UeJW5Uggv31EoZ8UqRVBzA1cBDEb5PSEoOJWKxFWSbOkbbFDQDIXxV7ZYXaVVGqYfs\nhN/DyqQZwUWyTH0UNAh4ApgT4fuEHshQYSCuqpk+yWrXUk2ihNsOnL2KiormZqRAEHG6h+QawRMR\nCilVHymlNgJF9o8AAVaKyG/NNiuBEyLys1SOJZuxF8wBM+2YQ4GaeJLVZQpTysuHbZEdzcAID3Rz\nDmabMaMiYwvdhN/DPAgpS2UvYjQYpFQoiMjMSN8rpeYBnwEui9ZXU1OT/3VVVRVVVVUDG1wWEU/k\nsZ5kNcOF8IjmeUAD0Iw1oXo8i2huzgzbgRPh9zAFuJmyshspLz8vLEo8Gi0tLbS0tAxsUPFuLZJ1\nAFcCrwLjY2ib0NZpqJBttgIndJCaJtk4R0x/UYyaCivF45klW7duT/cwI5Jq91gSUB8p47zBRyn1\nOjASeNf8qFVEvuLSVtI1zkzAsX5ARUVS6gekMkeR/RqpGr9meGN57uzb18/YsT0o1UtXV6G5ws6O\n+sj2e0j2uJVSiIiK65xsmGyHu1AA2+Rt2gqSMXkP1mS9ur6eFQ8/HOZ6ek9dnVZ1aTQpJBGhoLOk\nZgmpsBXEasAeKKnMxqrRaJJLFuVz1SSbwZqsrSA1OzpITaPJTLRQGMYM1mQ9r7mZxooK/7UsNdU8\nXf9Ao8k4tE1hGDOYBuBU2EQ0Gk1ktKFZEzd6stZohi5aKGg0Go3GTyJCQdsUNBqNRuNHCwWNRqPR\n+NFCQaPRaDR+tFDQaDQajR8tFDQajUbjRwsFjUaj0fjRQkGj0Wg0frRQ0Gg0Go0fLRQ0Go1G40cL\nBY1Go9H40UJBo9FoNH60UNBoNBqNHy0UNBqNRuNHCwWNRqPR+NFCQaPRaDR+tFDQaDQajR8tFDQa\njUbjRwsFjUaj0fjRQkGj0Wg0frRQ0Gg0Go0fLRQ0Go1G40cLBY1Go9H4SbtQUEotV0r1K6VOSfdY\nNBqNZriTVqGglDoNmAnsSec4Uk1LS0u6hzAgsnn82Tx20ONPN9k+/kRI907he8DX0jyGlJPtf1jZ\nPP5sHjvo8aebbB9/IqRNKCilaoAOEXk5XWPQaDSa/9/evcfYUZZxHP9+S2m4Wo2REkBKGwS1BGuj\nWKwYIigE02LUENBIK/yFl268JGI1ITHeo4kEvISIDZKiCZVETKzQtVGjsQK1tTfEJigtIGtUjHhv\n4fGPec/xULu7c5Zu58ye55Nsds7szJzfbs7OM+/MvO+kZ5s9nRtXNwLzemcBAXwcWEN16qj3Zyml\nlBpkRBz5N9VzgFHgH1TF4DTgMeC8iPjDIZY/8iFTSmkGiIi+DrgbKQr/F0J/CyyJiCebzpJSSsOs\n6QvNHUGePkoppcYNREshpZTSYBiUlkJtbe3spn5efVDdpn5HfV7TmSajXqr+Wv2N+pGm8/RDPU3d\npO5Sd6irm840Feos9Zfq3U1n6Zc6V72zfO53qa9pOlNd6gfUnep2dZ06p+lME1FvVcfU7T3zXqDe\nqz6k3qPOrbOtVhWFlnd2uxdYFBGLgT3ARxvOMyF1FnAzcAmwCLhKfWmzqfpyAPhgRCwCzgfe27L8\nHSPA7qZDTNGNwPcj4mXAK4AHG85Ti3oK8H6q65znUt2leWWzqSa1lup/tdf1wGhEnA1souY+p1VF\ngRZ3douI0Yh4przcTHXH1SA7D9gTEY9ExH7g28DlDWeqLSKeiIhtZfpvVDukU5tN1Z9yEHQZ8PWm\ns/SrtIQviIi1ABFxICL+2nCsfhwFHK/OBo4DHm84z4Qi4qfAwTfqXA7cVqZvA95SZ1utKQozrLPb\nNcCGpkNM4lRgX8/rR2nZTrVDPQNYDPyi2SR96xwEtfHC3wLgj+racvrrFvXYpkPVERGPA18E9lLd\nKv+XiBhtNtWUnBQRY1AdJAEn1VlpoIqCurGcw+t87SjfV1B1druhd/GGYo5rgvzLe5b5GLA/Iu5o\nMOrQUE8A1gMjpcXQCuqbgbHS2pEB/LxPYjawBPhyRCyh6pN0fbOR6lGfT3WUPR84BThBfUezqQ6L\nWgcX09qjuV8R8cZDzS+d3c4AfqV2OrttUQ/Z2a0p4+XvUFdRnQ54wxEJ9Nw8Bpze87rTwbA1StN/\nPXB7RHy36Tx9WgasUC8DjgVOVL8ZEVc3nKuuR6la9g+U1+uBttyscDHwcET8GUC9C3gt0LYDuTF1\nXkSMqScDtfaVA9VSGE9E7IyIkyNiYUQsoPrAvXKQCsJk1EupTgWsiIh/N52nhvuBM9X55c6LK4G2\n3QHzDWB3RNzYdJB+RcSaiDg9IhZS/e03taggUE5b7FPPKrMuoj0XzPcCS9VjykHoRbTjIvnBLcq7\ngVVleiVQ68BooFoKfWhjZ7ebgDnAxupzxuaIeE+zkcYXEU+r76O6a2oWcGtEtOEfAwB1GfBOYIe6\nleozsyYiftBssqGyGlinHg08DLy74Ty1RMR96npgK7C/fL+l2VQTU+8ALgReqO6lOtX+WeBO9Rqq\nOzavqLWt7LyWUkqpoxWnj1JKKR0ZWRRSSil1ZVFIKaXUlUUhpZRSVxaFlFJKXVkUUkopdWVRSENB\nXa3uVm+fwrrz1aumI1fZ/gXqFnW/+tbpep+U6siikIbFdcDFEfGuKay7AOh77Jsy/Hgdj1D1OF3X\n73ukdLhlUUgznvpVYCGwQR1RjysPJdlcjtCXl+Xmqz9RHyhfS8smPgO8roz2OaKuVG/q2f731NeX\n6afUL5Re1EvVJeqP1PvVDeq8g/NFxN6I2Ek7R0NNM0xbh7lIqbaIuE69BLgwIp5UPwX8MCKuLU+j\nuk8dBcaoWhP/Uc8EvgW8mmp0zw9FxAoAdSXj78CPB34eER8uA/L9mGq8qz+pVwCfBq6dzt83peci\ni0IaFr2Dhb0JWK52Htg0h2pE2N8DN6uLgaeBl0zhfQ4Ad5Xps4FzqMa7kqplPtAPa0kpi0IaVm+L\niD29M9QbgCci4lz1KOCf46x7gGefej2mZ/pf8b8BxQR2RsSywxU6pemW1xTSMLqHagRPAErLAGAu\nVWsB4GqqRzICPAWc2LP+74DFVl5M9ejS7uZ6ph8CXtS5NqHOVl8+Sba2jf6bZpgsCmlY9F4D+CRw\ndOfpeMAnyvyvAKvKReKzgL+X+duBZ9St6khE/IyqMOwCvgRsOdT7lGdbvx34nLqNagjm8w8Opr5K\n3VeW/VrJlFIjcujslFJKXdlSSCml1JVFIaWUUlcWhZRSSl1ZFFJKKXVlUUgppdSVRSGllFJXFoWU\nUkpdWRRSSil1/RfNu6Y5eXCpBgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<matplotlib.figure.Figure at 0x7f9a4282e9e8>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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/H6dxGjpf03+qP26lsfT6pWz+zGYGpg4Yh+8xgtVQvRr05IXez0j7RWJVjY1G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\nehYFSVLPoiBJ6n0FvBNBPOMBlEsAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<matplotlib.figure.Figure at 0x7f9a40d8bb70>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "def datashow(dataSet, k, centroids, clusterAssment):  # 二维空间显示聚类结果\n",
+    "def datashow(dataSet, k, centroids, clusterAssment, fnFig=None):  # 二维空间显示聚类结果\n",
     "    from matplotlib import pyplot as plt\n",
     "    num, dim = np.shape(dataSet)  # 样本数num ,维数dim\n",
     "\n",
@@ -462,16 +460,17 @@
     "    c = ['yellow', 'pink', 'red']\n",
     "    for i in range(k):\n",
     "        plt.plot(centroids[i, 0], centroids[i, 1], markcentroids[i], markersize=15, label=label[i], c=c[i])\n",
-    "        plt.legend(loc='upper left')  #图例\n",
+    "        #plt.legend(loc='upper left')  #图例\n",
     "    plt.xlabel('feature 1')\n",
     "    plt.ylabel('feature 2')\n",
     "\n",
     "    plt.title('k-means cluster result')  # 标题\n",
+    "    if fnFig != None: plt.savefig(fnFig)\n",
     "    plt.show()\n",
     "    \n",
     "    \n",
     "# 画出实际图像\n",
-    "def trgartshow(dataSet, k, labels):\n",
+    "def trgartshow(dataSet, k, labels, fnFig=None):\n",
     "    from matplotlib import pyplot as plt\n",
     "\n",
     "    num, dim = np.shape(dataSet)\n",
@@ -488,12 +487,13 @@
     "    plt.title('true result')  # 标题\n",
     "\n",
     "    # 显示图形\n",
+    "    if fnFig != None: plt.savefig(fnFig)\n",
     "    plt.show()\n",
     "    # label=labels.iat[i,0]\n",
     "    \n",
     "# 绘图显示\n",
-    "datashow(X, k, mycentroids, clusterAssment)\n",
-    "trgartshow(X, 3, y)"
+    "datashow(X, k, mycentroids, clusterAssment, \"fig-res/k-means_predict.pdf\")\n",
+    "trgartshow(X, 3, y, \"fig-res/k-means_groundtruth.pdf\")"
    ]
   },
   {
@@ -730,31 +730,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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QES9GxM4Kh1WqA4CpkiYCBwI9FY6naBHxY2BbQee3A9en768H3jGmQZWHc1gV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\nORimJEmScjBMSZIk5WCYkiRJyuH/AyMr8KzgYp8cAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<Figure size 720x720 with 6 Axes>"
+       "<matplotlib.figure.Figure at 0x7f497ea9f860>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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1kqEawZ13wpZbwkorFZ1Ealybbw6XXALDhsELLxSdRpLKp8NDAhHRGbg0pXRQ\n9SItvFo8JPCNb8A668B3v1t0EqnxnXNOLtTuvhuW84y5pBpXyiGBUk5x3g3s2v4+tFpTawVaSrD2\n2jB2bD7FKamyUoIRI/KJ6euvh86di04kSfNXrgLtUmATYCTwceehlNLZ5QhZDrVWoD3+OOy3H/z9\n7zaolapl+nQYNAi23x5+9rOi00jS/JVSoJUy6ukfbR+dADcPSjDn9KbFmVQ9XbvCtdfmmZ2bbAKH\nHlp0IkladAtcQfv4hRHLAqSUplU00SKotRW0HXaAn/wE+tdMK1+peUycCH36wHXXwS67FJ1Gkj6r\nXFucmwN/BFZsu/Qm8NWU0t/KkrIMaqlAe+21PBj99dfzT/SSqm/cOPja1+Cee2C99YpOI0mfVq5J\nAhcCx6eU1k4prQ2cAPyuHAEb0ZzpARZnUnF22w1OPTXfajBlStFpJGnhlVKgLZNSunPOg5RSK7BM\nxRLVOacHSLXhmGOgpQUOPBBmzSo6jSQtnFK2OG8AHiFvcwIcDGyTUtq3wtlKVitbnB9+CD17wj/+\nASuvXHQaSTNmwO6754a255xTdBpJysq1xXk4sApwPXAdsHLbNc2ltRW22MLiTKoVSywBV18Nt9wC\nF15YdBpJKt0C22yklN4B/qsKWeqe25tS7VlhBRg9Gnr3hl69oF+/ohNJ0oItcAUtIm6LiOXbPV4h\nIsZWNlb9SSn/R8ACTao9vXrBlVfC8OHw3HNFp5GkBStli3PllNK7cx60raitWrlI9enJJ/N4GUc7\nSbWpXz/4f/8v/xD1zjtFp5GkjpVSoM2OiLXmPIiItYHi78ivMU4PkGrfiBEwZAgccEA+QCBJtaqU\nAu37wN0R8ceIuAwYD5xS2Vj1x+1NqT78z//kwwPf/nbRSSRp/koa9RQRKwM7klfOHkgpvVnpYAuj\n6DYbr78OG27o9ACpXkyeDF/6EnzjG7lfmiRV02K12YiItSOiB0BbQfYesBvw1YiwDGnn5pth4ECL\nM6le9OiRb0v48Y/zWChJqjUdbXFeTdvEgIj4AnAN8G9gK+D8ykerH25vSvVnvfVyj7RDDskD1iWp\nlsx3izMinkgpbdn2+c+B2Sml70ZEJ+CxOc/VgiK3OD/6CFZdFf7+d1hllUIiSFoMl1wCZ54J998P\nK61UdBpJzWBxJwm0f+OuwJ8BUkqzy5CtYbS25jEyFmdSfTr0UNh3X9h/f5g+veg0kpR1VKDdERFX\nR8S5wAo9I/7oAAAgAElEQVTAHQARsTrgP2NtnB4g1b+f/hS6d4djj81NpyWpaB1tcQbwZWB14OqU\n0stt178IrJpSqplpAkVtcaYE666b70HbfPOqf3tJZTR1ah4HddhhcNxxRaeR1MgWa4szZVemlM6Z\nU5y1XX+0loqzIv31r7kx7WabFZ1E0uJabjkYORLOOisPV08pcfLJZ1FkCx9JzauURrWaD6cHSI1l\n7bXhuuvyfWnnnDOW88+fxPXX24dDUvVZoC2G0aNh6NCiU0gqpyeeuIyuXYdy0kkTmDr1bE45ZTyb\nbTaUCy64rOhokprIAgu0iPhWKdeazeuvw1NPQd++RSeRVE4jRhzEOeccw7LLzgaCadNmc8YZxzJi\nxEFFR5PUREpZQfvaPK4dWuo3iIjBETExIp6NiJPm8XzfiHg3Ih5p+zi13XPPR8TjEfFoRPyl1O9Z\nDbfcAgMGwJJLFp1EUjlFBBHBrFkfsvrqx/Pqqx/w73/na5JULV3m90REDAe+AqwbESPbPbUc8HYp\nX7ytqe3/Av2BV4AHI+KmlNLcfbvHp5T2mseXmA20pJTeKeX7VZPbm1Ljeu65F7n44sHst99uHH/8\nOE477UV694btty86maRm0VGbjbWBdYGfAie3e2oq8ERKaeYCv3jEjsBpKaUhbY9PJh8Q/Vm71/QF\nvpNS+kw3sYj4F7BtSumtBXyfqrbZ+Ogj6NkTnn02TxGQ1Nhuvjm337jqKujXr+g0kurd4rbZeCGl\n1JpS2imldFe7j0dKKc7arAG82O7xS23X5rZTRDwWETdHxKbtYwC3RcSDEXFkid+z4u66Czbd1OJM\nahZ77JHndn75y/n0tiRVWimHBPaLiOciYnJETImIqRExpYwZHgbWSil9gbwdemO753ZOKW0N7A4c\nExG9y/h9F5nD0aXm09KS/79/5JFwxRVFp5HU6OZ7D1o7ZwF7ppSeXoSv/zKwVrvHa7Zd+1hKaVq7\nz2+NiPMjYsWU0tsppUlt19+IiBuA7YG75/WNTj/99I8/b2lpoaWlZRHiLlhK+SfokSMX/FpJjWX7\n7eH222Hw4Dx5YMSIohNJqgetra20trYu1Hvmew/axy+IuCeltPOiBIqIzsAz5EMCk4C/AMPbF3sR\n0TOl9Frb59uTx0qtExFLA51SStMiYhlgHHBGSukzXSOreQ/aX/+aDwf86182qJWa1d//DgMHwje+\nASeeWHQaSfWmlHvQSllBeygiriJvPX4052JK6foFvTGlNCsijiUXV52Ai1JKT0fEUfnpdCGwf0Qc\nDcwAPiDP/wToCdwQEakt5+XzKs6qbc72psWZ1Lw22AAmTMhF2uTJ8KMf+W+CpPIqZQXt4nlcTiml\nwysTaeFVcwVt553hhz+EQYOq8u0k1bA33sj/FvTuDb/8JXRyNoukEpSygrbAAq0eVKtAe+MN6NUL\nXnvNBrWSsnffzbc99OoFv