From 648a4210f8973f25426ee7009a08d86bd98e7033 Mon Sep 17 00:00:00 2001 From: JERRYenSHU503 <1929891932@qq.com> Date: Sun, 28 Apr 2024 13:29:20 +0800 Subject: [PATCH] Update model-selection.ipynb --- .../ml-advanced/model-selection.ipynb | 47 +++++++++---------- 1 file changed, 21 insertions(+), 26 deletions(-) diff --git a/open-machine-learning-jupyter-book/ml-advanced/model-selection.ipynb b/open-machine-learning-jupyter-book/ml-advanced/model-selection.ipynb index b58b5d508..cef35b22f 100644 --- a/open-machine-learning-jupyter-book/ml-advanced/model-selection.ipynb +++ b/open-machine-learning-jupyter-book/ml-advanced/model-selection.ipynb @@ -404,9 +404,17 @@ "We'll look at a couple ways of getting more signal out of the training data while reducing the amount of noise later.\n" ] }, + { + "cell_type": "markdown", + "id": "1e5c8a70", + "metadata": {}, + "source": [ + "Let's first take a look at a learning curve. In this part we're using a datasets called iris in scikit-learn." + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "63830061", "metadata": { "tags": [ @@ -414,31 +422,6 @@ ] }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "d:\\software\\environment for paper\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:547: FitFailedWarning: \n", - "15 fits failed out of a total of 50.\n", - "The score on these train-test partitions for these parameters will be set to nan.\n", - "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n", - "\n", - "Below are more details about the failures:\n", - "--------------------------------------------------------------------------------\n", - "15 fits failed with the following error:\n", - "Traceback (most recent call last):\n", - " File \"d:\\software\\environment for paper\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n", - " estimator.fit(X_train, y_train, **fit_params)\n", - " File \"d:\\software\\environment for paper\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n", - " return fit_method(estimator, *args, **kwargs)\n", - " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - " File \"d:\\software\\environment for paper\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1246, in fit\n", - " raise ValueError(\n", - "ValueError: This solver needs samples of at least 2 classes in the data, but the data contains only one class: 0\n", - "\n", - " warnings.warn(some_fits_failed_message, FitFailedWarning)\n" - ] - }, { "data": { "image/png": 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4eLhJW1hYGE2bNgXA2tqahg0bmvTR6/WEh4cb++Q7rRbmzDF8/eRf4GnvQ0PN4hd6luRYZV9Wx0qjga+/hqFDwd4ejh2Dfv2gQgWYMQPu3s33kIUQoiBRtQCKi4vj2LFjHDt2DDBc5n7s2DEiIiIAw6mpfv36Gfu/9957XL58mVGjRnH27Fm+/fZbfv31V0aMGGHsExQUxIIFC1i6dClnzpxhyJAhxMfHG68KU0W3brB6NXh5mbaXKWNo79ZNnbgKIjlW2ZfVsRo2DL75Bv79F6ZNM9wk8cYNGDsWvL3hgw/g4kV1YhdCCJWpegrs77//5sUXXzS+T5uH079/f5YsWUJUVJSxGAKoUKEC69atY8SIEcyZM4cyZcrwww8/GC+BB3j99de5desWkyZNIjo6Gh8fHzZu3JhuYnS+69bNcEmy3LE3a/87VqnbtnFswwZ8Xn7ZbE575Vh2vq9KlDAUPR99BCtWwKxZcOKEoTiaOxe6dDGcHmvevFDMExJCiNygagHUpk0blKfMX8joLs9t2rTh6NGjT93usGHDGDZs2POGl/u0WmjTRu0oCgetFqV1ayLj46nXurUUP0+T3e8ra2vDabC+fWHrVsPk/PXr4fffDa8mTQyF0GuvgWWhmh4ohBA5VqjmAAkhcoFGA23bwrp18M8/MHgw2NjAwYPQq5fhPkuzZ0MOrqYQQojCRgogIcxZzZrw/fcQEQEhIVCqlOHrjz4yzCX66CPDHamFEKKIkQJICAFubhAcbCh+FiyAGjXg4UPDSFClSoaRoYLwUGEhhMglUgAJIf5jawtvvw2nThnmB/n5GZ47tnIl+PoaJlj//ruhTRRtOh1s3w7Llxv+lc9cFDFSAAkh0rOwgJdfhrAwwz2E+vc3PJh1927DlWfVqhmuIouLUztSkRfWrIHy5Q033OzTx/Bv+fKGdiGKCCmAhBBPV68eLFkCV6/CuHFQvDhcumS4j1DZsoZL7G/cUDtKkVvWrIHu3eHJhzhHRhrapQgSRYQUQEKI7CldGj791HBjxblzoXJluHfPcGfp8uUNl9j/76amopDS6SAwMOPHq6S1DR8up8NEkSAFkBAiZxwc4P334exZWLvWMC8oJQV++gnq1//vEnu9Xu1IRU7ExRlukvnkyM/jFMVQADdoAAMGGK4cXLoUdu40TKCXwkgUInK3MyHEs9FqDXeh7twZDh2CL7+EX3813GRx61aoXh1GjDDceNHOTu1oxZPi42HvXti2zTDJ+dAhSE3N3ronThheT7K0NJwWrVDB8Cpf3vRfd3fD/DIhCgApgIQQz69xY1i2zHA67OuvDfcWOnsW3n0Xxo83jBi9/77hF6BQR0KCoeDZvt1Q9Bw8mL7gcXeHmJistzVhgqGovXoVrlwxvCIiDCOBly8bXhmxtYVy5UyKIk2ZMrjcuAG3b4OHhzyOReQbKYCEELmnbFn4/HOYOBEWLYLQUMONFKdMgZkz4c03DaNCtWqpHWnR9+gR7Nv33wjPgQOGAuVxZcsarvBq08bw8vY2FCaRkRnPA9JoDDfIDAlJ/3ganc4wGf7xoijt66tXDafOEhPh3DnD638sgdYAH38MxYoZ9p82YvTkKJKLSy4cGCEMpAASQuQ+JyfDZNlhwwz3DZo1y/ALeOFCw6tDB8Nzx/z85C/+3JKYaCh40kZ4DhyA5GTTPmXKGAqetKKnfPn0x3/OHMPVXhqNaRGU1i80NONn82m1hgLK29swL+xJKSmGIujxoujKFfSXL5N07hx2d+8a5iGdOmV4ZcTFJfPiqEIFw/w0IbJJCiAhRN6xtIQePQyvvXsNhdDvv8PGjYZXnTqGQqh3b8PzyET2JSYaipy0EZ79+yEpybSPl9d/xc6LLxqKhKwKzm7dYPVqw9Vgj0+ILlPGUPx06/Zs8VpZQcWKhtdjdCkpbF6/no4vvYRVVFTGo0dXrsCtW3D/vuFKw8yuNixVKn1RlPZvuXKGU3B5QaeDXbsgKgo8PQ0FoDzAucCTAkgIkT+aNTO8Ll2Cr74yjASdPAkDBxruJTRsGLz1ltpRFlxJSYaCZ/t2w2vfPkMR9DhPT9MRnkqVnm2ErVs3w+T2/PylbmsLVasaXhmJj09fFD1+uu3+fUORdOuWYUJ3Rjw9TYuix7/29jYUaTm1Zk3GxeKcOc9eLIp8IQWQECJ/Vapk+OUQEmJ47thXXxnmnEyYgOWnn1K3dWvDPYbMfZ5QcrJhonLaKa29e9MXPB4epiM8lSvn3ilFrdaw3YLCwcHwPZHZ98X9+4aCKKM5SFeuGAqoqCjDa+/e9OtbWBgKl8xOsXl5pS8A024a+eR8qbSbRq5eLUVQASYFkBBCHcWLw6hRhrlCq1bBrFlojh6lwsaNKJs2QUCA4fRYq1bmMU8oOZkSZ85gcfy4YeRlzx7DRObHubv/V+y0aWMYLTGHY5MdLi7g42N4PUlR4M6dzEePrl41jLBFRBheO3em34aVlWHSeFpRVLas4ZSgoqDTwK5yEFUMPOOg5TUFLRrD93bnznI6rICSAkgIoS5ra3jjDejTh9TwcG6NG4fnoUPwxx+GV8OGhkKoR49nO0VRUKWkwN9/G0d4LPfsoWVCgmkfN7f/rtB68UXDM9ik4Mk5jQZcXQ2vxo3TL9frDZf/Z3YF27Vrhs/r0iXD6zFrakBgB7ju/F9bmQcwZ6NCtzP/Gk7tNm363/5LlTL8W6xYXmYsskEKICFEwaDRoLRuzcHx4+lYqRJW33xjuMvw4cOGAmn0aPjwQxg8uHBeDp2SYsglbQ7P7t2G0zL/owGSnJyw8vPDom1bQ9FTo4YUPPnBwsIwP8jT01CsPEmnM5zWerwoCg9nze1ddO8JT94wINIJuveE1b9Ct/nzYf789Nu0scHS1ZU2VlZov/76v8Io7d8nv3Z1LVp/ABQAUgAJIQqeatUMvzQ++cTw7zffGCaZjhpluKfQW28ZJp5WqKB2pJlLTYUjR/6bw7N7t+Ey78eVLGkc4Ulp0YKNV6/SsVMnLOQXXcGi1RpOeZUtazglC+hatSDwTz9D8fNEjapoQKPA8A7QufRLaLWWhsnZt28b/k1MhKQkNJGROIOhoMoOZ+enF0lPFlHOzgWvgNbp0OzYgdfOnWgcHAwjmyqdIpQCSAhRcLm6Gu46PHIkLF8Os2cb7hEzZ47hjtPduhlOj2X0V3t+S001XJ6ddln6rl3w8KFpnxIloHXr/+bw1Kr136MhUlIMp1pEobCrrOlprycpGvjXGaaOa06vun2oUqIKWov//aKPj4fbt0mJjubv9etpXKEClmlXsd2+/V+hlPb1nTuG03QPHhheT5yGy5SlpekIUlaFk6tr3t0qAIxXzFlev04jMPw8q3jFnBRAQoiCz9bWcLn8gAEQFma4n9DmzYarbFavNhRAQUHQtWv+/TWp0xkKnrQRnl27IDbWtE/x4oaCJ20OT+3a8iysIuJGQjYeGQJM3jWVybumYmtpS61StajrXpc6bnWo616XGtVqcDO6AUrHjk8/vaXTGa5ye7IwetrXcXGGojw62vDKrmLFsl8slSpl+B7Pzvd0AbxiTgogIUThodFA+/aG16lThgew/vyz4Z44PXoYTokFBsKgQeDomLv71ukMDwBNG+HZudPw1/jjXFwMp0jSRnjq1pWCpwjaemUrU3ZMyVbfGq41uPbgGgkpCRyOOszhqMMmy4tbFqfRg0bU9ahLXXfDq4ZrDWwsH7sxqFZrOF1asqTh9HB2JCYaRo4yKpIyK5xSUw2FU1xc9k/LWVgYRjafdiqueHEYMiTjx6soiuHnWoUr5qQAEkIUTrVrG26m+Omn8O23hteVK4b/SIOD4Z13DJOmy5T5b52c3LFXrzcUPGmTlnfsMPwV/jhnZ0PBkzbCU7euXPJchB2NOsrY8LFsurQJAA0alHRToDEuK+NUhpNDTqLRaLh87zInYk5wIuYEJ2+e5ETMCS7dvcS91HuEXQkj7EqYcV2tRkt11+rUca9DXbf/CqMyTmXQZHdOj62t4d5FXl7Z668ohoI+O6NLaV/fv2/4OUl7/6wUxfCYlF278vXeU1IACSEKNw8Pw8ToMWPgp58M8wrOnzc8lPXLL6FnT/joI8NftE+7Y69ebxhVShvh2bED7t0z3Zejo+kIj4+PFDxm4PK9y0zYOoHlp5YDYGVhxbsN36W+Z33e/uNtAJNCSPO/WdGhHUKN834ql6hM5RKV6Vbjv9M89+Lv8cP//YBTFSf+uf2PsUC6l3iPf279wz+3/mEFK4z9nW2cjcVQ2qm02m61cbTJhdFOjcYwguniYrihZnakpBhGmbIqmC5cyN78tqio58kgx6QAEkIUDfb28O67hsvk1683FELbtsGyZYZXRiIj4bXXwNcXLl40/Gf+OEdHwyhR2giPj49hYqkwCzfjbzJ1x1S+O/wdKfoUAHrX7s3UF6dSqUQlAFxsXQjcGMj12P8K6zJOZQjtEGpS7GSkmHUxqjpUpaNPR6z+NwdIURQiH0ZyMsYwSnTipqEoOnv7LA+SHrArYhe7InaZbKdi8YqGositrmHUyL0ulYpX+m/SdV6xsjL8AeLh8fR+27cbfn6y4umZK2Fll/wkCyGKFgsLeOUVw+vIEfjiC8MVZBlJm5Nw4IDhXwcHQ8GTNsLToIEUPGboYdJDZu2bxax9s4hLNty6wL+SP9PbTqe+Z32Tvt1qdKNztc7sithF1MMoPB09aVm25TMXHxqN4dRZGacyvFzlZWN7si6Zs7fPGkeJ0k6l3Xh4g8v3LnP53mXWnl1r7G9naUdtt9rGCdd13Q3Fkau96zPF9VxatjSMtkZGZjwPSKMxLG/ZMl/Dkp9sIUTR1aCBYS5QZgXQ4+bONYweyT14zFZSahLfHf6OT3Z+wq2EWwA0Kt2ImX4zeanCS5mup7XQ0qZ8mzyNzVprbSxkHnc74bZxtChtbtGpm6d4lPqIQzcOceiG6YNhSzuWNimK6rrXpbprday11nkXvFZrONXcvbuh2Hm8CEqb0xQamu+nk6UAEkIUbdmdV1C8uBQ/Zkqv6Fl+cjkTt03kyv0rAFQpUYVPX/qU7jW7Z3/isQpc7V15scKLvFjhv1NMOr2OS/cuGYqimJPG02iX713mxsMb3Hh4wziRG8DSwpLqrtWNp9HSRou8HL1yL/du3QyXumc0Dy80VO4DJIQQuS678wryef6BUJ+iKGy6tIkxW8ZwPOY4AJ7FPAluHcyg+oOw0hbOglhroaVqyapULVmV7jW7G9sfJj3kn1v/mJxGOxFzggdJDzh18xSnbp5iGf/NlytuW9xkwnVd97rUdquNg7XDswXWrRu6gFfY/sfX7D+4lReavESbVz9Aa5WHo09PIQWQEKJoK6DzD4S6DkYeZPSW0Wy/uh0AJxsnRjcfTaBv4LP/gi/gHG0ceaHMC7xQ5gVjm6IoXI+9/l9BdNMwanT29lnuJd5jx7Ud7Li2w9hfg4ZKJSqZFEV13etSsXhFLDRPv+fVmjNr/pswbg+cWk+ZiFDmdJiT5YTxvCAFkBCiaCug8w+EOs7dPsf4reP57cxvgGFuzQdNPmBsi7GUtC+pcnT5T6PR4O3sjbezN52qdjK2J6Umceb2mXT3LoqOi+bi3YtcvHuRNWfWGPvbW9lT2622yX2L6rjXoYRdCcBQ/HT/tXu6+yZFxkbS/dfurO65Ot+LICmAhBBFXwGcfyDy142HN5i8fTILjy5Ep+jQoKG/T38mt5lMWeeyaodX4NhY2uDj4YOPh49J+834m5yMOWksiE7EnOCfW/+QkJLAwciDHIw8aNLfy9GLOm512PPvngxvGqmgoEHD8I3D6Vytc95fuv8YKYCEEOahWzfDrfazeydoUSTcT7zPzN0zmXNgDo9SHwEQUDWAaW2nUduttsrRFT5uDm60rdiWthXbGttS9alcvHsx3b2Lrt6/SuTDSCIfRj51mwoK/8b+y66IXXl+Nd3jpAASQpgPrTZfb7Uv1JOYmsg3B79h2q5p3Es03NG7mXczZvrNpEXZFipHV7SkXUVW3bU6PWr1MLbHJsVy6uYpFh5ZyKJji7LcTtRDuRO0EEII8Ux0eh0/Hv+RSdsnGe/OXLNUTaa3nU5A1YACfUl7UeNk40Qz72Yk65KzVQB5OsqdoIUQQogcURSFP879wbit4zh96zQA3k7eTG4zmX71+uXr3BJhqmXZlpRxKkNkbGSG84DSHhzbsqzcCVoIIYTItt0Ruxm9ZTR7/90LGO5fM77leIY2GYqtpa3K0QmthZY5HebQ/dfuaNBk+eDY/PL0i/aFEEKIAurUzVO8uvxVWi5uyd5/92JnacfYFmO5HHiZj5p9JMVPAdKtRjdW91yNl5OXSXsZpzKqXAIPMgIkhBCikIl4EMGkbZP48fiPKChoNVreqv8WwW2CKe1YWu3wRCbSHhy77fI2NuzewMstXubFii+qdnpS9RGguXPnUr58eWxtbfH19eXgwYOZ9k1JSWHKlClUqlQJW1tb6tWrx8aNG036hISEoNFoTF7Vq1fP6zSEEELksTsJd/ho00dU/boqS48vRUHhtRqv8c/7//BdwHdS/BQCWgstrcu1plXxVrQu11rVuVmqjgCtXLmSoKAg5s+fj6+vL6Ghofj7+3Pu3Dnc3NzS9Z8wYQI///wzCxYsoHr16mzatImuXbuyd+9e6tevb+xXq1YttmzZYnxvaSkDXUIIUVjFJ8cTuj+Uz/Z+RmxSLABtyrdhpt9Mmng1UTk6UVipOgI0e/ZsBg8ezMCBA6lZsybz58/H3t6eRYsyvlzup59+Yty4cXTs2JGKFSsyZMgQOnbsyKxZs0z6WVpa4uHhYXy5urrmRzpCCCFyUYouhfl/z6fy15WZsG0CsUmx1HOvx4Y3NrC131YpfsRzUW1oJDk5mcOHDzN27Fhjm4WFBX5+fuzbty/DdZKSkrC1NZ3UZmdnx+7du03aLly4QOnSpbG1taVp06ZMnz6dsmUzv9V5UlISSUlJxvexsYa/MFJSUkhJSclxbrkpbf9qx6EGc83dXPMGyf3xf81FRnkrisJvZ39j0o5JXLx7EYAKLhUIbhVMr1q9sNBYkJqaqkq8uUk+89zPOyfb1ChKRo9Hzns3btzAy8uLvXv30rRpU2P7qFGj2LFjBwcOHEi3Tp8+fTh+/Dhr166lUqVKhIeH07lzZ3Q6nbGA2bBhA3FxcVSrVo2oqCgmT55MZGQkp06dwtHRMcNYQkJCmDx5crr2ZcuWYW9vn0sZCyGEyMqJhyf48caPXHxkKHycLZ3p4d4D/5L+WFlYqRydKOgSEhLo06cPDx48wMnJ6al9C1UBdOvWLQYPHsyff/6JRqOhUqVK+Pn5sWjRIh49epThfu7fv0+5cuWYPXs2b731VoZ9MhoB8vb25vbt21kewLyWkpJCWFgY7dq1w8rKvH74zTV3c80bJHdzzD0t71J1SxGyK4SwK2EAFLMuxvAmwxnhOwJHm4z/eC3szP0zz4u8Y2NjcXV1zVYBpNopMFdXV7RaLTExMSbtMTExeHh4ZLhOqVKlWLt2LYmJidy5c4fSpUszZswYKlasmOl+XFxcqFq1KhcvXsy0j42NDTY2NunaraysCsw3ZUGKJb+Za+7mmjdI7uaU+6V7l5h1dRa7ju0CwMrCivcavceEVhNwc0h/MUxRZG6feZq8yDsn21NtErS1tTUNGzYkPDzc2KbX6wkPDzcZEcqIra0tXl5epKam8ttvv9G5c+dM+8bFxXHp0iU8PfP3GSNCCCEyFxMXw7D1w6jzXR123TcUP33q9OHssLN89fJXZlP8CPWoen14UFAQ/fv3p1GjRjRp0oTQ0FDi4+MZOHAgAP369cPLy4vp06cDcODAASIjI/Hx8SEyMpKQkBD0ej2jRo0ybnPkyJEEBARQrlw5bty4QXBwMFqtlt69e6uSoxBCiP/EJsUya+8sZu2bRXxKPAD1Heszv8d8mnjLVV0i/6haAL3++uvcunWLSZMmER0djY+PDxs3bsTd3R2AiIgILCz+G6RKTExkwoQJXL58mWLFitGxY0d++uknXFxcjH2uX79O7969uXPnDqVKlaJFixbs37+fUqVK5Xd6Qggh/icpNYnvDn/H1J1TuZ1wG4DGpRvzSZtPeHT6EfU96mexBSFyl+p3CBw2bBjDhg3LcNn27dtN3rdu3ZrTp08/dXsrVqzIrdCEEEI8J72iZ9nJZUzcNpGr968CULVkVT596VNeq/EaqamprD+9Xt0ghVlSvQASQghR9CiKwsaLGxkbPpbjMccB8CzmSXDrYAbVH4SV1vwm/YqCRQogIYQQuerA9QOMCR/D9qvbAXCycWJ089EE+gbiYO2gbnBC/I8UQEIIIXLF2dtnGb91PGvOrAHARmvDsCbDGNtiLCXtS6ocnRCmpAASQgjxXCJjI5m8YzKLji5Cp+iw0FjQr14/JreZTFnnzB9DJISapAASQgjxTO49usfMPTOZc2AOiamJALxa7VWmvTSNWm61VI5OiKeTAkgIIUQ6Or2OXRG7iHoYhaejJy3LtkRroQXgUcojvjn4DdN3T+de4j0Amns3Z4bfDFqUbaFm2EJkmxRAQgghTKw5s4bAjYFcj71ubCvjVIbZ7WfzMPkhwduDjctqlqrJ9LbTCagagEajUStkIXJMCiAhhBBGa86sofuv3VEwfU729djr9Fzd0/je28mbKS9OoW/dvsaRISEKEymAhBBCAIbTXoEbA9MVP4/ToGGm30w+8P0AW0vbfIxOiNyl2sNQhRBCFCy7InaZnPbKiIJCY6/GUvyIQk8KICGEEABEPYzK1X5CFGRSAAkhhADA09EzV/sJUZBJASSEEAKAlmVbUsapTKbLNWjwdvKmZdmW+RiVEHlDCiAhhBAAaC20zOkwJ8NlGgyXuId2CJWrvkSRIAWQEEIII89iGZ/eKuNUhtU9V9OtRrd8jkiIvCGXwQshhDAK3h4MwIB6A+jv0z/DO0ELURRIASSEEAKAXdd2EXY5DEsLSya1nkSF4hXUDkmIPCOnwIQQQgD/jf4M8hkkxY8o8qQAEkIIwbYr29h2dRtWFlaMbzVe7XCEyHNSAAkhhJlTFIVJ2ycBMLjBYMo6l1U5IiHynhRAQghh5rZc3sLuiN3YaG0Y13Kc2uEIkS+kABJCCDP2+OjPe43ew8vJS+WIhMgfUgAJIYQZ23hxI/uv78fO0o4xLcaoHY4Q+UYKICGEMFOPj/683/h9PIp5qByREPlHCiAhhDBTf53/i79v/I29lT2jmo9SOxwh8pUUQEIIYYYeH/35oMkHuDm4qRyREPlLCiAhhDBDa8+u5Vj0MYpZF2Nks5FqhyNEvpMCSAghzIxe0RtHfwJ9A3G1d1U5IiHynxRAQghhZlafXs2pm6dwsnHio6YfqR2OEKqQAkgIIcyITq8jZHsIAEEvBFHcrri6AQmhEimAhBDCjKz8ZyVnbp/BxdaF4S8MVzscIVQjBZAQQpiJVH0qk3dMBmBk05E42zqrHJEQ6pECSAghzMSyk8s4f+c8JexK8KHvh2qHI4SqpAASQggzkKJLYcqOKQCMajYKRxtHlSMSQl1SAAkhhBn46cRPXLp3iVL2pRjaZKja4QihOimAhBCiiEvWJRtHf0Y3H00x62IqRySE+qQAEkKIIm7x0cVce3ANj2IeDGk8RO1whCgQpAASQogiLCk1iU92fQLA2BZjsbeyVzkiIQoGKYCEEKII++HID1yPvU5px9K80/AdtcMRosBQvQCaO3cu5cuXx9bWFl9fXw4ePJhp35SUFKZMmUKlSpWwtbWlXr16bNy48bm2KYQQRdWjlEdM2z0NgPEtx2NraatyREIUHKoWQCtXriQoKIjg4GCOHDlCvXr18Pf35+bNmxn2nzBhAt999x1ff/01p0+f5r333qNr164cPXr0mbcphBBF1feHv+fGwxt4O3nzVv231A5HiAJF1QJo9uzZDB48mIEDB1KzZk3mz5+Pvb09ixYtyrD/Tz/9xLhx4+jYsSMVK1ZkyJAhdOzYkVmzZj3zNoUQoihKSElg+u7pAExoNQEbSxuVIxKiYFGtAEpOTubw4cP4+fn9F4yFBX5+fuzbty/DdZKSkrC1NR3CtbOzY/fu3c+8TSGEKIrmHZpHTHwM5V3KM8BngNrhCFHgWKq149u3b6PT6XB3dzdpd3d35+zZsxmu4+/vz+zZs2nVqhWVKlUiPDycNWvWoNPpnnmbYCiskpKSjO9jY2MBw5yjlJSUZ8ovt6TtX+041GCuuZtr3iC5P/7v84hLjmPG7hkAjG8+Ho1eQ4q+YB5T+czNL/e8zDsn21StAHoWc+bMYfDgwVSvXh2NRkOlSpUYOHDgc5/emj59OpMnT07XvnnzZuztC8Ylo2FhYWqHoBpzzd1c8wbJ/Xn9FvMbtx/dxsPagxLXS7A+cn0uRJa35DM3P3mRd0JCQrb7qlYAubq6otVqiYmJMWmPiYnBw8Mjw3VKlSrF2rVrSUxM5M6dO5QuXZoxY8ZQsWLFZ94mwNixYwkKCjK+j42Nxdvbm/bt2+Pk5PSsKeaKlJQUwsLCaNeuHVZWVqrGkt/MNXdzzRsk99zIPTYplkHfDgJgmv80AuoE5FaIeUI+c/PLPS/zTjuDkx2qFUDW1tY0bNiQ8PBwunTpAoBeryc8PJxhw4Y9dV1bW1u8vLxISUnht99+o2fPns+1TRsbG2xs0k8QtLKyKjDflAUplvxmrrmba94guT9P7vP2zePuo7tUK1mNvj59sbQoHAP98pmbX+55kXdOtqfqT0ZQUBD9+/enUaNGNGnShNDQUOLj4xk4cCAA/fr1w8vLi+nTDVcyHDhwgMjISHx8fIiMjCQkJAS9Xs+oUaOyvU0hhCiq7ifeZ9Y+w1Wxwa2DC03xI4QaVP3peP3117l16xaTJk0iOjoaHx8fNm7caJzEHBERgYXFfxeqJSYmMmHCBC5fvkyxYsXo2LEjP/30Ey4uLtnephBCFFWh+0O5n3ifmqVq0rNWT7XDEaJAU/3Pg2HDhmV6emr79u0m71u3bs3p06efa5tCCFEU3X10ly/3fwlASOsQtBZalSMSomBT/VEYQgghnt/sfbOJTYqlrntdXqv5mtrhCFHgSQEkhBCF3O2E28w5MAeAyW0mY6GR/9qFyIr8lAghRCH3+Z7PiUuOo75HfTpX66x2OEIUClIACSFEIRYTF8M3h74BYMqLU9BoNCpHJEThIAWQEEIUYp/t+YyElAQal25Mpyqd1A5HiEJDCiAhhCikoh5G8e3f3wIy+iNETkkBJIQQhdSM3TNITE2kaZmm+FfyVzscIQoVKYCEEKIQuh57ne8OfwfI6I8Qz0IKICGEKISm7ZpGki6JVuVa0bZCW7XDEaLQkQJICCEKmWv3r/HDkR8AmNJGRn+EeBZSAAkhRCHz6a5PSdGn8FKFl2hdvrXa4QhRKEkBJIQQhcjle5dZfGwxYLjrsxDi2UgBJIQQhcgnOz8hVZ9K+0rtaVG2hdrhCFFoSQEkhBCFxIU7F/jx+I+AjP4I8bykABJCiEJi6s6p6BQdHat05IUyL6gdjhCFmhRAQghRCJy9fZZfTv4CyOiPELlBCiAhhCgEJu+YjF7R07laZxqVbqR2OEIUelIACSFEAXfq5ilWnloJQEibEHWDEaKIkAJICCEKuMk7JqOg8FqN1/Dx8FE7HCGKBCmAhBCiADsefZzVp1ejQSOjP0LkIimAhBCiAAvZEQJAz1o9qe1WW91ghChCpAASQogC6vCNw6w9uxYNGoJbB6sdjhBFihRAQghRQKWN/vSp04capWqoG4wQRYwUQEIIUQAdjDzIX+f/QqvRyuiPEHlACiAhhCiAJm2bBEDfen2pUrKKytEIUfRIASSEEAXMnog9bLq0Ca1Gy8RWE9UOR4giSQogIYQoYIK3G055DfQZSMXiFVWORoiiSQogIYQoQHZe20n4lXCsLKwY32q82uEIUWRJASSEEAWEoihM3ml40Olb9d+ivEt5dQMSogiTAkgIIQqIk3En2fXvLqy11oxrOU7tcIQo0qQAEkKIAkBRFJZHLwfg3Ybv4u3srXJEQhRtUgAJIUQBsOXKFs7En8HW0pYxLcaoHY4QRV6OC6Dy5cszZcoUIiIi8iIeIYQwO4qiELIzBIB3G7xLacfSqsYjhDnIcQE0fPhw1qxZQ8WKFWnXrh0rVqwgKSkpL2ITQgizsP7Ceg7dOIS1xpqRL4xUOxwhzMIzFUDHjh3j4MGD1KhRgw8++ABPT0+GDRvGkSNH8iJGIYQoshRFYdJ2w12fO5bqiHsxd5UjEsI8PPMcoAYNGvDVV19x48YNgoOD+eGHH2jcuDE+Pj4sWrQIRVFyM04hhCiS/jj3B0eijuBg5UBXt65qhyOE2bB81hVTUlL4/fffWbx4MWFhYbzwwgu89dZbXL9+nXHjxrFlyxaWLVuWm7EKIUSRolf0xrs+D208FOcEZ5UjEsJ85LgAOnLkCIsXL2b58uVYWFjQr18/vvzyS6pXr27s07VrVxo3bpyrgQohRFHz+5nfOR5zHEdrR0Y0GcGB7QfUDkkIs5HjAqhx48a0a9eOefPm0aVLF6ysrNL1qVChAr169cqVAIUQoih6fPRnxAsjKGlfUuWIhDAvOS6ALl++TLly5Z7ax8HBgcWLFz9zUEIIUdSt+mcV/9z6B2cbZ0Y0HaF2OEKYnRxPgr558yYHDqQfpj1w4AB///13jgOYO3cu5cuXx9bWFl9fXw4ePPjU/qGhoVSrVg07Ozu8vb0ZMWIEiYmJxuUhISFoNBqT1+On54QQQm06vY6QHSEAfNT0I1xsXVSNRwhzlOMCaOjQofz777/p2iMjIxk6dGiOtrVy5UqCgoIIDg7myJEj1KtXD39/f27evJlh/2XLljFmzBiCg4M5c+YMCxcuZOXKlYwbZ/rMnFq1ahEVFWV87d69O0dxCSFEXlp+ajlnb5+luG1xAl8IVDscIcxSjgug06dP06BBg3Tt9evX5/Tp0zna1uzZsxk8eDADBw6kZs2azJ8/H3t7exYtWpRh/71799K8eXP69OlD+fLlad++Pb179043amRpaYmHh4fx5erqmqO4hBAir6TqU5m8w/DE94+bfYyTjZPKEQlhnnI8B8jGxoaYmBgqVqxo0h4VFYWlZfY3l5yczOHDhxk7dqyxzcLCAj8/P/bt25fhOs2aNePnn3/m4MGDNGnShMuXL7N+/Xr69u1r0u/ChQuULl0aW1tbmjZtyvTp0ylbtmymsSQlJZnczTo2NhYwXOqfkpKS7ZzyQtr+1Y5DDeaau7nmDeaR+48nfuTi3Yu42rnybv130+VclHPPiLnmDeabe17mnZNtapQc3rGwd+/eREVF8X//9384OxvuWXH//n26dOmCm5sbv/76a7a2c+PGDby8vNi7dy9NmzY1to8aNYodO3ZkOM8I4KuvvmLkyJEoikJqairvvfce8+bNMy7fsGEDcXFxVKtWjaioKCZPnkxkZCSnTp3C0dExw22GhIQwefLkdO3Lli3D3t4+W/kIIURWUpVUhp4ZSkxyDP1L95cbHwqRyxISEujTpw8PHjzAyenpo6s5LoAiIyNp1aoVd+7coX79+gAcO3YMd3d3wsLC8Pb2ztZ2nqUA2r59O7169eKTTz7B19eXixcvEhgYyODBg5k4cWKG+7l//z7lypVj9uzZvPXWWxn2yWgEyNvbm9u3b2d5APNaSkoKYWFhtGvXLsNbDhRl5pq7ueYNRT/3RccW8d7693B3cOfc++ewt/rvD6yinntmzDVvMN/c8zLv2NhYXF1ds1UA5fgUmJeXFydOnOCXX37h+PHj2NnZMXDgQHr37p2jRFxdXdFqtcTExJi0x8TE4OHhkeE6EydOpG/fvrz99tsA1KlTh/j4eN555x3Gjx+PhUX6KU0uLi5UrVqVixcvZhqLjY0NNjY26dqtrKwKzDdlQYolv5lr7uaaNxTN3JN1yUzbMw2AMS3G4Gyf8V2fi2Lu2WGueYP55p4Xeedke8/0KAwHBwfeeeedZ1nVyNramoYNGxIeHk6XLl0A0Ov1hIeHM2zYsAzXSUhISFfkaLVagEyfPRYXF8elS5fSzRMSQoj8tPDIQiIeROBZzJN3G76rdjhCmL1nfhbY6dOniYiIIDk52aT91VdfzfY2goKC6N+/P40aNaJJkyaEhoYSHx/PwIEDAejXrx9eXl5