From 859485aa5a8778e31591cdd0956af2edc735be01 Mon Sep 17 00:00:00 2001 From: bushuhui Date: Tue, 8 Oct 2019 13:34:50 +0800 Subject: [PATCH] Translate some course to Chinese --- 2_knn/knn_classification.ipynb | 18 +++--- 3_kmeans/k-means.ipynb | 85 ++++++++++++++----------- 4_logistic_regression/Least_squares.ipynb | 83 ++++++++++++------------ 4_logistic_regression/Logistic_regression.ipynb | 41 ++++++------ 5_nn/Perceptron.ipynb | 6 +- 5_nn/mlp_bp.ipynb | 22 +++---- 5_nn/softmax_ce.ipynb | 14 ++-- 6_pytorch/PyTorch_quick_intro.ipynb | 45 +++++++------ 8 files changed, 166 insertions(+), 148 deletions(-) diff --git a/2_knn/knn_classification.ipynb b/2_knn/knn_classification.ipynb index e875791..f4970c7 100644 --- a/2_knn/knn_classification.ipynb +++ b/2_knn/knn_classification.ipynb @@ -48,7 +48,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -124,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -251,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -287,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -345,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -361,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { diff --git a/3_kmeans/k-means.ipynb b/3_kmeans/k-means.ipynb index fb1cbda..0b40cdc 100644 --- a/3_kmeans/k-means.ipynb +++ b/3_kmeans/k-means.ipynb @@ -4,16 +4,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# k-means" + "# k-Means" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Theory\n", + "## 方法\n", "\n", - "由于具有出色的速度和良好的可扩展性，K-Means聚类算法算得上是最著名的聚类方法。K-Means算法是一个重复移动类中心点的过程，把类的中心点，也称重心（centroids），移动到其包含成员的平均位置，然后重新划分其内部成员。\n", + "由于具有出色的速度和良好的可扩展性，K-Means聚类算法算得上是最著名的聚类方法。***K-Means算法是一个重复移动类中心点的过程，把类的中心点，也称重心（centroids），移动到其包含成员的平均位置，然后重新划分其内部成员。***\n", "\n", "K是算法计算出的超参数，表示类的数量；K-Means可以自动分配样本到不同的类，但是不能决定究竟要分几个类。\n", "\n", @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -304,7 +304,7 @@ "4 5.0 3.6 1.4 0.2 Iris-setosa" ] }, - "execution_count": 6, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -328,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": { "lines_to_next_cell": 2 }, @@ -364,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "metadata": { "lines_to_next_cell": 2 }, @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": { "lines_to_end_of_cell_marker": 2, "scrolled": true @@ -414,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -441,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -506,22 +506,32 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "最初的中心= [[6.2 2.8]\n", - " [6.7 3.1]\n", - " [5.1 3.8]]\n", - "the SSE of 1th iteration is 54.890000\n", - "the SSE of 2th iteration is 37.397339\n", - "the SSE of 3th iteration is 37.268236\n", - "the SSE of 4th iteration is 37.201302\n", - "the SSE of 5th iteration is 37.155048\n", - "the SSE of 6th iteration is 37.141172\n" + "最初的中心= [[6.4 2.7]\n", + " [5. 3.4]\n", + " [6.8 2.8]]\n", + "the SSE of 1th iteration is 52.450000\n", + "the SSE of 2th iteration is 38.174960\n", + "the SSE of 3th iteration is 38.055060\n", + "the SSE of 4th iteration is 37.980634\n", + "the SSE of 5th iteration is 37.859100\n", + "the SSE of 6th iteration is 37.783402\n", + "the SSE of 7th iteration is 37.694864\n", + "the SSE of 8th iteration is 37.636365\n", + "the SSE of 9th iteration is 37.535779\n", + "the SSE of 10th iteration is 37.454640\n", + "the SSE of 11th iteration is 37.355678\n", + "the SSE of 12th iteration is 37.290519\n", + "the SSE of 13th iteration is 37.229337\n", + "the SSE of 14th iteration is 37.201302\n", + "the SSE of 15th iteration is 37.155048\n", + "the SSE of 16th iteration is 37.141172\n" ] } ], @@ -533,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -595,12 +605,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -638,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -691,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -780,7 +790,7 @@ "source": [ "\n", "\n", - "方法2： 如果被用来评估的数据没有所属类别，则使用轮廓系数(Silhouette Coefficient)来度量聚类结果的质量，评估聚类的效果。轮廓系数同时兼顾了聚类的凝聚都和分离度，取值范围是[-1,1]，轮廓系数越大，表示聚类效果越好。 \n", + "方法2： 如果被用来评估的数据没有所属类别，则使用轮廓系数(Silhouette Coefficient)来度量聚类结果的质量，评估聚类的效果。**轮廓系数同时兼顾了聚类的凝聚度和分离度，取值范围是[-1,1]，轮廓系数越大，表示聚类效果越好。** \n", "\n", "轮廓系数的具体计算步骤： \n", "1. 对于已聚类数据中第i个样本$x_i$，计算$x_i$与其同一类簇内的所有其他样本距离的平均值，记作$a_i$，用于量化簇内的凝聚度 \n", @@ -791,12 +801,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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ZrVxY2ZQ5f/lS+o6YeUhb3xEzOX/50q7qw8zMrFYurGzKnHnKQi4+62QWzu1DwMK5fVx81smF7lTeij7MbPoZmD1QV3un9mGtp4hoWWeDg4MxOjrasv7MrL0kbYyIwXbHUQTnL7Ppp5Ec5i1WZmZmZgVxYWVmZmZWEBdWZmZmZgVxYWVmZmZWEBdWZmZmZgVxYWVmZmZWEBdWZmZmZgVxYWVmZmZWkFnNvFjSXOBy4KlAAH8UEf9RRGDWG667aTuXXL+ZHXv2smBuH+cvX1r4WdFb0Yf1Jucwq2b+pfPZ9cCuw9oHZg+w8907u6YPa72mCivgMuBfI+J3JR0J9BcQk/WI627azgXXbmLvvv0AbN+zlwuu3QRQWOHTij6spzmHWUWVCp6J2ju1D2u9hv8KlHQM8ALgCoCIeCgi9hQVmHW/S67ffKDgGbd3334uuX5zV/Vhvck5zMymQjP7WD0e2A2slXSTpMslzS4fSdKQpFFJo7t3726iO+s2O/bsrau9U/uwnjVpDnP+MrN6NVNYzQKeCXwiIk4BHgDeWz5SRAxHxGBEDM6bN6+J7qzbLJjbV1d7p/ZhPWvSHOb8ZWb1aqawugO4IyJuyM+vISUpMwDOX76UviNmHtLWd8RMzl++tKv6sJ7lHGZmhWu4sIqIncDtksa/wU4HflRIVNYTzjxlIRefdTIL5/YhYOHcPi4+6+RCdypvRR/Wm5zDbCIDswfqau/UPqz1FBGNv1h6BulQ5SOBW4FVEfHzauMPDg7G6Ohow/2ZWXeRtDEiBtsdRzX15DDnL7Ppp5Ec1tTpFiLi+0DHJk0zs4k4h5lZ0XzmdTMzM7OCuLAyMzMzK4gLKzMzM7OCuLAyMzMzK4gLKzMzM7OCuLAyMzMzK4gLKzMzM7OCuLAyMzMzK4gLKzMzM7OCuLAyMzMzK4gLKzMzM7OCuLAyMzMzK4gLKzMzM7OCuLAyMzMzK4gLKzMzM7OCuLAyMzMzK4gLKzMzM7OCuLAyMzMzK4gLKzMzM7OCuLAyMzMzK4gLKzMzM7OCuLAyMzMzK4gLKzMzM7OCNF1YSZop6SZJ64oIyMysVZy/zKxoRWyxejtwSwHvY2bWas5fZlaopgorSccBvwVcXkw4Zmat4fxlZlOh2S1WHwHeAzxSbQRJQ5JGJY3u3r27ye7MzArj/GVmhWu4sJK0ArgrIjZONF5EDEfEYEQMzps3r9HuzMwK4/xlZlOlmS1WzwVeIWkL8I/AiyRdVUhUZmZTy/nLzKZEw4VVRFwQEcdFxBLgbOAbEXFuYZGZmU0R5y8zmyo+j5WZmZlZQWYV8SYRsQHYUMR7mZm1kvOXmRXJW6zMzMzMCuLCyszMzKwgLqzMzMzMCuLCyszMzKwgLqzMzMzMCuLCyszMzKwgLqzMzMzMClLIeaymk+tu2s4l129mx569LJjbx/nLl3LmKQvbHZZZzbwMT0/zL53Prgd2HdY+MHuAne/e2YaIzOo3MgKrV8O2bbBoEaxZAytXtjuqQ7mwqsN1N23ngms3sXfffgC279nLBdduAvAXk3UFL8PTV6WiaqJ2s04zMgJDQzA2lp5v3ZqeQ2cVV/4rsA6XXL/5wBfSuL379nPJ9ZvbFJFZfbwMm1m3Wr36YFE1bmwstXcSF1Z12LFnb13tZp3Gy7CZdatt2+prbxcXVnVYMLevrnazTuNl2My61aJF9bW3iwurOpy/fCl9R8w8pK3viJmcv3xpmyIyq4+XYTPrVmvWQH//oW39/am9k7iwqsOZpyzk4rNOZuHcPgQsnNvHxWed7J1+rWt4GZ6+BmYP1NVu1mlWroThYVi8GKR0PzzcWTuuAygiWtbZ4OBgjI6Otqw/M2svSRsjYrDdcRTB+cts+mkkh3mLlZmZmVlBXFiZmZmZFcSFlZmZmVlBXFiZmZmZFcSFlZmZmVlBXFiZmZmZFcSFlZmZmVlBXFiZmZmZFWRWoy+UdDzwaWAACGA4Ii4rKrB6XXfTdi65fjM79uxlwdw+zl++tGvPJt1L02L18WffOp2Uw+ZfOp9dD+w6rH1g9gA7372zDRE1plemwxozMgKrV6eLIi9alC4102lnRW+Fhgsr4GHgXRFxo6SjgY2SvhYRPyootppdd9N2Lrh2E3v37Qdg+569XHDtJoCu+1LqpWmx+vizb7mOyWGVipGJ2jtVr0yH1W9kBIaGYGwsPd+6NT2H6VdcNfxXYETcGRE35sf3AbcAbcn+l1y/+cCX0bi9+/ZzyfWb2xFOU3ppWqw+/uxbq5NymFm3W736YFE1bmwstU83hexjJWkJcApwQ4VhQ5JGJY3u3r27iO4Os2PP3rraO1kvTYvVx599+1TLYa3IX2a9YNu2+tp7WdOFlaQ5wBeAd0TEveXDI2I4IgYjYnDevHnNdlfRgrl9dbV3sl6aFquPP/v2mCiHtSJ/mfWCRYvqa+9lTRVWko4gJaSRiLi2mJDqd/7ypfQdMfOQtr4jZnL+8qVtiqhxvTQtVh9/9q3XKTnMrNutWQP9/Ye29fen9umm4cJKkoArgFsi4sPFhVS/M09ZyMVnnczCuX0IWDi3j4vPOrkrd/jtpWmx+vizb61OymEDswfqau9UvTIdVr+VK2F4GBYvBindDw9Pvx3XARQRjb1Qeh7wbWAT8Ehufl9EfLnaawYHB2N0dLSh/sys+0jaGBGD7Y6jknpzmPOX2fTTSA5r+HQLEfEdQI2+3sysnZzDzGwq+MzrZmZmZgVxYWVmZmZWEBdWZmZmZgVxYWVmZmZWEBdWZmZmZgVxYWVmZmZWEBdWZmZmZgVp+DxWnea6m7ZzyfWb2bFnLwvm9nH+8qU+Y7UV5v3XbeLqG25nfwQzJc559vFcdObJ7Q6rIV5XOs/8S+ez64Fdh7UPzB5g57t3tiEi6zVveUs6E/r+/TBzJgwNwcc/3u6o6jcyAqtXp4s7L1qULpnTaWd374nC6rqbtnPBtZvYu28/ANv37OWCazcB+AvDmvb+6zZx1X8evET7/ogDz7utuPK60pkqFVUTtZvV4y1vgU984uDz/fsPPu+m4mpkJBWEY2Pp+dat6Tl0VnHVE38FXnL95gNfFOP27tvPJddvblNE1kuuvuH2uto7mdcVs+lneLi+9k61evXBomrc2Fhq7yQ9UVjt2LO3rnazeuyvcj3Nau2dzOuK2fSzf3997Z1q27b62tulJwqrBXP76mo3q8dMVb6cXLX2TuZ1xWz6mTmzvvZOtWhRfe3t0hOF1fnLl9J3xKFLSN8RMzl/+dI2RWS95JxnH19XeyfzumI2/Yzvh1Rre6daswb6+w9t6+9P7Z2kJwqrM09ZyMVnnczCuX0IWDi3j4vPOtk741ohLjrzZM49ddGBLVQzJc49dVHX7bgOXlc61cDsgbrazerx8Y/Dm998cAvVzJnpeTftuA5pB/XhYVi8GKR0PzzcWTuuAyhauJ/I4OBgjI6Otqw/M2svSRsjYrDdcRTB+cts+mkkh/XEFiszMzOzTuDCyszMzKwgLqzMzMzMCuLCyszMzKwgLqzMzMzMCuLCyszMzKwgLqzMzMzMCuLCyszMzKwgTRVWkl4mabOkn0p6b1FBmZm1gnOYmRWt4cJK0kzgY8AZwEnAOZJOKiowM7Op5BxmZlOhmS1WzwJ+GhG3RsRDwD8CrywmLDOzKeccZmaFm9XEaxcCt5c8vwN4dvlIkoaA8Wto/1LSD5vos5M8Dri73UEUoFemAzwtnWhpuwOYwKQ5zPmrK/TKtPTKdEBvTUvdOayZwqomETEMDANIGu2VC7L2yrT0ynSAp6UTSerqqxY7f3W+XpmWXpkO6L1pqfc1zfwVuB04vuT5cbnNzKwbOIeZWeGaKay+BzxZ0uMlHQmcDXypmLDMzKacc5iZFa7hvwIj4mFJbwWuB2YCn4yImyd52XCj/XWgXpmWXpkO8LR0oo6djgZyWMdOSwM8LZ2nV6YDpvm0KCKmIhAzMzOzacdnXjczMzMriAsrMzMzs4K0pLDqlctGSDpe0npJP5J0s6S3tzumZkmaKekmSevaHUszJM2VdI2kH0u6RdJz2h1TIyS9My9bP5R0taSj2h1TrSR9UtJdped6kvQYSV+T9JN8f2w7Y2xUL+Qw56/O1Sv5C5zDoAWFVY9dNuJh4F0RcRJwKvDHXTwt494O3NLuIApwGfCvEXEC8HS6cJokLQTeBgxGxFNJO1Sf3d6o6nIl8LKytvcCX4+IJwNfz8+7Sg/lMOevztX1+Qucw8a1YotVz1w2IiLujIgb8+P7SAv/wvZG1ThJxwG/BVze7liaIekY4AXAFQAR8VBE7GlvVA2bBfRJmgX0AzvaHE/NIuJbwM/Kml8JfCo//hRwZkuDKkZP5DDnr87UY/kLnMNaUlhVumxE167M4yQtAU4BbmhvJE35CPAe4JF2B9KkxwO7gbX5b4HLJc1ud1D1iojtwKXANuBO4BcR8dX2RtW0gYi4Mz/eCQy0M5gG9VwOc/7qKD2Rv8A5bJx3Xm+ApDnAF4B3RMS97Y6nEZJWAHdFxMZ2x1KAWcAzgU9ExCnAA3TnX07Hkn4dPR5YAMyWdG57oypOpHO7+Pwubeb81XF6In+Bc9i4VhRWPXXZCElHkJLSSERc2+54mvBc4BWStpD+2niRpKvaG1LD7gDuiIjxX9/XkBJVt3kxcFtE7I6IfcC1wG+2OaZm7ZL0qwD5/q42x9OInslhzl8dqVfyFziHAa0prHrmshGSRPof/JaI+HC742lGRFwQEcdFxBLSZ/KNiOjKXxYRsRO4XdL4VchPB37UxpAatQ04VVJ/XtZOp0t3Yi3xJeA1+fFrgC+2MZZG9UQOc/7qTD2Uv8A5DGjikja1avDSN53qucB5wCZJ389t74uIL7cxJkv+FzCSv/huBVa1OZ66RcQNkq4BbiQdwXUTXXRpCElXA6cBj5N0B/AB4EPA5yS9DtgK/H77ImxMD+Uw56/O1fX5C5zDDryPL2ljZmZmVoye3nld0hZJL+6AOBZJuj+fDwdJGyS9Pj9+raTvtDfCziWpT9I/S/qFpM/ntosk3S1pZ/m8neB9ni9pc2uiNitGB+WwQ9af0rgkXdjF+zdNOUkDkr4l6T5J/1fJWkk/l/TdWnOTpJWSuv0Iu2mhpwurIki6StKdku6V9N/jBVE9ImJbRMyJiP1TEWMjKiXD0oKvg/wu6fDWx0bE70laBLwLOCki5tc6byPi2xGxdKJxatXMl52kZ0jaKGks3z9jgnHvL7vtl/Q3edipSmcB/pmk3ZI+P76DZR5+vtKZj++TdJuk8xuJ13qHpCdLerCRIqjI9acokq6UdFFZW0cUomWGgLuBR0fEu4