/sddCllX0JSU1usNhvtvsiGEfHniPhr2+Mt23f7bya33gr9+1ucSfrE\n8svD2LHwyitw4IG5T6IkLa5SFuR/B5xCvkeMlNITwIGVDFWrRo1yeoCkz1pmmXyyOyXYay94772i\nE0mqd6UUaEunlOaeg1lqo9qGMX063HZbblgpSXNbcsk8aWD11fN9aZMnF51IUj0rpUB7MyLWJ3f1\nJyL2J7fMaCp33QWbbOL0AEnz16UL/P73sPXWeSTUG28UnUhSvSqlQDsGuADYOCJeBo4Djq5oqhrk\ncHRJpejUCc49N6+29+kDL71UdCJJ9ajkU5xtzWI7pZSmVjbSwqv0Kc6UYP314cYbYcstK/ZtJDWY\n//kfOP/8fHvEBhsUnUZSrVisRrURcXBK6bKIOH7uLwqQUjq7LCnrwFNPwaxZsMUWRSeRVE9OPBG6\nd4e+ffNJz803LzqRpHrRUceeZdp+Xa4aQWrZnO1NO4VLWlhHHZWLtAED8knw7bYrOpGkemCj2hL0\n7g2nnpoHJEvSohg1Co44Aq6+Glpaik4jqUjlalT7h4hYvt3jFSLi9+UIWA/efBOefNJ/UCUtnj33\nzG04DjgAbr656DSSal0ppzi3TCm9O+dBSukd4IuVi1Rbbr0Vdt0Vllqq6CSS6l2/fp+spF11VdFp\nJNWyUqbGdYqIFdoKMyJixRLf1xBGjco/+UpSOeywQz7VOXgwTJkCRx5ZdCJJtaiUQusXwH0RcQ0Q\nwP7AmRVNVSOmT4dx4+C884pOIqmRbLFFbn49cGAu0k44oehEkmrNAgu0lNKlEfEQsGvbpf1SSk9V\nNlZtmDABNtoIevYsOomkRrPBBjB+fD7dOXkynHGGJ8UlfaKjPmjdU0pT2rY0XwX+1O65FVNKb1cj\nYJHc3pRUSZ//fP5BcNCgvJJ29tl5EoEkzbfNRkSMTikNjYh/0TaHc85TQEoprVeNgKWoRJuNlPJP\nuNdfD1ttVdYvLUmf8u67eTTURhvB734HnTsXnUhSJZXSZqOjAq13SunuiFgqpfRhRRKWSSUKtKef\nzj/VvvCC2w6SKu+992DffaFHD7j8cujatehEkiplcfugndv2673li1Q/Ro1yeoCk6llmmfzvzqxZ\nsPfe8P77RSeSVKSOVtDuB54A9gGunPv5lNJ/VTZa6SqxgrbLLvC978GQIWX9spLUoZkz4fDD4fnn\nc8HWo0fRiSSV2+KuoA0F7gA+AB6ex0fDeusteOKJ3FRSkqqpSxe45BLYcsvcJPvNN4tOJKkIHbXZ\nODGldFJErJVS+kPVEtWAW2/NxZnTAyQVoVOn3H/x1FOhT5/c2HaNNYpOJamaOlpB2z0iAjiwWmFq\nhe01JBUtAs48Ew49NN9y8c9/Fp1IUjV1tII2BngHWDYiptDWXoNP2mx0r0K+qpszPeDccxf8Wkmq\ntO9+F5ZbLq+kjR0Lm21WdCJJ1TDfFbSU0okppeWBm1NK3VNKy7X/tYoZq+ruu6FXL1httaKTSFJ2\n9NHws59B//7w0ENFp5FUDQvsWZ1S2jsi1o6IAQAR0S0ilqt8tGK4vSmpFh10EFx4Iey+ex4RJamx\nLbBAi4gjgWuBC9ourQncWMlQRUnJAk1S7dprL7jiCth/f7jllqLTSKqkUqa+HQPsDEwBSCk9B6xa\nyVBFeeYZ+OgjRztJql39+8PIkXDYYXD11UWnkVQpHR0SmOOjlNL0aGupHxFd+PRszobh9ABJ9WDH\nHXPrjSFDYOpUOOKIohNJKrdSVtDuiojvAd0iYiBwDTCqsrGK4fampHqx5ZbQ2go/+hGcc07RaSSV\n23xHPX38gohOwBHAbuQWG2OB/yv7bKXFUI5RT2+/DeusA6+9Bt26lSeXJFXav/8NAwfC8OFw2mnu\nAEj1oJRRTwvc4kwpzY6IPwD3tV16ppaKs3KZMz3A4kxSPVlrrXyqc9AgmDwZzj7bIk1qBKWc4mwB\nngN+DZwPPBsRfSqcq+rc3pRUr3r2hDvvhPvvhyOPhFmzik4kaXGVssX5MPCVlNIzbY83BK5IKW1T\nhXwlWdwtzhkzYNVV4amnYPXVyxhMkqpo2jTYZx9YaSX44x+ha9eiE0mal1K2OEs5JLDEnOIMIKX0\nLLDE4oarJXffDRtsYHEmqb4tuyyMHp3bBe2zD7z/ftGJJC2qUgq0hyLi/yKipe3j/4CGGjbi9qak\nRrHUUnDNNXkVbcgQmDKl6ESSFkUpW5xLkpvV9m67NB74TUrpowpnK9nibnFuuCFceSVsvXUZQ0lS\ngWbPhmOPhQcfzIegVl656ESS5ihli3O+BVpErAKsklJ6aq7rmwGvp5TeKFvSxbQ4Bdozz+TO3C++\n6MknSY0lJfje9/Lkgdtug899ruhEkmDx70E7D5jXz1wrAucuTrBa4vQASY0qAn76UzjkENhlF/jn\nP4tOJKlUHRVoG6SUxs99MaU0AdiycpGqy/vPJDW6k0+GE06Avn3zaXVJta+jRrXLdfBcQ5zifPtt\nePRR2HXXopNIUmV94xvQvXu+pWP0aNimZholSZqXjlbQ/h4Ru899MSKGAA2xUD5mDLS0OD1AUnM4\n+GD4zW/y6c4JE4pOI6kjHa2gHQfcHBEHAA+3XdsW2AkYWulg1eD2pqRms88+uV/asGFw6aUweHDR\niSTNS4dtNtpabHwF2Lzt0t+AP6WUPqxCtpItyinOGTPyeJS//tWTTZKaz3335WLt17+G/fcvOo3U\nXBZ7WHpbr7OLy5qqRtxzD6y3nsWZpOa0004wblze7pw6FQ47rOhEktrrsEBrZG5vSmp2W20Fra0w\ncGCeOPCtbxWdSNIcTVugjR4Nf/pT0SkkqVgbbpgPDAwYAJMnww9+YF9IqRaUMouTiOgWERtVOky1\nPPssTJvmaCdJAlhrrVykXXstfOc7eQKBpGItsECLiD2Bx4AxbY+/EBEjKx2skpweIEmf1rNn3u68\n5x4YMQJmzSo6kdTcSllBOx3YHngXIKX0GLBuBTNV3OjRuUCTJH1ixRXzzM5//hMOOiifdpdUjFIK\ntBkppclzXavbBfB33oGHH87dtCVJn7bccnDzzfD++7DvvvDBB0UnkppTKQXa3yLiK0DniOgVEecB\n91Y4V8WMGZPn0S29dNFJJKk2LbUUXHcd9OiR23BMmVJ0Iqn5lFKgfRPYDPgI+BMwGajbw9hub0rS\ngi2xBPzxj7DJJvmE51tvFZ1Iai6lFGh7pJS+n1Laru3jVGCvUr9BRAyOiIkR8WxEnDSP5/tGxLsR\n8Ujbx6mlvndhzZyZV9As0CRpwTp1gvPPh3798s7DpElFJ5KaRykF2iklXvuMiOgE/C8wiLwKNzwi\nNp7HS8enlLZu+/jxQr63ZPfcA+usA2ussThfRZKaRwT87Gf50MAuu8C//lV0Iqk5zLdRbUQMAXYH\n1oiIX7V7qjsws8Svvz3wXErphbaveSWwNzBx7m+3GO8tmdubkrRoTjkFuneHPn3yiKhNNik6kdTY\nOlpBewV4CPgQeLjdx0jyqlYp1gBebPf4pbZrc9spIh6LiJsjYtOFfG/JHO8kSYvumGPgzDNh113h\nkUeKTiM1tvmuoKWUHgcej4ieKaU/tH8uIr4FnFumDA8Da6WU3m9btbsR2LBMX/tjzz2XTyI5PUCS\nFt1Xv5pbcQweDNdfD717F51IakylzOI8EDhrrmuHUlqB9jKwVrvHa7Zd+1hKaVq7z2+NiPMjYsVS\n3tve6aef/vHnLS0ttLS0fOr5UaNgjz3yTa+SpEW3776w7LKw3375pOegUvdUpCbV2tpKa2vrQr0n\n0nyGrkXEcOArQG9gQrunlgNmp5QW2Oo1IjoDzwD9gUnAX4DhKaWn272mZ0rptbbPtweuTimtU8p7\n232NNL/fxxy77grHHQd7lXz+VJLUkXvuyUXa+efDsGFFp5HqR0SQUupw4GRHK2j3kgujlYFftLs+\nFXiilAAppVkRcSwwjny/20Uppacj4qj8dLoQ2D8ijgZmAB8AX+7ovaV837m9+y489FDu5SNJKo+d\nd86ti/bYA6ZNg699rehEUuOY7wrap14UsTbQK6V0e0R0A7qklKZWPF2JFrSCduWVeRn+5purGEqS\nmsTEibDbbnDiifDNbxadRqp9paygLfCOrIg4ErgWuKDt0prkG/nrxujRnt6UpErZeGOYMAF+9Sv4\n8Y+hhJ/7JS3AAlfQIuIxck+yB1JKX2y79mRKaYsq5CtJRytoM2dCz57w+OOw5ppVDiZJTeTVV2Hg\nwHzC86yzcpNbSZ9VlhU04KOU0vR2X7QLUDc/H917L6y9tsWZJFXaaqvBXXfB+PHw9a/DrFlFJ5Lq\nVykF2l0R8T2gW0QMBK4BRlU2Vvk4PUCSqmfFFeH22+HZZ+GQQ2DGjKITSfWplC3OTsARwG7kkUxj\ngf9bYF+LKupoi3OTTeDSS2G77aocSpKa2AcfwAEH5M+vvhq6dSs2j1RLStniLOkUZ62bX4H297/n\n4b4vv2yDWkmqthkz8uSB116Dm27KEwgkle8U578i4p9zf5QvZuWMHu30AEkqyhJLwGWXQa9euQ/l\n228XnUiqH6WULtsC27V97AL8CriskqHKxeHoklSszp3ht7+Fvn3zx6RJRSeS6sMibXFGxMMppW0q\nkGeRzGuLc/Jk+Pzn8z8GyyxTUDBJEpB7o/3kJ3DJJXDbbbDOOkUnkoqzuKOe5nyRrds97EReUStl\nyHqhxo6F3r0tziSpFkTA978P3btDnz4wblxucCtp3koptNrP4ZwJPA8cUJE0ZeT2piTVnm9+Mxdp\n/frBLbfAF79YdCKpNjXkKc6ZM3PDxEcfzduckqTact118I1vwA03wJe+VHQaqbrKdYqzR0ScHREP\ntX38IiJ6lC9m+d1/f54cYHEmSbVp2LDco3KfffI9aZI+rZRTnL8HppK3NQ8ApgAXVzLU4nJ7U5Jq\n36BBcP31cNBBeSVN0idKuQdt/ZTSsHaPz2gboF6zRo3KJ4UkSbWtd28YMyb3rJw6NTe2lVRagfZB\nRPROKd0NEBE7Ax9UNtai+8c/cjPEbbctOokkqRRbbw133JFX1KZOhWOOKTqRVLxSCrSvA5e23XcW\nwNvAoZUMtTicHiBJ9WeTTWD8+DxxYPJkOOWU3JpDalYLLNBSSo8DW0VE97bHUyqeajGMGgXHHlt0\nCknSwlpnHZgwAQYOzEXaf/+3RZqa1wLbbETEksAwYB3aFXQppf9X0WQLYU6bjcmT8+nNSZNg2WWL\nTiVJWhRvvQVDhuStz1//Oo+LkhpJWdpsADcBe5Ob1L7X7qPmjBuXbzi1OJOk+rXSSnD77TBxYj40\nMGNG0Ymk6itlBe2vKaXNq5RnkcxZQfvqV2HHHXPzQ0lSffvgA/iP/8graFddBUstVXQiqTzKtYJ2\nb0RsUaZMFTNrVh4bssceRSeRJJVDt265T1q3bvnf9mnTik4kVc98C7SIeDIingB6A49ExDMR8US7\n6zXl/vthjTVg7bWLTiJJKpeuXeHyy2G99fIJz7ffLjqRVB3z3eKMiA5LnZTSCxVJtAgiIp10UqJL\nF/jxj4tOI0kqt5TgxBPzvcbjxuV5y1K9KmWLs6M2G1PLnKeiRo2C3/++6BSSpEqIgP/5H+jRA/r0\nyfM73TFRI+uoQHsYSOTmtHNLwHoVSbSI3nwTttuu6BSSpEqJgB/8ALp3z0XauHGw0UZFp5IqY74F\nWkpp3WoGWVxOD5Ck5vCtb+UirV+/fDjsC18oOpFUfvMt0CJi45TSxIjYel7Pp5QeqVyshbfnnkUn\nkCRVy2GH5Z6XgwbBjTfCTjsVnUgqr44OCfwupXRkRNw5j6dTSmnXykYrXUSkqVOTDWolqcmMGZOb\n2f7pT/mUp1QPSjkksMBGtfUgItLs2bMJh7ZJUtOZMAGGDYPf/Q723rvoNNKCLVaj2ojYLiJWa/f4\nqxFxU0T8KiJWLGfQcrj++nFFR5AkFWCXXeDWW+HrX4fLLis6jVQeHd1WfwEwHSAi+gD/DVwKTAYu\nrHy0hXPKKePZbLOhXHCB/++UpGazzTbw5z/DKafA+ecXnUZafB212eicUprTs/nLwIUppeuA6yLi\nscpHWzgffjibn/zkWIYNG1R0FElSATbdFMaPz/eiTZkCJ59cdCJp0XW0gtY5IuYUcP2BO9o911Fh\nV4h33/2AiPA+NElqYuuum+9J++Mfc4HWALdZq0l1VKBdAdwVETcBHwATACJiA/I2Z025+OIhPPfc\ni0XHkCQV7HOfg7vuyluexxwDs2cXnUhaeB2e4oyIHYHVgXEppffarm0ILFtLfdAiIjXCaVRJUvlM\nmZJ7ZK61Flx8MXSpub0fNaumarPRCL8PSVJ5vf8+7L8/dO0KV14JSy1VdCJpMdtsSJJU75ZeOk8a\n6NoVhg6FadOKTiSVxgJNktTQunaFK66AddaB3XaDd94pOpG0YBZokqSG17lznjSw4455yPprrxWd\nSOqYBZokqSlEwC9+AfvuC336wL//XXQiaf480yJJahoRcNpp0KNHLtLGjYMNNyw6lfRZFmiSpKZz\n3HHQvTu0tMCYMbDllkUnkj7NAk2S1JQOPxyWXRYGDoSbbsr3p0m1wnvQJElN64ADchPbvfaCO+5Y\n8OularFAkyQ1td13h2uugQMPhJEji04jZW5xSpKaXt++cMstnzSz/cpXik6kZmeBJkkSsO22ecD6\noEF5jufXv150IjUzCzRJktpsthmMHw8DBsDkyXDSSUUnUrOyQJMkqZ311oMJE/LpzsmT4cwzc/80\nqZoipVR0hsUWEakRfh+SpNrx5psweHBuv/GrX0Enj9WpTCKClFKHZb9/3SRJmoeVV873pD3+OBx6\nKMycWXQiNRMLNEmS5qNHDxg7Ft54I/dM++ijohOpWVS8QIuIwRExMSKejYj53m4ZEdtFxIyI2K/d\ntecj4vGIeDQi/lLprJIkzW3ppfOkgc6dYc894b33ik6kZlDRAi0iOgH/CwwCNgOGR8TG83ndfwNj\n53pqNtCSUvpiSmn7SmaVJGl+unaFK66ANdeE3XaDd98tOpEaXaVX0LYHnkspvZBSmgFcCew9j9d9\nE7gWeH2u64HbsJKkGtClC/zf/8F220G/fvD63P/Fksqo0sXPGsCL7R6/1HbtYxHxOWCflNJvyAVZ\newm4LSIejIgjK5pUkqQF6NQJzjknz+7s0wdefHHB75EWRS30Qfsl0P7etPZF2s4ppUkRsQq5UHs6\npXR3deNJkvSJCDjjjHyAYJdd4LbboFevolOp0VS6QHsZWKvd4zXbrrW3LXBlRASwMjAkImaklEam\nlCYBpJTeiIgbyFum8yzQTj/99I8/b2lpoaWlpVy/B0mSPuP446F7d2hpgTFjYIstik6kWtXa2kpr\na+tCvaeijWojojPwDNAfmAT8BRieUnp6Pq+/GBiVUro+IpYGOqWUpkXEMsA44IyU0rh5vM9GtZKk\nQlx1FfzXf8HIkbDDDkWnUT0opVFtRVfQUkqzIuJYcnHVCbgopfR0RByVn04Xzv2Wdp/3BG6IiNSW\n8/J5FWeSJBXpy1+GZZfNLTiuuiofIJAWl6OeJEkqg9bW3Mz2ootysSbNj6OeJEmqkpYWGD0ajjwy\n90yTFkctnOKUJKkhbL893H57HrI+dSqMGFF0ItUrCzRJkspo883hrrtg4ECYPBlOPLHoRKpHFmiS\nJJXZ+uvD+PGfFGk/+lHunyaVykMCkiRVyBtvwKBB0Ls3/PKXeRKB5CEBSZIKtMoqcOed8OijcPjh\nMHNm0YlULyzQJEmqoB49YOxYmDQp90z76KOiE6keWKBJklRhSy+dJw1AHrT+3nvF5lHts0CTJKkK\nllwyTxpYffV8X9rkyUUnUi2zQJMkqUq6dIHf/x623jqPhHrjjaITqVZZoEmSVEWdOsG558Iee0Cf\nPvDSS0UnUi2yD5okSVUWkXuj9egBu+wCt90GG2xQdCrVEgs0SZIK8p3vQPfueY7nmDF5CoEEFmiS\nJBVqxAhYbjkYMCCf9Nx++6ITqRZYoEmSVLDhw3ORNnQoXH11XlFTc/OQgCRJNWDo0NyG44AD4Oab\ni06jolmgSZJUI/r1g1Gj4IgjcrGm5uUWpyRJNWSHHfKpzsGDYcoUOPLIohOpCBZokiTVmC22gLvu\ngoEDc5F2wglFJ1K1WaBJklSDNtgAxo/PRdrkyXDGGbl/mppDpJSKzrDYIiI1wu9DkqS5vf56nt3Z\npw+cc06eRKD6FhGklDost/2fWZKkGrbqqnDnnfDQQ/nwwMyZRSdSNVigSZJU45ZfHsaNg5dfzj3T\npk8vOpEqzQJNkqQ6sMwyuQXHrFmw997w/vtFJ1IlWaBJklQnllwyTxpYZZXchmPy5KITqVIs0CRJ\nqiNdusAll8CWW8Kuu8KbbxadSJVggSZJUp3p1AnOOy+vovXpk+9NU2OxD5okSXUoAs48E3r0gF12\ngdtvh/XWKzqVysUCTZKkOvbd70L37nklbexY2GyzohOpHCzQJEmqc1//ei7S+vfPJz23267oRFpc\nFmiSJDWAr3wFllsO9tgDrr02r6ipfnlIQJKkBrHnnnDllbD//nDLLUWn0eKwQJMkqYHsuiuMHAmH\nHZZ7pqk+ucUpSVKD2XFHuO02GDIEpk7NMzxVXyzQJElqQFtuCa2tMHAgTJkC3/520Ym0MCzQJElq\nUL16wfjxuUibPBlOOy33T1Pti5RS0RkWW0SkRvh9SJJUCa+9BoMGQb9+cPbZFmlFiwhSSh3+r+Ah\nAUmSGlzPnnDnnfDAA/Cf/wmzZhWdSAtigSZJUhNYYQUYNw7+/W8YPhymTy86kTpigSZJUpNYdtk8\naWD6dNhnH3j//aITaX4s0CRJaiJLLQXXXAMrrZTbcEyZUnQizYsFmiRJTWaJJeAPf8iD1fv3hzff\nLDqR5maBJklSE+rUCX79axgwAPr2hVdeKTqR2rMPmiRJTSoCfvpT6NEDdtklTx9Yb72iUwks0CRJ\nanonn5yLtL59YexY2HTTohPJAk2SJHH00bDccnnY+s03wzbbFJ2ouVmgSZIkAA4+OBdpQ4bAddfl\nbU8Vw0MCkiTpY3vvDX/6EwwbBmPGFJ2meVmgSZKkTxkwAG66Cb72Nbj22qLTNCe3OCVJ0mfstFMe\nDTVkCEydCocdVnSi5mKBJkmS5mmrraC1FQYOzBMHvvWtohM1Dws0SZI0XxtuCBMm5G3PyZPhBz/I\n/dNUWZFSKjrDYouI1Ai/D0mSatVrr8Fuu+VC7ec/t0hbHBFBSqnDP8GKHxKIiMERMTEino2Ikzp4\n3XYRMSMi9lvY90qSpMrq2TNvd957L4wYAbNmFZ2osVV0BS0iOgHPAv2BV4AHgQNTShPn8brbgA+A\n36eUri/1vW3vdwVNkqQqmDYtt+JYZRW49FLo2rXoRPWnFlbQtgeeSym9kFKaAVwJ7D2P130TuBZ4\nfRHeK0mSqmTZZfOkgQ8+gP32y7+q/CpdoK0BvNju8Utt1z4WEZ8D9kkp/QaIhXmvJEmqvqWWyv3R\nencbLLkAABL7SURBVPTIbTimTCk6UeOphUa1vwS8v0ySpDqyxP9v796D7qrre4+/PwnXQBIHcYoV\nuUhBwGolgnK4hIeDQCgWQaU2QEO9oQeMKGeOxVqPOIcOAuOc41SdDpSih4LQckcoJIgx2E4FFQWR\nIMVjuQjeuAQUJJDv+WOtwCaTy5OQ/az1PM/7NZPJ3nv91trfvUmefPj91u/32xguuAB2262ZOPDr\nX3dd0cQy7GU2HgS2G3i+bfvaoD2Bi5ME2Bo4LMmzozz3eaeddtrzj0dGRhgZGXkpdUuSpLWYMgW+\n9CU49VQ44ABYuBBe+cquq+qfRYsWsWjRonU6Z9iTBKYCd9Pc6P8QcAswt6ruWk3784Fr2kkCoz7X\nSQKSJHXrjDPgvPOakLbjjl1X02+jmSQw1B60qnouyYeBBTTDqedV1V1JPtgcrnNWPmVt5w6zXkmS\ntH4+8QmYMQNmz262iNptt64rGt9cqFaSJG0wF1wAH/94M9Nz1qyuq+mnznvQJEnS5PLnf94sxTFn\nDlx+Oey3X9cVjU99mMUpSZImkKOOggsvbNZJu+GGrqsZnwxokiRpgzv4YLjySpg3Dy67rOtqxh+H\nOCVJ0lDssw9cfz0cfnizRdTxx3dd0fhhQJMkSUOzxx5w001wyCHNjgPz53dd0fhgQJMkSUO1665w\n883NjgOPPw6f/CRkjXMY5TIbkiRpTDz8cHNv2pw5cNZZkzekjWaZDScJSJKkMbHNNvDNb8LixfCh\nD8Fzz3VdUX8Z0CRJ0pjZaiu48Ua45x447jhYtqzrivrJgCZJksbU9Olw3XXwm980a6U99VTXFfWP\nAU2SJI25zTZr1kebPr1ZhuOJJ7quqF8MaJIkqRMbb9zs3bnzzs0Mz0ce6bqi/jCgSZKkzkydCn/3\nd3DAAc2vhx7quqJ+cB00SZLUqQTOPBNmzoTZs2HhQthhh66r6pYBTZIkdS5pFrCdMaMJaQsWNAvc\nTlYGNEmS1Bvz5zch7cADm5mee+zRdUXdMKBJkqReOf74ZnbnnDlw+eWw775dVzT2nCQgSZJ65x3v\naGZ4HnVUc0/aZGNAkyRJvXTIIU0P2rHHwhVXdF3N2HKIU5Ik9dZ++8H117+wmO28eV1XNDYMaJIk\nqddmzYKbboJDD21C2kkndV3R8BnQJElS7+22Gyxe3Ow48Pjj8IlPNEtzTFQGNEmSNC7ssAPcfDMc\nfHAT0j772Ykb0lJVXdfwkiWpifA5JEnS2v3613DYYc3Q5xe/2GwXNZ4koarWGC2dxSlJksaVl78c\nvv51WLKkmTSwbFnXFW14BjRJkjTuTJ8O//IvzVDnO98JTz/ddUUblgFNkiSNS5tv3qyPNm1aswzH\nk092XdGGY0CTJEnj1sYbw4UXwmte08zwfOSRrivaMAxokiRpXJs6Fc45p1nUdmQEHn6464peOpfZ\nkCRJ414CZ58NM2fC7NnN/p3bb991VevPgCZJkiaEBD71KZgxowlpCxbAa1/bdVXrx4AmSZImlJNP\nbkLagQfCddfBG9/YdUXrzoAmSZImnPe8p1mK49BDm5me++zTdUXrxkkCkiRpQnrXu+ArX4Ejj4Qb\nb+y6mnVjQJMkSRPWnDlw2WVw7LFw1VVdVzN6DnFKkqQJbf/9m10HDj8cnngCjjuu64rWzoAmSZIm\nvFmzmv07Dz0Uli6FE0/suqI1M6BJkqRJYffdYfHiZseBpUvh1FO7rmj1DGiSJGnS2HFHuPlmOPhg\neOwxOOOMZv20vklVdV3DS5akJsLnkCRJY+NXv4LDDoO99oIvfAGmjOG0ySRU1RpjobM4JUnSpLP1\n1s09aXfeCfPmwbJlXVf0YgY0SZI0Kc2YAddfD48+CkcfDU8/3XVFLzCgSZKkSWvzzZudBjbdFN72\nNnjyya4rahjQJEnSpLbJJnDRRbDDDnDIIU2PWtcMaJIkadKbOhXOPRf23rvZZP3nP++2HgOaJEkS\nzXIbn/scHHUUzJ4N993XXS2ugyZJktRK4NOfhpkzm5C2YAHsssvY12FAkyRJWslHP9rM8hwZaWZ6\nvuENY/v+BjRJkqRVeO97Yfr0ZteBq65q7k8bK96DJkmStBpHHw1f/jIccUSzsO1YMaBJkiStwWGH\nwaWXwty5cPXVY/OeDnFKkiStxezZcN11Lyxme8wxw30/A5okSdIo7LlnM8x56KGwdCl86EPDe6+h\nD3EmmZNkSZIfJ/nLVRw/IskPktyW5JYk+w4c++ngsWHXKkmStCavex0sXgxnnQVnnjm890lVDe/i\nyRTgx8BBwM+AW4E/q6olA22mVdVv28evB/6pqnZrn/8EeFNVrXHThSQ1zM8hSZI06MEHm9mdRx4J\nf/M3zfppo5WEqlrjGcPuQXszcE9V/WdVLQMuBt4+2GBFOGttCSwfeJ4xqFGSJGmdvOpVTU/aggUw\nfz4sX772c9bFsMPPq4D7B54/0L72IkmOTHIXcA3w3oFDBSxMcmuSDwy1UkmSpHWw9dbNPWm33w5/\n8Rfw7LMb7tq96J2qqivbYc0jgdMHDu1bVbOAPwZOSrJfJwVKkiStwsyZzU4Dv/xls2ba7363Ya47\n7FmcDwLbDTzftn1tlarqW0lek2Srqnqkqh5qX/9lkitohky/tapzTzvttOcfj4yMMDIy8tKrlyRJ\nWotp05qdBo47Dv7kT+CKK2CLLV44vmjRIhYtWrRO1xz2JIGpwN00kwQeAm4B5lbVXQNtdqqqe9vH\ns4CrqurVSaYBU6rqySRbAAuAz1TVglW8j5MEJElSp557Dk44AZYsgWuvhZe9bNXtOp8kUFXPAR+m\nCVd3AhdX1V1JPpjkhLbZO5P8MMn3gL8F/rR9/feAbyW5Dfh34JpVhTNJkqQ+mDoVzj0X9toLDjwQ\nfvGL9b/WUHvQxoo9aJIkqS+q4LTT4JJLYOFCePWrX3x8ND1o7iQgSZK0ASXwmc80Ewj2378JaTvv\nvG7XMKBJkiQNwSmnwIwZMDLSzPR8/etHf64BTZIkaUje/36YPh3e+la4+mp485tHd0uWAU2SJGmI\n3v1u2HLLZgmOE0+8YVTnGNAkSZKG7IEH/pFp0y7m9NP/aFTte7GTgCRJ0kR2wgnHcvbZJ7HNNqPb\ntNOAJkmSNGRJSMLSpU+Pqr0BTZIkaQzcc8/9nH/+nFG1daFaSZKkMdT5Vk+SJEladwY0SZKknjGg\nSZIk9YwBTZIkqWcMaJIkST1jQJMkSeoZA5okSVLPGNAkSZJ6xoAmSZLUMwY0SZKknjGgSZIk9YwB\nTZIkqWcMaJIkST1jQJMkSeoZA5okSVLPGNAkSZJ6xoAmSZLUMwY0SZKknjGgSZIk9YwBTZIkqWcM\naJIkST1jQJMkSeoZA5okSVLPGNAkSZJ6xoAmSZLUMwY0SZKknjGgSZIk9YwBTZIkqWcMaJIkST1j\nQJMkSeoZA5okSVLPGNAkSZJ6xoAmSZLUMwY0SZKknjGgSZIk9YwBTZIkqWcMaJIkST1jQJMkSeoZ\nA5okSVLPGNAkSZJ6xoAmSZLUMwY0SZKknjGgSZIk9YwBTZIkqWeGHtCSzEmyJMmPk/zlKo4fkeQH\nSW5LckuSfUd7riRJ0kQ01ICWZArwBeBQ4HXA3CS7rtTsxqr6o6raA3gf8PfrcK46smjRoq5LmHT8\nzsee3/nY8zsfe37n/TTsHrQ3A/dU1X9W1TLgYuDtgw2q6rcDT7cElo/2XHXHv9Bjz+987Pmdjz2/\n87Hnd95Pww5orwLuH3j+QPvaiyQ5MsldwDXAe9flXEmSpImmF5MEqurKqtoNOBI4vet6JEmSupSq\nGt7Fk72B06pqTvv8VKCq6sw1nHMvsBewy2jPTTK8DyFJkrSBVVXWdHyjIb//rcAfJNkeeAj4M2Du\nYIMkO1XVve3jWcAmVfVIkrWeu8LaPqQkSdJ4MtSAVlXPJfkwsIBmOPW8qroryQebw3UO8M4k84Bn\ngKeAP13TucOsV5IkqQ+GOsQpSZKkddeLSQLrI8m2SW5KcmeSO5J8pOuaJrokmyb5druo8B1JPt11\nTZNFkilJvpfk6q5rmQyS/HRwAe2u65kMksxM8s9J7mp/rr+l65omsiS7tH++v9f+/rj/jg5fko8l\n+WGS25NcmGST1bYdrz1oSbYBtqmq7yfZEvgu8PaqWtJxaRNakmlV9dskU4F/BT5SVf4DNmRJPga8\nCZhRVUd0Xc9El+QnwJuq6tGua5ksknwZ+GZVnZ9kI2BaVS3tuKxJoV0Y/gHgLVV1/9raa/0k+X3g\nW8CuVfVMkkuAa6vq/66q/bjtQauqh6vq++3jJ4G7cJ20oRtYWHhTmnsYx2fCH0eSbAv8Me0uGxoT\nYRz/fBxvkswA9q+q8wGq6lnD2Zh6K3Cv4WxMTAW2WPE/IcDPVtdwQvwASrID8Ebg291WMvG1Q223\nAQ8DC6vq1q5rmgT+N/A/MAyPpQIWJrk1yQe6LmYS2BH4VZLz2yG3c5Js3nVRk8i7ga92XcREV1U/\nAz4H3Ac8CDxWVTeurv24D2jt8OalwMltT5qGqKqWt/umbgu8JcnuXdc0kSU5HPh521uc9peGb9+q\nmkXTc3lSkv26LmiC2wiYBXyx/d5/C5zabUmTQ5KNgSOAf+66lokuyctotqzcHvh9YMskx6yu/bgO\naG0X4aXABVV1Vdf1TCbt8MM3gDld1zLB7Qsc0d4T9VXgwCSrvF9BG05VPdT+/kvgCpq9gTU8DwD3\nV9V32ueX0gQ2Dd9hwHfbP+sarrcCP6mqR6rqOeByYJ/VNR7XAQ34B+BHVfX5rguZDJJsnWRm+3hz\n4GDASRlDVFV/VVXbVdVraBZrvqmq5nVd10SWZFrbM0+SLYBDgB92W9XEVlU/B+5Pskv70kHAjzos\naTKZi8ObY+U+YO8kmyUJzZ/z1a7vOuydBIYmyb7AscAd7T1RBfxVVV3fbWUT2iuBr7QzfqYAl1TV\ndR3XJG1ovwdc0W4htxFwYVUt6LimyeAjwIXtkNtPgPd0XM+El2QaTa/OCV3XMhlU1S1JLgVuA5a1\nv5+zuvbjdpkNSZKkiWq8D3FKkiRNOAY0SZKknjGgSZIk9YwBTZIkqWcMaJIkST1jQJMkSeoZA5qk\noUmyPMnZA8//e5L/uYGufX6Sd2yIa63lfd6V5EdJvr6KYzsnuTbJ3Um+k+TiJK9IckCSa9bz/U5O\nstlLr1zSeGZAkzRMvwPekWSrrgsZlGTqOjR/H/D+qjpopWtsClxLs3/ka6tqT+BLwCvaJuu7yORH\ngWnrckK7eLSkCcS/1JKG6VmalbJPWfnAyj1gSZ5ofz8gyaIkVyb5jyRnJDkmybeT/CDJjgOXOTjJ\nrUmWtBvLk2RKkrPa9t9P8oGB6y5OchVw5yrqmZvk9vbXGe1rnwL2A85LcuZKpxwD/NvgbhpVtbiq\nXrRFUZJPJzll4PkdSbZrt5T6WpLb2vc8Osl8mk2Uv7Gixy7JIUn+re2hu6Rd/Z0k/y/JZ5N8B3hX\nkvlJ7mw/80Vr+e8iqefG7VZPksaFAr5IsyXbygFnVW1XeAOwK/AYzbY/51bVW5J8BJjPC4Fv+6ra\nK8kf0ISanYDjgcfa9psA/5pkxVZNewCvq6r7Bt84ySuBz7bHHwMWJjmiqv5Xkv8KnFJVt61U7x8C\n3x3tF7GKzzkHeLCq3tbWML2qnkjyMWCkqh5N8nLgk8BBVfVUko+3n/309hq/anvuSPIgsENVLUsy\nYz3qktQj9qBJGqqqehL4CnDyOpx2a1X9oqqeAe4FVgSsO4AdBtr9U/se/9G225Vmc/N57R693wa2\nAnZu29+ycjhr7QV8o6oeqarlwIXA7IHjWYfa12bFte6g6QE8I8l+VfXEwPEVbfYGdqcJmbcB84Dt\nBq51ycDjHwAXJTkWeG4D1iupAwY0SWPh8zT3cm0x8NqztD+DkgTYZODY7wYeLx94vpwX9/wP9rql\nfR5gflXt0f7aqapubNv8Zg01rmsIuxPYcxTtnv+crc0AquoeYBZNUDs9yV+vpqYFVTWr/Sx/WFWD\nG1sPfp7DgS+017zV+9Kk8c2/wJKGKQBV9ShNb9f7Bo79lBcCztuBjdfj+kensROwI3A3cANwYpKN\n4PmZlmu76f4WYHaSrdoJBHOBRWs55yLgvyQ5bMULSfZPsvtK7X5KE5pIMqutc8Ww6lNVdRFw9oo2\nwFJgxRDlvwP7tp+P9r61nVlJG3C3q6pvAqe252+5lvol9Zj3oEkapsEers8BJw28di5wVTt0dwOr\n791a02zI+2jC1XTgg1X1TJK/pxkG/V4bXH4BHLnGIqseTnIqL4Syr1XV19b0/lX1dJK3AZ9P8n+A\nZcDtNEO5rxhoehnNkOsdNEOud7evvx44O8ly4Bngv7Wvnwtcn+TBqjooyXuAr7azRgv4a+Celeqa\nCvxje+9ZgM9X1dI1fWZJ/Zaq9Z0JLkmSpGFwiFOSJKlnDGiSJEk9Y0CTJEnqGQOaJElSzxjQJEmS\nesaAJkmS1DMGNEmSpJ4xoEmSJPXM/wdqO7tvTbzrzgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
+       "<matplotlib.figure.Figure at 0x7f497c161f60>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -802,7 +798,7 @@
     "plt.plot(clusters,sc_scores,'*-')   #绘制类簇数量与对应轮廓系数关系\n",
     "plt.xlabel('Number of Clusters')\n",
     "plt.ylabel('Silhouette Coefficient Score')\n",
-    "\n",
+    "plt.savefig('fig-res/k-means_silhouette_coef.pdf')\n",
     "plt.show()   "
    ]
   },
@@ -920,7 +916,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/3_kmeans/2-kmeans-color-vq.ipynb b/3_kmeans/2-kmeans-color-vq.ipynb
index 15e8a28..055cecd 100644
--- a/3_kmeans/2-kmeans-color-vq.ipynb
+++ b/3_kmeans/2-kmeans-color-vq.ipynb
@@ -16,7 +16,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -206,7 +208,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/3_kmeans/README.md b/3_kmeans/README.md
index daa3c06..a6d2f2e 100644
--- a/3_kmeans/README.md
+++ b/3_kmeans/README.md
@@ -1,8 +1,19 @@
+## k-Means
+
+k-Means算法是无监督学习领域最为经典的算法之一。k-means算法就是将n个数据点进行聚类分析，得到 k  个聚类，使得每个数据点到聚类中心的距离最小。而实际上，这个问题往往是NP-hard的，以此有许多启发式的方法求解，从而避开局部最小值。
+
+![cluster illustration](images/kmeans-illustration.jpeg)
+
 ## 内容
 