Mnz4dgICAAGbPnk39+vWNp8AmTpxIQECAsRAaOXIkAQEBlCtXzvigVq1WS+/evZ81VSGEeC6JqYl8uutTAMa1HIedlZ3KEQkhnulO0F27duXkyZNoNBrjyItGowFAp9Nle1uvv/46t27dYtKkSURHR+Pj48PGjRtxd3cHICIiwmTEZ8KECWg0GiZMmEBkZCSlSpUiICCATz/91Njn+vXr9O7dmzt37lCqVClatGjB/v37KVWqVE5TFUKIXLHg8AIiH0ZSxqkMbzd4W+1whBA8QwEUGBhIhQoVCA8Pp0KFChw8eJA7d+7w0Ucf8cUXX+Q4gGHDhmV6ymv79u2mwVpaEhwcTHBwcKbbW7FiRY5jEEKIvPIo5RHTdhvm/oxvOR5bS1uVIxJCwDMUQPv27WPr1q24urpiYWGBhYUFLVq0YPr06Xz44YccPXo0L+IUQohCaf7f84mOi6asc1kG1R+kdjhCiP/J8Z2gdTqd8X46rq6u3LhxA4By5cpx7ty53I1OCCEKsfjkeGbsmQHAxFYTsdZaqxyRECJNjkeAateuzfHjx6lQoQK+vr589tlnWFtb8/3336e7O7QQQpizbw99y834m1QsXpH+9fqrHY4Q4jE5LoAmTJhAfHw8AFOmTOGVV16hZcuWlCxZkpUrV+Z6gEIIURg9THrIzD0zAZjUahJWWvO7z4sQBVmOCyB/f3/j15UrV+bs2bPcvXuX4sWLG68EE0IIc/f1wa+58+gOVUpU4Y26b6gdjhDiCTmaA5SSkoKlpSWnTp0yaS9RooQUP0II8T8PEh/wxV7DVbHBrYOxtHjmW64JIfJIjgogKysrypYtm6N7/QghhLmZc2AO9xLvUd21Or1q91I7HCFEBnJ8Fdj48eMZN24cd+/ezYt4hBCiULv36B6z980GIKR1CFoLrcoRCSEykuNx2W+++YaLFy9SunRpypUrh4ODg8nyI0eO5FpwQghR2Hy5/0seJD2gtlttetTqoXY4QohM5LgASntwqRBCCFN3Eu4Quj8UgMltJmOhyfEguxAin+S4AHraYyiEEMKczdo3i4fJD/Hx8KFL9S5qhyOEeAr580QIIXLBrfhbfHXgK0BGf4QoDHI8AmRhYfHUS97lCjEhhDn6bM9nxKfE09CzIQFVA9QORwiRhRwXQL///rvJ+5SUFI4ePcrSpUuZPHlyrgUmhBCFRXRcNHMPzQVgyotT5L5oQhQCOS6AOnfunK6te/fu1KpVi5UrV/LWW2/lSmBCCFFYzNw9k0epj/D18uXlyi+rHY4QIhty7ST1Cy+8QHh4eG5tTgghCoUbD28w7+95gIz+CFGY5EoB9OjRI7766iu8vLxyY3NCCFFoTN81nSRdEs29m9OuYju1wxFCZFOOT4E9+dBTRVF4+PAh9vb2/Pzzz7kanBBCFGT/PviX7498D8DUF6fK6I8QhUiOC6Avv/zS5IfcwsKCUqVK4evrS/HixXM1OCGEKMim7ZpGsi6ZNuXb8GKFF9UORwiRAzkugAYMGJAHYQghROFy9f5VFh5dCBju+yOEKFxyPAdo8eLFrFq1Kl37qlWrWLp0aa4EJYQQBd0nOz8hRZ+CX0U/WpVrpXY4QogcynEBNH36dFxdXdO1u7m5MW3atFwJSgghCrJLdy+x5NgSQEZ/hCisclwARUREUKFChXTt5cqVIyIiIleCEkKIgmzqzqnoFB0dKnegmXcztcMRQjyDHBdAbm5unDhxIl378ePHKVmyZK4EJYQQBdX5O+f56cRPgIz+CFGY5bgA6t27Nx9++CHbtm1Dp9Oh0+nYunUrgYGB9OrVKy9iFEKIAmPKjinoFT0BVQNo4tVE7XCEEM8ox1eBTZ06latXr9K2bVssLQ2r6/V6+vXrJ3OAhBBF2plbZ1h2chkgoz9CFHY5LoCsra1ZuXIln3zyCceOHcPOzo46depQrly5vIhPCCEKjMk7JqOg0LV6V+p71lc7HCHEc8hxAZSmSpUqVKlSJTdjEUKIAutkzEl+/edXAELahKgbjBDiueV4DtBrr73GzJkz07V/9tln9OjRI1eCEkKIgiZkRwgKCj1q9qCue121wxFCPKccF0A7d+6kY8eO6dpffvlldu7cmStBCSFEQXI06ihrzqxBg4bg1sFqhyOEyAU5LoDi4uKwtrZO125lZUVsbGyuBCWEEAVJyI4QAHrV7kUtt1rqBiOEyBU5LoDq1KnDypUr07WvWLGCmjVr5kpQQghRUPx942/+OPcHFhoLGf0RogjJ8SToiRMn0q1bNy5dusRLL70EQHh4OMuWLWP16tW5HqAQQqgpeLuh6Hmz7ptUc62mcjRCiNyS4wIoICCAtWvXMm3aNFavXo2dnR316tVj69atlChRIi9iFEIIVey/vp/1F9aj1WiZ2Gqi2uEIIXLRM10G36lTJzp16gRAbGwsy5cvZ+TIkRw+fBidTperAQohhFrSRn/61+tP5RKVVY5GCJGbcjwHKM3OnTvp378/pUuXZtasWbz00kvs378/N2MTQgjV7I7YzeZLm7G0sGRCqwlqhyOEyGU5GgGKjo5myZIlLFy4kNjYWHr27ElSUhJr166VCdBCiCJl0rZJAAzyGUSF4hVUjkYIkduyPQIUEBBAtWrVOHHiBKGhody4cYOvv/46L2MTQghVbLuyjW1Xt2FlYcX4VuPVDkcIkQeyPQK0YcMGPvzwQ4YMGSKPwBBCFFmKohjn/gxuMJiyzmVVjkgIkReyPQK0e/duHj58SMOGDfH19eWbb77h9u3bzx3A3LlzKV++PLa2tvj6+nLw4MGn9g8NDaVatWrY2dnh7e3NiBEjSExMfK5tCiFEmvAr4eyK2IWN1oZxLcepHY4QIo9kuwB64YUXWLBgAVFRUbz77rusWLGC0qVLo9frCQsL4+HDhzne+cqVKwkKCiI4OJgjR45Qr149/P39uXnzZob9ly1bxpgxYwgODubMmTMsXLiQlStXMm7cuGfephBCpFEUxTj3571G7+Hl5KVyREKIvJLjq8AcHBwYNGgQu3fv5uTJk3z00UfMmDEDNzc3Xn311Rxta/bs2QwePJiBAwdSs2ZN5s+fj729PYsWLcqw/969e2nevDl9+vShfPnytG/fnt69e5uM8OR0m0IIkWbTpU3su74PO0s7xrQYo3Y4Qog89MyXwQNUq1aNzz77jOvXr7N8+fIcrZucnMzhw4fx8/P7LxgLC/z8/Ni3b1+G6zRr1ozDhw8bC57Lly+zfv1648NZn2WbQggBpqM/7zd+H49iHipHJITIS890I8QnabVaunTpQpcuXbK9zu3bt9HpdLi7u5u0u7u7c/bs2QzX6dOnD7dv36ZFixYoikJqairvvfee8RTYs2wTICkpiaSkJOP7tIe6pqSkkJKSku2c8kLa/tWOQw3mmru55g3q5v7Xhb84dOMQ9lb2jGgyIt9jMNfP3VzzBvPNPS/zzsk2c6UAyi/bt29n2rRpfPvtt/j6+nLx4kUCAwOZOnUqEyc++23qp0+fzuTJk9O1b968GXt7++cJOdeEhYWpHYJqzDV3c80b8j93RVH46PxHAHQo3oG/d/ydr/t/nLl+7uaaN5hv7nmRd0JCQrb7qlYAubq6otVqiYmJMWmPiYnBwyPjoeeJEyfSt29f3n77bcDwZPr4+Hjeeecdxo8f/0zbBBg7dixBQUHG97GxsXh7e9O+fXucnJyeNcVckZKSQlhYGO3atcPKykrVWPKbueZurnmDermvPbeWK8evUMy6GN/0+QZXe9d823cac/3czTVvMN/c8zLvtDM42aFaAWRtbU3Dhg0JDw83njrT6/WEh4czbNiwDNdJSEjAwsJ02pJWqwUMf8E9yzYBbGxssLGxSdduZWVVYL4pC1Is+c1cczfXvCF/c9creqbumgrAcN/heDp75st+M2Oun7u55g3mm3te5J2T7al6CiwoKIj+/fvTqFEjmjRpQmhoKPHx8QwcOBCAfv364eXlxfTp0wHD3ahnz55N/fr1jafAJk6cSEBAgLEQymqbQgjxuN9O/8bJmydxsnEiqGlQ1isIIYoEVQug119/nVu3bjFp0iSio6Px8fFh48aNxknMERERJiM+EyZMQKPRMGHCBCIjIylVqhQBAQF8+umn2d6mEEKk0el1hOwIASDohSCK2xVXNyAhRL5RfRL0sGHDMj09tX37dpP3lpaWBAcHExwc/MzbFEKINL/+8yunb53GxdaF4S8MVzscIUQ+eq77AAkhRGGVqk81jv6MbDoSZ1tndQMSQuQrKYCEEGZp2cllnL9znhJ2JfjQ90O1wxFC5DPVT4EJIUR+0el17IrYxb8P/mXslrEAjGo2CkcbR5UjE0LkNymAhBBmYc2ZNQRuDOR67HVjm4XGgjLOZVSMSgihFimAhBBF3poza+j+a3cUFJN2vaKn75q+2Fna0a1GN5WiE0KoQeYACSGKNJ1eR+DGwHTFz+OGbxyOTq/Lx6iEEGqTAkgIUaTtithlctrrSQoK/8b+y66IXfkYlRBCbVIACSGKtKiHUbnaTwhRNEgBJIQo0jwds/dsr+z2E0IUDVIACSGKtJZlW1LGKfMrvTRo8HbypmXZlvkYlRBCbVIACSGKNK2Flk5VOmW4TIMGgNAOoWgttPkZlhBCZVIACSGKtJi4GFb+sxIAFxsXk2VlnMqwuudquQReCDMk9wESQhRpH23+iPuJ92ng2YB9g/ax9/peoh5G4enoScuyLWXkRwgzJQWQEKLI2nJ5C7+c/AULjQXfvfId1pbWtCnfRu2whBAFgJwCE0IUSYmpiQxZNwSAoY2H0qh0I5UjEkIUJFIACSGKpGm7pnHx7kVKO5bmk5c+UTscIUQBIwWQEKLIOXv7LDN2zwBgToc5ONk4qRyREKKgkQJICFGkKIrCe3+9R4o+hY5VOvJajdfUDkkIUQBJASSEKFJ+PP4jO67twM7Sjrkd56LRaNQOSQhRAEkBJIQoMu4k3GFk2EgAglsHU96lvLoBCSEKLCmAhBBFxqiwUdxOuE1tt9oENQ1SOxwhRAEmBZAQokjYdW0Xi44tAuC7V77DSmulckRCiIJMCiAhRKGXrEvm3b/eBWBwg8E0826mckRCiIJOCiAhRKH3xd4vOHP7DKXsSzHDb4ba4QghCgEpgIQQhdqlu5eYunMqALP9Z1PCroTKEQkhCgMpgIQQhZaiKAxdP5TE1ETaVmjLG3XeUDskIUQhIQWQEKLQ+vWfX9l0aRPWWmu+7fSt3PNHCJFtUgAJIQql+4n3Gb5pOADjWoyjasmq6gYkhChUpAASQhRK48PHEx0XTdWSVRnTYoza4QghChkpgIQQhc7ByIPM+3seAPM7zcfG0kbliIQQhY0UQEKIQiVVn8q7f72LgkK/ev14scKLaockhCiEpAASQhQqXx34imPRxyhhV4Iv2n2hdjhCiEJKCiAhRKER8SCCSdsmAfCZ32eUciilckRCiMJKCiAhRKHxwYYPiE+Jp0XZFgysP1DtcIQQhZgUQEKIQmHt2bX8ce4PLC0smd9pPhYa+e9LCPHs5H8QIUSB9zDpIR9s+ACAj5t9TC23