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机器学习 - 逻辑回归.ipynb 184.66 KB
Cynhard 提交于 2018-07-08 11:44 . write a little
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import numpy as np\n",
"from sklearn.linear_model import LogisticRegression"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def plot_dataset(splitline=False):\n",
" np.random.seed(42)\n",
" x = 10 * np.random.rand(100, 2)\n",
" y = (x[:,0] + x[:,1] > 10).astype(np.int)\n",
" log_reg = LogisticRegression(C=10**10, random_state=42)\n",
" log_reg.fit(x, y)\n",
"\n",
" left_right = np.array([0, 10])\n",
" boundary = -(log_reg.coef_[0][0] * left_right + log_reg.intercept_[0]) / log_reg.coef_[0][1]\n",
"\n",
" plt.figure(figsize=(15,6))\n",
" plt.plot(x[y==1][:,0], x[y==1][:,1], 'g^', markersize=14)\n",
" plt.plot(x[y==0][:,0], x[y==0][:,1], 'bs', markersize=14)\n",
" if splitline:\n",
" plt.plot(left_right, boundary, 'r-', linewidth=2)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x24559802cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_dataset()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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dB1ttBZtuGrpvn+Q+zFGSJJWOQRhSFRdqqU48VMu0bw8DBsDo0fD66yHaf/XV4ZVX4IQToFOn0GV7/PFw3k2SJKWKW2WlKt76WFHb/vJUVY/B/PkhOXLIkDCHLfMFrLsuHHEEHH44dOmS6BIlSZJKrX52Pf3v6s/I/UaWZNtoqe8vjdz6KC2qbVvYd1946CF4//0wg23tteG99+CPf4R11gmz2kaNgm++SXq1kiRJJTH4mcGMnjq6ZNtGS31/5cyOWh7Krpu0BGl9DEoRpAKEAdqPPx66bPfc01igdegAhx0WYv67d899IZIkSWVg0dCWUoSxlPr+0sqOmspWyc4P1tTAj38Mt98O06eHaP+ePcMnOu882HBD2H57uOEGmDs3v0VJkiSl1KKhLaUIYyn1/ZU7O2p5sKNW3Mcg0XXFMTz3XOiyjRwJX34Zbl9xRTjooBDzv9lmLbwTSZKkZDU1AqGYXa5S31+a2VGT8hFFsPXWMHRoiPnPRPt/8QVcdRVsvjn06QNXXAGffpr0aiVJkvLS1AiEYna5Sn1/laBFhVoURSdGUfRaFEWvRlF0axRFyxZqYSq+Dh1K+3FlZ4UVQgftuecao/1XXRUmToRjjw0x/4ccAk89VTltVkmSVPHqZ9czfOLw741AmN8wn+EThzNzTmEH85b6/ipF3oVaFEWdgeOAvnEc9wJqgP6FWpiKz6HaOejVCy66CGbMgNtug112gXnz4KabYMcdQ+jIueeGLpyk76mfXU+/Ef38ZSxJKbC0geLF6HKV+v4qRUu3PrYG2kVR1BpYDpjR8iVJKbbMMnDAAfDoozB5coj279wZ3nkHTjsN1loL9torzGxbsCDp1UqpYRyzJKXDkrpbGYXucpX6/ipJ3oVaHMfTgfOBqUA98Hkcx48UamFS6q27LvzlLzBlCjz4IOyzTzjjdt99sOeeYU7bGWfAu+8mvVIpUZlf0gvjhf4ylqTFlHrHwdK6WxmF7HKV+v4qSUu2Pq4C7AWsC3QClo+i6OAm3u+oKIrGRVE07qOPPsp/pVJa1dTAz34Gd90F06aFaP/u3cM2yL/9DdZfH3baCW65JWyXlKqMccyStGSl3HHQXHcro1BdrlLfX6VpydbHXYD34jj+KI7jb4C7gW0Wf6c4jq+N47hvHMd911hjjRbcnVQGOnSA3/0O3nwTnn02DM5u1w6efDLE+3fqBL/9Lbz0UtIrlUpi8V/S/jKWpEal3nGQTXcroxAvrJX6/ipNSwq1qcAPoyhaLoqiCNgZeKMwyyo+Ew9VVFEE220HI0aEztpVV0HfviHS//LLoXdv2GILuOYa+PzzpFcrFY1xzJK0ZKXccZBtdyujpS+slfr+KlFLzqg9D9wJvAi88u3nurZA6yo6Ew9VMiutBL/+NbzwAkyYEKL9V14Zxo0Lt3fsCIcfDqNHG/OvimIcsyQtWal3HOTS3cpoSfFY6vurRC1KfYzj+E9xHPeI47hXHMeHxHH8daEWJlWk3r3hsstCzP/NN4do/6++guuvhx/9CH7wA/jHP2DWrKRXKrWYccxKK8dFKA1KveNg7LSxWXe3MuY3zGfMtDFlcX+VKIpL+Ap+375943HjxpXs/lSeoij/jy3LhtQ778CwYY3bJAFatw7JkXV18OMfh8ASqYzUz66n26XdmLdgyQE67Vq3Y/Lxk6ltX1vClUkw6MFBXDP+Gn69+a+5Yvcrkl6OqtDSfkZWw8/GQQ8OYuiEod8p5NrWtKWuT11VPCejKBofx3Hf5t6vpXPUpIKruvOD668f0iGnTm2M9o9juPvukCbZtSv83//B++8nvVKVmSS7BsYxK60cF6E0qOYdB26Lz56FmlKnas8Ptm4NP/853HsvfPABnHMOrLdeiPwfPBi6dQvdtZEj4Wt3Gat5SQ2ZNo5Zaea4CCWt2gdAV3ORmisLNSmNOnaEP/wBJk1qjPZv2xYefRT694fOneHEE+HVV5NeqVIqya6BccxKK8dFKA2qecdBtRepubJQk9KsVSvYYQe46aZwfu2yy2DTTeGTT+Dii2HjjeGHP4QhQ2D27KRXqxRJqmtgHLPSzHERSlq17zio5iI1HxZqUrlYZZUQ7T9hQmO0/4orwvPPw5FHhi5cXR0891yZpqqoUJLsGhjHrLTyXIzSoJp3HFR7kZoPCzWp3EQRbL55GKJdX98Y7f/llzB0KGy9NfTqBRddBB9/nPRqlYAkuwbGMSutPBejpFX7joNqLlLzZTy/VCneeisUatdfDx9+GG5r0wZ+8QsYOBB22SVspVRFq/bIZ6kpjotQGjQVSd+cSomsz+Y5uLhKfk4azy9Vmw03hPPOCymRmWj/hga4/Xb4yU9CauRf/hISJVWx7BpI3+e5GKVBNe84cFt8fuyoSWWqthZmzVr6+3RmGoczgoEMZV3eB2AhEa12+0k4z/bzn4c0SVUEuwbS9+XySn4xnh/1s+vpf1d/Ru430uedqlafa/owcebEnD+ud21vJhw9oQgrSla2HTULNalMRVEO78tCduIJ6hjCLxjFMnz7it4aa8Bhh4WtkT16FGehKplsttVUyjYaKVu5bDcrxvNj0IODuGb8Nfx681/7vJMEWKhJFS+XQm1Rq/IJn1x8U4j0X3QO27bbhi7b/vvD8ssXZpEqmaS7BlIaJX0uZtH793knKcMzapKa9F9Wg+OPh5dfDlH+Rx4J7dvDv/8NAwaEmP+jj4YXXjDmv4yYpiV9X9LnYpKaZyipMlioqeBqa0O3J9e3Wl9kLK0ogq22gmuvDTH/mWj/2bPDbVtuCb17hyHb//1v0qvVUlR75LO0JEmGNyQ5z1BSZXDrowou3y15YAMnF0V7nF9/PRRtN9zQOIdtmWVgn33C1sgddjDmP2WqOfJZSqumnpc+7ySBWx8l5atnT7jgghDzn4n2nz8fbr0Vdt4ZNtgAzj4bpk9PeqX6VjVHPqu46mfX029EP7tAOVpSl9uumqRc2FFTwdlRK42SPs5TpsDw4TBsWOMctlatwqy2urrw3zZt8l+QlHLVGrFuYmF+ltbltqsmyY6apMJZZx046yx47z34179gv/2gpgYeeAD23hvWWgv+8Ad4++2kVyoVxeBnBjN66uiqCoPIdIUWxgvtAuWguTOjdtUkZctCTVL2amrCVsg77ghbI88/H37wgzB5++9/h+7dwxm2G2+EuXOTXq1UENVasJhYmJ9skiZ9PCVlw0JNUn7WXBNOPhlee60x2n+55eDpp+HQQ6FTJzjmGJgwIemVSi1SjQWLiYX5yTaB1cdTUjYs1CS1TBTBNtuE82v19Y3R/p9/DldeCZttFt6uvBI++yzp1Uo5qdaCpamuULUUqS3hPENJhWShJqlwVlwxDNB+/nl46SU47jhYddXQVTvmmDBM+9BDQ9fN5BiVgWosWEwszI/zDCUVmoWapOLYZBO45JIQ45+J9p83L5xf22EH2HDDcK5tphcpSqdqLViW1hWq9CK1JXLppmX4eEpaGgs1qUx16FDaj8vbsstC//7w2GPw7rtw5pnh/Nrbb4ekyC5dQnLkAw/AggUlXpy0ZNVYsJhYmD/nGUoqNOeoqeCco6ZmLVgADz8MQ4Z8t0Dr1CmEkhxxBHTrluwaVdXqZ9fT7dJuzFswb4nv0651OyYfP7mi5qotbf5XhnPAJKllnKMmKb1at4bdd4dRo8IA7b//HTbYAGbMgLPPhvXWC1slb701bJeUSqwaI9ZNLJSkdLFQk5Ss2lo49VR4660QMnLIIWG75BNPwIEHhi7bccfByy8nvVJViWotWEwslKR0sVBTwZXN2SmlSxTB9tvDDTeEmP9MtP+nn8Jll8Gmm4bY/2uvhS++SHq1qmDVWLCYWChJ6WOhpoKbOTOcNcv1zfC/0qqtDbVRrm+1pTiOs/LK8JvfwPjx8OKLIdp/pZXghRfg6KNDzP+AAWHQtgcbVUDVWrCYWChJ6WOhJlWh2lqYNSu/j8334/LWpw9cfnnost14I/TrB3PnwogRsN120LMnXHABfPhhiRemSlStBYuJhZKUPqY+SlWoJcmckIIm1ttvw7BhoVjLtGLbtIE994S6Oth1V6ipSXSJKk99runDxJkTc/643rW9mXD0hCKsSJJUabJNfbRQk6pQ2RdqGd98A//8JwwdCg8+CAu/7YSstVaI+B8wANZZJ9k1SpIkLcJ4fkmVr00b2GsvuO8+mDo1RPt36xYi///8Z1h3XfjJT+COO+Drr5NerSRJUtYs1CRVhs6d4fTTw7bITLR/27bwyCPwy19Cly5w0knw2mtJr1SSJKlZFmqSKkurVrDjjnDzzWGA9qWXwiabwMcfw0UXQa9esM024YzbnDlJr1aSJKlJFmqSKteqq8JvfwsTJzZG+6+wAowdCwMHhpj/I4+E559P0cE7SZIkC7WCSfVMKqnaRRH07QtXXx1i/jPR/nPmwJAh8MMfhq7bxReHzpskpUj97Hr6jehX9vP6JOXGQq1AymYmlVTtll8eDjsMnn0W3ngDTjkF1lgDXn0VTjwxnHXr3x8ee6wxRTJPXlxJKoTBzwxm9NTRZT+vTy3j75TqY6EmqXr16AH/+AdMmwZ33QU//WmI/B85MsxiW289GDw4/H0evLiS1FL1s+sZPnE4C+OFDJ843Iv0KubvlOpjoSZJbdvCPvuEmWxTpsBf/hLmr73/Pvzf/4U/77473H13KOSy4MWVpEIY/MxgFsahu98QN3iRXqX8nVKdLNQkaVFrrQV//CNMntwY7d+6dSji9t03xPyfeiq89dZSP40XV5JaKnNxPr9hPgDzG+Z7kZ4ipdyK6O+U6mShJklNadUqbH8cORKmTw/R/httBB9+GLZL9ugBP/oRXH89fPnldz7UiytJhbDoxXmGF+npUaqtiP5OqV4WapJy0qFD0itIwOqrwwknwCuvhGj/uroQSjJ6NBx+OHTqBL/5DYwbB3HsxZWkFlv84jzDi/R0KOVWRH+nVC8LNakK5VtsdegAM6v52iCKQpT/ddeFmP9MtP8XX4To/y224JtNe7HM1dex3BwvriTlr6mL8wwv0pNXqq2IFuzVzUJNqkIzZ4b5zrm+VXWRtrgVVghDs8eObYz2X2012rzyOhc9sID68+Gmu2CH9yD69lrLiytJ2VjSxXmGF+nJKuVWRAv26mahJkkttdFGcOGF1L85joMPaMPD60HbBjjoFXjyeph0GfzhWVjtUy+uJDVvaRfnGV6kJ6dUWxEt2GWhJkkFMvj587ijV8Ruh0C34+HP/eCDFWH9T+Gcx2HqRXD7jfO457wjYMGCpJcrKYWauzjP8CI9GaXcimjBLgs1SSqAxX95T1kFztoRup4Aux0Ed/4A4gj2eCvm14MfomGtLnDaafDOOwmvXFKaZHNxnuFFeumVaiuiBbvAQk2SCmJJv7wXtoKHN4D9D4DOJ8Epu8Kbq0PNzFlw7rmwwQaw445w883w1VcJrFxSWmR7cZ7hRXpplXIrogW7wEJNklos24urj9rDBdvCD46BnY5sy9yDDoB27eCpp+Dgg0PM/7HHwsSJpVm4pFTJ5eI8w4v00inVVkQLdmW0qFCLomjlKIrujKLozSiK3oiiaOtCLazctGS2VBQ1/VZbW7j1SSqenC+uIvj32vC7X60WYv6vvhr69oXPPoMrroA+fcL/X3UVfP558RYuKVXGThub9cV5xvyG+YyZNqZIK1JGKbciWrAro3ULP/4S4F9xHO8XRVFbYLkCrKksLS22PIry+5yzZuX3cZJKq0UXVyutBEcfHd5eegmGDoUbb4Tx48PbySfD/vuHIdvbbZf/DxRJqTfh6AlN3l4/u55ul3Zj3oJ5/7utXet2TD5+MrXtfVW3FPLZinjF7lfkdV8W7MqI4jjO7wOjaEXgJaBbnOUn6du3bzxu3Li87q+cteS6Ks9vj6Ry9tVXMGpUKNqeeKLx9u7dw+y2ww5rWRtfUlkZ9OAghk4Y+p2L97Y1banrU5d3MaDsNVUoN8dCWksTRdH4OI77Nvd+Ldn62A34CBgeRdGEKIqGRFG0fAs+nyQJwrm1Aw+Exx8PqZCnnw4dO8KkSfD730OXLrDPPvDPf0JDQ9KrlVREpYyDV9PciqiktKRQaw1sBlwVx3Ef4EtDA8kiAAAgAElEQVTgD4u/UxRFR0VRNC6KonEfffRRC+5OkqrQeuvB2WfD1Klw//2w