diff --git a/hyperopt/.ipynb_checkpoints/bayes_optimize-checkpoint (lubus in Konflikt stehende Kopie 2018-03-26).ipynb b/hyperopt/.ipynb_checkpoints/bayes_optimize-checkpoint (lubus in Konflikt stehende Kopie 2018-03-26).ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ae4ac65a4e22c9845bcc18aa111d3759a9df76b3 --- /dev/null +++ b/hyperopt/.ipynb_checkpoints/bayes_optimize-checkpoint (lubus in Konflikt stehende Kopie 2018-03-26).ipynb @@ -0,0 +1,432 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "import sklearn\n", + "import numpy as np\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import load_data as ld\n", + "\n", + "%matplotlib inline\n", + "colors = plt.rcParams['axes.color_cycle']" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.text.Text at 0x7fb249f02390>" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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RuvaZ+NUToyDsDkQHyTnMowPl32X0E4gNhlKVZSGv2kVexUhP2RALBPHhcGwm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\nrRm21xX5vjbpKTxwWVdWFO1l0tx8r8upUm99tpV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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x7fb249f3e1d0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as pl\n", + "from sklearn.gaussian_process.kernels import RBF as skRBF\n", + "\n", + "# generate some data\n", + "n = 50\n", + "Xtest = np.linspace(-5, 5, n).reshape(-1,1)\n", + "Xtest_ = np.concatenate((Xtest,Xtest),axis=1)\n", + "\n", + "# get a RBF kernel\n", + "kernel = skRBF(length_scale=1.)\n", + "# Compute the training data covariance matrix\n", + "K_ss = kernel(Xtest,Xtest)\n", + "\n", + "# get cholesky decomposition (square root) of the\n", + "# covariance matrix\n", + "L = np.linalg.cholesky(K_ss + 1e-12*np.eye(n))\n", + "\n", + "# sample 3 sets of standard normals for our test points,\n", + "# multiply them by the square root of the covariance matrix\n", + "f_prior = np.dot(L, np.random.normal(size=(n,3)))\n", + "\n", + "# plot sampled functions.\n", + "pl.plot(Xtest, f_prior)\n", + "pl.axis([-5, 5, -3, 3])\n", + "pl.title('Three samples from the gp prior')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<matplotlib.text.Text at 0x7fb249e21550>" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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g2Tc+49zx0+l6sJA9j42mx4BQLBvmozdrj+Pyl5ARHYvtKfDzi0HTatAvqPAp\ntWKtOwuw/PYOzFmA/LYQq1MSF+nH3SPuxNFpOC9c3qICuV1KTy1QGXSpM2PLhyza+x13BHfiju0r\nkcHNcV72AnqrPqDrmD5bguWZV9ESU3CPHYnz3SnVqhNg9rrDvPJLOme0CeCt61sSZNUwbfsG6y9T\nkf5hOK+cWn8iU6SO+Y/ZWH57D9m8I44RM5Ch0XXdqjKp18LeXwj5l8mEe+xI3I/fjYyOYlPadh5Z\nNwWPrnNbj1GM7HApZq3s+HaReQjriumYDqxDD2yG+4yxuE+7utQn78lfdSURIzbh3yGNedc2pe/z\njxGyfDUAG28+l+4P3YLfj8+CPRfX+ffj7nPdUUtDCAt+fq2Uz70BUBvWuha/GeuSp8j+/ABNNhv9\nN9/3a8vj5z/L6As6cevAsnOuaFoANlsEHo8dXS/E4ylEShdVFXpd6jz/11t8d2glD7W9hJs3L0dk\nJ+E+cwKus281OisLCjG/Mwf3TddCi+YAiH92I1s0h/AmVTkNLNmazZNLkzm7eR4zw+bhf2gtntan\n47jsJQgsv2+sttH2r8W27AkwWXBc8yZ6i2513aSTUq+FvV/LFvL3b+cjOxnW+Ir4tTy94TVaBkbx\n2rmTiQ4sJ4bVkY9l/YeYN34CZpsxmKHvdWWEMAk0zYbNFllqnpjsghyueW8D6XkuvpjYhphl32C7\n+0lMHp2/h3am3eyZBP06BdOBdbg7nIdz+DPgHwoITKYArNZIFe9ez3E40vF4cmqmcLcDy2/vYvrr\nY3b+YaHHjxnYLRrPXnI1WwdP4sUro+kcWb6VbrNFnxBaK6WOrju8MztVfmo8t+7hifXTWJ30J0/3\nuZ2r9qzH/M8yPC164LzwcWRU1xI7uPE78zJEagauZx7EPe46MFViAKHRaPauWESbTW9iFW6yz7qL\n4HNGQTmGWl0iDu/HtvheREEmjstfRu8wqK6bVCr1WtiLd55+/t9yXtvyET3Du/DKwCcItZbRKSkl\npn9/wLLqdSMmtcflOAfdUyJ6pSQCs7kJFktYmeL7X/IhrvlgJ5EhFj6d0Jrg1b+j3Xg71kIn//SJ\nInLJYkL3/4Jl9ZvokV1w3DDT63cXWCxNS50BR1E/kNJDYeEhasJa15J3Yv1uMp7DB3ipUz+W5qfy\n5JwCPux5Nxdecy4Tzm5aocR2xttfzEnvUZcru8qTWTs9Lh78/QU2pm1nytmPMCQ3B+vPL0NhNp4e\nl+E69y4rpNiOAAAgAElEQVRksGGtk5GJbcx9mFavB0Dv3R3njKfRz+hbobpEbirWH57HdGAd2RGn\nMSp9PCmmFnx4U0wFHm51TF4Gti/vQ0vbjWvoo7j7jKzrFp1AvRf2Nb99xbvbF7Bg99ecF30Gz535\nAH6mkw8aEGl7sP4yFVPC33giu+Ia+kiFfHUVzcMupc6Kf3Yw6dM4hnYJZsa10Zi27EBceTN+h3P5\ncEJXhkydTXTCdqzfPIyn81CcV0zxDlMW2Gwt1CxN9RSn8whu98knrqgSHpfx1rh+NlmE8WCHjvzl\nTsKRcR6dLFfy0pUt6RBR0UEwAqs1osxIKyklhYUHqep8rAXuQu5Z/Qy7s/YxY+BkTg9rh2X9bMyb\nPgXNZORVOX2skVdFSkzf/IDlsZfQEpIBcI++GudzD0PUiSO2vQ3EtH0J1l9ngHTjGnQv7r7XsTfD\nxa0fx+NwS+aMaQDi7izEtuwxTPt+w3X6WFzn3VOvUhHUa2Hv07eHHPrqYH6IW8017YfxYJ9bMYmT\nvKblZWD9/T1M25eAXwjOQffg6XVlBU+2hr9/LOJkZZfA4ylg5uodTP0pjbvOa8Zd5zVDHIgj/e23\nuP7sBPyt/sw490m67fkd66rXcZ05Adegu4/WZXSmqkFM9YnqCmJpiMw4bEsfQ0vbxd7Is7G8sIa4\ncBN3Xnc/k864lLFnVcxKL1Yi/v5ty3XnVXdCkGxnLneuepLEvFTeOu8ZeoZ3QWQlYlnzFuZdP6EH\nNsM16G48PS4zfl/5BVheeR/z67MQThd6u1jsW34+zjUjMg9h2rsa0+5fMCX/gyemH85hTyGbxBzd\n5lCmk3Hz4nB6JHPHxNZIHnmforux/DIdy5ZFuLtcjPOSZ8FcPyYcqdfCHtEpXEb9L5rbe4xmXJdr\nS7+hXYWYN36C5Y+54HHi7nMdrnMmgV9IBWsp3woqDbs9lce+/o9vtmbz6jXRDO9u1Lcv+xD3//Y8\nlvQjvNDyRk7T/8W87Wscw5/G0/NKo8ZyXqcVtY/bnY/TmYqvrHXT7l+wfv8smCysjLmMDvd/RGya\ng/3RLbEv/5w2nSs/mtFsDsNqDS93Oyl170Oq6sdy2H6ESb8+TrYzlzlDXyEmyEiIpyVuxbJyBqak\n7ehhMehRXZFN26CHt0XmWLFM/Rj3lRfjufkatKRtmHavxLT/N7QjRmy93rwT7tOuxd376lKNroOH\nnYybH4fbI5nTEMRdSswb5mFd/SaeVn1xjHgF/Ove3VqvhT2gbYCcvfw1rmg79MSVUse083ssa95G\ny03F3fF8XOfdh2waW6k6NM2vSnnXpdTJzj3AhAWH2JFsZ/7YWHq2NFwsGWnxuC+4kojUfPYvmk6P\n1O/R4jfhGPkOeusBGJ2pQdhszStVp6LmsNsTfDOXqceFZdXrWDYtxB3dg9fpzagnZhF92EVyhw4E\n/fIppoiqRHwI/PxiK/ym53Rm4nZnUR1xT8pPZczPDxATFM2sC6ZgLqpbSky7fsK841tE5kFEViKi\nWD16cBTCbUcUZiHXuCAtAPcjo3FfeG2FwgQPHnYydl4cujTEveKuqrrD9O+PWL97ChkUgePK6Sd2\nNtcy9VrYO/VsL7f8+dMJy7W4TVhWvYYpZafhR7/gAfSYflWooXI/lpJ4PAUkZyZw/UcHcboln09s\nTVSIBVwuuPkuApb9SmaImeSvXqfrzg8R+RnYR89Fhrfx1t3yuBnnFXWDrjuw2xOprrUuclKwLn0U\nU9J2CnvfwP37XDz7wgIis9zk9umFaflcCKvom+TxaFoAfn4VnxDCVx3BKxLW8cT6aYzpcg139by5\n9I3cDsSROLTDBxGZB9EOHwTNhCf2LCwjnkdLTkNqGu5bbsD1v/uOpgMpi/0ZDsbNj0NKmDsmlvYN\nQNy15B1Yv3kYUXgE50VP4OlxeZ21pV4L+3G5YqREO7gey/rZmBI2owdHGn6+bsOr2GnhmygVp/Mw\n/yalMWr2QWKbWlgwrjUBVg0cDrh6AgGr/iSlmZWcxa/R8c9pSGsg9pvmQUAThLB649uVS6YucThS\nqz1DknZgHbblT4LHRdLAx5n05zbenz6PmHQX7oGn41w8E4KrOiy+9BDH8nA6M3C7s6tY5zFe2vgO\nSw/8wtvnPUv/5pVM7Xv4CJaX3sQ861NjoGFoMK6H78R9203l5p/Zl26IO8C8sbG0a1b/xZ2CI9iW\nPoYp7i9cvUfiGvJQzSYtOwkVFfa66+6VOqY9v2KbfxN+i+5GZCXgvOAh7BO/wtP90ir3RAthwWyu\n/uxIFktTOkeF8eo1Ldmd6uDRr5PQpTQm+PhiFgX9uxOV4cRv1EMcPOP/ELlp2L5+ENwOpHTh8eRX\nuw2KqiOlp3rXQErMaz/AtugeZFAz1pz7AcO27CQtdB2ZbVrh6dsD55ezqiHqRlroqrzZGUZL9Y2G\n+3vfQuvgljzz5+tkOSoZ4x/eBNerT2P/czmeoecisnOxPjkV/+7nI5LLnsKvfYSNOWMM1+q4+XEc\nPOwsc/t6QUATHNe9g+v0MVi2LMK28FZEblqtNkGk7anwtnUi7KIwG7/Z12H75iGEIxfHxZOxT1pq\nzJhSrXwNwjsIqfo3vRBGWed1CuXRi5qzYncer/+abqwMDIBv5lPYrR2xSYV4xj9F0jn3YUrcgmX1\nm4DE6UxHSt9FYigqh8tVjcFIuhvrD89hXfsB7u6XMiPmFSZtXAahv3NBh8vo8PN3OJbOg6DqTA8n\nvJlCK3+vCmHGZKp+8ix/sx/PnfEAWc4cXtz4NlV5e5ddO+JYMgf71x/h6dsTvVNbZFSxPiZP6YOq\nOkTYmH1zLLoO4+fHEZfZAMRdM+Ma/H84rpiKlv4ffvNHo8Vvqvl6dTfm9R/hN/+mCu9SN8KenQhC\nw3HZS4aFftoIH4QTCczmMDTNd2FJQmjYbNHcdHo41/UL48O1mSzZ6n0FbhKKXPYJ9jbRbOroz51H\n1pLW51osmxaiHVgPSFyuIz5ri6LiSCmr3sHodmBd+hjm7UvIHTCRcUcmMGvfx9y0ZTmjYy7lqTNu\nRVgs0KT6b4XVEWeLpQm+sNo7N2nHXT3HsCZpA1/t/6HK5egXnYdjzVc4Fr53LOXGP7vx63EBptmf\ngfNE4e7Y3MZHN8dgd+uMXxBHYlYDEHfA0+VC7DfPR1qDsH12G5afXoL80qcwrC4iMw7bp7dg/e0d\nPB3Pr/B+dSLsskkM9vGf4ek2DHwU9y2EyXuz+xZNs+Dn14L/DYvizLYBPLU8hY2HCoyVURHovy8j\n+v33SSxI4y4yyGnWDtt3T0NBJm53Nrpe2uQdiprE4ymgSqLuyMe2+F7Me34l5Yz7uXj7hWy2f8yD\nvy5j8vxkHpr6hw+k1MBsDq3Wm6WmWdA032QXvb7jZZwV1Zc3tsxhf3Zc1QsSApoe69syz1uEFpeI\n7Z4n8et2PuaX34ZiMzsBdI7046ObYsl36IyfH09ydsP4vchm7bGPmY+797WYt36N/6yrMK//CFx2\nH1WgY978OX5zb0DLPITj8ik4r5xa4d3rRthtwT4ezSWwWpvXWGelyeRPgF8zXru2FTFNLNy+MIFN\ncV5xbxJKv8hevHT2I6Qn7OP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yT+QATBLe+vZdrjjzLe789B8Opv6H03lECXwpeDz5lGat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\nB6irW02Tf6G9Ndhn/J25sz7i2oN64DdMxg8cy5k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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x7fb249edf860>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Evaluated emphirical risks \n", + "Xtrain = np.array([-4, -3, -2, -1, 1]).reshape(5,1)\n", + "ytrain = np.sin(Xtrain)\n", + "\n", + "# Apply the kernel function to emphirical risks\n", + "K = kernel(Xtrain, Xtrain)\n", + "L = np.linalg.cholesky(K + 0.00005*np.eye(len(Xtrain)))\n", + "\n", + "# Compute the mean of the surrogate model\n", + "K_s = kernel(Xtrain, Xtest)\n", + "Lk = np.linalg.solve(L, K_s)\n", + "mu = np.dot(Lk.T, np.linalg.solve(L, ytrain)).reshape((n,))\n", + "\n", + "# Compute the standard deviation so we can plot it\n", + "s2 = np.diag(K_ss) - np.sum(Lk**2, axis=0)\n", + "stdv = np.sqrt(s2)\n", + "# Draw samples from the posterior at our test points.\n", + "L = np.linalg.cholesky(K_ss + 1e-6*np.eye(n) - np.dot(Lk.T, Lk))\n", + "f_post = mu.reshape(-1,1) + np.dot(L, np.random.normal(size=(n,3)))\n", + "\n", + "pl.plot(Xtrain, ytrain, 'bs', ms=8)\n", + "pl.plot(Xtest, f_post)\n", + "pl.gca().fill_between(Xtest.flat, mu-2*stdv, mu+2*stdv, color=\"beige\")\n", + "pl.plot(Xtest, mu, 'r--', lw=2)\n", + "pl.axis([-5, 5, -3, 3])\n", + "pl.title('Three samples from the gp posterior')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "# artifical expected risk to be minimized\n", + "def fkt(vx):\n", + " return 0.004*(vx-100.)**2+40" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bayesian Optimization" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": true, + "scrolled": false + }, + "outputs": [], + "source": [ + "from sklearn.gaussian_process import GaussianProcess, GaussianProcessRegressor\n", + "from sklearn.gaussian_process import correlation_models as correlation\n", + "\n", + "def plot_gp_bounds(x, y, x_predict, y_predict, y_std, ax=None):\n", + " if ax is None:\n", + " ax = plt.gca()\n", + " \n", + " bound1 = y_predict + 1.96 * y_std\n", + " bound2 = y_predict - 1.96 * y_std\n", + "\n", + " ax.plot(x_predict, y_predict, color='b', lw=3.3)\n", + " \n", + " \n", + " ax.fill_between(\n", + " x_predict, \n", + " bound1.reshape(len(bound1)),\n", + " bound2.reshape(len(bound2)),\n", + " alpha=0.3, color='gray'\n", + " )\n", + " ax.scatter(x, y, color='k', s=120)\n", + " \n", + " return ax\n", + "\n", + "def gp(x, y, index, nugget=1e-5):\n", + " gp = GaussianProcess(corr='squared_exponential', theta0=1e-1,\n", + " thetaL=1e-3, thetaU=1, nugget=nugget)\n", + " \n", + " \n", + " xi = x.reshape((-1, 1))\n", + " yi = y.reshape((-1, 1))\n", + " gp.fit(xi, yi)\n", + "\n", + " x_predict = np.linspace(0, 200, 200)\n", + " y_predict, y_var = gp.predict(np.atleast_2d(x_predict).T, eval_MSE=True)\n", + " y_std = np.sqrt(y_var.reshape((-1, 1)))\n", + "\n", + " \n", + " return xi, yi, x_predict, y_predict, y_std\n", + "\n", + "def plot_gp_example1(xi, yi, x_predict, y_predict, y_std, index, ax=None):\n", + " if ax is None:\n", + " ax = plt.gca()\n", + "\n", + " ax.set_title(\"Gaussian Process regression after {} iterations\".format(index))\n", + " ax.set_xlabel(\"Hyperparameter\")\n", + " ax.set_ylabel(\"Emphirical risk\")\n", + "\n", + " plot_gp_bounds(xi, yi, x_predict, y_predict, y_std, ax=ax)\n", + "\n", + " ax.set_ylim(0, 100)\n", + " ax.set_xlim(0, 200)\n", + " \n", + " return ax" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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w5Eicz342tuy0116v8rrX5bjttgxvetM84fC5/ztt/NvweDwMDAyQTCat/MKY\ndVJVarUatVqNarW64HK1WqVSqTQvV6tVHMdpruM4TjMIr36cgzbXWXwfr9e74OTz+fD5fPj9/uZ5\n622Nc/s8MMZVLBaZnJxkcnIS4Iz5EXYyC8if/CQej4dCoUClUiEUCjEwMGCjxRuoUCgwPj5OKpVa\n88F8SymVhC9/OcqRI3EeeCBKpbJ0yUs0WuOmm7LccUeGK64494P7Wssvkskk/f399Wm4jTFwOvxW\nKpVm0C2VShQKhWZ/+Eql0ly39f+/quLxeJonEWmet3MEuDHq3BiJXnzeGJ1eis/nax6BHwwGCYVC\n+P3+5sl+YTK7maqSy+UYHx9ndnZ227ZpO1cbHpBF5C5V/dCiZX+gqu9a70bb5dChQ/rhD38YEWnW\nmcZiMRsd2CTFYrEZlFfro7yaTMbD5z4X49574zz6aGTZGfyGh8vccUeGO+7IcOBA5Vx2H1Uln89T\nrVZJJBIMDQ3Zvx+zZzSmeK9UKs3+74VCgXw+T6lUwnGcBSO3Ho9nwWhtI/TuNI0A3RjJboxutz6X\nxvS44XCYaDRKIBBonnbL6JrZe1SVTCbDqVOnyGaz+P3+Xd2kYDMC8n3AR1T1o/Xrfw0EVfWu9W60\nXV7xilfo0aNHrVPBFiuVSkxMTDR/olluZr61mpjwct99ce65J86TT4aWXe+yywrceWeaN785Szy+\n/GjRalrrlKPRKENDQyQSiV37pmH2ltZZQUulErlcjnw+3/wVRUSaAbhRhtAIwHtVIzg3SkbgdHlH\nMBgkGo0SjUYJh8PNUWh7vzDbleM4zM7ONg+8CwQChELLf7buFpsRkMPAPcDfAW8GZlT1l9e7wXa6\n4oor9LHHHtvq3TB1jZn5JiYmmr0Sz3W05dlnA9xzjxuWJyeXLpkJBByuv36eO+7IcO21Oc7l19FG\niAiFQoyMjNDZ2WkffGbHaJRCFItFcrkc8/PzzR7hjVIIr9fbrM3dyyF4PRqlJ40a69bykmg0SiwW\nIxaLEQqFCAaD9vqaLVWtVpmZmeHUqVPNEtSdfuDd2diwgCwi3S1XO4DDwMPAbwOo6sx6N9ouFpC3\np0qlQiqVYmxsjFqt1pam4rUaPPJIhMOHE3z+8zGKxaU/eJLJKrfckuEtb8lw8cXr769cLpcpFAqE\nw2GGh4fp6uqyoGy2lcaocD6fZ35+nvn5+WZdMNAMwnZQ2sZrLVep1WoLQnM8Hm+G5kAgYH8Ls+HK\n5XLzM9hasLcxAAAgAElEQVRxnB0zsUe7bWRA/gGggLScN6iqnrfejbaLBeTtrVarNdvGlMvltn17\nnZ/3cPRojMOHE3zjG8sfXHfxxUXuuCPDbbdlSCbX1zKuUZsZCoUsKJstU61WmzXCmUyGTCZDrXb6\n33Tj4DKrj90+GqG5XC43R/B9Ph+JRIJ4PE4kEiEUCtkos2mbUqnU/BUXdldHivXY010sLCDvDI7j\nMDc3x9jYGLlcrln/1I6gefKkn3vuiXP4cJwTJ5YO316vcu21Od7yljQ//MM5gsGz/zffGpSt9MJs\nJMdxFowMp9NpyuVy83YLwztXrVajXC5TrVabdd8dHR10dnY2a5otMJuztbiz1E6b8W6jbEYN8o8C\n96tqVkR+E7gc+O+q+s31brRdLCDvLKrK/Pw8Y2NjbZ+6UhW++c0Qhw8nuO++DrLZpcNDPF7jzW/O\ncuedaS69tMjZZtxGUI5EIoyMjBCPxy0om3NSrVYpFArkcjnS6TTZbLZ5m9frJRAI7MmfR/cCx3Ga\no8wN8Xicrq6uZmC29xeznFwux9jYGDMzM/h8vnXNTbCbbUZA/raqvlJErgXeC/wJ8Buq+pr1brRd\nLCDvXIVCgcnJSaampgAIh8NtCwGlkvDFL8Y4fDjOQw9FqdWWfsM4eLDMnXemuf32DENDZzfFdeNA\nqI6ODvbt20csFmvHrptdrtFRolAokM1mmZubo1Q6XSsfCATw+/02+rNHOY5DuVxuHgDo9Xrp6upq\njjDvpQOszNIaA02nTp0ik8ng8/nsi9QyNiMgf1NVLxOR9wLfUdV/aCxb70bbxQLyzrd4estGy6R2\nmZrycu+9bgnGM88s3dZGRHnNa/LceWeGG27IEo2uvQSjWCxSKpXo7OxkZGTEJhwxCzRaCObzeTKZ\nDHNzc822YY3RYZvQyCxncUlGOBymu7u7WcNsX6T2DlUlm81y8uRJ5ufn21qquFttRkC+FxgF3gS8\nGigAX1fVV613o+1iAXn3cByHdDrdrFPeiDqqp54KcvhwnCNH4szMLD1aHYk4/Kt/5ZZgXHXV2mbt\nU9XmTI69vb0MDQ1ZX+49qvFvIZ/PMzc31zyYTkTw+Xw20YQ5J5VKhWKx2BxdTiQSdHd3E4vF7IvW\nLqWqpNNpRkdHyefze6aHcTtsRkCOADfhjh4/JyKDwCtU9bPr3Wi7WEDenfL5PFNTU6RSKRzHIRwO\nt/XNv1KBBx+Mcvhwgi99afkproeGKtx+e4Y770xz8ODqs/Y1Zuar1WoMDg7S399vH1q7nOM4zZ7D\njUDcmHzD7/cTCARslM9siMbBnJVKBREhGo3S09NDPB63ALULNA5uHx0dpVAoNHtrm7XbyDZvcVXN\nLOqH3GR9kM1Gq1arzM7OMj4+TrFY3JD54ufmPHzmM24Jxre/HV52vUsvdWftu/nm1WftcxyHXC6H\niDAyMkIymbRRw12iNRDPzs6SzWYtEJst16htb7SUCwaD9Pb2Nksx7Gf4naMx693o6Ghz0iqrPV+f\njQzI96rqrYv6ITdYH2SzaVSVXC7H1NQU09PTqOqGvGm88EKAf/5nd9a+iYmVZ+27884Mr3vdyrP2\n1Wq1Zlu7/fv3W2u4HWipQNx4z7RAbLarSqVCqVTCcRwCgQA9PT3NA/3sPWh7qtVqzWBcLpfb/svp\nXrShJRbi/k/ap6on1ruBjWQBee+pVqvMzc0xMTFBPp/H4/EQDofbOkJbq8Gjj7qz9n3uc8vP2tfb\nW+XWW90SjJe+tLzkOuB+WOXzeet4sQNYIDa7TWOSGcdx8Pl8JJPJZhs5C8tbrzGhVmM6aAvG7bMZ\nNciPq+qr17uBjWQBeW8rFArMzMwwOTlJtVrF5/O1fWaqtc7ad8klRe68M8Ott2bo6Vl61r5Gx4tk\nMsnw8LDVk20DjuM0D6qzQGx2u1qtRqFQaIbl3t5eG1neIo1gPDo6SrVa3bPTQW+kzQjIfwX8vap+\nY70bWeIxO4EPAi/HLd/4OeAZ4B+Bg8Ax4N+o6uxKj2MB2YAbcubn55menmZmZgbHcfD7/W1vgXPy\npJ9PfcqtV37xxaXLO3w+5fWvz3HHHWne+MYcodDC/1+NA/kcx2F4eJi+vj6rT95EjUDcelBdgwVi\ns5csDst9fX10dXVZT90NVqvVSKVSnDp1yoLxBtuMgPwkcBFwHMjh1iKrqr5y3RsV+TDwoKp+UEQC\nQAT4DWBGVf9ARN4FdKnqr6/0OBaQzWK1Wo1MJsP09DRzc3OoKoFAgGAw2LY3fVV44okwhw/Hue++\nDubnlw64kYjDDTdkufXWDNdck19Qr9w4kM/n87F//366urrsQ2kDNEJAIxA3RohFxCblMKautQwj\nGAzS19dHZ2endcNoo2q12gzGtVrNgvEm2IyAfGCp5ap6fF0bFIkD3wLO05aNi8gzwBtVdazeSu4B\nVX3pSo9lAdmspFqtNsNyOp1uHrASDAbbFoqKReHzn3dLML761QiOs3TI7e6uctNNbli+7LLTU1y3\n1ifv37+faDTalv3aqyqVyoJAnMvlUFU8Hk9zhNi+iBizvNZey9FolL6+PhKJhNXFrlNrMHYch0gk\nYr8abpIND8jtJiKXAh8AngReBTwO/CdgVFU7W9abVdWuJe7/DuAdAPv373/18ePryulmj6lWq80y\njLm5ORzHwev1EgqF2vZmNTHh5ciRBEeOdCw7ax/A8HCZW25xw/JFF7kH9y2eaMTa+qxOVZtTfmcy\nGdLpNMViEQCPx9McIbZAbMz6lMtlisUiIkIikaC3t5eOjg4LeGvQCMajo6OoqgXjLbATA/IVwKPA\n61T1ayLyPiAD/Me1BORWNoJs1qPRfi2dTjM9Pd1stB8IBNo2wvjsswE+/ek4997bwejo8mH3ootK\n3HJLhltuyTA8XCGXywGwb98+ksmk/fzfolEuUSgUSKfTzUk5wA3EwWDQfrI0ZgM0pkyvVCp4vV6S\nySTd3d12cN8SqtUqU1NTzRHjaDRqwXiL7MSAPAA8qqoH69dfD7wLuAArsTCbrPHGn81mF3QxaA1c\n5/IBoArf+laIe+9165Wnp5cPcK96VYEbb8xyww1pEok5QqEQBw8epKOjY93b36kas4Q1/jaZTIZC\noQBgk3IYs4UaB7rWajWCwSD9/f10dnbu+a48Foy3nx0XkAFE5EHgblV9RkTeAzQKL6dbDtLrVtV3\nrvQ4FpBNu9VqNfL5PJlMhrm5OQqFQjMwn+tP9tWq21/53nvjfO5zMXK55d88X/nKAtdfP8cb3jDB\nK17RwcjIyJIfQOPj49xzzz3Mzs7S3d3NbbfdxsDAwLr2b6u0huFcLkc2m23WDgN4vV4CgcA5f1kx\nxrRXa71yowQjHo/vqWBowXj72siZ9LK4LdjOuAm3i0V83Rt165A/CASAF4CfBTzAJ4D9wAngR1eb\nztoCstlojYO+stksc3Nz5PP5Zkjz+XwEAoF1vRkWi8IDD0T59KfjPPBAlEpl+ZHQQ4dyXH/9LG99\na5ArrujG6/WSSqW46667OHr0KF6vl3K5TCAQoFarceONN/KhD32IZDK57ue9URozfDXC8Pz8fHNk\nWFXxer34/X6rHTZmB2kcD1Aul/F4PPT19dHd3b2rp7luDcZWY7w97cgR5HaxgGw2W61Wo1gsNkeZ\ns9ks1Wq1ebvP58Pv9+P1etf8wZDJePjCF2IcPdrBww9HVgzLl1yS56abCvzDP/w409NfplqtnLGO\n3+9nYGCAJ554YktCsqpSrVYpl8uUy2Xy+Ty5XI5cLketVmu2WWuEYRsZNmb3cByn2es9FAoxMDBA\nZ2fnrumCYSPGO8emBWQR6QOah+Zvh+mnLSCb7aDxM2OjXjaXy1EqlRas4/P58Pl8eL3eFetms1kP\nX/xijPvv7+Chh1YOy/A8cBj4FPAw4DRv8fv93HzzzRw+fPhcntqyVJVKpdI8lUql5ox0jX6qDR6P\nZ8HzN8bsDa1dMLq7u+nt7SUWi+3IL8Q2YrzzbEYf5NuBPwWGgEngAPCUqr5svRttFwvIZruq1WrN\nEdRisdjszdsIj40PiEZ9s9frbYbnxuhzNuvhgQei3H9/Bw8+GKVcXiksTwH34oblzwIFgsEgx44d\nO6uaZFWlVqs1T9VqtTka3Jgqu/FTamP91pKTtXwJMMbsLY7jUCwWqVarBAIB+vv76e7u3hHtLC0Y\nb2/lsjA352FuzrvglE57ef/7X7LhAflbwHXA51X1MhH5YeAnVPUd691ou1hANjtNI4C2jr6Wy2UK\nhUIzUFcqlQUhGmB+3sNDD3Xy0Y/mePrpg8BKnS3ywOfw+z/Pu999Kb/8y/8aVcVxHGq1WvO8Wq02\n96VarVKpVKjVagv2VUSa5z6fb0GY34mjQMaYrVWtVpsHP3d2dtLf308sFtt2X6otGG8ux3HLDRvh\ndmHgXW65l3x++X830WhswwPyY6p6RT0oX6aqjoh8XVWvWu9G28UCstmtWoNs47LjOPz5n/85v/u7\nf0yt9gbgTuB2YHDFxzrvvDyvfW2aa65Jc9llOQIBt1Wax+NpnrdetuBrjNlorb2VfT4fAwMDdHd3\nb3m7OAvG565QkCVGdN2QOzt75kjv3JyHTMa77Ey063WuAXktXfXnRCQGfAX4qIhMAtVV7mOMOQeN\nUdrFB7YMDQ0RDEI+fz9wP/DvgKuAO3AD8yVnPNYLL0R44YUIH/nIIJGIw2tfm+P1r8/xhjfkGBy0\n/8rGmM0nIoTDYcLhMNVqldHRUU6ePElnZyd9fX10dHRs6qiyHXy3kCrkcp5meE2n3ZCbTnvJZLzL\njPa6t5dKW/drgMejxOM1OjsdpqbO7bHWMoIcBQq4bdjeCiSAj6rq9Llt+tzZCLLZa8bHxzl48OAZ\nBwGediFuWL4ZuBZY+cjxCy4ocfXVea6+Os9VV+WJx50V1zfGmI3SOqrs9/ubtcprHVVeT1/43R6M\ni0Vphtszg66nfn3h5XTaQzbrpVbb2l8TIxGHzs7aGadEwg3ASy2Pxx0a36s24yC9lwBjqlqsXw8D\n/ap6bL0bbRcLyGYvuuOOO7jvvvuoVM5s8dbg9/u55pobufnmP+Mb30jy0EMxJiZWDssej3LoUJFr\nrsnzmtfkefWrC4TDO7cNpDFm52rUKgMkEgn6+/vp6OhYsgRsPX3hd0IwVoVSyT1gO5v1MD/vBtn5\neTfAusvc4JvNNgLwwiC88sHdm8Pn0yUC7pkh9/Ry97ZA4Nw+fzYjID8GvFZVy/XrAeBhVb1yvRtt\nFwvIZi9KpVJcfvnljI+PLxmSG32QH3/8cTweD8ePH8dxlJMnu3jooQ6+8pUoTzwRXnV0wO9XXvWq\nAtdck+fKK/O84hVFC8zGmE3VOqocCAQYGBigq6ur2QFjre+Hjb7wmxWMVd0OC7mcpx5e3YDbCLut\nAbdxubHcXeZerlS2zzEhPp9bvhCPOyQStfrJWSbgng660ajDVhzashkB+V9U9dJFy76lqq9a70bb\nxQKy2atSqRR33303R48exePxNEdMHMfhxhtv5IMf/GBzxKRSqTA2Nsb4+DjBYJBQKEQ26+GRRyI8\n+qh7euGF1X/C9PncEebLLy80T8lkbdX7GWNMO7R2wOju7qavr4+3ve1ta/pF7aabbuJv//ZvVzz4\nrlx2625bT/m854xlq93eWFatbp9w2yCidHQ4xONuuG0E3Xjcqdfung7ArevE4zWiUd2SoLtemxGQ\nPwf8f6p6T/36HcAvqer1691ou1hANnvd+Pg4R44cYXZ2lq6uLm6//Xb6+/uXXDeXy3H8+HHm5+eJ\nRqP4fKeP0Z2Y8PG1r0WaoXlsbG2zXh04UOayy04H5vPOK7PNujUZY3aJRslBPi/MzpZ48cUZ/sN/\neCfVqh+IApGW88iCZR5PjDe84c14PDGKRS+FQiPQSjPYrjwx0/bh9yuxWI2ODqd+qjVD7+nR3aVD\nbkeHwzarJNkwmxGQzwc+ijtRiAAvAj+tqt9f70bbxQKyMWdHVUmlUrz44ouoKtFo9IyaPlU4ccLf\nHF3+2tcizMyspeENRKM1XvayEi9/ebF52revsqNGHYwx61OpQKHgoVAQikVP83Kh4KFYlPr11Zfl\n8+55sehpXm7cprqz30w8HncEd6mAG4u5ITcWc864rXGfeNwhGNxZI7lbZTOnmo7V18+ud2PtZgHZ\nmPWpVCqMjo4yOTnZLLtYjiocP+7niSfCzdNaSjIa4vEaL3uZG5bd8xJDQxUbaTZmEzmOO/paKHjI\n588MsKcD6tJhtRFkVwqw27GkoB28XiUadZY8RSIrX49GnQUBd6eVKexkGxaQReRtqvoREfmVpW5X\n1T9b70bbxQKyMedmfn6e48ePk8/nz+pgldlZL9/8ZqgZmL/zndBZ/TwZiThceGGJiy4q1c/LXHRR\nie5uq2k2e5PjsCCkNkJsPn/msoUh98xR2MYIbj6/cDR396sCOdzZRPMtl3N4PCUOHTrARReNEAo5\nhMPLh97FgddGbHemcw3IK/1uGq2frzSnrTFmB4vFYlxyySXNsgtgybKLxbq6alx3XY7rrssB7sjU\nU08F+d73Qnz3u+7p+ecDy86MlM97+Na3wnzrW+EFy3t6qs3QfP75ZQ4cKHPwYIW+vqqNOJttpVo9\n84Cuszk1DuRqBOCtnFxhs4TDTjOcupfd88blSOTMZe7l0+fu/d3LhUKKt7/9R6lU5nDD8PIH6nm9\nfv7kT46wf79n201rbbanZQOyqv6tiHiBjKr++SbukzFmE3k8Hvr6+ujs7GR0dJSpqSlCodBZTfka\nDCqXXlrk0kuLzWX5vPD00yG++91gMzT/4AeBFWsIp6d9PPKIj0ceiS5YHgo57N9f4cCBcv10+nJf\nX81Gd8yqHMf9N3m2XQqWO+22QOv365IB1g2tS4Xa5YNs6/qN82BQ2/olt1qtks8rr33tAb761dFV\nu1jceOONXHLJJUxNTaGqBIPBZqs4Y5ayloP0vqSqP7xJ+3NWrMTCmPabn5/n2LFj5PN5YrFYW3uE\nzs97ePbZAM8+G+S559zTs88GmJtb20GASwkEHAYHqwwOVhgYqDI0VGFwsMrAQIWhIfc8GrX+zTuN\nqjsL2NmMxq62zk4WCCwMpYsDaGtQbR2JjUTcUdellrWOyvrX1rhmy1UqFfL5PIFAgOHhYRzH4cor\nrzyrPsjpdJrx8XHy+Txer5dwOGyjyrvQZnSx+D3c6aX/Efc3DABU9Yn1brRdLCAbszEcxznrsov1\nUoVUylsPy0Gee84N0MeOBchm2xPOE4kayWSVnp7T5z09p8+TyRrd3e55KGRhej2qVbd0pnFqtNBq\nvb749nxelg23+bxny6e6XQ+PZ221ra0HdC0VXFsvh0KKb/3fIXeFSqVCoVAgEAgwMjJCV1dXM9Se\nTV/4BlUln88zNTVFKpVCVQmHw/h3yjcFs6rNCMhfWmKxqup1691ou1hANmZjlcvlZtnFat0u2k3V\nPRjw2DE/x48H6if38rFjgQ0bEQwEHOJxt71So3m+22P0dJ/RWKzWHHlbWDe58DwQaO/PymdD1Q2t\n1ao0T6WSUCwKpVKjC8Hpy+5tnhWWSbN37OmQezoA7+SSg0ZQXaoDwdmeQiE7oKudyuUyxWKRYDDI\n8PDwgmC82Nn0hW9VrVaZm5tjbGyMYrFoo8q7xKa1eduOLCAbszlau11EIpEFk4xshcao84kTAU6d\n8jE25mdsbOF5JrM9uuF7PIrPp/j97iige67Nc5+PZQPVSm/Pi8NvpbLw+m5tuQXul5i1BdbVR3Mj\nEccOAN2GSqUSpVKJUCjEyMgIiURiwwOrqpLL5ZiammJ6ehqAUChko8o7lLV5s4BszKZwHIfp6Wle\nfPFFHMchFottWNlFO8zPC+PjpwPz9LSX6Wkf09NeUilf83o6vT2C9G7WqIttBNLTl3XB9bWUI0Sj\nO6de1py9YrFIqVQiFosxNDREIpHYkveZSqXC7Ows4+PjlEolfD4foVDIRpV3EGvzZozZFB6Ph97e\nXjo7OxkbG2N8fJxAIEA4HF79zlsgFlMuuKDMBReUV1yvXIbZWR+plJeZGS/ZrJdMxtM8z2S8ZLMe\n0mn3PJPxksud7jm7E+tkF/N6lWDQLRkJhZRgsHHuLnPP3cuLA+vikNu43LgeDuuemdrWrI+qUiwW\nKZfLdHR08JKXvISOjo4t/QLu9/vp6+ujt7eXXC7H5OQkMzMzVqu8h1iJhTFmXXK5HCdOnCCbzRKJ\nRPbkB4bq6el1l5rIoVJpLYFYqgwCKpX1hQCfj3qJxsISjsXXvd7Gdbc0wQ26p0PwTupgYHYXVaVQ\nKFCpVOjs7GRoaGhDDwg+V4tHla1WeXvbyBFkAESkF/h54GDr+qr6c+vdqDFm54tGo1x88cXMzs5y\n4sQJisUi0Wh0T31YiEAg4AbPRMLZ6t0xZkdwHId8Po/jOHR1dTE4OEg0Gl39jlts8ahyo1bZRpV3\np7UcafMp4EHg84DNA2uMaRIRuru7SSQSTExMcOrUKTweD5FIZNuOAhljtkZrMO7t7aW/v3/blmit\nRESIxWLEYjFGRkaYm5tjfHycTCZjo8q7yFoCckRVf33D98QYs2N5vV6Ghobo6enh5MmTTE9Pn/Vs\nfMaY3alWq5HP5wHo7++nr69v17w3+P1+ent7SSaT5PN5UqkUqVQKx3EIBoO75nnuRWsJyPeKyM2q\n+pl2brg+jfVjwKiq3ioiLwE+DnQDTwA/paorH11jjNlWgsEg559/Pn19fZw4cYJ0Ok00Gt3ytnDG\nmM3nTgftzlY3PDxMMpnctWUIIkI0GiUajTI8PEw6nWZiYoJMJoPH4yEcDrd1VlKz8Zb91BKRLKCA\nAL8hImWgMY+jqmr8HLf9n4CngMbj/CHw56r6cRF5P3AX8DfnuA1jzBbo6Ojg0KFDzMzMcOLEiea0\n1fazozG7X2NyD7/fz4EDB+ju7t5TX5J9Ph89PT309PRQKBSYnp5mamqKarWK3+8nFApZCdoOsOy/\nWFXdsPZuIjIC3AL8HvAr4v5LuQ74yfoqHwbegwVkY3YsEaGnp6dZnzw2Nmb1ycbsUqpKqVSiXC4T\nCoU4//zz6ezs3PNfisPhMCMjIwwNDZHNZpmcnGRubg4RsUlItrk1faUTkX8NXIs7ovygqh4+x+3+\nBfBOTvdY7gHmVLVav34SGD7HbRhjtgGfz9f8eXV0dJRUKrWt+ycbY9ZOVcnn89RqNeLx+LboYbwd\neTweEokEiUSCcrnM7OzsghKMSCSy579MbDdrafP218AFwMfqi/6tiNygqv9+PRsUkVuBSVV9XETe\n2Fi8xKpLNmgWkXcA7wDYv3//enbBGLMFgsEg5513Hv39/Zw8eZJ0Ok04HCYQCGz1rhljzlJrR4qe\nnh76+/t3RKu27SAQCDQPVszn880SDMdxCAQCBINB+4KxDaxlBPmHgJdrfUYREfkw8J1z2ObrgNtF\n5GYghFuD/BdAp4j46qPII8Cppe6sqh8APgDuRCHnsB/GmC0QjUa56KKLSKfTvPjii2QyGSKRyJ6q\nUTRmp6pWqxQKBUSE/v5+ent7rVPDOi0+sC+TyTA1NUU6nQYgFArZAMIWWssn0jPAfuB4/fo+4Nvr\n3aCqvht4N0B9BPnXVPWtIvJPwI/gdrJ4O27/ZWPMLiQidHZ2Eo/HmZmZ4eTJk+TzeaLRqB3pbcw2\nVCqVKJVKBAIB9u/fv+cOvNtoXq+Xrq4uurq6KJfLzM3NWReMLbaWf909wFMi8vX69SuBR0TkHgBV\nvb1N+/LrwMdF5HeBbwIfatPjGmO2KY/HQzKZpKuri6mpKUZHR1HVPTcjnzHbUWMq6Gq1SjQaZd++\nfSQSCfu/ucECgUBzxr5CocDs7CyTk5NUq1WbiGQTrSUg//ZGbVxVHwAeqF9+Abhqo7ZljNm+vF4v\nAwMD9PT0MDExwfj4OCJiB64YswUaE3uoarO+2LrPbL7Ge2AkEmFoaIj5+Xmmp6eZnp7GcRxrGbfB\nVg3IqvplABGJt66vqjMbuF/GmD3I7/czMjJCb28v4+PjTE5O4vF4iEaj9iFgzAZr9C/2er0MDg6S\nTCatvnibEBE6Ojro6Ohg3759ZLNZUqkUc3NzgPveaQf3tddauli8A/jvQAFwcDtOKHDexu6aMWav\nCgaDHDhwgP7+fsbGxkilUni9XhvFMqbNWssowuEw5513Hp2dnVbvuo15vV46Ozvp7OykWq2STqeZ\nmpoim80iIgQCAQKBgL1XnqO1lFj8F+Blqpra6J0xxphWoVCIl7zkJQwMDDA2Nsb09DQ+n49wOGxv\n/sacg0Y3ClWlu7u72abN/l/tLK2z9pXL5WYnjGw2C1gnjHOxloD8PJDf6B0xxpjlNEa2BgYGOHXq\nFLOzszaibMxZap3trjGBT09PjwWoXSIQCJBMJkkmk5RKJdLpNKlUikwmA9Ccuc/eM9dmLQH53cBX\nReRrQKmxUFV/acP2yhhjlhCJRLjgggvI5/MWlI1Zo1qtRqFQwHEcOjo62L9/v3Wj2OWCwSB9fX30\n9fVRKpXOGFkOBoNWhrGKtQTkvwW+iDs5iLOxu2OMMatrDcrj4+NMT0/bwXzGLFIqlSgWi/h8Pvr7\n++np6bEp3vegYDBIb28vvb29lEql5gF+jbBss/ctbS0Buaqqv7Lhe2KMMWcpEolw3nnnMTg4yMTE\nBKlUytrDmT2tdbQ4Go1ywQUXkEgk7KA7A7hhORgMkkwmKZfLZLNZpqenyWQyqGqzG4a9f64tIH+p\n3sniCAtLLKzNmzFmWwiHwxw8eJDBwUEmJyeZmJgA3ABtwcDsdq21xV6v10aLzZoEAoHmAX7VapX5\n+XlmZmaYnZ3FcZw9P4PfWgLyT9bP392yzNq8GWO2nWAwyL59+xgYGCCVSjE2NobjOM2DU4zZTVo7\nUcTjcfbv3088Ht+zgcasn8/na7aOcxyHXC7H3NwcMzMz5HI5RIRgMLinDvJby0QhL9mMHTHGmHbx\n+yg6K4oAACAASURBVP0MDg7S19fH7Owsp06dIp1OEwwGCYVCW717xqyb4zgUCgVqtRqBQIDh4WG6\nu7ttQg/TNh6PpzkpycjICIVCoVmK0ahb3gulGMsGZBF5p6r+Uf3yj6rqP7Xc9vuq+hubsYPGGLNe\nXq+XZDJJT08P6XSasbExMpkMXq+XcDi8q9/cze7RWkLh8Xjo7u4mmUwSi8X2zGie2Rqt01339/dT\nqVTIZrPMzc0xNzdHrVbD4/E0R5d3k5VGkH8c+KP65XcD/9Ry202ABWRjzI4gIs2fD3O5HJOTk0xP\nTwNu/bLPt5ZqM2M2V6VSoVgsAhCLxdi3bx/xeNz+vZot4/f76e7u/r/s3XecXGXZ//HPtWW2l+xu\neqcECAgBQvlJEamCSBQBQQGxgD5SxIaCioig6AOCgg88IDyIFAHpTYoFQWoCAUICJKT3bO9lZq7f\nH+fsMrs7m2Szm50t3/frNa+Zue8zZ645Oztz7b3XuW9KSkqIx+M0NjZSV1dHZWVlx3zLGRkZZGVl\nDflSn839llkPt5PdFxEZEvLy8pg+fToTJ06ksrKSdevW0djYqKmOZFCIRqM0NzcTj8fJyclhypQp\nFBcXazEPGXTS0tLIz88nPz+f8ePH09raSmNjI9XV1VRVVRGNRjEzMjMziUQiQ+4/dptLkL2H28nu\ni4gMKZFIhHHjxjFmzBjq6urYsGEDNTU1I/7MbRl4iUlxJBJhwoQJFBcXk52drT/YZMiIRCJEIhGK\ni4uZOnUqzc3NNDQ0dIwuuztm1rHdYH9vby5B3svMaglGi3PC24T3dZaLiAwLaWlpFBUVUVRURHNz\nMxUVFWzcuJFoNKpRZdluEpPijIwMxo0bR3FxsVaFlGHBzMjJySEnJ4eysrKOk0vr6+upqqqivr4e\ndyctLY1IJDIoZ8foMUF2dw2fiMiIkp2dzcSJExk/fjx1dXVs3LiR6upqzExTxUmfJU7L1r66XXFx\nsVaAlGGvfaXTvLw8xo4d27GgTdeEuX2EOTMzM+UlGar0FxHpInFUubW1laqqKjZu3Ehtba1KMKRX\nWltbaWlpwd3Jyspi/PjxGimWES89Pb2jfnncuHEdCXP7/Mt1dXUdCXNGRgaRSGTAP3OVIIuIbEYk\nEmHs2LGMGTOGpqYmKisr2bRpE9FoVNPFSTeJU7JBsJpj++wTqikWSS4xYR47dizxeJzm5mYaGxup\nra2lpqaGWCwG0GmUeXv+PilBFhHZConzgU6YMKHj5JOKigpisZiS5REsGo3S0tJCLBbDzCgqKmLS\npEkUFBRo9gmRbZCWltbxeVtWVoa709raSnNzM3V1ddTW1lJfX9+xfUZGBpmZmf06BaISZBGRXkpc\naWry5MnU19dTWVlJZWVlx8T5KsMYvuLxOK2trR2jxJFIhNGjR1NUVEReXp5+7iL9rH2p66ysLIqK\nigCIxWI0Nzd31DLX1tbS1NSU+Jg+DS8rQRYR6YO0tDQKCwspLCxkypQpHTV05eXlHfOAtq8ypX+v\nD03uTltbG62trcTj8Y4/kCZOnEh+fr5mOhFJgfT09I4T/8rKyoCPZodpamqira2tsS/7V4IsItJP\nEkeWJ02aRFNTE7W1tVRUVFBXVwcMn1WmhrP2hLj95DozIy8vj9GjR5Ofn09ubq5KaUQGoYyMjI5a\n5paWloY+7au/ghIRkY8k1iyPGzeuY5WpqqoqqqqqOupVh+oqU8NJPB7vGCF2D9bBys/Pp6ysjPz8\nfC1HLjIC6TdeRGQAJK4yNW3atI5Vpqqrq6mpqelIzFI1pdFI4e5Eo1FaW1s7zopvH/kfO3YseXl5\nqh8XESXIIiIDLdkqU+1TGtXU1FBbW0s0GgUY1CtNDXbtyXBbW1vH8QQ6jntBQQHZ2dmafk1EulGC\nLCKSYj1NadQ+cX77lEbt9bDtpRmDYbWpwcDdicViHclwe1v7HyIlJSXk5+d3JMMaHRaRLRnwBNnM\nJgN3AOOAOHCzu//OzEqAe4FpwHLgFHevGuj4RERSLXFKo+LiYiZOnEg8HqelpYWWlhYaGhqor6+n\nsbGxo0yg/XEZGRlkZGSQnp4+7EZFY7FYRyKc+LohKGHJz8/vKJGIRCJkZWXpDwgR2SapGEGOAt9z\n9zfMrACYZ2bPAmcBf3f3q8zsR8CPgB+mID4RkUGnfW7lnJwciouLO9rbTy5rPwmwsbGRpqamTvOB\nQjCimp6e3ukyWJJHdycejxOLxTqu209ibB8JBsjMzCQ7O5uioiJycnLIysrqqO0eLK9FRIaHAU+Q\n3X0dsC68XWdmi4CJwBzgsHCzPwH/QgmyiMhmtZda5OXlMWrUqI729qnKEi/Nzc0dyyA3NzcTi8U6\nEtDE63bt5RxdL5vT/nh373ZJ3G/itomzebSP/LbPHd2+QpbKSURkIKW0BtnMpgF7A68CY8PkGXdf\nZ2ZjenjMOcA5AFOmTBmYQEVEhhgz60g4e9Jeu5t46TqSG41GO7ZrH+ntmvC2P1/iJS0trWOUOiMj\ng7S0tE5tXUeyh1s5iIgMbSlLkM0sH3gAuNDda7f2w9HdbwZuBpg9e7ZvYXMREelBYs2yiIh8JCX/\nrzKzTILk+C53fzBs3mBm48P+8cDGVMQmIiIiIiPbgCfIFgwV3woscvffJnQ9Cnw5vP1l4JGBjk1E\nREREJBX/VzsIOAN4x8zmh22XAFcB95nZ14CVwMkpiE1ERERERrhUzGLxItBTwfERAxmLiIiIiEhX\nmjNHRERERCSBEmQRERERkQRKkEVEREREEihBFhERERFJoARZRERERCSBEmQRERERkQRKkEVERERE\nEihBFhERERFJoARZRERERCSBEmQRERERkQRKkEVEREREEihBFhERERFJoARZRERERCSBEmQRERER\nkQRKkEVEREREEihBFhERERFJoARZRERERCSBEmQRERERkQRKkEVEREREEihBFhERERFJoARZRERE\nRCSBEmQRERERkQRKkEVEREREEihBFhERERFJoARZRERERCSBEmQRERERkQSDKkE2s0+Z2ftmtsTM\nfpTqeERERERk5Bk0CbKZpQN/AI4FZgKnmdnM1EYlIiIiIiPNoEmQgf2BJe6+1N1bgb8Ac1Ick4iI\niIiMMBmpDiDBRGBVwv3VwAFdNzKzc4BzwrstZrZgAGIbzsqA8lQHMcTpGPadjmH/0HHsOx3DvtMx\n7Dsdw77bpS8PHkwJsiVp824N7jcDNwOY2Vx3n729AxvOdAz7Tsew73QM+4eOY9/pGPadjmHf6Rj2\nnZnN7cvjB1OJxWpgcsL9ScDaFMUiIiIiIiPUYEqQXwd2NrPpZhYBTgUeTXFMIiIiIjLCDJoSC3eP\nmtl5wNNAOnCbu7+7hYfdvP0jG/Z0DPtOx7DvdAz7h45j3+kY9p2OYd/pGPZdn46huXcr8xURERER\nGbEGU4mFiIiIiEjKKUEWEREREUkwZBNkLUvde2Y22cz+aWaLzOxdM/t22H6Zma0xs/nh5bhUxzqY\nmdlyM3snPFZzw7YSM3vWzBaH16NSHedgZWa7JLzX5ptZrZldqPfh5pnZbWa2MXHu957edxb4ffj5\n+LaZ7ZO6yAePHo7hf5vZe+FxesjMisP2aWbWlPB+vCl1kQ8uPRzHHn9/zezi8L34vpkdk5qoB5ce\njuG9CcdvuZnND9v1XuxiM/lMv30mDska5HBZ6g+Aowimh3sdOM3dF6Y0sEHOzMYD4939DTMrAOYB\nnwVOAerd/eqUBjhEmNlyYLa7lye0/QaodPerwj/YRrn7D1MV41AR/i6vIVgU6CvofdgjMzsUqAfu\ncPc9wrak77swOTkfOI7g2P7O3bstvDTS9HAMjwb+EZ4o/muA8BhOAx5v304+0sNxvIwkv79mNhO4\nh2C13AnAc8AMd48NaNCDTLJj2KX/GqDG3S/Xe7G7zeQzZ9FPn4lDdQRZy1JvA3df5+5vhLfrgEUE\nKxhK380B/hTe/hPBL6ps2RHAh+6+ItWBDHbu/m+gsktzT++7OQRfvO7urwDF4RfKiJbsGLr7M+4e\nDe++QjAHv2xGD+/FnswB/uLuLe6+DFhC8B0+om3uGJqZEQxc3TOgQQ0hm8ln+u0zcagmyMmWpVai\n1wvhX6R7A6+GTeeF/3a4TeUBW+TAM2Y2z4KlzwHGuvs6CH5xgTEpi25oOZXOXwJ6H/ZOT+87fUZu\nm68CTyXcn25mb5rZ82Z2SKqCGkKS/f7qvdh7hwAb3H1xQpveiz3oks/022fiUE2Qt2pZaknOzPKB\nB4AL3b0WuBHYEZgFrAOuSWF4Q8FB7r4PcCxwbvivMuklCxYEOgG4P2zS+7D/6DOyl8zsx0AUuCts\nWgdMcfe9ge8Cd5tZYariGwJ6+v3Ve7H3TqPzwIHeiz1Iks/0uGmSts2+D4dqgqxlqbeRmWUSvJnu\ncvcHAdx9g7vH3D0O3IL+/bVZ7r42vN4IPERwvDa0/7smvN6YugiHjGOBN9x9A+h9uI16et/pM7IX\nzOzLwPHAlzw8MScsCagIb88DPgRmpC7KwW0zv796L/aCmWUAJwL3trfpvZhcsnyGfvxMHKoJspal\n3gZhXdOtwCJ3/21Ce2IdzueABV0fKwEzywtPCMDM8oCjCY7Xo8CXw82+DDySmgiHlE6jJHofbpOe\n3nePAmeGZ24fSHCyz7pUBDjYmdmngB8CJ7h7Y0L76PAkUsxsB2BnYGlqohz8NvP7+yhwqpllmdl0\nguP42kDHN4QcCbzn7qvbG/Re7K6nfIZ+/EwcNEtN98Y2LkstcBBwBvBO+/QxwCXAaWY2i+DfDcuB\nb6QmvCFhLPBQ8LtJBnC3u//NzF4H7jOzrwErgZNTGOOgZ2a5BLPQJL7XfqP3Yc/M7B7gMKDMzFYD\nPwOuIvn77kmCs7WXAI0EM4SMeD0cw4uBLODZ8Pf6FXf/JnAocLmZRYEY8E1339oT04a1Ho7jYcl+\nf939XTO7D1hIUMJy7kifwQKSH0N3v5Xu52WA3ovJ9JTP9Ntn4pCc5k1EREREZHsZqiUWIiIiIiLb\nhRJkEREREZEESpBFRERERBIoQRYRERERSaAEWUREREQkgRJkEZGtYGb1Xe6fZWY3pCqeVDOzC8Pp\n+kREhh0lyCIig1C4olZf95HeH7H04EKgVwnydo5HRKTfKEEWEekDMysws2XhsqeYWaGZLTezTDP7\nl5ldZ2YvmdkCM9s/3CbPzG4zs9fN7E0zmxO2n2Vm95vZY8AzZnaYmf3bzB4ys4VmdpOZpYXb3mhm\nc83sXTP7eUI8y83sUjN7ETjZzM4On+ctM3ugfdTXzG4P9/FPM1tqZp8IY1pkZrcn7O9oM3vZzN4I\nY8s3swuACcA/zeyfPW2XLJ7t/xMREek7JcgiIlsnx8zmt1+AywHcvQ74F/DpcLtTgQfcvS28n+fu\nHwe+BdwWtv0Y+Ie77wd8EvjvcOlygP8HfNndDw/v7w98D/gYsCNwYvs+3H02sCfwCTPbMyHWZnc/\n2N3/Ajzo7vu5+17AIuBrCduNAg4HvgM8BlwL7A58zMxmmVkZ8BPgSHffB5gLfNfdfw+sBT7p7p/s\nabse4hERGfSG5FLTIiIp0OTus9rvmNlZwOzw7h+Bi4CHCZYwPTvhcfcAuPu/w9HlYuBo4AQz+364\nTTYwJbz9bJdlZF9z96Xhc94DHAz8FTjFzM4h+BwfD8wE3g4fc2/C4/cwsyuAYiAfeDqh7zF3dzN7\nB9jg7u+Ez/MuMA2YFO73P+EyzBHg5STH5sAtbHdvkseIiAxaSpBFRPrI3f9jZtPM7BNAursvSOzu\nujlgwOfd/f3EDjM7AGhIsn2n+2Y2Hfg+sJ+7V4UlEdkJ2yTu43bgs+7+VpjUH5bQ1xJexxNut9/P\nAGIECftpbJ5tYbuur0lEZFBTiYWISP+4g2C0+P+6tH8BwMwOBmrcvYZgFPd8C4dbzWzvzex3fzOb\nHtYefwF4ESgkSDprzGwscOxmHl8ArAtrpL/Uy9f0CnCQme0UxplrZjPCvrpw31vaTkRkyFGCLCLS\nP+4iqOm9p0t7lZm9BNzER/W/vwAygbfNbEF4vycvA1cBC4BlwEPu/hbwJvAuQV3zfzbz+J8CrwLP\nAu/15gW5+ybgLOAeM3ubIBHeNey+GXjKzP65he1ERIYcc+/63zsREektMzsJmOPuZyS0/Qv4vrvP\n3cZ9HhY+/vh+CVJERLaKapBFRPrIzK4nKHM4LtWxiIhI32kEWUREREQkgWqQRUREREQSKEEWERER\nEUmgBFlEREREJIESZBERERGRBEqQRUREREQSKEEWEREREUmgBFlEREREJIESZBERERGRBEqQRURE\nREQSKEEWEREREUmgBFlEJMXM7CwzezHVcYiISEAJsogMOWa23MyazKw+4XJDCuP5l5l9fTvte5qZ\nuZllbI/9D0fb8+chIiODPnBFZKj6jLs/l+oghhMzy3D3aKrjEBFJNY0gi8iwYmY3mtlfE+7/2sz+\nboHDzGy1mV1iZuXhSPSXErbNMrOrzWylmW0ws5vMLCehf46ZzTezWjP70Mw+ZWZXAocANySOZJvZ\nrmb2rJlVmtn7ZnZKwn5KzezRcD+vATv24vXdbmb/Y2ZPhc/3HzMbZ2bXmVmVmb1nZnsnbL/czC42\ns4Vh//+ZWXbY1348fmhm64H/C9vPNrMlYeyPmtmEsP0mM7u6SzyPmNl3w9sTzOwBM9tkZsvM7IKE\n7S4zs/vN7E4zqzOzd8xsRhjbRjNbZWZHJ2xfZGa3mtk6M1tjZleYWXrYd5aZvRj+rKrC5zo27Ev6\n8xAR6Q0lyCIy3HwP2DNMog4BvgZ82d097B8HlAETgS8DN5vZLmHfr4EZwCxgp3CbSwHMbH/gDuAH\nQDFwKLDc3X8MvACc5+757n6emeUBzwJ3A2OA04D/MbPdw+f5A9AMjAe+Gl564xTgJ+HraAFeBt4I\n7/8V+G2X7b8EHEOQiM8IH9tuHFACTAXOMbPDgV+FzzEeWAH8Jdz2buALZmbhMRkFHA38xczSgMeA\nt8LjdgRwoZkdk/BcnwH+DIwC3gSeJvgemghcDvxvwrZ/AqIEP4e9w+dJLJs4AHg/fM2/AW41M0v2\n89jMcRQRSUoJsogMVQ+bWXXC5WwAd28ETidIEu8Eznf31V0e+1N3b3H354EngFPCpO9s4DvuXunu\ndcAvgVPDx3wNuM3dn3X3uLuvcff3eojteILk+f/cPerubwAPACeFo6CfBy519wZ3X0CQDPbGQ+4+\nz92bgYeAZne/w91jwL0ECWWiG9x9lbtXAlcSJOzt4sDPwuPRRJBM3+bub7h7C3Ax8P/MbBpB4ukE\nI7QAJwEvu/taYD9gtLtf7u6t7r4UuCXh+AG84O5Ph2Uc9wOjgavcvY0gCZ9mZsVmNhY4FrgwPEYb\ngWu77GuFu98SvuY/ESTzY3t5HEVEklINsogMVZ/tqQbZ3V8zs6UEo7f3demucveGhPsrgAkEyVou\nMC8cIAUwID28PRl4citjmwocYGbVCW0ZBKOno8Pbq7rE0BsbEm43Jbmf32X7rs81IeH+pjDRbjeB\nYDQaAHevN7MKYKK7LzezvxAk2P8GvkjwRwgEr3lCl9ecTpBU9xR3eZjgtt8njH0CkAmsS/hZpHV5\nHesTYmwMt+v6ukVEtokSZBEZdszsXCALWAtcRFAy0G6UmeUlJMlTgAVAOUGStru7r0my21X0XCvs\nXe6vAp5396OSxJZOUDowGWgfgZ6yxRfVN5MTbk8hOC7tusa+liDZBSAsFykF2o/JPcAzZnYVQZnD\n58L2VcAyd9+5H+JdRVA6UraNJw12fU0iIr2iEgsRGVbMbAZwBUGZxRnARWY2q8tmPzezSFijfDxw\nv7vHCUoCrjWzMeG+JibU0N4KfMXMjjCztLBv17BvA7BDwv4fB2aY2Rlmlhle9jOz3cIR0weBy8ws\n18xmEtRCb0/nmtkkMysBLiEow+jJ3QSvc5aZZRGUmbzq7ssB3P1NYBPwR+Bpd28fMX4NqA1P+Msx\ns3Qz28PM9uttsO6+DngGuMbMCsPjvaOZfWIrd9H15yEi0itKkEVkqHrMOs+D/JAFcwXfCfza3d9y\n98UECeGfw2QPgn/NVxGMlN4FfDOhlviHwBLgFTOrBZ4DdoGgbAP4CkEtbA3wPB+NtP6OoL64ysx+\nH9YvH01QM7s2fM5fE4xqA5xHUA6wHridcPaI7ehugoRzaXi5oqcN3f3vwE8JaqbXEYyan9pls3uA\nI8P9tj8uRnAS3ixgGcGI/B+Bom2M+UwgAiwk+Hn9laDOeGt0+nls4/OLyAhmH53YLSIyvJnZYcCd\n7j4p1bEMFDNbDnxdc0aLiGw9jSCLiIiIiCRQgiwiIiIikkAlFiIiIiIiCTSCLCIiIiKSQAmyiMgW\nmNkVZlZuZuu3vPX2Z2Y3mdlPUxzDu+FJjyIiw45KLERENsPMJgMfAFPdfWO45PIyIHMbF7FI9hwz\ngP8GPk6w+tzrwAXu/v5WPPYwtvPMHGZ2O7Da3X+yvZ5DRGQw0QiyiMjmTQUq3H1jf+wsnKu5q2Lg\nUYI5l8cSLLrxSH883zbGIyIyoilBFpERz8x+ZGYfmlmdmS00s8+F7UcCzwITwsVIbgf+HT6sOmz7\nf+G2XzWzReHiFE+bWeJyzW5m55rZYmBx1+d399fc/VZ3r3T3NoLFSHYxs9Ie4r09LPvIA55KiK/e\nzCaEK8+1v6YKM7svXEUPM5sWxvM1M1sJ/CNsv9/M1ptZjZn928x2D9vPAb5EsCJhvZk9FrYvD48P\nZpZlZteZ2drwcl37wixmdpiZrTaz75nZRjNbZ2ZfSXgtx4XHvM7M1pjZ97fphygi0o+UIIuIwIfA\nIQSrvv0cuNPMxoeLaxwLrHX3fHc/Czg0fExx2PaymX2WYMW+E4HRwAsEq80l+ixwADBzK+I5FFjv\n7hWb28jdG7rEl+/ua4ELwuf7BDCBYCW6P3R5+CeA3YD2pbSfAnYGxgBvEKwyiLvfHN7+Tbj/zyQJ\n5cfAgQSr6O0F7A8klmOMIzi2E4GvAX8ws1Fh363AN9y9ANiDMGEXEUklJcgiMuK5+/3uvtbd4+5+\nL8Eo7/692MU3gF+5+6KwLvmXwKzEUeSwv9Ldmza3IzObRJDMfreXL6NrPD9299Xu3gJcRrD0cmI5\nxWXu3tAej7vf5u51CdvvZWZbu0z0l4DL3X2ju28i+CPjjIT+trC/zd2fBOoJl/AO+2aaWaG7V7n7\nG9v2kkVE+o8SZBEZ8czsTDObb2bVZlZNMJJZ1otdTAV+l/D4SsAIRkzbrdqKOEYDzwD/4+5dR6B7\nYyrwUEI8i4AYQX1zt3jMLN3MrgpLMmqB5WHX1h6DCcCKhPsrwrZ2FV1OaGwE8sPbnweOA1aY2fPt\nJSsiIqmkBFlERrRwlPcW4Dyg1N2LgQUECW4yyab+WUVQJlCccMlx95e28LjEOEYRJMePuvuVvXgJ\nPcVzbJd4st19TQ+P+yIwBziSoBRiWntYWxM7sJYgKW83JWzbcvDur7v7HILSjoeB+7bmcSIi25MS\nZBEZ6fIIEsBNAOEJZHtsZvtNQBzYIaHtJuDihBPbiszs5K0NwMwKgaeB/7j7j3oXPhuA0i7lEDcB\nV7aXeJjZaDObs5l9FAAtQAWQS1Ai0vU5duj6oAT3AD8Jn6cMuBS4c0uBm1nEzL5kZkXhyYm1BCPd\nIiIppQRZREY0d18IXAO8TJAIfgz4z2a2bwSuBP4TljAc6O4PAb8G/hKWKCwgOHlua30O2A/4SsJs\nFPVmNmUr4n+PIEFdGsYzAfgdwbRxz5hZHfAKwQmCPbmDoCxiDbAw3D7RrQR1wtVm9nCSx18BzAXe\nBt4hOMnvii3FHjoDWB4et28Cp2/l40REthstFCIiIiIikkAjyCIiIiIiCZQgi4iIiIgkUIIsIiIi\nIpJACbKIiIiISIKMLW8yeJWVlfm0adNSHYaIiIiIDCLz5s0rd/fR2/r4IZ0gT5s2jblz56Y6DBER\nEREZRMxsxZa36plKLEREREREEihBFhERERFJoARZRERERCTBkK5BTqatrY3Vq1fT3Nyc6lCGvOzs\nbCZNmkRmZmaqQxEREREZMMMuQV69ejUFBQVMmzYNM0t1OEOWu1NRUcHq1auZPn16qsMRERERGTDD\nrsSiubmZ0tJSJcd9ZGaUlpZqJF5kEHJ3Hn/8cdw91aGIiAxLwy5BBnqdHOvLJjn9kSEyOL311lt8\n5jOf4e233051KCIiw9KwTJB7S182IjKU3HvvvZ2uRUSkfylBpv+/bMyM733vex33r776ai677LJt\n2tfDDz/MwoULt3r7+fPn8+STT27Tc4nI0HDnnXd2uhYRkf417E7S2xaJXza//OUv+7y/rKwsHnzw\nQS6++GLKysr6tK+HH36Y448/npkzZ27V9vPnz2fu3Lkcd9xxfXpeEek/7k5Da4zK+lYqGlqobmyj\nriVKQ3ip77iO0RKN0RZzWsPrhrpaXrjlUtqaG4N9xeNUbdgIwJr1GyjbaS/S0tJJS4OsnDw+dd6V\nFBQWkZWRRlZGOlmZaR2387LSKcjOoCArM7jODq4LczLJz8ogPU1lVSIiMAIT5JqaGk4//XTq6uoA\niMVilJeXA1BeXs4hhxxCeno6AAUFBdx5550UFRX16jkyMjI455xzuPbaa7nyyis79W3atIlvfvOb\nrFy5EoDrrruOgw46iAsuuICysjIuvfRSnn76aa688kquuuoqHn30UZ5//nmuuOIKHnjgAXbccceO\nfd1///38/Oc/Jz09naKiIp577jkuvfRSmpqaePHFF7n44os5/vjjOf/883nnnXeIRqNcdtllzJkz\nh9tvv52HHnqIlpYWli1bxhe/+EV+9rOfbfNxFRmpmttirK9pZm1NE2urm1lX3dRxu7y+hcqGVioa