WipHJIQo7KQAEkIUeMHbg7kee50KLhWY0GqC2uEIIYoAKYCEEAXa0aijzDkwB4BvO32LvZW9yhEJIYoCKYCEEAWWTq/j3b/eRa/o6VmrJx0qd1A7JCFEESEFkBCiwJr/93wO3TiEk40Tof6haocjhChCCkQBNHfuXMqXL4+trS2+vr4cPHgw075t2rRBo9Gke3Xq1MnYZ8CAAemWd+ggfzkKUZjceHiDcVvHATDtpWl4OnqqHJEQoiixVDuAlStXEhQUxPz58/H19SU0NBR/f3/OnTuHm5tbuv5r1qwhOTnZ+P7OnTvUq1ePHj16mPTr0KEDixcvNr63sZFnBQlRmIzYNILYpFgal27Me43eUzscIUQRo/oI0OzZsxk8eDADBw6kZs2azJ8/H3t7exYtWpRh/xIlSuDh4WF8hYWFYW9vn64AsrGxMelXvHjx/EhHCJELNl3axK///IqFxoLvXvkOrYVW7ZCEEEWMqiNAycnJHD58mLFjxxrbLCws8PPzY9++fdnaxsKFC+nVqxcODg4m7du3b8fNzY3ixYvz0ksv8cknn1CyZMkMt5GUlERSUpLxfWxsLAApKSmkpKTkNK1clbZ/teNQg7nmbq55gyHnJH0SozeOBuCDxh9Q27W2WRwLc/3czTVvMN/c8zLvnGxToyiKkusRZNONGzfw8vJi7969NG3a1Ng+atQoduzYwYEDB566/sGDB/H19eXAgQM0adLE2L5ixQrs7e2pUKECly5dYty4cRQrVox9+/ah1ab/SzIkJITJkyena1+2bBn29nLJrRD56acbP/Hbzd8oaVWSb6p/g53WTu2QhBCFREJCAn369OHBgwc4OTk9ta/qc4Cex8KFC6lTp45J8QPQq1cv49d16tShbt26VKpUie3bt9O2bdt02xk7dixBQUHG97GxsXh7e9O+ffssD2BeS0lJISwsjHbt2mFlZaVqLPnNXHM317wBjkcdZ+2xtQDMf3U+nat1VjegfGSun7u55g3mm3te5p12Bic7VC2AXF1d0Wq1xMTEmLTHxMTg4eHx1HXj4+NZsWIFU6ZMyXI/FStWxNXVlYsXL2ZYANnY2GQ4SdrKyqrAfFMWpFjym7nmbm556xU9w8OGo0PHK1VeoXvt7mqHpApz+9zTmGveYL6550XeOdmeqpOgra2tadiwIeHh4cY2vV5PeHi4ySmxjKxatYqkpCTefPPNLPdz/fp17ty5g6enXEYrREG1+Ohi9lzfg62FLaHtQ9UORwhRxKl+FVhQUBALFixg6dKlnDlzhiFDhhAfH8/AgQMB6Nevn8kk6TQLFy6kS5cu6SY2x8XF8fHHH7N//36uXr1KeHg4nTt3pnLlyvj7++dLTkKInLkVf4tRW0YB0MujF2Wdy6ockRCiqFN9DtDrr7/OrVu3mDRpEtHR0fj4+LBx40bc3d0BiIiIwMLCtE47d+4cu3fvZvPmzem2p9VqOXHiBEuXLuX+/fuULl2a9u3bM3XqVLkXkBAF1Miwkdx9dJe6bnUJKBWgdjhCCDOgegEEMGzYMIYNG5bhsu3bt6drq1atGpldvGZnZ8emTZtyMzwhRB7admUbPx7/EQ0avn35W24fv612SEIIM6D6KTAhhPlKSk3ivXWGuzwPaTSEJl5NslhDCCFyhxRAQgjVzNg9g/N3zuNRzINpbaepHY4QwoxIASSEUMX5O+eZtttQ9IT6h+Js66xyREIIcyIFkBAi3ymKwpB1Q0jWJeNfyZ+etXqqHZIQwsxIASSEyHe/nPyFrVe2Ymtpy7edvkWj0agdkhDCzEgBJITIV3cf3SVok+HRMxNbTaRi8YoqRySEMEdSAAkh8tWYLWO4lXCLmqVqMrLZSLXDEUKYKSmAhBD5Zk/EHhYcWQDA/E7zsdZaqxyREMJcSQEkhMgXKboU4z1/BvkMomW5lipHJIQwZ1IACSHyxex9szl18xSu9q581u4ztcMRQpg5KYCEEHnuyr0rTN4xGYAv2n1BSfuSWawhhBB5SwogIUSeUhSFYRuG8Sj1EW3Kt6FfvX5qhySEEFIACSHy1m9nfmP9hfVYWVgxr9M8ueePEKJAkAJICJFnYpNiCdwYCMCYFmOo7lpd5YiEEMJACiAhRJ6ZsHUCNx7eoHKJyoxrOU7tcIQQwkgKICFEnvj7xt98c/AbAOZ1moetpa3KEQkhxH+kABJC5LpUfSrv/vUuCgp96vTBr6Kf2iEJIYQJKYCEELlu7sG5HIk6goutC7Pbz1Y7HCGESEcKICFErroee50J2yYAMKPtDNyLuasckRBCpCcFkBAiV3244UPikuNoWqYpgxsOVjscIYTIkBRAQohc8+e5P/n97O9YWljy3SvfYaGR/2KEEAWT/O8khMgV8cnxDNswDICgF4Ko415H5YiEECJzUgAJIXJFyPYQIh5EUM65HJNaT1I7HCGEeCopgIQQz+149HG+3P8lAHM7zsXB2kHliIQQ4umkABJCPBe9oue9de+hU3S8VuM1OlXtpHZIQgiRJSmAhBDP5fvD37P/+n4crR2Z02GO2uEIIUS2SAEkhHhm0XHRjNkyBoBPXvoELycvlSMSQojskQJICPHMgjYF8SDpAQ09GzK08VC1wxFCiGyTAkgI8Uw2X9rM8lPLsdBY8N0r36G10KodkhBCZJsUQEKIHHuU8oj3170PwLDGw2hYuqHKEQkhRM5IASSEyLFpu6Zx6d4lSjuWZupLU9UORwghckwKICFEjpy5dYaZe2YC8FWHr3CycVI5IiGEyDkpgIQQ2aYoCkPWDSFFn0KnKp3oVqOb2iEJIcQzkQJICJFtS48vZce1HdhZ2vFNx2/QaDRqhySEEM9ECiAhRLbcTrjNyM0jAQhpE0J5l/LqBiSEEM9BCiAhRLaMChvFnUd3qONWhxEvjFA7HCGEeC5SAAkhsrTj6g4WH1sMwPxX5mOltVI5IiGEeD5SAAkhniopNYn31r0HwDsN3qGZdzOVIxJCiOcnBZAQ4qk+3/s5Z2+fxc3BjRl+M9QORwghckWBKIDmzp1L+fLlsbW1xdfXl4MHD2bat02bNmg0mnSvTp06GfsoisKkSZPw9PTEzs4OPz8/Lly4kB+pCFGkXLx7kU92fgLA7PazKW5XXOWIhBAid1iqHcDKlSsJCgpi/vz5+Pr6Ehoair+/P+fOncPNzS1d/zVr1pCcnGx8f+fOHerVq0ePHj2MbZ999hlfffUVS5cupUKFCkycOBF/f39Onz6Nra1tvuQlRGGnKArvr3ufJF0SfhX96FOnj9ohqU6n05GSkpIn205JScHS0pLExER0Ol2e7KMgMte8wXxzf568rays0Gpz57mDqhdAs2fPZvDgwQwcOBCA+fPns27dOhYtWsSYMWPS9S9RooTJ+xUrVmBvb28sgBRFITQ0lAkTJtC5c2cAfvzxR9zd3Vm7di29evXK44yEKBpWnFpB2OUwbLQ2zOs0z6zv+aMoCtHR0dy/fz9P9+Hh4cG///5rVsfaXPMG8839efN2cXHBw8PjuY+ZqgVQcnIyhw8fZuzYscY2CwsL/Pz82LdvX7a2sXDhQnr16oWDgwMAV65cITo6Gj8/P2MfZ2dnfH192bdvX4YFUFJSEklJScb3sbGxgKFKzau/9rIrbf9qx6EGc829IOR9P/E+IzYZLnUf03wM5RzL5Us8BSH3jMTExBAbG0upUqWwt7fPk19WiqIQHx+Pg4OD2f0yNMe8wXxzf9a8FUUhISGBW7duodPpcHd3T9cnJ/93qFoA3b59O8Mk3N3dOXv2bJbrHzx4kFOnTrFw4UJjW3R0tHEbT24zbdmTpk+fzuTJk9O1b968GXt7+yzjyA9hYWFqh6Aac81dzbzn/zufmPgYvGy8qH2/NuvXr8/X/Rekz1yj0eDp6YmHhwdWVlZ5WpxZW1sXuOIvP5hr3mC+uT9r3lZWVjg6OhIVFcWRI0dQFMVkeUJCQra3pfopsOexcOFC6tSpQ5MmTZ5rO2PHjiUoKMj4PjY2Fm9vb9q3b4+Tk7oPekxJSSEsLIx27dphZWVe914x19zVzvtA5AE2HdsEwJLuS2hdrnW+7Vvt3DOSlJREREQEJUqUwM7OLs/2oygKDx8+xNHR0exGA8wxbzDf3J83bysrKx4+fMhLL72EjY2NybK0MzjZoWoB5OrqilarJSYmxqQ9JiYGDw+Pp64bHx/PihUrmDJlikl72noxMTF4enqabNPHxyfDbdnY2KQ7iGA4yAXlP+GCFEt+M9fc1cg7RZfC0I1DUVDoX68/fpX9sl4pDxSkz1yn06HRaNBqtVhY5N2Fs3q9HjCMOOXlfgoac80bzDf3581bq9Wi0WiwtLRM9/9ETv7fUPWIW1tb07BhQ8LDw41ter2e8PBwmjZt+tR1V61aRVJSEm+++aZJe4UKFfDw8DDZZmxsLAcOHMhym0KYuzkH5nAi5gQl7ErwRfsv1A5HFDDly5cnNDQ02/23b9+ORqPJ08njQjwr1UvOoKAgFixYwNKlSzlz5gxDhgwhPj7eeFVYv379TCZJp1m4cCFdunShZMmSJu0ajYbhw4fzySef8Mcff3Dy5En69etH6dKl6dKlS36kJEShdO3+NYK3BwPwebvPcbV3VTmiIkang+3bYflyw795eNlzRvdKe/wVEhLyTNs9dOgQ77zzTrb7N2vWjKioKJydnZ9pf8+ievXq2NjYZDrnU4g0qs8Bev3117l16xaTJk0iOjoaHx8fNm7caJzEHBERkW6I7Ny5c+zevZvNmzdnuM1Ro0YRHx/PO++8w/3792nRogUbN26UewAJkQlFUfhgwwckpCTQsmxLBvoMVDukomXNGggMhOvX/2srUwbmzIFu3XJ9d1FRUcavV65cyaRJkzh37pyxrVixYsavFUVBp9NhaZn1r4NSpUrlKA5ra+sspzPkpt27d/Po0SO6d+/O0qVLGT16dL7tOyMpKSkF5lSuSE/1ESCAYcOGce3aNZKSkjhw4AC+vr7GZdu3b2fJkiUm/atVq4aiKLRr1y7D7Wk0GqZMmUJ0dDSJiYls2bKFqlWr5mUKQhRqa8+u5c/zf2JlYcX8V+ab1YTMPLdmDXTvblr8AERGGtrXrMn1XXp4eBhfzs7OaDQa4/uzZ8/i6OjIhg0baNiwITY2NuzevZtLly7RuXNn3N3dKVasGI0bN2bLli0m233yFJhGo+GHH36ga9eu2NvbU6VKFf744w/j8idPgS1ZsgQXFxc2bdpEjRo1KFasGC+//LLJaE1qaioffvghLi4ulCxZktGjR9O/f/9sjeAvXLiQPn360LdvXxYtWpRu+fXr1+nduzclSpTAwcGBRo0aceDAAePyP//8k8aNG2Nra4urqytdu3Y1yXXt2rUm23NxcTH+frp69SoajYaVK1fSunVrbG1t+eWXX7hz5w69e/fGy8sLe3t76tSpw/Lly022o9fr+eyzz6hcuTI2NjaULVuWTz/9FICXXnqJYcOGmfS/desW1tbWJlM9RM4ViAJICKGeh0kP+WDDBwB83OxjapaqqXJEhYCiQHx81q/YWPjwQ0P/jLYBhpGh2NjsbS+