-增加一个Bag of Words的说明和例子程序 （https://blog.csdn.net/wsj998689aa/article/details/47089153）
+* [k-Means原理、算法](1-k-means.ipynb)
+* [应用-图像压缩](2-kmeans-color-vq.ipynb)
+* [聚类算法对比](3-ClusteringAlgorithms.ipynb)
 
 
 
 ## References
+
 * [如何使用 Keras 实现无监督聚类](http://m.sohu.com/a/236221126_717210)
+
+* [Bag-of-words模型入门](https://blog.csdn.net/wsj998689aa/article/details/47089153)
diff --git a/4_logistic_regression/README.md b/4_logistic_regression/README.md
new file mode 100644
index 0000000..ca7cb33
--- /dev/null
+++ b/4_logistic_regression/README.md
@@ -0,0 +1,17 @@
+# 逻辑回归
+
+
+
+逻辑回归(Logistic Regression, LR)模型其实仅在线性回归的基础上，套用了一个逻辑函数，但也就由于这个逻辑函数，使得逻辑回归模型能够输出类别的概率。逻辑回归的本质是：假设数据服从这个分布，然后使用极大似然估计做参数的估计。
+
+![theory](images/linear_logistic_regression.png)
+
+
+
+## 内容
+
+* [线性回归-最小二乘法](1-Least_squares.ipynb)
+* [逻辑回归](2-Logistic_regression.ipynb)
+
+* [特征处理 - 降维](3-PCA_and_Logistic_Regression.ipynb)
+
diff --git a/5_nn/1-Perceptron.ipynb b/5_nn/1-Perceptron.ipynb
index c908e87..24e2692 100644
--- a/5_nn/1-Perceptron.ipynb
+++ b/5_nn/1-Perceptron.ipynb
@@ -197,19 +197,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2 2 2 2 2 1 1 2 1 1 1 1 2 1 1 1 1 2 2 2]\n"
+     ]
+    },
+    {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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jiZui/hQdHc3hwoKT7lNUnEd0dPnz3EVEgkGBfpyePXtSUHiAfQe2ldmeX5DD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\nAElFTkSuQmCC\n",
       "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
+       "<matplotlib.figure.Figure at 0x7f2187fecc88>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -238,12 +243,16 @@
     "x = x[shuffled_index]\n",
     "y = y[shuffled_index]\n",
     "\n",
-    "\n",
     "train_data = np.concatenate((x, y[:, np.newaxis]), axis=1)\n",
     "\n",
+    "label_y = y.copy()\n",
+    "label_y[label_y==-1] = 2;\n",
+    "print(label_y)\n",
     "# plot data\n",
-    "plt.scatter(train_data[:,0], train_data[:,1], c=train_data[:,2], marker='.')\n",
+    "plt.scatter(train_data[:,0], train_data[:,1], marker='.',  s = 300,\n",
+    "            c=label_y, cmap=plt.cm.Spectral)\n",
     "plt.title(\"Data\")\n",
+    "plt.savefig(\"perceptron_sample_data.pdf\")\n",
     "plt.show()\n"
    ]
   },
@@ -256,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 21,
    "metadata": {
     "lines_to_end_of_cell_marker": 2
    },
@@ -265,37 +274,47 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "update weight/bias:  3.662519607024163 3.038628576040485 0.5\n",
-      "update weight/bias:  3.1283482021588505 1.595298136548129 0.0\n",
-      "update weight/bias:  1.7022056555578562 0.9488336160189257 -0.5\n",
-      "update weight/bias:  0.6064534385664728 0.25821553124912766 -1.0\n",
-      "update weight/bias:  -0.0072588404447664345 -1.1732780618880432 -1.5\n",
-      "update weight/bias:  3.56979581700504 4.157268901515593 -1.0\n",
-      "update weight/bias:  2.7104258301108155 3.6232316160976543 -1.5\n",
-      "update weight/bias:  1.6146736131194321 2.9326135313278563 -2.0\n",
-      "update weight/bias:  1.0805022082541196 1.4892830918355 -2.5\n",
-      "update weight/bias:  -0.3456403383468747 0.8428185713062968 -3.0\n",
-      "update weight/bias:  3.4230241861565407 3.6955935657768997 -2.5\n",
-      "update weight/bias:  2.5636541992623156 3.161556280358961 -3.0\n",
-      "update weight/bias:  1.1375116526613214 2.5150917598297577 -3.5\n",
-      "update weight/bias:  0.38678027777241897 1.1329290169473543 -4.0\n",
-      "update weight/bias:  -1.446079265832824 -0.7361650837497964 -4.5\n",
-      "update weight/bias:  1.7930043074867144 5.9278879714909145 -4.0\n",
-      "update weight/bias:  1.2588329026214018 4.484557531998558 -4.5\n",
-      "update weight/bias:  0.3697277316535954 3.2936957431536 -5.0\n",
-      "update weight/bias:  -0.519377439314211 2.1028339543086423 -5.5\n",
-      "update weight/bias:  -2.352236982919454 0.23373985361149163 -6.0\n",
-      "update weight/bias:  1.8159337720901148 4.415105700242464 -5.5\n",
-      "update weight/bias:  0.7328910527198487 3.024123458986641 -6.0\n",
-      "update weight/bias:  -0.3501516666504174 1.6331412177308182 -6.5\n",
-      "w =  [-0.3501516666504174, 1.6331412177308182]\n",
-      "b =  -6.5\n",
+      "update weight/bias:  2.9024433699190153 3.129619118339762 0.5\n",
+      "update weight/bias:  2.013338198951209 1.9387573294948042 0.0\n",
+      "update weight/bias:  0.4222250668142189 0.37906946899829386 -0.5\n",
+      "update weight/bias:  -0.4668801041535875 -0.8117923198466639 -1.0\n",
+      "update weight/bias:  1.6670650562137663 2.551793701566567 -0.5\n",
+      "update weight/bias:  0.8076950693195415 2.0177564161486283 -1.0\n",
+      "update weight/bias:  -0.05167491757468334 1.4837191307306896 -1.5\n",
+      "update weight/bias:  -0.8024062924635857 0.10155638784828613 -2.0\n",
+      "update weight/bias:  2.4366772808559527 6.765609443088997 -1.5\n",
+      "update weight/bias:  1.6859459059670503 5.383446700206593 -2.0\n",
+      "update weight/bias:  0.25980335936605603 4.7369821796773905 -2.5\n",
+      "update weight/bias:  -0.6293018116017504 3.546120390832433 -3.0\n",
+      "update weight/bias:  -1.7123445309720164 2.1551381495766098 -3.5\n",
+      "update weight/bias:  -2.246515935837329 0.7118077100842535 -4.0\n",
+      "update weight/bias:  0.9925676374822094 7.375860765324964 -3.5\n",
+      "update weight/bias:  0.1331976505879846 6.841823479907025 -4.0\n",
+      "update weight/bias:  -0.48051462842325465 5.410329886769854 -4.5\n",
+      "update weight/bias:  -1.906657175024249 4.763865366240651 -5.0\n",
+      "update weight/bias:  -2.6573885499131515 3.3817026233582475 -5.5\n",
+      "update weight/bias:  1.0051310571110115 6.420331199398733 -5.0\n",
+      "update weight/bias:  -0.07791166225925461 5.0293489581429105 -5.5\n",
+      "update weight/bias:  -1.1609543816295207 3.638366716887088 -6.0\n",
+      "update weight/bias:  -1.6951257864948333 2.1950362773947316 -6.5\n",
+      "update weight/bias:  3.626233232216561 6.632855560776491 -6.0\n",
+      "update weight/bias:  2.5304810152251775 5.942237476006693 -6.5\n",
+      "update weight/bias:  1.1043384686241833 5.29577295547749 -7.0\n",
+      "update weight/bias:  -0.7285210749810598 3.4266788547803397 -7.5\n",
+      "update weight/bias:  -1.342233353992299 1.995185261643169 -8.0\n",
+      "update weight/bias:  1.5602100159267163 5.124804379982931 -7.5\n",
+      "update weight/bias:  1.0260386110614037 3.6814739404905743 -8.0\n",
+      "update weight/bias:  0.13693344009359731 2.4906121516456166 -8.5\n",
+      "w =  [0.13693344009359731, 2.4906121516456166]\n",
+      "b =  -8.5\n",
+      "\n",
       "\n",
       "\n",
       "ground_truth:  [-1. -1. -1. -1. -1.  1.  1. -1.  1.  1.  1.  1. -1.  1.  1.  1.  1. -1.\n",
       " -1. -1.]\n",
-      "predicted:     [-1. -1. -1. -1. -1. -1.  1. -1.  1.  1.  1.  1. -1.  1.  1.  1.  1. -1.\n",
-      " -1. -1.]\n"
+      "predicted:     [-1. -1. -1. -1.  1.  1.  1. -1.  1.  1.  1.  1. -1.  1.  1.  1.  1. -1.\n",
+      " -1. -1.]\n",
+      "accuracy:      0.95\n"
      ]
     }
    ],
@@ -303,12 +322,14 @@
     "import random\n",
     "import numpy as np\n",
     "\n",
-    "# 符号函数\n",
+    "\n",
     "def sign(v):\n",
+    "    \"\"\"符号函数\"\"\"\n",
     "    if v > 0:  return 1\n",
     "    else:      return -1\n",
     "    \n",
     "def perceptron_train(train_data, eta=0.5, n_iter=100):\n",
+    "    \"\"\"对感知机模型进行训练\"\"\"\n",
     "    weight = [0, 0]      # 权重\n",
     "    bias = 0             # 偏置量\n",
     "    learning_rate = eta  # 学习速率\n",
@@ -331,6 +352,7 @@
     "    return weight, bias\n",
     "\n",
     "def perceptron_pred(data, w, b):\n",
+    "    \"\"\"输入数据，模型，对数据进行分类\"\"\"\n",
     "    y_pred = []\n",
     "    for d in data:\n",
     "        x1, x2, y = d\n",
@@ -340,17 +362,24 @@
     "    return np.array(y_pred, dtype=float)\n",
     "\n",
     "\n",
-    "# do training\n",
+    "# 训练感知机\n",
     "w, b = perceptron_train(train_data)\n",
     "print(\"w = \", w)\n",
     "print(\"b = \", b)\n",
     "\n",
-    "# predict \n",
+    "# 预测 \n",
     "y_pred = perceptron_pred(train_data, w, b)\n",
     "\n",
+    "# 计算分类精度\n",
+    "c = y_pred == y\n",
+    "cn = np.sum(c == True)\n",
+    "acc = cn / len(y_pred)\n",
+    "print()\n",
+    "\n",
     "print(\"\\n\")\n",
     "print(\"ground_truth: \", train_data[:, 2])\n",
-    "print(\"predicted:    \", y_pred)"
+    "print(\"predicted:    \", y_pred)\n",
+    "print(\"accuracy:     \", acc)"
    ]
   },
   {
@@ -380,7 +409,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/5_nn/2-mlp_bp.ipynb b/5_nn/2-mlp_bp.ipynb
index b7ef799..7a79ea5 100644
--- a/5_nn/2-mlp_bp.ipynb
+++ b/5_nn/2-mlp_bp.ipynb
@@ -719,7 +719,9 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -1024,7 +1026,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/5_nn/3-softmax_ce.ipynb b/5_nn/3-softmax_ce.ipynb
index 651302d..76a294e 100644
--- a/5_nn/3-softmax_ce.ipynb
+++ b/5_nn/3-softmax_ce.ipynb
@@ -11,13 +11,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "softmax经常被添加在分类任务的神经网络中的输出层，神经网络的反向传播中关键的步骤就是求导，从这个过程也可以更深刻地理解反向传播的过程，还可以对梯度传播的问题有更多的思考。\n",
+    "`Softmax`经常被添加在分类任务的神经网络中的输出层，神经网络的反向传播中关键的步骤就是求导，从这个过程也可以更深刻地理解反向传播的过程，还可以对梯度传播的问题有更多的思考。\n",
     "\n",
     "## 1. softmax 函数\n",
     "\n",
-    "softmax(柔性最大值)函数，一般在神经网络中， softmax可以作为分类任务的输出层。其实可以认为softmax输出的是几个类别选择的概率，比如我有一个分类任务，要分为三个类，softmax函数可以根据它们相对的大小，输出三个类别选取的概率，并且概率和为1。\n",
+    "`softmax`(柔性最大值)函数，一般在神经网络中， `softmax`可以作为分类任务的输出层。其实可以认为`softmax`输出的是几个类别选择的概率，比如有一个分类任务，要分为三个类，softmax函数可以根据它们相对的大小，输出三个类别选取的概率，并且概率和为1。\n",
     "\n",
-    "Softmax从字面上来说，可以分成`soft`和`max`两个部分。`max`故名思议就是最大值的意思。Softmax的核心在于`soft`，而`soft`有软的含义，与之相对的是`hard`硬。很多场景中需要我们找出数组所有元素中值最大的元素，实质上都是求的`hardmax`。下面使用`Numpy`模块实现hardmax。"
+    "Softmax从字面上来说，可以分成`soft`和`max`两个部分。`max`故名思议就是最大值的意思。Softmax的核心在于`soft`，而`soft`有软的含义，与之相对的是`hard`硬。很多场景中需要找出数组所有元素中值最大的元素，实质上都是求的`hardmax`。下面使用`Numpy`模块实现hardmax。"
    ]
   },
   {
@@ -62,7 +62,7 @@
     "\n",
     "![softmax_demo](images/softmax_demo.png)\n",
     "\n",
-    "softmax直白来说就是将原来输出是$[3,1,-3]$通过softmax函数作用，就映射成为(0,1)的值，而这些值的累和为1（满足概率的性质），那么我们就可以将它理解成概率，在最后选取输出结点的时候，我们就可以选取概率最大（也就是值对应最大的）结点，作为我们的预测目标！\n"
+    "softmax直白来说就是将原来输出是$[3,1,-3]$通过softmax函数作用，就映射成为(0,1)的值，而这些值的累和为1（满足概率的性质），那么我们就可以将它理解成概率，在最后选取输出结点的时候，选取概率最大（也就是值对应最大的）结点，作为预测目标！\n"
    ]
   },
   {
@@ -78,12 +78,12 @@
     "神经元的输出设为：\n",
     "\n",
     "$$\n",
-    "z_i = sigmoid( \\sum_{j} w_{ij} x_{j} + b )\n",
+    "z_i = \\sum_{j} w_{ij} x_{j} + w_b\n",
     "$$\n",
     "\n",
-    "其中$W_{ij}$是第$i$个神经元的第$j$个权重，$b$是偏置。$z_i$表示该网络的第$i$个输出。\n",
+    "其中$W_{ij}$是第$i$个神经元的第$j$个权重，$w_b$是偏置。$z_i$表示该网络的第$i$个输出。**请注意这里没有使用sigmoid等激活函数。**\n",
     "\n",
-    "给这个输出加上一个softmax函数，那就变成了这样：\n",
+    "给这个网络输出加上一个softmax函数，那就变成了这样：\n",
     "\n",
     "$$\n",
     "a_i = \\frac{e^{z_i}}{\\sum_k e^{z_k}}\n",
@@ -108,7 +108,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "以一个神经元的二类分类训练为例，进行两次实验（神经网络常用的激活函数为`sigmoid`函数，该实验也采用该函数）：输入一个相同的样本数据x=1.0（该样本对应的实际分类y=0）；两次实验各自随机初始化参数，从而在各自的第一次前向传播后得到不同的输出值，形成不同的代价（误差）：\n",
+    "以一个神经元的二类分类训练为例，进行两次实验（神经网络常用的激活函数为`sigmoid`函数，该实验也采用该函数）：输入一个相同的样本数据$x=1.0$（该样本对应的实际分类$y=0$）；两次实验各自随机初始化参数，从而在各自的第一次前向传播后得到不同的输出值，形成不同的代价（误差）：\n",
     "\n",
     "![cross_entropy_loss_1](images/cross_entropy_loss_1.png)\n",
     "实验1：第一次输出值为0.82\n",
@@ -143,7 +143,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 2. 推导过程\n",
+    "## 3. 推导过程\n",
     "\n",
     "首先，我们要明确一下我们要求什么，我们要求的是我们的$loss$对于神经元输出($z_i$)的梯度，即：\n",
     "\n",
@@ -158,14 +158,26 @@
     "$$\n",
     "\n",
     "有个人可能有疑问了，这里为什么是$a_j$而不是$a_i$，这里要看一下$softmax$的公式了，因为$softmax$公式的特性，它的分母包含了所有神经元的输出，所以，对于不等于$i$的其他输出里面，也包含着$z_i$，所有的$a$都要纳入到计算范围中，并且后面的计算可以看到需要分为$i = j$和$i \\ne j$两种情况求导。\n",
-    "\n",
-    "### 2.1 针对$a_j$的偏导\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.1 针对$a_j$的偏导\n",
     "\n",
     "$$\n",
     "\\frac{\\partial C}{\\partial a_j} = \\frac{(\\partial -\\sum_j y_j ln a_j)}{\\partial a_j} = -\\sum_j y_j \\frac{1}{a_j}\n",
     "$$\n",
-    "\n",
-    "### 2.2 针对$z_i$的偏导\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.2 针对$z_i$的偏导\n",
     "\n",
     "如果 $i=j$ :\n",
     "\n",
@@ -188,8 +200,14 @@
     "$$\n",
     "(\\frac{u}{v})' = \\frac{u'v - uv'}{v^2} \n",
     "$$\n",
-    "\n",
-    "### 2.3 整体的推导\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3.3 整体的推导\n",
     "\n",
     "\\begin{eqnarray}\n",
     "\\frac{\\partial C}{\\partial z_i} & = & (-\\sum_j y_j \\frac{1}{a_j} ) \\frac{\\partial a_j}{\\partial z_i} \\\\\n",
@@ -211,7 +229,7 @@
     "\n",
     "其中\n",
     "$$\n",
-    "z_i = \\sum_{j} w_{ij} x_{j} + b\n",
+    "z_i = \\sum_{j} w_{ij} x_{j} + w_b\n",
     "$$\n"
    ]
   },
@@ -219,7 +237,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "对于使用二次代价函数的更新方程为：\n",
+    "最为对比，使用二次代价函数的更新方程为：\n",
     "\n",
     "$$\n",
     "\\delta_i = a_i (1-a_i) (y_i - a_i)\n",
@@ -234,7 +252,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 3. 问题\n",
+    "## 4. 问题\n",
     "如何将本节所讲的softmax，交叉熵代价函数应用到上节所讲的BP方法中？"
    ]
   },
@@ -245,6 +263,7 @@
     "## 参考资料\n",
     "\n",
     "* [一文详解Softmax函数](https://zhuanlan.zhihu.com/p/105722023)\n",
+    "* [损失函数：交叉熵详解](https://zhuanlan.zhihu.com/p/115277553)\n",
     "* [交叉熵代价函数（作用及公式推导）](https://blog.csdn.net/u014313009/article/details/51043064)\n",
     "* [手打例子一步一步带你看懂softmax函数以及相关求导过程](https://www.jianshu.com/p/ffa51250ba2e)\n",
     "* [简单易懂的softmax交叉熵损失函数求导](https://www.jianshu.com/p/c02a1fbffad6)"
@@ -267,7 +286,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/5_nn/README.md b/5_nn/README.md
index 4123ec1..8c67b69 100644
--- a/5_nn/README.md
+++ b/5_nn/README.md
@@ -1,4 +1,21 @@
+# 神经网络
+
+人工神经网络（artificial neural network，ANN），简称神经网络（neural network，NN），是一种模仿生物神经网络的结构和功能的数学模型或计算模型。神经网络由大量的人工神经元联结进行计算。大多数情况下人工神经网络能在外界信息的基础上改变内部结构，是一种自适应系统。现代神经网络是一种非线性统计性数据建模工具，常用来对输入和输出间复杂的关系进行建模，或用来探索数据的模式。
+
+![mlp_theory](images/mlp_theory.gif)
+
+## 内容
+
+* [感知机](1-Perceptron.ipynb)
+
+* [多层神经网络和反向传播](2-mlp_bp.ipynb)
+
+* [Softmax和交叉熵](3-softmax_ce.ipynb)
+
+    
+
 ## References
+
 * https://iamtrask.github.io/2015/07/12/basic-python-network/
 * http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/
 