\nWmmNxrdp/x6PUWv5NCx+qVtfvK2Vig8/Kg3L2/No/vZ+NZZWt03PlZ+VQWF2BsW5EUrzI5Tmzkjo\nXgAAIABJREFURSjJy6I0P0JJXnA/uB20FWRl6FwFERmWRlyCnJ+fz7hx43j88ce79bUnlu2+/vWv\nk5+fv03Pc+6557Lnnnty0UUXdWr/9re/zXe+8x0OPvhgVq5cyTHHHMOiRYu46qqr2G+//TjkkEO4\n4IILePLJJ9lxxx054YQTOP744znppJO6Pcfll1/O008/zcSJE6muriYSiXD55Zczd+5cbrjhBgAu\nueQSDj/8cG677Taqq6vZf//9OfLIIwF47bXXWLBgAbm5uey33358+tOfZvbs2dv0ekWGs1jcWV3V\nyNLyBpZuamBZeX143cC6mu0704ulpVN27AXk7rQ/5Y9fg0dbIR77aIO0dCwjQtnx3yd35wP69Fz1\n4Wj22q18TTmZ6YwrymZMQRbjirIZW9h+yWJceHtMYRZZGel9iktEZKCNuAQ5PT2dW265heOPP54z\nzjiDpqYmotFoR39GRga5ubn8+c9/5oQTTtjm5yksLOTMM8/k97//PTk5OR3tzz33XKea4traWurq\n6igoKOCWW27h0EMP5dprr+00UtyTgw46iLPOOotTTjmFE088Mek2zzzzDI8++ihXX301EEyD1z56\nfdRRR1FaWgrAiSeeyIsvvqgEWUa8uuY23ltfx8K1tcFlXS3vb6jb5hHg/pK784GMOfnnbLz3p3hC\ngmxpGYw5+edkT9q6Mqz+1NQWY1l58IfC5ozKzWRsYTbjirKZUJzDxOIcJo0KrieOymFMQbbKO0Rk\nUBlxCXK7OXPm8NRTT3H00Ud3SpAjkQhPPvkkBx10UJ+f48ILL2SfffbhK1/5SkdbPB7n5Zdf7pQ0\nt3vnnXcoLS1l7dq1W7X/m266iVdffZUnnniCWbNmMX/+/G7buDsPPPAAu+yyS6f2V199tdu/RvWv\nUhlp2mJx3ltXx7wVlcxbWc3bq6tZUdG4XZ4rKyMtKFnIjzAqN0JhdiZ5WenkZWWQn5VBXnjJzkgj\nkpFGJD2NzPTgdmZ6Ghnpxp9ufJVb0o3WKOTk5tHU2EBmuvG5CXWcfNZsWqNxWqJxWtritERjwe2O\nthj1LVFqm6PUNbdRl3Bd29RGQ2tsyy9iG1U1tlHVGPzhkUxmujGuKDtImItzg+R5VA6TwgR6fFEO\nkQydUy4iA2fEJsgAL7zwAvF4MCqUn59PfX098XicF198sV8S5JKSEk455RRuvfVWvvrVrwJw9NFH\nc8MNN/CDH/wACE6qmzVrFitWrOCaa67hzTff5LjjjuOzn/0sBxxwAAUFBR310l19+OGHHHDAARxw\nwAE89thjrFq1qtv2xxxzDNdffz3XX389Zsabb77J3nvvDcCzzz5LZWUlOTk5PPzww9x22219fs0i\ng1ldcxuvL69k3ooq5q2o4q1VNTS19S0xTDMYW5jN+HB0dEJxTsftsYXZYR1vhNxIep//CD3z0fuJ\nRdsYM2YMV1xxBT/+8Y+prKzk+cf/yo2/ubxP+47FnfrmKDVNbVQ2tlLZ0EJFfSuVDcGlvD5oa6+p\nLq9vobmtf0bV22LOqsomVlU2AZXd+s1gTEEWE4tzmFySy5SSXCaX5DK1JJcppbmMLcgmTSPQItKP\nbCgvjjF79myfO3dup7ZFixax2267bdXjd9ttNxYvXkxpaWmnL5sZM2b0amq1rtqTbYANGzYwffp0\nLrroIi677DLKy8s599xzWbRoEdFolEMPPZQbb7yRo446igsuuIATTjiBefPmcdZZZ/H6668zb948\nzj77bLKysvjrX//aqfTixBNPZPHixbg7RxxxBNdddx1VVVUcc8wxtLW1cfHFF3PCCSdw4YUX8tJL\nL+HuTJs2jccff5zbb7+dJ598koaGBpYsWdLjSXq9OZ4ig01bLM5bq6p5YXE5/1lSzpurqonFt+0z\nrzQvwg6j89ihLJ/po/PYoSyPHUbnM6Ukd0BGN9etW8eECRP49Kc/zZ133klxcTHV1dWcfvrpPPHE\nE6xdu5bx48dv9zjauTu1TVE21DWzvqaZDbXBZX1tMxtqWzrub6prYRsP+VaLpKcxqSSHKWHy3HEp\nzWXyqFzyskb0WJDIiGRm89x9m+tGR2yCPNi+bAba7bff3ulkvp4oQZahZm11E88t2sC/P9jEK0sr\nqW+JbvlBCdIMdhidz8zxhcycUMjM8YXsNr6Q0QVZ2ynirdPa2srzzz/PkUce2Wkk2t159tlnOeyw\nw4hEIimMMLloLE5FQyvra5pZV9PMmuom1lQ1sbqqMbhd3UR1Y9t2jaEsP9Ix8qzRZ5GRoa8J8oj9\ns7q0tJRnnnmm05dNcXExjz32GM8++2zHyWsiMri5O++tr+OZdzfw7KL1LFhT26vHl+VnMXvqKPad\nOop9phYzc3wROZHBN+tCJBLhqKOO6tZuZhx99NEpiGjrZKSndcxusdfk5Ns0tEQ/SpzD6+B+kERv\nqG3pUwzl9UGJyJsrq7v1JRt9nlySy1SNPouMaCN2BFm2jo6nDEbuzhsrq3ni7XU8s3A9q6uatvqx\nO47O4//tWMrsqSXsO3UUk0bl6ATVQa4lGmNddTD6vLKyseOyKrzeniPQXUefE8s3NPosMnhpBDkJ\nd9cXXj8Yyn88yfD0wYY6Hpm/hkfmr93qpLgsP8JBO5Vx8E5lHLRTGROKu88gI4NbVkY608rymFaW\nR7LTp2ua2jqS5a7J85qqJqJ9KILe2tHnqeHIs2qfRYaHYffbm52dTUVFBaWlpUqS+8DdqaioIDs7\nO9WhyAi3trqJR99ay8NvrulxmrBEZrDPlFEcudtYDttlNLuOK9BnwTBXlJNJ0cQi9pjYfdXTaCzO\nuprmjoR5RT+OPrfG4izdFCwek0z76PPUhNINjT6LDA3DrsSira2N1atX09y8fVe3Ggmys7OZNGkS\nmZmZqQ5FRpi2WJy/L9rIPa+t5N+LN7Glj6lIRhqH7FTGUTPHcsRuY1N+Qp0MHe2jz50S6IpGVlX1\nffR5cyIZaUweldOp7rk9eZ5SkktuZNiNX4kMKM1i0SVBFpGha2VFI395fSX3z1vNprrNn5iVlZHG\nkbuN5fg9x3PojNH6d7b0u+05+rwlZflZTCnJ6XLiYB5TSnIZU5Cl0WeRLVANsogMabG48+zCDdz5\nygpeXFK+2W3TDA7aqYw5syZyzO5jKcjWfzdk+8lIT2NymJx+PEn/9q19bqG8voU3ktU+dxl9nhIm\nzkEinaPRZ5F+oN8iEUmJuuY27pu7mttfWhauoNaz3ScUctK+k/j0nuMZU6C6eBkctqb2eWWSBHpF\nRSM1TX2ofY7G+XBTAx/2WPvcffRZtc8ivaMEWUQG1KrKRm5/aTn3vr5qs4t45GdlcMKsCZy23xQ+\nNql7AiIymCWOPiedeaOxjVVVyZPnNdVN27ziI2xh9LmHeZ/br/NVqiQCKEEWkQHyzuoa/udfS3j6\n3fWbXXp41uRivrj/FD6953jVFcuwVZSbSVFuL0afKz663afR5y3MvFGa133e58nh6PO4wmzSNfos\nI4S+fURku5q7vJLr/7GE5z/Y1OM2kfQ05syawFcOms7MCYUDGJ3I4NOb0ecVFQmjz5UNrK1u7tPo\nc0VDKxUNrcxf1X30OTPdmDSqfcQ5h8mjOifQhTonQIYRJcgi0u/cnZc+rOD6fyzmlaWVPW5Xlh/h\n9AOn8qUDpmpqNpGt1OvR536aeaMt5iwrb2BZefLR5+LczM41zwmX8UXZZKSnbfNziww0Jcgi0m/c\nnec/2MTv/r446cpj7XYdV8BXD57OCXtNIDszfQAjFBnetjj63GXe58QEenUfZ96obmyjurGGt1fX\ndOtLTzMmFuf0mEAX5Wr0WQYXJcgi0i/mLq/kN397n9eW9zxiPHvqKM47fCc+MWO0VrcTSYHNzbwR\nizvrapo6jTivrPzofmVD6zY/byzuHcl4MqNyM5lamsf0sjymleYxrSw3vM6jKEfJsww8Jcgi0icL\n19ZyzTPv8/f3Nva4zcE7lXHe4TtxwPQSJcYig1R6WlBjPGlULuzYvb+uuY1VlV0T6I9Gn1tj8W1+\n7qrGNqoaq5PWPpfkRZhamsv00jymhsnz9LLgtpJn2V4GNEE2s8uAs4H2s3Uucfcnw76Lga8BMeAC\nd396IGMTkd5ZXt7Ab5/9gMfeXtvjUtBH7DqGcw/fiX2mjBrY4ESk3xVkZzJzQmbSE2ljcWdDbXO3\nmuf22+X12z76XNnQSmVDa9KyrZK8CNNKPxptnlaWR1l+hMLsTAqyMygIrzNV/yy9lIoR5Gvd/erE\nBjObCZwK7A5MAJ4zsxnuHktBfCKyGdWNrVz33GLufGVFj/WKn5gxmh8cs0vSf+OKyPCTnmZMKM5h\nQnEOB+5Q2q2/oSUazLxR0TmBXlHZyOrKbR99bk+ek835nCgnM52C7AzyszMoyGq/ziQ/O4P8rIww\nmc4gP2zr2Ka9PyuTvKx0nWg4ggyWEos5wF/cvQVYZmZLgP2Bl1Mbloi0a4vFueuVFVz73OIe52Hd\nd+ooLjpmFw5I8gUpIiNXXlYGu44rZNdxyUef19U0sby8kWUVDawob2B5RTBbxqo+JM+JmtpiNLXF\n2FjX0qf95GdlBHXcOZkU5350XZiTSXFOpFN74nb5WRkqLxtiUpEgn2dmZwJzge+5exUwEXglYZvV\nYVs3ZnYOcA7AlClTtnOoIuLu/Ov9TVzxxMIel7bddVwBPzhmFw7fdYy+BESkVxJrnw/euaxTXyzu\nrK1uYnlFA8srGlle3hBcKhpYWdlIW2zbZ93YFvUtUepboqypburV49LTrCNZLs2LUJIXoTQ/q+N2\nSV6E0rwsSvIilOVHGJUXUVlIipn3VDy4rTs0ew4Yl6TrxwRJcDngwC+A8e7+VTP7A/Cyu98Z7uNW\n4El3f2BzzzV79myfO3duv8YvIh9ZvKGOXzyxiH/3sMjHxOIcLvrULnxmzwmkaYUtERlAnZLn8iCB\nXlHRSG1TG7XNbdQ1R6ltbqO+JdrjeRKDWWF2BqX5WWHyHKE0P0iiRxdkMaYgizGFWYwpyGZ0QZam\ny0zCzOa5++xtfXy/jyC7+5Fbs52Z3QI8Ht5dDUxO6J4ErO3n0ERkKzW0RPn93xdz64vLktYZ50XS\nOffwnfjqQdP1wSwiKZGeZh1zPh+y8+get4vHnYbWKHXNURpaotQ2B6PA9c1R6luCRLq+Jeivb7/d\nEqU+TK472lsHNtGubQ5i7WlhlkSF2RmMKcwOEueCrI7bQTKdHSbTWSr16IWBnsVivLuvC+9+DlgQ\n3n4UuNvMfktwkt7OwGsDGZuIBOUUT7+7np8/tpB1Nc3d+s3glH0n871jZjCmIDsFEYqI9E5amoWz\nWfRtSrh43KlrjlLT1EZ1U2tw3dhGdVMbtU1tVDd+1FbT9NGlurGNprbtO+dAkEzXs2Rj/Wa3y42k\nM64wm/HF2YwrzGF8UTbjirKZkHC/ODdTSTQDX4P8GzObRVBisRz4BoC7v2tm9wELgShwrmawEBlY\nKyoa+Nmj7/Kv95OXUxy4Qwk/PX4mu0/QzBQiMvKkpVm4zHcmU8jt1WNbojFqmtqoamijoqGFivpg\n9o2KhlYqG1qobGilPGyrbGilqrF1u4xWN7bGWFrewNLNjEpnZ6YxvignSKSLwmS6KIfxhUEyPbE4\nZ0Qk0f1egzyQVIMs0nct0Rj/+/xS/vDPJbREu58tPr4om0uPn8mn9hg37D8QRUQGg1jcqW4MEuiK\njsS5hU31rWyqa2ZjbQsb61rYWNdMeX0rsT4sEb4t8iLpTByVw8TiHCaNymXiqBwmhfcnjsphdH5W\nyr8vBl0NsogMHfNWVPHDB95O+m+5jDTjawdP54IjdiYvSx8VIiIDJT3Nglku8rNg7Oa3jcWdyoZW\nNtY1s7GuhU21LR23NybermuhNckgyLZoaI3xwYZ6PtiQvKQjKyOtI1me1CWRnlicw9jCbNIH+Ynd\n+tYTGYEaWqJc/cz73P7S8qT/xtt/Wgm/+Owe7DKuYOCDExGRrZaeZowOT8jbfTPbuTs1TW2sr21m\nXXUz62qaWVfTxLqaZtbXNLO2pol11c39Ui/dEo1vtpQjI80YX5zNpOJcJpfkMCU82bL9ujQvkvIR\naCXIIiPMC4s3cfGD77C6qvs8nqV5ES45bjdO3Gdiyj+cRESk/5gZxbkRinMjSRdsgSCJrm2Odkqc\n11U3hcl0kESvrW6iua1vI9HRuLOqsolVlU28vLR7f24knSklwdzYU0pymVKSw5TSXCaPChLogZg9\nSQmyyAhR09jGFU8s5P55q5P2nzJ7EpcctxvFuZEBjkxERAYDM+tYAXBzSXRFQytrqppYU93E6qpG\n1lQ1sbrjfhP1LdE+xdHYGuO99XW8t74uaf+YgqyO0eaOkedRQRI9tiC7X+blV4IsMgL8872N/PCB\nt5MuszppVA5XnbhntxWsREREujIzyvKzKMvPYq/Jxd363Z3apiirqzsnzmuqmjraqhrb+hRDe031\n3BVV3foi6WlMKsnp0/5BCbLIsFbfEuXKJxZyz2uruvWZwVc+Pp3vHzOD3Ig+CkREpO/M2qfDK+px\nWtCGcLnuVZWNwaWqiZXh7ZWVjTS2bnsddGssztJNW15cZUv0rSgyTL26tILv//UtVlV2rzXeeUw+\nV31+T/adOioFkYmIyEiWl5XBjLEFzBjb/URw92BWjpVhsry6qomVFY0d99fVNDEQs9opQRYZZprb\nYlzzzPv88cVl3WaoSDP41mE7cf4RO5GVoSWiRURkcDH7aIq7vad0H8Rpi8VZW93UkTAHJ/uFt6sa\nqe5j+UY7Jcgiw8iCNTV85975LE4yr/EOZXlcc8peST9wREREhoLM9DSmluYxtTQvaX9NUxurKhv5\n2K/79jxKkEWGgWgszo3/+pDf/X0x0ST/ezrr49P44ad2JSeiUWMRERm+inIyKZqYvPa5N5Qgiwxx\nSzfV89373mL+qupufROLc/jvk/bk4ztphgoREZGtpQRZZIhyd/78ygp++eSipJO2n7TvJC79zEwK\nszNTEJ2IiMjQpQRZZAhaX9PMD/76Fi8sLu/WV5oX4Vcnfoyjdx+XgshERESGPiXIIkPMI/PX8NOH\nF1Db3H2loqNmjuVXJ36MsvysFEQmIiIyPChBFhkiqhpa+ekjC3j87XXd+vKzMvjZZ2Zy0r6TMOv7\nEpsiIiIjmRJkkSHg+Q828YP730q6VPSBO5Rw9cl7MWlUbgoiExERGX6UIIsMYs1tMa566j1uf2l5\nt75IRhoXHbMLXz1oOmlpGjUWERHpL0qQRQapzS36sfuEQq79wqyky3SKiIhI3yhBFhlkYnHnlheW\ncs0z79MW67zoR5rBuZ/cifMP35lIRlqKIhQRERnelCCLDCJrqpv47r3zeXVZZbe+ySU5XPeFWew7\ntSQFkYmIiIwcSpBFBomn3lnHDx94O+n0bSfvO4mfnbA7+Vn6lRUREdne9G0rkmLNbTGueGIhd76y\nsltfcW4mv/rcxzj2Y+NTEJmIiMjIpARZJIWWbKzjvLvf5L31dd36Dtm5jKtP3ouxhdkpiExERGTk\nUoIskgLuzr2vr+Kyx96luS3eqS8z3fjhp3bV9G0iIiIpogRZZIDVNbdxyUMLeOyttd36ppbmcv1p\ne7PnpOIURCYiIiKgBFlkQC1aV8u37nqDZeUN3frmzJrAFZ/dg4LszBREJiIiIu2UIIsMkPvnruIn\nDy+gJdq5pCInM53L5+zOSftOwkwlFSIiIqmmBFlkO2tui3HZo+/yl9dXdevbdVwBN3xxH3Yak5+C\nyERERCQZJcgi29GKigb+6843WLiutlvfaftP5mef2Z3szPQURCYiIiI9GfC1as3sfDN738zeNbPf\nJLRfbGZLwr5jBjoukf72twXrOf73L3ZLjrMz07j65L341Yl7KjkWEREZhAZ0BNnMPgnMAfZ09xYz\nGxO2zwROBXYHJgDPmdkMd48NZHwi/aEtFue/n36fm/+9tFvf9LI8bjx9H3YdV5iCyERERGRrDHSJ\nxX8BV7l7C4C7bwzb5wB/CduXmdkSYH/g5QGOT6RP1tc0c/49b/D68qpufcd9bBy//vyemqVCRERk\nkBvoEosZwCFm9qqZPW9m+4XtE4HEM5hWh20iQ8YrSys4/voXuiXHGWnGpcfP5A9f3EfJsYiIyBDQ\n7yPIZvYcMC5J14/D5xsFHAjsB9xnZjsAyea28h72fw5wDsCUKVP6I2SRPnF3bn9pOVc8sYhYvPPb\ndnxRNjd8cR/2nToqRdGJiIhIb/V7guzuR/bUZ2b/BTzo7g68ZmZxoIxgxHhywqaTgO7LjAX7vxm4\nGWD27NlJk2iRgdLcFuOSB9/hwTfXdOs7ZOcyrvvCLErzs1IQmYiIiGyrgS6xeBg4HMDMZgARoBx4\nFDjVzLLMbDqwM/DaAMcm0iurqxo56aaXkibHFxyxM7d/ZX8lxyIiIkPQQJ+kdxtwm5ktAFqBL4ej\nye+a2X3AQiAKnKsZLGQwe+nDcs67+00qG1o7tednZXDtF2Zx1MyxKYpMRERE+mpAE2R3bwVO76Hv\nSuDKgYxHpLfcnVtfXMavnnqvW73xjqPz+N8zZmtVPBERkSFOK+mJbKWm1hg/evBtHpnfvTz+qJlj\n+e0pe2mWChERkWFACbLIVlhV2cg3/jyv26p4ZvCdI2dw3id3Ii0t2WQsIiIiMtQoQRbZgpc/rOBb\nd82jqrGtU3tBVgbXnTqLI3ZTvbGIiMhwogRZZDPuenUFP3vkXaJd6o13HpPP/56xLzuMVr2xiIjI\ncKMEWSSJtlicXzy+kDteXtGt71O7j+PqU/YiP0u/PiIiIsORvuFFuqhubOXcu9/gP0squvV996gZ\nnH/4Tpip3lhERGS4UoIskmDJxjq+/qe5LK9o7NSek5nOtV/Yi0/tMT5FkYmIiMhAUYIsEvrn+xu5\n4O43qWuJdmqfWJzDzWfuy+4TilIUmYiIiAwkJcgy4rk7f3xhGb96ahFdzsVj9tRR3HTGvpRpyWgR\nEZERQwmyjGgt0Rg/fmgBf523ulvfyftO4orP7UFWRnoKIhMREZFUUYIsI9bGuma++ed5vLGyulN7\nmsElx+3G1w6erpPxRERERiAlyDIiLVhTwzl3zGVtTXOn9oLsDK4/bW8O22VMiiITERGRVFOCLCPO\nk++s43v3vUVTW6xT+/SyPP745dnsqMU/RERERjQlyDJixOPO7/+xmOueW9yt7+CdyvjDF/ehKDcz\nBZGJiIjIYKIEWUaExtYo37//LZ58Z323vrM+Po2ffHo3MtLTUhCZiIiIDDZKkGXYW1vdxNl3zOXd\ntbWd2jPSjF98dg9O239KiiITERGRwUgJsgxr81ZU8Y0/z6O8vqVTe0lehBu/tA8H7FCaoshERERk\nsFKCLMPWX+et5pIH36E1Fu/UvsvYAv745dlMLslNUWQiIiIymClBlmEnFnd+/bf3uPnfS7v1Hbnb\nWK47dRb5WXrri4iISHLKEmRYqW1u49v3vMk/39/Ure9bh+3I94/ehbQ0Lf4hIiIiPVOCLMPG8vIG\nvn7HXJZsrO/UnpWRxm9O2pM5syamKDIREREZSpQgy7Dw0pJy/uuuN6hpauvUPqYgi1vOnM1ek4tT\nFJmIiIgMNUqQZcj788vLueyxhcTi3ql9r0lF3HzmbMYWZqcmMBERERmSlCDLkNUWi3PZo+9y16sr\nu/WdsNcEfnPSnmRnpqcgMhERERnKlCDLkFRR38K37nqDV5dVduv7wTG78K3DdsRMJ+OJiIhI7ylB\nliFn0bpazr5jLqurmjq150bSue4Lszh693EpikxERESGAyXIMqT8bcE6vnvfWzS2xjq1TxqVwy1n\nzma38YUpikxERESGCyXIMiTE4871/1jCtc990K3vgOkl3Hj6vpTkRVIQmYiIiAw3SpBl0GtsjfK9\n+97iqQXru/WdfuAUfvaZ3clMT0tBZCIiIjIcKUGWQW11VSNn3zGPRetqO7VnpBmXnbA7px84NUWR\niYiIyHA1oAmymd0L7BLeLQaq3X1W2Hcx8DUgBlzg7k8PZGwy+Ly2rJJv3jmPyobWTu0leRH+50v7\ncOAOpSmKTERERIazAU2Q3f0L7bfN7BqgJrw9EzgV2B2YADxnZjPcPZZ0RzLs3f3qSi59ZAHRLot/\n7DqugFvOnM3kktwURSYiIiLDXUpKLCyYoPYU4PCwaQ7wF3dvAZaZ2RJgf+DlVMQnqdMWi/OLxxdy\nx8sruvV9avdxXHPKXuRlqTJIREREtp9UZRqHABvcfXF4fyLwSkL/6rCtGzM7BzgHYMqUKdszRhlg\nlQ2tnHvXG7y8tKJb34VH7swFh+9MWpoW/xAREZHtq98TZDN7Dki2UsOP3f2R8PZpwD2JD0uyvSdp\nw91vBm4GmD17dtJtZOhZuLaWb9w5l1WVnRf/yMlM57en7MWxHxufoshERERkpOn3BNndj9xcv5ll\nACcC+yY0rwYmJ9yfBKzt79hkcHpk/hp++MDbNLfFO7VPLA4W/5g5QYt/iIiIyMBJRYnFkcB77r46\noe1R4G4z+y3BSXo7A6+lIDYZQNFYnF899R63vrisW9/+00u48Uv7UJqflYLIREREZCRLRYJ8Kp3L\nK3D3d83sPmAhEAXO1QwWw1t5fQvn3f0Gryyt7Nb3pQOCxT8iGVr8Q0RERAbegCfI7n5WD+1XAlcO\nbDSSCm+tquabd85jXU1zp/ZIRhpXzNmDU/ab3MMjRURERLY/zZclA+q+11fxk0cW0BrtXG88oSib\nG0/fl70mF6coMhEREZGAEmQZEK3ROD9/7F3uenVlt74Ddyjhhi/uQ5nqjUVERGQQUIIs292G2mb+\n6855vLGyulvf1w+ezo+O3ZWMdNUbi4iIyOCgBFm2q5c+LOeCe+ZTXt/SqT07M41ff35P5sxKuh6M\niIiISMooQZbtIh53/vDPJVz73AfEuyznMrkkh/89XfMbi4iIyOCkBFn6XUV9C9+57y1tnL2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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x7fb249e819b0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(10, 7))\n", + "\n", + "from scipy.stats import norm\n", + "\n", + "x = np.array([20, 60,160])\n", + "y = np.array([80, 55, 50])\n", + "\n", + "xlist = list(x)\n", + "ylist = list(y)\n", + "\n", + "num_of_eval = 1\n", + "\n", + "# hyperparameter of the u method \n", + "kappa = 0.1\n", + "\n", + "acquisition_function_flag = 3\n", + "\n", + "for column_id, index in enumerate(range(2,num_of_eval+2)):\n", + " _, idx = np.unique(x, return_index=True)\n", + " _, idy = np.unique(y, return_index=True)\n", + " x=x[np.sort(idx)]\n", + " y=y[np.sort(idy)]\n", + " \n", + " # generate a surrogate gp model\n", + " xi, yi, x_predict, y_predict, y_std = gp(x, y, index)\n", + " \n", + " # get the best emphirical risk value \n", + " y_min = min(yi)\n", + " \n", + " z = (y_min-y_predict)/y_std\n", + " \n", + " # compute an aquisition \n", + " if acquisition_function_flag==1:\n", + " acquisition_function = norm.cdf(z)\n", + " \n", + " elif acquisition_function_flag==2:\n", + " acquisition_function = (y_min-y_predict) * norm.cdf(z) + y_std * norm.pdf(z)\n", + " \n", + " elif acquisition_function_flag==3: \n", + " acquisition_function = -y_predict + kappa * y_std\n", + " \n", + " max_index = acquisition_function.argmax()\n", + " \n", + " # evaluate emphirical risk at new position \n", + " max_fkt = fkt(max_index)\n", + " \n", + " # add new position and emphirical risk value to the set of evaluated values \n", + " xlist.append(max_index)\n", + " ylist.append(max_fkt)\n", + " x=np.array(xlist)\n", + " y=np.array(ylist)\n", + " \n", + " \n", + " \n", + "# plotting settings \n", + "ax1 = plt.subplot2grid((2, 1), (0, 0))\n", + "ax2 = plt.subplot2grid((2, 1), (1, 0), sharex=ax1)\n", + "\n", + "ax1 = plot_gp_example1(xi, yi, x_predict, y_predict, y_std, index, ax1)\n", + "ax1.set_title(\"Gaussian Process regression\\nafter {} iterations\".format(index)) \n", + " \n", + "ax2.set_title(\"Acquisition_function\\nafter {} iterations\".format(index))\n", + "ax2.set_xlabel('Hyperparameter')\n", + "ax2.plot(x_predict, acquisition_function, lw=4) \n", + "\n", + "ax2.scatter(x_predict[max_index], acquisition_function[max_index],\n", + " marker='*', s=120, color='k',\n", + " label='Next step', zorder=10)\n", + "\n", + "ax2.legend(loc='upper left')\n", + "\n", + " \n", + " \n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1347, 64)\n", + "10\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m<ipython-input-7-3798909daaf3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m \u001b[0mnested_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 50\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnested_score\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.6/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)\u001b[0m\n\u001b[1;32m 319\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0mfit_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 321\u001b[0;31m pre_dispatch=pre_dispatch)\n\u001b[0m\u001b[1;32m 322\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcv_results\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test_score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.6/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score)\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0mfit_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_train_score\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m return_times=True)\n\u001b[0;32m--> 195\u001b[0;31m for train, test in cv.split(X, y, groups))\n\u001b[0m\u001b[1;32m 196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 777\u001b[0m \u001b[0;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 778\u001b[0m \u001b[0;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 779\u001b[0;31m \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 780\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 781\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m 623\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 624\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 625\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 626\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m 586\u001b[0m \u001b[0mdispatch_timestamp\u001b[0m \u001b[0;34m=\u001b[0m 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options, run_metadata)\u001b[0m\n\u001b[1;32m 1295\u001b[0m run_metadata):\n\u001b[1;32m 1296\u001b[0m \u001b[0;31m# Ensure any changes to the graph are reflected in the runtime.