j7TyjMWPGMGPGDM6cOUPdunWJi4ujY8eOhIeHc/ToUTp06EBAQAARERFP3c7kyZPp2bMnJ06coGPHjrzxxhvcvXs30/4JCQl88cUX/PTTT+zcuZN///2XiRMnGpfPnDmTX375hcWLF7Nnzx5iY2PTFR4ZefjwIatWreLNN9+kXbt2PHjwgF27dhmXx8XF0bp1ayIjI/njjz84fvw4o0aNMk7IXbduHV27dqVjx44cPXqU8PDwZ7rCeMyYMQQGBnLmzBn8/f1JTEykYcOGrFu3jlOnTvHOO+/Qt29fk0c+jRs3jhkzZjBx4kROnz7NsmXLjGdB3n77bZYtW2Zyr7qff/4ZLy8vXnrppRzHJx6jiHQePHigAMqDBw/UDkVJTk5W1q5dqyQnJ6sdSr4z19zzO+/hG4YrhKBUnFNRSUhOyJd9ZqYgfuaPHj1STp8+rTx69Oi/xrg4RTGUI/n7iovLcfyLFy9WnJ2dje+3bdumAMratWuzXLdWrVrK119/bXxfrlw55csvvzS+B5QJEyY8dljiFEDZsGGDyb7u3btnjAVQLl68aFznm2++Udzc3BSdTqcoiqK4u7srn3/+uXF5amqqUrZsWaVz585PjfX7779XfHx8jO8DAwOV/v37G99/9913iqOjo3Lnzp0M12/atKnyxhtvZLp9QPn9999N2pydnZXFixcriqIoV65cUQAlNDT0qXEqiqJ06tRJ+eijjxSdTqdEREQoNjY2yoIFCzLs++jRI6V48eLKypUrjW1169ZVQkJCstxPQaXT6ZR79+4ZP/OcyvBn8n9y8vtbRoCEMGNHoo7w1cGvAPi247fYWeXdfW5EwdKoUSOT93FxcYwcOZIaNWrg4uJCsWLFOHPmTJYjQHXr1jV+7eDggJOTEzdv3sy0v729PZUqVTK+9/Dw4NatWwA8ePCAmJgYk5EXrVZLw4YNs8xn0aJFJlcFv/nmm6xatYqHDx8CcOzYMerXr5/ucUppjh07Rtu2bbPcT1aePK46nY6pU6dSp04dSpQoQbFixdi0aZPxuJ4/f56kpKRM921ra2tySu/IkSOcOnWKAQMGPHes5k71SdBCCHXo9Dre/etd9Iqe12u9jn9lf7VDKjzs7SEuLut+O3dCx45Z91u/Hn2LFsTGxuLk5JT5vVFy8c70aY8PSjNy5EjCwsL44osvqFy5MnZ2dnTv3t3k4dMZeXKSr0ajMZ5Wym5/5TlP7Z0+fZr9+/dz8OBBk4nPOp2OFStWMHjw4CxvYpnV8ozizOhOxk8e188//5w5c+YQGhpKnTp1cHBwYPjw4cbjmp2Lc95++218fHy4fv06ixcv5qWXXqJcuXJZrieeTkaAhDBT3x76lr9v/I2TjRNf+n+pdjiFi0YDDg5Zv9q3N1ztldmkco0GvL0N/bKzvTycnL5nzx4GDBhA165dqVOnDh4eHly9ejXP9pcRZ2dn3N3dOXTokLFNp9Nx5MiRp663cOFCWrVqxfHjxzl27JjxFRQUZHxUUt26dTl27Fim85Pq1q371EnFpUqVMrm67sKFC9l67MKePXvo3Lkzb775JvXq1aNixYqcP3/euLxSpUrY2dk9dd916tShUaNGLFiwgGXLljFo0KAs9yuyJgWQEGYoMjaS8VvHAzC97XQ8HT2zWEM8E63WcKk7pC9e0t6Hhhr6qaxKlSqsWbOGY8eOcfz4cfr06fPUkZy88sEHHzB9+nT+7//+j3PnzhEYGMi9e/cyvTIxJSWFn376id69e1O7dm2T19tvv82BAwf4559/6N27Nx4eHnTp0oU9e/Zw+fJlfvvtN+ODt4ODg1m+fDnBwcGcOXOGkydPMnPmTON+XnrpJb755huOHj3K33//zXvvvZetS9yrVKlCWFgYe/fu5cyZM7z77rsmTz+wtbVl1KhRjBo1ih9//JFLly6xf/9+k2dcgmEUaMaMGSiKYnJ1mnh2UgAJYYaGbxrOw+SHNPFqwrsN31U7nKKtWzdYvRq8vEzby5QxtOfBfYCexezZsylevDjNmjUjICAAf39/GjRokO9xjB49mt69e9OvXz+aNm1KsWLF8Pf3z/RU0R9//MGdO3cyLApq1KhBjRo1WLhwIdbW1mzevBk3Nzc6duxInTp1mDFjBtr/FZ9t2rRh1apV/PHHH/j4+PDSSy+ZXKk1a9YsvL29admyJX369GHkyJHZelj2hAkTaNCgAf7+/rRp08ZYhD3Z56OPPmLSpEnUqFGD119/Pd08qt69e2NpaUnv3r3lnna5RKM878nXIig2NhZnZ2cePHhQIB6Gun79ejp27Gh2N9Qy19zzOu/1F9bTaVkntBotf7/zNz4ePrm+j2dVED/zxMRErly5QoUKFZ7vF49OB7t2QVQUeHpCy5YmIz96vT7rOUBFUFZ56/V6atSoQc+ePZk6daoKEeadnHzmV69epVKlShw6dEiVwjQ3Pe/3+tN+JnPy+1smQQthRhJSEhi6figAgb6BBar4KfK0WmjTRu0oCrxr166xefNmWrduTVJSEt988w1XrlyhT58+aoemipSUFO7cucOECRN44YUXCn3xU5CYz58ZQgim7JjC1ftX8XbyZvKLk9UOR4h0LCwsWLJkCY0bN6Z58+acPHmSLVu2UKNGDbVDU8WePXvw9PTk0KFDzJ8/X+1wihQZARLCTJy6eYpZ+2YB8E3HbyhmXSyLNYTIf97e3uzZs0ftMAqMNm3aPPdtAkTGZARICDOgV/S8+9e7pOpT6VK9C69We1XtkIQQQlVSAAlhBhYeWcjef/dSzLoYX3X4Su1whBBCdVIACVHE3Yy/yegthrvjTmkzBW9nb5UjEkII9UkBJEQR99Hmj7iXeA8fDx8+8P1A7XCEEKJAkAJIiCIs/HI4P5/4GQ0avnvlOywt5LoHIYQAKYCEKLISUxMZsm4IAO83fp8mXk2yWEMIIcyHFEBCFFEzds/gwt0LeBbz5NOXPlU7HCHyzJIlS3BxcTG+DwkJwcfH56nrDBgwIN0jKZ5Fbm1H5D8pgIQogs7dPsf03dMBCO0QirOts8oRCZ1ex/ar21l+cjnbr25Hp9fl+T6jo6P54IMPqFixIjY2Nnh7exMQEPDUJ48XBSNHjsz1HK9evYpGo+HYsWMm7XPmzGHJkiW5uq+neffdd9FqtaxatSrf9llUyYQAIYoYRVF4f/37JOuS6VC5Az1q9lA7JLO35swaAjcGcj32urGtjFMZ5nSYQ7caefMw1KtXr9K8eXNcXFz4/PPPqVOnDikpKWzatImhQ4dy9uzZDNdLSUkpMM9he1bFihWjWLH8udGns3P+/XGRkJDAihUrGDVqFIsWLaJHD3V/tpOTk7G2tlY1huchI0BCFDE/n/iZrVe2Ymtpy9yOc9FoNGqHZNbWnFlD91+7mxQ/AJGxkXT/tTtrzqzJk/2+//77aDQaDh48yGuvvUbVqlWpVasWQUFB7N+/39hPo9Ewb948Xn31VRwcHPj0U8Pp0nnz5lGpUiWsra2pVq0aP/30k3EdRVEICQmhbNmy2NjYULp0aT788EPj8m+//ZYqVapga2uLu7s73bt3zzBGvV5P2bJlWbhwoUn70aNHsbCw4Nq1a4DhSfV16tTBwcEBb29v3n//feLi4jLN/clTYDqdjqCgIFxcXChZsiSjRo1Kd3fljRs30qJFC2OfV155hUuXLhmXV6hQAYD69euj0Who87/nuj15CiwpKYkPP/wQNzc3bG1tadGiBYcOHTIu3759OxqNhvDwcJo0aULp0qVp0aIF586dyzSfNKtWraJmzZqMGTOGnTt38u+//5osT0pKYvTo0Xh7e2NjY0PlypVNju0///zDK6+8gpOTE46OjrRs2dKYY5s2bRg+fLjJ9rp06cKAAQOM78uXL8/UqVPp168fTk5OvPPOOwCMHj2aqlWrYm9vT8WKFZk4cSIpKSkm2/rzzz9p3Lgxtra2uLm58eabbwIwZcoUateunS5XHx8fJk6cmOUxeR5SAAlRhNxJuEPQ5iAAJrWaRMXiFVWOqGhSFIX45PgsX7GJsXy44UMU0j/KIK0tcEMgsYmxhnVSnr697D4S4e7du2zcuJGhQ4fi4OCQbvnj82XAUDB07dqVkydPMmjQIH7//XcCAwP56KOPOHXqFO+++y4DBw5k27ZtAPz22298+eWXfPfdd1y4cIG1a9dSp04dAP7++28+/PBDpkyZwrlz59i4cSOtWrXKME4LCwt69erF6tWrTdp/+eUXmjdvTrly5Yz9vvrqK/755x+WLl3K1q1bGTVqVLaOBcCsWbNYsmQJixYtYvfu3dy9e5fff//dpE98fDxBQUH8/fffhIeHY2FhQdeuXdHr9QAcPHgQgC1bthAVFcWaNRkXrqNGjeK3335j6dKlHDlyhMqVK+Pv78/du3dN+o0fP57PP/+crVu3YmlpyaBBg7LMY+HChbz55ps4Ozvz8ssvpzv11q9fP5YvX85XX33FmTNn+O6774wjYZGRkbRq1QobGxu2bt3K4cOHGTRoEKmpqdk6hmm++OIL6tWrx9GjR40FiqOjI0uWLOH06dPMmTOHBQsW8OWXXxrXWbduHV27dqVjx44cPXqUsLAw40NdBw0axJkzZ0yKxKNHj3LixAkGDhyYo9hyTBHpPHjwQAGUBw8e5Op2U3WpyrYr25RlJ5Yp265sU1J1qVmuk5ycrKxdu1ZJTk7O1VgKulRdqhJ2IUwJWhykhF0Iy9axKipy+pk//n3V8eeOCiEoNefWVJJSk/I40txXEL/fHz16pJw+fVp59OiRsS0uKU4hhHx/xSXFZSvmAwcOKICyZs2aLPsCyvDhw03amjVrpgwePNikrUePHkrHjh0VRVGUWbNmKVWrVs3wc/rtt98UJycnJTY2NluxHj58WNFoNMqVK1cURVEUnU6neHl5KfPmzct0nVWrViklS5Y0vl+8eLHi7OxsfB8cHKzUq1fP+N7T01P57LPPjO9TUlKUMmXKKJ07d850H7du3VIA5eTJk4qiKMqVK1cUQDl69KhJv/79+xu3ExcXp1hZWSm//PKLcXlycrJSunRp4/63bdumAMqWLVsUnU6n3Lt3T/nzzz8VwOR77Ennz59XrKyslFu3bimKoii///67UqFCBUWv1yuKoijnzp1TACUsLCzD9ceOHatUqFAh05+t1q1bK4GBgSZtnTt3Vvr37298X65cOaVLly6Zxpjm888/Vxo2bGh837RpU+WNN94wvk/LW6fTKYqiKC+//LIyZMgQ4/IPPvhAadOmTabbz+hnMk1Ofn/LCFA+WXNmDeXnlOfFpS/SZ00fXlz6IuXnlM+z4e/CLO1YtfulHbOvzabdL+3kWGXiye+r9RfXA9Cndh+stYX33Lx4PkoOH57ZqFEjk/dnzpyhefPmJm3NmzfnzJkzAPTo0YNHjx5RsWJFBg8ezO+//24cSWjXrh3lypWjYsWK9O3bl19++YWEhATAMLKTNj+nWLFi7Nq1Cx8fH6pVq8by5csB2LFjBzdv3jSZ37Jlyxbatm2Ll5cXjo6O9O3blzt37hi3+zQPHjwgKioKX19fY5ulpWW