116h1T5qFOy+O6yzDvzxj/Dee0mvVFIRlCoOXkvmVkQlpSVbH2uB5+I47vrt//8I+EMcx7sv6WPc+pg7tz5K+p76erj++rA1ctE5bLvsErZG7r03LLtscuuTVBDZbLlzi51Ufoq+9TGO45nAB1EUbfjtTTsDr+f7+SRJWerYEf7wh7AVMhPtv+yy8Nhj8KtfQefOcMIJ8MorSa9UUguUKg5eUjrl3VEDiKKoNzAEaAtMBgbEcfzpkt7fjlru7KhJyspnn8Ett8B11313DtuWW4bEyP79YYUVklufpJzkEmBhV00qL6UIEyGO44lxHPeN43iTOI73XlqRJkkqopVXhkGDYMKEEOv/m9+E6P///AeOOip04QYOhDFjfAVIKgP5xMHnq352Pf1G9DOYREqZFhVqkqQU2mwzuPJKmDEDbrgBtt8evvwShg2DbbeFjTaCCy8EA56kVMp2uHJGSxMgBz8zmNFTR7uFUkoZCzVJqlTLLQeHHAJPPw1vvRWi/Tt0gDfeCIO0O3cOw7QfftiYfylFShkHnykKF8YLyyLu3+6fqomFmiRVg+7d4dxz4YMPGqP9Gxrgzjtht92gWzf485/DGABJiSplHPyiRWE5BJPY/VM1aVGYSK4ME8mdR0kkFc306TBiRIj5z8xhiyL48Y9DAMmee0LbtokuUVLxNBVYkuZgkkXXm+Z1Ss0pSZiIJKmMde4MZ5wRZrFlov3btAlbIfffP/z9ySfD605ekSpRU1ss09xVK7fun9RSFmol0KFDaT9OKpXa2tCAyfWt1hdA06VVK9h55xDvP2MGXHIJbLwxfPxxCB3ZaKMQQjJ8OMyZk/RqJRXAkgJLWhpMUiyLrzet65QKyUKtBGbODFsYc32b6c8epdysWaX9OJXAaqvBccfBSy81Rvu3bx9i/Y84IsT8H3VU+Dv3Zktla2mBJWnsVpVb968lDExRhoWaJOn7ogi22AKuuQbq6xuj/efMCUO1t9oKNt00dN8++STp1aqMeVFaes3F/6etW1Vu3b+WMjBFGRZqkqSla98eBgyA0aPDebWTT4bVV4dXXoETToBOncL5tscfh4W5RYpLXpSWXjbx/2nqVpVb968lym1cgorLQk2SlL0f/ADOPz8kRmai/b/5Bm67DXbZBdZfH/76V5g2LemVqgx4UVp62Q7TTku3qty6fy1lYIoWZaEmKVEGkpSptm1h333hoYfg/ffhrLNg7bVDzP8f/wjrrAN77BFmtn3zTdKrVUp5UVp6uQzTTsP3pNy6fy1hYIoWZ6EmKVEGklSAtdeGP/0JJk9ujPavqYEHH4R99oG11oLf/x4mTUp6pUoRL0pLL9tuWkbS35Ny6/61VDUFpig7FmqSypadtpSpqQnDsm+/PWyNvPBC6NkzVNXnnQcbbgjbbw833ABz5ya9WiXMi9LSy6WblpHk96Tcun8tUW2BKcqOhZpUAG7fSw87bSmxxhpw4onw6quN0f7LLQfPPguHHRZi/gcNghdfTHqlSoAXpckYO21s1t20jPkN8xkzbUyRVrRk5db9a6lqCkxR9qK4hHNw+vbtG48bN65k99ec2tr8Luo6dHDGmb4rivL/2HIeRVWIr7sln6O5z62U+eILGDkShg6F559vvL13b6irgwMPhFVWSW59KplBDw5i6IShTV6Et61pS12fOq7Y/YoEVqa0WNq/kSUp13879bPr6XZpN+YtmLfE92nXuh2Tj59MbXtf4a0EURSNj+O4b3PvV9UdNc/GSJXJbmVKrbgiHHkkPPccvPwyHH88rLoqTJwIxx4bYv4POQSeftpqu4JVW4qf8lNO3b+WqqbAFOWmqjtq1doFUeFV67+ltHbUFr8Ppdi8eXDvvTBkCDz2WOPt668PAwc2bpNUxcimU1KunREpV9l00zLsqlUOO2qSpPRbdlk44AB49NGQGnnmmdC5M7zzDpx2WkiM3HtvuP9+WLAg6dVqEfWz6+k3ol9Ona9qS/GTmlNNgSnKnYWaJCkd1l0XBg+GKVNCtP8vfhFarvfeC3vuGcYAnHEGvPtu0isV4QJz9NTROV04elEqNaq2wBTlzkJNRWMSYuXr0KG0H6cqUVMDP/sZ3H03TJsWov27d4f6evjb38K2yJ12gltuCVsnVXKZC8yF8cKsLxy9KC1f+XRP1bxyG5eg0rNQU9EY1lL5Zs4M58ByfTM1VVnr0AF+9zt480145hk49FBo1w6efBIOOigEkBx3HLz0UtIrrSqLXmBme+HoRWn5yqd7quZVU2CK8mOYSJ4MKWheIR7fchmh4L+l/Bkmopx9/jncemsIIBk/vvH2vn1DzP+vfhUSJlUUTYUfZBNy0OeaPkycOTHn++td25sJR0/Ia61quUW/34ZZSIWRbZiIhVqevABsXtKJgKX8HpXLOtPIQk0tMnFimMt2003w2WfhtuWWg/33D0XbttsW9x9ZFWoqtdGUxsq16Pfb77NUGBZqWfDiurgs1LJT7f+WLNRUEF99Fc60DRkCTz3VePuGG4aC7dBDYc01E1tepVhalLjdlsqTb/dU0tIZzy+pLBgsooJo1y6cWXvySXj77RDtX1sLb70Vzrh17gz77gsPPQQNDUmvtmwt7ZyZ58kqT1Pfb7/PUunYUcuTr9Q3z45advy31LRCdNp8bKvcggXwz3+GrZEPPthYoHXpAgMGwBFHQNeuiS6xnGQzmNduS+WweyoVjx01SWXNTptarHXrMH/t3nth6tQQ7b/eeiHyf/Bg6NYNfvxjuP12+PrrpFebetmkNtptqRx2T0vH8QdaEgs1qQCcJ1Z4meh/qSA6dQrbISdNaoz2b9sWHn0UDjggbI088UR49dWkV5pK2c5Ac/ZZZWju++33ubAcf6AlqepCzYtrFYrzxKQy0aoV7LBDSImsr4fLLoNNN4VPPoGLL4aNN4attw6hJLNnJ73a1MhlBprdlvJn97R08hker+pR1YWaF9dS+vmCiopmlVXg2GNhwgQYNw5+/eswf+255+DII6Fjx5AY+dxzVd3ezbablmG3pbzZPS2tfIbHq3pUdaEmKf18QUVFF0Ww+eZw1VWhy3b99fCjH8GXX4Ygkq23hl694KKL4OOPk15tyeXSTcvwgrN82T0tncWLYotfLc5CTZKkjOWWCzPXnnkG3nwzRPuvuSa8/jqcdFI463bAAfDII7Awt+KlXI2dNjbrblrG/Ib5jJk2pkgrUrHYPS0txx+oOVUdz6/iqqZ4fkkV7Jtv4IEHwrm1f/2rsUBbZ50Q8T9gAKy1VrJrlApg0IODGDphaE6FeduattT1qeOK3a8o4soqj+MPqpvx/EqcZ4skVYQ2beAXvwiz2KZMCdH+XbuGP//pT6Fg++lP4a67YH5unScpTeyelo7jD5QNO2pKNTtqklJp4UJ44onQZRs1qrFAW2MNOOwwGDgQevRIdo2SUsnh8bKjpopgV05SKrVqBbvsArfdBjNmhGj/Xr3go4/g/PPhBz+A7baDESNCKInKkoOIVQyOP1C2LNSUaib+SUq91VaD44+Hl19ujPZv3x7+/e9wfq1jRzj6aHjhBVv9ZcZBxCo0xx8oFxZqkiQVQhTBVlvBtdeGmP9MtP/s2eG2LbeE3r3DkO3//jfp1aoZDiJWMTj+QLmwUJMkqdDatw+JkGPGwGuvhWj/1VYLXbfjjgsx