DnAS8BjouIZ9U6byNiJCJeWkRAkkLSkxp87elKZ4gfUzqT/+JJxn97zkEPKJ1V/ikVxvlktZiaWW7bxYXV5C4GlkTEo4FXABdJ+vU2xzSdLAb+OyIezs8XAfdERNcdXZb3n/gicBVwLOlkc1/M7YfJBeOciJgDzAf2Ap/Pg48l7buwhDSP7gPWlnYHvDqP9zLgrZK66QzIVryPkXbEt9ZaDPwoDu53sxjYEhEPtDGmhkh6HOlIv/8NPAYYBT47wfivB15HOpHrHGAFqcgsHed5wBMn6Lb7ltuI6NkbsAV4cX58InAbcE4T77eUdNKz368y/FmkBe1eYBfw4dy+hHTui1n5+Qbg9fnxa4HvkE6q9vMc4xkl77mAdFTCz4CfAm8oGXYlcFHJ89NIh+2WvvYLpJPP3Qa8Lbe/DHgI2AfcD/wAWAPsBx7MbR/N454AfC33v7natOdxH0P6ct+Rp+W6kmFvyPH/LE/PgpJhFfsAPlgW5xtJxcUj+fmVFeZtxRhqnTd52IXA54BPkwqWm0mXaAD4TO5/b47hPXUsPy8lHaavkrZtwMtqeO1rSDu1qsrwZwL3TfD6vwb+pt3rpG/13Sgoh5GOnPtcXravmmC8l5OOSLsvL6vvzu3l609pXFXXl5K4NwB78rBXlAzbQM6F+flrge+UPK+WG4ZyXngor4f/XG3dJF2+599z/z8ATptg+o8nFQ67gXs4mAdnAO8n7bx8V57WY0peV7EPUo4qjfONpBy7Pz//YIV5Wy2GmuZNSb8fA/4lfyY3AE8IElgSAAAgAElEQVTMw75FypkP5Bj+oI7laAj495Lns/P8PqHCuDNIJ9Y9fYL3m0Xawf1pOaYnNbLcdtqt7QFM6cTllZ/0pbMNWFEybF1eCSrd1pW9z8eBsfzB3wjMqdLffwDn5cdzgFPz4yVMXFjtIxUeM4E3k4qC8QMLvpX7Pwp4Rl7ZXpSHXUmVwiov1BuB/wMcCTyB9MW8PA8/bEHl8CQ3O68Yq/IKcArp18ZJVab/X0i/Xo4FjgBemNtflF/3TOBRwN8A36qlj/I4OTwJlc/bajHUO28eJH3JzCRttfzP8uWqbNqrLUt7gPfmcd4JfKXsdetI12+bbFn+BnDhBMPfURpj2TCRkteb2r1O+lbfjQJyGPBo4L9J5986bL0v6+9O4Pn58bHAM/Pj8vXuwDow0fqS18GfAu/L69qLSF/0S/PwDVQprJg8N1xJSf4rjys/X0gqTl6e1/uX5OfzKkz7TFJR9Fe576OA5+Vhf5Sn4wmk3H4t8Jla+iiPk8MLpAPzdpIY6p0395B+7M8CRoB/LOnzkCKG9E/ARDnsD/N4l5FOZFo6334IvKrC/FyU+3l7jvU2UiE5o2Sc84HLqsRU83Lbabfp8Ffg80lbSF4dEQeugB4RKyJibpXbitI3iIi3AEfn97oW+GWVvvYBT5L0uIi4PyL+s8YYt0bE30faT+hTwK8CA5KOJx0i/ScR8WBEfJ90XaxX1/Cev0Fasf800rWnbgX+nvouiLmCtMl6bUQ8HBE3kbby/F75iHn/njNIX94/j4h9EfHNPHgl6RD1GyPil8AFwHOULqtRcx+TmSSGUrXMm+9ExJfzZ/IZ0oVRq5pgWZobER/Ko80BflH20l+Qlq2Jpmsx8EIOXq+qfPjTSEVitf2oLiQl/LUT9WMdq9kc9mfAFRFxRw197QNOkvTovA7dWGOM1daXU0nL/YfyuvYNUkF4Tg3vWURuOBf4co7tkYj4GulfhZdXGPdZpC3Z50fEAznnjh9YtJL0D8StEXE/KYedrXQ9vHr6mMxEMZSqZd78U0R8N9JuFCOkH+YVRdpXdaIc9g951Hpy2HH5/qXAycAy0uf+OoD8/fZGUu6qpJ7ltqNMh8LqTaRNlxuaeZOI2J8X8ONIW5UqeR3wFODHkr6ndNmFWuws6WcsP5xDWsF+FumCqeO2Utt1yhYDCyTtGb+RfjXWc622xcCzy95jJWl/n3LH51h/XmHYghw3ADkx3ZOno54+JjNRDKVqmTc7Sx6PAUflJNqM+0m/wko9mvQLfiLnkb64bisfkHf2/Arw9oj4doXhbyUV4r+Vi1rrPg3nsHxwxItJW0Bq8SpSQbBV0jclPafG11VbXxYAt0dE6fX86slhzeaGxcDvlb3H80g/XssdT/qR+3CFYYfksPx4Filn1NPHZCaKoVQt86b8M5nTQDzl6slhe/P9X0bEnojYAvwdBwvOjwB/GhHlhVojy21HmfIThHaANwF/IumvIuKd442SvkL6JVjJtyPijCrDZlFlR7uI+AlwjqQZwFnANZIe23jo7AAeI+nokuJqEQcvp/EA6erh40pXqttJlxZ4cpX3jhrabge+GREvqSHW23Osc+PwK7PvICUCAJQuMPpY0nTU00czMZSPN9G8mcxh807S/ROM/+cR8eek/UveJUkRMf4eTyPtCzGRV5NOUlfe52Lg34A/i4jPVBj+R6Rrjr2gG3/12QHN5LDTSH+Xb5ME6ct1pqSTIuKwy6ZExPeAVypd+uatpP1bji8frw47gOMlzSgprhaR/uKByXPYRLmh1hz2mYh4Qw2x3g4skjSrQmFzSA4jTcPDpH1p6+mjmRjKxysqb5KPtp7obO9vjIgRUg57TcnrZpO+DyudMHczad+y0s+k9PHpwPMk/WVJ239IejvwK9Sx3Haa6bDF6j7SztovkHTgyykizoiSo67KbmcASPoVSWdLmiNppqTlpE2ZXx9/n3yI6Gn58bmS5uUEMv7F3vCV1yPidtIOkRdLOir/5fM60lFlAN8HXi7pMZLmk/azGfdd4D5Jf6J0LqiZkp4q6Tfy8F3AklwEUtL2hJLn64CnSDpP0hH59huSTqwQ652kLScfl3RsHvcFefDVwCqlUw08Cvhz4Ib8C6bmPmqYXxPFUGqyeTOZ8vnEBMvSnFxUQdqfZD/wNkmPyluTIO0/VZGk3yT9uv98WfvC/LqPRsTfVnjdStJ8fkmkvzqtezWcw0hHjj6R9DfQM4C/Je2HuBxA0pKcw5ZIOlLpXEnHRLrO2700kb+yG0hbS96T18fTgN8mXd8PUg47S+kSKE8i/02UTZYbDlsPK7RdBfy2pOV5PT9K0mmSjuNw3yXtY/YhSbPzuM/Nw64G3ql0WaM5pHXrs7n4qaePyUwUQ6lm8+Yh8ykOnram2m0kj/pPwFMlvUrSUaS/8f4rIn5c3kH+9+WzpM/+6Dw/hnLskP7deToHl01Iy8Y/Mcly2+mmQ2FF3nrxEuAMSX9Wz0tJf/vdQTrC7FLSFeG/BAf+I74P2JTHfxlwc956cRlwdkTsPexd63MOqXLfQVrgPhAR/5aHfYa0o+MW4KuUHPYaaV+HFaSF8jbSjo2XA8fkUca/qO+RNL4fxWXA7yqduO6v81ayl5L2PdpB2rT8F6Qd0Cs5j7SPxo9JR868I8fyb6TDc79AShpPzO9JA31MpmIMpWqYN5O5GHi/0ib4d9caWEQ8BJxJ2gK1h7RD7Jm5HUnvy1shSr0GuLbs72CA15MS44UqOddVyfCLSFsFv1cy/LACzLpDozksIsYiYuf4jfRXzoMRsTuPcjzpb63xreDnAVsk3UvaUrayybgfIn1ZnkFazz5O2lds/Iv4r0hbNXaR9iEcKXntZLnhCtL+YHskXZfbDlk384/TV5L+6t9N2tJzPhW++3Je+G3gSaQDBe4A/iAP/iQp336LlDMeJF2Ghnr6qGF+TRRD6XjN5s0LgU/l+VTzZabycvMq0lHkPweeTcm+qZL+tizPvJW0zO0gHdz1D6R5SUTcVbZsAtwdEXtrWG47mi9p0wRJ5wK/FhEXtDsWM7N6SXo/sDsi/q7dsZj1ChdWZmZmZgWZdFOl0qnm75L0w5K2xyhdTuMn+f7YqQ3TzKwxzmFm1kq1/Ad8JWnfoVLvBb6ej6r6en5uZtaJrsQ5zMxapKa/ApVO5LguIp6an28mnbL/TqWTMm6IDrtAp5nZOOcwM2uVRs9jNZAPbYd0NELVk05KGiIdYsns2bN//YQTTmiwSzPrNhs3brw7Iua1O44Kasphzl9m01sjOazpE4RGREiqutkrIoZJ56RgcHAwRkdHm+3SzLqEpK2Tj9VeE+Uw5y+z6a2RHNboeax25c3n49dnu6vB9zEzawfnMDObEo0WVl/i4GntXwN8sZhwzMxawjnMzKZELadbuJp0xtSlku6Q9DrSdcteIuknpAslHnYdMzOzTuAcZmatNOk+VhFxTpVBpxcci5lZ4ZzDzKyVpsW1As3MzMxawYWVmZmZWUFcWJmZmZkVxIWVmZmZWUFcWJmZmZkVxIWVmZmZWUFcWJmZmZkVxIWVmZmZWUFcWJmZmZkVxIWVmZmZWUFcWJmZmZkVxIWVmZmZWUFcWJmZmZkVxIWVmZmZWUFcWJmZmZkVxIWVmZmZWUFcWJmZmZkVxIWVmZmZWUFcWJmZmZkVxIWVmZmZWUFcWJmZmZkVxIWVmZmZWUFcWNnUW78elixJ92ZmZj2sqcJK0jsl3Szph5KulnRUUYFZj1i/HlasgK1b072LK+sgzmE2kZGR9Jtwxox0PzLS7oisGzRcWElaCLwNGIyIpwIzgbOLCsx6wHhRNTaWno+NubiyjuEcZhMZGYGhofSbMCLdDw25uLLJNftX4CygT9IsoB/Y0XxI1hPKi6pxLq6ssziHWUWrV1dOX6tXtyce6x4NF1YRsR24FNgG3An8IiK+Wj6epCFJo5JGd+/e3Xik1l1WrTo8K40bG0vDzdqolhzm/DV9bdtWX7vZuGb+CjwWeCXweGABMFvSueXjRcRwRAxGxOC8efMaj9S6y9q10N9feVh/fxpu1ka15DDnr+lr0aL62s3GNfNX4IuB2yJid0TsA64FfrOYsKzrLVsG69YdXlz196f2ZcvaE5fZQc5hVtWaNZXT15o17YnHukczhdU24FRJ/ZIEnA7cUkxY1hPKiysXVdZZnMOsqpUrYXgYFi8GKd0PD6d2s4k0s4/VDcA1wI3ApvxewwXFZb1ivLhavNhFlXUU5zCbzMqVsGULPPJIundRZbWY1cyLI+IDwAcKisV61bJlKSuZdRjnMDMrms+8bmZmZlYQF1ZmZmZmBXFhZWZmZlYQF1ZmZmZmBXFhZWZmZlYQF1ZmZmZmBXFhZVNv/XpYsmRqL7zcij7MzMwm4cLKptb69bBiBWzdmu6novBpRR9mNu2MjKTfazNmpPuRke7sw1rLhZVNnfGCZ2wsPR8bK77waUUfZjbtjIzA0FD6vRaR7oeGii18WtGHtZ4LK5sa5QXPuCILn1b0YWbT0urVlVPL6tXd1Ye1ngsrmxqrVh2eMcaNjaXh3dCHmU1L27bV196pfVjrubCyqbF2LfT3Vx7W35+Gd0MfZjYtLVpUX3un9mGt58LKpsayZbBu3eGFT39/al+2rDv6MLNpac2ayqllzZru6sNaz4WVTZ3ywmcqCp5W9GFm087KlTA8DIsXg5Tuh4dTezf1Ya2niGhZZ4ODgzE6Otqy/qxDrF+f9ndau3bqCp5W9GF1k7QxIgbbHUcRnL/Mpp9GctisqQrG7IBly2DLlu7vw8zMbBL+K9DMzMysIC6szMzMzAriwsrMzMysIC6szMzMzAriwsrMzMysIC6szMzMzAriwsqm3vr1sGTJ1F4UuRV9mJmZTaKpwkrSXEnXSPqxpFskPaeowKxHrF8PK1bA1q3pfioKn1b0YT3JOcwmMjKSfq/NmJHuR0a6sw9rrWa3WF0G/GtEnAA8Hbil+ZCsZ4wXPGNj6fnYWPGFTyv6sF7mHGYVjYzA0FD6vRaR7oeGii18WtGHtV7DhZWkY4AXAFcARMRDEbGnqMCsy5UXPOOKLHxa0Yf1LOcwm8jq1ZVTy+rV3dWHtV4zW6weD+wG1kq6SdLlkmaXjyRpSNKopNHdu3c30Z11lVWrDs8Y48bG0vBu6MN62aQ5zPlr+tq2rb72Tu3DWq+ZwmoW8EzgExFxCvAA8N7ykSJiOCIGI2Jw3rx5TXRnXWXtWujvrzysvz8N74Y+rJdNmsOcv6avRYvqa+/UPqz1mims7gDuiIgb8vNrSEnKLF0Ued26wwuf/v7UvmxZd/Rhvcw5zKpas6Zyalmzprv6sNZruLCKiJ3A7ZKW5qbTgR8VEpX1hvLCZyoKnlb0YT3JOcwmsnIlDA/D4sUgpfvh4dTeTX1Y6ykiGn+x9AzgcuBI4FZgVUT8vNr4g4ODMTo62nB/1qXWr0/7O61dO3UFTyv6sLpJ2hgRg+2Oo5p6cpjzl9n000gOm9VMhxHxfaBjk6Z1iGXLYMuW7u/Deo5zmJkVzWdeNzMzMyuICyszMzOzgriwMjMzMyuICyszMzOzgriwMjMzMyuICyszMzOzgriwMjMzMyuICyszMzOzgriwMjMzMyuICyszMzOzgriwMjMzMyuICyszMzOzgriwMjMzMyuICyszMzOzgriwMjMzMyuICyszMzOzgriwMjMzMyuICyszMzOzgriwMjMzMyuICyszMzOzgriwMjMzMyuICyszMzOzgriwMjMzMytI04WVpJmSbpK0roiAzMxaxfnLzIpWxBartwO3FPA+Zmat5vxlZoVqqrCSdBzwW8DlxYRjZtYazl9mNhWa3WL1EeA9wCPVRpA0JGlU0uju3bub7M7MrDDOX2ZWuIYLK0krgLsiYuNE40XEcEQMRsTgvHnzGu3OzKwwzl9mNlWa2WL1XOAVkrYA/wi8SNJVhURlZja1nL/MbEo0XFhFxAURcVxELAHOBr4REecWFpmZ2RRx/jKzqeLzWJmZmZkVZFYRbxIRG4ANRbyXmVkrOX+ZWZG8xcrMzMysIC6szMzMzAriwsrMzMysIC6szMzMzAriwsrMzMysIC6szMzMzAriwsrMzMy6x/r1sGRJuu9AhZzHajoZGYHVq2HbNli0CNasgZUr2x2VWW3mXzqfXQ/sOqx9YPYAO9+9sw0RWav4s7eesH49rFgBY2Ppft06WLas3VEdwlus6jAyAkNDsHUrRKT7oaHUbtYNKn2xTtRuvcOfvXW90qIKDhZXHbblyoVVHVavPvh5jhsbS+1mZmY2RcqLqnEdWFy5sKrDtm31tZuZmVkBVq06vKgaNzaWhncIF1Z1WLSovnYzMzMrwNq10N9feVh/fxreIVxY1WHNmsM/1/7+1G5mZmZTZNmytKN6pS/hDtuB3YVVHVauhOFhWLwYpHQ/POyjAq17DMweqKvdeoc/e+t65cVVBxZV4NMt1G3lShdS1r18WP305c/eesJ4cbVqVfr7r8OKKnBhZWZmZt1k2TLYsqXdUVTlvwLNzMzMCuLCyszMzKwgLqzMzMzMCuLCyszMzKwgLqzMzMzMCuLCyszMzKwgLqzMzMysGOvXw5IlHXVR5FZr+DxWko4HPg0MAAEMR8RlRQVWr5ERWL06XRB50aJ0mZluPZFnL02L1W7+pfPZ9cCuw9oHZg/45I5ToJNyWK989r0yHdag9ethxYp0UeQVKzryrOit0MwWq4eBd0XEScCpwB9LOqmYsOozMgJDQ7B1K0Sk+6Gh1N5temlarD6VvpAmaremdUwO65XPvlemwxpQWlTBweJqGm65ariwiog7I+LG/Pg+4BZgYVGB1WP16oOf5bixsdTebXppWsw6WSflMLOuVl5UjZumxVUh+1hJWgKcAtxQYdiQpFFJo7t37y6iu8Ns21ZfeyfrpWkx6xbVclgr8pdZ11u16vCiatzYWBo+jTRdWEmaA3wBeEdE3Fs+PCKGI2IwIgbnzZvXbHcVLVpUX3sn66VpMesGE+WwVuQvs663di3091ce1t+fhk8jTRVWko4gJaSRiLi2mJDqt2bN4Z9pf39q7za9NC1mna5TcphZV1u2LO2oXunLaxruwN5wYSVJwBXALRHx4eJCqt/KlTA8DIsXg5Tuh4e780i6XpoWq8/A7IG62q05nZTDeuWz75XpsAaUF1fTtKgCUEQ09kLpecC3gU3AI7n5fRHx5WqvGRwcjNHR0Yb6M7PuI2ljRAy2O45K6s1hzl9mNVi/Pu1TtXZtTxRVjeSwhs9jFRHfAdTo683M2sk5zGwKLFsGW7a0O4q28pnXzczMzAriwsrMzMysIC6szMzMzAriwsrMzMysIC6szMzMzAriwsrMzMysIC6szMzMrHusXw9LlnTsxZ0bPo9VpxkZgdWr08WKFy1Kl4Dx2cqtCDM/OJNHDpw/8qAZzGD/B/a3IaLGzb90Prse2HVY+8DsAXa+e2cbIjLw52JTq++b3+TBCicDP0pi7wtf2IaImrB+PaxYkS7uvGJFR57dvSe2WI2MwNAQbN0KEel+aCi1mzWrUlE1UXsnq/TlPVG7tYY/F5tKT+zrq6u9Y5UWVXCwuOqwLVc9UVitXn1wPo8bG0vtZmZm09lVJ55Ysf0fqrR3pPKialwHFlc9UVht21Zfu5mZ2XTxjKOP5tfGL46c/Vp/P087+ug2RdSAVasOL6rGjY2l4R2iJwqrRYvqazczM5tOyrdaddXWKkgXdS4rDg/o70/DO0RPFFZr1hw+v/v7U7uZmdl0V7rVquu2VkHaQX3duspf9h22A3tPFFYrV8LwMCxeDFK6Hx72UYFWjBlVVpNq7Z1sYPZAXe3WGv5crBWuOvFEjpk5s/u2Vo0rL646sKgCUFQ4BHOqDA4OxujoaMv6M7P2krQxIgbbHUcRnL/MOsT69WmfqrVrp7yoaiSH9cx5rMzMzGwaWLYMtmxpdxRVdd9/GWZmZmYdyoWVmZmZWUFcWJmZmZkVxIWVmZmZWUFcWJmZmZkVxIWVmZmZWUFcWJmZmZkVpKnCStLLJG2W9FNJ7y0qKDOzVnAOM7OiNVxYSZoJfAw4AzgJOEfSSUUFZmY2lZzDzGwqNLPF6lnATyPi1oh4CPhH4JXFhGVmNuWcw8yscM1c0mYhcHvJ8zuAZ5ePJGkIGMpPfynph0302UkeB9zd7iAK0CvTAZ6WTrS03QFMYNIc5vzVFXplWnplOqC3pqXuHDbl1wqMiGFgGEDSaK9ckLVXpqVXpgM8LZ1IUldftdj5q/P1yrT0ynRA701Lva9p5q/A7cDxJc+Py21mZt3AOczMCtdMYfU94MmSHi/pSOBs4EvFhGVmNuWcw8yscA3/FRgRD0t6K3A9MBP4ZETcPMnLhhvtrwP1yrT0ynSAp6UTdex0NJDDOnZaGuBp6Ty9Mh0wzadFETEVgZiZmZlNOz7zupmZmVlBXFiZmZmZFaQlhVWvXDZC0vGS1kv6kaSbJb293TE1S9JMSTdJWtfuWJohaa6kayT9WNItkp7T7pgaIemdedn6oaSrJR3V7phqJemTku4qPdeTpMdI+pqkn+T7Y9sZY6N6IYc5f3WuXslf4BwGLSiseuyyEQ8D74qIk4BTgT/u4mkZ93bglnYHUYDLgH+NiBOAp9OF0yRpIfA2YDAinkraofrs9kZVlyuBl5W1vRf4ekQ8Gfh6ft5VeiiHOX91rq7PX+AcNq4VW6x65rIREXFnRNyYH99HWvgXtjeqxkk6Dvgt4PJ2x9IMSccALwCuAIiIhyJiT3ujatgsoE/SLKAf2NHmeGoWEd8CflbW/ErgU/nxp4AzWxpUMXoihzl/daYey1/gHNaSwqrSZSO6dmUeJ2kJcApwQ3sjacpHgPcAj7Q7kCY9HtgNrM1/C1wuaXa7g6pXRGwHLgW2AXcCv4iIr7Y3qqYNRMSd+fFOYKCdwTSo53KY81dH6Yn8Bc5h47zzegMkzQG+ALwjIu5tdzyNkLQCuCsiNrY7lgLMAp4JfCIiTgEeoDv/cjqW9Ovo8cACYLakc9sbVXEindvF53dpM+evjtMT+Qucw8a1orDqqctGSDqClJRGIuLadsfThOcCr5C0hfTXxoskXdXekBp2B3BHRIz/+r6GlKi6zYuB2yJid0TsA64FfrPNMTVrl6RfBcj3d7U5nkb0TA5z/upIvZK/wDkMaE1h1TOXjZAk0v/gt0TEh9sdTzMi4oKIOC4ilpA+k29ERFf+soiIncDtksavQn468KM2htSobcCpkvrzsnY6XboTa4kvAa/Jj18DfLGNsTSqJ3KY81dn6qH8Bc5hQBOXtKlVg5e+6VTPBc4DNkn6fm57X0R8uY0xWfK/gJH8xXcrsKrN8dQtIm6QdA1wI+kIrpvooktDSLoaOA14nKQ7gA8AHwI+J+l1wFbg99sXYWN6KIc5f3Wurs9f4Bx24H18SRszMzOzYvT0zuuStkh6cQfE8XxJm0ueH4hL0oVdvG/AlJM0IOlbku6T9H+VrJX0c0nfLZ+3E7zPSkndfnSKTTOdksMAJN0v6Qn58ZWSLsqPT8u/7q2C8pyV294saVeep48tnbcTvM+iPN7M1kRujerpwqoIkjZIejAv0PfX8iVeLiK+HRFLJx+zdUoTY0lbxyTxEkPA3cCjI+JdwPOAlwDHRcSzap23ETESES8tIiBJIelJDb72dKWzK48pnQV78QTjbpG0t2TZ+2rJsNdK2l8y7H5Jp9XyWpteJC2R9OX8xb5T0keVzjFUl4iYExG3TkWMjcjrwHfK2g7Lax3gkJyVDyD4MPDSPE/vqWXeRsS2PN7+ZgPK32uvb/C1S3LuGsu5rOp3hqS/lHS7pHslbZX0virjvTrn1deXtL1T0q35tTsk/VUjy207uLCqzVvzAj2n0wqkaWAx8KM4+J/1YmBLRDzQxpgaIulxpKNk/jfwGGAU+OwkL/vtkmWvvDD8j5JhcyJiQx2vtenj46QjmX4VeAbwQuAtbY1oeinPWQPAUUA37qcHcDVp36nHAquBayTNqzLuFcAJEfFo0tGBKyWdVTqC0ika3sfh8+NLwDPza59KOiP92wqbiik0bQorSSdKuk3SOVPYx8uVrsN1n6Ttkt6d2yfbVH6kpE/n190sabAs7g2S9uRhrygZtqGswj/kF5ykE5SubfQzpeuc/X5uHwJWAu/JWzP+WdJngEXAP+e29+RxT5X077n/H5RuFakw/cdLulbSbkn3SPpobp8h6f35F8tdeVqPKXldxT4kXUk6CmM8zjeSzrL8nPz8g+XzdoIYapo34/1K+pikf8mfyQ2SnpiHfSuP9oMcwx9M8LmWOwu4OSI+HxEPAhcCT5d0Qh3vYdNUEzns8cDnIuLBfATavwK/VqWPJ0n6pqRfSLpb0mdLhk24pVbSu/L6faekVSXtx+R1fnfOAe+XNCMPO2RXCKWtITG+ZSK/9or8ntslXaR0fcATgb/lYC7YUymv5fdYIOkLuf/bJFX9cpbUp7TLwdY8D74jqS8Pe0XOwXty7j2x5HUV+1Da4bk0Z10NjP/rsUfSN8rnbbUYap03edhr8+suVdpSeZukM/KwNcDzgY/mmD5abX5UmD9PIZ0K4gMRsTcivgBsAl5VafyI2Fz2I/gRoHwZuhj4a9I/E6Wv/Z+SM9Cryms7U0T07A3YQjqvxjNJh4GuKBm2DthT5bauZLwNpLPi3g38P+C0Cfq7E3h+fnwsqdqGdJTBHeVx5ccXAg8CLycdcXQx8J952BHAT0nV/JHAi4D7gKUlsb2+5H1fC3wnP55NOlv0KtLRn6fkaTgpD78SuKjS/Cp5vhC4J8c2g7Q5+x5gXoVpnwn8APir3PdRwPPysD/K0/EEYA5pq81naumjPM7SaSyft5PEUO+8uYd0KZNZwAjwjyV9BvCkkueLqL4s7QH+MI93GekkgKXz7YfAqyZYfneRlh8DGzMAABvUSURBVL+vAk8vmw8P5Lj/m7QVbFYtr/Wte24Uk8PeCHyadHmRhXmZ+50q/V1N2goxo3T9ycMOLPel62VeBx8G/pSUs14OjAHH5uGfJh2ifjSwJC+vr8vDLgSuKuljSe5nVn7+T8Df5XX2V4DvAm/Mw15LSS4ojys/nwFsBP4PKYc+gXTU3fIq0/8xUl5dSMonvwk8CnhKXt9ekqfxPaScduRkfZTHWT6NFeZttRjqnTf7gDfk93gz6dIy4wesbaDkuyO3/RfVl6eP53F+h3S6jtLXfRT4mwmW4fcC9+fYbyX9JTo+7FmkLfczqsT0h8C9+bW76ZI81vYApnTiUlL6IOkEbKc1+B7PJiWER5G2ntwHPLHKuNtISezRZe2nMXFh9W8lw04C9ubHzyedQn9GyfCrgQvz40MWRA4tHv4A+HZZHH9H+qUBtRVWf0IugErargdeU2Han5MX/FkVhn0deEvJ86V5pZ81WR/lcTJxYTVRDPXOm8tLhr0c+HHJ80MKqzqWpSuAD5W1/T/gtVXGfy7QR/pCvCAvC3PzsCeQtkTMAE4mnffmglpe61v33Cgmh51I+uJ/OC+7V5K/YCuM+2nS4fHHVRg2UWG1l0MLhbtIF3qeCTxE/tGSh70R2JAfX0iVwor0l9kvgb6S4ecA6/PjQ3JBeVz5+bOBbWXjXACsrTB9M/J0HPblTfrh8rmycbfnaZ+wj/I4maCwmiSGeufNT0uG9efXzs/PN1BWxNS4LJ1H/uFf0rYGuHKS14n0A/aDwNG5bSapqDp1spiA/7+9+4+Sq6zvOP75khDCLCRgk+7yK1m0VIocJXajQCi6oi3URbRqT0pQu2LTWIpIRYogJyBF8Zz4g1r8sQdZad0iHqTqyUGrB1ctKpENSUGIFE02CZAsiwqEDBFjvv3juZOdzP6aH8/Mnbn7fp2zZ2fvnZnne2dnvvO99z73eU6QdF0h/mb/mQmnAldJ+rGP739SFndf5+673P237n6rwhfhX05y97cm67Ymh9NPK7OZnUW385LmJod7j5a03d2L58LaqvLmKVss6dXJYeunzexphcPkHWXGVHiOt5c8xxkKfTVKHSdpq7vvnWDd0UncxdtQSA6VtDGdqWIoVs5rU/o/OayKeEo9J2leybJ5CsX6OO7+Iw+H2/Pu/jGFPcc/S9Ztdvct7r7P3R9UOFrwtnIei5ZTdQ5LTrl9W+EocZukBQpH0z8+yUMuV/gS/Gly2uvdZTb1q5LPXeEzs0DhCE/p57/cHHawpB1Fn9MvKBydKddiSUeXfNav1MTzvS1QOEr3ywnWHZDDkpy8PdmOStqYzlQxFCvntdmfw9w9n9ysNY9VlMOK2nd336BQNF6bLP4HSQ+4+73TNerujyr0wfpsxRGnYKYUVovM7FPFC83sW3bgFVXFP9+a4vlcIfGMX+F+n7ufp/Dm/rqkr9YY+xOSjiv0R0gs0th0GrsV9kQKiguD7ZJ+4O5HFP0c5u7vLdqOcZtQ8vd2haNJxc/R5u43TPDY7Qqv80RXbTyhkAiKt2GvwqmqStqYzlQxlN5vqtemIjZ2GfRkPyuSuz6k0AGz8Lg2SS9R+Z1YJ33vTbOunPVoXrXksBcpfN7+Ldk5/JWkfk2yc+juO93979z9aIUjS5+1Kq+ATTylcHS69PNfbg77raQFRZ/Tee5e6B9Wbg7bUvJZP9zdJ9r+pxS6ZbxkgnUH5DAzM4UduccrbGM6U8VQul1TvTbTGffaJYX0ZO+nzyd3e0jSi83s8KKHvkLl57DZGtu2syS9xcKVqjsVTnl+Yoo+X8WPbWozobDaJelsSWea2f4va3c/xw+8oqr4p9DJ7wgz+wszm2tms5MvyDMV9gCLO1p2mtkcC2MlzfcwR9Kzqn3W9XUKe36Xm9nBFjp1n6swN5YkbZT0VxamD/gjSRcWPXatpD82s3ckjz3YzJbaWIfLEYXTScVKl31Z0rnJazAreR1ea2bHThDrTxX6mN1gZm3JfZcl626TdKmFKUEOk/RRSbcne7iVtDGdqWIoNt1rM50DXicfuwx6sp+B5K7/JelkM3urmc1V6JPxgLv/vLSBpFhblryv5prZBxX2Zn+UrD/HzNqT2ycqnKr4RjmPRcupOoe5+1OStkh6b5LDjlDo0vBA4XksDM3xt8nttxd99n6j8AVcdR7zMDTAVyVdb2aHWxhe5J8UPvdSyGFnJu/Z+Qqn0AqP3aHQP/ATZjbPwkUwLzGz1yR3GZF0rIXRylW0rDiH/VTSLjP7ZwsdwGeZ2clmtnSCWPdJukXSJy10Rp9lZqeZ2SHJNrzRwnApB0v6gEJh8+NK2ijj9ZoqhuL7TffaTGdc/nf3l03xflqV3Of/FP5nq5Pc8hZJL1eYf/IASUx/b2ZHWvAqSRcpdA2RwunKP1G4UvUUhdOC1yr08ZOZvcfM/jC5fZLCe+NutYCZUFjJw5UFb5B0jpldV8FDD5b0LxrrvH6xpDcnby4pOfWksb2vd0gaNrNnFfYyV6gG7v6CQiF1TtL+ZyW9s+iL+FMK/RdGJN2q0Mm68Nhdkv5cYR6tJxQOC39coa+YFPr7nGThMPLXk2Ufk/ThZNll7r5dYabyK5PXYLukD2qC902SQM9V6CewTaFPSOGKuVsk/YekHyok+T0Kr6UqaaOM12uqGIrvN91rM51rJN2avE5lT9Hi7qMKp4uvV/jSenUSgyTJzD5ftGd4uKTPJfd7XOGL9ZzkiIMU9vYeMLPdku5SONXz0TIfixZTQw6TwtWoZyt8vn6hcATpUklKipI/kFQ4HbNU0joze07hcvdLvPaxqy5WODK1WdI9kv5TISfI3b+rMOTIAwr9wNaWPPadCh3CH1Z4P9+hsW4C31M4UrLTzApXlB2Q15Kc0KPwxb1FIY/eLGm+JnaZwlVu90n6tUJeOMjdH5F0gaTPJM9xrsJwJi9U0cZ0JoxhgvtN9dpM50ZJb7NwxeC/VhjfckldSZs3SHpbktsKAzEXH716i8JpzV0KxfRnkh+5+9PJEdKdHq5WfUHSs+7+TPLYZQrTLxVy3F0K3xNNjyltamBmH5Y06u5fSDsWAKiUmZ0h6SJ3r9swNMBMQ2EFAAAQybSnW8zsFguDvv2saNmLLAyu+Gjy+8j6hgkA1SGHAWikcvqxfEnh/HyxKyTd7e4nKHQmuyJyXAAQy5dEDgPQIGWdCjSzToWRfE9O/n5EYbC6HWZ2lMJgb8yhB6ApkcMANEq1M0W3J5d7SuGKqkkHQrMwf9NKSWpra/vTE09kWjRgpli/fv1T7j7ZBK1pKiuHkb+Ama2aHFZtYbWfu7uZTXrYy937FKZIUFdXlw8NDdXaJIAWYWZbp79XuqbKYeQvYGarJodVO47VSHL4XMnvJ6t8HgBIAzkMQF1UW1h9U2H0XiW/vxEnHABoCHIYgLooZ7iF2yT9RNJLzewxM7tQYbTVN5jZo5Jen/wNAE2HHAagkabtYzXFiLxnRY4FAKIjhwFopBkxVyAAAEAjUFgBAABEQmEFAAAQCYUVAABAJBRWAAAAkVBYAQAAREJhBQAAEAmFFQAAQCQUVgAAAJFQWAEAAERCYQUAABAJhRUAAEAkFFYAAACRUFgBAABEQmEFAAAQCYUVAABAJBRWAAAAkVBYAQAAREJhBQAAEAmFFQAAQCQUVgAAAJFQWAEAAERCYQUAwGQGB6XOzvAbKMPsWh5sZpdKeo8kl/SgpF533xMjMLS+jjUdGtk9Mm55e1u7dl62M4WIgAORwzClwUGpp0fK58PvtWul7u60o0KTq/qIlZkdI+l9krrc/WRJsyQtjxUYWt9ERdVUy4FGIodhSsVFlTRWXHHkCtOo9VTgbEmHmtlsSTlJT9QeEgA0DDkM45UWVQUUVyhD1YWVuz8uaY2kbZJ2SHrG3b9Tej8zW2lmQ2Y2NDo6Wn2kABBROTmM/DVD9faOL6oK8vmwHphELacCj5R0nqTjJR0tqc3MLii9n7v3uXuXu3ctXLiw+kgBIKJychj5a4bq75dyuYnX5XJhPTCJWk4Fvl7SFncfdfffSbpT0ulxwgKAuiOHYWLd3aGjemlxlcvRgR3TqqWw2ibpVDPLmZlJOkvSpjhhIQva29orWg40GDkMkystriiqUKaqh1tw93Vmdoek+yXtlbRBUl+swND6GFIBzYwchmkViqve3nD6j6IKZahpHCt3Xy1pdaRYAKChyGGYVne3NDycdhRoIYy8DgAAEAmFFQAAQCQUVgAAAJFQWAEAAERCYQUAABAJhRUAAEAkFFYAAExmcFDq7KzvxMuNaAMNU9M4VsBUOtZ0aGT3yLjl7W3t0QYPbUQbAGaowUGppydMvNzTU5+R1xvRBhqKI1aom4kKnqmWN2sbAGag4oJHGit8Yh5VakQbaDgKKwAAipUWPAUxC59GtIFUUFgBAFCst3d8wVOQz4f1rdAGUkFhBQBAsf5+KZebeF0uF9a3QhtIBYUVAADFurtDJ/LSwieXi9e5vBFtIBUUVqib9rb2ipY3axsAZqDSwqceBU8j2kDDMdwC6qYRwx0wpAKAuikUPr294dRcPQqeRrSBhqKwAgBgMt3d0vBw67eBhuFUIAAAQCQUVgAAAJFQWAEAAERCYQUAABAJhRUAAEAkFFYAAACRUFgBAABEUtM4VmZ2hKSbJZ0sySW9291/EiMwtL6ONR0a2T0ybnl7W3u0gT0b0QayixyG6ezYsUPLly/X7bffro6OjpZtA41T6xGrGyV9291PlPQKSZtqDwlZMVHBM9XyZm0DmUYOw5Suu+463XPPPfrIRz7S0m2gcczdq3ug2XxJGyW92Mt8kq6uLh8aGqqqPbQeu9YmXeerq3vfpdEGqmdm6929K+04JlJpDiN/zSyHHnqo9uzZM2753Llz9fzzz7dMG6hNNTmsliNWx0saldRvZhvM7GYza5sgqJVmNmRmQ6OjozU0BwBRTZvDyF8z1+bNm3X++ecrl0yQnMvltGLFCm3ZsqWl2kDj1VJYzZb0Skmfc/clknZLuqL0Tu7e5+5d7t61cOHCGpoDgKimzWHkr5nrqKOO0rx587Rnzx7NnTtXe/bs0bx586L2gWpEG2i8WgqrxyQ95u7rkr/vUEhSANAKyGGY0sjIiFatWqV7771Xq1at0s6d8S+IaUQbaKyqrwp0951mtt3MXuruj0g6S9LD8UJDq2tva5/0ir1WagPZRA7DdO688879t2+66aaWbQONVdNwC5IuljRgZnMkbZbUW3tIyIpGDHfAkAqoETkMQFQ1FVbuvlFSU17xAwDTIYcBiI2R1wEAACKhsAIAAIiEwgoAACASCisAAIBIKKwAAAAiobACAACIhMIKAAAgEgorAACASCisAAAAIqGwAgAAiITCCgAAIBIKKwAAgEgorAAAACKhsAIAAIiEwgoAACASCisAAIBIKKwAAAAiobACAACIhMIKAAAgEgorAACASCisAAAAIqGwAgAAiITCCgAAIJKaCyszm2VmG8xsbYyAAKBRyF8AYotxxOoSSZsiPA8ANBr5C0BUNRVWZnaspDdKujlOOADQGOQvAPVQ6xGrT0u6XNK+ye5gZivNbMjMhkZHR2tsDgCiIX8BiK7qwsrMeiQ96e7rp7qfu/e5e5e7dy1cuLDa5gAgGvIXgHqp5YjVMklvMrNhSV+R9Doz+3KUqACgvshfAOqi6sLK3T/k7se6e6ek5ZK+5+4XRIsMAOqE/AWgXhjHCgAAIJLZMZ7E3b8v6fsxngsAGon8BSAmjlgBAABEQmEFAAAQCYUVAABAJBRWAAAAkVBYAQAAREJhBQAAEAmFFTATDQ5KnZ3hNwAgGgqrCg0MhO+jgw4KvwcG0o4IqNDgoNTTI23dGn5TXM0IHWs6ZNfauJ+ONR1phwZU7Jpr0o5gchRWFRgYkFauDN9H7uH3ypUUV2ghhaIqnw9/5/MUVzPEyO6RipYDzezaa9OOYHIUVhW46qqx76OCfD4sB5peaVFVQHEFANFQWFVg27bKlgNNpbd3fFFVkM+H9QDQpK65RjILP9LY7WY7LUhhVYFFiypbDjSV/n4pl5t4XS4X1gNAk7rmmtANxz38XbhNYdXCrr9+/PdSLheWA02vu1tau3biN/HatWE9AKAmFFYVWLFC6uuTFi8Ohx8XLw5/r1iRdmRAmUqLK4qqGaO9rb2i5UAzW7067QgmZ144ptYAXV1dPjQ01LD2AExicDD0qervr2tRZWbr3b2rbg00EPkLmHmqyWGz6xUMgCbW3S0ND6cdBQBkDqcCAQAAIqGwAgAAiITCCgAAIBIKKwAAgEgorAAAACKhsAIAAIiEwgpoNoODUmcnkyIDQAuqehwrMztO0r9Lapfkkvrc/cZYgVViyX33aePu3eOWn9LWpg1Ll6YQUW0GBqSrrgqTOy9aFKbMYXT3GWJwUOrpCZMi9/QwKnodkcPi61jToZHdI+OWt7e1a+dlO1OICI3Ed1dQyxGrvZI+4O4nSTpV0kVmdlKcsCpz2rx5mlOY7joxx0ynz5+fRjg1GRiQVq6Utm4Nk0tu3Rr+HhhIOzLUXXFRJY0VVxy5qhdyWGQTFVVTLUd28N01purCyt13uPv9ye1dkjZJOiZWYJW4urNTB5UkpVlmunrx4jTCqclVV419rxbk82E5Mqy0qCqguKobchgQD99dY6L0sTKzTklLJK2bYN1KMxsys6HR0dEYzY1z1CGHqLe9ff8e3xwz9XZ0qOOQQ+rSXj1t21bZcmREb+/4rFSQz4f1qJvJclgj8peUrRyGmYnvrjE1F1Zmdpikr0l6v7s/W7re3fvcvcvduxYuXFhrc5Mq3uNr5T29RYsqW46M6O+XcrmJ1+VyYT3qYqoc1qj8JWUnh2Fm4rtrTE2FlZkdrJCQBtz9zjghVaewx3eQ1NJ7etdfP/77NZcLy5Fh3d2ho/pE/3w6sNcNOQyIg++uMVUXVmZmkr4oaZO7fzJeSNW7urNTZ8yf39J7eitWSH190uLFkln43dc3M6+smHFKiyuKqroih8XX3tZe0XJkB99dY8zdq3ug2RmS/kfSg5L2JYuvdPe7JntMV1eXDw0NVdUeMGMMDoY+Vf39LV9Umdl6d+9KO46JVJrDyF/AzFNNDqt6HCt3v0eSTXtHAJXp7paGh9OOIvPIYQDqgZHXAQAAIqGwAgAAiITCCgAAIBIKKwAAgEgorAAAACKhsAIAAIiEwgqYiQYHpc5OJncGgMiqHseqmSy57z5t3L173PJT2tq0YenSFCJCFu3auEsbX7tRp/zwFB3+8sPTDqd6g4NST0+Y3Lmnh9HdmwA5DI2QhRzm/ntt3/4pbdt2gxYt+pCOO+79MpuVdlgHyMQRq9Pmzds/K3zBHDOdPn9+ShEhizZdsEm/f+b32nT+prRDqV5xUSWNFVccuUoVOQyN0Oo5LJ9/VENDXRoevkZ79/5Kw8OrtX79UuXzj6Yd2gEyUVgVzwpfwOzwiGnXxl3KPxSKkfxDee16YFfKEVWhtKgqoLhKHTkM9ZaFHLZhwzLt3v2A9u0LR3f37dut5577X23YsCzlyA6UicKqMCt8YY9vjhmzwyOqTRccuIfXknt8vb3ji6qCfD6sRyrIYai3LOSwtraXaWxaz4J9ams7OY1wJpWJwko6cI+PPT3EVLynV9CSe3z9/VIuN/G6XC