diff --git a/5_nn/images/figures.pptx b/5_nn/images/figures.pptx
new file mode 100644
index 0000000..a16b029
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diff --git a/5_nn/images/softmax_neuron.0.png b/5_nn/images/softmax_neuron.0.png
new file mode 100644
index 0000000..f7eca53
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diff --git a/5_nn/images/softmax_neuron.png b/5_nn/images/softmax_neuron.png
index f7eca53..da10ee4 100644
Binary files a/5_nn/images/softmax_neuron.png and b/5_nn/images/softmax_neuron.png differ
diff --git a/5_nn/perceptron_sample_data.pdf b/5_nn/perceptron_sample_data.pdf
new file mode 100644
index 0000000..d3a801d
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diff --git a/6_pytorch/1-tensor.ipynb b/6_pytorch/1-tensor.ipynb
index 896558a..d52c4f3 100644
--- a/6_pytorch/1-tensor.ipynb
+++ b/6_pytorch/1-tensor.ipynb
@@ -42,7 +42,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import torch\n",
@@ -52,7 +54,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# 创建一个 numpy ndarray\n",
@@ -69,7 +73,9 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "pytorch_tensor1 = torch.tensor(numpy_tensor)\n",
@@ -100,7 +106,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# 如果 pytorch tensor 在 cpu 上\n",
@@ -129,7 +137,9 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# 第一种方式是定义 cuda 数据类型\n",
@@ -160,7 +170,9 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "cpu_tensor = gpu_tensor.cpu()"
@@ -716,7 +728,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/6_pytorch/2-autograd.ipynb b/6_pytorch/2-autograd.ipynb
index 644bd57..c93f799 100644
--- a/6_pytorch/2-autograd.ipynb
+++ b/6_pytorch/2-autograd.ipynb
@@ -119,7 +119,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "z = torch.mean(torch.matmul(w, x) + b) # torch.matmul 是做矩阵乘法\n",
@@ -275,7 +277,9 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "n.backward(torch.ones_like(n)) # 将 (w0, w1) 取成 (1, 1)"
@@ -349,7 +353,9 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "y.backward(retain_graph=True) # 设置 retain_graph 为 True 来保留计算图"
@@ -375,7 +381,9 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "y.backward() # 再做一次自动求导，这次不保留计算图"
@@ -455,7 +463,9 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "x = torch.tensor([2, 3], dtype=torch.float, requires_grad=True)\n",
@@ -553,7 +563,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -567,7 +577,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/6_pytorch/3-linear-regression.ipynb b/6_pytorch/3-linear-regression.ipynb
index 2db4de0..c1ad752 100644
--- a/6_pytorch/3-linear-regression.ipynb
+++ b/6_pytorch/3-linear-regression.ipynb
@@ -151,7 +151,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# 转换成 Tensor\n",
@@ -166,7 +168,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# 构建线性回归模型\n",
@@ -180,7 +184,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "y_ = linear_model(x_train)"
@@ -275,7 +281,9 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# 自动求导\n",
@@ -305,7 +313,9 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# 更新一次参数\n",
@@ -542,7 +552,9 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# 构建数据 x 和 y\n",
@@ -582,7 +594,9 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# 定义参数\n",
@@ -670,7 +684,9 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# 自动求导\n",
@@ -702,7 +718,9 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# 更新一下参数\n",
@@ -853,7 +871,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -867,7 +885,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/6_pytorch/README.md b/6_pytorch/README.md
index 60be4da..32780e8 100644
--- a/6_pytorch/README.md
+++ b/6_pytorch/README.md
@@ -11,8 +11,20 @@ PyTorch的简洁设计使得它入门很简单，本部分内容在深入介绍P
 