/wceGM65VUnMf7lxELEKzfEHypWFmpRStbXhBfpc32o9dywVRMGegz17wgUXwPTpcPvt8OMfh/CRW2+FnXeGDTaAv/0tnHVTKjiIWMXg8HjlykJNSqlZs0r7cZK+q+DPwWWWgf33h4cfhvfeC9H+a60FkyfDGWeEP//853DvvWF2mxLjIGIVg+MPlCvj+aWUcjSBlKySPAcbGuDRR8N5tkULtNpaOPzwsH1ygw3yX4hy5iBiScVmPL8kSWlXUwO77QZ33AHTpoVo/x49QnTtuedC9+6www5w003w1VdJr7YqOIhYUlrYUZNSyo6alKzEnoNxDGPHhmHaI0fC3Lnh9pVWgoMOgro66NOnBXegJXEQsaRSsKMmSVI5iiLYZhsYNizE/F9zTYj2//xzuPJK2Gwz2HxzuOoq+OyzpFdbURxELClN7KhJKWVHTUpW6p6DL78czrLdeCN8+mm4bdllQ0BJXR386EctW3SVy6ablmFXTVJL2FGTJKmSbLIJXHJJiPHPRPvPmxcKt379YMMN4e9/D+fblDMHEUtKGztqUkql7tV8qcqUxXNw8uSwRXL48MY5bDU1Iea/rg5+8hNo3bpEiylfuXTTMuyqScqXHTVJkipdt27w17/ClCnwwAOw997h9nvugT32gHXWgTPPDAWdlshBxJLSyEJNkqRy17o17L47jBoVYv7//vcwf23GDDj7bFhvPdhll7Blcl72XaNq4SBiSWnU4q2PURTVAOOA6XEc77G093Xro5S9sth2JVWwsn8OxjE8+2yI+b/jjsYCbZVV4JBDYODAcO5NklRSpdz6eDzwRgE+jyRJKpQogu23hxtuCDH/mWj/Tz+FSy+FTTcNsf/XXgtffJH0astG/ex6+o3ox8w5hrZIKq4WFWpRFHUBdgeGFGY5kiSp4FZeGX7zGxg/Hl58EY45JgzQfuEFOPpo6NgRjjgC/v3vlLQD02vwM4MZPXW059MkFV1LO2oXA6cCuZ3AldSsDh1K+3GSvqtin4N9+sDll4cuWybaf+7ckBy53XbQsydccAF8+GHSK02d+tn1DJ84nIXxQoZPHG5XTVJR5V2oRVG0B/BhHMfjm3m/o6IoGhdF0biPPvoo37uTqs7MmeGF7VzfHKEkFUbFPwfbtYODD4annoJJk+APf4DaWnjzTTjlFOjSBfbbD/71L2hoSHq1qbBoOqSpj5KKLe8wkSiKzgEOARYAywIrAnfHcXzwkj7GMBFJklLsm2/gn/+EoUPhwQdh4bcbZtZaK2yNHDAgRP5XoaZmrTlLTVI+ih4mEsfxaXEcd4njuCvQH3hiaUWaJElKuTZtYK+94L77YOrUEO3frRt88AH8+c+w7rphiPYdd8DXXye92pJqataaXTVJxeQcNUlFU1sbgudyfav1xWkpeZ07w+mnw9tvwxNPwIEHQtu28Mgj8Mtfhq2RJ50Er72W9EqLLnM2bfFZa/Mb5ntWTVLRFKRQi+P4qeZmqEmqPrNmlfbjJBVBq1aw445w881hgPall8LGG8PHH8NFF0GvXrDNNjBsGMyZk/Rqi6KpblqGXTVJxdLigde58IyaVF3KfmCwpKbFMYwbF86y3XILzJ4dbm/fHvr3h7q6MKOtJT8EUqKps2mL86yapFyUcuC1JEmqJlEEW2wBV18dYv6HD4dttw0dtSFD4Ic/hE02gYsvDp23Mra0blqGXbX0cCC5KomFmiRJyt/yy8Phh8Po0fDGGyHaf4014NVX4cQTw1m3/v3hsccaUyTLxJLOpi3Os2rp4UByVRILNUlVw3ATqch69IB//AOmTYM774Sf/jRE/o8cCbvuCuutB4MHh78vA9l00zLsqiXPgeSqNBZqkqqG4SZSibRtC/vuG2ayvf9+iPZfZ53w5//7v/Dn3XeHUaNCIZdC2XbTMuyqJc+B5Ko0FmqSJKl41l47FGeTJzdG+9fUhCJun31CzP+pp8JbbyW90u/IpZuWYXGQnMULawtnVQILNUmSVHytWoXtjyNHhpj/Cy+Enj3hww/DdskePWD77eH662Hu3KRXy9hpY7PupmXMb5jPmGljirQiLY0DyVWJjOdXwdXW5rdVrEMHmOkLXxUlbfH8aVuPVPXiGJ5/PiRF3nYbfPlluH3FFcOA7bo62Gyzioj5V/EsbYSCoxOURsbzKzGeA5IkZSWKQpT/kCEh5j8T7f/FFyH6v29f6NMHLr8cPv006dUqpRxIrkplR00FZ9dCGWn7t5C29UhagldfDcO0b7wRPvkk3LbMMrDffjBwIPTrF7ZSquo5kFzlyI5aShkPLmXH54lUxXr1gosugunTG6P9v/4abr4ZdtoJuneHc84JZ91U1RxIrkpmoVZibguUisfniVRhllkmpEQ+8gi8915Ij+zSBd59F04/PSRK7rkn3HcfLFiQ9GpVYg4kL6362fX0G9HPx7GELNQkFU2HDkmvQFLF6No1zGN7//3GaP8ogvvvh732CkXb6afDO+8kvVKViAPJS2vwM4MZPXW0j2MJeUatxKrhjEw1fI0qrmL9G/LfplRhZs0K59iGDPnuHLYddgiJkfvsA+3aJbY8FU82Z9MW51m1/C36ePs4tpxn1CRJUmXr0AFOOQXeeAOefRYOPzwUZk89BQcfDJ06wbHHwsSJSa9UBeZA8tJa9PH2cSwdCzVJklTeogi22w6GDw8x/5lo/88+gyuuCBH/ffuG2z//POnVqgAcSF46i58F9Mxf6bj1scSqYetVNXyNKi63PkoqiJdeaoz5/+yzcFu7drD//mFr5HbbOUxbasagBwcxdMLQ7xTGbWvaUtenjit2vyLBlZUvtz5K0mLyDTcxFEUqU5tuCpdeGmL8M9H+X30FN9wA228PPXrAeecZGSstwZKSNe2qlYaFmqSqMXNm6Izl+jbT30NlwTmVWqJ27eDAA+Hxx0Mq5OmnQ8eOMGkS/P73IfJ/n31CmmRDQ9KrlVJjaWcBPatWfG59LLFq2HpVDV+jist/Q8qH/26UkwUL4F//ComRDzzQWKB17gwDBsARR8C66ya7RilB2SRrmgCZH7c+SpKyZjdKVad1a9hjD7jnHvjgAzjnHFh/fZg+Hf76V+jWDXbdFW67DeZlHwEvVYpskjXtqhWXHbUSq4ZXfGtr89vu36GDW8wUVMPzJG0q4TGvhK9BCYtjeOaZ0GW7887GAm3VVeGQQ2DgQNh442TXKJVALnPq7Krlzo6aEuM5IElSWYoi6NcvpETW14do/9694b//hUsugU02ga22guuug9mzk16tVDS5zKmzq1Y8dtRKzFd8peb5PCm9SnjMK+FrUEq9+GLost18M3zxRbht+eXhgANCl23rrY35V8XIpZuWYVctN3bUUqolMd+eDVG1MEZfUqpsthlceWXosl1/fYj2//JLGDYMtt0WNtoILrwQPvoo6ZVKLZZLNy3DrlpxWKiVWFPbAvPl2BdVKrfPSkql5ZaDQw+Fp5+GN9+EU0+FNdeEN96Ak08OiZG//CU88ggszO1CV0qLsdPGfm9uWnPmN8xnzLQxRVpR9XLrYwq4XUeqbmkI4KmEn0OV8DWoDH3zDTz4YNga+dBDjQXa2muHiP8BA8KfJelb2W59tFBLAS8upOqWhp8BaVhDS1XC16AyN20ajBgRtkS+9164LYrgJz8JZ9n23BPatk10iZKS5xk1SZKkUurSBc48E955Bx57DPr3hzZtwmDt/fcPf3/KKWGrpCQ1w0JNkiSpkFq1gp13hltvhRkzQrR/r14hbOSCC6BnzxBCMnx4CCWRpCZYqEmSJBXLaqvBccfByy/D88/DkUdC+/YwZkw4w9axIxx9NPznP+7BlfQdFmqSJEnFFkWw5ZZw7bUh5n/YMNhmmzA4+9prwyDtTTeFSy8NA7YlVT0LNUlSRXD+nspG+/YhDfLf/4bXXw/R/quvDq+8AscfD506wa9+BY8/bsy/KkL97Hr6jejHzDnO0cmFhZokqSI4f09l6Qc/gPPPh+nT4Y47QkLk/Plw222wyy6w/vpw9tnh76XFlEsBNPiZwYyeOtqh2DmyUJMk2Y2Skta2Ley3X0iIfP99OOusMH/tvfdCkuTaa8Mee8A994TZbRLlUQDVz65n+MThLIwXMnzi8NQXlWlioSZJshuVpdracNQo17fa2qRXrrKy9trwpz/B5MmN0f41NWGw9i9+AWutBb//PUyalPRKlaByKYAGPzOYhXHYwtsQN7SoqCyXDmKhWKhJkpSlWbNK+3GqcjU1YSvk7beHrY8XXBC2Ss6aBeedBxtuCP36wY03wty5Sa9WJbZoATRvwTxOe+y0hFf0fZlicn7DfADmN8xvUVFZDh3EQrJQkyRJSrs11oCTToLXXmuM9l9uOXjmGTj00BDzP2gQvPhi0itVCSxeAMXE3PjyjanrNC1aTGbk21Urlw5iIVmopYBnQyRJUlaiCLbeGoYODTH/mWj/L76Aq66CzTeHzTaDK66ATz9NerUqkiUVQGnqqi1eTGbk21Ur5BbKcmGhlgKeDZEkSTlbccUwQPu558JA7eOPh1VXhQkT4NhjQ8z/IYfA0087TLuCLKkAAlLVVWuqmMzItdAq9BbKcmGhJkkJs6uejHyCQaTU2nhjuPjicJYtE+0/bx7cdBPssAN07w7nnhu6cCprzRVAaeiqLa2YhNwLrUJuoSwnFmqSlLBidNVNJ2yeAR+qSMsuCwccAI8+GlIjzzwTOneGd96B004LiZF77w333w8LFiS9WuWouQII0tFVW1oxmZFtoVXoLZTlxEJNkiqQ6YSSWHddGDwYpkxpjPaPIrj3XthzzzAG4Iwz4N13k16pspRtAZRkVy2bYhKyL7QKuYWy3FioSZIkVbKaGvjZz+Duu2HatBDt37172Ab5t7/B+uvDTjvBLbeE7ZJKpWwLIEi2q5ZNMZnRXKFV6C2U5cZCTZIkqVp06AC/+x28+WZjtH+7dvDkk3DQQSGA5Ljj4KWXkl5pi1XacORcC6Akumq5FJPQfKFVyC2U5chCTZIkqdpEEfzoR3D99aGzlon2//RTuOwy6N0bttgCrrkmRP+XoUoajpxrAQTJdNVyKSYzllRoFXoLZTmyUJMkSapmK60Ev/41jBsXBmYfcwysvHL4/1//OgzTPvxwGD26bGL+K204cr4FUKm7amOnjc2pmIRQaI2ZNuZ7txdyC2W5iuISPuH69u0bjxs3rmT3J0nVqiVR8mVyHdZipY7br5bHVRXiq6/CmbYhQ+Cppxpv33BDqKsLWybXXDOx5TVn0IODGDphKPMb5tO2pi11feq4Yvcrkl5W3vpc04eJMyfm/HGrtVuNj0/9uAgrKq762fV0u7Qb8xZkf2ayXet2TD5+MrXt0x9fHEXR+DiO+zb3fnbUJEmS9N2xHsu1Izr4IKKnnmR93uZvnEY9tfDWW/C73/FNh87cGe3HT6OH6NShIemlf0clDkeecPQEZpw0g2VbL5vTx839Zm5Zft2F3EJZzizUqpDzlSSptBxOrnKwpPEc77I+Z/A31uID9uRe7uPnRMTsx108xM94/sOu8Kc/wfvvl3K5S1Spw5GrqXgp5BbKcpb31scoitYCbgBqgYXAtXEcX7K0j3HrYzq4JUqqfD7Pm+djJH1XLs+JjszgMK5nIENZn3cbP8Euu4StkXvtBcssU5yFLsXStsyV09a4puS7/bF3bW8mHD2hCCtSvrLd+tiSQq0j0DGO4xejKFoBGA/sHcfx60v6GAu1dPDiROWutja/wcwdOsDM8tsBkhef583zMZK+K5/nRMRC+vE0Tx40FO68E77+OvzFaquFc2wDB8JGGwGl+dm96Nm0xVXCWTVVhqKfUYvjuD6O4xe//fNs4A2gc76fT0oDt4WWh3x+0bfk46SW8OeKKllMK55iR7jpphDzf9llsOmm8MkncNFF0KsXbL01DB3KnFlz8rqPbH92V/twZFWegpxRi6KoK9AHeL4Qn09KigWApELz54qqxiqrwLHHwoQJjdH+K64Izz0HdXXU05HrqGMrngMK35au9uHIqjwtjuePoqg98DRwdhzHdzfx90cBRwGsvfbam0+ZMqVF96eWc7vPkvnYlAe/T83zMWpeqbbQ+r1QuSjKv9UvvwxbIocOhWef/d/Nr7IRQ6jjJg7mE1bP//N/K5c493I/q6byV5J4/iiK2gB3ATc3VaQBxHF8bRzHfeM47rvGGmu05O4kSVnKN2WwmtIJZ84MF3+5vlXLOcckuE20Ai2/PBx2GDzzDD14g/P4HbNYk168xsWcyHQ6cxsHsAuPEpFbouGiHI6sStSSMJEIuB74bxzHJ2TzMYaJpIOv7i6Zj0158PukclKu/16TCO0p18eqUhT78c98/tZ8wx48QB1D2I1/UfNtgfY+6zCMIxjOAKaxVtafv9KHI6vylKKjti1wCLBTFEUTv337WQs+nyRJSgnP1qlYFtCGe/gFe/AgXXmfP/IX3qMrXZnCX/gTU1iHB/kZv+Bu2tD8LK1qmi+m6tKS1MfRcRxHcRxvEsdx72/f/lnIxUmSJKlyTWMt/sofWY932YVHuY0D+IY2/IyHuJt9mUYXzuN38OabS/wcDkdWpWpxmEgu3PqYDm4tWTIfm8Io9pYpv08qJ+X67zWJdZfrY1UpSrX1sTmr8gkHcxNHch29eK3xL7bbLsxl23//cPZNKlMlCROR0sDD5+njlilJUr7+y2pcyvFszCtsxXNcRx20bw+jR8OAAdCxY4j+HzfOCl0VzUJNZc+iQJKkShTxH7biKK4Lw7SHDg3Ds2fPhmuugS22gN69w5Dt//436cVKBWehJkmSpHSP9WjfHo44AsaMgddeg5NOgtVWg5dfhuOOg06d4KCD4IknYGH+Mf9SmlioVaFU/yCWJEmJKJvZgj17wgUXwPTpcPvt8OMfw/z5cMstsPPOsMEG8Le/wYwZJV6YVFiGiajsFfLwswfZCyMtB9Lz/fxSIZXrv1fDRFRoRf3+TpkCw4fDsGHwwQfhtlat4Gc/g7q68N82bfJfgFRAholIqlh2haV0M7RJTSnqz+511oGzzoL33oOHHoL99oOaGnjgAdh7b1h7bTjtNHj77fwWISXAQk1ahAVAeSib7TkS1f1zxdAmLaokP7tramC33eCOO2DaNDj/fOjRI3ySc8+F7t1hhx3gppvgq6+K9aVWhPrZ9fQb0Y+Zc/zlmRQLNWkRFgCSCs2fK1JC1qhr1o4AAA6HSURBVFwTTj4ZXn8d/v3vEO2/3HLw9NNwyCEh5v+YY2DChKRXmkqDnxnM6KmjGfz04KSXUrUs1CRJklS5ogi22SacX6uvb4z2//xzuPJK2Gwz2HxzuOoq+OyzpFebCvWz6xk+cTgL44UMnzjcrlpCLNQkSVIqVPM2UZXIiivCUUfBf/4DL70Uov1XWQVefBEGDQpdtkMPhWeeqeqEmsHPDGZhHMYcNMQNdtUSYuqjyp4pYenj90Qqf7W1hTlj5nNaqTdvHowaBUOGhDlsGRtsEBIjDz20qpJx6mfX0+3SbsxbMO9/t7Vr3Y7Jx0+mtn31PA7FZOqjJEnK26Jn66SKtuyy8KtfweOPw7vvwhlnhAHab78Nv/89dOkCv/gFPPggLFiQ9GqLbtFuWoZdtWRYqElSFmprGyPHc3mrohdhJan8desGf/1rmMt2//0h2h/gnntgjz2ga1c480yYPDnRZRZL5mza/Ib537l9fsN8z6olwEJNUsGl+ZxJvgVXvlvAjCeXpDLUunUozEaNCjH/554L668P06fD2WfDeuvBLrvAbbeFrZMVoqluWoZdtdLzjJrKXr7nKDp0MP663BTie92S83P5cuuYyp3nTiXCP+Znnw1n2e64o7FAW2WVEPdfVwcbb5zsGlugqbNpi/OsWmF4Rq2I3AKVLs4oqh52tSRJiYki2H57uOGGEPN/xRUh2v/TT+HSS2GTTWCrreDaa+GLL5Jebc6W1k3LsKtWWnbU8uAri1IyCvHcs6Mm5c7fe9JSvPgiDB0KN98cZrNBGKx9wAGhy7b11sn88slBNt20DLtqLWdHTZIkSSq2zTYL3bUZM+DGG6FfP5g7F4YPh223hZ494YIL4KOPkl7pEmXTTcuwq1Y6dtTy4CuLUmEVal7T0thRk/Ln7z0pR5MmwbBhMGJE4y+4Nm1gr71g4EDYdVeoqUl0iRm5dNMy7Kq1jB01SWXDM2RSuqU5yVVKpe7dQ1LkBx80Rvs3NMCdd8JPfwrrrgtnnRXGACQsl25ahl210rBQkyRJS2Vok5SnTBft/vth6tQQ7d+tWyjg/vznULD95CchRfLrrxNZ4thpY783N6058xvmM2bamCKtSBlufcyDW0CkwirFdkS3PkqSUmHhQnjqqRDzf/fdjQXa6qvDoYeGrZE9eya6RBWXWx8lSZKktGnVCnbaCW65JQSQXHppmL/28cdw4YWw0UawzTbhjNucOUmvVgmyUJMkSZKSsOqq8NvfwksvwX/+A0cfDSusAGPHhs5ax45w5JHw/PNuz6hCFmpSijlcXZKkKhBFsMUWcPXVYZh2Jtp/zpywRfKHPwwDtS+5BD75JOnVqkQs1KQUyzcN0RRFSVKufHEwJZZfHg4/HEaPhjfegFNOgTXWgFdfhRNOgE6doH9/eOyxcN5NFctCTZKKyHhySeXCFwdTqEcP+Mc/YNq0xmj/b76BkSPDLLb11oPBg8Pfq+JYqEmqKi2ZB2U8uSQpEW3bwr77wj//Ce+/H6L911kn/Pn//i/8effdYdSoUMipIlioSaoKmS06S3vld2nFmAWXJCkV1l47FGeTJ8Mjj8Avfwk1NaGI22cf6NIFTj0V3nor6ZWqhSzU8tCSV+QlFVZTRVW+3L4jSSobrVqF7Y8jR4aY/wsvDPPXPvwwbJfs0QO23x6uvx7mzk16tcqDA6+lFKuW4eqF/jqr5XGTpELyZ2cFiOMQ5T9kCNx2G3z5Zbh9xRXhwAOhrg4226xl32y1mAOvJUmSpGoSRSHKf8iQEPOfifb/4osQ/d+3L/TpA5dfDp9+mvRq1QwLNUmJczuxJEkFtsIKYWj22LHwyish2n/VVcNw7d/+NgzTPvhgePJJY/5TykJNUuJmzjRRUSoF52RJVapXL7joonCW7bbbwtm2r7+Gm2+GnXaC7t3hnHNCF06p4Rk1KcU8L5AfHzepaT43tDT++6gy770Hw4fDsGEwfXq4raYmxPzX1YWZba1bJ7vGCuUZNUmSJElNW3dd+MtfYMqUxmj/KIL77oM99wxjAE4/Hd55J+mVVi0LNUkF4ZYqSZLKUE1N6J7ddRdMmwbnnQcbbhi2QZ5zDmywQdgeefPN8NVXSa+2qlioSSqIfGeQObtMkqSU6NABfvc7eOMNePZZOOwwaNcuBI4cfDB06hSCSF56KemVVgULNSnFTEOUJEklF0Ww3XYwYkTorGWi/T/7LET79+4d/v/qq+Hzz5NebcWyUJNSzDRESVKp+OKgmrTSSnD00fDCCzBhAhx7LKy8MowfD7/5TYj5P/zw0IEzVaagTH2UVBBpSgtL01qkNPG5IakgvvoKRo0KA7WffLLx9u7dQ2LkoYdawS+FqY+SJEmSCq9dOzjwQHjiiZAKefrpobM2aRL/3979h1p913Ecf7696myTkU0XpiNtiE0cMRFnCiK52MRI/wkc1Ib70f5wOVsg1h9rYwwaNJd/tEBMJ2s4mgmNIa22gmAbbmuLpTPTWer1R96QSvzH1Hd/fI9cM6Nd7zn3873f83yA3HO+3HvPS/l47n2d7+f7PqxZA5MnV1Mkd+yAc+dKpx22LGqSGsftO5Iux+m0UgfceCM88QQcOtQ/2j+zOuO2ZAlMmQKPPFK9b5sGxK2PktrCLVVS/XX7/9Nu//tLQ+bYMdiypdoa+eGH/cdvu63aGrlsGVx1Vbl8hbn1UZIkSdLQmzgR1q6Fffv6R/uPGQOvvgrLl1dj/levhl27SietNYuaJEmSpPaLgIUL4bnn4OjR/tH+J0/C+vVw880wd2515u3UqdJpa8eiJkmSJKmzxo2DlSurEf8XRvtfey3s3An331+dhbv3XnjzTfcat1jUJEnqEg7akVQLs2bBM8/0X8u2YAGcPg2bNsG8eTBzJqxbB319pZMW5TARSW3hRfqS6s7nKanG9u6titqzz8KJE9WxUaOqwSP33VcNIhnRjHNMDhORJEmSNDxMnw5PPgm9vf2j/c+dgxdfhNtvh6lT4bHHqrcB6BKDKmoRcUdE7I2I/RGxtl2hJA0/bqmSJEmDduEs2ssvw8GD8PjjVUk7dAgefbR6X7bFi2HbNjhzpnTajrrirY8R0QP8Cfgi0Au8DdyZmR/8r69x66MkSSrFrY/SMHX+fDXmf+NG2L69v6BNmAB33VUNIbnpprIZB2Aotj7OAfZn5oHMPAO8ACwdxPeTJEmSpP80YgQsWgRbt1Zj/tevrwaO9PXBU0/BjBkwfz5s3lwNJWmIwRS1ScDhi+73to5JkiRJUvtddx2sWgXvv98/2n/sWHjjDbjnnmrM/wMPwFtvDftT4YMpapfbQPBf/xoR8fWIeCci3unr8hGbkiRJktogAubMgQ0bqjH/F0b7nzpVHbv11urPuXOlk16xwRS1XuCGi+5PBo5e+kmZuSEzZ2fm7AkTJgzi4SRJkiTpEmPHwooV8PrrsHs3PPwwjB8P06ZBT0/pdFdsMEXtbWBaREyNiNHAcuCl9sSSJElqL6fTSl1gxozqurUjR+Dpp0unGZSRV/qFmXk2Ih4EXgF6gE2ZubttySRJktro+PHSCSQNmdGj4frrS6cYlCsuagCZuQPY0aYskiRJkiQG+YbXkiRJkqT2s6hJkiRJUs1Y1CRJkiSpZixqkiRJklQzFjVJkiRJqhmLmiRJkiTVjEVNkiRJkmrGoiZJkiRJNWNRkyRJkqSasahJkiRJUs1Y1CRJkiSpZiIzh+7BIvqAg0P2gB/deOBvpUOosVxf6iTXlzrJ9aVOc42pk+q6vj6dmRP+3ycNaVGrq4h4JzNnl86hZnJ9qZNcX+ok15