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1297\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1298\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_exception_on_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1299\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_with_new_api\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_extend_graph\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1356\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_exception_on_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1357\u001b[0m tf_session.TF_ExtendGraph(\n\u001b[0;32m-> 1358\u001b[0;31m self._session, graph_def.SerializeToString(), status)\n\u001b[0m\u001b[1;32m 1359\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_opened\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "from skopt import BayesSearchCV\n", + "from sklearn.datasets import load_digits\n", + "from sklearn.svm import SVC\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn import linear_model\n", + "from sklearn.model_selection import cross_val_score\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, Dropout\n", + "from keras.optimizers import RMSprop \n", + "from keras.wrappers.scikit_learn import KerasClassifier\n", + "\n", + "X, y = load_digits(10, True)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, random_state=0)\n", + "\n", + "num_classes = 10\n", + "\n", + "# Function to create model, required for KerasClassifier\n", + "def create_model(C=64):\n", + "\n", + " model = Sequential()\n", + " model.add(Dense(C, activation='relu', input_shape=(64,)))\n", + " model.add(Dropout(0.2))\n", + " model.add(Dense(num_classes, activation='softmax'))\n", + "\n", + " model.compile(loss='categorical_crossentropy',\n", + " optimizer=RMSprop(), metrics=['accuracy'])\n", + "\n", + " return model\n", + "\n", + "estimator = KerasClassifier(build_fn=create_model, epochs=2, batch_size=10, verbose=0)#\n", + "\n", + "opt = BayesSearchCV(\n", + " estimator,\n", + " {\n", + " 'C': (32,64)\n", + " },\n", + " n_iter=10\n", + ")\n", + "\n", + "\n", + "opt.fit(X_train, y_train)\n", + "#print(opt.total_iterations)\n", + "\n", + "\n", + "nested_score = cross_val_score(opt, X=X_train, y=y_train)\n", + "#print(nested_score.mean()) \n", + "\n", + "print(\"val. score: %s\" % opt.best_score_)\n", + "print(\"test score: %s\" % opt.score(X_test, y_test))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/hyperopt/.ipynb_checkpoints/bayes_optimize-checkpoint.ipynb b/hyperopt/.ipynb_checkpoints/bayes_optimize-checkpoint.ipynb index 280c710091d67860bac463b7ba345b060f2272ce..bdca7fd17b57b620717c7af1f9bf6bc029e216a0 100644 --- a/hyperopt/.ipynb_checkpoints/bayes_optimize-checkpoint.ipynb +++ b/hyperopt/.ipynb_checkpoints/bayes_optimize-checkpoint.ipynb @@ -1,14 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": 1, @@ -29,23 +20,25 @@ "import os\n", "import matplotlib.pyplot as plt\n", "import load_data as ld\n", + "from scipy.stats import norm\n", + "from sklearn.gaussian_process.kernels import RBF as skRBF\n", + "from sklearn.gaussian_process import GaussianProcess, GaussianProcessRegressor\n", + "from sklearn.gaussian_process import correlation_models as correlation\n", "\n", - "%matplotlib inline\n", - "\n", - "#plt.style.use('ggplot')\n", "warnings.filterwarnings(\"ignore\")\n", "\n", - "colors = plt.rcParams['axes.color_cycle']" + "%matplotlib inline\n", + "colors = plt.rcParams['axes.prop_cycle']" ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Gaussian process is a stochastic process such that every finite subset of size $n$ follows a multivariate Gaussian distribution $p(\\mu, \\Sigma)$ and thus defines a probability distribution over functions $x \\mapsto f(x)$. As a multivariate distribution is uniquely defined by its mean vector $\\mu \\in \\mathbb{R}^n$ and covariance matrix $\\Sigma\n", + "\\in \\mathbb{R}^{n \\times n}$, a Gaussian distribution is uniquely defined by its mean function $x \\mapsto m(x)$ and covariance kernel $x \\mapsto K(x)$. We can make use of the GP definition to sample finite subset from a GP. In a first step, we sample the mean function $m(x)$ and covariance kernel $K(x)$ at finite number of input values and build a corresponding mean vector $\\mu$ and corresponding covariance matrix $\\Sigma$. In a second step we apply a Cholesky decomposition to the covariance matrix $\\Sigma=L L^T$ and sample from the multivariate Gaussian distribution according to \n", + "$y = L z + \\mu$ where $z$ denotes a sample from a zero mean Gaussian distribution with unit variance." + ] }, { "cell_type": "code", @@ -55,7 +48,7 @@ { "data": { "text/plain": [ - "<matplotlib.text.Text at 0x7fb286723080>" + "<matplotlib.text.Text at 0x7fbc8700d6d8>" ] }, "execution_count": 2, @@ -64,9 +57,9 @@ }, { "data": { - "image/png": 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8ylggSFWaVkcVR0kuXUOjmtw4s5VTfacYHB1MqK9PoWtqD/Ytji0oKLze+nri\nx9tWQOmCzMgxrk7ILpTadrqolkctOns4sM+NfUzlaukeCqQ2lOf7B79Pj7uHh695mCxDVviqqmUo\n83KMOud0MtlZRgqyTXGlmP1d+1lYvJCi7KKknvOiCOw1pXkIIXBYHTS7msNVeEsrCyjINrGrfno3\nULW0E4jEnmen0z17Mvb2QW/MoDynKIeqopy4fvbWNPuwT0bt8qj2r7kUUfX1jfaNyT2w8ygYTFC+\nOHzTkpIlVOZV8mrLq4kfr8oxjTthuCe5557McGdm9HUYL1LS4ozpb4TcUjkGMBaVq6Vjp+dU0ks5\n0nOEX536Fe9b8j5Wl0u9vqagBsj8BqovEKS5zz3FEaNSUZAd08vuC/o42H2QKyqS09fhIgjsTb0j\nYaujw+rA4/fQ65WXnEaD4Mq60mnvG6OlnUAk9tzZVaTUPuCZUJw0mY3zSuIOuE53ctJk1LLqlktY\nZ9/dsZu6wjpNTqsJdB6D8iXSIhhCCMENjhvY1b4Lj1/Da7b8bjlS7+TzSa56EulMTpqM2lZAa2CP\nJcOoVK6Rn5PU2X1BHw+//TAVuRV8Zt1nwreX55STbczOeGBv7ffgDyrMK4vuaIk3+/RU7yk8fg/r\n7cnp6zDDgT0YVGjp94SLk2qs8l0zcnf66rpSmvvc4Q3A6aDH00OWIYtCS6Gm4215NgZGB7T9k80w\niqLQPjC1OCmSDbUlOIfHaOydqnt3DHp4cm8zQqRQdXrmj9C8e8rNasZ+qersvoCPA90HkpdhIGYr\ngS2OLXgD3ugNwyZTsQzKFqUvx7gymLFnF4LZqs0ZoyWwl9TJ6VJJBvafH/85Z/vP8tCVD5GXNZ5F\nCyFwFDgyLsU0OIcBYmfscfrFqPp6so4YmOHA3hnqGVKjBnb1cihCZ1f97NNpe1Stjlo3ucKWx1mg\nsw95/LjHAnGD8sZ50s8eOeA6EFT42ZsNvPPfd7CvsZ9/um0Z1uysWKeYSjAIz34M/vjQlLsqrNlk\nGS9dL/vhnsN4/J7kA/uIU25YRmklsN6+HmuWlddaNMw3VeWYpjfHnS3Jos46zcTGqbomLV72gF8e\nkyiwGwzS3plEYG8eauZHh3/ETXNvYkvNlin311hrMp6xn4/hYVcpDwX2aFfL+7v2U1tQq6lwcjIz\nGtjVro5zS+QPPSdvDkZhnPDiLrZZKc7Nmt7AHmonoBXV8pgJL3vv8ChHWqdprBmRPV5iZ+zzy/Mp\nzs0KT1QE99F4AAAgAElEQVQ62jrIXY+9yT9vPcH62hK2/e31PLgpxtzNWHQelj0+Og5PaddqNAgq\nrNl0XaKzT/d07sEgDGywb0jugXGGV2cZsthcvZnXW18f7ykTj6W3Szmm/pXk1qDi7pOTmTIV2CHk\nZU8Q2IdapasnUWCHkDPmKGh5PYBv7/s2WYYsvrjxi1Hvr7HW0OJq0fb6aqTBOUJRbhbFMaaOVViz\nGfMHGfJOtFkGggEOdB1IKVuHGQ7szaE+7KoUk2XMojKvcsLlkMEguHJe6bT62bUWJ6moGXs6Orui\nKGw93M47v7OD9/zwLYa8qe3uJ0JttlUZpxWAEIL1tbJm4OHnj3PnY2/QOeTlBx9Yy+MPbghfUSVF\nfWijLzAGHVNtaZWF2ZfsUOvdHbtZVrIsYUO5KXSFhmvYow/XeIfjHfR5+zjcoyFLrVgGppzxN4tk\nyaTVUaXQkTiw9zfJz1oDu88Nvec0Pf2pvlNscWyJue/hKHDgC/rodndrOp8WGqL0iIlELVLqmaSz\nnx04i8vnmp2BvanXjckgxoPOUAc11uopl0NXzy+lbcAzbf1Fks3Y020r4Bwe5a9/eYBP/eogZpMB\nf1DhbJcrpXMlol3jAOqNtSW09nv4+duNfPCqubzy2eu5bdWc5DzYkdS/Nt78qXXvlLtthdl0XaA+\nQBcSt8/NkZ4jqenrncekpp0X/dJ7c9VmTAaTNjnGYJRDOlIN7OrkpExp7CAzdk8/jA7HPiaRhz2S\ncAWqNjlmaGwo7j6auseXSTkmUWAPe9knOWP2d+0HSP6qL8TMBvY+N9XFOZgEsPPf4ZHlOFx9NA81\nT9CcVD/7dGTtXr8X15grqYzdYrRQkl2StOVRzdJv+s4OXjnZzRduXcKvP3o1AKc74/yxp0H7oJcs\no6As3xL3uGuXWFi24hV+/bH1fOXOFRQko6dPZnQYmnfByvdCQTW07plyiL1AZuyxnDizlb2de/Er\nfjZWJmlzhIQ92PPN+Wy0b+S1lte0vW62FfKcqbzG6qzTTLliQA4DgfgbqP2N0u6pJgXxKFsEpmxN\ngd0f9DPiG4l7FZXpwO4e89Mx6I2pr0Psodb7OvdRlV8VVgeSJSOBXQjxUyFEtxDiWDKPa+51s7TQ\nB/97L7zyFUDB4XHh8rkYHB0MH7ewIp98i4nD06BFJ+thV7Hl2pLK2COz9JrSPF789GY+fsN8akpy\nyTMbOTONGbu9MDvhAOrXO1+kJbANQ3YGXAFNb0p9dv47wLEBWvdNOaSyMBuPLzBFW5ztvNjwIoWW\nwqRLwPGPQc/phD3Ytzi20DTUpLF3zEqZISczuUhlWqQY1cueILAXOuQVRyKMJvnmpSGwu8bk/1eB\nJXZgt+XZMBvMGXPGNDqlwhDL6giR/WLGr14VRWF/1/6UZRjIXMb+OHBrsg8q6j3AN3o+AQ07ZL/l\nBTdR45bBO/Jd02AQrKwq5EjrYKxTpUyyHnaVZAZutPS5ufmR18NZ+m//6moW2mTxhcEgWGCzTltg\n7xjwMieOh11lR8sOgMwUXtW/KvVdx1VQvUHqqkMTS7VtBTJTme52ERcS15iLV5tf5dbaWzEbo2+W\nxaTnlHwzjKGvq9zguAFAW7GSeq7OpPItiatL9mPJRNWpStjLHicj1mJ1jKRytQzsCXrQDI3Jlhnx\nMnaDMFAdRQpOlQbnxDmn0bBaTGRnGSZIMecHz9M/2p98chBBRgK7oiivA0mVh3q2P8JPlYcRRjN8\neJvst1xSR02/DACTX9xVjkJOdgwx6s9sGXqqGXsybQVeO91N38gYv/34NXz8hvmYjBNf9sW2/GkL\n7G0JPOwATo+To06pxWak8Kr+VajdBFnZUB2SJCbp7Oq+SuclpLNva9rGaGCUO+bfkfyD1Y1TW/zA\nbs+zs6x0mTad3RYapdaVgs6eiVmnk7HapcySKGNPKrCvgtEhGGiMe9jQaOLADpm1PKoe9tqy2OYD\nIaRDLFKKUfX1GQ/sWhBCfFQIsU8IsW+s8xQ52x9mW/AK9t76O5gTqiIrqaPaO4RATGmhuaa6CF9A\n4WRHZgNgqhm7LdeGy+dixDeS8NiTHS6KcrNYURX9j2qRzYpzeCzjw7sDQYXOIe+4hz0YhLcfm9L5\nb2frThQUBCJ9b/5ACzjPSBkG5D+e0TxFZx/P2C8dL/vz9c9TW1DLyrL4wTkqncfkVU7p/ISHbnFs\n4WjPUZyeBD2ULFYZJFPZQM2kh13FYISCObG97N5BaZFNNmOHhHJMOGOPI8UA4SKlTOz9nHeOUFmY\nTa45/hDqydWn+zr3UZFTQbW1OuXnvmCBXVGUHyuKsl5RlPVmxcvRVQ/x177PUGWP2JwpmYdFAZul\naIrOtcohm+Ac1jDtJxl6PD2YDCaKLMk12UmmSOl05xCLbdaYDpPFdinLZDpr73HJsVvhdgI9J+EP\n/yA/Itjesh17np0FxQvSl2LOhzJJNbCbLPKfb5LOPh7YM/tmNlO0ulrZ37Wf2+ffnpqTqL9RVlNq\n0JbVpmDbW7YnPq99ZWpSzHBXZh0xKmr73mgkY3VUqVgmrwKiWGoj0SLFgMzYvQFv+Eo+HRpCXR0T\nUR5RfRrW1+1XpO5IY6ZcMaWLeL34PYCgJnIkXkkdADUm65TLoTmF2ZTlWzK+gdrj7qEipyLpF1Gr\nlz0YVDjd6WJpZew/qEUhvf1sV2adMVP6sKsbYkd+DW0HABgNjPJ2x9tcX3099twMdK2sf1UGhPIl\n47dVb4T2g3KDMITZZKAs30zn0KWRsb9w/gUAbqu7LbUTjPRAvra+MouKF1GVX6VRjlkJfedhLPGV\nZZhgMLN9YiKJ52VPxuqoYrJAxdLEGXtIiknUNiTsjBlKX45pcI5MGa4RjQqrhZ5QYG9xtdDt6U5L\nhoGZCuzmXJp6Ryi3WiZephQ6QBhwYJoixQghWF1dmPGMvdvTTVlu8iW74cCeIMNtG/AwMhYIZ+XR\nqLBaKMzJ4nSGM3a1OCmssavl5aYc+OM/gqKwu2M3Hr+HGxw3JLUhHJVgQM7cnP8OWUKuUr0e/N5x\nHTmEvTD7ktg8VRSFrfVb2WDfwJz8OamdZKQH8rTJgZFNwdy+BLUd9hWAktwYOY9adToNGXuRA4ba\nZeuAyaQS2GF8AzWOfKI1Y3cUhNr3pjlNqX9kjAG3L67VUaWiIBuX14/XF8iIvg6Zszv+CngbWCyE\naBVCfDjRY5p6pw6wxmSGQgc1Y2P0efvCFiWVVdVFnHeO4MpglabT7aQiJ8kOfEBFbgUCkTAQqgMs\nlsQJ7EIIFtusnOnMbGBvnzwSTy06ecdD0PQGnP49O1p2kGPKYYN9A7ZcG/2j/Xj9KQbbjsPSXqfK\nMCqO6Buoqpd9tnO45zDNrubUNk1VRpyaAztIOWYsOMZb7W/FPzDsjEliKMV0WB1VCqtlywBXlIEW\n/Y3SiZOTnCxK5Ro5wGOoPeYhQ2NDZBuzE7qVKvMqMQlT2huoDb2JHTEqkUVK+7r2UZJdwrzCJFt4\nTCJTrpj3K4pSqShKlqIo1Yqi/Heix8g+7FF2i0vm4XBLW+Pkd83VjkIUBY62Zc722O3pTqnJTpYh\ni7KcsoSB/XQoWKtySywW2fM53eXKaMFO+4AXq8U0Xmzk6gJLAVz5cShbhLLtH9nRuoNNczZhMVrC\nVyEpl1SrbQTqbph4e0GVzP5aJm6g2i+R6tOt9VvJNmZz09ybUjuBzyOHM8eoOI3GOts6rGYNTcEK\nHbKzYlcSOnsmR+JFWw9El2OiDbDWgoYN1MHRQU0tHkwGE1XWqrSlGHWAtZbAHjnU+nDPYdZWrE1L\nX4cZkmIURdrc1OZfEyipo2YghuWxWt1AzUxgV6tOk+6ZHUKLdHGq08Xc0lzyLPF3xhfZrLi8frri\nzD9MlvYBz8QB1sMh3dRogpu+yilXM13uLq53XA9koAdO/Wuy497kACWE9LNHydj73b5ZPUlpLDDG\nS40vcePcGye0gU0K1aWURMaeZcjiuurr2NG6I/6cTiFkEU8yG6jTmrGrgT3KBmp/Y/ypSbGwLQdh\niBvYh8aGEjpiVBxWR9pSTFPvCAYxPnsgHmr1advgEC2uFhYVL0rruWGGAvtYIIiijDf/mkDxPBwj\n/cDUMVUleWZqSnIz1g1R3flOKmOPyKi1TFI6FXLEJELN6DOps7cPTvKwD3eP/7MuuoXtVUsRisK1\nZdJuqnXfICqjLmjZPVWGUaneIDOy4fGrAXvIrTObs/YdrTtwjbm4oy4dGSbkwEgisIOUYwZHBznY\nfTD+gfaVcoyclkHSENFOYJqkGICBSRlxMCBvSyVjN+fJ9gKJArvGpmyqlz2dq+eOQS/lVgtZxsQh\nVq0+Pd17nqASpK6oLuXnVZmZwB4qMoouxdSRqyiUmQujvmuuyuAGquoD1pyx9zXAd1fCL++B/qZw\nW4FYfwBeX4AG5whL4jhiVNTAnkmdvWPAO3FyUqTTQQi2FxSzanSM0r0/BaQ3H1LM2Bsj2ghEI4rO\nbg9ZHmezzv58/fOU55Sn1vRLJYWMHcabgr3R9kb8A20rwDcC/RraEIC8sssukgVmmcacK8feTc7Y\nXR2yE2gqgR3GN1BjMDSaRGAvqGHEN0KfN/WRnJ1D3nDikoiSXDMmg6BhsB6ABYULUn5elRkJ7KN+\nmTlM2TyFcctjVmHUDYw1jiLaB71xJ3trRdWSNWXsI074xZ/KKrfGN+GHV2Hvqcfj9+DyRQ/G57qH\nCSrxN05VSvLMlFstGfOye30BekfGxmedKor0JocCe7e7mxNDDdxQuAh2/Qj6m8g2ZVNkKUrN8qi2\nEai5Kvr9laul3zhCZ7cXykxltmbsfd4+3mh9g9vqbsOopbdJLMIZe3J7PXlZeSwtWcqh7kPxD1T7\nz2gtVMrk5KRoRLM8puqIUalcDa72CVeEkSQrxUB6zpjOQS/2gviN91QMBtmkr93dhFEYmVuQghw1\n+ZxpnyEFxvxB8i0mSqI1nw/9Yh0iK2ozHlVnP5IBnV1zxj42IhuVDbXBB56CT+yGeddhO7EVgM7G\n6NPjVUdMPKtjJIsy2FpAzYLDUsyoS/autsrAvqNV9oa5ftP/k/rkK18B0hjUXf8q1G6eMKtzAlk5\nUn+PKFRSM5qLMWMPBhWu/darPLGrKeYxLzW8hF/xc/v829N7sgSB/WBzP43O6D701eWrOdF7Al8w\njlOsfCkIYxKBfRraCURSWD01Y89EYIeYhUrJSjGQXpfHziFv+IpUC+VWC72+FmoKasgyptFZNcSM\nBfaaktzoO7/mXLBWUuPz0+3pnuLTXVFVgEGQEZ29292duOo04IenHpQFNu/9KdRcKb24738S+7Vy\nEkvn7z4ifeFjE9d6utOFxWTQVH0GUo450zVMMJi+MyZsdVQvB4dDWXhIN93RsoOq/CoWVF8D13wS\njj0NrftTG9Q90Ay9Z2PLMCqOjdB+IOxhzreYyLeYLkove5fLS0ufh11xJndtrd/K0pKlLCxemN6T\njfRAVq7Uiifx+6MdvPdHb/OpX0XX0VeXr8Yb8HKm70zs82dlSw1aqzPG1TW9gb2oRrYViJQw+xtl\ngqFuriaLauvsmHr1oqVlbyRV+VVykluKzpiRUT8ur1+zFAPSGTMSbGVBUfoyDMzg5mnUjVOV4nnU\neGS22zo88Z0912xikc3KoQx0enR6nPFnnSoKvPAZOPsH2X1yyZ+M3ycE9pV/BkDnvGvgre/BD6+C\nN78HvVIrO9XpYpHNijFBy1yVxTYrHl8gI7NApwzYUDfErDY8fg+7OnZxg+MG+bNv+gzkVcAfH8KW\nl1w7YkC6YSBxYK/eIK8auo+Hb7pYi5TUsY1nu6NfQdUP1HO893j62TqEPOxTs/VtJ7r49K8Okms2\ncrRtkHNR1rK6XGaqh3o0yDFanDHBoNTYpztj943ImgeV/kZ5e6rZanYhFM+LqrNradkbiTrJLdWM\nXW1sp0qNWii1GvAbeqgrTH/jFGYyY48X2EvqcAxKrSyaHLO6uogjrQNpe7673d3xm3+99jU4+Au4\n7u9h/V9Mubs8pxyjMNI5/3p44EXIKYZt/wjfXwc/vJob2n7MjYXtmgcdLMpgz5j2AfnHZVP/uCIy\n9l3tuxgNjHJ9tbQ5YrHC9Z+H5rexBwVDY0OJKxojqX8VrHOgfHH846pD02AidfaC7Iuyw2NTqMCk\nwTmCLzDVTbK1fitGYeRd896V/pNFqTrdfrqbT/zyAMvnFPD8JzdjNAieOTC1r7o9z05FbkXicXm2\nFXKeqDvBhqC7F4L+6dfYYaLOnmxXx2jE2EDVWnUaSU1BTcp92dVZvrYkpBhzTi8IhdqC9AqTVGbG\nxw7RPewqJbVUD8msMdq75ipHIQNuH81pjspzepyx2/Xu+ym8/i1Y+0HY8lDUQ4wGI+W55XKode1m\n+NgO+MwRuPUb+MxFPBj8LX9z/iPwyAr4w0MTeqVEY2GFbMifCctj+4CHcqsFiym0qacGdquNHa07\nyM/Kn1i2HHKt2EKOJc2DumO1EYhGUY28Mpigs1+cGXtjKGP3BZRwkFcJBANsPb+VTVWbUipum8Kk\nwP7mOScffWI/C235/M9fXMm8sjyuXVjGc4fap8h0QghWl6/mSE+CylJVqkgkx6jVydPRJ0Yl2sCN\nTAX2gaaJVwJo7xMTicPqSD9jTyKwB03ydS/NSn/jFGZwNF5NPON+SR2FQYWirKnNwEBm7ACH05Rj\nut0xqk5PvQgvfhYW3gy3fTduwJqiSRfPhas+zp4bnmD96H9w9upvQtlCePsH0LIr7nqs2VlUFeVk\nJmMf9DAncoC1qxOMFoKWAna07uCaOddM3KQJZVF2nyyQ0izHtB8C7wDM35L4WCHkG0jrxIy9Z1h2\nobyYaO51hyW0yc3Z9nTuodvdze11GZBhYIIUs/t8Lx/++V7qyvJ44sNXUpgrf0d3r62ibcDD7oap\nGffq8tW0DbeFW1BHRevQjbBkN40ZuzoiT23fOzYi39yK0gxq6gbqpE3ilDJ2aw1DY0MTJrlpRTUD\n2OMMkJ+Ml3YURWAmtWLJycxYYE+ksQPUmIuiWo4W261YTIa0/Oxev5ehsaGpjpgxNzz/aenguOfx\nhJpfrOrTkx1D9FNA8aYH4a7/kDd2n0q4rkW2/HAbgnRonzxgI2R1PNF3EqfHGZ7EEyanGMz52D3y\nuTUH9vpXAQF1GgI7yIZgfedhRG5K2guzCQSVjPeiT5fG3hGumFsMwNnuiYF9a/1WrFnWqa9hKihK\nOGPf39TPXzy+l6qiHH7xl1dOcI3dvMxOvsXEMwemVmyqOntcOSa/Ql4tJXLGTGfVqUpuqbTGqlJM\nKu16oxGjtYAanJOVYiC1Lo9dQ14Ksk0J+7BH0u9vRRkrpX84MwnOjAR2wfgEnaiUyMDuENHnD2YZ\nDSyfU5CWM0a1Ok7R2A8+IRsK3fr1qC6Fydjz7HS5u6bo/ac7XZTlW+QQaatdbu70nEx4vkV2K+d7\nRvBH0XW1oigKHYNRipOsNra3bMcgDFxbde3EBwkBhQ5sLpn1abY8tu2X2npeqbbjJ01UuhiLlBRF\noanXzbLKAqqLcyYEdrfPzcvNL3Nz7c1kmzJQwOMdhKCPDr+VB366h3Krhf/9yFVTho/nmI28a4Wd\nl4514hmb2IJhWekysgxZiXV2+8rE05S6jkt3ynRKMUKELI9qYG+Un4vT1JfzyuTw9EmBXeuQjUjS\nsTx2DnqTytYBujxNBEZtU4Zap8qMBPYso2HKeLgJ5BRDTjE1/iAdIx2MBaZq06uqizjWNpRyAIw6\nEi/gg7e+L2d1zr1G03lsuTZGA6MMjE58kznV6RovTBJCeom1ZOwVVsYCwbDGmwqDHh/uscD45CQI\nZ+w7WnewpnwNRdlRLJ5FDsyDrZRml2ovUhpoCheVaWLOGumpDskx6j/AxaSz942MMTzqZ25pLgsr\n8jkbIY293PwyHr+HOxfcmZknC1Wd/u7MKDlmI//7katibrrdva6K4VE/205O/N2YjWaWli7VENhX\nyIHZgRie96F22P84rLxneqpOI4n0sqfrYY+kai2ce2XCjN1UpJgqaxUCkVJg70qi6hTAF/DRPtJC\ncKwiI4WXMEOB3WzS8LQldTi8wygoUyyPIDs9enyBKZfJWok6Eu/oUzKLuPbvNJ8nWuOsQFDhTJdr\nYsVpxRKZsSdwyGRimpLqiFGtjm6fm5d8Tj6tdHCq71RsCSFUEWjLs2nL2BVF/lMmo42a82SAUTP2\ncGC/eAZuqG+qc0tzWWSbeAX1/Lnnqc6vZk35msw8Wag4aXePkdtXz4k7n/aqeaXMKczm2RhyzHHn\ncXwRQft0p2viZqttpSzbd8bwvG//htwM3/IPUe/+5p5vcv9L92emA2mRY1xj728EsxVyS9I/75aH\nwD8KT90fNisMjWpr2RuJ2u00FWdMRxJVpwCNQ40ElAA5ypzZnbGrbSrjUlJHzZD8o49leYTUC5Wm\nZOzBILzxXWkLW3iz5vNEC+yNvSOM+oMTK07Ll8rd+hglzyoLKvIRgrR09vYBDwg/7b59fH7H57nh\n19fz+eJcjgeGuX/Z/bxvyfuiP7DIAZ5+7Nll2jL2Eaf0pSfbka96o5zgFAxQkmvGbDTQmcGulumi\numDmluaxoCKfsUCQ5j43HcMd7Oncwx3z70i7rWqYUGDv8lu5dmF8h43BILhzbRWvn3WGJ+6orC5f\nzVhwjFN98qrwmQOt3PLd1/n2H0+PHxTeQI0ixzjPSmvvhg9HzZyDSpDfN/yeA90HEl8ZaKGwBka6\nwecdd8Rk4jWtWAp3PSYb0oVGQCZTdRpJKoOt/YEgzuHRpBwx9aEeMaXmGroz9H8wI4E9UQtbQHZ5\nHJCN86O9uLWleVizTRxKsbVAj3vSrNPTL4LzNGz+26T+wKJ1RFSD8hJ7xB9TRWhUXHf8STbZWUZq\nS/NiFsZo4df1/0n+wn/hu0f/gbc73uZ2xzv4aUcX25Z+gs9t+Bw5phhZoeqMMeVq2zwdCG16Jetm\nqN4AY8PQfRKDQVBRYLmoMvamXjcGAdXFOSxUm7N1DfNiw4soKNw2P8Xxd9EIBfYhQxFXzku8T/Ge\ntVUEggpbD08cKhG5gdrS5+afnjuO2Wjgx6+f50S7lCIoXQBGS/TA/spXZNuHaz8X9XlP9p4MN8V6\n6sxTWn+62KiWx6G21Nv1xmL53XDNp2DvT+DQr5LqExOJoyD59r09w6MEFbAlobGfHziPQRiozHPQ\nM5ulGE2U1FEc8JNvyo26M20wiHChUip0jHRQnlOOQRikpLDzOzJrWHZXcsvMLsFkME0IhKc6hjAI\nWGjLHz+wfKn83JNYZ19Ykboz5kTvCXb1PUXQU8tj73iMV+99lX+sey8bvKMYElnYQjY0u2Ji2DfM\n8FgCmSusjSb5T+kIFSqpOvtFVqTU1DtCZWEOFpORBaHagrNdQzxf/zzrKtaFm0RlhJDGPr+2hhxz\n4kZiC21WVlYV8uzBicVK9jw79jw7B7sP8Te/PoQQ8MxfX0NRbhZffOaItJMaTTKjnexlb90PJ5+H\nqz8J+dHrOna27UQguGnuTfxfw/+lZAOcQFHoNRxoSnrAxuGWAR5+/viUTeQJ3Pgw1F4LL/wNQ672\nlDP2aJPc4qGaAOKaQyZxbuAc1fnV2KzWKVdiqXIRB/Z5CMBhKYn5rrmqupBTna6UBjUc6TnC0pJQ\nsG3YIXuYbPqM/ONPAoMwhNv3qpzqdFFblkd2VsQ/an6F3BTuTuyMWWy30tjrTunneubsMxjIosT9\nANc5riPLkBVRdJLAI6tm7KFeLgmz9lQz9uJ5kFMSHqh9sRUpNfa6qS2Tdtx8i4mqohwOdB2lYbAh\nvfF3UXD3d9Kv5HPNIu2+8bvXVnG0bXDCpi7IrP2t1v3sb+rnX+5awYqqQr58+3KOtA7yszdDLXvt\nK2XGrurkigIvfxlyy2TPoBjsbNvJirIVfGzVxxgLjvHcueeS/lknoGbsrfvlPNwkAvs3XjrF4281\n8tEn9jHqj/E/YjTBe38GuaUM9ZykINZVahxUZ0wyWXsqVafnB84zv2g+FVZZ05GJPYyLOLCH2vca\nsmO+sKsdRQSCCsfVS02NdLu7aR1uZZ1tnbxh53ekvWv1B1Ja6mQv+6lOF0vtkzIE1RmjIWNfZLMS\nCCqc70lisjzSm//7878n37+WqoKIjahw0UkCb3K+DYxmbF65eZiw+rS/SQYES3784yYjxITybzVj\nz+RYwHRo7nMzN6Jx24KKfE4Nv4bZYObmWu37L1ro62mjVylIqK9HcseaObLFwKSsvdS0kOGAk3ev\nyeXONVUA3LaqkhuXVPDvfzxDS59bBnZ37/jfRP2r0LhTts2wRO9COuAd4GjPUTZXbWZxyWJWla/i\nqTNPpff7KqgChHxu0Gx1bHSO8Pb5XjbWlrDzrJNP/e/BqC0fAHn1ce//MESAgvYjcmM4CdTB1sno\n7MlWnfqCPpqGmkKB3YIvoNDvTn+m88Ub2PPKISuPmoBC+3B71Lak4QrUJAuVDnTJTPEK2xXSh92w\nA67+RMoWL9XLDrKzW3OfO3qr3ool0vKo0RmTrM6+rWkbLp8L/+DG8eZfIK2OwpB4kIPBAAVV2N3y\n9UyYsaejjVaullcv/lHshdl4fUEGPZkbUh7G1SnfuPf8RNPhgx4ffSNjE2YFzK/IxmXayxbHO7Ca\ntbVg1op3oItBQ9HURCAOZfkWrltYxnMH28Kul+FRP1t3S9fHLVeMX84LIfjqXSswCPiHZ4+i2JbL\nO7qOScPAyw9LCW79gzGf7632t1BQ2Fy1GYB7F91L41Aj+7r2xXxMQoxZE+fgavw7enJvC0aD4Psf\nWMuXb1/GH0908bmnDseuXK5ez5A5h4LBNtn7KQmq8+VVRTLOmM4hL2ajIXpL8ig0DzXjV/zUFdaF\nJyllwvJ48QZ2IUKWRzd+xU/n8NQgYy/MxlZgSVpn39+1nxxTDktKlsh/+uzCqE2+tGLPlYE9qATD\nfUQUrxQAACAASURBVF6iDtcoXwqjg9EntEdQW5qHySCS1tmfOfsM1fkOnM5qqosnBfa8ctAyDKKw\nmvKhLgQiseVxoCn1MvDK1XLiUvfJcctjpnT2YADOboMn74PvLINX/hl+/znZejkBzarVsSQbXvoC\nnHsZck4ijG6uqshsth4MKhjcTgzWcgwaO4Cq3L2umvZBL7saZAXvw88fp7OnBJMwc3ZgooY+pyiH\nL7xrCTvPOnmhK3Ql13kEjj8jP2/5Uuw++sAbbW9QZClieal8U7il9hasZiu/Of2bpNY8hSIH+D2A\n0NSu1xcI8vT+VrYsrsBWkM2Dm+bx97cs5rlD7Xzpd0ejXkH4gj5GgmMU2FbCzm/D6Zc0Ly83K5eK\nnIrkMvZBL7ZCi2bXVP1AaGpS0YLw7NNMOGMu3sAOUFJLjUtuLsV6cVdVFyXdM+ZA9wFWl6/G1FsP\np16AjR+NeRmqBVueDX/QT5+3L7ojRiXsjImvs5tNBurK85LysjcNNbGvax/rS28hEIS1oXJ4QPbX\n1lpJWFRD1mAr5Tnl8TP2YEAWmKSTsQN0HA5vNKVTfTro8XHo+HHY/k14dDX88r3QvEteiX3kNVnG\n/ocvJbxaagxZHdd0/RZ2/wh+/SEaXb8n6M8nX1me8vqicarTRZEygLU0+b4sNy+zkW8x8eyBNl48\n0sHT+1v5xJYlrChbFtWO+MEr57Kupoh//EMrgYIa+Sb36r9Ie+/Ke2I+T1AJ8mb7m1wz55rwlKhs\nUzZ3zr+Tl5tfptcTu199QlSdvWCOpqvlV0524Rwe5f0bx98EPrFlAZ/YMp9f7Wnhqy+cnBLcwy17\nl98je9K/8UhSS3QUOGgeOK+5Q6ucnJSc1VEgqC2sDdvAM+Flv8gDex01/VJHjBXY1ziKaHCOaL6M\nHxob4mz/Wamvv/Fd2bPiyr9Ka5n23HEv+6mOIfLMxokZs0oSzphFNmtSXR6fPfssBmEgZ/QqhIB1\nNRGBPZn+2oUOcHViz62IH9iH2mR711Qz9pI6sBRCx+HwRlNXGoF9x/OPs/I3m2D716St757H4e9O\nws1fhap1suim6Q3Z4C0OzX1uyhjEtvffwHElA5Y8Dg4cwDC4gvru9LqJTuaN0x2UiGHslcm7bLKz\njLx7pZ3fH+3gH549ympHEZ++cWF4otLkam2DQfDNP13FyKif40EHnHxBzkC98ctSgouBanNUZRiV\nexbdgz/o53fnfpf02sOoWbrGjdNf7WnBXpDN9YsmSoqfu3kxD1xTy0/fbOCRbROLr9TOjgU5JbD0\nDtlZ1Ks9EazJKqKl86C8AtRA15A3qY3T+oF6qvKryDHlUG69HKQYgOJ5lPm85BgtMZvxrKqWrTiP\naszaD3UfQkHhilwHHP0NXHF/0rMmJxNZpHSq08UiuzX6pXV+ucwctThjbFZa+jy4x/wJj/UH/TxX\n/xzXVV3HyVZ5tVCYE9G8LKmM3QEo2MzW+Jun6TZuEgIqV0HHYSqs2QiRXsZe3bKVHoq4dvQRfrn4\nUellNkXonOsegPIlsl9+nPbJjc4RvpLzJMLngTsf46WrH8Av4IujZ6nvSm6TPhGHz5wHIL8ktYZb\nd6+tZmQsgC8Q5NE/W0OW0cDqClmodLJv6t/YQpuVT2xZwGsDNkCBmmtg4U1xn0O1OW6q2jTh9rqi\nOtbb1vP0macJKin2NVIzdg1/Q639bl4/28O966untCMRQvBPty3jz9Y7+N6r5/jRjvrwfWo7gUJL\noexAqgSgYafmJda4B+kxGXE3bE947HiPpuQC+/yi+YCs78kzGy8HKaYOAVRbSmM6Y5bPkYH9eLu2\nwL6/az8mg4mVp0LvwFfHtnhpRQ3sHSMdE3vEREOjM0YtjJncMjYaO1t34vQ4uWP+XRxo6mdDbUS2\nHgzICj+tgT2URdlENp0jnbGdD6rVMZ3CksrV0HUMswhSmmdJfai1olAzcpTzuWtYuHglDz17jKf3\nTyq7N5rg5n+VnSX3/lfMU+V27OLdyg7Y9GkoW8jWgeMsspRy3+hBVjf9LLX1RcEzFqC5JfQaJtrU\njsGV80p490o733rvKmrLpIsnXKjUHb069OM3zKetYC1+jIze8E8Ji/FUm2NJ9tRy/3sX30vrcCu7\n2uO3o46J2r5XQ2D/zT75+7x3Q/SrG4NB8LX3rOT21XP4xkuneGqfjBcT+sRUb4SsPDj/muYlOnob\nAWjp3J/w2EGPj1F/UHPG7g/6aRxqDAd2kDbJSz9jD3V5rDHmxpRiSvLMVBXlcEyj5fFA1wGWWeeR\nc+AJuOKB8UKJNCiyFGExWjjf38agxxddX1epWCIbMWl0xmiRY5459wxlOWWUGdYwMhZgfW3EP+GI\nE5Sgdikm9HrYFfD4PeF/jCmkO6MSZGD3e8F5hsrC7JQz9mB/E2VKHwNl6/jhfevYvKCMzz99mOcn\nVWey8J0w/0bY8c3ok4QCPu7v/x59WXa49nOcHzzPUedR7lj5IEdLbubP3U8QPPNySmuczJ7GPgqC\noU3/FAO7wSD44X1XcNuqOeHbKnIrqMyrjFn2bzEZufFP7mWN9z85xKK454+0OUbjxpobKcku4Tdn\nYm+inu47zce2fYxXml+ZemdJKKCVxV9HIKjw1L4Wrl1YTnVx7HbfRoPg3+9ZzeYFZXzxmaO8drp7\nXIoxF8gruNrN46McE+EdoqZDVoo3951N+D87PhJPW2BvdjXjD/onBPbqkty0BwjBxR7YC6rAaKYm\nCK2uVgIxfKgrqgo41pY4Y/f6vRzrPcYVA12QXRBzMlKyCCGw59mp75P7AVGtjirlS2B0SGrUcagp\nycViMkwpQplMj7uHna07uWP+HRxsln/EEzL2ZCfiFFQDArtP7lnE1Nn7m0K/nzQmqoc3UA9hK8hO\nOWMfPPMGAIrjSrKzjPzkQ+tZX1vC3/76EP93bNL6b/4X+frv+NaU84y9+QPqlFbeWvR5MOfyQv0L\nGISBP6n7E06t/yqnlWqUZ/5yvOI2DXae6cFmDP1uUwzssVhTviZuP5fFNivD5NKUIIBMtjlOxmw0\nc+eCO9nesp1u98QeSP6gn/86+l+878X38Vb7W3xrz7cmNCgDoGwBfHS71L7jsONMNx2DXt4fI1uf\nsCaTgf/44DqW2K389S8OcKJLyonhlgLzt0Bf/biUGI+GHdSMSVmkCZ+82otDZ5JVp+cH5PnmF44H\n9trSXJqc7rRrOjIS2IUQtwohTgshzgkhvpiJcwLSnldci2PUiy/oC//x+AI+Tved5vn65/m3vf9G\nd/bjNPT14vLG30A96jyKP+hnXVc9vONLmekmF8Kea6cjFATjSjEVoQ3UBC18jQbBQls+pxNIMc/V\nP0dACfCehe9hX1Mf1cU5E/uwq03HtGbsJjNY7dg98nlj6uzpWB1VShdAVi50HMZeaEk5Y/fWv4VL\nyaF4nuy4mGM28tMHNrC6upBP/eoAr56K+Blsy2Dd/bKPiPPc+O2DrZhe/ybbAutQFr0LX9DH1vNb\nuWbONZTllFFXVcHHfH9HMBCAX39QDmRJg51nnawtCe2fpLnHM5nVFavpcnfFfFOuKs7BaBBha2cs\nJtsco3HPwnsIKAGeOftM+LamoSbu/7/7efTAo7zD8Q6+tvlrtI+083z981NPMGdt3M1bkJumZflm\nblyqLTmxZmfxswc3UGY184s9cq8h3FJAHQijRY45u408s5UKSxENWSboiD8wvFNr1enbj8H/3EV9\n43YA5hWOF2fNLc3DNeqnbyT+GM1EpB3YhRBG4DHgXcAy4P1CiGXpnjdM8TxqhuVl88NvP8w9W+9h\n4/9u5L1b38tDbzzEk6ee5Jx7B1mF+8abHcVgf0gLXFtQB1fELshIBVuejf7RbuwF2RTlxilOCDtj\nEm+gLrXLYSKxes4risIzZ5/5/+29d3ib53X3/7mxCIIb3HtLooa1ZdnWsGx5r3jVI9tNHNtJm6YZ\nfp027vs2TdO8Sfqm+SWNR5Laieu4nomXbEvykCxZskRZ1qIGxSXuTZAgCWI8vz9uAARJTAISSen5\nXJcvmSQA3gTBg/Oc8z3fw8rslRQlFfFxfR+rSya9WQ1GmLGDXLjh9jAJmrFHa9yk0cpJyLZPyU2J\nZ2DEHtz/IwBx7fv5xFVBWdb4TsvEOB1P3beGBTnJPPDMAXae8lkbt+n7Ug219dHxz731CIqi8H8c\nX6A43cRrp1+j3drOXfPvAqAiK4kmJZutC34oR/Jf/1bYErjJdFhGOdExyOIUG2h04M8bPwpCbVTS\nazXkp8Z7pZ3+8Cdz9EdhciGX5l3KiydfxO6y82zNs9zx6h00DDTwk/U/4Wcbf8aNZTeyOH0xTx5+\n0u+gYTA6LaO8e7yT21cWhGf37SYrycjTX16DohkBxcDAsPt3lTlfLl4PVY5RFDnDULaRktQKGgyG\nkHMQnlKMR48ekH2/hbr3OH3kWfLRY+of7x+WuDfLhbqaCkUsMvY1QK2iKHWKoowBzwEx2kIAmMuo\n7G0mQZ/Ayb6TpBvT+cLCL/CT9T/hz7f8mb2f3cuCtEXo0z7mcIhyzIFTr1I5NkbK9T8Lb1gnAnIS\ncrAp/czPDbF1KSFdXnqHsXTjyqps+oftfvdcAuzv2M+ZwTPcXnk7jT3DdA/Zpgb26SwnTi0ks78V\nrdD6D+z2Efm4sViMkLsU2g6RnSTfDCMeUhq1kDp4ikNiPtmTPLCTjXr++NdrKEk38d0XDo1f3iZm\nSc/9E29IhcSprVDzKtXFf02zkkVeqoHHP32cJRlL2FiwEYCUeD3ZyXFscy6Dyx+BQ8+51wJGzs5T\n8k2zJH5EvhZiZQHsZn7afOK0cUHLMcXpJhqDZOyBZI7++Kt5f0XHcAd3vHoHP/74x6zMWckrt7zC\n9WXXI4RACMGDyx6kZaiF106/FtHP8kJ1M06Xwt2riyK6H0BZZiLr5iegOOP58lMfM2RzyOe6fJOc\nNg9mMdB5TJZLK66iOKWURkOc3O8bhA7LKBmJhuBvQP1nZEnnih9w2lxA2egw/OdauY7T0ua1spi8\nQD1SYhHY8wFfyUqz+3MTEELcL4TYL4TY39UVZOnuZMylmG1D7LrxZd77q/d47KrH+NbKb3F92fWU\np5aj0+i4e8GdaOM62d0ceMTZ0XOaT4dbWWEqCHs7UiRkGLNAuCjKDC1PJHNBWBn7xnmZxOu1vHnY\n/6Tqy6deJlGfyObizexrkMF/Qn0dpNTRmBqZXUJKIVpLK5nxmf5LMZ4MI9pSDMjAbrdSJuQbSMRm\nYM370OCiLWWZ32m/VJOBr6wro90yynHfSd61D0lP8LcfgTe/C+kVvJZwO2kmPdubX6PV2srXl319\nwmNWZiVR2zk0frUXouYaiA9PdZGRGEeq0h/zMgyAXqtnUfqioIG9JD2Bhh5rwFpuIJmjPzYUbiDb\nlE2btY1HL3mU31z5mym7hNfnr2dR+iKeOPRE2Fm7y6Xw3L4m1paZKc0IvabSH3r9KHlJZmraBnnw\nmWrGHC5Zjhnpm7JCbwIe3XrlVZQklzAgFPo6DkkbhgC0hbMSz+2N46i8mgaHlYqLPicHJA8+C79c\nTsmn/06iGKGhe+Yzdn/pxpRXi6IoTyiKskpRlFWZmRE0i9xmYNr+wGO915Veh0aJ44jlnYC3OfH2\ndxjWaFi5bPrWAcGwjcoaXlZaGL7iWVVhKWPiDVo2Lcjk7aMdU7wwLGMWtjZu5YayG4jXxbOvoZdU\nk57yzEmGXEPtke+vTC0E5xg5RrP/jH26dr3+cDdQ80blYEm7JUJf9jN7caLBlrMi4E02uAdaPjjp\nk1DojbD5n2RZpa8erv8Z9X0OCtL1PHHoCZZnLefSvIkJQEVWIrWdQ7g8pZORvsjOigxWH9Z2s64i\nHWHtjnnj1MPSzKXU9NT4XSsJMmMfHHXQH8BwKpjMcTJ6jZ6nr3ua1299nTvn3en3DVYIwYNLZdb+\n+unXw/oZdp/u4UzvCPesiTxb92AZs5CblMa/3baEnae6+fk7J6DscvnFYHX22m1yKjc5j5KUEgAa\nXCNB38zDmjqt3wGmDJrjExlzjVGWuRiu+wl842NYcD26XT/n1/FPzIqMvRnwbVcXAK0Bbhs5Hte3\nIE+oSW+iImEDw4YDdAz5+WM7/S7V7XIV24qSzYAceNjf4L/EMR1Ot0q732xzGO+0WVVy0cRAaHOh\naxfn0j1ko7px4s/1Zt2b2Jw2bq28FYD9DX2sKjZPHYwa7ICkCAN7ituXXZcQIGOfpl2vPzIXgDYO\ns0VewbQPRDac4Wzcw3FXIflZgQNkToqRBTlJfHBi0pXi4tth/vUyYyrfREOPFX3KHjpHOvnGsm9M\nCVDzspMYHnPSMuiUq9yGIx+nr2m30D00xvrKTLlk4ywGdrvLzmOfPsbhrsNTsmTvJb+fWm4omaM/\n8hPzp2Tpk9lQsIGF6QvDztr/tK+JVJOeaxZNb4ALYMA2QHJcMneuKuSKBVlsPdYhBwVzlgSus49a\noOkjqJCxoiS5BIAGvT5oAzXk1KmiyMBeso7TFmmj7FXEmMvgjt/Dur9nvetjrJ31Ef+svsQisO8D\nKoUQpUIIA3A34Kf9PU1Si6Reujf4D3p98S0IjZ2nD70y8QtOO2z5XxxISiM/IY/sBBnk/vHPR7jj\nsY/4yVvHAzvDRcD+Wj0aJZ5jfaGNprwN1DDq7FcsyMKg07DlyMRyzMunXmZ+2nwWmhfSPWSjrts6\ntQwD7iXWEf5huLXs2ej8Dyn1NchNPLHYZK/VQ/YiDJ2HSIrTRbZJyelANO9jv2seZZnBL9U3zstk\nf2OvrLN6EALu+RNc/1PGHC5aBwY4o7zBmpw1rMldM+UxPItTajuHpKLKnxY+BJ76+vrKDDljcJYC\n+6qcVZSmlPLk4Se59817ufTZS7nv7fv45YFfsrN5JxnJsr7sLzMMJXOcLp6svXmomTfqgls79AzZ\neOdoO7cuz5+41yBCfNfirSk1U9dtpWfIJssxTXtgzE9mXP+BtMtwT+XmJeah0+hoiDMGbKCO2p30\nDduDZ+y9dbJuX7rBK3UsS520CN7tsrmmN7yrmkBEHdgVRXEA3wDeBmqA5xVFORrt43rRGeTocYh6\n5rWVq3GO5vFW058nBqKPn0TpPsEnpgRW5qwC5C9hT10POclGfvP+ae57ah8DUXggt/aPUNNmpTRh\nOTubd4bWoHrMwMKosyfG6dhQmclbR9q9Fq1He45S01vD7fNuRwjhvfJYNblxqigysEecsXsWbriw\nOW302ya5Z/Y3yjfcEDK1sHF7s+ckx0XWPO04gsYxTLVrPmUZwT3hN87LxO5U+Oi0/yy7uW8YXepH\njLoG+MZy/9PIlZ5tSp2DMrCPTCewd7EgJ4ksoxPs1rNSYwc5Qv/qZ15l+53b+dnGn3HHvDsYtg/z\n+yO/56HtD/Hld2/AmP8se1urp7xew5E5TpeNBRupMlfxxKEncLgC96N2nOrC7lS4fUVBVN/PYhsP\n7Kvcxnj7G/tkA9Vlh8bdU+90aivEJUPhxQDoNDqKkopoSEwL2EDtCGc4qX6H/Ld0I7X9teQm5JKg\nn5SQpBZxJv0ybla2MzA0/Tp7TP4yFUV5U1GUeYqilCuK8qNYPOYEzGWyDhqEvNR4DMOX0GWr52iP\n+31lqBPe/zH15RvodVhZkSXrsPsaehm1u/jxbUv411uXsPt0Nzf/+sOI3BR92X5casWvL99E50gn\nJ/pOBL9DfJrMosPI2AGuW5xD28Aon7rtiV88+SJGrZEbym5w/zx9xOk0LMlPmXjH0QE52Rlpxh6X\nCPFp5Nhk9jylzt4X2SqzkOQuhdEBliT2R7bU+sxeAPa75nk3Hk2me6Sbb7//bR4/9R1MJgsfnPS/\nTPxEZzf69A9YYl7D8qzlfm+TajKQmRQnbR7iI8/YR8ac7KvvG8/W4axl7B6yTFlcU3IND695mOdu\nfI7d9+zmt1f/ls9W3Ys+8RSvdf0Df/X6X/HKqVcYdYyGLXOcLkIIHlj6AGcGzwTN2uu6rGiELH9N\nF7vLzrBj2DuctDg/BYNWI8uaRZfIq87J5RgfmaPv8F1xcjGNer1suPppoHqa/iEDe1IepJdTN1A3\nNVt30z3/s2SLfvo/mX7hY3ZPnnpIKw2ZsQshWJRyOUIx8OLJF+Und/0HjFk5sPBaAO/GpB0nuzBo\nNVxcZubei4t47v61DI85+cyvd7ElgAIlGNtrOihON3HbAlmT29G8I/SdssJTxgBsrspGpxG8daSd\nYfswb9a9yTUl13gzkf0NvSwrTJ0qsxpy18fDHU7yJaWQnBEpH50S2PtjoGH3xd1AXaZtjKwU07SH\nfl0mjqR8kowTJ2AVRWFL/RZu/cutvH/mfU70HSeu+Jdsq9/t94rqz3XPodEN89DSrwf9lpVZiZz0\nlmIiq7Hvre9hzOly19fPTWCfjElv4uLci/nu6u+yYOz/kjX2WRwuB4/ufpSrXryKH+z6Qdgyx+my\nqXATC8wLgmbtdd1WCs2miLTrk/Fa9rr/Tox6LRcVpMgrXH28VMdNlqz6yBx9KUkpock1inNsUE6u\nTiLk5iRPfb10A07FRf1APRUpFX5vmrD4OlqUdEyHn47kx53A3Ajs5jKpQAihQlial4PdchFb6rdg\ntXbCJ3+EhbdwYLgFs9HsbYLsPNXN6tI0TAbZ8FxZbOb1v1nH/JwkHvzvA/zs7RNh192HxxzsPt3D\nlQuyyTBlsCh9ETubw3CPy3QrY4LIpzykmPRcVpHBliPtvFn/JsOOYe6YdwcgNzYdabVM1a/D9IaT\nPKQWkWOR2e2EBupIn7wSiEXj1EPWQtDomK/U0TVoCziQNYUzezmirZpSX+8Z6eHbH3yb7+34HkVJ\nRbxw8ws8e8OzJBtSGUr7T36x77cTgrtlzMK+vj+jWBdyWaH/bN1DZVYitR2DKPHmiFUx7xzrwKjX\nsKbULBuncNZKMeFQlm5moGMVL9/8Mr+7+neszF7J63WvoxXasGSO08WTtTcNNvFm/Zt+b9PQbZ22\nxNHDBJ8YNytL0jjcMiD3CZdvksmVxSeZ88gc3Y1TD6XJpdgVJ606nd86e8iMvbMGhruhdD0tQy3Y\nnLYJHjG+FGUk8ZxjE5mdu6Fn6ptIOMyRwO5RxgQvxyzOT8HWt5phxzBbdv1YBqA191PdUc2KrBUI\nIehw65nXV07MlLKTjTx3/1ruXl3Ir96r5X+/Gl6bYOepbsYcLjZXSUXAhoINHOo+RP9oiK1OWQvA\nPgwD4W1nuW5xDk29wzxz9HkqUiu804UHz/TjdCmsLvUT2D0Z+3QCe0oh5v4WdBrdxIzda9cbw8Cu\nN0JmFYW2k7gU6BoKoxzTfwYsLewaq6DUp77+dsPb3iz9Wyu/xdPXPU1ZShllKWU8fsUfcAxW8fua\nX/LwzocZccirgz8c/QN2xUqO8zMhN99UZCdhHXMyqEmWnjOT/U8CMGp38vqnrVy7KEc2A72B/dxm\n7L4UpZvoHrJhHXOyJncNv9j0C7bctoU/XvfHsGSO0XBF4RUBs3ZFUaiPRWD3tex1s6rYjN2pcKh5\nwMde4P3xO9Vug6xFkDJxFKc4Wb7eG4wJfuvs7ZZREgzaKVeOXjy7XUs3eLcmBSrFmAw6tsdfgxMt\nVD8V4qf0zxwJ7O53tvZDQW+2JD8F10gRmYZiXmp+F3KW0J5eQqu1dUIZBmBD5dQ/qDidlh/ftkSW\nZ/Y1hWVKtb2mgySjzhtY1+ev99YpgxKBMgbgqoXZ6IytnLbUcHvl7d4AtK+hF42AFUV+xtK9S6yn\nk7EXohkbItuYMXFFXiyljr7kLiVj8DighDek5K6v7xgtpzwzAcuYhe9+8F2+88F3yEvM4/kbn+e+\nxfeh0+i8d1mQnUmu7WvkuW7jrfq3+Nybn+NI9xGeqXkGg20Z88zBXQYB5rkbqO12d00/zDr7tpoO\nLKMObl/pbgZ6Artp5jL2Erfk0dczJi8xjyWZS8769xZC8MBFD9BoaWRr48QlFp2DNobHnJTFKLBP\nyNi9DdReqVM3ZYzr2T0yx8rNUx7Lq2U3F/jN2Dsso2SHqq+nlUBqEacHZGD3Nf+aTGJGIfvi1sIn\nz4Ajcn/2uRHYMxdA9hK51irIkoSCtHiSjXqqxso5onVxfMlnqO6Ui6s9gX3nqW4yEuOoyvXflBFC\n8MCGcpwuhad3NwQ9lsul8O7xTjbOy0TvNv9flLEIs9Ecus6eOV/+G2adPT0xjvyiw6DouKn8Ju/n\n9zX0siAn2X+mMNQhPVHikqd+LRQeX3ZDSoCMvSTyxwxG7lIMtl5y6A0vsDftwakzcVwpojQjgV8e\n+CXbGrfxt8v/lmeuf4aKNP/1y43zsmisW8svLv8V7dZ27nnjHobtwwy2baLIHDqQeHzym21uo7Uw\nlTEvVTeTk2zk0nJ3ILd2gyERDIFtaM82xR5fkiiHYabLpqJNFCUV8WzNsxM+X9clz1MaQukUCm8p\nxuf1b04wUJ6ZQHVDn1R1lV0uG6iK4iNznLrbNi0ujWRDMg0JqTLBnGRHEHQ4yeWUGXvpBgBq+2vJ\nMmWRaAj88xWnm3jGcaV8fR2LvIk6NwK7RgNXPir105/8IeDNhBAszk/hltbjGBSFF4WVAx0HMOlM\nzE+b753621CZEfSSuyjdxDWLcvjvvU1BNxgdbO6ne2iMqxaOZ8QaoWFd/jp2te4KaDMMQHyq7JCH\nsU0JpDf6kH4vdstiOvvlr83udPFJU7+s2frDI3WcjheJx5dda5wY2Psb5fLv+OkbV/WN9vHz/T8f\nVy8B5ElnxsWahvAkj2f20J2yBCdaslPh1dOvcmP5jXz1oq9OyNIns3F+JqN2FzpbFc/d+BxLM5dy\nY8kdjI1kew2YgmFOMJCeYOD0kNuXJoyMvXNwlB2nurl1RT5azwCZtWtG6+sgX+cADSFcHs8WGqHh\n7gV3c7DrIMd6jnk/X9/tDuwhZhNC4S9jB1mO2d/YJ+XD5VfIRTQdR6fIHH0RQlCSXEKjTiOH8Vc4\nPAAAIABJREFUC3tqJ3y9PZidQPshWRYulb5DBzsPclHGRUHPXpKRwBvWebjSSqE68gUvcyOwgxwW\nKLpU+mj7Gypwc0nGKFeMfsxV8YW80fAWe9r2sCxrGTqNjiOtA/Rax7wj5sH4yvpSBkbsUzfx+LC9\npgOtRnD5vEm+GAXrGbANcLj7cPBvkrUg7MD+dsPbjLmGsfev4c3DMtDWtFkYHnOyyt9gEshSTKRS\nRw+e6VNFQ+dw5/j6s77o7Hp3NO/gtldv46mjT/HCiRfGv5C9CEVoWKptCJ2x2wah4yi1xkXoNIID\nve8w4hjhngX3hPz+a0vTMeg0fHCyi8KkQp65/hmuz3sIGJ/GDEVldiI1A+43jzCUMX/5pBWna5Im\n+yxOnYZLslFPeoKBpt6ZydgBbqm4hXhdPH86/ifv5+q7h4jTaciNYHeoP7w1dsNEGfDKkjQGRuyc\n7hqSDVSQ5Rg/MkdfSlJKqLe7JdE+5RiXS6Fz0BY4Y/fo10vW0W5tp2WohZXZK4OevTjdhIKGrnn3\nQuOusEu2HuZOYBdCensMdcjt8QG4ZuRNBApry/6aIfsQTYNN3ifRU19fVxk6U1pZbGZ5USq//7A+\noEJme00nq4rTSDFNfCFcknsJWqENoxxTBd0ng7vMuXnp5EuUJJewLHOFdwr143qP8VeIjH06mMyg\nN5Ftt2N32ekddWemfQ3TapwO24f554/+ma9v/zppxjTKUsqo7ffJegwJiIx5LDc0hc7Ym/eD4qLa\nNZ/CdCMvnHyepZlLWZge2i063qDl4lLzBN+YRndgKw4jYwdpBnao163xDlGKURSFlw40s7QwlYos\nn0vvszh1GglF6aaoDaeiIdmQzE1lN/Fm3Zv0jUqVUX23lZL0BP97gyNgwDZAvC4e/aRA7fl72d/Y\nB8l5kDEfPn7Sr8zRl5LkEjptvQwbTBMaqN1WGw6XEjhjr98hv0dSDtUdcsVeqMDu6X8cyboBtIaI\ns/a5E9gBitbCvOvgw//wfwlsH6W86QW2u1ZgY41X3ugZTNpxqptFeclkJMZNva8fvrKujIaeYbbX\nTPVLae4b5nj7IJv9mP+nxKWwNHMpO1tCyB6zFsgBohAbeWr7ajnYdZA75t3BdYtzOd4+SEO3lf0N\nfRSZTYH9KQanYSfgQQipZR91L9ywdkhpZn9TxBn7wc6D3PHaHbx48kW+vOjLPHfDc6zOWc3p/tMT\nNeW5S1mo1IVeuHFmLyB411pEekYTDZaGsLJ1DxvnZVLbOURznwxojT3DGHSa0AZObiqzE8dr7CFK\nMcfaLBxvH+SOFZMMT2dBKQZkAInFKrZouGfBPYy5xrzLOupioIgBmbEnGab20krSTaQnGNjf4Jar\nlm8aFwVUTG2ceu/nbqA2ZldNyNi9Ukd/rx+nHRo/8tbXqzuqSdQnMi8teKPeUyY7NWSUG6YO/imi\n5S5zK7ADXPkDKTPb9YupXzv6CtrRXp4T13G01cKXFn2JbFM2izMWMzhq50BjX1hlGA/XLMomPzWe\n3+6cKrPcXiM13ldW+Tc+2lCwgeO9x2VADIR36Ubwy6yXTr2EXqPn5vKbuXaxDNRbjrSzv7E3cBnG\nPgK2Aek9Pl1SC8lxLzlpt7bLKwCnLezGqd1p55cHfskX3/oiLsXF76/5PX+/6u8xaA1UpFYwZB+a\nqJHPXYrZ1YO9P8SQWNMelOyFHOsVDBneJ92YztXFUxtegbh8vnwN7Dgph4Qauq0Um01hZ4iVWUmM\nEodTGxeyFPNSdQsGrYablo7vJcXlkprm2ZCxm020Doxgc0S+4CRWVKRVsCZnDf9z4n8YtY/R1DMc\ndX0dJtoJ+CKEYGVxmlTGgKyzg1+Zoy9eyaM5f0IDNaiGveWAtI7wCezLs5aHnOpNNuoxJxhkY3vV\nffJv+ejLQe/jy9wL7NmL4KK7YO/jYPExkVQU+PhxyJjPUO5lHGkZ4PZ5t7Ptzm0YdUY+Ot2Dw6X4\nlTkGQqfV8OXLSvi4oZdPz0zUpW+r6aAsI4GyyTa5btYXrAek70ZAPMqYIHV2m9PGq6df5cqiK0kz\nplGQZmJpQQpP7a6ne2gseBkGpjd16iGlkJwBGWTbh9vHs5owA/sjHz7Ck4ef5Obym3nxphdZ5fbq\nAbzDGR5NL+CdQE0fOh7Yb8flhOb9WLNWYRfdtIwd4I55d0y53A5GeWYi+anxXnuBpt7hsMswAEsK\nUshINNDrSsRpDRzY7U4XfznYwpVVWRO3ao32S/XFLAjsJRkmFAXO9EZolxxj7l1wL23WNl45sQ2H\nS4lZxu4vsAOsKkmjsWeYrkEbFF8G+gRYcEPQxytKKkIgaDAlyRmUbmk13RFs6rR+ByCgZB09Iz3U\nDdR5FXqhKPaUyYovlaWc/b8P634wFwM7wKZH5B/4Bz8Z/1zzfnl5tOarLC5I5VibZUJtfMepLkwG\nrVfHGi53rS4kKU7H7z4cz9qHbA721vUGzNYBKlMryUnICV6OMSbL5dFBMvatjVuxjFm8k6YgrXw7\n3J4qAQP7oGc4KYrAnlpImrUHg8Ygrzz6wtewK4rC7tbd3Fx+Mz+87IdTpF0VqVKOOKHOniP10/Nd\n9QF9wuk4CmODNCUuwZC2Bw0a7px3Z0Q/lhCCDfMy2VXbw5jDRUOPNezGKUhjtv97x0V0ORM53RTY\nevmDE130WMe4bbKRlSfLnwWB3SPxnCnJo4eNhRvJScjhxVPPAUStYQd3YA8g9fUY5lU39kpvpG98\nDBu+E/TxjDojeYl5NAh3XHHX2dsto2g1gnR/Jd76DyBnMZjMfNIpyzersldNvZ0fvGUyIWTW3lId\n1v1grgb2tBL5gx744/hC4o+fkB7ZS+9mcX4yo3YXdV3ji6B3nurmkrL0iL0nkox67l5TyBuH22jp\nl1nNzpNdjDldQZfrCiFYn7+ej1o/CrjsAOD9zAK+MVDNU0ee4mTfySmZ6osnX6QwqZDVOau9n7vO\nXY5JM+kpD3TJOhTFcJKHlEIEkO1ZuOEdTgq9+KDf1s/g2CDz0+b7/XqaMQ2z0TwxYzemYE0oZrGm\nPnCd3T2YdFCUo0/dx/r8TV4r5kjYOC+TIZuDt462M2p3hSV19OWKBdnEJWdg6e1k9+luv7d56UAz\n6QkGb+nHyyywE/BQEiPJ4966Hk60T89ED6SD4l3z7+Kk5RM0ho6YZeyTFTEeFuelEKfTjNfZUwpA\nF7r3VpxcTIOtV2b47jp728Ao2Ulx41JWD/ZROPOxV+ZY3VFNnDYubNfM4nRZJhu1O2HpXXImJUzm\nZmAH+e6qM8J7/yKz06OvwPLPQlwSi90uh0dapYlVY4+Vxp7hiOrrvnzpMmlp4BlY2lbTSUq83msD\nGvCIBRsYdgxzwD0kNZndLbv5lquNA2KMn1f/nNtfvZ3NL2zmB7t+wFv1b3Gw8yDVHdXcXnk7GjH+\nqyrJSGBFUSqXz88KrMePRcbuse/VJcpSTF+DfLww1uw1WuSbgKfh5I+K1IqJgR2wZS5msWgIvEmp\naQ8k5vBW31GEdoQvLr43rB9lMpdWpKPTCP74UQMARRFk7B6KCwrI0g7x7ec/nWL73D88xvaaTm5e\nlucdXvMyC+wEPJgTDCTF6WiKImPvGrTxpf/ax7+8cSz0jYNwe+XtaNCTkLkHc0KQhfBhYrEFztgN\nOg1LC1LZ1xiZ309JcgkNlgaU3CXepRsBp06bP5Y9KZ/6+tLMpWGXDUvSE1AUKdQgPg3ueyvsc87d\nwJ6YBZd8XQb0N78tvZVXfxWQl3FGvYbDzVLH6pE5rg9D5uiP/NR4rl+Sy5/2NjEwYue9E51cPj8T\n3eQ/2EmsyVmDQWPwK3v8pPMTvvneNylPKmJL5xBb+xT+eenfsCxrGe82vct3d3yXz2/5PDqh45aK\nqbvBn/3qWn5ye5Ahh6F20OjAlB7xz+vFM6Qk9DJj7wvf1bHB0gCMN5z8UZ5azumBicqY+OIVFGq6\nOHA8gJvnmb0ohWuoGdmCwZkf9mXtZJKNelYUp7HPnbFFmrED6BIzyDOM0Dlo4wd/OTLha6992sqY\n0+XfT3wWBXYhhJQ8RpGx/+b904zYnRxrtYTeRRCENGMaKa7ViKQDDNmHQt8hCF7L3gA1dpB19qMt\nA4yMhd84LkkpYdgxTHf2Qmg7BE5H4KnT+h0gtFB0CYNjg5zoOxFS5uiLp+/jlaO6h/jCYe4GdoBL\nvyF9sWteg/IrIUPWbXVaDVW5yd6MfcepbgrS4qO6vPvrdaUM2hx8/5XD9FrHgpZhPJj0JlbnrJ7i\n9ljTU8ND2x4iJyGHx657ipQvvEqObYhb3/0FP1/yEDvu2sEz1z/Dg0sf5Ptrv09G/NQ3JKNeG7ys\nNNgBCVnRLcNIygWNjhynQudwJ87+8IeTGi2N6ISOvMS8gLepSK3AardOmGyNL5Iv/MsOPozz1W/C\ntv8j7ZcP/EEu/B04w8HMEkbFGcrirg5p2hWMje4rOJ1GkJ8a/mWuF5MZ3dgAf3dFGa9+2spfDrZ4\nv/TigRYW5CSxKM9PYLF2A0K+dmcB0Uge2wZGeGZvI8lGHT3WMdmMjIKxnktRhI2/1P4lqseZbNnr\nj1UlaThcinfPQTh4lTEpOeAYge4TgadO63dA/gowJnOw8yAuxRVhYJfxqmEaV1NzO7AbU2D9t+X/\nX/zAhC8tyU/hWKsFm8PJR6d72DAvM6ogsKwwldUlabxxqA2dRniDQijWF6ynwdLAGYtsstUN1PG1\nrV8jyZDEE1c9IYN27lL44mtS0/7UDWh761iauZSHlj0UcWPQy1BHdFJHAI0WkvMoso/hVJw0D3eE\nrYhptDRSkFSAXhP4stOjjJnQQC1cQ0/OerJcnTiOviqD+tZH4dW/gT8/CMAztnYUp5FLsgIPk4SD\n53eYnxYf8urLL/FmUFw8eHEGK4pS+cc/H6Glf4TaziE+PdPP7SsK/L/mrF1yAEwb2PrgXFKUbuJM\n73D4dsk+/HJ7LYqi8OhNsm58rM0y7XOM2p20d2eQZZjPcyeeG592ngb+fGIms6LIbQgWwe7j0mRZ\nlq2Pl0F3pLEa65hzasZuG5TNzhKpjqvuqEYndFyUGdxKwJc0k54ko47GaVxNze3ADrD2Qbjvbe9+\nQg+L81IYsjn48yctDNkcbJhmGcaXr6yXNpurS8ykxIdXJ1ufL3+xO1p20DLUwlff+SoaoeHJq58k\nNzF3/IY5S+CLr8uBhqdukF7t0TDUHp3U0UNKEeVW+UdSq9dGVIrxDIgFwqOMmVBnNySQev9rfC7+\n19yf/T/waA880gx/dxi+tpOuL7/G9q592PtXMS87ijITsDA3mcykOO+UX8S4y1w6Wz//765luFwK\n337+IC9Un0GrEdyyPMDVyiywE/ClJN2Ew6XQ2h/BWkJk7+qF/We4Z00RV7mvYGvapt9A9WSml+d+\nhkZLI7tb/aytC5NAPjG+pJoMVGYlygnUMMlOyMaoNdLoHAZDIram/YAfDfvp96Sk1ae+vihjEfER\nNECFEJSkJ/hdOB6KuR/YNVo5kTopM1qUL3+hj31Qh1YjuLQi+sC+uSqb65fk8KXLSsK+T1FyESXJ\nJbxZ9yZffeerjDhGePyqx/3XnrMXwpfekJr8p24I20fGL4MdsVk2nVpI+YBsxJ7W68MqxbgUF02W\npqD1dZATuhnxGRMzdkCrEdy5soAdp7poGRiFuCSpxMm9iBf7juJUnIz1rQ255zQUGo3gic+v5B9v\nqJreA5jcpZSRXorTE/inmxaxp66X3+6sZ0NlBllJAZrMs8ROwIPnkr8xQs+YX2w7hU4r+MamClJM\nevJSjNREkbHXu10dbyq/lnRj+gT/mEgZsMkybLDADlL2eMBjCBYGGqGhKLmIhsFGyF2Kxm0lnp1k\nkBn6uz+C36yD5z8vG56FFzPqGOVIz5Gw9eu+FKebpiVFnfuBPQCVWUkYtBrqu60sL0wlOZABfgRo\nNYL//OxKrlkUWSa8vmA9h7oP0T3SzWObH2O+2b8EEJA2A196QzZdnroB2o8Evm0gnA6ZFcYkYy/E\nNNhGnj6J0wZ9WBl7h7UDm9NGcUro25anlk9RxgDcuUo2bl/cP27CZnfaef7k8xQaV6DYM2IiiVte\nlOa14o0YT43crUu/c1UBVy/MloZfK4MsYZ4ldgIeiqcheTzVMcifD7bwxUtKyHKXIapyk6MK7HVu\nV8fKrFTunH8nO5t3Tl3LGCbejD2EZfWq4jQsow5OdYbfrC1JLqFhoAFyl5HQe4x/1f2WVS9fBk9e\nATt/JudTrvohfG0HGEwc7j6Mw+WYVqO/JD2B5r4R7BGWyc7bwG7QaVjg9lyfvC3pXHNT2U0UJxfz\nqyt+FV6NLXMefPlNuWz3+S/IDD4SrF2AErOMHcVFuUvDaYMBkgOPXHuot8hhrlClGHBLHgdOT6mn\nFppNXFaewfP7z3izqQ9bPqR7pJt05ybyUozEG2K/bDkiPBm72y9GCMFP71jK/75pYfA3f2vXjC7Y\nmEx2kpE4nSYiyeO/bz1JgkHHAxvHl0VU5SZT122VuutpUN9tJSspjoQ4HWty1qCgUDcQfNdxIMIp\nxQBeS459EdTZS1JKaBlqwV6wEq3Lxk3ajxBFa+HWJ+C7p+Xf7mV/65332N+xH4FgWVb4qhYPxekm\nnC6Flr7IJoPP28AOsChP6tk3zJvZP6Kq9Cpev/V11uSuCf9O6eWw8XtycW6kJRnvcFJsMnaA8sEe\n6vV6wvmT9WjYQ5ViQGbsI44R2qxT/WHuWl1IS/8Iu9wDQLtbdxOvi2egtySglcM5xacU4yHFpOdL\nl5VO1a57cNrlrtRZVIrRaARF5vAlj0daBthypJ371pWS5qM3r8pNxulSONUxPami7zq8/ESZQLQN\nRb5cHsabp4EGlDwUmU1kJsVRHUGdvSS5BKfi5Ez+Mn4177/YpPkd2rv+IIeITFOVTtUd1cw3zw/5\nJuOP6SpjzuvAfuvyfG5ZlsdFBdNfCjGjzLtG/nsy/MEEIDbDSR7cWUe5dYAxAWcGA4/Qe2i0NBKv\niyczPnTw8ttAdXP1omxSTXqe2ye/5972vazIWkF9ty0mZZioiUuWswJhrscDfOwEZk/GDjKANIUZ\n2H/2zglS4vV8ZX3phM97tpJNtxzT0G31LibPMmWhFVpahlpC3Ms/ljGLX8veyQghWOVrCBYGnivR\nhsFGDtqLyEgJXMqzO+182vlpRDLHCd/Lu+UqsgbqeR3Y15Sa+Y+7l08d9Z0rJOdJKWSkgd27xDpK\nuSN4Sy8VY3Ky0l8AnoxHEROOvNSv5NFNnE7Lrcvz2Xq0gxPdzdQP1LPYvIrBUYc3AMwoQsgGWRjL\nNrzMouEkX4rTTTT2WkM2Efc39PL+iS4e2Fg+pW9VnJ5AvF47LcnjwLCdHuuY9w1bp9GRbcr2eyUX\nDsEMwCazsjiNM70jYe04Bry9o0ZLI+2WkcA+7MCx3mOMOkenHdgzk+KI12vVwH7eMe866Tdh9e9H\n4hdvYI9BjV1vhMRsyuzuwD4QOrA3DjSGVYYBWQPNis8K+IZx1+pCxpwuntz3DgDZhsUAs6MUA1Ly\nGObeU2DWBvaSdBOjdhedQQaMFEXhp2+fICMxji9eOvX3q9UI5uckTStjr++Zuuc0NzGX1qHWQHcJ\nSjA7gcl4DMG8vjEhSDYkYzaaabA00D4QZHMSeBdreHZCRIoQYlrKGDWwz3bmXwsocOqd8O8z2C4V\nG7ro/TYAqYxRFPIMKX4za1/GnGO0WlvDDuwgs/ZAj7sgJ5mlhansOLOb1LhUHMOyvBQL97+YEG+G\n4Qj8Rjxv0LMssHslj0ECyK7aHvbW9/L1TeWYDP6HqxbmSWVMpNYC9d2yLu9bYstLyKPVOs3AHkHG\nvigvmXi9lr314V95lSSXUNdfT4/VFnjRDTKwl6aUkh4//ZmLkvQEtcZ+3pG7TI72R1KOGeqITePU\ng9szpjypOGQppnmwGZfiijiw1w/UB5w0vGtVAVbNcSqTl1HfM4JBpyFvOhYAZwOTeZqlmNlWYw9e\ny3W6FP7trRryUozce3Fgd8+q3GQsow5aQ23BmkR9lxWNkM1MD3mJeXQOd2J3BbBwDkIkgV2v1XBx\nmZkPa8O/Ki5NKaVhoAFFgdwApRiny8knHZ9MuwzjoTjDxJnekYArOv0RVWAXQtwphDgqhHAJIabn\nxqQSHCGg8mqofRccge1/JzDYHpsyjAe3MqYivYr6gXocLkfAm3rMv8KROnqoSK1gxDESsFG2tMyO\nRj/AqKWUui4rJemm2dM3MZkjL8Vo9NIOYxaRnxqPTiMCDim9WH2GIy0WHr5uAXG6wDLThZ4Gamtk\n5Zi6biuFZtME/6O8xDxciiv4FrIARBLYAdZVZFDXZaW1PzxZYXFyMf1jfaAZ8e/siOwbDdoHp12G\n8VCSnsCY00XbQPiSx2gz9iPAbUCIrc0qUTH/OhgbhMYg25h8iXXGvuxeuPz7lGcuxu6y0zzYHPCm\nXqljGMNJHvxuU/LhaK+0Pf70VCY1bZaoJ05jSrxZqmLCLT147ASi8C06G+i0GvLT4v1KHi2jdn76\n9glWFadx89LApm4A83NkMI20gVrvZ89pboK03JhOAzWSGjuML7gPN2v3JC4aQxd5Kf6vHvd3SLuB\n6TqQeig2R66MiSqwK4pSoyhKlKYmKiEp3Si950++Hfq21h4YbJOKmliRVQWXP0x5EGmih0ZLI2aj\nOaJsKZgyBmBP2x7McVlYrWm09I/MDkWMB5NZWkbbwvRIsXbPujKMh+L0BL819l+9W0uPdYx/umlR\nSKVTYpyO4nRTRA1URVH8BnaPlj1SyWM4lr2TmZ+dREZiHLvCDezuPQMpyf1UZvlPNKo7qslLyJvo\nCTUNijMi17Kfsxq7EOJ+IcR+IcT+rq6uc/Vtzw8MJii7HE5sCZ0ZfvT/ybWBF90V82OUpUgTtGAN\n1HDMvyaTZEgi25Tt9w3DpbjY176P9QWXUO5WwswKDbsHj999uOWYWWYA5ktJuonGnuEJjc+6riH+\na1c9d64sYElBeOWjqpzIrAU6B20MjzmnNMRzEuRVZ6RDSl5nxwgCuxCCdRXp7KrtDss3JsuYB4qG\n4hyr3yXoiqJQ3VEddX0dIDfZiEGnCXvOAMII7EKIbUKII37+m7r9IQiKojyhKMoqRVFWZWbOzhf2\nrGbeNXI1XZD9qAz3wsdPwuLbxhdlxxCT3kR+Yn7IjD2SxqkHf9uUAE70nqDf1s/FuRdz92rZtKsI\nkCHNCPETbQVCMosDe5HZxOCogz6fbVA/eqOGOJ2W71wT/uupKjeZxt5hrLbAvRhf6rqmSh0BDFoD\nWfFZEStjwvWJmcy6yky6h8Y40RH66utAkwXXmJnERP+KqAZLA72jvTEJ7OOTweFn7CENoRVF2RzV\nqVRiw7xrgW/JrD0rgBvhR7+GMSts+N5ZO4Zn65E/hsaG6B7pnlZgL08tZ/+J/ThdTrSa8ebc3ja5\n4/Ti3ItJLkonO8XIssJZNEnsx1YgKLO4FFPiI3k0Jxh4/0Qn24938sh1CwI7VfqhKjcJRYHj7YNh\nLY+vd5t/lfopsU1Hy+4J7KHsBCazzu0A++Gpbqpyg78pbDvWAY5MrMpEk7IRxwjVHdW8cuoVgJgE\ndhi/mgoXVe44V/BOoQaosw/3wt7HYdFnpEPkWcIjTfSnjGkcdO85jbAU43lcm9M2pZ66p30PpSml\nZJmyMOq13Lw0L6qFKTEnkox9zAr24VmbsftKHu1OFz98/Rgl6aaIbKoBb1AMtxzT0GMlTqch148e\nPC8hL/LAHsaSDX/kpBipyEoM2UBVFIVtNZ3kJRRxZrCRoz1H+d3h3/GVt7/CZX+6jAe3Pch7Z97j\nlvJbppXk+KM4Qi17tHLHW4UQzcAlwBtCiDC6eyrTZt61ckGu1Y9ues9vpHJmw3fP6hHKU8qxu+x+\nPWMaB8I3/5ryuH4aqHannQMdB7g45+JpnvYc4KmxhxPYZ+nUqYdCswkhZGD/40eNnO6y8o83LAwq\nb/RHQVo8SUZd2IFdSlgT/Naq8xLzaLe243SF7xgZrrOjP9ZVZLC3vgebI/D3O9ZmoaV/hJW587A5\nbdz9+t384sAv6LX1cu+Ce3ls82PsumcX/7LuX2KWhBS7J4PDJardXIqivAK8Es1jqETAvGvhg5/I\nKdRl94x/fqQP9j4GVTdD9qKzegRf067SlIkmUA2WBgSCwuTCiB+3PGVc8nhF0RUAHOo+xIhjhLW5\na6M89VkkPhUQ4ZViZunUqQejXktOspFPzvRxoLGP9ZUZXFkVud+QECKiBmp99xCVWf6NtPIS83Ao\nDrpGurzN1FBEG9if2t3AgcZ+Lin3Py267VgnQsBXVtyCub6fKnMVa3PXkmk6e7/X4gi3fKmlmLlE\n7jLp2Dh5CnXPY2CzSJvfs4wnmPtrdDZYGshLzCNOGxfx4yYaEslJyJmQse9t24tGaFiVM4tn3zRa\nOWwUzvTpLJ069aU43cT7J7qwjjl59MaF0844q3KTON4+GFJh4nC6aOod9ltfB7zL0CPRsk+3FANw\ncZkZrUbwYW1g5d7WmnZWFKVRkZHN91Z/j5vKbzqrQR3GXR7DRQ3scwmNRqpjarePT6GODsgyzIIb\n5d7Us0wwZcx0FTEeJm9T2tu2lypzFSlxs2tKcwqm9POiFAPjDdTPry2e/mYpZJ19eMxJU4h9nS39\nI9idSkAJa16CDOyRaNm9lr1BFqkHIsmoZ3lhKh/W+n+jbhsY4UiLhc1VMZzsDgPPZHC4qIF9rjHv\nWvcU6i758d7HwTZwTrJ1D+Wp5dQOTNSyK4oSdWCvSKmgfqAep8vJsH2YQ12HuDh3FtfXPYRrKzDU\nKf+dxRn72rJ0SjMS+LvNlVE9TrgNVM86vECmbtPRskdqJzCZyyoyONzcz8DwVI+abcekvcFVC2Ng\niR0BOq2GLd9cH/bt1cA+1yi73D2F+haMWqTEcf71UjFzjihPLadhoGGCMqZntAer3RqJpUfFAAAO\nbklEQVR1xj7mGuPM4BmqO6pxKI65EdjjwzQC662XpTT9LDEw88Nnlufz3ncuJ9UUnTPo/JwkNCJ0\nYK/3atj9B3aT3oTZaI5Iyx6pncBk1ldm4FLgo7qp6pitNZ2UZiR4h+XOJZFcQamBfa5hMEmLgRNb\n4OPHYbT/nGbrIBuodpedpsEm7+caBhqA6UkdfR8XpOf73ra96DV6lmctj+ao5wZTenjWvT21kF5x\n9s8zCzDqtZRmJHCsLfiwT323lWSjDnNC4DeS3ITItOwDYwNRZexLC1NJjNOx89TEwD44auej091s\nrsqaXZJbP6iBfS4y/1o5hbrj51B5DeSd2+DnkSbW9Y8vGvaYf3k8NKJ53NP9p9nbvpdlWcuI183e\n7NZLuKWYnlq5y/YCoSo3tDKmvttKaWZi0ECZlxiZlj3aUoxeq2FtmXmKb8yOk93YnQpXLYyhwd5Z\nQg3sc5FK9y5Uxwhc/vA5//alyVIZ46tgabQ0YtAYyDFN/0Vv0pvIS8ijuqOa473HZ7d+3Zf4NDl4\nZA9iqzrSB8PdkBFd7XouUZWbTEv/CAMjgf3U67utIZem5CXk0WZtC3t5h8UWXWAHWWdv6BnmjE/z\nd1tNB2kmPSuKZtHkcwDUwD4XScmHokulEiY/NiPLkeBPGdNgaaAouWiCHcB0KE8tZ3frboC5UV+H\n8IaUetzP1QVSigFY6G6gHg+QtY/anbQOjIQ0dctNzMXmtNEzGt5CE8uYJWol1fpJNr52p4t3j3ey\naUEWOu3sD5uz/4Qq/vniq3Dn0zP27StSK6Zk7LEYn/bU2U06E4syzu6wVcwIxy+mx/1cXUCBPZgy\nxulS+NEbNSgKLMgJ3hT02PeGo4yxu+yMOEaiztjLMxPJTo7zBvb9DX0MjNi5euG5lTlOFzWwz1W0\netBGNTgcFeWp5TRYpDLG6XLSNNgUk8DuqbOvylk1LR3yjBCOX0z3KRBaSCs5J0eaDWQnx5Fm0lMz\nqYE6Mubka3+s5o97GvnahrKQmnDPwo0Wa2gtezTDSb5IG99MdrttfLfVdGDQaVhfOXtnEHyZucig\nMqcpTy3H4XLQNNiEXqPH4XJEpYjxUJkma9Bzpr4O4xl7MMljT60M6to58mYVA4QQsoHaPp6xdw/Z\n+Oun93O4uZ9/vmURX7ikJOTjeKdPw8jYo7ETmMy6ynReOtDM0VYLW491cFl5OglxcyNkzo1Tqsw6\nfBUsHuVKLDL2KnMV/7ruX7my6MqoH+ucEc6yjQtI6uhLVW4yz+xp9FoHfOm/9tE5OMpjn1vJ1YvC\na7QnGZJIMiSFNX0ay8B+mdvG97921dPUO8zXNpZF/ZjnCjWwq0yLspQyBILa/lrvH1EsArsQgpvK\nb4r6cc4p3lJMAC27yyWbp2WXn6sTzRoW5iZjc7h46UAzP95yHK0Q/Omra1leFNqn3RePMiYUsSrF\nAGQlGZmfncTLn8g3lHNtIxANao1dZVrE6+LJT8ynrr+OhoEGkvRJmI3mmT7WzKAzgCExcClmsFVK\nUy8gDbsHTwP14ZcOk2Yy8PJDl0Yc1CF8LXssM3YYX3K9tCCFbD9+8bMVNbCrTJvy1HJq+2tpsDRQ\nnFw866fxzirBhpS6T8l/0y8cDbuHiqxEr/b7pQcvjdh+1oMnsIfSssc8sLvLMXMpWwe1FKMSBeWp\n5exq3YXFZmF17uqZPs7MEm8OrIq5AKWOHgw6De9953IS43RR6b9zE3IZdgyH1KgP2AaA2JRiQNbZ\nv3llJfdeXBSTxztXqIFdZdpUpFbgcDnoHOmM2QqwOYspiBFYz2lZqkma/aPoZ4NoDcVgXMveMtQS\nNLBHY9nrD4NOw7eumheTxzqXqKUYlWnjUcZAdOZf5wWm9MClmJ5Tsr5+IZeqoiQ3UWrZQ0keLbbo\np07PB9TArjJtSlNKEchgdcEH9nhzYFXMBSp1jCX5CTJjD2Xfe7LvpHc5x4WMGthVpo1HGQOxkTrO\naUxmufDEOcnwymGD/iY1sEdJSlwK8br4oMqY5sFmanpr2FS46RyebHai1thVoqIyrZIx1xgmfWQ7\nGc87PFr2kT5I9Nmu01sPiuuCVMTEEiEEeQnBJY/bm7YDcGXxHBpuO0uogV0lKr618lv0jYaxZOJ8\nx+TjF+Mb2L2KmAtPwx5r8hLzgpZitjdtZ4F5AYVJhefwVLMTtRSjEhWlKaWsyF4x08eYeQI5PF7A\nUsdYE2xIqWu4i4OdB+eWFcVZRA3sKiqxID6AEVjPKUjMBmNsdNUXMnmJeVjGLAyNDU352ntn3kNB\nYXPR5hk42exDDewqKrEg0LKNntNqth4jPGoXf+WYbY3bKEkumSDBvZBRA7uKSiwIVIrpPqXW12NE\nIC37gG2Afe37uLLoygvb1sIHNbCrqMQCvQm0cRNLMZ49p6oiJiZ4pLWTM/YPmj/AoTjYXKyWYTxE\nFdiFED8VQhwXQhwSQrwihJj9W15VVM4GQshyjO+QUk+d/FctxcQEs9GMQWOY0kDd1riNnIQcFqXP\nkVWK54BoM/atwGJFUS4CTgKPRH8kFZU5ymSHxx63q2OGmrHHAo3QTFHGDNuH2d26m81Fm9UyjA9R\nBXZFUd5RFMXh/nAPUBD9kVRU5ijxaRObpz21cs9p6gU+lRtDchNyJwT2nS07sTltqsxxErGssd8H\nbInh46mozC0mOzz21EJasVzEoRITJg8pbW/cjtloZnnW8hk81ewj5OSpEGIb4M9v9B8URfmL+zb/\nADiA/w7yOPcD9wMUFc0tb2MVlbCY7PDYXas2TmNMXmIevaO9jDpGEULwQfMHXFd6HVqNdqaPNqsI\nGdgVRQnaahZCfBG4EbhSCbLeRFGUJ4AnAFatWhV8DYqKylwk3iyVMC6X/Lj3NJRumNkznWfkJkjJ\nY6u1lebBZoYdw6oaxg9RecUIIa4FHgY2KooyHJsjqajMUUxmafg12g/2EbAPqxr2GOORPLYNtbGt\ncRuJ+kQuzrl4hk81+4jWBOxXQByw1d2R3qMoygNRn0pFZS7imT4d6YOBM/L/VUVMTMlLlNOnTYNN\nvHfmPTYWbkSvjc22pPOJqAK7oiiqQFdFxUO8j8Ojav51VsiMz0QndLxe9zr9tn6uKrpqpo80K1En\nT1VUYoXJxwis5zToEyApd2bPdJ6h1WjJTsjmUNchjFojl+ZfOtNHmpWogV1FJVb4+sV0q3tOzxae\ncsy6/HXE6+Jn+DSzEzWwq6jEismlGLUMc1bwKGPUTUmBUQO7ikqsMKbISdPBNuhvVBunZ4nK1Eri\ndfFsKFClpIFQV+OpqMQKIWQ5puWAe8+pmrGfDT5b9VmuL7ueZIO6vCQQasauohJL4s3Q+on8f1XD\nflbQa/VkmbJC3/ACRg3sKiqxxGQGx4j8fzVjV5kh1MCuohJLPA3UhCxZc1dRmQHUwK6iEks8kkc1\nW1eZQdTArqISSzyBPUMN7CozhxrYVVRiSbyasavMPGpgV1GJJR4jMDWwq8wgamBXUYklOYtl1p6n\nbvRRmTnUASUVlViStxwerp/pU6hc4KgZu4qKisp5hhrYVVRUVM4z1MCuoqKicp6hBnYVFRWV8ww1\nsKuoqKicZ6iBXUVFReU8Qw3sKioqKucZamBXUVFROc9QA7uKiorKeYYa2FVUVFTOM9TArqKionKe\noQZ2FRUVlfMMNbCrqKionGeogV1FRUXlPCOqwC6E+KEQ4pAQ4qAQ4h0hRF6sDqaioqKiMj2izdh/\nqijKRYqiLANeBx6NwZlUVFRUVKIgqsCuKIrF58MEQInuOCoqKioq0RL1BiUhxI+ALwADwKYgt7sf\nuN/94ZAQ4kS03ztKMoDuGT7DbEF9LsZRn4tx1OdinNnyXBSHcyOhKMGTbCHENiDHz5f+QVGUv/jc\n7hHAqCjKP0VyyplCCLFfUZRVM32O2YD6XIyjPhfjqM/FOHPtuQiZsSuKsjnMx3oWeAOYE4FdRUVF\n5XwlWlVMpc+HNwPHozuOioqKikq0RFtj/zchxHzABTQCD0R/pHPGEzN9gFmE+lyMoz4X46jPxThz\n6rkIWWNXUVFRUZlbqJOnKioqKucZamBXUVFROc9QAzsghPiOEEIRQmTM9FlmCiHET4UQx90WEa8I\nIVJn+kznGiHEtUKIE0KIWiHE/5rp88wUQohCIcR7QogaIcRRIcQ3Z/pMM40QQiuE+EQI8fpMnyUc\nLvjALoQoBK4Cmmb6LDPMVmCxoigXASeBR2b4POcUIYQW+DVwHbAQuEcIsXBmTzVjOIBvK4pSBawF\nvn4BPxcevgnUzPQhwuWCD+zA/wO+xwVuh6AoyjuKojjcH+4BCmbyPDPAGqBWUZQ6RVHGgOeAW2b4\nTDOCoihtiqIccP//IDKg5c/sqWYOIUQBcAPw25k+S7hc0IFdCHEz0KIoyqczfZZZxn3Alpk+xDkm\nHzjj83EzF3Aw8yCEKAGWA3tn9iQzyi+QyZ9rpg8SLlF7xcx2glkiAN8Hrj63J5o5wrGHEEL8A/JS\n/L/P5dlmAcLP5y7oqzghRCLwEvB3kwz/LhiEEDcCnYqiVAshLp/p84TLeR/YA1kiCCGWAKXAp0II\nkKWHA0KINYqitJ/DI54zQtlDCCG+CNwIXKlceAMOzUChz8cFQOsMnWXGEULokUH9vxVFeXmmzzOD\nXAbcLIS4HjACyUKIZxRF+dwMnyso6oCSGyFEA7BKUZTZ4OB2zhFCXAv8O7BRUZSumT7PuUYIoUM2\nja8EWoB9wL2Kohyd0YPNAEJmOk8DvYqi/N1Mn2e24M7Yv6Moyo0zfZZQXNA1dpUJ/ApIAra6N2I9\nNtMHOpe4G8ffAN5GNgufvxCDupvLgM8DV7hfCwfdGavKHEHN2FVUVFTOM9SMXUVFReU8Qw3sKioq\nKucZamBXUVFROc9QA7uKiorKeYYa2FVUVFTOM9TArqKionKeoQZ2FRUVlfOM/x9sJKJIE1JgZQAA\nAABJRU5ErkJggg==\n", 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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fb2893d6c50>" + "<matplotlib.figure.Figure at 0x7fbc89c0d6a0>" ] }, "metadata": {}, @@ -74,34 +67,42 @@ } ], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as pl\n", - "from sklearn.gaussian_process.kernels import RBF as skRBF\n", - "\n", - "# Generate some data\n", + "# Generate some input data\n", "n = 50\n", - "Xtest = np.linspace(-5, 5, n).reshape(-1,1)\n", - "Xtest_ = np.concatenate((Xtest,Xtest),axis=1)\n", + "Xtest = np.linspace(0, 1, n).reshape(-1,1)\n", "\n", "# Get a RBF kernel\n", "K = skRBF(length_scale=.2)\n", - "# Compute the training data covariance matrix\n", + "# Build the covariance matrix from the \n", "K_ss = K(Xtest,Xtest)\n", "kernel=K\n", "\n", - "# Get cholesky decomposition (square root) of the\n", + "# compute the cholesky decomposition of the\n", "# covariance matrix\n", "L = np.linalg.cholesky(K_ss + 1e-15*np.eye(n))\n", + "\n", "# Sample 3 sets of standard normals for our test points,\n", "# multiply them by the square root of the covariance matrix\n", + "# We assume a zero mean vector \n", "f_prior = np.dot(L, np.random.normal(size=(n,3)))\n", "\n", "# Now let's plot the 3 sampled functions.\n", - "pl.plot(Xtest, f_prior)\n", - "pl.axis([-5, 5, -3, 3])\n", - "pl.title('Three samples from the GP prior')\n" + "plt.plot(Xtest, f_prior)\n", + "plt.axis([0, 1, -3, 3])\n", + "plt.xticks([])\n", + "plt.yticks([])\n", + "plt.title('Three samples from the GP prior')\n" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": 3, @@ -110,7 +111,7 @@ { "data": { "text/plain": [ - "<matplotlib.text.Text at 0x7fb28668d780>" + "<matplotlib.text.Text at 0x7fbc86ecfcf8>" ] }, "execution_count": 3, @@ -119,9 +120,9 @@ }, { "data": { - "image/png": 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yDWOzlWC15qfc/pSl5wA89UWoPgHO/a6+La9S/30Me+xSSsMJ8KCqbjTNhZR+oht10L3r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\n", 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QJp2MMJMrK5nib0OXbe3Qv11L6G9PYp06xrZ89QaE37+/k9eRIdA0r9OTW72o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\np6AXEQk4Bb2ISMAp6EVEAu7/AU4ts+8WalCdAAAAAElFTkSuQmCC\n", "text/plain": [ - "<matplotlib.figure.Figure at 0x7fb2866d5400>" + "<matplotlib.figure.Figure at 0x7fbc89c0d668>" ] }, "metadata": {}, @@ -130,8 +131,8 @@ ], "source": [ "# Noiseless training data\n", - "Xtrain = np.array([-4, -3, -2, -1, 1]).reshape(5,1)\n", - "ytrain = np.sin(Xtrain)\n", + "Xtrain = np.array([0.1, 0.4,0.6, 0.9]).reshape(4,1)\n", + "ytrain = np.sin(5*Xtrain)\n", "\n", "# Apply the kernel function to our training points\n", "K = kernel(Xtrain, Xtrain)\n", @@ -149,12 +150,19 @@ "L = np.linalg.cholesky(K_ss + 1e-6*np.eye(n) - np.dot(Lk.T, Lk))\n", "f_post = mu.reshape(-1,1) + np.dot(L, np.random.normal(size=(n,3)))\n", "\n", - "pl.plot(Xtrain, ytrain, 'bs', ms=8)\n", - "pl.plot(Xtest, f_post)\n", - "pl.gca().fill_between(Xtest.flat, mu-2*stdv, mu+2*stdv, color=\"beige\")\n", - "pl.plot(Xtest, mu, 'r--', lw=2)\n", - "pl.axis([-5, 5, -3, 3])\n", - "pl.title('Three samples from the GP posterior')\n" + "plt.plot(Xtrain, ytrain, 'bs', ms=8)\n", + "plt.plot(Xtest, f_post)\n", + "plt.gca().fill_between(Xtest.flat, mu-2*stdv, mu+2*stdv, color=\"beige\")\n", + "plt.plot(Xtest, mu, 'r--', lw=2)\n", + "plt.axis([0, 1, -3, 3])\n", + "plt.title('Three samples from the GP posterior')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We set up a synthetic emphirical risk function that should be minimized by Bayesian optimization." ] }, { @@ -166,19 +174,19 @@ "outputs": [ { "data": { - "image/png": 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AYKWZPWlmfyU4lbZ/aQt0B5x5VEvG/vxoUlPEeX+bzBvTVyU6JOcAGDN1JRf8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\ncZJSgfT4huWccy5ZRXPEcQG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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fb2866aa0f0>" + "(0, 250)" ] }, + "execution_count": 4, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" }, { "data": { - "image/png": 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1grOpLgTui2tU1Vjt9FQev7gXHZvU5YnJS1m2eTd/vbQ3zevXTnRozn3Lpl17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\nYuKJwznnXEz+HxoUxbbSfCMuAAAAAElFTkSuQmCC\n", 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TowoOgI5pKTx2cSYb9uRz88tLdLBcpJErLinlpy8sZkfeYSZfOkQHwyOg0QUHwKhjW/PL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\n0/vQuVVTv0uTerB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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fb2867705f8>" + "<matplotlib.figure.Figure at 0x7fbc86fefcc0>" ] }, "metadata": {}, @@ -186,27 +194,20 @@ } ], "source": [ - "x = np.array([40, 170])\n", - "y = np.array([65, 84])\n", - "vx= np.linspace(1., 250., num=200)\n", "def fkt(vx):\n", " return 0.004*(vx-100.)**2+40\n", "\n", + "x = np.array([40, 170])\n", + "y = [fkt(x_) for x_ in x]\n", "\n", - "for i in range(1, 3):\n", - " plt.scatter(x[:i], y[:i], color=colors[1])\n", - "\n", - " plt.title(\"Hyperparameter optimization, Iteration #{}\".format(i))\n", - " plt.xlabel(\"Number of hidden units\")\n", - " plt.ylabel(\"Classification Accuracy (%)\")\n", - "\n", - " plt.plot(vx,fkt(vx))\n", - " \n", - " plt.ylim(0, 100)\n", - " plt.xlim(0, 250)\n", - " \n", - " #saveas('hid-units-vs-accuracy-iter{}'.format(i))\n", - " plt.show()" + "vx= np.linspace(1., 250., num=200)\n", + "plt.scatter(x, y)\n", + "plt.title(\"Hyperparameter optimization\")\n", + "plt.xlabel(\"hyperparameter\")\n", + "plt.ylabel(\"Empirical risk\")\n", + "plt.plot(vx,fkt(vx))\n", + "plt.ylim(0, 100)\n", + "plt.xlim(0, 250)" ] }, { @@ -218,16 +219,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ - "from sklearn.gaussian_process import GaussianProcess, GaussianProcessRegressor\n", - "from sklearn.gaussian_process import correlation_models as correlation\n", - "\n", "def plot_gp_bounds(x, y, x_predict, y_predict, y_std, ax=None):\n", " if ax is None:\n", " ax = plt.gca()\n", @@ -235,7 +233,7 @@ " bound1 = y_predict + 1.96 * y_std\n", " bound2 = y_predict - 1.96 * y_std\n", "\n", - " ax.plot(x_predict, y_predict, color='b', lw=3.3)\n", + " ax.plot(x_predict, y_predict, color='k', lw=4.3)\n", " \n", " \n", " ax.fill_between(\n", @@ -244,7 +242,14 @@ " bound2.reshape(len(bound2)),\n", " alpha=0.3, color='gray'\n", " )\n", - " ax.scatter(x, y, color='k', s=120)\n", + " ax.scatter(x, y, color='b', s=220)\n", + " \n", + " ax.tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom='off', # ticks along the bottom edge are off\n", + " top='off', # ticks along the top edge are off\n", + " labelbottom='off') # labels along the bottom edge are off\n", " \n", " return ax\n", "\n", @@ -271,7 +276,7 @@ " ax.set_title(\"Gaussian Process regression after {} iterations\".format(index))\n", " ax.set_xlabel(\"Hyperparameter\")\n", " ax.set_ylabel(\"Emphirical risk\")\n", - "\n", + " \n", " plot_gp_bounds(xi, yi, x_predict, y_predict, y_std, ax=ax)\n", "\n", " ax.set_ylim(0, 100)\n", @@ -282,9 +287,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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tdHV1LbX55S9GHo+HLVu20NLSYlcLjDHGmGnS6TSDg4MMDAwArMmBdwthAfIM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\nh8P2t2kVrbsAGUBEvgf8qqo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C+/YVl4XOLDrz/qR8RWDr1q2Mjo6We5Wd9Av7UrU5WIqF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+ "text/plain": [ + "<matplotlib.figure.Figure at 0x7fbc4f9079b0>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(10, 7))\n", "\n", @@ -296,7 +312,12 @@ "xlist = list(x)\n", "ylist = list(y)\n", "\n", - "num_of_eval = 12\n", + "num_of_eval = 1\n", + "\n", + "# hyperparameter of the u method \n", + "kappa = 0.1\n", + "\n", + "acquisition_function_flag = 1\n", "\n", "for column_id, index in enumerate(range(2,num_of_eval+2)):\n", " _, idx = np.unique(x, return_index=True)\n", @@ -304,39 +325,52 @@ " x=x[np.sort(idx)]\n", " y=y[np.sort(idy)]\n", " \n", + " # generate a surrogate gp model\n", " xi, yi, x_predict, y_predict, y_std = gp(x, y, index)\n", " \n", + " # get the best emphirical risk value \n", " y_min = min(yi)\n", + " \n", " z = (y_min-y_predict)/y_std\n", " \n", - " #expected_improvement = norm.cdf(z)\n", - " acquisition_function = (y_min-y_predict) * norm.cdf(z) + y_std * norm.pdf(z)\n", - " #print(y_predict.shape, y_std.shape)\n", - " #expected_improvement = -y_predict + 0.01 * y_std\n", + " # compute an aquisition \n", + " if acquisition_function_flag==1:\n", + " acquisition_function = norm.cdf(z)\n", + " \n", + " elif acquisition_function_flag==2:\n", + " acquisition_function = (y_min-y_predict) * norm.cdf(z) + y_std * norm.pdf(z)\n", + " \n", + " elif acquisition_function_flag==3: \n", + " acquisition_function = -y_predict + kappa * y_std + 100\n", " \n", " max_index = acquisition_function.argmax()\n", " \n", + " # evaluate emphirical risk at new position \n", " max_fkt = fkt(max_index)\n", - " \n", + " \n", + " # add new position and emphirical risk value to the set of evaluated values \n", " xlist.append(max_index)\n", " ylist.append(max_fkt)\n", " x=np.array(xlist)\n", " y=np.array(ylist)\n", " \n", " \n", + " \n", + "# plotting settings \n", "ax1 = plt.subplot2grid((2, 1), (0, 0))\n", "ax2 = plt.subplot2grid((2, 1), (1, 0), sharex=ax1)\n", "\n", - "\n", "ax1 = plot_gp_example1(xi, yi, x_predict, y_predict, y_std, index, ax1)\n", "ax1.set_title(\"Gaussian Process regression\\nafter {} iterations\".format(index)) \n", " \n", - "ax2.set_title(\"Expected Improvement\\nafter {} iterations\".format(index))\n", + "ax2.set_title(\"Acquisition_function\\nafter {} iterations\".format(index))\n", "ax2.set_xlabel('Hyperparameter')\n", - "ax2.plot(x_predict, acquisition_function, lw=4) \n", + "ax2.plot(x_predict, acquisition_function, lw=1)\n", + "ax2.fill_between(x_predict, acquisition_function[:,0]*0, acquisition_function[:,0], label='acquisition_function')\n", + "\n", "\n", "ax2.scatter(x_predict[max_index], acquisition_function[max_index],\n", - " marker='*', s=120, color='k',\n", + " s=180, color='k',\n", " label='Next step', zorder=10)\n", "\n", "ax2.legend(loc='upper left')\n", @@ -348,24 +382,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1347, 64)\n", - "10\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m<ipython-input-6-dc4591004739>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;31m# perform nested cross validation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0mnested_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m 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"\u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1295\u001b[0m run_metadata):\n\u001b[1;32m 1296\u001b[0m \u001b[0;31m# Ensure any changes to the graph are reflected in the runtime.