6sR2rIYaiXrOSwjo4LNWvWYQcsmzXrMHV0vDuliCaWmcKqsMd3kMSeHqIq3dPbv7zV9vi6u0NH9dLiKpejA3sTIIehXrKSwxYsOFdmB15zZzZbCxacm1JEE8vEVYEFV3d26qF8nj09RPX8L5+vaHlTKxRXhb5WFFVNhRyGeshKDps9e77OOOM3aYcxrUwVVkcdcoh+sGRJ2mEgY17z/GvSDiGuQnHV2xtO/1FUNQ1yGOohczmsyWWqsAJQpu5uaXg47SgAIHMy08cKAAAgbRRWAAAAkVBYAQAAREJhBQAAEAmFFQAAQCQUVgAAAJFQWAEAAERSU2FlZmeb2SNm9gszuyJWUADQCOQwALFVXViZ2SxJN0k6R9JJkv7GzE6KFRgA1BM5DEA91HLE6lWSfuHum939BUlfkXRenLAAoO7IYQCiq2VKm2MkbS/6+zFJry69k5mtlLQy+fO3ZvazGtpsJgskPZV2EBFkZTsktqUZvTTtAKYwbQ4jf7WErGxLVrZDyta2VJzD6j5XoLv3SeqTJDMbcveuerfZCFnZlqxsh8S2NCMzG0o7hlqQv5pfVrYlK9shZW9bKn1MLacCH5d0XNHfxybLAKAVkMMARFdLYXWfpBPM7HgzmyNpuaRvxgkLAOqOHAYguqpPBbr7XjP7R0n/LWmWpFvc/aFpHtZXbXtNKCvbkpXtkNiWZtS021FFDmvabakC29J8srId0gzfFnP3egQCAAAw4zDyOgAAQCQUVgAAAJE0pLDKyrQRZnacmQ2a2cNm9pCZXZJ2TLUys1lmtsHM1qYdSy3M7Agzu8PMfm5mm8zstLRjqoaZXZq8t35mZreZ2dy0YyqXmd1iZk8Wj/VkZi8ys++a2aPJ7yPTjLFaWchh5K/mlZX8JZHDpAYUVhmbNmKvpA+4+0mSTpV0UQtvS8ElkjalHUQEN0r6trufKOkVasFtMrNjJL1PUpe7n6zQoXp5ulFV5EuSzi5ZdoWku939BEl3J3+3lAzlMPJX82r5/CWRwwoaccQqM9NGuPsOd78/ub1L4c1/TLpRVc/MjpX0Rkk3px1LLcxsvqQzJX1Rktz9BXd/Ot2oqjZb0qFmNltSTtITKcdTNnf/oaRflyw+T9Ktye1bJb25oUHFkYkcRv5qThnLXxI5rCGF1UTTRrTsh7nAzDolLZG0Lt1IavJpSZdL2pd2IDU6XtKopP7ktMDNZtaWdlCVcvfHJa2RtE3SDknPuPt30o2qZu3uviO5vVNSe5rBVClzOYz81VQykb8kclgBnderYGaHSfqapPe7+7Npx1MNM+uR9KS7r087lghmS3qlpM+5+xJJu9Wap5yOVNg7Ol7S0ZLazOyCdKOKx8PYLozvkjLyV9PJRP6SyGEFjSisMjVthJkdrJCUBtz9zrTjqcEySW8ys2GFUxuvM7MvpxtS1R6T9Ji7F/a+71BIVK3m9ZK2uPuou/9O0p2STk85plqNmNlRkpT8fjLleKqRmRxG/mpKWclfEjlMUmMKq8xMG2FmpnAefJO7fzLteGrh7h9y92PdvVPhf/I9d2/JPQt33ylpu5kVZiE/S9LDKYZUrW2STjWzXPJeO0st2om1yDclvSu5/S5J30gxlmplIoeRv5pThvKXRA6TVMOUNuWqcuqbZrVM0jskPWhmG5NlV7r7XSnGhOBiSQPJF99mSb0px1Mxd19nZndIul/hCq4NaqGpIczsNkmvlbTAzB6TtFrSDZK+amYXStoq6a/Ti7A6Gcph5K/m1fL5SyKH7X8eprQBAACIg87rAAAAkVBYAQAAREJhBQAAEAmFFQAAQCQUVgAAAJFQWAEAAERCYQUAABDJ/wNdRP3mCnqx7wAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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xZlZhZm+a2VmdXss0s8rw8fOimFMOwR8Ikp7qo7S4n9dRJExD30VEko/XW3ekAmOBjwEXAw+aWd/wayOcc6XAJcDdZvah2+DM7OpwoavcuXNnb2VOGhVVIU4c3o/MtBSvo0gnGvouIpJcolnWtgHDOj0fGj7W2Vbgeedci3NuA7COjvKGc25b+Gs18CpQcuAPcM494Jwrdc6V9u/fv+d/B0mspr6Z1e/s0Xq1GHVFeTFfOGk4979WzR8XbfY6joiIRFE0y9oiYKyZjTSzdOAi4MC7Op+j46waZlZEx2XRajPrZ2YZnY6XA6ujmFUO8GZ1x4ipsjFarxaLNPRdRCR5RK2sOedaga8BLwFvA08551aZ2W1mtv/uzpeAkJmtBv4NfNs5FwKOByrNbFn4+B3OOZW1XlRRFSQ3I5UpQ/O9jiKHoKHvIiLJwRJlhE1paamrrKz0OkbCOOWXrzKyKIe5s6d5HUW6sTnUwHm/rSA/K40HLjuR7z+3kt9cUqJZriIiMczMFofX5nfL6xsMJAZtr21kQ7Be69XiROeh75f+fgGLNtZwzz/Xex1LRER6iMZNyYf4Ax3r1cq1Xi1uXPr7BTS3tbNjbxMAjy3YzGMLNpOR6mPt7Wd7nE5ERI6GzqzJh/irghTkpHPcwD5eR5EIvfGdUzhn6mBSfAZAWopx7tTBvHHjKR4nExGRo6WyJh/gnKMiEGTm6EJ84T/4JfYNyMukT0Yq7c5hQEtbx1etWxMRiX8qa/IB1cF63tvTpBFTcShY18SlM0bw0KxppPqMV9fupLWt3etYIiJylFTW5AP8VR37dZWP0c0F8eb+y0q5/byJnHr8AO68cAq1jS3c8y/daCAiEu9U1uQDKqpCDOmbxfCCbK+jyFE4d+oQLjhxKPf+u4r54RtGREQkPqmsyfva2x3zq0OUjS7ETOvV4t0Pz5nAyMIcrv/jEmrqm72OIyIiR0hlTd63+p097G5s0ZYdCSInI5V7Li5hV30L33lmGYmyAbaISLJRWZP3VYTXq83UZrgJY+KQfG48exz/fHsHj87f5HUcERE5Aipr8r6KQIgxA3IZmKftHhLJnPJiTh03gB///W1Wb9/jdRwRETlMKmsCQHNrO4s21FCus2oJx8z4xQWTyc9K49on3qKhudXrSCIichhU1gSApVtqaWxpo0zr1RJSYW4Gd39+KtXBem7762qv44iIyGFQWROgY72az+CkkTqzlqjKxxTx5ZNH8+SiLfx12Xav44iISIRU1gSA+YEQE4fkk5+d5nUUiaJvfPxYSob35Xt/WsGWmgav44iISARU1oSG5laWbNlFmUZMJby0FB/3XFQCwHVPLqFF46hERGKeypqwcEMNLW2OMt1ckBSGFWTzk89MYsnmWu7+5zqv44iISDdU1gR/IER6io9pxQVeR5Fe8ukpg7mwdCi/fTXw/jxYERGJTSprgj8QpGR4X7LSU7yOIr3o1nMmMLIoh+v/uJRQXZPXcURE5BBU1pJcbUMzq7bv0YipJJSdnsq9F5dQ29DCt59ZrnFUIiIxSmUtyc0PhHAOrVdLUhMG5/O9T4zjlTU7eMS/0es4IiJyECprSc4fCJGTnsKUYX29jiIemVVWzGnjBvDTv69h5bbdXscREZEDqKwluYpAkOkjC0hL0f8UkpWZ8YvPTaFfThrXPbGE+iaNoxIRiSX6EzqJvbt7H9U767W/mlCQk86vPj+VDaF6bn1+lddxRESkE5W1JFYR3rKhbIzWqwmUjS7iqx8bw9OLt/KXpdu8jiMiImEqa0nMHwjRLzuN44/J8zqKxIjrTx/LCcP7cvOfV7I5pHFUIiKxQGUtSTnn8AeCzBxdiM9nXseRGJGa4uPXF5WAaRyViEisUFlLUhuC9byze5/Wq8mHDCvI5o7PTGbpllruelnjqEREvKaylqT8gRCANsOVg/rk5EFcPH0Yv3stwH/WaxyViIiXVNaSlD8QZHB+JsWF2V5HkRj1g09NYHT/XL7x1FKCGkclIuIZlbUk1N7umB8IMXN0EWZaryYHl5Wewr0Xl7C7sYVvPb2M9naNoxIR8YLKWhJ6+9097GpooVxbdkg3jh+Ux82fPJ5X1+5kbsUGr+OIiCQllbUk5K/qWK+mmwskEpedNIKPjx/Iz17UOCoRES+orCWhikCQUf1zOCY/0+soEgfMjJ9/djKFORlc+8QS6jSOSkSkV6msJZnm1nYWbqihXGfV5DD0y0nn7oumsilUzy1/0TgqEZHepLKWZJZvraWhuU3r1eSwnTSqkK+dOpZn39rKc0s0jkpEpLeorCWZiqoQZh1/8IocrutOHcO04n7c/NxKNoXqvY4jIpIUVNaSTEUgyITBefTNTvc6isSh1BQfd19Ugs/guieW0NyqcVQiItGmspZEGpvbWLJ5l9aryVEZ0jeLn312Msu27ubOf6z1Oo6ISMJTWUsiizbW0NLmKNOIKTlKZ08axCUzhnP/69W8vm6n13FERBKayloSqQgESUsxphX38zqKJIAffGo8xw7M5ZtPLWPnXo2jEhGJFpW1JOKvClEyrB/Z6aleR5EEkJmWwr0Xn8DefS3coHFUIiJRo7KWJHY3tLBy+27KtGWH9KDjjunDf39qPK+v28lD/9E4KhGRaFBZSxLzq0M4B+VaryY97NIZwzlzwkB+/tIalm+t9TqOiEjCUVlLEv5AkKy0FKYM7et1FEkwZsbPPjuZ/rkaRyUiEg0qa0nCHwgxfWQB6an6Vy49r292OndfVMKWmgZ+8NxKr+OIiCQU/cmdBN7bs4+qHXUaMSVRNX1kAdedNpY/LdnGn97a6nUcEZGEobKWBPyBIABl2gxXouzaU8cyfWQB//3cSjYENY5KRKQnqKwlgYqqEH2z0xg/KM/rKJLgUnzG3Z+fSmqKT+OoRER6iMpagnPOMT8QYuaoQnw+8zqOJIHBfbP4+QWTWbFtN794aY3XcURE4p7KWoLbFGpgW20jZaO1Xk16z5kTjuGyk0bw4BsbeHXtDq/jiIjENZW1BFexf72a9leTXvb9Tx7PuGP68K2nl7Fj7z6v44iIxC2VtQTnD4Q4Ji+TUUU5XkeRJNMxjqqEuqZWbnhK46hERI6UyloCa2/vWK9WNqYQM61Xk943dmAffvCpCbyxPsgDb1R7HUdEJC6prCWwNe/upaa+WVt2iKcunj6MT0w6hl++tJalWzSOSkTkcKmsJbD9+6tpM1zxkpnx0/MnMzAvk+ueWMLefS1eRxIRiStRLWtmdpaZrTWzKjO76RDvudDMVpvZKjN7vNPxWWa2PvyYFc2cicofCDGqKIdB+VleR5Ekl5+dxq8vmsq22kZufm4lzmn9mohIpKJW1swsBbgPOBsYD1xsZuMPeM9Y4LtAuXNuAnB9+HgBcAswA5gO3GJm/aKVNRG1tLWzoDrETG3ZITGitLiA608by1+WbufZt7Z5HUdEJG5E88zadKDKOVftnGsGngTOPeA9VwH3Oed2ATjn9m/IdCbwsnOuJvzay8BZUcyacJZvraW+uY1ybdkhMeQrp4zhpFEF/OAvK6neWed1HBGRuBDNsjYE2NLp+dbwsc6OBY41swoze9PMzjqMz2JmV5tZpZlV7ty5swejxz9/VQgzmDlKZ9YkdnSMoyohPdXHtU8soam1zetIIiIxz+sbDFKBscDHgIuBB82sb6Qfds494Jwrdc6V9u/fP0oR41NFIMj4QXn0y0n3OorIBxyTn8kvLpjCqu17+PmLa72OIyIS86JZ1rYBwzo9Hxo+1tlW4HnnXItzbgOwjo7yFsln5RAam9t4a1OtRkxJzPr4+IHMmjmCh/6zgX+v0TgqEZGuRLOsLQLGmtlIM0sHLgKeP+A9z9FxVg0zK6Ljsmg18BJwhpn1C99YcEb4mERg8aZdNLe1a8SUxLTvfqJjHNUNTy9jxx6NoxIROZSolTXnXCvwNTpK1tvAU865VWZ2m5mdE37bS0DIzFYD/wa+7ZwLOedqgB/RUfgWAbeFj0kEKgJBUn3G9OICr6OIHFJmWgq/uaSExuY2vvHUUo2jEhE5BEuU/Y5KS0tdZWWl1zFiwrm/+Q9pKT6e+XKZ11FEuvXHRZu58dkVfOes4/jKx8Z4HUdEpFeY2WLnXGkk7/X6BgPpYbsbW1ixbbcugUrcuLB0GJ+cPIg7/7GOtzbv8jqOiEjMUVlLMAuqQ7Q7KNfNBRInzIyfnD+JY8LjqPZoHJWIyAeorCUYfyBEZpqPkuEa+CDxIz8rjXsuLuGd3fv43p9WaByViEgnKmsJpqIqyLTiAtJT9a9W4suJI/rxzY8fy9+Wv8PTlVu9jiMiEjP0J3oC2bF3H+t31GnElMSta04eTdnoQm55fhVVOzSOSkQEVNYSyvxACIDy0SprEp9SfMavPj+VzLSOcVT7WjSOSkREZS2BVFQFyctMZfzgPK+jiByxgXmZ/PJzU3j7nT3c8cIar+OIiHhOZS1BOOeoqAoxc3QhKT7zOo7IUTnt+IFcUV7MI/6N/HP1e17HERHxlMpagthS08i22katV5OEcdPZ4xg/KI9vP7OMd3drHJWIJC+VtQRREQgCaHi7JIyM1BTuvaSEfS3tfOOPS2nTOCoRSVIqawmioirIgD4ZjO6f63UUkR4zun8uPzx3AvOrQ/zutYDXcUREPKGylgCcc8wPhCgfU4SZ1qtJYvnciUP59JTB3PXyOhZvqvE6johIr1NZSwBr39tLqL5Zl0AlIZkZPz5/IoP7ZnLdE0vZ3ahxVCKSXFTWEkBFVcf+ahreLokqLzONey4q4b09GkclIslHZS0B+KuCFBdmM6RvltdRRKKmZHg/bjjjOP53xTv8cdEWr+OIiPQalbU419rWzoINNTqrJknhSx8dxUfGFHHrX1dRtWOv13FERHpFRGXNzEaY2enhX2eZWZ/oxpJILd+2m7qmVq1Xk6Tg8xl3XTiFnPRUvva4xlGJSHLotqyZ2VXAM8D94UNDgeeiGUoi56/q2F9t5iiVNUkOA8LjqNa8u5ef/v1tr+OIiERdJGfWvgqUA3sAnHPrgQHRDCWR8wdCHD8oj8LcDK+jiPSaU8YN4MqPjGTe/E28rHFUIpLgIilrTc655v1PzCwV0K1YMWBfSxuVm3ZRrkugkoS+c9ZxTBzSMY7qnd2NXscREYmaSMraa2b2PSDLzD4OPA38NbqxJBKLN+2iubWdsjEqa5J8MlJTuOeiEppb27n+SY2jEpHEFUlZuwnYCawAvgT8Hbg5mqEkMv5AkFSfMX2kypokp1H9c/nRuRNZsKGG+/5d5XUcEZGoSO3qRTNLAR51zl0KPNg7kSRSFVUhpgzrS25Gl/8aRRLaZ04Ywhvrd3L3P9dRNrqQ0uICryOJiPSoLs+sOefagBFmlt5LeSRCe/a1sHxrrbbskKRnZvzovIkMK8jm608uZXeDxlGJSGKJ5DJoNVBhZv9tZt/c/4h2MOnaguoa2h2UjdZmuCJ9Oo2juulPyzWOSkQSSiRlLQD8LfzePp0e4iF/IEhmmo8TRvT1OopITJgyrC/fPvM4Xlj5Lk8s1DgqEUkc3S52cs79EMDMcsPP66IdSrrnrwoxrbiAjNQUr6OIxIyr/msU/6kK8sO/rqK0uB/HDtTfK0Uk/kUywWCimS0BVgGrzGyxmU2IfjQ5lJ17m1j73l5mar2ayAf4fMadF06hT2Yq12oclYgkiEgugz4AfNM5N8I5NwK4Ad0Z6qn51SEAyrVeTeRDBvTJ5M4Lp7L2vb38+H81jkpE4l8kZS3HOffv/U+cc68COVFLJN3yVwXpk5nKxCH5XkcRiUknH9ufqz86ij+8uYkXV77rdRwRkaMS0d2g4TtBi8OPm+m4Q1Q8UhEIctKoQlJ85nUUkZj1rTOOY/LQfG58djnbazWOSkTiVyRlbQ7QH/gT8CxQFD4mHthS08CWmkbNAxXpRnqqj3suKqG1rWMcVWtbu9eRRESOSLdlzTm3yzl3nXPuBOfcic65651zu3ojnHyYPxAEoHyM1quJdKe4KIfbz5/Iwo01/EbjqEQkTkVyN+jLZta30/N+ZvZSdGPJoVRUhejfJ4MxA3K9jiISF84vGcpnSoZwz7/Ws3BDjddxREQOWySXQYucc7X7n4Qr9zZ2AAAgAElEQVTPqg2IXiQ5FOcc/kCIstGFmGm9mkikbjtvIsMLsrn+ySXUNjR7HUdE5LBEUtbazWz4/idmNgLQLBcPrN9RR7CuSVt2iBym3IxU7r34BHbWNXHjsxpHJSLxJZKy9n3gP2b2BzN7DHgd+G50Y8nBVFR1rFcrG6ObC0QO16Sh+dx41jheWvUejy3Y7HUcEZGIRTJu6kUzOwE4iY4zatc754JRTyYfUlEVYnhBNkP7ZXsdRSQuzSkfyRvrg/zob6uZVtyPccfkeR1JRKRbhzyzZmYjzCwfIFzO6oEzgMvNLL2X8klYa1s7C6pDlOusmsgR8/mMX35uCnmZaVz3xBIamzWOSkRiX1eXQZ8iPKnAzKYCTwObgSnAb6MfTTpbuX0Pe5taKdN6NZGj0r9PBr/6/BTWvVfHj/53tddxRES61VVZy3LObQ//+gvAXOfcncAVwPSoJ5MP2L9eTcPbRY7ef43tz5dOHsXjCzbzwop3vI4jItKlrspa570hTgX+BeCc0zbgHvAHgow7pg9FuRleRxFJCN864zimDOvLjc8uZ+uuBq/jiIgcUldl7RUze8rMfg30A14BMLNBgDYq6kX7Wtqo3LhLl0BFelBaio97Lyqh3aFxVCIS07oqa9fTMQ90I/AR51xL+PgxdGznIb3krc27aGpt180FIj1seGE2Pz5/IpWbdnHPKxpHJSKx6ZBbd7iOXSOfPMjxJVFNJB/irwqR4jOmjyzwOopIwjl36hDeWB/kN6+sp2x0ISeN0l+KRCS2RLIprnisIhBk8tB8+mSmeR1FJCH98JwJFBfmcP2TS9lVr1UeIhJbVNZi3N59LSzfulsjpkSiKCcjlXsuLqGmvplvP6NxVCISW7ota2b29UiOSXQs3FBDW7ujTFt2iETVxCH53Hj2OP759nv84c1NXscREXlfJGfWZh3k2OweziGHUFEVIiPVxwkj+nkdRSThzSkv5tRxA7j9f9/m7Xf2eB1HRAToetzUxWb2V2CkmT3f6fFvoKb3IiY3fyBIaXE/MtNSvI4ikvDMjF9cMJn8rDSufWIJDc2tXkcSEelykLsfeAcoAu7sdHwvsDyaoaRDsK6JNe/u5dtnHud1FJGkUZibwd2fn8oXHlrA9/60gu279/GbS0oY0CfT62gikqS62rpjE7AJmNl7caSz+YEQgNarifSy8jFFfPnk0fz21QAG3PPP9dx+/iSvY4lIkurqzBoAZvYZ4GfAADpGUBkd27DlRTlb0vMHQvTJSGXSkHyvo4gkleNufoGm1o6JBg54bMFmHluwmYxUH2tvP9vbcCKSdCK5weDnwDnOuXznXJ5zro+KWu/wB4LMGFVIaop2WBHpTW985xTOmTqYjNT/+2/vzAkDeePGUzxMJSLJKpIW8J5z7u2oJ5EP2LqrgU2hBl0CFfHAgLxM+mSk0tzWTlqKAfBmdQ0ZKbrRR0R6XyRlrdLM/hi+O/Qz+x9RT5bk/FUd69XKx2gzXBEvBOuauHTGCP7y1Y9w+vED2NPYwpXzFtHY3OZ1NBFJMpGUtTygATgD+HT48alIvrmZnWVma82sysxuOsjrs81sp5ktDT++2Om1tk7Hn4/st5M4/IEgRbnpHDsw1+soIknp/stKuf28iYwfnMfvZ03jvktPYPHmXXzt8bdoaWv3Op6IJJFubzBwzl1xJN/YzFKA+4CPA1uBRWb2vHNu9QFv/aNz7msH+RaNzrmpR/Kz451zjopAiJmjizAzr+OICPCJSYO47dyJ/PdzK/nun1bwiwsm679PEekVkYybOtbM/mVmK8PPJ5vZzRF87+lAlXOu2jnXDDwJnHt0cZND1Y46du5tolzr1URiymUnjeD608fyzOKt3PHiGq/jiEiSiOQy6IPAd4EWAOfccuCiCD43BNjS6fnW8LEDfdbMlpvZM2Y2rNPxTDOrNLM3zey8g/0AM7s6/J7KnTt3RhApPvgDWq8mEqu+ftpYLjtpBPe/Vs2Dr1d7HUdEkkAkZS3bObfwgGM9NYPlr0Cxc24y8DIwr9NrI5xzpcAlwN1mNvrADzvnHnDOlTrnSvv3799DkbxXURVkWEEWwwqyvY4iIgcwM249ZwKfnDSIH//9bZ5dvNXrSCKS4CIpa8FwUXIAZnYBHWOourMN6HymbGj42PuccyHnXFP46e+BEzu9ti38tRp4FSiJ4GfGvbZ2x5vVIcpG6ayaSKxK8Rl3fX4K5WMK+c6zy3llzXteRxKRBBZJWfsqcD8wzsy2AdcDX47gc4uAsWY20szS6bh0+oG7Os1sUKen5wBvh4/3M7OM8K+LgHLgwBsTEtLKbbvZs6+VsjFaryYSyzJSU7j/slLGD8rjK//zFos31XgdSUQSVLdlLXyDwOlAf2Ccc+4jzrmNEXyuFfga8BIdJewp59wqM7vNzM4Jv+06M1tlZsuA64DZ4ePH07G/2zLg38AdB7mLNCH5358HqjNrIrEuNyOVh6+YxqD8LOY8Usm69/Z6HUlEEpA55w7+gtkXnHOPmdk3D/a6c+6uqCY7TKWlpa6ystLrGEftsocW8N6effzjGyd7HUVEIrSlpoHP/j8/PjOe+fJMhvbTelMR6ZqZLQ6vze9WV2fWcsJf+xziIT2sqbWNRRtrdFZNJM4MK8hm3pzp1De3cvnchdTUN3sdSUQSyCE3xXXO3R/++sPei5PclmyuZV9Lu7bsEIlDxw/K46FZ07jsoQVc8fBCHr/qJHIyut13XESkW5FsijvPzPp2et7PzOZGN1Zy8lcF8RnMGFXgdRQROQLTRxbwm0tOYOX2PVzz2GKaWzWWSkSOXiR3g052ztXuf+Kc20WSbKPR2yoCISYN7UteZprXUUTkCH18/EB+ev4k3lgf5FtPL6O9/eDrgkVEIhVJWfOZWb/9T8ysgAhmisrhqWtqZdmWWo2YEkkAF04bxo1njeP5Zdu57W+rOdSNXCIikYikdN0JzDezpwEDLgB+HNVUSWjRhhpa253Wq4kkiGtOHkWwromH/rOBotx0vnbqWK8jiUic6rasOeceNbNK4NTwoc8ky55nvamiKkh6qo8TR/Tr/s0iEvPMjO9/4nhq6pv55T/WUZibwcXTh3sdS0Ti0CHLmpnlOef2hC97vgs83um1AuectuvuQRWBECcO70dmWorXUUSkh/h8xs8vmMyuhma+/+cV9MtO46yJg7r/oIhIJ12tWdtfzhYDlZ0e+59LD6mpb+btd/ZQrhFTIgknLcXHby89gSnD+nLdk0t5szrkdSQRiTNdlbU7wl+Pd86N6vQY6Zwb1RvhksX88IipmdoMVyQhZaenMnfWNIYXZHPVvEpWbd/tdSQRiSNdlbVfh7/6eyNIMqsIBMnNSGXK0Hyvo4hIlPTLSefROdPJzUxl1txFbArVex1JROJEV2WtxcweAIaa2T0HPnorYDLwVwWZMbKA1JRIdlIRkXg1uG8Wf7hyOq3t7Vw+dyE79u7zOpKIxIGu2sGngFeARjrWqR34kB6wrbaRjaEGyrRlh0hSGDOgDw/PnsaOPU3MnruIPftavI4kIjGuq607vu2cu9HMhjvn5vVaoiTjrwoCUKbNcEWSRsnwfvy/L5zAF+dVcvWjlTxyxXTdCS4ih9TVmbVPmJkBF/VWmGTkD4QozEnnuIF9vI4iIr3oY8cN4Jefm8Kb1TVc/+RS2jSWSkQOoauy9iKwC5hsZnvMbG/nr72UL6E55/AHgswcXYjPZ17HEZFedl7JEH7wqfG8uOpdbn5upcZSichBHbKsOee+7ZzrC/yvcy7POden89dezJiwAjvreW9PE2XaskMkac35yEi+8rHRPLFwM796eZ3XcUQkBkUybupcMxsBjHXO/dPMsoBU59ze6MdLbP5Ax3o1bYYrkty+feZxhOqaueeVKgpy0pldPtLrSCISQ7ota2Z2FXA1UACMBoYCvwNOi260xOevCjGkbxbDC7K9jiIiHjIzfnz+RHY1NPPDv62mIDeDc6YM9jqWiMSISDb2+ipQDuwBcM6tBwZEM1QyaGt3zK8OUT6mkI77OEQkmaWm+Ljn4hKmjSjghqeW8sb6nV5HEpEYEUlZa3LONe9/YmapgFbBHqXV2/ewu7FF69VE5H2ZaSk8OKuU0f1z+dIfFrNsS63XkUQkBkRS1l4zs+8BWWb2ceBp4K/RjZX4KgLaX01EPiw/K41H50ynMDed2Q8vJLCzzutIIuKxSMraTcBOYAXwJeDvwM3RDJUM/IEQYwfkMiAv0+soIhJjBuRl8uicGaT4jMsfWsi7uzWWSiSZdVvWnHPtwDzgh8CtwCNOmwEdlebWdhZtqNFZNRE5pJFFOTxyxXR2N7Zw+dwF1DY0d/8hEUlI3ZY1M/sYsB64D/gtsM7MPhrlXAltyeZdNLa0aR6oiHRp4pB8HrjsRDYGG7hyXiWNzW1eRxIRD0RyGfRO4Azn3MnOuY8CZwK/im6sxOYPhPAZnDRKZ9ZEpGtlY4q4+6KpvLV5F199/C1a2tq9jiQivSySspbmnFu7/4lzbh2QFr1Iic8fCDJpSD75WfrHKCLd+8SkQfzo3Im8smYHNz27QmOpRJJMt5viApVm9nvgsfDzLwCV0YuU2OqbWlmyuZYv/tcor6OISBz5wkkjCNU186t/rqMoN53vfuJ4ryOJSC+JpKx9mY6Nca8LP38d+H9RS5TgFm6sobXdacSUiBy2604bQ6i+iftfr6YwN52rPzra60gi0gsOWdbMrD/Q3zm3Grgr/MDMJgB5dGznIYdpfiBEeoqP0hEFXkcRkThjZtzy6QmE6pv5yd/XUJCTwQUnDvU6lohEWVdr1u4FDna7YgHw6+jESXwVVUFKhvclKz3F6ygiEodSfMZdF06hfEwhNz67nH+9/Z7XkUQkyroqa2Occ68feNA59wYwOXqREteu+mZWv7OHcm3ZISJHISM1hfsvK2X8oDy++vhbLN5U43UkEYmirspany5e022MR+DN6hDOofVqInLUcjNSeeSKaQzKz+KKhxex9t29XkcSkSjpqqxVmdknDjxoZmcD1dGLlLgqAkFy0lOYPLSv11FEJAEU5mbw6JzpZKalcPncBWzd1eB1JBGJgq7K2vXA3Wb2iJldG37Mo2O92td7J15i8VeFmD6ygLSUSLa3ExHp3rCCbB69cjqNzW1c/tBCQnVNXkcSkR52yNbgnFsPTAJeA4rDj9eAyeGNceUwvLO7kepgvdariUiPG3dMHg/Nnsa22kbmPLKI+qZWryOJSA/q8hSPc67JOfewc+6G8GOuc25fb4VLJP6qEABlo1XWRKTnTSsu4L5LTmDl9j1c89himls1lkokUeh6XC+pCAQpyEln3DFd3bchInLkTh8/kJ9+ZhJvrA9yw9PLaG/XWCqRRBDJBAM5Ss45/FUhZo4qxOczr+OISAK7sHQYNfXN3PHCGgpz0rnl0+Mx0//viMSziMqamWUBwzsPdJfIbQjW8+6efZRpyw4R6QVf+ugognub+P1/NlCUm87XTh3rdSQROQrdXgY1s08DS4EXw8+nmtnz0Q6WSCoCWq8mIr3HzPjeJ47nMyVD+OU/1vH4gs1eRxKRoxDJmbVbgenAqwDOuaVmNjKKmRKOvyrI4PxMiguzvY4iIknC5zN+dsFkdjU0c/NzKyjISeOsiYO8jiUiRyCSGwxanHO7DzimVasRam93zK8OUTamSOtGRKRXpaX4uO/SE5gyrC/XPbGU+eGz/CISXyIpa6vM7BIgxczGmtm9gD/KuRLG6nf2UNvQohFTIuKJ7PRUHp49jRGF2Vz1aCUrtx34d28RiXWRlLVrgQlAE/A4sBtNMIiYPxAEtF5NRLzTNzudR6+cTl5mKrMfXsSmUL3XkUTkMERS1j7pnPu+c25a+HEzcE60gyWKiqoQo/vnMDAv0+soIpLEBuVn8eiVM2hrb+eyhxayY6/2NxeJF5GUte9GeEwO0NzazqKNNRoxJSIxYcyAXObOnsbOvU3MnruIPftavI4kIhE4ZFkzs7PD69OGmNk9nR6PABo8F4FlW2tpaG6jbLTWq4lIbCgZ3o/fXXYi697by1XzKtnX0uZ1JBHpRldn1rYDlcA+YHGnx/PAmdGPFv8qqoKYwUmjVNZEJHacfGx/7rxwCgs21PD1J5fQprFUIjHtkPusOeeWAcvMbKBzbl7n18zs68Cvox0u3vkDISYOzqdvdrrXUUREPuDcqUMI1TVz299Wc/NzK/jJ+ZO0vZBIjIpkzdpFBzk2u4dzJJyG5laWbN6lEVMiErPmfGQkXz1lNE8s3MJdL6/zOo6IHMIhz6yZ2cXAJcDIA8ZL9QFqoh0s3i3auIuWNqctO0Qkpn3rjOMI1TVz7ytVFOakM7tcA2pEYk1X46b8wDtAEXBnp+N7geXRDJUI/FVB0lKMacX9vI4iInJIZsbt502kpr6ZH/5tNQW5GZwzZbDXsUSkk0NeBnXObXLOveqcmwlsBNKcc68BbwNZvZQvbvkDIUqG9yM7PZLxqyIi3klN8XHPxSVMKy7ghqeW8vq6nV5HEpFOul2zZmZXAc8A94cPDQWei2aoeFfb0MzK7bu1ZYeIxI3MtBR+P6uUMQP6cM1ji1m6pdbrSCISFskNBl8FyoE9AM659cCASL65mZ1lZmvNrMrMbjrI67PNbKeZLQ0/vtjptVlmtj78mBXZbyc2vFkdwjm0Ga6IxJW8zDTmXTGNwtx0rnh4IVU76ryOJCJEVtaanHPN+5+YWSrQ7aY8ZpYC3AecDYwHLjaz8Qd56x+dc1PDj9+HP1sA3ALMAKYDt5hZ3Cz+8gdCZKenMGVoX6+jiIgclgF5mfxhzgxSfMasuQt5Z3ej15FEkl4kZe01M/sekGVmHweeBv4aweemA1XOuepw2XsSODfCXGcCLzvnapxzu4CXgbMi/KznKqqCTCsuID01kn+8IiKxpbgoh0eumM7uxhZmzV1IbUNz9x8SkaiJpE3cBOwEVgBfAv4O3BzB54YAWzo93xo+dqDPmtlyM3vGzIYd5mdjzru79xHYWU+59lcTkTg2cUg+D1x+IhuDDVw5r5LGZo2lEvFKt2XNOdfunHvQOfc559wF4V/31GySvwLFzrnJdJw9m9fN+z/AzK42s0ozq9y5MzbuXvIHggDaX01E4l7Z6CJ+fdFU3tq8i68+/hYtbe1eRxJJSpHcDbrBzKoPfETwvbcBwzo9Hxo+9j7nXMg51xR++nvgxEg/G/78A865Uudcaf/+/SOIFH3+QIi+2WmMH5TndRQRkaN29qRB3H7eRF5Zs4Mbn11Ou+aIivS6SDYBK+3060zgc0BBBJ9bBIw1s5F0FK2L6JiI8D4zG+Sceyf89Bw69nADeAn4SaebCs4AvhvBz/SUcw5/VZCZowrx+TRjT0QSw6UzRhCqa+aul9dRlJvB9z5xvNeRRJJKt2XNORc64NDdZrYY+EE3n2s1s6/RUbxSgLnOuVVmdhtQ6Zx7HrjOzM4BWukYYTU7/NkaM/sRHYUP4DbnXMyPuNoYamD77n18+RRdAhWRxHLtqWMI1jXxwOvVFOak86WTR3sdSSRpdFvWzOyETk99dJxpi2hbfufc3+m4IaHzsR90+vV3OcQZM+fcXGBuJD8nVuxfr1auzXBFJMGYGbd+egI19c389IU1FOZmcMGJQ72OJZIUIildneeCttIxeurCqKSJc/6qEMfkZTKyKMfrKCIiPc7nM+68cAq1DS3c+Oxy+mWncdrxA72OJZLwIrkb9JROj487565yzq3tjXDxpL3d4Q8EKRtTiJnWq4lIYspITeF3l53IhMF5fOV/3qJyY8yvUBGJe5HcDZpvZnft3yLDzO40s/zeCBdP3n53D7saWijXlh0ikuByM1J5ePY0hvTNYs4ji1j77l6vI4kktEg2xZ0L7KXj0ueFdMwIfTiaoeLR/EDHfRhl2gxXRJJAYW4G8+ZMJys9hcvnLmDrrgavI4kkrEjK2mjn3C3hsVHVzrkfAqOiHSzeVFQFGVWUw6D8LK+jiIj0imEF2cybM53G5jYuf2ghobqm7j8kIoctkrLWaGYf2f/EzMoBTfbtpKWtnYUbanRWTUSSzrhj8nho9jS21TZyxSOLqGtq9TqSSMKJpKxdA9xnZhvNbBPwm/AxCVu+tZb65jatVxORpDStuID7LjmBVdv3cM0fFtPcqrFUIj0pkrtBlznnpgCTgUnOuRLn3LLoR4sfFVUhzOCkUTqzJiLJ6fTxA7njM5P4T1WQbz61VGOpRHpQJJviZgCfBYqB1P3bUjjnbotqsjhSURVk/KA8+uWkex1FRMQznysdRqi+mTteWENhTjq3njNBWxmJ9IBINsX9C7AbWAxo9egBGpvbWLK5ltnlxV5HERHx3Jc+OopQXRMPvrGBotwMrj1trNeRROJeJGVtqHPurKgniVOVm2pobmtnpkZMiYhgZnz37OMJ1TVz58vrKMhN59IZI7yOJRLXIilrfjOb5JxbEfU0caiiKkSqz5heXOB1FBGRmODzGT+7YDK7Gpr57+dWUpiTzlkTB3kdSyRuHfIGAzNbYWbLgY8Ab5nZWjNb3um40DG8vWR4X3IyIpptLyKSFNJSfPz20hOZOqwv1z2xFH8g6HUkkbjV1d2gnwI+DZwNjAHOCD/ffzzp7W5oYeW23ZRpyw4RkQ/JSk9h7uxpjCjM5upHF7Ny226vI4nEpa7K2t5uHknvzQ0h2h2Uab2aiMhB9c1O59Erp5OXmcrshxeyMVjvdSSRuNNVWVsMVIa/HviojH602OevCpKVlkLJ8H5eRxERiVmD8rN49MoZtLU7Lp+7kB1793kdSSSuHLKsOedGOudGhb8e+NBsUMAfCDFtZAHpqZEMghARSV5jBuTy8BXTCdY1MWvuIvbsa/E6kkjc6OoGg3Hhrycc7NF7EWPTjj37WL+jTpdARUQiNHVYX373hRNZ/95erppXyb6WNq8jicSFrm5hvAG4CrjzIK854NSoJIoT/kAIQPNARUQOw0eP7c+dF07h608u5etPLuG3l55Iik9TDkS6csiy5py7Kvz1lN6LEz8qqoLkZ6UxfnCe11FEROLKuVOHUFPfzA//upqbn1vBT86fpLFUIl04ZFkzs2nAFufcu+Hnl9MxI3QTcKtzrqZ3IsYe5xz+QIiZowr1N0IRkSNwRflIQnXN/ObfVRTmZPCtM4/zOpJIzOpqZfz9QDOAmX0UuAN4lI45oQ9EP1rs2lzTwLbaRsrGaL2aiMiRuuGMY7l4+jB+8+8qHq7Y4HUckZjV1Zq1lE5nzz4PPOCcexZ41syWRj9a7Kqo6livps1wRUSOnJnxo3Mnvn9JtCAnnXOnDvE6lkjM6erMWoqZ7S9zpwGvdHotqWcr+QNBBuZlMLp/jtdRRETiWmqKj19fVMKMkQXc8NQyXlu30+tIIjGnq7L2BPCamf0FaATeADCzMXRcCk1K7e2O+YEQZaOLtCBWRKQHZKal8OCsUsYO7MOXH1vM0i21XkcSiSldbYr7Yzq273gE+IhzznX6zLXRjxab1r63l1B9s/ZXExHpQXmZacybM42i3AyueHghVTvqvI4kEjO63HrfOfemc+7Pzrn6TsfWOefein602FRRFQSgfIzWq4mI9KQBfTL5w5XTSfEZs+Yu5J3djV5HEokJmpN0mOYHQowsymFw3yyvo4iIJJwRhTk8csV0dje2cPlDC6ltaPY6kojnVNYOQ2tbOws21DBTl0BFRKJm4pB8Hrj8RDaFGpjzyCIamzWWSpKbytpheHXtTuqaWpk4ON/rKCIiCa1sdBH3XDyVpVtq+cr/LKalrd3rSCKeUVk7DPe+sh6AJZt3eZxERCTxnTVxELefN4l/r93Jjc8sp73ddf8hkQSU1PulReq4m1+gqfX//lb39OKtPL14KxmpPtbefraHyUREEtslM4YTrGvirpfXUZibzvc/Od7rSCK9TmfWIvDGd07h05MHvf88M83HuVMH88aNmnEvIhJt1546hlkzR/DgGxu4/7WA13FEep3OrEVgQF4meVlpmEFaio+m1nb6ZKQyoE+m19FERBKemXHLpycQqm/mpy+soSAnnc+VDvM6lkivUVmLULCuiUtnjOCS6cN5fOFmdu7d53UkEZGk4fMZd104ld2NLdz0pxX0y07n9PEDvY4l0ivs/wYTxLfS0lJXWVnpdQwREYmiuqZWLnnwTda+u5fHvjiDacUFXkcSOSJmttg5VxrJe7VmTURE4kZuRioPz57GkL5ZXPnIIta8u8frSCJRp7ImIiJxpTA3g0evnE5Wegqz5i5kS02D15FEokplTURE4s7Qftk8OmcGjc1tzJq7kFBdk9eRRKJGZU1EROLSccf0Ye7saWyrbeSKRxZR19TqdSSRqFBZExGRuFVaXMBvLz2BVdv3cM0fFtPUqjmiknhU1kREJK6ddvxAfvbZyfynKsgNTy3TWCpJONpnTURE4t4FJw4lVNfET19YQ2FOOreeMwEz8zqWSI9QWRMRkYTwpZNHE6pv5oHXqynMzeC608Z6HUmkR6isiYhIwrjprHEfGPx+6YwRXkcSOWoqayIikjB8PuNnn51MbUMLNz+3koLsdM6eNMjrWCJHRTcYiIhIQklL8XHfJSdwwvB+fP3JpfgDQa8jiRwVlTUREUk4WekpPDSrlOKibK5+dDErt+32OpLIEVNZExGRhNQ3O515c6aTn5XG7IcXsjFY73UkkSOisiYiIglrUH4W8+ZMp63dcfnchezYs8/rSCKHTWVNREQS2pgBuTx8xXSCdU3MengRe/a1eB3p/7d35+FR1vf6x+9PEiaBJGwhguxLQWWRLQSt1aNWq1YPWK2KC7t7rVsXbU/radXT1qXVY2td2RXX6tFT+1PrqdpqW5KwCAiiIBRQKlnYkkCGkM/5I4O/yEHZZvKdmbxf15XLeZ55nsmdRy64832WL3BAKGsAgLQ3rEd7PXjJSDhwBCcAAB2VSURBVK3cuE2XzirTjp1MS4XUQVkDALQIJwwo1N3nDVXpmipd+8RC1e9qCB0J2C+UNQBAizF2WDf9+1kD9eqyT/Sj/1oqd+YRRfLjobgAgBZl0nF9VFEd1W9eX6mCvIi+d9qRoSMBX4iyBgBocb7ztQGqrKnT/a+vUqe8bE0+rk/oSMDnSuhpUDM73cxWmNlKM7v5C7Y718zczIpiy73NbLuZLYp9PZjInACAlsXMdPvZQ3TaoM766X8v0wuLPgodCfhcCStrZpYp6X5JZ0gaKOlCMxu4l+3yJV0nad4eb61y92GxrysTlRMA0DJlZpj+c9xwje7TUd95+h29+X556EjAXiVyZK1Y0kp3/9Ddo5KelDR2L9vdJukOSTypEADQrHJaZeqRiUXq3zlfVz02XwvXbgodCfg/ElnWukla12R5fWzdp8xshKQe7v7SXvbvY2YLzexNMzs+gTkBAC1Y25xWmjVllDrlZWvKzFKt3FgdOhLwGcEe3WFmGZJ+Jek7e3l7g6Se7j5c0o2S5ppZ2718xuVmVmZmZeXlDF8DAA7OYfk5mjO1WJkZGZowbZ42bNkeOhLwqUSWtY8k9Wiy3D22brd8SYMlvWFmayQdI+lFMyty9zp3r5Qkd58vaZWkAXt+A3d/2N2L3L2osLAwQT8GAKAl6FWQq5mTR2nbjnpNmFaizbXR0JEASYkta6WS+ptZHzOLSBon6cXdb7r7Fnfv5O693b23pL9LGuPuZWZWGLtBQWbWV1J/SR8mMCsAABrcrZ0enlCkf1TVasrMUtVG60NHAhJX1ty9XtI1kl6RtFzS0+7+rpndamZj9rH7CZIWm9kiSc9KutLdqxKVFQCA3Y7tV6D7xg3TonWbdfXjC7STaakQmKXLVBtFRUVeVlYWOgYAIE3MnbdWP3x+ic4Z3k13nzdUGRkWOhLSiJnNd/ei/dmWGQwAANiLi0b3VGV1nX75x/fVMTeifzvzKJlR2ND8KGsAAHyOa07+kipronr0rdXqlJ+tK/+lX+hIaIEoawAAfA4z0y1nDVRlTVS/+H/vqWNuROcX9dj3jkAcUdYAAPgCGRmmX543VJtro/rBc0vUsU1EpwzsHDoWWpBgD8UFACBVRLIy9OAlIzW4a1t9a+4Cla7hAQVoPpQ1AAD2Q252lqZPGqVu7VtrysxSvffPraEjoYWgrAEAsJ8K8rI1e2qx2kQyNWFaidZV1YaOhBaAsgYAwAHo3qGNZk8ZrR07d2nC9BJVVNeFjoQ0R1kDAOAAHdElX9MnjdKGLds1eUapquuYlgqJQ1kDAOAgFPXuqN9ePELLNmzVFXPKVFe/K3QkpCnKGgAAB+nkIzvrznOP1tsrK3Xj0+9oV0N6TOGI5MJz1gAAOATnjuyuypo6/ewP76kgN6KfjhnEtFSIK8oaAACH6PIT+qmyOqqH/vyhCnKzdd0p/UNHQhqhrAEAEAc3n3GkKqqjuue191WQF9Elx/QKHQlpgrIGAEAcmJl+ce4QbaqN6scvLFXH3Ii+PuTw0LGQBrjBAACAOGmVmaH7LxqhET076PonF+mvKytCR0IaoKwBABBHrSOZmj5xlHp3aqPL58zX0o+2hI6EFEdZAwAgztq1aaXZU0arXetWmjSjRGsqakJHQgqjrAEAkABd2uVo9tRiNbg0fvo8bdy6I3QkpCjKGgAACdKvME8zJo1SZXVUE6aXaMv2naEjIQVR1gAASKChPdrrofEjtaq8WpfNLtOOnUxLhQNDWQMAIMGO71+oX54/TKVrqnTtEwtVv6shdCSkEMoaAADNYMzQrvr3swbq1WWf6N+eXyp35hHF/uGhuAAANJNJx/VRZU1Uv/7TSnXKj+h7px0ZOhJSAGUNAIBmdOOpA1RRHdX9r69SQW62pnylT+hISHKUNQAAmpGZ6fazB2tTTVS3/n6ZOuZGdPbwbqFjIYlxzRoAAM0sM8N077hhOqZvR333mXf0xoqNoSMhiVHWAAAIIKdVph6ZUKQBnfN11WMLtHDtptCRkKQoawAABJKf00ozp4xSYX62psws1cqN20JHQhKirAEAENBh+TmaM7VYmRkZmjCtRB9v3h46EpIMZQ0AgMB6FeRq1pRR2rajXhOml2hTTTR0JCQRyhoAAElgUNd2enhCkdZW1WrKrFLVRutDR0KSoKwBAJAkju1XoPvGDdc76zbr6scXaCfTUkGUNQAAksrpg7voP74xRG+sKNf3n12shgampWrpeCguAABJ5sLinqqsrtPdr76vjrkR/ejMo2RmoWMhEMoaAABJ6FsnfUkV1VFNe2u1OuVl66oT+4WOhEAoawAAJCEz0y1nDVRVTVR3vPyeCvIiOr+oR+hYCICyBgBAksrIMN193lBtqo3qB88tUYc2EZ06sHPoWGhm3GAAAEASi2Rl6MFLRmpwt3a6Zu4ClayuCh0JzYyyBgBAksvNztKMSaPUrUNrTZ1VquUbtoaOhGZEWQMAIAV0zI1o9pRi5UayNHF6idZV1YaOhGZCWQMAIEV079BGs6cWq66+QROml6iiui50JDQDyhoAAClkQOd8TZ9UpA1btmvyjFJV1zEtVbqjrAEAkGJG9uqo3148Qss2bNUVc8pUV78rdCQkEGUNAIAUdPKRnXXnuUfr7ZWVuvGpd7SLaanSFs9ZAwAgRZ07sruqaqL6jz8sV8fciG4dO4hpqdIQZQ0AgBR22Ql9VVFdp4f+/KE65WXrulP6h46EOKOsAQCQ4m4+40hV1kR1z2vvqyAvokuO6RU6EuKIsgYAQIozM/3inCHaVBPVj19Yqo65EX19yOGhYyFOuMEAAIA0kJWZod9cNEIje3bQ9U8u0l9XVoSOhDihrAEAkCZaRzI1beIo9emUq8tml2npR1tCR0IcUNYAAEgj7dq00qwpxWrfJqKJ00u0uqImdCQcIsoaAABppku7HM2eWiyXNH7aPG3cuiN0JBwCyhoAAGmoX2GeZkwapaqaqCZML9GW7TtDR8JBoqwBAJCmhvZor4fGj9Sq8mpdNqtMO3YyLVUqoqwBAJDGju9fqF+dP0yl/6jSt59YqPpdDaEj4QAltKyZ2elmtsLMVprZzV+w3blm5mZW1GTdD2L7rTCz0xKZEwCAdPavQ7vqJ/86SH9c9ol++PwSuTOPaCpJ2ENxzSxT0v2STpW0XlKpmb3o7sv22C5f0nWS5jVZN1DSOEmDJHWV9JqZDXB3xm8BADgIE7/cW5XVdbrvTyvVKS9b3z/9yNCRsJ8SObJWLGmlu3/o7lFJT0oau5ftbpN0h6Smt6qMlfSku9e5+2pJK2OfBwAADtINpw7QRaN76rdvrNK0t1aHjoP9lMiy1k3SuibL62PrPmVmIyT1cPeXDnRfAABwYMxMt40drDMGd9Ftv1+m/1r4UehI2A/BbjAwswxJv5L0nUP4jMvNrMzMysrLy+MXDgCANJWZYbrngmE6pm9HffeZd/TGio2hI2EfElnWPpLUo8ly99i63fIlDZb0hpmtkXSMpBdjNxnsa19Jkrs/7O5F7l5UWFgY5/gAAKSnnFaZemRCkY7okq+rHlughWs3hY6EL5DIslYqqb+Z9TGziBpvGHhx95vuvsXdO7l7b3fvLenvksa4e1lsu3Fmlm1mfST1l1SSwKwAALQo+TmtNHNysQ5rm63JM0u1cuO20JHwORJW1ty9XtI1kl6RtFzS0+7+rpndamZj9rHvu5KelrRM0suSvsWdoAAAxFdhfrbmTBmtVpkZGj+tRB9v3h46EvbC0uVZK0VFRV5WVhY6BgAAKWfZx1t1wUN/U+d2OXrmimPVITcSOlLaM7P57l607y2ZwQAAgBZvYNe2emRikdZW1WryzFLVRutDR0ITlDUAAKBj+hbo1xcO1+L1m3X14wu0k2mpkgZlDQAASJJOG9RFP/vGEL2xolzff3axGhrS41KpVJew6aYAAEDqGVfcU5U1Ud31ygp1zI3oR2ceJTMLHatFo6wBAIDPuPrEfirfVqdpb61Wp7xsXXViv9CRWjTKGgAA+Awz0y1nDVRVTVR3vPyeCnIjOn9Uj33viISgrAEAgP8jI8N093lDtXn7Tt383GJ1yI3o1IGdQ8dqkbjBAAAA7FUkK0MPXDxCQ7q31zVzF6hkdVXoSC0SZQ0AAHyu3OwszZg0St06tNbUWaVavmFr6EgtDmUNAAB8oY65Ec2ZOlq5kSxNnF6idVW1oSO1KJQ1AACwT93at9acqcWqq2/Q+GnzVFFdFzpSi0FZAwAA+6V/53xNnzRK/9y6Q5NmlGjbjp2hI7UIlDUAALDfRvbqoAcuHqnlG7bpijnzVVe/K3SktEdZAwAAB+SkIw/TXd88Wn9dVakbnlqkXUxLlVA8Zw0AABywc0Z0V1VNVLe/tFwdc5fqtrGDmZYqQShrAADgoFx6fF+VV9fpoTc/VKe8bF1/yoDQkdISZQ0AABy0m08/UlXVUd372gcqyMvW+GN6hY6UdihrAADgoJmZfn7OEG2qjeqWF5aqY5uIzjz68NCx0go3GAAAgEOSlZmhX184QiN7dtANTy3S2ysrQkdKK5Q1AABwyFpHMjVt4ij16ZSry2eXacn6LaEjpQ3KGgAAiIt2bVpp9tRitW8T0aQZJVpdURM6UlqgrAEAgLjp3DZHc6YWyyWNnzZPn2zdETpSyqOsAQCAuOpbmKeZk0dpU01UE6eXaMt2pqU6FJQ1AAAQd0d3b6+HxhdpVXm1LptVph07mZbqYFHWAABAQnylfyfdc8Ewlf6jStfMXaj6XQ2hI6UkyhoAAEiYs47uqp+OGaTXln+iHz6/RO7MI3qgeCguAABIqAnH9lZFdVT3/U/jLAc3nX5k6EgphbIGAAAS7oZT+quiuk4PvLFKBbkRXXp839CRUgZlDQAAJJyZ6baxg7WpJqrbX1qugryIvjG8e+hYKYFr1gAAQLPIzDDdO26Yju1boO89s1ivr9gYOlJKoKwBAIBmk52VqYcnjNQRXfJ19WMLtGDtptCRkh5lDQAANKv8nFaaOblYh7XN1pSZpVq5cVvoSEmNsgYAAJpdYX625kwZrVaZGRo/rUQfb94eOlLSoqwBAIAgeha00azJxareUa/x0+ZpU000dKSkRFkDAADBDOzaVo9OLNK6Tds1eWapaqP1oSMlHcoaAAAIanTfAv36wuFavH6zrnpsgXYyLdVnUNYAAEBwpw3qop99Y4jefL9c33vmHTU0MC3VbjwUFwAAJIVxxT1VWRPVXa+sUMfcbP34rKNkZqFjBUdZAwAASePqE/uporpO099erU75EV194pdCRwqOsgYAAJKGmenHZw5UVU1Ud768QgW5EV0wqmfoWEFR1gAAQFLJyDDd9c2h2lS7Uz94bok6tInoa4O6hI4VDDcYAACApBPJytADF4/QkO7t9e0nFmreh5WhIwVDWQMAAEkpNztLMyaNUvcOrXXp7DIt37A1dKQgKGsAACBpdcyNaPbU0crLztKE6SVaV1UbOlKzo6wBAICk1q19a82eUqxofYPGT5uniuq60JGaFWUNAAAkvf6d8zV90ij9c+sOTZpRom07doaO1GwoawAAICWM7NVBD1wyUu9t2KYr5sxXXf2u0JGaBWUNAACkjJOOOEx3nXe0/rqqUjc8tUi7WsC0VDxnDQAApJRvDO+uyuqobn9puTq0Warbzx6c1tNSUdYAAEDKufT4vqqojurBN1epU162bjh1QOhICUNZAwAAKemm049QZXWd/vN/PlCnvIjGH9s7dKSEoKwBAICUZGb6+TlDtKl2p2558V11yI3orKO7ho4Vd9xgAAAAUlZWZoZ+c9FwFfXqoBueWqS3V1aEjhR3lDUAAJDSclpl6tEJo9SvME+Xzy7TkvVbQkeKK8oaAABIee3atNKsKcXqkBvRpBklWl1REzpS3FDWAABAWujcNkezpxRLksZPm6dPtu4InCg+ElrWzOx0M1thZivN7Oa9vH+lmS0xs0Vm9paZDYyt721m22PrF5nZg4nMCQAA0kPfwjzNnFysTTVRTZxeoi3bU39aqoSVNTPLlHS/pDMkDZR04e4y1sRcdx/i7sMk3SnpV03eW+Xuw2JfVyYqJwAASC9DurfTQ+OLtKq8WpfOKtWOnak9LVUiR9aKJa109w/dPSrpSUljm27g7lubLOZKSv85IwAAQMJ9pX8n3XPBMJX9Y5OumbtA9bsaQkc6aIksa90krWuyvD627jPM7FtmtkqNI2vXNnmrj5ktNLM3zez4vX0DM7vczMrMrKy8vDye2QEAQIo76+iuunXMIL22fKN++PwSuafmmFDwGwzc/X537yfpJkk/iq3eIKmnuw+XdKOkuWbWdi/7PuzuRe5eVFhY2HyhAQBAShh/bG9d99X+erpsve58ZUXoOAclkTMYfCSpR5Pl7rF1n+dJSQ9IkrvXSaqLvZ4fG3kbIKksMVEBAEC6uv6U/qqortMDb6xSQW5Elx7fN3SkA5LIkbVSSf3NrI+ZRSSNk/Ri0w3MrH+TxTMlfRBbXxi7QUFm1ldSf0kfJjArAABIU2amW8cO1teHdNHtLy3X8wvXh450QBI2subu9WZ2jaRXJGVKmu7u75rZrZLK3P1FSdeY2SmSdkraJGlibPcTJN1qZjslNUi60t2rEpUVAACkt8wM0z0XDNPm2lJ975nFat8mopOOOCx0rP1iqXqx3Z6Kioq8rIyzpAAA4PNt27FTFz7yd63cWK3HLz1GI3t1CJLDzOa7e9H+bBv8BgMAAIDmkp/TSjMmFatL2xxNmVmqDz7ZFjrSPlHWAABAi1KYn605U0crkpWhCdNL9NHm7aEjfSHKGgAAaHF6dGyj2VOKVV1XrwnT5qmqJho60ueirAEAgBbpqMPb6tEJRVq3abumzCxVbbQ+dKS9oqwBAIAWa3TfAv3mwuFavH6zrnxsgdZvqtX5D/1NG7ftCB3tU5Q1AADQon1tUBf9/Jwh+vP75brk0XkqXVOl+177IHSsTyVyBgMAAICUcMsL70qS1lTWSpIem7dWj81bq+ysDK24/YyQ0RhZAwAA+Mv3T9KYoV2VmWGSpJxWGRo7rKv+ctNJgZNR1gAAAHRY2xzl52SpwV3ZWRmqq29QfnaWDsvPCR2N06AAAACSVFFdp4tH99JFxT01t2StypPkJgOmmwIAAGhmTDcFAACQJihrAAAASYyyBgAAkMQoawAAAEmMsgYAAJDEKGsAAABJjLIGAACQxChrAAAASYyyBgAAkMQoawAAAEmMsgYAAJDEKGsAAABJjLIGAACQxChrAAAASYyyBgAAkMQoawAAAEmMsgYAAJDEKGsAAABJjLIGAACQxChrAAAASYyyBgAAkMTM3UNniAszK5f0j2b4Vp0kVTTD92kpOJ7xxzGNL45n/HFM449jGl/NcTx7uXvh/myYNmWtuZhZmbsXhc6RLjie8ccxjS+OZ/xxTOOPYxpfyXY8OQ0KAACQxChrAAAASYyyduAeDh0gzXA8449jGl8cz/jjmMYfxzS+kup4cs0aAABAEmNkDQAAIIlR1vaDmfUws9fNbJmZvWtm14XOlOrMLMfMSszsndgx/WnoTOnAzDLNbKGZ/T50lnRgZmvMbImZLTKzstB50oGZtTezZ83sPTNbbmbHhs6UqszsiNifzd1fW83s+tC5Up2Z3RD7d2mpmT1hZjnBM3EadN/M7HBJh7v7AjPLlzRf0tnuvixwtJRlZiYp192rzayVpLckXefufw8cLaWZ2Y2SiiS1dfezQudJdWa2RlKRu/P8qjgxs1mS/uLuj5pZRFIbd98cOleqM7NMSR9JGu3uzfHM0bRkZt3U+O/RQHffbmZPS/qDu88MmYuRtf3g7hvcfUHs9TZJyyV1C5sqtXmj6thiq9gXvzkcAjPrLulMSY+GzgLsjZm1k3SCpGmS5O5RilrcfFXSKopaXGRJam1mWZLaSPo4cB7K2oEys96ShkuaFzZJ6oudslskaaOkP7o7x/TQ3Cvp+5IaQgdJIy7pVTObb2aXhw6TBvpIKpc0I3a6/lEzyw0dKk2Mk/RE6BCpzt0/knS3pLWSNkja4u6vhk1FWTsgZpYn6XeSrnf3raHzpDp33+XuwyR1l1RsZoNDZ0pVZnaWpI3uPj90ljTzFXcfIekMSd8ysxNCB0pxWZJGSHrA3YdLqpF0c9hIqS92OnmMpGdCZ0l1ZtZB0lg1/mLRVVKumV0SNhVlbb/Frqv6naTH3f250HnSSew0yOuSTg+dJYUdJ2lM7BqrJyWdbGaPhY2U+mK/ZcvdN0p6XlJx2EQpb72k9U1G0Z9VY3nDoTlD0gJ3/yR0kDRwiqTV7l7u7jslPSfpy4EzUdb2R+xi+GmSlrv7r0LnSQdmVmhm7WOvW0s6VdJ7YVOlLnf/gbt3d/feajwd8id3D/7bYCozs9zYDUWKnar7mqSlYVOlNnf/p6R1ZnZEbNVXJXGj1qG7UJwCjZe1ko4xszaxf/u/qsbr1IPKCh0gRRwnabykJbFrrCTph+7+h4CZUt3hkmbF7mDKkPS0u/O4CSSTzpKeb/z7WlmS5rr7y2EjpYVvS3o8duruQ0mTA+dJabFfJE6VdEXoLOnA3eeZ2bOSFkiql7RQSTCbAY/uAAAASGKcBgUAAEhilDUAAIAkRlkDAABIYpQ1AACAJEZZAwAASGKUNQAJZ2ZuZr9ssvxdM/tJnD57ppl9Mx6ftY/vc56ZLTez1/fy3gAz+4OZfWBmC8zsaTPrbGYnmtlBPZLGzK43szaHnhxAqqOsAWgOdZLOMbNOoYM0FZuoeX9NlXSZu5+0x2fkSHpJjVMo9Y9NT/VbSYWHGO96NU4ivd9izy0EkGYoawCaQ70aHyx5w55v7DkyZmbVsf+eaGZvmtkLZvahmf3CzC42sxIzW2Jm/Zp8zClmVmZm78fmSZWZZZrZXWZWamaLzeyKJp/7FzN7UXt5er6ZXRj7/KVmdkds3S2SviJpmpndtccuF0n6m7v/9+4V7v6Gu39mtgMz+4mZfbfJ8lIz6x2bKeElM3sntu4CM7tWjfMSvr57JM/MvmZmf4uN3D0Tm6tYZrbGzO4wswWSzjOza81sWexnfnIf/18ApABmMADQXO6XtNjM7jyAfYZKOkpSlRqfdv+ouxeb2XVqfBL+9bHteqtx3s5+aiw4X5I0QdIWdx9lZtmS3jazV2Pbj5A02N1XN/1mZtZV0h2SRkraJOlVMzvb3W81s5Mlfdfdy/bIOFjS/AP4mfZ0uqSP3f3MWIZ27r7FzG6UdJK7V8RGJH8k6RR3rzGzmyTdKOnW2GdUxkb0ZGYfS+rj7nW7p3QDkNoYWQPQLNx9q6TZkq49gN1K3X2Du9dJWiVpd9laosaCttvT7t7g7h+osdQdqca5PCfEpoibJ6lAUv/Y9iV7FrWYUZLeiE3iXC/pcUknHEDeg7FE0qmx0bHj3X3LXrY5RtJANRbORZImSurV5P2nmrxerMbpnC5R44gmgBRHWQPQnO5V47VfuU3W1Sv2d5GZZUiKNHmvrsnrhibLDfrsmYE9581zSSbp2+4+LPbVx913l72aQ/opPutdNY7E7cunP2dMjiS5+/tqHOlbIun22CnXPZmkPzb5WQa6+9Qm7zf9ec5U4yjmCEmlB3hdHoAkRFkD0GzcvUrS02osbLut0f8vO2MktTqIjz7PzDJi17H1lbRC0iuSrjKzVtKnd2zmftGHSCqR9C9m1il2sf6Fkt7cxz5zJX3ZzM7cvcLMTjCzwXtst0aNBUpmNkJSn9jrrpJq3f0xSXft3kbSNkn5sdd/l3Rc7PSuYte5DdgzSKzs9nD31yXdJKmdpLx95AeQ5PiNC0Bz+6Wka5osPyLpBTN7R9LLOrhRr7VqLFptJV3p7jvM7FE1nipdYGYmqVzS2V/0Ie6+wcxulvS6GkezXnL3F/axz/bYTQ33mtm9knaq8VTkdZKa3v36OzWeln1Xjadl34+tHyLpLjNriO17VWz9w5JeNrOP3f0kM5sk6YnY9XdS4zVs7+uzMiU9ZmbtYvnvc/fNX5QfQPIz9z3PHgAAACBZcBoUAAAgiVHWAAAAkhhlDQAAIIlR1gAAAJIYZQ0AACCJUdYAAACSGGUNAAAgiVHWAAAAktj/Atf4T0IOQpEyAAAAAElFTkSuQmCC\n", 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" ] @@ -879,14 +889,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" + "