c6zTWmThru68utj5IkSZJUMxY1SZIkSaoZi1plQ+kAajTXlzrJ9aVOcn2p01xj6qRhvb68Rk2SJEmSasYzapIkSZJUM11d1CLijojYGxH7I2Jt6Txqloi4ISJ+ExF7ImJ3RDxUOpOaJyJ6IuK9iHi5dBY1S0R8PCK2RcQfW89jny+dSc0REd9s/WzcFRFbI2JM6Uwa3iJiU0SciIhdFx37RET8KiL2tT6OK5lxoLq2qEVED/BDYDEwA7gzImaUTaWGOQt8KzNvAuYCK11j6oCHgD2lQ6iR1gO/yMzPAp/DdaY2iYhJwCpgdmbOBHqA5WVTqQGeBe645Nha4LXMnAa81ro/bHRtUQPmAPsz80BmngFeAJYWzqQGycxjmflu6/Ypql9yJpVNpSaJiMnAEmBj6Sxqloi4FlgA/BggM89k5t/LplLDjAQ+FhEjgauBo4XzaJjLzN8CJy85vBTY0rq9BVg2pKEGqZuL2iTg8EX3e/GXaHVIREwBbgF2lk2ihvkBsAY4XzqIGuczQB+wubW1dmNEXFM6lJohM48A3wcOAceAf2TmL8umUkN9MjOPQfUCOnB94TwD0s1FLS5zzBGYaruIGAv8DFidmf8snUfNEBFfAk5k5u9KZ1EjjQRmAT/KzFuA0wyzLUOqr9Z1QkuBqcCngGsi4qtlU0n1081FrRe44aL7k/G0u9osIkZRlbTnM3N76TxqlPnAlyPiL1Rbt78QET8pG0kN0gv0ZuaFXQDbqIqb1A63AX/OzL7M/BewHZhXOJOa6a8RMRGg9fFE4TwD0s1F7W1gWkRMjYjRVBexvlQ4kxokIoLq+o49mbmudB41S2Z+OzMnZ+YUquevX2emr0irLTLzOHA4Iqa3Di0CPigYSc1yCJgbEVe3flYuwmE16oyXgLtbt+8Gfl4wy4CNLB2glMw8GxEPAq9QTRvalJm7C8dSs8wHvgb8ISJ+3zr2nczcUTCTJH1U3wCeb72YeQBYUTiPGiIzd0bENuBdqgnJ7wEbyqbScBcRW4GFwPiI6AW+C3wP+GlE3Ev1AsFXyiUcuMj0sixJkiRJqpNu3vooSZIkSbVkUZMkSZKkmrGoSZIkSVLNWNQkSZIkqWYsapIkSZJUMxY1SZIkSaoZi5okSZIk1YxFTZIkSZJq5t/IDOY7lTPBBwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2455a563780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_dataset(splitline=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2455f3b0a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,6))\n",
"y_pt = np.array([0.07, 0.1, 0.3, 0.4, 0.5, 0.6, 0.7, 0.9, 0.93])\n",
"x_pt = np.log(y_pt / (1 - y_pt))\n",
"plt.plot(x_pt, y_pt, 'bs-', markerfacecolor='r', markeredgecolor='r')\n",
"plt.hlines(1.0, -6.0, 6.0, linestyle='dashed', alpha=0.5)\n",
"plt.hlines(0.5, -6.0, 6.0, linestyle='dashed', alpha=0.5)\n",
"plt.hlines(0.0, -6.0, 6.0, linestyle='dashed', alpha=0.5)\n",
"plt.yticks(np.linspace(0.0,1.0,11))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2455f39eb00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(-6, 6, 1000)\n",
"y = 1 / (1 + np.exp(-x))\n",
"plt.figure(figsize=(18,4))\n",
"\n",
"plt.subplot(121)\n",
"plt.plot(x, y, 'b-')\n",
"plt.hlines(1.0, -6.0, 6.0, linestyle='dashed', alpha=0.5)\n",
"plt.hlines(0.5, -6.0, 6.0, linestyle='dashed', alpha=0.5)\n",
"plt.hlines(0.0, -6.0, 6.0, linestyle='dashed', alpha=0.5)\n",
"plt.yticks(np.linspace(0.0,1.0,11))\n",
"\n",
"plt.subplot(122)\n",
"plt.plot(x_pt, y_pt, 'bs-', markerfacecolor='r', markeredgecolor='r')\n",
"plt.hlines(1.0, -6.0, 6.0, linestyle='dashed', alpha=0.5)\n",
"plt.hlines(0.5, -6.0, 6.0, linestyle='dashed', alpha=0.5)\n",
"plt.hlines(0.0, -6.0, 6.0, linestyle='dashed', alpha=0.5)\n",
"plt.yticks(np.linspace(0.0,1.0,11))\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 示例"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iris Plants Database\n",
"====================\n",
"\n",
"Notes\n",
"-----\n",
"Data Set Characteristics:\n",
" :Number of Instances: 150 (50 in each of three classes)\n",
" :Number of Attributes: 4 numeric, predictive attributes and the class\n",
" :Attribute Information:\n",
" - sepal length in cm\n",
" - sepal width in cm\n",
" - petal length in cm\n",
" - petal width in cm\n",
" - class:\n",
" - Iris-Setosa\n",
" - Iris-Versicolour\n",
" - Iris-Virginica\n",
" :Summary Statistics:\n",
"\n",
" ============== ==== ==== ======= ===== ====================\n",
" Min Max Mean SD Class Correlation\n",
" ============== ==== ==== ======= ===== ====================\n",
" sepal length: 4.3 7.9 5.84 0.83 0.7826\n",
" sepal width: 2.0 4.4 3.05 0.43 -0.4194\n",
" petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n",
" petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n",