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1297\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1298\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_exception_on_not_ok_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1299\u001b[0m \u001b[0;32mif\u001b[0m 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"\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "from skopt import BayesSearchCV\n", "from sklearn.datasets import load_digits\n", - "from sklearn.svm import SVC\n", "from sklearn.model_selection import train_test_split\n", - "from sklearn import linear_model\n", "from sklearn.model_selection import cross_val_score\n", "from keras.models import Sequential\n", "from keras.layers import Dense, Dropout\n", @@ -384,14 +453,15 @@ " model.add(Dense(C, activation='relu', input_shape=(64,)))\n", " model.add(Dropout(0.2))\n", " model.add(Dense(num_classes, activation='softmax'))\n", - "\n", " model.compile(loss='categorical_crossentropy',\n", " optimizer=RMSprop(), metrics=['accuracy'])\n", "\n", " return model\n", "\n", - "estimator = KerasClassifier(build_fn=create_model, epochs=2, batch_size=10, verbose=0)#\n", + "# create a scikit-learn estimator from a keras model\n", + "estimator = KerasClassifier(build_fn=create_model, epochs=2, batch_size=10, verbose=0)\n", "\n", + "# set the optimization parameter\n", "opt = BayesSearchCV(\n", " estimator,\n", " {\n", @@ -402,11 +472,9 @@ "\n", "\n", "opt.fit(X_train, y_train)\n", - "#print(opt.total_iterations)\n", - "\n", "\n", - "nested_score = cross_val_score(opt, X=X_train, y=y_train)\n", - "#print(nested_score.mean()) \n", + "# perform nested cross validation \n", + "nested_score = cross_val_score(opt, X=X_train, y=y_train) \n", "\n", "print(\"val. score: %s\" % opt.best_score_)\n", "print(\"test score: %s\" % opt.score(X_test, y_test))" diff --git a/hyperopt/.ipynb_checkpoints/crosval-checkpoint.ipynb b/hyperopt/.ipynb_checkpoints/crosval-checkpoint.ipynb index 85d27f10b24f62d59e51d13a4a2682878d19d34e..bd44c8f739ae5b3ea9b36d0a10853075299bfc4c 100644 --- a/hyperopt/.ipynb_checkpoints/crosval-checkpoint.ipynb +++ b/hyperopt/.ipynb_checkpoints/crosval-checkpoint.ipynb @@ -11,6 +11,8 @@ "name": "stderr", "output_type": "stream", "text": [ + "C:\\Users\\kk\\Anaconda3\\envs\\haf_workshop\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n", "Using TensorFlow backend.\n" ] } @@ -23,13 +25,14 @@ "import load_data as ld\n", "import os\n", "\n", - "%matplotlib inline\n", - "\n", - "#plt.style.use('ggplot')\n", "warnings.filterwarnings(\"ignore\")\n", "\n", - "colors = plt.rcParams['axes.color_cycle']\n", + "%matplotlib inline\n", "\n", + "# set colour scheme \n", + "colors = plt.rcParams['axes.prop_cycle']\n", + "\n", + "# make random variables reproducible \n", "RANDOM_STATE = 123" ] }, @@ -39,16 +42,14 @@ "source": [ "## Validation Curve\n", "\n", - "The validation curve plots the influence of a single hyperparameter on the training score and the validation score to find out whether the estimator is overfitting or underfitting. We demonstrate the concept of the learning curve by means of the regularization parameter of the ridge logistic regression classificator applied to the mnist dataset. We use the scikit-learn module to implement the valdation curve.\n", + "The validation curve plots the influence of a single hyperparameter on the training score and the validation score in order to detect overfitting or underfitting. We demonstrate the concept of the learning curve by means of the regularization parameter of the ridge logistic regression classificator applied to the mnist dataset. We use the scikit-learn module to implement the valdation curve.\n", "We start with loading the necessary scikit-learn modules:" ] }, { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", @@ -57,7 +58,7 @@ "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import ShuffleSplit\n", - "from sklearn.pipeline import make_pipeline\n" + "from sklearn.pipeline import make_pipeline" ] }, { @@ -76,14 +77,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "3.0\n" + "label: 2.0\n" ] }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fc67004b3c8>" + "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, @@ -100,14 +101,17 @@ "y=y[0:num_of_samples].astype(float)\n", "\n", "# choose example number 12\n", - "example_num=12\n", + "example_num=122\n", + "\n", "aa=X[example_num,:]\n", "aa=aa.reshape(28,28)\n", "plt.imshow(aa, cmap='gray')\n", "plt.grid(False)\n", "plt.xticks([])\n", "plt.yticks([])\n", - "print(y[example_num])" + "\n", + "# print the label \n", + "print('label:', y[example_num])" ] }, { @@ -129,14 +133,16 @@ "Size of original input data: (500, 784)\n", "Size of training input data: (450, 784)\n", "Size of test input data: (50, 784)\n", - "First 5 labels of original data: [ 5. 0. 4. 1. 9.]\n", - "First 5 labels of train data: [ 4. 9. 8. 8. 5.]\n", - "First 5 labels of test data: [ 6. 3. 0. 8. 4.]\n" + "First 5 labels of original data: [5. 0. 4. 1. 9.]\n", + "First 5 labels of train data: [4. 9. 8. 8. 5.]\n", + "First 5 labels of test data: [6. 3. 0. 8. 4.]\n" ] } ], "source": [ + "# shuffle and split the data into test set and validation set\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n", + "\n", "# print the size of the original data and the training/test data \n", "print('Size of original input data:', X.shape)\n", "print('Size of training input data:', X_train.shape)\n", @@ -185,7 +191,7 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x7fc652648160>" + "<matplotlib.legend.Legend at 0xcc08e48>" ] }, "execution_count": 6, @@ -194,9 +200,9 @@ }, { "data": { - "image/png": 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HyciQ7RYLNzfcUI7Z7Mcbb/RvXDrWRl+RmppHRoZvr6EEQ9HzKC6GCy6A+no4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\n8m8i8msR+W8R+Waq45kKEWkQkYdE5OlUxzJR8e/GY/F9cXG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+ "image/png": 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\n", "text/plain": [ - "<matplotlib.figure.Figure at 0x7fc6526480b8>" + "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, @@ -217,7 +223,7 @@ "if cv_type==2:\n", " rs = KFold(n_splits=10)\n", "\n", - "\n", + "# make the estimate pipeline: \n", "for tt1, index in enumerate(hprange):\n", " if class_method == 1:\n", " estimator = make_pipeline(scaler, linear_model.LogisticRegression(C=index))\n", @@ -245,7 +251,7 @@ " score_array[:,tt1,0] + score_array[:,tt1,1], alpha=0.2, color=color_list[tt1], lw=2)\n", "\n", "plt.ylabel('Score')\n", - "plt.xlabel('C=$\\lambda$')\n", + "plt.xlabel('C=1/$\\lambda$')\n", "plt.legend()" ] }, @@ -255,8 +261,8 @@ "source": [ "## Learning Curve\n", "\n", - "A learning curve shows the validation and training score of an estimator for varying numbers of training samples. It is a tool to find out how much we benefit from adding more training data and whether the estimator suffers more from a variance error or a bias error. If both the validation score and the training score converge to a value that is too low with increasing size of the training set, we will not benefit much from more training data.\n", - "If the training score is much greater than the validation score for the maximum number of training samples, adding more training samples will most likely increase generalization." + "A learning curve shows the validation and training score of an estimator for varying numbers of training samples. It is a technique to find out how much we benefit from adding more training data and whether the estimator suffers more from a variance error or a bias error. If both the validation score and the training score converge to a value that is too low with increasing size of the training set, we will not benefit much from more training data.\n", + "If the training score is much greater than the validation score for the maximum number of training samples, the estimator might benefit from more training data." ] }, { @@ -266,9 +272,9 @@ "outputs": [ { "data": { - "image/png": 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4vkQMkezWLe2sFJfXhcvrotpZTbW7OiAmVosVh9WRtJtMa1gnbYHwteZNMYiw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\n8suGNRFtxnFjOD3ORickunwuZm2YxZw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tJxIJWVRURG5uLitWrIjYt2fPHrZt2xYIbEgm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\nG0ldCjwiqZ+ZbW8wZ+DkqC53Az8BPgt8BdhHCLUOgKTPAr+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\n", "text/plain": [ - "<matplotlib.figure.Figure at 0x7fc68af34a90>" + "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, @@ -276,9 +282,9 @@ }, { "data": { - "image/png": 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rozkCMV25FFcVR3Tl8uvuXxk9ezQ7SncEhTdNbsobQ9/glKNOqdV9PV6PsahR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\nj+boo4/mlVde4bbbbqNPnz6Ul5cDcMEFF/Dqq68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TxhJ6mtn/JIkQDXr/KIp7F3ANYQD+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\n21O97Yo8AxcAAAAASUVORK5CYII=\n", + "image/png": 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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fc65009d320>" + "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, @@ -286,9 +292,9 @@ }, { "data": { - "image/png": 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AcBFh2tJp3PHRHSHCcnPnm5l64VQVFkVRYkZltznugLXNcRLW6scrPEHtgX7A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\njo2LMcaYjmPjYowxpuP8H1P15R5mWd8AAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "<matplotlib.figure.Figure at 0x7fc62b6e7d30>" + "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, @@ -296,9 +302,9 @@ }, { "data": { - "image/png": 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BGA/0BcaFUpAxpiPwN2ChiFxdJj0T191oNwDvhlBUgYi8E0K+EUAPXHe0zXCn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\n", 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\n", "text/plain": [ - "<matplotlib.figure.Figure at 0x7fc62b7696d8>" + "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, @@ -306,9 +312,9 @@ }, { "data": { - "image/png": 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1G+0S4I0ImioUkdcjqDcG6IM1o+0JT9lzxpj3gLuNMS/V9308eSVW6pY4W1xE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\nBsc9smkEYDcKVuhH29cX4sslCIIgaDgx5hIEQRA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\n", "text/plain": [ - "<matplotlib.figure.Figure at 0x7fc629db1908>" + "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, @@ -321,26 +327,29 @@ "from sklearn.model_selection import learning_curve\n", "import plot_learn_curve as plc\n", "\n", - "\n", "# read mnist data set\n", "X, y = ld.read_data('mnist')\n", "\n", "# samples from the nmist data\n", - "num_of_samples=600\n", + "num_of_samples = 600\n", "X=X[0:num_of_samples,:].astype(float)\n", "y=y[0:num_of_samples].astype(float)\n", "\n", - "\n", "# fix random seed for reproducibility\n", - "seed = 7\n", - "np.random.seed(seed)\n", + "np.random.seed(RANDOM_STATE)\n", "train_sizes=np.linspace(0.04, 1, 5)\n", + "\n", "cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n", "hyperpara_range=np.logspace(-3.,1.,5)\n", + "\n", + "\n", + "# loop of hyperparameter range \n", "for hyperpara in hyperpara_range: \n", " estimator = make_pipeline(scaler,linear_model.LogisticRegression(C=hyperpara))\n", - " train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=4, \n", - " train_sizes=train_sizes)\n", + " train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, \n", + " n_jobs=4, train_sizes=train_sizes)\n", + " \n", + " # plot the learning curves for the current hyperparameter \n", " plc.plot_learn_curve(train_sizes, train_scores, test_scores, r'C =' + str(hyperpara))\n", " \n", " " @@ -363,7 +372,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/hyperopt/bayes_optimize.ipynb b/hyperopt/bayes_optimize.ipynb index 1617b13580f89f2384d5ca6b5fa667c401cd0af5..bdca7fd17b57b620717c7af1f9bf6bc029e216a0 100644 --- a/hyperopt/bayes_optimize.ipynb +++ b/hyperopt/bayes_optimize.ipynb @@ -2,9 +2,17 @@ "cells": [ { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], "source": [ "import warnings\n", "import sklearn\n", @@ -12,35 +20,46 @@ "import os\n", "import matplotlib.pyplot as plt\n", "import load_data as ld\n", + "from scipy.stats import norm\n", + "from sklearn.gaussian_process.kernels import RBF as skRBF\n", + "from sklearn.gaussian_process import GaussianProcess, GaussianProcessRegressor\n", + "from sklearn.gaussian_process import correlation_models as correlation\n", "\n", - "%matplotlib inline\n", - "\n", - "#plt.style.use('ggplot')\n", "warnings.filterwarnings(\"ignore\")\n", "\n", - "colors = plt.rcParams['axes.color_cycle']" + "%matplotlib inline\n", + "colors = plt.rcParams['axes.prop_cycle']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Gaussian process is a stochastic process such that every finite subset of size $n$ follows a multivariate Gaussian distribution $p(\\mu, \\Sigma)$ and thus defines a probability distribution over functions $x \\mapsto f(x)$. As a multivariate distribution is uniquely defined by its mean vector $\\mu \\in \\mathbb{R}^n$ and covariance matrix $\\Sigma\n", + "\\in \\mathbb{R}^{n \\times n}$, a Gaussian distribution is uniquely defined by its mean function $x \\mapsto m(x)$ and covariance kernel $x \\mapsto K(x)$. We can make use of the GP definition to sample finite subset from a GP. In a first step, we sample the mean function $m(x)$ and covariance kernel $K(x)$ at finite number of input values and build a corresponding mean vector $\\mu$ and corresponding covariance matrix $\\Sigma$. In a second step we apply a Cholesky decomposition to the covariance matrix $\\Sigma=L L^T$ and sample from the multivariate Gaussian distribution according to \n", + "$y = L z + \\mu$ where $z$ denotes a sample from a zero mean Gaussian distribution with unit variance." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.text.Text at 0x7fb24b2b13c8>" + "<matplotlib.text.Text at 0x7fbc8700d6d8>" ] }, - "execution_count": 10, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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XTjw+zwKObnHg9rl5t+Nd/mPPf/DBZz7I+59+P7f/3+2M+cYWemhhGXQP0jTYNOen0E0l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\nARIJKcY47rywhItKs8Ma2JqRrLWw7RNySbalnuKRI+xzbcLujHBptR9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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fb24b2f5550>" + "<matplotlib.figure.Figure at 0x7fbc89c0d6a0>" ] }, "metadata": {}, @@ -48,54 +67,62 @@ } ], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as pl\n", - "from sklearn.gaussian_process.kernels import RBF as skRBF\n", - "\n", - "# Generate some data\n", + "# Generate some input data\n", "n = 50\n", - "Xtest = np.linspace(-5, 5, n).reshape(-1,1)\n", - "Xtest_ = np.concatenate((Xtest,Xtest),axis=1)\n", + "Xtest = np.linspace(0, 1, n).reshape(-1,1)\n", "\n", "# Get a RBF kernel\n", "K = skRBF(length_scale=.2)\n", - "# Compute the training data covariance matrix\n", + "# Build the covariance matrix from the \n", "K_ss = K(Xtest,Xtest)\n", "kernel=K\n", "\n", - "# Get cholesky decomposition (square root) of the\n", + "# compute the cholesky decomposition of the\n", "# covariance matrix\n", "L = np.linalg.cholesky(K_ss + 1e-15*np.eye(n))\n", + "\n", "# Sample 3 sets of standard normals for our test points,\n", "# multiply them by the square root of the covariance matrix\n", + "# We assume a zero mean vector \n", "f_prior = np.dot(L, np.random.normal(size=(n,3)))\n", "\n", "# Now let's plot the 3 sampled functions.