 ![PyTorch Demo](imgs/PyTorch.png)
 
+## 内容
+
+- [Tensor](1-tensor.ipynb)
+- [autograd](2-autograd.ipynb)
+- [linear-regression](3-linear-regression.ipynb)
+- [logistic-regression](4-logistic-regression.ipynb)
+- [nn-sequential-module](5-nn-sequential-module.ipynb)
+- [deep-nn](6-deep-nn.ipynb)
+- [param_initialize](7-param_initialize.ipynb)
+- [optim/sgd](optimizer/6_1-sgd.ipynb)
+- [optim/adam](optimizer/6_6-adam.ipynb)
 
 ## References
+
 * [code of book "Learn Deep Learning with PyTorch"](https://github.com/L1aoXingyu/code-of-learn-deep-learning-with-pytorch)
 * [PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation](https://github.com/chenyuntc/pytorch-book)
 * [Awesome-Pytorch-list](https://github.com/bharathgs/Awesome-pytorch-list)
diff --git a/6_pytorch/optimizer/6_6-adam.ipynb b/6_pytorch/optimizer/6_6-adam.ipynb
index 48ff972..15cb6df 100644
--- a/6_pytorch/optimizer/6_6-adam.ipynb
+++ b/6_pytorch/optimizer/6_6-adam.ipynb
@@ -47,7 +47,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def adam(parameters, vs, sqrs, lr, t, beta1=0.9, beta2=0.999):\n",
@@ -63,7 +65,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -267,7 +271,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -281,7 +285,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/7_deep_learning/1_CNN/1-basic_conv.ipynb b/7_deep_learning/1_CNN/01-basic_conv.ipynb
similarity index 100%
rename from 7_deep_learning/1_CNN/1-basic_conv.ipynb
rename to 7_deep_learning/1_CNN/01-basic_conv.ipynb
diff --git a/7_deep_learning/1_CNN/02-LeNet5.ipynb b/7_deep_learning/1_CNN/02-LeNet5.ipynb
new file mode 100644
index 0000000..a4c66a2
--- /dev/null
+++ b/7_deep_learning/1_CNN/02-LeNet5.ipynb
@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LeNet5\n",
+    "\n",
+    "LeNet 诞生于 1994 年，是最早的卷积神经网络之一，并且推动了深度学习领域的发展。自从 1988 年开始，在多次迭代后这个开拓性成果被命名为 LeNet5。LeNet5 的架构的提出是基于如下的观点：图像的特征分布在整张图像上，通过带有可学习参数的卷积，从而有效的减少了参数数量，能够在多个位置上提取相似特征。\n",
+    "\n",
+    "在LeNet5提出的时候，没有 GPU 帮助训练，甚至 CPU 的速度也很慢，因此，LeNet5的规模并不大。其包含七个处理层，每一层都包含可训练参数（权重），当时使用的输入数据是 $32 \\times 32$ 像素的图像。LeNet-5 这个网络虽然很小，但是它包含了深度学习的基本模块：卷积层，池化层，全连接层。它是其他深度学习模型的基础，这里对LeNet5进行深入分析和讲解，通过实例分析，加深对与卷积层和池化层的理解。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "from torch.autograd import Variable\n",
+    "from torchvision.datasets import CIFAR10\n",
+    "from torchvision import transforms as tfs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from torch import nn\n",
+    "\n",
+    "lenet5 = nn.Sequential(\n",
+    "    nn.Conv2d(1, 6, kernel_size=5, padding=2), nn.Sigmoid(),\n",
+    "    nn.AvgPool2d(kernel_size=2, stride=2),\n",
+    "    nn.Conv2d(6, 16, kernel_size=5), nn.Sigmoid(),\n",
+    "    nn.AvgPool2d(kernel_size=2, stride=2),\n",
+    "    nn.Flatten(),\n",
+    "    nn.Linear(16 * 5 * 5, 120), nn.Sigmoid(),\n",
+    "    nn.Linear(120, 84), nn.Sigmoid(),\n",
+    "    nn.Linear(84, 10) )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from utils import train\n",
+    "\n",
+    "# 使用数据增强\n",
+    "def train_tf(x):\n",
+    "    im_aug = tfs.Compose([\n",
+    "        tfs.Resize(224),\n",
+    "        tfs.ToTensor(),\n",
+    "        tfs.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
+    "    ])\n",
+    "    x = im_aug(x)\n",
+    "    return x\n",
+    "\n",
+    "def test_tf(x):\n",
+    "    im_aug = tfs.Compose([\n",
+    "        tfs.Resize(224),\n",
+    "        tfs.ToTensor(),\n",
+    "        tfs.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
+    "    ])\n",
+    "    x = im_aug(x)\n",
+    "    return x\n",
+    "     \n",
+    "train_set = CIFAR10('../../data', train=True, transform=train_tf)\n",
+    "train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)\n",
+    "test_set  = CIFAR10('../../data', train=False, transform=test_tf)\n",
+    "test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)\n",
+    "\n",
+    "net = lenet5\n",
+    "optimizer = torch.optim.SGD(net.parameters(), lr=1e-1)\n",
+    "criterion = nn.CrossEntropyLoss()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "(l_train_loss, l_train_acc, l_valid_loss, l_valid_acc) = train(net, \n",
+    "                                                               train_data, test_data, \n",
+    "                                                               20, \n",
+    "                                                               optimizer, criterion,\n",
+    "                                                               use_cuda=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "plt.plot(l_train_loss, label='train')\n",
+    "plt.plot(l_valid_loss, label='valid')\n",
+    "plt.xlabel('epoch')\n",
+    "plt.legend(loc='best')\n",
+    "plt.savefig('fig-res-lenet5-train-validate-loss.pdf')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.plot(l_train_acc, label='train')\n",
+    "plt.plot(l_valid_acc, label='valid')\n",
+    "plt.xlabel('epoch')\n",
+    "plt.legend(loc='best')\n",
+    "plt.savefig('fig-res-lenet5-train-validate-acc.pdf')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/7_deep_learning/1_CNN/03-AlexNet.ipynb b/7_deep_learning/1_CNN/03-AlexNet.ipynb
new file mode 100644
index 0000000..aeaf5ac
--- /dev/null
+++ b/7_deep_learning/1_CNN/03-AlexNet.ipynb
@@ -0,0 +1,99 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AlexNet\n",
+    "\n",
+    "\n",
+    "第一个典型的卷积神经网络是 LeNet5 ，但是第一个开启深度学习的网络却是 AlexNet，这个网络在2012年的ImageNet竞赛中取得冠军。这网络提出了深度学习常用的技术：ReLU和Dropout。AlexNet网络结构在整体上类似于LeNet，都是先卷积然后在全连接，但在细节上有很大不同，AlexNet更为复杂，Alexnet模型由5个卷积层和3个池化Pooling层，其中还有3个全连接层构成，共有$6 \\times 10^7$个参数和65000个神经元，最终的输出层是1000通道的Softmax。AlexNet 跟 LeNet 结构类似，但使⽤了更多的卷积层和更⼤的参数空间来拟合⼤规模数据集 ImageNet，它是浅层神经⽹络和深度神经⽹络的分界线。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import torch.nn as nn\n",
+    "import torch\n",
+    "\n",
+    "class AlexNet(nn.Module):\n",
+    "    def __init__(self, num_classes=1000, init_weights=False):   \n",
+    "        super(AlexNet, self).__init__()\n",
+    "        self.features = nn.Sequential( \n",
+    "            nn.Conv2d(3, 96, kernel_size=11, stride=4, padding=2),  \n",
+    "            nn.ReLU(inplace=True), #inplace 可以载入更大模型\n",
+    "            nn.MaxPool2d(kernel_size=3, stride=2),       \n",
+    "\n",
+    "            nn.Conv2d(96, 256, kernel_size=5, padding=2),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
+    "\n",
+    "            nn.Conv2d(256, 384, kernel_size=3, padding=1),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "\n",
+    "            nn.Conv2d(384, 384, kernel_size=3, padding=1),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "\n",
+    "            nn.Conv2d(384, 256, kernel_size=3, padding=1),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
+    "        )\n",
+    "        self.classifier = nn.Sequential(\n",
+    "            nn.Dropout(p=0.5),\n",
+    "            nn.Linear(256*6*6, 4096),  #全链接\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.Dropout(p=0.5),\n",
+    "            nn.Linear(4096, 4096),\n",
+    "            nn.ReLU(inplace=True),\n",
+    "            nn.Linear(4096, num_classes),\n",
+    "        )\n",
+    "        if init_weights:\n",
+    "            self._initialize_weights()\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.features(x)\n",
+    "        x = torch.flatten(x, start_dim=1) #展平或者view()\n",
+    "        x = self.classifier(x)\n",
+    "        return x\n",
+    "\n",
+    "    def _initialize_weights(self):\n",
+    "        for m in self.modules():\n",
+    "            if isinstance(m, nn.Conv2d):\n",
+    "                #何教授方法\n",
+    "                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') \n",
+    "                if m.bias is not None:\n",
+    "                    nn.init.constant_(m.bias, 0)\n",
+    "            elif isinstance(m, nn.Linear):\n",
+    "                #正态分布赋值\n",
+    "                nn.init.normal_(m.weight, 0, 0.01) \n",
+    "                nn.init.constant_(m.bias, 0)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/7_deep_learning/1_CNN/2-vgg.ipynb b/7_deep_learning/1_CNN/04-vgg.ipynb
similarity index 78%
rename from 7_deep_learning/1_CNN/2-vgg.ipynb
rename to 7_deep_learning/1_CNN/04-vgg.ipynb
index 1c22d58..8149c64 100644
--- a/7_deep_learning/1_CNN/2-vgg.ipynb
+++ b/7_deep_learning/1_CNN/04-vgg.ipynb
@@ -48,7 +48,7 @@
     "VGG网络的特点：\n",
     "* 小卷积核和连续的卷积层: VGG中使用的都是3×3卷积核，并且使用了连续多个卷积层。这样做的好处主要有，\n",
     "    - 使用连续的的多个小卷积核(3×3)，来代替一个大的卷积核（例如(5×5)。使用小的卷积核的问题是，其感受野必然变小。所以，VGG中就使用连续的3×3卷积核，来增大感受野。VGG认为2个连续的3×3卷积核能够替代一个5×5卷积核，三个连续的3×3能够代替一个7×7。\n",
-    "    - 小卷积核的参数较少。3个3×3的卷积核参数为3×3×=27，而一个7×7的卷积核参数为7×7=49\n",
+    "    - 小卷积核的参数较少。3个3×3的卷积核参数为3×3×3=27，而一个7×7的卷积核参数为7×7=49\n",
     "    - 由于每个卷积层都有一个非线性的激活函数，多个卷积层增加了非线性映射。\n",
     "* 小池化核，使用的是2×2\n",
     "* 通道数更多，特征度更宽:  每个通道代表着一个FeatureMap，更多的通道数表示更丰富的图像特征。VGG网络第一层的通道数为64，后面每层都进行了翻倍，最多到512个通道，通道数的增加，使得更多的信息可以被提取出来。\n",
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-12-22T09:01:51.296457Z",
@@ -81,7 +81,8 @@
     "import torch\n",
     "from torch import nn\n",
     "from torch.autograd import Variable\n",
-    "from torchvision.datasets import CIFAR10"
+    "from torchvision.datasets import CIFAR10\n",
+    "from torchvision import transforms as tfs"
    ]
   },
   {
@@ -98,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-12-22T09:01:51.312500Z",
@@ -108,10 +109,8 @@
    },
    "outputs": [],
    "source": [
-    "def vgg_block(num_convs, in_channels, out_channels):\n",
-    "    net = [nn.Conv2d(in_channels, out_channels, \n",
-    "                     kernel_size=3, padding=1), \n",
-    "           nn.ReLU(True)] # 定义第一层\n",
+    "def VGG_Block(num_convs, in_channels, out_channels):\n",
+    "    net = [nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), nn.ReLU(True)] # 定义第一层\n",
     "\n",
     "    for i in range(num_convs-1): # 定义后面的很多层\n",
     "        net.append(nn.Conv2d(out_channels, out_channels, \n",
@@ -131,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-12-22T08:20:40.819497Z",
@@ -156,13 +155,13 @@
     }
    ],
    "source": [
-    "block_demo = vgg_block(3, 64, 128)\n",
+    "block_demo = VGG_Block(3, 64, 128)\n",
     "print(block_demo)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-12-22T07:52:04.632406Z",
@@ -196,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-12-22T09:01:54.497712Z",
@@ -206,12 +205,12 @@
    },
    "outputs": [],
    "source": [
-    "def vgg_stack(num_convs, channels):\n",
+    "def VGG_Stack(num_convs, channels):\n",
     "    net = []\n",
     "    for n, c in zip(num_convs, channels):\n",
     "        in_c = c[0]\n",
     "        out_c = c[1]\n",
-    "        net.append(vgg_block(n, in_c, out_c))\n",
+    "        net.append(VGG_Block(n, in_c, out_c))\n",
     "    return nn.Sequential(*net)"
    ]
   },
@@ -224,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-12-22T09:01:55.149378Z",
@@ -283,7 +282,7 @@
     }
    ],
    "source": [
-    "vgg_net = vgg_stack((2, 2, 3, 3, 3), ((3, 64), (64, 128), (128, 256), (256, 512), (512, 512)))\n",
+    "vgg_net = VGG_Stack((2, 2, 3, 3, 3), ((3, 64), (64, 128), (128, 256), (256, 512), (512, 512)))\n",
     "print(vgg_net)"
    ]
   },
@@ -291,12 +290,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "可以看到网络结构中有个 5 个 最大池化，说明图片的大小会减少 5 倍。可以验证一下，输入一张 256 x 256 的图片看看结果是什么"
+    "可以看到网络结构中有个 5 个 最大池化，说明图片的大小会减少 5 倍。可以验证一下，输入一张 224 x 224 的图片看看结果是什么"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-12-22T08:52:44.049650Z",
@@ -308,12 +307,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "torch.Size([1, 512, 8, 8])\n"
+      "torch.Size([1, 512, 7, 7])\n"
      ]
     }
    ],
    "source": [
-    "test_x = Variable(torch.zeros(1, 3, 256, 256))\n",
+    "test_x = Variable(torch.zeros(1, 3, 224, 224))\n",
     "test_y = vgg_net(test_x)\n",
     "print(test_y.shape)"
    ]
@@ -327,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-12-22T09:01:57.323034Z",
@@ -337,14 +336,14 @@
    },
    "outputs": [],
    "source": [
-    "class vgg(nn.Module):\n",
+    "class VGG_Net(nn.Module):\n",
     "    def __init__(self):\n",
-    "        super(vgg, self).__init__()\n",
-    "        self.feature = vgg_net\n",
+    "        super(VGG_Net, self).__init__()\n",