" ============== ==== ==== ======= ===== ====================\n",
"\n",
" :Missing Attribute Values: None\n",
" :Class Distribution: 33.3% for each of 3 classes.\n",
" :Creator: R.A. Fisher\n",
" :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
" :Date: July, 1988\n",
"\n",
"This is a copy of UCI ML iris datasets.\n",
"http://archive.ics.uci.edu/ml/datasets/Iris\n",
"\n",
"The famous Iris database, first used by Sir R.A Fisher\n",
"\n",
"This is perhaps the best known database to be found in the\n",
"pattern recognition literature. Fisher's paper is a classic in the field and\n",
"is referenced frequently to this day. (See Duda & Hart, for example.) The\n",
"data set contains 3 classes of 50 instances each, where each class refers to a\n",
"type of iris plant. One class is linearly separable from the other 2; the\n",
"latter are NOT linearly separable from each other.\n",
"\n",
"References\n",
"----------\n",
" - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n",
" Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
" Mathematical Statistics\" (John Wiley, NY, 1950).\n",
" - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n",
" (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n",
" - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
" Structure and Classification Rule for Recognition in Partially Exposed\n",
" Environments\". IEEE Transactions on Pattern Analysis and Machine\n",
" Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
" - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n",
" on Information Theory, May 1972, 431-433.\n",
" - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n",
" conceptual clustering system finds 3 classes in the data.\n",
" - Many, many more ...\n",
"\n"
]
}
],
"source": [
"from sklearn.datasets import load_iris\n",
"\n",
"iris = load_iris()\n",
"print(iris.DESCR)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2455f555d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_iris(iris_data):\n",
" plt.figure(figsize=(15, 8))\n",
" for index, style in zip((1,2), ('bs', 'g^')):\n",
" data = iris_data[iris.target == index]\n",
" # 第 2、3 列分别表示花瓣长、宽\n",
" plt.plot(data[:,2], data[:,3], style, label=iris.target_names[index])\n",
" plt.xticks(fontsize=18); plt.xlabel(\"petal length\", fontsize=18)\n",
" plt.yticks(fontsize=18); plt.ylabel(\"petal width\", fontsize=18)\n",
" plt.legend(loc='best', fontsize=18)\n",
" \n",
"plot_iris(iris.data)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def sigmoid(z):\n",
" return 1 / (1 + np.exp(-z))\n",
"\n",
"x = iris.data[:,(2,3)]\n",
"y = (iris.target == 2).astype(np.int).reshape(-1, 1)\n",
"m = x.shape[0]\n",
"X = np.c_[np.ones([m, 1]), x]\n",
"theta = np.random.randn(3, 1)\n",
"\n",
"epoches = 10000\n",
"eta = 0.01\n",
"\n",
"for epoch in range(epoches):\n",
" theta += eta * X.T.dot(y - sigmoid(X.dot(theta)))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2455f2d71d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"left_right = np.array([2.9, 7])\n",
"boundary = -(theta[1] * left_right + theta[0]) / theta[2]\n",
"\n",
"plot_iris(iris.data)\n",
"plt.plot(left_right, boundary, 'r-')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2455f596cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"x = iris.data[:,(2,3)]\n",
"y = (iris.target == 2).astype(np.int)\n",
"\n",
"log_reg = LogisticRegression(C=10**10)\n",
"\n",
"log_reg.fit(x, y)\n",
" \n",
"left_right = np.array([2.9, 7])\n",
"boundary = -(log_reg.coef_[0][0] * left_right + log_reg.intercept_[0]) / log_reg.coef_[0][1]\n",
"\n",
"plot_iris(iris.data)\n",
"plt.plot(left_right, boundary, 'r-')\n",
"plt.show()"
]
}
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