6nC9cuEDv3r2pWLEiTk5OlC9fHoCIiIjsHEIALl26REpKismxs7KyokmTJsZjl6Zu3brGrz09PQG4efNmpttetGgR/v7+uLq6AtCxY0cePHjA1q1bATh27BharZbWrVtnuP6xY8do2bLlc8/vevK4AaxcuZLmzZvj4eFBsWLFmDBhgslxO3bsGG3bts10m4MHD2b58uUkJiaSnJzMsmXLsjUi9rxkEnQ+SJsD8OQweNocgNU9V+fZRMjCRo5V9mV2rAAmbptIjVI15FjlEXsre+LGZj4HJc3OazvpuKxjlv3W91lPC+8WxD6MxcnRCQuLjP82tbeyz1Z8VapUQaPRZDrR+UkZnSZ7Gm9vb86dO8eWLVsICwvj/fff5/PPP2fHjh04Ojpy5MgRtm/fzubNm5k0aRIhISEcOnSIV1991aQQ8fLyAqB79+4sX76csWPHsmzZMjp06EDJkiUBw2TuV155hSFDhvDpp59SokQJdu/ezVtvvUVycjL29tk7JlkJCAigXLlyLFiwgNKlS6PX66lduzbJycm5sv0nPV6IpM3TSzvd9iSdTsfSpUuJjo7G0tLSpH3RokW0bdsWOzu7p+4vq+UWFhbpCucn5/FA+u+Vffv28cYbbzB58mT8/f1xdnZmxYoVzJo1K9v7DggIwMbGht9//x1ra2tSUlIynTeWm6QAymM6vY7AjYGZzgHQoGHY+mH4uPugtdCm65OSmsLN5Jtce3ANK8vCfWVGVnR6HcPWD3vmY1VUZOczf9qxSjN843A6V+tcpI+VWjQaDQ7WWRcN7Su1p4xTGSJjIzP8rDRoKONUhvaV2qNBg85Kh4O1Q6YFUHaVKFECf39/5s6dy4cffpjul9b9+/fTzQN6XI0aNdizZw/9+/c3tu3Zs4eaNWsa39vZ2REQEEBAQABDhw6levXqnDx5kgYNGmBpaYmfnx9+fn4EBwfj4uLC1q1b6datG46Ojib70uv19OjRg08//ZTDhw+zevVq5s+fb1x++PBh9Ho9s2bNMh6XX3/9NdvHwtnZGU9PTw4cOGCci5Samsrhw4eN81Du3LnDuXPnWLBgAS1btgRg9+7dJttJu9pJp8v89gVpk8b37NljnL+UkpLCoUOH0k0wzon169fz8OFDjh49ilb738/zqVOnGDhwIPfv36dOnTro9Xp27NiBn59fum3UrVuXpUuXZnqVX6lSpYiKijK+1+l0nDp1ihdffPGpse3du5dy5coxfvx4Y1va5PXH9x0eHp7pnB5LS0v69+/P4sWLsba2plevXlkWTblBCqA8titiV7qrPx6noBAVF0Wlrys9fUOnczmwQijbx6qoeI7PXEHh39h/2RWxizbl2+RaSCJntBZa5nSYQ/dfu6NBY1IEaTD81R/aIRSthTbTv/6f1dy5c2nevDlNmjRhypQp1K1bl9TUVMLCwpg3b166UzKP+/jjj+nZsyf169fHz8+PP//8kzVr1rBlyxbAcONBnU6Hr68v9vb2/Pzzz9jZ2VGuXDn++usvLl++TKtWrShevDjr169Hr9dTrVq1TPdXtmxZmjVrxltvvYVOp+PVV181LqtcuTIpKSl8/fXXBAQEsGfPHpMCKTsCAwOZMWMGVapUoXr16syePZv79+8blxcvXpySJUvy/fff4+npSUREBGPGjDHZhpubG3Z2dmzcuJEyZcpga2ub7hJ4BwcHhgwZwscff0yJEiUoW7Ysn332GQkJCbz11ls5ivlxCxcupFOnTtSrV8+kvWbNmowYMYJffvmFoUOH0r9/fwYNGsRXX31FvXr1uHbtGjdv3qRnz54MGzaMr7/+ml69ejF27FicnZ3Zv38/TZo0oVq1arz00ksEBQWxbt06KlWqlO4YZaZKlSpERESwYsUKGjduzLp169JNMA8ODqZt27ZUqlSJXr16kZyczO+//86kSZOMfd5++21q1KgBGIrt/CAFUB6LehiVdSfAysIq07/U9To9FtqiP11Lp9eRok8/5Pqkpx2roiKrzzy7xyq7338i73Sr0Y3VPVdneB+g0A6heXaasmLFihw5coRPP/2Ujz76iKioKEqVKkXDhg2ZN2/eU9ft0qULc+bM4YsvviAwMJAKFSqwePFi46XfLi4uzJgxg6CgIHQ6HXXq1OHPP/+kZMmSuLi4sGbNGkJCQkhMTKRKlSosX76cWrVqPXWfvXv3ZtiwYfTr18/kr/969eoxe/ZsZs6cydixY2nVqhXTp0+nX79+2T4Wafn3798fCwsLBg0aRNeuXXnw4AFgOP2zYsUKPvzwQ2rXrk21atX46quvjPmCYZTiq6++YsqUKUyaNImWLVuyffv2dPuaMWMGer2evn378vDhQxo1asSmTZsoXrx4tuN9XExMDOvWrWPZsmXplqVdqbZw4UKGDh3KvHnzGDduHO+//z537tyhbNmyjBs3DoCSJUuydetWPv74Y1q3bo1Wq8XHx8c4X2nQoEEcP36cfv36YWlpyYgRI7Ic/QF49dVXGTFiBMOGDSMpKYlOnToxceJEQkJCjH3atGnDqlWrmDp1KjNmzMDJyYmmTZuabKdKlSo0a9aMu3fvmpwmzUsaJaez5cxAbGwszs7OPHjwACcnp+fa1var23lxadbfRNv6b8vwL/WUlBTWr19Px44dC/3NybLyvMeqqMjOZ15Uj1VB/H5PTEzkypUrVKhQAVtb22fejk6vY1fELqIeRuHp6EnLsi1NCnm9Xk9sbCxOTpnPASqKzDVvMN/cM8pbURSqVKnC+++/T1BQ0FPXf9rPZE5+f8sIUB5rWbZltuYAtCzbUoXoChY5Vtknx6rw0VpoC1UxKkR+uXXrFitWrCA6Ojrv7/3zGPMpOVWSNgcA/jvnn+bJOQDmTo5V9smxEkIUFW5ubkyZMoXvv//+mU8VPgspgPJB2hwALycvk/YyTmXksu4nyLHKPjlWQoiiQFEUbt26RZ8+ffJ1v3IKLJ90q9GNztU6P3UOgDBIO1bbLm9jw+4NvNziZV6s+KIcqwzI95UQQjwbKYDykcwByD6thZbW5VoT/088rcu1ll/oTyHfV0IIkXNyCkwIIbJBLpgVomDIrZ9FKYCEEOIp0i7Hz85zp4QQeS/tZ/F5b5Uhp8CEEOIptFotLi4uxgdV2tvbG5/dlJv0ej3JyckkJiaa3T1hzDFvMN/cnzVvRVFISEjg5s2buLi4mDwW5FlIASSEEFnw8PAAnv607uelKAqPHj3Czs4uTwqsgspc8wbzzf1583ZxcTH+TD4PKYCEECILGo0GT09P3NzcMnxCdm5ISUlh586dtGrVqsDcBTs/mGveYL65P0/eVlZWzz3yk0b1Amju3Ll8/vnnREdHU69ePb7++muaNGmSaf/79+8zfvx41qxZw927dylXrhyhoaF07NgRgJCQECZPnmyyTrVq1Th79mye5iGEKPq0Wm2u/eeb0bZTU1OxtbU1q1+G5po3mG/uBSVvVQuglStXEhQUxPz58/H19SU0NBR/f3/OnTuHm5tbuv7Jycm0a9cONzc3Vq9ejZeXF9euXcPFxcWkX61atYxPLQbDQ+yEEEIIIdKoWhnMnj2bwYMHG5/9MX/+fNatW8eiRYsYM2ZMuv6LFi3i7t277N2711g1li9fPl0/S0vLXDk/KIQQQoiiSbVp58nJyRw+fBg/P7//grGwwM/Pj3379mW4zh9//EHTpk0ZOnQo7u7u1K5dm2nTpqHT6Uz6XbhwgdKlS1OxYkXeeOMNIiIi8jQXIYQQQhQuqo0A3b59G51Oh7u7u0m7u7t7pvN1Ll++zNatW3njjTdYv349Fy9e5P333yclJYXg4GAAfH19WbJkCdWqVSMqKorJkyfTsmVLTp06haOjY4bbTUpKIikpyfj+wYMHANy9ezfPJjxmV0pKCgkJCdy5c8eszhGD+eZurnmD5G6OuZtr3mC+uedl3g8fPgSyebNERSWRkZEKoOzdu9ek/eOPP1aaNGmS4TpVqlRRvL29ldTUVGPbrFmzFA8Pj0z3c+/ePcXJyUn54YcfMu0THBysAPKSl7zkJS95yasIvP79998s6xDVRoBcXV3RarXExMSYtMfExGQ6f8fT0zPdJXA1atQgOjqa5ORkrK2t063j4uJC1apVuXjxYqaxjB07lqCgION7vV7P3bt3KVmypOr3ZoiNjcXb25t///0XJycnVWPJb+aau7nmDZK7OeZurnmD+eael3krisLDhw8pXbp0ln1VK4Csra1p2LAh4eHhdOnSBTAUHuHh4QwbNizDdZo3b86yZcvQ6/XGu0eeP38eT0/PDIsfgLi4OC5dukTfvn0zjcXGxgYbGxuTtievLFObk5OTWf2APM5cczfXvEFyN8fczTVvMN/c8ypvZ2fnbPVT9d7bQUFBLFiwgKVLl3LmzBmGDBlCfHy88aqwfv36MXbsWGP/IUOGcPfuXQIDAzl//jzr1q1j2rRpDB061Nhn5MiR7Nixg6tXr7J37166du2KVquld+/e+Z6fEEIIIQomVS+Df/3117l16xaTJk0iOjoaHx8fNm7caJwYHRERYfKcEG9vbzZt2sSIESOoW7cuXl5eBAYGMnr0aGOf69ev07t3b+7cuUOpUqVo0aIF+/fvp1SpUvmenxBCCCEKJtXvEDhs2LBMT3lt3749XVvTpk3Zv39/pttbsWJFboVWINjY2BAcHJzuFJ05MNfczTVvkNzNMXdzzRvMN/eCkrdGUbJzrZgQQgghRNGh6hwgIYQQQgg1SAEkhBBCCLMjBZAQQgghzI4UQEIIIYQwO1IAFQAhISFoNBqTV/Xq1Y3LExMTGTp0KCVLlqRYsWK89tpr6e6gXZhFRkby5ptvUrJkSezs7KhTpw5///23cbmiKEyaNAlPT0/s7Ozw8/PjwoULKkacO8qXL5/uc9doNMb7WhXVz12n0zFx4kQqVKiAnZ0dlSpVYurUqSbP7imqnzkYnlU0fPhwypUrh52dHc2aNePQoUPG5UUl9507dxIQEEDp0qXRaDSsXbvWZHl28rx79y5vvPEGTk5OuLi48NZbbxEXF5ePWeRcVnmvWbOG9u3bG580cOzYsXTbKKw/+0/LPSUlhdGjR1OnTh0cHBwoXbo0/fr148aNGybbyM/PXAqgAqJWrVpERUUZX7t37zYuGzFiBH/++SerVq1ix44d3Lhxg27duqkYbe65d+8ezZs3x8rKig0bNnD69GlmzZpF8eLFjX0+++wzvvrqK+bPn8+BAwdwcHDA39+fxMREFSN/focOHTL5zMPCwgDo0aMHUHQ/95kzZzJv3jy++eYbzpw5w8yZM/nss8/4+uuvjX2K6mcO8PbbbxMWFsZPP/3EyZMnad++PX5+fkRGRgJFJ/f4+Hjq1avH3LlzM1yenTzfeOMN/vnnH8LCwvjrr7/YuXMn77zzTn6l8Eyyyjs+Pp4WLVowc+bMTLdRWH/2n5Z7QkICR44cYeLEiRw5coQ1a9Zw7tw5Xn31VZN++fqZZ/m0MJHngoODlXr16mW47P79+4qVlZWyatUqY9uZM2cUQNm3b18+RZh3Ro8erbRo0SLT5Xq9XvHw8FA