+    "        self.feature = VGG_Stack((2, 2, 3, 3, 3), ((3, 64), (64, 128), (128, 256), (256, 512), (512, 512)))\n",
     "        self.fc = nn.Sequential(\n",
-    "            nn.Linear(512, 100),\n",
+    "            nn.Linear(512*7*7, 4096),\n",
     "            nn.ReLU(True),\n",
-    "            nn.Linear(100, 10)\n",
+    "            nn.Linear(4096, 10)\n",
     "        )\n",
     "    def forward(self, x):\n",
     "        x = self.feature(x)\n",
@@ -362,74 +361,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-12-22T09:01:59.921373Z",
      "start_time": "2017-12-22T09:01:58.709531Z"
-    },
-    "collapsed": true
+    }
    },
    "outputs": [],
    "source": [
     "from utils import train\n",
     "\n",
-    "def data_tf(x):\n",
-    "    x = np.array(x, dtype='float32') / 255\n",
-    "    x = (x - 0.5) / 0.5 # 标准化，这个技巧之后会讲到\n",
-    "    x = x.transpose((2, 0, 1)) # 将 channel 放到第一维，只是 pytorch 要求的输入方式\n",
-    "    x = torch.from_numpy(x)\n",
+    "# 使用数据增强\n",
+    "def train_tf(x):\n",
+    "    im_aug = tfs.Compose([\n",
+    "        tfs.Resize(224),\n",
+    "        tfs.ToTensor(),\n",
+    "        tfs.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
+    "    ])\n",
+    "    x = im_aug(x)\n",
+    "    return x\n",
+    "\n",
+    "def test_tf(x):\n",
+    "    im_aug = tfs.Compose([\n",
+    "        tfs.Resize(224),\n",
+    "        tfs.ToTensor(),\n",
+    "        tfs.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
+    "    ])\n",
+    "    x = im_aug(x)\n",
     "    return x\n",
     "     \n",
-    "train_set = CIFAR10('../../data', train=True, transform=data_tf)\n",
+    "train_set = CIFAR10('../../data', train=True, transform=train_tf)\n",
     "train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)\n",
-    "test_set  = CIFAR10('../../data', train=False, transform=data_tf)\n",
+    "test_set  = CIFAR10('../../data', train=False, transform=test_tf)\n",
     "test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)\n",
     "\n",
-    "net = vgg()\n",
+    "net = VGG_Net()\n",
     "optimizer = torch.optim.SGD(net.parameters(), lr=1e-1)\n",
     "criterion = nn.CrossEntropyLoss()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-12-22T09:12:46.868967Z",
      "start_time": "2017-12-22T09:01:59.924086Z"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 0. Train Loss: 2.303118, Train Acc: 0.098186, Valid Loss: 2.302944, Valid Acc: 0.099585, Time 00:00:32\n",
-      "Epoch 1. Train Loss: 2.303085, Train Acc: 0.096907, Valid Loss: 2.302762, Valid Acc: 0.100969, Time 00:00:33\n",
-      "Epoch 2. Train Loss: 2.302916, Train Acc: 0.097287, Valid Loss: 2.302740, Valid Acc: 0.099585, Time 00:00:33\n",
-      "Epoch 3. Train Loss: 2.302395, Train Acc: 0.102042, Valid Loss: 2.297652, Valid Acc: 0.108782, Time 00:00:32\n",
-      "Epoch 4. Train Loss: 2.079523, Train Acc: 0.202026, Valid Loss: 1.868179, Valid Acc: 0.255736, Time 00:00:31\n",
-      "Epoch 5. Train Loss: 1.781262, Train Acc: 0.307625, Valid Loss: 1.735122, Valid Acc: 0.323279, Time 00:00:31\n",
-      "Epoch 6. Train Loss: 1.565095, Train Acc: 0.400975, Valid Loss: 1.463914, Valid Acc: 0.449565, Time 00:00:31\n",
-      "Epoch 7. Train Loss: 1.360450, Train Acc: 0.495225, Valid Loss: 1.374488, Valid Acc: 0.490803, Time 00:00:31\n",
-      "Epoch 8. Train Loss: 1.144470, Train Acc: 0.585758, Valid Loss: 1.384803, Valid Acc: 0.524624, Time 00:00:31\n",
-      "Epoch 9. Train Loss: 0.954556, Train Acc: 0.659287, Valid Loss: 1.113850, Valid Acc: 0.609968, Time 00:00:32\n",
-      "Epoch 10. Train Loss: 0.801952, Train Acc: 0.718131, Valid Loss: 1.080254, Valid Acc: 0.639933, Time 00:00:31\n",
-      "Epoch 11. Train Loss: 0.665018, Train Acc: 0.765945, Valid Loss: 0.916277, Valid Acc: 0.698972, Time 00:00:31\n",
-      "Epoch 12. Train Loss: 0.547411, Train Acc: 0.811241, Valid Loss: 1.030948, Valid Acc: 0.678896, Time 00:00:32\n",
-      "Epoch 13. Train Loss: 0.442779, Train Acc: 0.846228, Valid Loss: 0.869791, Valid Acc: 0.732496, Time 00:00:32\n",
-      "Epoch 14. Train Loss: 0.357279, Train Acc: 0.875440, Valid Loss: 1.233777, Valid Acc: 0.671677, Time 00:00:31\n",
-      "Epoch 15. Train Loss: 0.285171, Train Acc: 0.900096, Valid Loss: 0.852879, Valid Acc: 0.765131, Time 00:00:32\n",
-      "Epoch 16. Train Loss: 0.222431, Train Acc: 0.923374, Valid Loss: 1.848096, Valid Acc: 0.614023, Time 00:00:31\n",
-      "Epoch 17. Train Loss: 0.174834, Train Acc: 0.939478, Valid Loss: 1.137286, Valid Acc: 0.728639, Time 00:00:31\n",
-      "Epoch 18. Train Loss: 0.144375, Train Acc: 0.950587, Valid Loss: 0.907310, Valid Acc: 0.776800, Time 00:00:31\n",
-      "Epoch 19. Train Loss: 0.115332, Train Acc: 0.960878, Valid Loss: 1.009886, Valid Acc: 0.761175, Time 00:00:31\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "train(net, train_data, test_data, 20, optimizer, criterion)"
+    "(l_train_loss, l_train_acc, l_valid_loss, l_valid_acc) = train(net, \n",
+    "                                                               train_data, test_data, \n",
+    "                                                               20, \n",
+    "                                                               optimizer, criterion,\n",
+    "                                                               use_cuda=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "plt.plot(l_train_loss, label='train')\n",
+    "plt.plot(l_valid_loss, label='valid')\n",
+    "plt.xlabel('epoch')\n",
+    "plt.legend(loc='best')\n",
+    "plt.savefig('fig-res-vgg-train-validate-loss.pdf')\n",
+    "plt.show()\n",
+    "\n",
+    "plt.plot(l_train_acc, label='train')\n",
+    "plt.plot(l_valid_acc, label='valid')\n",
+    "plt.xlabel('epoch')\n",
+    "plt.legend(loc='best')\n",
+    "plt.savefig('fig-res-vgg-train-validate-acc.pdf')\n",
+    "plt.show()"
    ]
   },
   {
@@ -465,7 +478,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/7_deep_learning/1_CNN/3-googlenet.ipynb b/7_deep_learning/1_CNN/05-googlenet.ipynb
similarity index 100%
rename from 7_deep_learning/1_CNN/3-googlenet.ipynb
rename to 7_deep_learning/1_CNN/05-googlenet.ipynb
diff --git a/7_deep_learning/1_CNN/4-resnet.ipynb b/7_deep_learning/1_CNN/06-resnet.ipynb
similarity index 98%
rename from 7_deep_learning/1_CNN/4-resnet.ipynb
rename to 7_deep_learning/1_CNN/06-resnet.ipynb
index 779fb3b..df09e12 100644
--- a/7_deep_learning/1_CNN/4-resnet.ipynb
+++ b/7_deep_learning/1_CNN/06-resnet.ipynb
@@ -47,7 +47,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T12:56:06.772059Z",
      "start_time": "2017-12-22T12:56:06.766027Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -69,7 +70,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T12:47:49.222432Z",
      "start_time": "2017-12-22T12:47:49.217940Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -85,7 +87,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T13:14:02.429145Z",
      "start_time": "2017-12-22T13:14:02.383322Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -203,13 +206,14 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T13:27:46.099404Z",
      "start_time": "2017-12-22T13:27:45.986235Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
     "class ResNet(nn.Module):\n",
     "    def __init__(self, in_channel, num_classes, verbose=False):\n",
-    "        super(resnet, self).__init__()\n",
+    "        super(ResNet, self).__init__()\n",
     "        self.verbose = verbose\n",
     "        \n",
     "        self.block1 = nn.Conv2d(in_channel, 64, 7, 2)\n",
@@ -290,7 +294,7 @@
     }
    ],
    "source": [
-    "test_net = resnet(3, 10, True)\n",
+    "test_net = ResNet(3, 10, True)\n",
     "test_x = Variable(torch.zeros(1, 3, 96, 96))\n",
     "test_y = test_net(test_x)\n",
     "print('output: {}'.format(test_y.shape))"
@@ -414,7 +418,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/7_deep_learning/1_CNN/5-densenet.ipynb b/7_deep_learning/1_CNN/07-densenet.ipynb
similarity index 97%
rename from 7_deep_learning/1_CNN/5-densenet.ipynb
rename to 7_deep_learning/1_CNN/07-densenet.ipynb
index 677c119..45bdaec 100644
--- a/7_deep_learning/1_CNN/5-densenet.ipynb
+++ b/7_deep_learning/1_CNN/07-densenet.ipynb
@@ -45,7 +45,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T15:38:31.113030Z",
      "start_time": "2017-12-22T15:38:30.612922Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -73,11 +74,12 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T15:38:31.121249Z",
      "start_time": "2017-12-22T15:38:31.115369Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
-    "def conv_block(in_channel, out_channel):\n",
+    "def Conv_Block(in_channel, out_channel):\n",
     "    layer = nn.Sequential(\n",
     "        nn.BatchNorm2d(in_channel),\n",
     "        nn.ReLU(True),\n",
@@ -100,17 +102,18 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T15:38:31.145274Z",
      "start_time": "2017-12-22T15:38:31.123363Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
-    "class dense_block(nn.Module):\n",
+    "class Dense_Block(nn.Module):\n",
     "    def __init__(self, in_channel, growth_rate, num_layers):\n",
-    "        super(dense_block, self).__init__()\n",
+    "        super(Dense_Block, self).__init__()\n",
     "        block = []\n",
     "        channel = in_channel\n",
     "        for i in range(num_layers):\n",
-    "            block.append(conv_block(channel, growth_rate))\n",
+    "            block.append(Conv_Block(channel, growth_rate))\n",
     "            channel += growth_rate\n",
     "            \n",
     "        self.net = nn.Sequential(*block)\n",
@@ -170,7 +173,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T15:38:31.222120Z",
      "start_time": "2017-12-22T15:38:31.215770Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -234,7 +238,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T15:38:31.318822Z",
      "start_time": "2017-12-22T15:38:31.236857Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -305,7 +310,8 @@
     "ExecuteTime": {
      "end_time": "2017-12-22T15:38:32.894729Z",
      "start_time": "2017-12-22T15:38:31.656356Z"
-    }
+    },
+    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -403,7 +409,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/7_deep_learning/1_CNN/6-batch-normalization.ipynb b/7_deep_learning/1_CNN/08-batch-normalization.ipynb
similarity index 99%
rename from 7_deep_learning/1_CNN/6-batch-normalization.ipynb
rename to 7_deep_learning/1_CNN/08-batch-normalization.ipynb
index 62f5b76..61a2f7f 100644
--- a/7_deep_learning/1_CNN/6-batch-normalization.ipynb
+++ b/7_deep_learning/1_CNN/08-batch-normalization.ipynb
@@ -601,7 +601,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/7_deep_learning/1_CNN/7-lr-decay.ipynb b/7_deep_learning/1_CNN/09-lr-decay.ipynb
similarity index 99%
rename from 7_deep_learning/1_CNN/7-lr-decay.ipynb
rename to 7_deep_learning/1_CNN/09-lr-decay.ipynb
index 16ab203..9f0de3d 100644
--- a/7_deep_learning/1_CNN/7-lr-decay.ipynb
+++ b/7_deep_learning/1_CNN/09-lr-decay.ipynb
@@ -403,7 +403,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.9"
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/7_deep_learning/1_CNN/8-regularization.ipynb b/7_deep_learning/1_CNN/10-regularization.ipynb
similarity index 100%
rename from 7_deep_learning/1_CNN/8-regularization.ipynb
rename to 7_deep_learning/1_CNN/10-regularization.ipynb
diff --git a/7_deep_learning/1_CNN/9-data-augumentation.ipynb b/7_deep_learning/1_CNN/11-data-augumentation.ipynb
similarity index 100%
rename from 7_deep_learning/1_CNN/9-data-augumentation.ipynb
rename to 7_deep_learning/1_CNN/11-data-augumentation.ipynb
diff --git a/7_deep_learning/1_CNN/CNN_Introduction.pptx b/7_deep_learning/1_CNN/CNN_Introduction.pptx
index 843c650..52e5bcc 100644
Binary files a/7_deep_learning/1_CNN/CNN_Introduction.pptx and b/7_deep_learning/1_CNN/CNN_Introduction.pptx differ
diff --git a/7_deep_learning/1_CNN/utils.py b/7_deep_learning/1_CNN/utils.py
index 72cc1f3..f4b5a33 100644
--- a/7_deep_learning/1_CNN/utils.py
+++ b/7_deep_learning/1_CNN/utils.py
@@ -13,16 +13,22 @@ def get_acc(output, label):
     return num_correct / total
 