" ] }, "metadata": { @@ -896,6 +906,7 @@ } ], "source": [ + "%matplotlib inline\n", "import numpy as np\n", "from sklearn.cluster import KMeans\n", "from scipy.spatial.distance import cdist\n", @@ -914,14 +925,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "
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" ] }, "metadata": { @@ -937,7 +948,9 @@ "for k in K:\n", " kmeans=KMeans(n_clusters=k)\n", " kmeans.fit(X)\n", - " meandistortions.append(sum(np.min(cdist(X,kmeans.cluster_centers_,'euclidean'),axis=1))/X.shape[0])\n", + " meandistortions.append(\\\n", + " sum(np.min(cdist(X,kmeans.cluster_centers_,'euclidean'),\\\n", + " axis=1))/X.shape[0])\n", "\n", "plt.plot(K,meandistortions,'bx-')\n", "plt.xlabel('k')\n", diff --git a/4_logistic_regression/Least_squares.ipynb b/4_logistic_regression/Least_squares.ipynb index 5ed5f93..2ac2386 100644 --- a/4_logistic_regression/Least_squares.ipynb +++ b/4_logistic_regression/Least_squares.ipynb @@ -4,21 +4,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Least squares\n", + "# 最小二乘（Generalized Least Squares）\n", "\n", - "A mathematical procedure for finding the best-fitting curve to a given set of points by minimizing the sum of the squares of the offsets (\"the residuals\") of the points from the curve. The sum of the squares of the offsets is used instead of the offset absolute values because this allows the residuals to be treated as a continuous differentiable quantity. However, because squares of the offsets are used, outlying points can have a disproportionate effect on the fit, a property which may or may not be desirable depending on the problem at hand. \n" + "## 1. 最小二乘的基本\n", + "\n", + "最小二乘法（generalized least squares）是一种数学优化技术，它通过最小化误差的平方和找到一组数据的最佳函数匹配。 最小二乘法通常用于曲线拟合、求解模型。很多其他的优化问题也可通过最小化能量或最大化熵用最小二乘形式表达。最小二乘原理的一般形式为：\n", + "$$\n", + "L = \\sum (V_{obv} - V_{target}(\\theta))^2\n", + "$$\n", + "其中$V_{obv}$是我们观测的多组样本值，$V_{target}$是我们假设拟合函数的输出值，$\\theta$为构造模型的参数。$L$是目标函数，如果通过调整模型参数$\\theta$，使得$L$下降到最小则表明，拟合函数与观测最为接近，也就是找到了最优的模型。\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Show the data\n" + "### 1.1 示例\n", + "\n", + "假设我们有下面的一些观测数据，我们希望找到他们内在的规律。" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -59,49 +67,49 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Theory\n", - "For $N$ observation data:\n", + "### 1.2 数学原理\n", + "有$N$个观测数据为：\n", "$$\n", "\\mathbf{X} = \\{x_1, x_2, ..., x_N \\} \\\\\n", "\\mathbf{Y} = \\{y_1, y_2, ..., y_N \\}\n", "$$\n", + "其中$\\mathbf{X}$为自变量，$\\mathbf{Y}$为因变量。\n", "\n", - "We want to find the model which can predict the data. The simplest model is linear model, which has the form of \n", + "我们希望找到一个模型能够解释这些数据，假设我们使用最简单的线性模型来拟合数据：\n", "$$\n", "y = ax + b\n", "$$\n", + "那么问题就变成求解参数$a$, $b$能够使得模型输出尽可能和观测数据有比较小的误差。\n", "\n", - "The purpose is to find parameters $a, b$ which best fit the model to the observation data. \n", - "\n", - "We use the sum of squares to measure the differences (loss function) between the model's prediction and observation data:\n", + "如何构建函数来评估模型输出与观测数据之间的误差是一个关键问题，这里我们使用观测数据与模型输出的平方和来作为评估函数（也被称为损失函数Loss function):\n", "$$\n", "L = \\sum_{i=1}^{N} (y_i - a x_i - b)^2\n", "$$\n", "\n", - "To make the loss function minimize, we can find the parameters:\n", + "使误差函数最小，那么我们就可以求出模型的参数:\n", "$$\n", "\\frac{\\partial L}{\\partial a} = -2 \\sum_{i=1}^{N} (y_i - a x_i - b) x_i \\\\\n", "\\frac{\\partial L}{\\partial b} = -2 \\sum_{i=1}^{N} (y_i - a x_i - b)\n", "$$\n", - "When the loss is minimized, therefore the partial difference is zero, then we can get:\n", + "既当偏微分为0时，误差函数为最小，因此我们可以得到:\n", "$$\n", "-2 \\sum_{i=1}^{N} (y_i - a x_i - b) x_i = 0 \\\\\n", "-2 \\sum_{i=1}^{N} (y_i - a x_i - b) = 0 \\\\\n", "$$\n", "\n", - "We reoder the items as:\n", + "将上式调整一下顺序可以得到：\n", "$$\n", "a \\sum x_i^2 + b \\sum x_i = \\sum y_i x_i \\\\\n", "a \\sum x_i + b N = \\sum y_i\n", "$$\n", - "By solving the linear equation we can obtain the model parameters." + "通过求解二元一次方程组，我们即可求出模型的最优参数。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Program" + "### 1.3 求解程序" ] }, { @@ -160,14 +168,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 如何使用迭代的方法求出模型参数\n", + "## 2. 如何使用迭代的方法求出模型参数\n", "\n", "当数据比较多的时候，或者模型比较复杂，无法直接使用解析的方式求出模型参数。因此更为常用的方式是，通过迭代的方式逐步逼近模型的参数。\n", "\n", - "### 梯度下降法\n", + "### 2.1 梯度下降法\n", "在机器学习算法中，对于很多监督学习模型，需要对原始的模型构建损失函数，接下来便是通过优化算法对损失函数进行优化，以便寻找到最优的参数。在求解机器学习参数的优化算法中，使用较多的是基于梯度下降的优化算法(Gradient Descent, GD)。\n", "\n", - "梯度下降法有很多优点，其中，在梯度下降法的求解过程中，只需求解损失函数的一阶导数，计算的代价比较小，这使得梯度下降法能在很多大规模数据集上得到应用。梯度下降法的含义是通过当前点的梯度方向寻找到新的迭代点。\n", + "梯度下降法有很多优点，其中最主要的优点是，在梯度下降法的求解过程中只需求解损失函数的一阶导数，计算的代价比较小，这使得梯度下降法能在很多大规模数据集上得到应用。梯度下降法的含义是通过当前点的梯度方向寻找到新的迭代点。\n", "\n", "梯度下降法的基本思想可以类比为一个下山的过程。假设这样一个场景：一个人被困在山上，需要从山上下来(i.e. 找到山的最低点，也就是山谷)。但此时山上的浓雾很大，导致可视度很低。因此，下山的路径就无法确定，他必须利用自己周围的信息去找到下山的路径。这个时候，他就可以利用梯度下降算法来帮助自己下山。具体来说就是，以他当前的所处的位置为基准，寻找这个位置最陡峭的地方，然后朝着山的高度下降的地方走，同理，如果我们的目标是上山，也就是爬到山顶，那么此时应该是朝着最陡峭的方向往上走。然后每走一段距离，都反复采用同一个方法，最后就能成功的抵达山谷。\n", "\n", @@ -190,16 +198,16 @@ "$$\n", "其中$\\theta$代表了模型中的参数，例如$a$, $b$\n", "\n", - "此公式的意义是：L是关于$\\theta$的一个函数，我们当前所处的位置为$\\theta_0$点，要从这个点走到L的最小值点，也就是山底。首先我们先确定前进的方向，也就是梯度的反向，然后走一段距离的步长，也就是$\\alpha$，走完这个段步长，就到达了$\\theta_1$这个点！\n", + "此公式的意义是：$L$是关于$\\theta$的一个函数，我们当前所处的位置为$\\theta_0$点，要从这个点走到L的最小值点，也就是山底。首先我们先确定前进的方向，也就是梯度的反向，然后走一段距离的步长，也就是$\\alpha$，走完这个段步长，就到达了$\\theta_1$这个点！\n", "\n", "下面就这个公式的几个常见的疑问：\n", "\n", "* **$\\alpha$是什么含义？**\n", - "$\\alpha$在梯度下降算法中被称作为学习率或者步长，意味着我们可以通过$\\alpha$来控制每一步走的距离，以保证不要步子跨的太大扯着蛋，哈哈，其实就是不要走太快，错过了最低点。同时也要保证不要走的太慢，导致太阳下山了，还没有走到山下。所以$\\alpha$的选择在梯度下降法中往往是很重要的！$\\alpha$不能太大也不能太小，太小的话，可能导致迟迟走不到最低点，太大的话，会导致错过最低点！\n", + "$\\alpha$在梯度下降算法中被称作为学习率或者步长，意味着我们可以通过$\\alpha$来控制每一步走的距离，以保证不要步子跨的太大，错过了最低点。同时也要保证不要走的太慢，导致太阳下山了，还没有走到山下。所以$\\alpha$的选择在梯度下降法中往往是很重要的。\n", "![gd_stepsize](images/gd_stepsize.png)\n", "\n", "* **为什么要梯度要乘以一个负号？**\n", - "梯度前加一个负号，就意味着朝着梯度相反的方向前进！我们在前文提到，梯度的方向实际就是函数在此点上升最快的方向！而我们需要朝着下降最快的方向走，自然就是负的梯度的方向，所以此处需要加上负号\n", + "梯度前加一个负号，就意味着朝着梯度相反的方向前进！梯度的方向实际就是函数在此点上升最快的方向，而我们需要朝着下降最快的方向走，自然就是负的梯度的方向，所以此处需要加上负号。\n", "\n" ] }, @@ -207,7 +215,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Program" + "### 2.2 示例代码" ] }, { @@ -3294,7 +3302,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## How to show the iterative process" + "## 3. 如何可视化迭代过程" ] }, { @@ -4136,9 +4144,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## How to use batch update method?\n", + "## 4. 如何使用批次更新的方法？\n", "\n", - "If some data is outliear, then only use one data can make the learning inaccuracy and slow.\n", + "如果有一些数据包含比较大的错误（异常数据），因此每次更新仅仅使用一个数据会导致不精确，同时每次仅仅使用一个数据来计算更新也导致计算效率比较低。\n", "\n", "\n", "* [梯度下降方法的几种形式](https://blog.csdn.net/u010402786/article/details/51188876)" @@ -4148,14 +4156,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## How to fit polynomial function?\n", + "## 5. 如何拟合多项式函数？\n", "\n", - "If we observe a missle at some time, then how to estimate the trajectory? Acoording the physical theory, the trajectory can be formulated as:\n", + "需要设计一个弹道导弹防御系统，通过观测导弹的飞行路径，预测未来导弹的飞行轨迹，从而完成摧毁的任务。按照物理学，可以得知模型为:\n", "$$\n", "y = at^2 + bt + c\n", "$$\n", - "The we need at least three data to compute the parameters $a, b, c$.\n", + "我们需要求解三个模型参数$a, b, c$。\n", "\n", + "损失函数的定义为：\n", "$$\n", "L = \\sum_{i=1}^N (y_i - at^2 - bt - c)^2\n", "$$\n" @@ -4198,7 +4207,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### How to get the update items?\n", + "### 5.1 如何得到更新项?\n", "\n", "$$\n", "L = \\sum_{i=1}^N (y_i - at^2 - bt - c)^2\n", @@ -4215,8 +4224,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## How to use sklearn to solve linear problem?\n", - "\n" + "## 6. 如何使用sklearn求解线性问题？\n" ] }, { @@ -4274,7 +4282,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## How to use sklearn to fit polynomial function?" + "## 7. 如何使用sklearn拟合多项式函数？" ] }, { @@ -4318,7 +4326,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## How to estimate some missing value by the model?\n" + "## 8. 如何通过模型来估计缺失的值？\n" ] }, { @@ -4405,15 +4413,6 @@ "plt.legend()\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "lines_to_next_cell": 2 - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/4_logistic_regression/Logistic_regression.ipynb b/4_logistic_regression/Logistic_regression.ipynb index 0349a7f..39f177f 100644 --- a/4_logistic_regression/Logistic_regression.ipynb +++ b/4_logistic_regression/Logistic_regression.ipynb @@ -4,12 +4,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Logistic Regression\n", + "# 逻辑回归 Logistic Regression\n", "\n", "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，使得逻辑回归模型成为了机器学习领域一颗耀眼的明星，更是计算广告学的核心。本节主要详述逻辑回归模型的基础。\n", "\n", "\n", - "## 1 逻辑回归模型\n", + "## 1. 逻辑回归模型\n", "回归是一种比较容易理解的模型，就相当于$y=f(x)$，表明自变量$x$与因变量$y$的关系。最常见问题有如医生治病时的望、闻、问、切，之后判定病人是否生病或生了什么病，其中的望闻问切就是获取自变量$x$，即特征数据，判断是否生病就相当于获取因变量$y$，即预测分类。\n", "\n", "最简单的回归是线性回归，在此借用Andrew NG的讲义，有如图所示，$X$为数据点——肿瘤的大小，$Y$为观测值——是否是恶性肿瘤。通过构建线性回归模型，如$h_\\theta(x)$所示，构建线性回归模型后，即可以根据肿瘤大小，预测是否为恶性肿瘤$h_\\theta(x)) \\ge 0.5$为恶性，$h_\\theta(x) \\lt 0.5$为良性。\n", @@ -59,7 +59,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 逻辑回归表达式\n", + "### 1.1 逻辑回归表达式\n", "\n", "这个函数称为Logistic函数(logistic function)，也称为Sigmoid函数(sigmoid function)。函数公式如下：\n", "\n", @@ -125,7 +125,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 逻辑回归的软分类\n", + "### 1.2 逻辑回归的软分类\n", "\n", "现在我们将y的取值$h_\\theta(x)$通过Logistic函数归一化到(0,1)间，$y$的取值有特殊的含义，它表示结果取1的概率，因此对于输入$x$分类结果为类别1和类别0的概率分别为：\n", "\n", @@ -146,7 +146,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 梯度上升\n", + "### 1.3 梯度上升\n", "\n", "得到了逻辑回归的表达式，下一步跟线性回归类似，构建似然函数，然后最大似然估计，最终推导出$\\theta$的迭代更新表达式。