+//xzY9v9+/cVGxsbZfny5fkRYr4JDAxUKlWqpOj1+iL9uXfq1EkZNGiQSVu3bt2UN954Q1GUov2ZJyQkKFqtVvnrr79M2hs0aKCMHz++yOYOKL///rvxfXbyPH36tAIohw4dMvbZsGGDotFolMjIyHyL/Xk8mffjrly5ogDK0aNHTdqLys/+03JPc/DgQQVQrl27pihK/n/mMgJUQFy4cIHSpUtTsWJF3njjDSIiIgA4fPgwKSkp+Pn5GftWr16dsmXLsm/fPrXCzTV//PEHjRo1okePHri5uVG/fn0WLFhgXH7lyhWio6NN8nd2dsbX17dI5J8mOTmZn3/+mUGDBqHRaIr0596sWTPCw8M5f/48AMePH2f37t28/PLLQNH+zFNTU9HpdNja2pq029nZsXv37iKd++Oyk+e+fftwcXGhUaNGxj5+fn5YWFhw4MCBfI85vxTln/0nPXjwAI1GY3z2Zn5/5lIAFQC+vr4sWbKEjRs3Mm/ePK5cuULLli15+PAh0dHRWFtbp3s4q7u7O9HR0eoEnIsuX77MvHnzqFKlCps2bWLIkCF8+OGHLF26FMCYY9rjUdIUlfzTrF27lvv37zNgwACAIv25jxkzhl69elG9enWsrKyoX78+w4cP54033gCK9mfu6OhI06ZNmTp1Kjdu3ECn0/Hzzz+zb98+oqKiinTuj8tOntHR0bi5uZkst7S0pESJEkXqWDypKP/sPy4xMZHRo0fTu3dv4wNR8/szV/1RGALjX74AdevWxdfXl3LlyvHrr79iZ2enYmR5T6/X06hRI6ZNmwZA/fr1OXXqFPPnz6d///4qR5d/Fi5cyMsvv0zp0qXVDiXP/frrr/zyyy8sW7aMWrVqcezYMYYPH07p0qXN4jP/6aefGDRoEF5eXmi1Who0aEDv3r05fPiw2qEJkS9SUlLo2bMniqIwb9481eKQEaACyMXFhapVq3Lx4kU8PDxITk7m/v37Jn1iYmLw8PBQJ8Bc5OnpSc2aNU3aatSoYTwFmJbjk1dAFJX8Aa5du8aWLVt4++23jW1F+XP/+OOPjaNAderUoW/fvowYMYLp06cDRf8zr1SpEjt27CAuLo5///2XgwcPkpKSQsWKFYt87mmyk6eHhwc3b940WZ6amsrdu3eL1LF4UlH+2Yf/ip9r164RFhZmHP2B/P/MpQAqgOLi4rh06RKenp40bNgQKysrwsPDjcvPnTtHREQETZs2VTHK3NG8eXPOnTtn0nb+/HnKlSsHQIUKFfDw8DDJPzY2lgMHDhSJ/AEWL16Mm5sbnTp1MrYV5c89ISEBCwvT/3q0Wi16vR4wj88cwMHBAU9PT+7du8emTZvo3Lmz2eSenTybNm3K/fv3TUbGtm7dil6vx9fXN99jzi9F+Wc/rfi5cOECW7ZsoWTJkibL8/0zz/Vp1SLHPvroI2X79u3KlStXlD179ih+fn6Kq6urcvPmTUVRFOW9995TypYtq2zdulX5+++/laZNmypNmzZVOerccfDgQcXS0lL59NNPlQsXLii//PKLYm9vr/z888/GPjNmzFBcXFyU//u//1NOnDihdO7cWalQoYLy6NEjFSPPHTqdTilbtqwyevTodMuK6ufev39/xcvLS/nrr7+UK1euKGvWrFFcXV2VUaNGGfsU5c9848aNyoYNG5TLly8rmzdvVurVq6f4+voqycnJiqIUndwfPnyoHD16VDl69KgCKLNnz1aOHj1qvOInO3l26NBBqV+/vnLgwAFl9+7dSpUqVZTevXurlVK2ZJX3nTt3lKNHjyrr1q1TAGXFihXK0aNHlaioKOM2CuvP/tNyT05OVl599VWlTJkyyrFjx5SoqCjjKykpybiN/PzMpQAqAF5//XXF09NTsba2Vry8vJTXX39duXjxonH5o0ePlPfff18pXry4Ym9vr3Tt2tXkh6Ww+/PPP5XatWsrNjY2SvXq1ZXvv//eZLler1cmTpyouLu7KzY2Nkrbtm2Vc+fOqRRt7tq0aZMCZJhPUf3cY2NjlcDAQKVs2bKKra2tUrFiRWX8+PEm/wkW5c985cqVSsWKFRVra2vFw8NDGTp0qHL//n3j8qKS+7Zt2xQg3at///6KomQvzzt37ii9e/dWihUrpjg5OSkDBw5UHj58qEI22ZdV3osXL85weXBwsHEbhfVn/2m5p132n9Fr27Ztxm3k52euUZTHbr8qhBBCCGEGZA6QEEIIIcyOFEBCCCGEMDtSAAkhhBDC7EgBJIQQQgizIwWQEEIIIcyOFEBCCCGEMDtSAAkhhBDC7EgBJITIVeXLlyc0NDTb/bdv345Go0n37CNhasmSJemeEC6EeHZSAAlhpjQazVNfISEhz7TdQ4cO8c4772S7f7NmzYiKisLZ2fmZ9pddaYVWRq/o6Og83bcQouCxVDsAIYQ6oqKijF+vXLmSSZMmmTyYtlixYsavFUVBp9NhaZn1fxmlSpXKURzW1tb5+pTrc+fOmTyBGsDNzS3f9i+EKBhkBEgIM+Xh4WF8OTs7o9FojO/Pnj2Lo6MjGzZsoGHDhtjY2LB7924uXbpE586dcXd3p1ixYjRu3JgtW7aYbPfJU2AajYYffviBrl27Ym9vT5UqVfjjjz+My588BZZ2qmfTpk3UqFGDYsWK0aFDB5OCLTU1lQ8//BAXFxdKlizJ6NGj6d+/P126dMkybzc3N5PcPTw8sLCwIDExkVq1apmMXl26dAlHR0cWLVoEwJ07d+jduzdeXl7Y29tTp04dli9fbrL9Nm3a8MEHHzB8+HCKFy+Ou7s7CxYsID4+noEDB+Lo6EjlypXZsGFDumOwbt066tati62tLS+88AKnTp16ai7/93//R4MGDbC1taVixYpMnjyZ1NRUwFC0hoSEULZsWWxsbChdujQffvhhlsdHCHMhBZAQIlNjxoxhxowZnDlzhrp16xIXF0fHjh0JDw/n6NGjdOjQgYCAACIiIp66ncmTJ9OzZ09OnDhBx44deeONN7h7926m/RMSEvjiiy/46aef2LlzJxEREYwcOdK4fObMmfzyyy8sXryYPXv2EBsby9q1a58rV1tbW3755ReWLl3K//3f/6HT6XjzzTdp164dgwYNAiAxMZGGDRuybt06Tp06xTvvvEPfvn05ePCgybaWLl2Kq6srBw8e5IMPPmDIkCH06NGDZs2aceTIEdq3b0/fvn1JSEgwWe/jjz9m1qxZHDp0iFKlShEQEEBKSkqG8e7atYt+/foRGBjI6dOn+e6771iyZAmffvopAL/99htffvkl3333HRcuXGDt2rXUqVPnuY6REEVKnjxiVQhRqCxevFhxdnY2vk97qvPatWuzXLdWrVrK119/bXxfrlw55csvvzS+B5QJEyYY38fFxSmAsmHDBpN93bt3zxgLoFy8eNG4zty5cxV3d3fje3d3d+Xzzz83vk9NTVXKli2rdO7cOdM40/bj4OBg8qpZs6ZJv88++0xxdXVVhg0bpnh6eiq3b99+av6dOnVSPvroI+P71q1bKy1atDCJzcHBQenbt6+xLSoqSgGUffv2mcS2YsUKY587d+4odnZ2ysqVK43H5fHPqG3btsq0adNMYvnpp58UT09PRVEUZdasWUrVqlWV5OTkp8YvhLmSOUBCiEw1atTI5H1cXBwhISGsW7eOqKgoUlNTefToUZYjQHXr1jV+7eDggJOTEzdv3sy0v729PZUqVTK+9/T0NPZ/8OABMTExNGnSxLhcq9XSsGFD9Hp9ljnt2rULR0dH43srKyuT5R999BFr167lm2++YcOGDZQsWdK4TKfTMW3aNH799VciIyNJTk4mKSkJe3v7TPPVarWULFnSZPTF3d0dIN0xaNq0qfHrEiVKUK1aNc6cOZNhHsePH2fPnj3GEZ+0+BITE0lISKBHjx6EhoZSsWJFOnToQMeOHQkICMjWPC4hzIH8JAghMuXg4GDyfuTIkYSFhfHFF19QuXJl7Ozs6N69O8nJyU/dzpNFhkajeWqxklF/RVFyGH3GKlSo8NTLyW/evMn58+fRarVcuHCBDh06GJd9/vnnzJkzh9DQUOrUqYODgwPDhw9Pl39G8T/eptFoALJVsGUmLi6OyZMn061bt3TLbG1t8fb25ty5c2zZsoWwsDDef/99Pv/8c3bs2JEuPiHMkRRAQohs27NnDwMGDKBr166A4Zfw1atX8zUGZ2dn3N3dOXToEK1atQIMIx9HjhzBx8fnubc/aNAg6tSpw1tvvcXgwYPx8/OjRo0agCH/zp078+abbwKGAub8+fPUrFnzufcLsH//fsqWLQvAvXv3OH/+vHHfT2rQoAHnzp2jcuXKmW7Pzs6OgIAAAgICGDp0KNWrV+fkyZM0aNAgV+IVojCTAkgIkW1VqlRhzZo1BAQEoNFomDhx4nONYjyrDz74gOnTp1O5cmWqV6/O119/zb1794wjK09z8+ZNEhMTTdpKliyJlZUVc+fOZd++fZw4cQJvb2/WrVvHG2+8wf79+7G2tqZKlSqsXr2avXv3Urx4cWbPnk1MTEyuFUBTpkyhZMmSuLu7M378eFxdXTO9sm3SpEm88sorlC1blu7du2NhYcHx48c5deoUn3zyCUuWLEGn0+Hr64u9vT0///wzdnZ2lCtXLldiFaKwk6vAhBDZNnv2bIoXL06zZs0ICAjA399fldGE0aNH07t3b/r160fTpk0pVqwY/v7+2NraZrlutWrV8PT0NHkdPnyYs2fP8vHHH/Ptt9/i7e0NwLfffsvt27eZOHEiABMmTKBBgwb4+/vTpk0bPDw8snXpfXbNmDGDwMBAGjZsSHR0NH/++SfW1tYZ9vX39+evv/5i8+bNNG7cmBdeeIEvv/zSWOC4uLiwYMECmjdvTt26ddmyZQt//vmnyZwmIcyZRsmtE+tCCKESvV5PjRo16NmzJ1OnTlU7nBzbvn07L774Ivfu3ZPHXQiRT+QUmBCi0Ll27RqbN2+mdevWJCUl8c0333DlyhX69OmjdmhCiEJCToEJIQodCwsLlixZQuPGjWnevDknT55ky5YtmU4YFkKIJ8kpMCGEEEKYHRkBEkIIIYTZkQJICCGEEGZHCiAhhBBCmB0pgIQQQghhdqQAEkIIIYTZkQJICCGEEGZHCiAhhBBCmB0pgIQQQghhdqQAEkIIIYTZ+X8wnBCrSU5BbwAAAABJRU5ErkJggg==", @@ -452,6 +435,18 @@ ], "source": [ "#This is a note of a learning curve by using the iris dataset in sklearn\n", + "\n", + "import warnings\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import learning_curve\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# Ignore warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.datasets import load_iris\n",