 
-def train(net, train_data, valid_data, num_epochs, optimizer, criterion):
-    if torch.cuda.is_available():
+def train(net, train_data, valid_data, num_epochs, optimizer, criterion, use_cuda=True):
+    if use_cuda and torch.cuda.is_available():
         net = net.cuda()
+        
+    l_train_loss = []
+    l_train_acc  = []
+    l_valid_loss = []
+    l_valid_acc  = []
+    
     prev_time = datetime.now()
     for epoch in range(num_epochs):
         train_loss = 0
         train_acc = 0
         net = net.train()
         for im, label in train_data:
-            if torch.cuda.is_available():
+            if use_cuda and torch.cuda.is_available():
                 im = Variable(im.cuda())  # (bs, 3, h, w)
                 label = Variable(label.cuda())  # (bs, h, w)
             else:
@@ -50,7 +56,7 @@ def train(net, train_data, valid_data, num_epochs, optimizer, criterion):
             valid_acc = 0
             net = net.eval()
             for im, label in valid_data:
-                if torch.cuda.is_available():
+                if use_cuda and torch.cuda.is_available():
                     im = Variable(im.cuda(), volatile=True)
                     label = Variable(label.cuda(), volatile=True)
                 else:
@@ -65,13 +71,21 @@ def train(net, train_data, valid_data, num_epochs, optimizer, criterion):
                 % (epoch, train_loss / len(train_data),
                    train_acc / len(train_data), valid_loss / len(valid_data),
                    valid_acc / len(valid_data)))
+                   
+            l_valid_acc.append(valid_acc / len(valid_data))
+            l_valid_loss.append(valid_loss / len(valid_data))
         else:
             epoch_str = ("Epoch %d. Train Loss: %f, Train Acc: %f, " %
                          (epoch, train_loss / len(train_data),
                           train_acc / len(train_data)))
+
+        l_train_acc.append(train_acc / len(train_data))
+        l_train_loss.append(train_loss / len(train_data))
+            
         prev_time = cur_time
         print(epoch_str + time_str)
-
+    
+    return (l_train_loss, l_train_acc, l_valid_loss, l_valid_acc)
 
 def conv3x3(in_channel, out_channel, stride=1):
     return nn.Conv2d(
diff --git a/7_deep_learning/README.md b/7_deep_learning/README.md
index 0e486b7..3ede604 100644
--- a/7_deep_learning/README.md
+++ b/7_deep_learning/README.md
@@ -19,7 +19,36 @@
 ![resnet-development.png](imgs/resnet-development.png)
 
 
+
+## 内容
+
+   - CNN
+      - [CNN Introduction](1_CNN/CNN_Introduction.pptx)
+      - [CNN simple demo](../demo_code/3_CNN_MNIST.py)
+      - [Basic of Conv](1_CNN/01-basic_conv.ipynb)
+      - [LeNet5](1_CNN/02-LeNet5.ipynb)
+      - [AlexNet](1_CNN/03-AlexNet.ipynb)
+      - [VGG Network](1_CNN/04-vgg.ipynb)
+      - [GoogleNet](1_CNN/05-googlenet.ipynb)
+      - [ResNet](1_CNN/06-resnet.ipynb)
+      - [DenseNet](1_CNN/07-densenet.ipynb)
+      - [Batch Normalization](1_CNN/08-batch-normalization.ipynb)
+      - [Learning Rate Decay](1_CNN/09-lr-decay.ipynb)
+      - [Regularization](1_CNN/10-regularization.ipynb)
+      - [Data Augumentation](1_CNN/11-data-augumentation.ipynb)
+   - RNN
+      - [rnn/pytorch-rnn](2_RNN/pytorch-rnn.ipynb)
+      - [rnn/rnn-for-image](2_RNN/rnn-for-image.ipynb)
+      - [rnn/lstm-time-series](2_RNN/time-series/lstm-time-series.ipynb)
+   - GAN
+      - [gan/autoencoder](3_GAN/autoencoder.ipynb)
+      - [gan/vae](3_GAN/vae.ipynb)
+      - [gan/gan](3_GAN/gan.ipynb)
+
+
+
 ## 参考资料
+
 * [深度学习 – Deep learning](https://easyai.tech/ai-definition/deep-learning/)
 * [深度学习](https://www.jiqizhixin.com/graph/technologies/01946acc-d031-4c0e-909c-f062643b7273)
 
diff --git a/README.md b/README.md
index ce3b1c3..18fa6dd 100644
--- a/README.md
+++ b/README.md
@@ -52,15 +52,17 @@
    - CNN
       - [CNN Introduction](7_deep_learning/1_CNN/CNN_Introduction.pptx)
       - [CNN simple demo](demo_code/3_CNN_MNIST.py)
-      - [Basic of Conv](7_deep_learning/1_CNN/1-basic_conv.ipynb)
-      - [VGG Network](7_deep_learning/1_CNN/2-vgg.ipynb)
-      - [GoogleNet](7_deep_learning/1_CNN/3-googlenet.ipynb)
-      - [ResNet](7_deep_learning/1_CNN/4-resnet.ipynb)
-      - [DenseNet](7_deep_learning/1_CNN/5-densenet.ipynb)
-      - [Batch Normalization](7_deep_learning/1_CNN/6-batch-normalization.ipynb)
-      - [Learning Rate Decay](7_deep_learning/2_CNN/7-lr-decay.ipynb)
-      - [Regularization](7_deep_learning/1_CNN/8-regularization.ipynb)
-      - [Data Augumentation](7_deep_learning/1_CNN/9-data-augumentation.ipynb)
+      - [Basic of Conv](7_deep_learning/1_CNN/01-basic_conv.ipynb)
+      - [LeNet5](7_deep_learning/1_CNN/02-LeNet5.ipynb)
+      - [AlexNet](7_deep_learning/1_CNN/03-AlexNet.ipynb)
+      - [VGG Network](7_deep_learning/1_CNN/04-vgg.ipynb)
+      - [GoogleNet](7_deep_learning/1_CNN/05-googlenet.ipynb)
+      - [ResNet](7_deep_learning/1_CNN/06-resnet.ipynb)
+      - [DenseNet](7_deep_learning/1_CNN/07-densenet.ipynb)
+      - [Batch Normalization](7_deep_learning/1_CNN/08-batch-normalization.ipynb)
+      - [Learning Rate Decay](7_deep_learning/1_CNN/09-lr-decay.ipynb)
+      - [Regularization](7_deep_learning/1_CNN/10-regularization.ipynb)
+      - [Data Augumentation](7_deep_learning/1_CNN/11-data-augumentation.ipynb)
    - RNN
       - [rnn/pytorch-rnn](7_deep_learning/2_RNN/pytorch-rnn.ipynb)
       - [rnn/rnn-for-image](7_deep_learning/2_RNN/rnn-for-image.ipynb)
@@ -81,21 +83,19 @@
 
 
 
-## 3. 参考资料
+## 3. [参考资料](References.md)
+* [教程、代码](References.md)
 * 资料速查
   * [相关学习参考资料汇总](References.md)
   * [一些速查手册](references_tips/cheatsheet)
-
 * 机器学习方面技巧等
   * [Confusion Matrix](references_tips/confusion_matrix.ipynb)
   * [Datasets](references_tips/datasets.ipynb)
   * [构建深度神经网络的一些实战建议](references_tips/构建深度神经网络的一些实战建议.md)
   * [Intro to Deep Learning](references_tips/Intro_to_Deep_Learning.pdf)
-
 * Python技巧等
   * [安装Python环境](references_tips/InstallPython.md)
   * [Python tips](references_tips/python)
-
 * [Git教程](https://gitee.com/pi-lab/learn_programming/blob/master/6_tools/git/README.md)
 * [Markdown教程](https://gitee.com/pi-lab/learn_programming/blob/master/6_tools/markdown/README.md)
 
diff --git a/References.md b/References.md
index 679455d..f5846f2 100644
--- a/References.md
+++ b/References.md
@@ -1,11 +1,27 @@
-# References
+# 参考资料
 可以自行在下属列表找找到适合自己的学习资料，虽然罗列的比较多，但是个人最好选择一个深入阅读、练习。当练习到一定程度，可以再看看其他的资料，这样弥补单一学习资料可能存在的欠缺。
 
 列表等在 https://gitee.com/pi-lab/pilab_research_fields/blob/master/references/ML_References.md
 
 
+## 1. 教程、代码
 
-## References
+### 1.1 教程
+
+* [《动手学深度学习》- PyTorch版本](https://tangshusen.me/Dive-into-DL-PyTorch/#/)
+* [Introduction — Neuromatch Academy: Deep Learning](https://deeplearning.neuromatch.io/tutorials/intro.html)
+
+
+### 1.2 代码
+
+* [《统计学习方法》的代码](https://gitee.com/afishoutis/MachineLearning)
+* [《统计学习方法》PyTorch实现](https://github.com/fengdu78/lihang-code)
+*  [pytorch-cifar100](https://github.com/weiaicunzai/pytorch-cifar100)  实现ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet
+* [Attention:  xmu-xiaoma666/External-Attention-pytorch: Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐ (github.com)](https://github.com/xmu-xiaoma666/External-Attention-pytorch)   注意力机制，多层神经网络，重参数。
+* [Python  TheAlgorithms/Python: All Algorithms implemented in Python (github.com)](https://github.com/TheAlgorithms/Python)
+* PytTorch 训练手册  https://github.com/zergtant/pytorch-handbook 
+
+## 2. 工具、技巧
 
 * [形象直观了解谷歌大脑新型优化器LAMB](https://www.toutiao.com/i6687162064395305475/)
 * [梯度下降方法的视觉解释（动量，AdaGrad，RMSProp，Adam）](https://www.toutiao.com/i6836422484028293640/)
@@ -35,10 +51,8 @@
 
 
 
-## Course & Code
-* [《统计学习方法》的代码](https://gitee.com/afishoutis/MachineLearning)
 
-## Exercise
+## 3. 练习
 * http://sofasofa.io/competitions.php?type=practice
 * https://www.kaggle.com/competitions
 * Machine learning project ideas
@@ -50,10 +64,12 @@
 * Titanic: notebooks/data-science-ipython-notebooks/kaggle/titanic.ipynb
 * 使用神经网络解决拼图游戏 https://www.toutiao.com/a6855437347463365133/
 * [Sudoku-Solver](https://github.com/shivaverma/Sudoku-Solver)
+* Python 小项目 https://github.com/kyclark/tiny_python_projects
 
 
-## Method
+## 4. 机器学习方法
 
+### 4.1 经典机器学习方法
 * Programming Multiclass Logistic Regression
 notebooks/MachineLearningNotebooks/05.%20Logistic%20Regression.ipynb
 
@@ -74,7 +90,7 @@ http://localhost:8889/notebooks/machineLearning/10_digits_classification.ipynb
 http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selection%20and%20Assessment.ipynb
 
 
-## NN
+### 4.2 NN
 * 神经网络——梯度下降&反向传播 https://blog.csdn.net/skullfang/article/details/78634317
 * 零基础入门深度学习(3) - 神经网络和反向传播算法 https://www.zybuluo.com/hanbingtao/note/476663
 * 如何直观地解释 backpropagation 算法？ https://www.zhihu.com/question/27239198
@@ -85,10 +101,10 @@ http://localhost:8889/notebooks/machineLearning/notebooks/01%20-%20Model%20Selec
 * https://www.python-course.eu/neural_networks_with_python_numpy.php
 
 
-## k-Means
+### 4.3 k-Means
 * [如何使用 Keras 实现无监督聚类](http://m.sohu.com/a/236221126_717210)
 
-## AutoEncoder (自编码/非监督学习)
+### 4.4 AutoEncoder (自编码/非监督学习)
 * https://morvanzhou.github.io/tutorials/machine-learning/torch/4-04-autoencoder/
 * https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/404_autoencoder.py
 * pytorch AutoEncoder 自编码 https://www.jianshu.com/p/f0929f427d03