只不过这里用的不是梯度下降，而是梯度上升，因为这里是最大化似然函数。\n", "\n", @@ -175,7 +175,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Program" + "### 1.4 示例程序" ] }, { @@ -350,7 +350,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## How to use sklearn to resolve the problem\n" + "## 2. 如何使用sklearn求解逻辑回归" ] }, { @@ -424,14 +424,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Multi-class recognition" + "## 3. 多类识别问题" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Load & show the data" + "### 3.1 加载显示数据" ] }, { @@ -474,11 +474,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Visualizing the Data\n", + "### 3.2 可视化特征\n", "\n", - "A good first-step for many problems is to visualize the data using one of the Dimensionality Reduction techniques we saw earlier. We'll start with the most straightforward one, Principal Component Analysis (PCA).\n", + "针对机器学习的问题，一个比较好的方法是通过降维的方法将原始的高维特征降到2-3维并可视化处理，通过这样的方法可以对所要处理的数据有一个初步的认识。这里介绍最简单的降维方法主成分分析(Principal Component Analysis, PCA).\n", "\n", - "PCA seeks orthogonal linear combinations of the features which show the greatest variance, and as such, can help give you a good idea of the structure of the data set. Here we'll use RandomizedPCA, because it's faster for large N." + "PCA寻求具有最大方差的特征的正交线性组合，因此可以更好地了解数据的结构。在这里，我们将使用Randomized PCA，因为当数据个数$N$比较大是，计算的效率更好。\n" ] }, { @@ -522,12 +522,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "A weakness of PCA is that it produces a linear dimensionality reduction:\n", - "this may miss some interesting relationships in the data. If we want to\n", - "see a nonlinear mapping of the data, we can use one of the several\n", - "methods in the `manifold` module. Here we'll use [Isomap](https://blog.csdn.net/VictoriaW/article/details/78497316) (a concatenation\n", - "of Isometric Mapping) which is a manifold learning method based on\n", - "graph theory:" + "PCA的一个缺点是它可能会丢失数据中一些有趣的相互关系。如果想看到非线性的降维与映射\n", + "我们可以使用几种流形模块中的方法。在这里，我们将使用[Isomap](https://blog.csdn.net/VictoriaW/article/details/78497316)（串联\n", + "等距映射）是一种基于图论的流形降维方法。" ] }, { @@ -571,7 +568,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Program" + "### 3.3 示例程序" ] }, { @@ -665,10 +662,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Exercise - How to draw mis-classfied data?\n", + "## 4. 练习 - 如何画出错误分类的数据？\n", "\n", - "1. How to obtain the mis-classified index?\n", - "2. How to draw them?" + "1. 如何得到错误分类数据的下标？\n", + "2. 如何根据下标，将这些错误的数据可视化出来？" ] }, { diff --git a/5_nn/Perceptron.ipynb b/5_nn/Perceptron.ipynb index 237bd44..3ae9d9a 100644 --- a/5_nn/Perceptron.ipynb +++ b/5_nn/Perceptron.ipynb @@ -41,11 +41,11 @@ "称为感知机。其中，参数w叫做权值向量，b称为偏置。w·x表示w和x的内积。sign为符号函数，即\n", "![sign_function](images/sign.png)\n", "\n", - "### 几何解释 \n", + "### 1.1 几何解释 \n", "感知机模型是线性分类模型，感知机模型的假设空间是定义在特征空间中的所有线性分类模型，即函数集合{f|f(x)=w·x+b}。线性方程 w·x+b=0对应于特征空间Rn中的一个超平面S，其中w是超平面的法向量，b是超平面的截踞。这个超平面把特征空间划分为两部分。位于两侧的点分别为正负两类。超平面S称为分离超平面，如下图：\n", "![perceptron_geometry_def](images/perceptron_geometry_def.png)\n", "\n", - "### 生物学类比\n", + "### 1.2 生物学类比\n", "![perceptron_2](images/perceptron_2.PNG)\n", "\n", "\n" @@ -136,7 +136,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 4. Program\n" + "## 4. 示例程序\n" ] }, { diff --git a/5_nn/mlp_bp.ipynb b/5_nn/mlp_bp.ipynb index 025c41b..d79ea84 100644 --- a/5_nn/mlp_bp.ipynb +++ b/5_nn/mlp_bp.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 神经元\n", + "## 1. 神经元\n", "\n", "神经元和感知器本质上是一样的，只不过我们说感知器的时候，它的激活函数是阶跃函数；而当我们说神经元时，激活函数往往选择为sigmoid函数或tanh函数。如下图所示：\n", "\n", @@ -49,7 +49,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 神经网络是啥?\n", + "## 2. 神经网络是啥?\n", "\n", "![nn1](images/nn1.jpeg)\n", "\n", @@ -67,7 +67,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 计算神经网络的输出\n", + "## 3. 计算神经网络的输出\n", "\n", "神经网络实际上就是一个输入向量$\\vec{x}$到输出向量$\\vec{y}$的函数，即：\n", "\n", @@ -103,7 +103,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 神经网络的矩阵表示\n", + "## 4. 神经网络的矩阵表示\n", "\n", "神经网络的计算如果用矩阵来表示会很方便（当然逼格也更高），我们先来看看隐藏层的矩阵表示。\n", "\n", @@ -145,7 +145,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 神经网络的训练 - 反向传播算法\n", + "## 5. 神经网络的训练 - 反向传播算法\n", "\n", "现在，我们需要知道一个神经网络的每个连接上的权值是如何得到的。我们可以说神经网络是一个模型，那么这些权值就是模型的参数，也就是模型要学习的东西。然而，一个神经网络的连接方式、网络的层数、每层的节点数这些参数，则不是学习出来的，而是人为事先设置的。对于这些人为设置的参数，我们称之为超参数(Hyper-Parameters)。\n", "\n", @@ -188,7 +188,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 输出层权值训练\n", + "### 5.1 输出层权值训练\n", "\n", "![nn3](images/nn3.png)\n", "\n", @@ -222,7 +222,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 隐藏层权值训练\n", + "### 5.2 隐藏层权值训练\n", "\n", "现在我们要推导出隐藏层的$\\frac{\\partial E_d}{\\partial net_j}$。\n", "\n", @@ -244,7 +244,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 具体解释\n", + "### 5.3 具体解释\n", "\n", "我们假设每个训练样本为$(\\vec{x}, \\vec{t})$，其中向量$\\vec{x}$是训练样本的特征，而$\\vec{t}$是样本的目标值。\n", "\n", @@ -297,7 +297,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Program" + "## 6. 示例程序" ] }, { @@ -2511,7 +2511,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 如何使用类的方法封装多层神经网络?" + "## 7. 如何使用类的方法封装多层神经网络?" ] }, { @@ -4730,7 +4730,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 深入分析" + "## 8. 深入分析与问题" ] }, { diff --git a/5_nn/softmax_ce.ipynb b/5_nn/softmax_ce.ipynb index fc635f1..d67f147 100644 --- a/5_nn/softmax_ce.ipynb +++ b/5_nn/softmax_ce.ipynb @@ -13,7 +13,7 @@ "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", "\n", - "## softmax 函数\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", "\n", @@ -56,7 +56,7 @@ "$a_i$代表softmax的第$i$个输出值，右侧套用了softmax函数。\n", "\n", "\n", - "### 损失函数 loss function\n", + "### 1.1 损失函数 loss function\n", "\n", "在神经网络反向传播中，要求一个损失函数，这个损失函数其实表示的是真实值与网络的估计值的误差，知道误差了，才能知道怎样去修改网络中的权重。\n", "\n", @@ -74,7 +74,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 推导过程\n", + "## 2. 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", @@ -90,13 +90,13 @@ "\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", - "### 针对$a_j$的偏导\n", + "### 2.1 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", - "### 针对$z_i$的偏导\n", + "### 2.2 针对$z_i$的偏导\n", "\n", "如果 $i=j$ :\n", "\n", @@ -120,7 +120,7 @@ "(\\frac{u}{v})' = \\frac{u'v - uv'}{v^2} \n", "$$\n", "\n", - "### 整体的推导\n", + "### 2.3 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", @@ -134,7 +134,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 问题\n", + "## 3. 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？" ] }, diff --git a/6_pytorch/PyTorch_quick_intro.ipynb b/6_pytorch/PyTorch_quick_intro.ipynb index 347d331..0559c89 100644 --- a/6_pytorch/PyTorch_quick_intro.ipynb +++ b/6_pytorch/PyTorch_quick_intro.ipynb @@ -18,7 +18,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Tensor\n", + "## 1. Tensor\n", "\n", "Tensor是PyTorch中重要的数据结构，可认为是一个高维数组。它可以是一个数（标量）、一维数组（向量）、二维数组（矩阵）以及更高维的数组。Tensor和Numpy的ndarrays类似，但Tensor可以使用GPU进行加速。Tensor的使用和Numpy及Matlab的接口十分相似，下面通过几个例子来看看Tensor的基本使用。" ] @@ -421,9 +421,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "此处可能发现GPU运算的速度并未提升太多，这是因为x和y太小且运算也较为简单，而且将数据从内存转移到显存还需要花费额外的开销。GPU的优势需在大规模数据和复杂运算下才能体现出来。\n", - "\n", - "### Autograd: 自动微分\n", + "此处可能发现GPU运算的速度并未提升太多，这是因为x和y太小且运算也较为简单，而且将数据从内存转移到显存还需要花费额外的开销。GPU的优势需在大规模数据和复杂运算下才能体现出来。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Autograd: 自动微分\n", "\n", "深度学习的算法本质上是通过反向传播求导数，而PyTorch的**`Autograd`**模块则实现了此功能。在Tensor上的所有操作，Autograd都能为它们自动提供微分，避免了手动计算导数的复杂过程。\n", " \n", @@ -693,7 +698,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 神经网络\n", + "## 3. 神经网络\n", "\n", "Autograd实现了反向传播功能，但是直接用来写深度学习的代码在很多情况下还是稍显复杂，torch.nn是专门为神经网络设计的模块化接口。nn构建于 Autograd之上，可用来定义和运行神经网络。nn.Module是nn中最重要的类，可把它看成是一个网络的封装，包含网络各层定义以及forward方法，调用forward(input)方法，可返回前向传播的结果。下面就以最早的卷积神经网络：LeNet为例，来看看如何用`nn.Module`实现。LeNet的网络结构如图2-7所示。\n", "\n", @@ -701,7 +706,7 @@ "\n", "这是一个基础的前向传播(feed-forward)网络: 接收输入，经过层层传递运算，得到输出。\n", "\n", - "#### 定义网络\n", + "### 3.1 定义网络\n", "\n", "定义网络时，需要继承`nn.Module`，并实现它的forward方法，把网络中具有可学习参数的层放在构造函数`__init__`中。如果某一层(如ReLU)不具有可学习的参数，则既可以放在构造函数中，也可以不放，但建议不放在其中，而在forward中使用`nn.functional`代替。" ] @@ -865,7 +870,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 损失函数\n", + "### 3.2 损失函数\n", "\n", "nn实现了神经网络中大多数的损失函数，例如nn.MSELoss用来计算均方误差，nn.CrossEntropyLoss用来计算交叉熵损失。" ] @@ -960,7 +965,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 优化器" + "### 3.3 优化器" ] }, { @@ -1014,14 +1019,18 @@ "source": [ "\n", "\n", - "#### 数据加载与预处理\n", + "### 3.4 数据加载与预处理\n", "\n", "在深度学习中数据加载及预处理是非常复杂繁琐的，但PyTorch提供了一些可极大简化和加快数据处理流程的工具。同时，对于常用的数据集，PyTorch也提供了封装好的接口供用户快速调用，这些数据集主要保存在torchvison中。\n", "\n", - "`torchvision`实现了常用的图像数据加载功能，例如Imagenet、CIFAR10、MNIST等，以及常用的数据转换操作，这极大地方便了数据加载，并且代码具有可重用性。\n", - "\n", - "\n", - "### 小试牛刀：CIFAR-10分类\n", + "`torchvision`实现了常用的图像数据加载功能，例如Imagenet、CIFAR10、MNIST等，以及常用的数据转换操作，这极大地方便了数据加载，并且代码具有可重用性。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. 小试牛刀：CIFAR-10分类\n", "\n", "下面我们来尝试实现对CIFAR-10数据集的分类，步骤如下: \n", "\n", @@ -1031,7 +1040,7 @@ "4. 训练网络并更新网络参数\n", "5. 测试网络\n", "\n", - "#### CIFAR-10数据加载及预处理\n", + "### 4.1 CIFAR-10数据加载及预处理\n", "\n", "CIFAR-10[^3]是一个常用的彩色图片数据集，它有10个类别: 'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'。每张图片都是$3\\times32\\times32$，也即3-通道彩色图片，分辨率为$32\\times32$。\n", "\n", @@ -1187,7 +1196,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 定义网络\n", + "### 4.2 定义网络\n", "\n", "拷贝上面的LeNet网络，修改self.conv1第一个参数为3通道，因CIFAR-10是3通道彩图。" ] @@ -1242,7 +1251,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 定义损失函数和优化器(loss和optimizer)" + "### 4.3 定义损失函数和优化器(loss和optimizer)" ] }, { @@ -1260,7 +1269,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 训练网络\n", + "### 4.4 训练网络\n", "\n", "所有网络的训练流程都是类似的，不断地执行如下流程：\n", "\n", @@ -1430,7 +1439,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 在GPU训练\n", + "### 4.5 在GPU训练\n", "就像之前把Tensor从CPU转到GPU一样，模型也可以类似地从CPU转到GPU。" ] },