\n", - "pl.plot(Xtest, f_prior)\n", - "pl.axis([-5, 5, -3, 3])\n", - "pl.title('Three samples from the GP prior')\n" + "plt.plot(Xtest, f_prior)\n", + "plt.axis([0, 1, -3, 3])\n", + "plt.xticks([])\n", + "plt.yticks([])\n", + "plt.title('Three samples from the GP prior')\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<matplotlib.text.Text at 0x7fb24b2016a0>" + "<matplotlib.text.Text at 0x7fbc86ecfcf8>" ] }, - "execution_count": 11, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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eHOGFqomGnlZ5KgBq7ctRHyu9/R1s6GjmIPW5SzPY0ThC6yTG4nju3vMwG5te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\n", 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QJp2MMJMrK5nib0OXbe3Qv11L6G9PYp06xrZ89QaE37+/k9eRIdA0r9OTW72o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\np6AXEQk4Bb2ISMAp6EVEAu7/AU4ts+8WalCdAAAAAElFTkSuQmCC\n", "text/plain": [ - "<matplotlib.figure.Figure at 0x7fb24b301f98>" + "<matplotlib.figure.Figure at 0x7fbc89c0d668>" ] }, "metadata": {}, @@ -104,8 +131,8 @@ ], "source": [ "# Noiseless training data\n", - "Xtrain = np.array([-4, -3, -2, -1, 1]).reshape(5,1)\n", - "ytrain = np.sin(Xtrain)\n", + "Xtrain = np.array([0.1, 0.4,0.6, 0.9]).reshape(4,1)\n", + "ytrain = np.sin(5*Xtrain)\n", "\n", "# Apply the kernel function to our training points\n", "K = kernel(Xtrain, Xtrain)\n", @@ -123,36 +150,43 @@ "L = np.linalg.cholesky(K_ss + 1e-6*np.eye(n) - np.dot(Lk.T, Lk))\n", "f_post = mu.reshape(-1,1) + np.dot(L, np.random.normal(size=(n,3)))\n", "\n", - "pl.plot(Xtrain, ytrain, 'bs', ms=8)\n", - "pl.plot(Xtest, f_post)\n", - "pl.gca().fill_between(Xtest.flat, mu-2*stdv, mu+2*stdv, color=\"beige\")\n", - "pl.plot(Xtest, mu, 'r--', lw=2)\n", - "pl.axis([-5, 5, -3, 3])\n", - "pl.title('Three samples from the GP posterior')\n" + "plt.plot(Xtrain, ytrain, 'bs', ms=8)\n", + "plt.plot(Xtest, f_post)\n", + "plt.gca().fill_between(Xtest.flat, mu-2*stdv, mu+2*stdv, color=\"beige\")\n", + "plt.plot(Xtest, mu, 'r--', lw=2)\n", + "plt.axis([0, 1, -3, 3])\n", + "plt.title('Three samples from the GP posterior')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We set up a synthetic emphirical risk function that should be minimized by Bayesian optimization." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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AYKWZPWlmfyU4lbZ/aQt0B5x5VEvG/vxoUlPEeX+bzBvTVyU6JOcAGDN1JRf8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\ncZJSgfT4huWccy5ZRXPEcQG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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fb24b292160>" + "(0, 250)" ] }, + "execution_count": 4, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" }, { "data": { - "image/png": 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1grOpLgTui2tU1Vjt9FQev7gXHZvU5YnJS1m2eTd/vbQ3zevXTnRozn3Lpl17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\nYuKJwznnXEz+HxoUxbbSfCMuAAAAAElFTkSuQmCC\n", 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TowoOgI5pKTx2cSYb9uRz88tLdLBcpJErLinlpy8sZkfeYSZfOkQHwyOg0QUHwKhjW/PL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\n8ay3rEu9L4UOQeP/wlsXq4E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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fb24b0e38d0>" + "<matplotlib.figure.Figure at 0x7fbc86fefcc0>" ] }, "metadata": {}, @@ -160,27 +194,20 @@ } ], "source": [ - "x = np.array([40, 170])\n", - "y = np.array([65, 84])\n", - "vx= np.linspace(1., 250., num=200)\n", "def fkt(vx):\n", " return 0.004*(vx-100.)**2+40\n", "\n", + "x = np.array([40, 170])\n", + "y = [fkt(x_) for x_ in x]\n", "\n", - "for i in range(1, 3):\n", - " plt.scatter(x[:i], y[:i], color=colors[1])\n", - "\n", - " plt.title(\"Hyperparameter optimization, Iteration #{}\".format(i))\n", - " plt.xlabel(\"Number of hidden units\")\n", - " plt.ylabel(\"Classification Accuracy (%)\")\n", - "\n", - " plt.plot(vx,fkt(vx))\n", - " \n", - " plt.ylim(0, 100)\n", - " plt.xlim(0, 250)\n", - " \n", - " #saveas('hid-units-vs-accuracy-iter{}'.format(i))\n", - " plt.show()" + "vx= np.linspace(1., 250., num=200)\n", + "plt.scatter(x, y)\n", + "plt.title(\"Hyperparameter optimization\")\n", + "plt.xlabel(\"hyperparameter\")\n", + "plt.ylabel(\"Empirical risk\")\n", + "plt.plot(vx,fkt(vx))\n", + "plt.ylim(0, 100)\n", + "plt.xlim(0, 250)" ] }, { @@ -192,16 +219,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ - "from sklearn.gaussian_process import GaussianProcess, GaussianProcessRegressor\n", - "from sklearn.gaussian_process import correlation_models as correlation\n", - "\n", "def plot_gp_bounds(x, y, x_predict, y_predict, y_std, ax=None):\n", " if ax is None:\n", " ax = plt.gca()\n", @@ -209,7 +233,7 @@ " bound1 = y_predict + 1.96 * y_std\n", " bound2 = y_predict - 1.96 * y_std\n", "\n", - " ax.plot(x_predict, y_predict, color='b', lw=3.3)\n", + " ax.plot(x_predict, y_predict, color='k', lw=4.3)\n", " \n", " \n", " ax.fill_between(\n", @@ -218,7 +242,14 @@ " bound2.reshape(len(bound2)),\n", " alpha=0.3, color='gray'\n", " )\n", - " ax.scatter(x, y, color='k', s=120)\n", + " ax.scatter(x, y, color='b', s=220)\n", + " \n", + " ax.tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom='off', # ticks along the bottom edge are off\n", + " top='off', # ticks along the top edge are off\n", + " labelbottom='off') # labels along the bottom edge are off\n", " \n", " return ax\n", "\n", @@ -245,7 +276,7 @@ " ax.set_title(\"Gaussian Process regression after {} iterations\".format(index))\n", " ax.set_xlabel(\"Hyperparameter\")\n", " ax.set_ylabel(\"Emphirical risk\")\n", - "\n", + " \n", " plot_gp_bounds(xi, yi, x_predict, y_predict, y_std, ax=ax)\n", "\n", " ax.set_ylim(0, 100)\n", @@ -256,14 +287,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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CAS9cZzIZEokEM2bMoKSk+PRuw8kCMhaQx7Oenp7eMoz+R5YjLFtWxmOPxWlq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tdHV1LbX55S9GHo+HLVu20NLSYlcLjDHGmGnS6TSDg4MMDAwArMmBdwthAfIM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\nh8P2t2kVrbsAGUBEvgf8qqo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C+/YVl4XOLDrz/qR8RWDr1q2Mjo6We5Wd9Av7UrU5WIqF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\nkM3MzMzMMlwgm5mZmZll/B61mImQovifbwAAAABJRU5ErkJggg==\n", "text/plain": [ - "<matplotlib.figure.Figure at 0x7fb24b143748>" + "<matplotlib.figure.Figure at 0x7fbc4f9079b0>" ] }, "metadata": {}, @@ -281,7 +312,12 @@ "xlist = list(x)\n", "ylist = list(y)\n", "\n", - "num_of_eval = 12\n", + "num_of_eval = 1\n", + "\n", + "# hyperparameter of the u method \n", + "kappa = 0.1\n", + "\n", + "acquisition_function_flag = 1\n", "\n", "for column_id, index in enumerate(range(2,num_of_eval+2)):\n", " _, idx = np.unique(x, return_index=True)\n", @@ -289,39 +325,52 @@ " x=x[np.sort(idx)]\n", " y=y[np.sort(idy)]\n", " \n", + " # generate a surrogate gp model\n", " xi, yi, x_predict, y_predict, y_std = gp(x, y, index)\n", " \n", + " # get the best emphirical risk value \n", " y_min = min(yi)\n", + " \n", " z = (y_min-y_predict)/y_std\n", " \n", - " #expected_improvement = norm.cdf(z)\n", - " acquisition_function = (y_min-y_predict) * norm.cdf(z) + y_std * norm.pdf(z)\n", - " #print(y_predict.shape, y_std.shape)\n", - " #expected_improvement = -y_predict + 0.01 * y_std\n", + " # compute an aquisition \n", + " if acquisition_function_flag==1:\n", + " acquisition_function = norm.cdf(z)\n", + " \n", + " elif acquisition_function_flag==2:\n", + " acquisition_function = (y_min-y_predict) * norm.cdf(z) + y_std * norm.pdf(z)\n", + " \n", + " elif acquisition_function_flag==3: \n", + " acquisition_function = -y_predict + kappa * y_std + 100\n", " \n", " max_index = acquisition_function.argmax()\n", " \n", + " # evaluate emphirical risk at new position \n", " max_fkt = fkt(max_index)\n", - " \n", + " \n", + " # add new position and emphirical risk value to the set of evaluated values \n", " xlist.append(max_index)\n", " ylist.append(max_fkt)\n", " x=np.array(xlist)\n", " y=np.array(ylist)\n", " \n", " \n", + " \n", + "# plotting settings \n", "ax1 = plt.subplot2grid((2, 1), (0, 0))\n", "ax2 = plt.subplot2grid((2, 1), (1, 0), sharex=ax1)\n", "\n", - "\n", "ax1 = plot_gp_example1(xi, yi, x_predict, y_predict, y_std, index, ax1)\n", "ax1.set_title(\"Gaussian Process regression\\nafter {} iterations\".format(index)) \n", " \n", - "ax2.set_title(\"Expected Improvement\\nafter {} iterations\".format(index))\n", + "ax2.set_title(\"Acquisition_function\\nafter {} iterations\".format(index))\n", "ax2.set_xlabel('Hyperparameter')\n", - "ax2.plot(x_predict, acquisition_function, lw=4) \n", + "ax2.plot(x_predict, acquisition_function, lw=1)\n", + "ax2.fill_between(x_predict, acquisition_function[:,0]*0, acquisition_function[:,0], label='acquisition_function')\n", + "\n", "\n", "ax2.scatter(x_predict[max_index], acquisition_function[max_index],\n", - " marker='*', s=120, color='k',\n", + " s=180, color='k',\n", " label='Next step', zorder=10)\n", "\n", "ax2.legend(loc='upper left')\n", @@ -333,17 +382,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1347, 64)\n", - "10\n" - ] - }, { "ename": "KeyboardInterrupt", "evalue": "", @@ -351,7 +392,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m<ipython-input-7-3798909daaf3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 49\u001b[0;31m \u001b[0mnested_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 50\u001b[0m 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\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/miniconda3/envs/jupyter/lib/python3.6/site-packages/keras/models.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps)\u001b[0m\n\u001b[1;32m 1002\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1003\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m steps=steps)\n\u001b[0m\u001b[1;32m 1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1006\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", @@ -385,9 +434,7 @@ "source": [ "from skopt import BayesSearchCV\n", "from sklearn.datasets import load_digits\n", - "from sklearn.svm import SVC\n", "from sklearn.model_selection import train_test_split\n", - "from sklearn import linear_model\n", "from sklearn.model_selection import cross_val_score\n", "from keras.models import Sequential\n", "from keras.layers import Dense, Dropout\n", @@ -406,14 +453,15 @@ " model.add(Dense(C, activation='relu', input_shape=(64,)))\n", " model.add(Dropout(0.2))\n", " model.add(Dense(num_classes, activation='softmax'))\n", - "\n", " model.compile(loss='categorical_crossentropy',\n", " optimizer=RMSprop(), metrics=['accuracy'])\n", "\n", " return model\n", "\n", - "estimator = KerasClassifier(build_fn=create_model, epochs=2, batch_size=10, verbose=0)#\n", + "# create a scikit-learn estimator from a keras model\n", + "estimator = KerasClassifier(build_fn=create_model, epochs=2, batch_size=10, verbose=0)\n", "\n", + "# set the optimization parameter\n", "opt = BayesSearchCV(\n", " estimator,\n", " {\n", @@ -424,11 +472,9 @@ "\n", "\n", "opt.fit(X_train, y_train)\n", - "#print(opt.total_iterations)\n", - "\n", "\n", - "nested_score = cross_val_score(opt, X=X_train, y=y_train)\n", - "#print(nested_score.mean()) \n", + "# perform nested cross validation \n", + "nested_score = cross_val_score(opt, X=X_train, y=y_train) \n", "\n", "print(\"val. score: %s\" % opt.best_score_)\n", "print(\"test score: %s\" % opt.score(X_test, y_test))" diff --git a/hyperopt/crosval.ipynb b/hyperopt/crosval.ipynb index 7e550fb534a414f74589f88680b37fd66a098623..4a421b8785d3f357df30e44dca71f8ee849addf7 100644 --- a/hyperopt/crosval.ipynb +++ b/hyperopt/crosval.ipynb @@ -23,13 +23,14 @@ "import load_data as ld\n", "import os\n", "\n", - "%matplotlib inline\n", - "\n", - "#plt.style.use('ggplot')\n", "warnings.filterwarnings(\"ignore\")\n", "\n", - "colors = plt.rcParams['axes.color_cycle']\n", + "%matplotlib inline\n", + "\n", + "# set colour scheme \n", + "colors = plt.rcParams['axes.prop_cycle']\n", "\n", + "# make random variables reproducible \n", "RANDOM_STATE = 123" ] }, @@ -39,7 +40,7 @@ "source": [ "## Validation Curve\n", "\n", - "The validation curve plots the influence of a single hyperparameter on the training score and the validation score to find out whether the estimator is overfitting or underfitting. We demonstrate the concept of the learning curve by means of the regularization parameter of the ridge logistic regression classificator applied to the mnist dataset. We use the scikit-learn module to implement the valdation curve.\n", + "The validation curve plots the influence of a single hyperparameter on the training score and the validation score in order to detect overfitting or underfitting. We demonstrate the concept of the learning curve by means of the regularization parameter of the ridge logistic regression classificator applied to the mnist dataset. We use the scikit-learn module to implement the valdation curve.\n", "We start with loading the necessary scikit-learn modules:" ] }, @@ -57,7 +58,7 @@ "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import ShuffleSplit\n", - "from sklearn.pipeline import make_pipeline\n" + "from sklearn.pipeline import make_pipeline" ] }, { @@ -76,14 +77,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "3.0\n" + "label: 2.0\n" ] }, { "data": { - "image/png": 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+ "image/png": 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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fc67004b3c8>" + "<matplotlib.figure.Figure at 0x7f5d3e935978>" ] }, "metadata": {}, @@ -100,14 +101,17 @@ "y=y[0:num_of_samples].astype(float)\n", "\n", "# choose example number 12\n", - "example_num=12\n", + "example_num=122\n", + "\n", "aa=X[example_num,:]\n", "aa=aa.reshape(28,28)\n", "plt.imshow(aa, cmap='gray')\n", "plt.grid(False)\n", "plt.xticks([])\n", "plt.yticks([])\n", - "print(y[example_num])" + "\n", + "# print the label \n", + "print('label:', y[example_num])" ] }, { @@ -136,7 +140,9 @@ } ], "source": [ + "# shuffle and split the data into test set and validation set\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=0)\n", + "\n", "# print the size of the original data and the training/test data \n", "print('Size of original input data:', X.shape)\n", "print('Size of training input data:', X_train.shape)\n", @@ -177,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": { "scrolled": true }, @@ -185,18 +191,18 @@ { "data": { "text/plain": [ - "<matplotlib.legend.Legend at 0x7fc629b92208>" + "<matplotlib.legend.Legend at 0x7f5d3bca4780>" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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xwCmnDKWsLJzFi7cwc6a1VX2rr69nzZo1fPTRR6xevdrvFgsPDw/YAryifZSX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\ner4V0zyybNGiRZx11lnBnhag/WjYbDb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moNquGi5jTIN81YaXlxfp6dUfPzNh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\ndqWBRimllF1poFFKKWVX/x9kqY1V6dwUPQAAAABJRU5ErkJggg==\n", 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U8d57iTzzTBYBAQcZMsR+7B3rUH7/brvdHoqKvKMwK+HhOq68skqJhULRgs8+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\n/o73L7uMz1JSYu76RLD169ezf/9+qqqquPHGGx1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hWbEwxhgTUtw8wS0incBfgmZlAp3j\nTA+/Dp6XC7Qc4+pHr2sybcaaf7TYg6fHyimcPI4W50TaTDaXUK+jtU3Ge2865hLO/6/g19Pxu+Lk\nNjlanBNpE0u5zFHVzJCtVDUufoANE50efj1q3vZIrXsybcaaP9FcxsnpmPOY6lxCvY7WNomnXML5\n/3WU/2vTIhcnt0k85TKRPFQ1rk5DvTKJ6VfGaROpdU+mzVjzJ5rLWDmFaypzmcjrYxVOHuO9Nx1z\nCef/V/Br+/81sXgm2iaWcpnQZ8TNaahwich2nUBnWrEuXvIAyyVWxUsu8ZIHTE0u8XRkEa4N0Q4g\nQuIlD7BcYlW85BIvecAU5GJHFsYYY0KyIwtjjDEhWbEwxhgTkhULY4wxIVmxmAARSROR90RkcbRj\nCYeInCQiT4jIiyKyKtrxhENELhWRp0TkZRH5SrTjCYeIVIvIMyLyYrRjmayh78azQ9vi69GOJxzT\neTuM5sT3I66LhYhsFJEmEdk5av4FIvKJiNSKyO0T+KjbgBeciXJiIpGLqu5W1RuBrwFRu2UwQrn8\nSlWvB/4JuMLBcI8qQrnsVdUVzkY6cZPMaQnw4tC2uHjKgw1hMrnE2nYYbZK5RP77Ec4TjLH+A/xf\n4HRgZ9C8RGAPUA3MBP4HOBn4AvDqqJ984Fxg6dA/+uLpnMvQMhcD/wVcOd1zGVrux8DpcZLLi9HK\nI4yc7gDmDbV5Ltqxh5NLrG2HCOUSse9HXI/Brapvi0jlqNlnALWquhdARDYDl6jqD4C/Os0kIguB\nNAJfjD4ReU1VBx0NfAyRyGXoc7YAW0TkP4HnnIt4fBHaLgI8CPxaVd93NuLxRWq7xJLJ5ATUA6XA\nB8TgmYpJ5rJraqObnMnkIiK7ifD3I+Y27hQoAeqCpuuH5o1JVdeo6q0EdqxPRaNQHMWkchGRs0Xk\nERF5EnjN6eAmaVK5AKsJHPV9VURudDKwYzDZ7ZIjIk8A80XkDqeDO0bj5fRL4DIRWU/kutFw2pi5\nTJPtMNp42yXi34+4PrIYh4wxL+STiaq6KfKhhG1Suajqm8CbTgUTpsnm8gjwiHPhhGWyubQCsVbw\nRhszJ1XtAa6d6mDCNF4u02E7jDZeLhH/fhyPRxb1QFnQdClwKEqxhMtyiU3xlMuweMrJcjkGx2Ox\n2AbMEZEqEZlJ4OL1lijHdKwsl9gUT7kMi6ecLJdjEe0r/A7fPfA80AAMEKjAK4bmXwh8SuAugjXR\njtNysVxi6SeecrJcIvdjHQkaY4wJ6Xg8DWWMMWaSrFgYY4wJyYqFMcaYkKxYGGOMCcmKhTHGmJCs\nWBhjjAnJioUxxpiQrFgYY4wJyYqFMZMgIoUisllE9ojILhF5TUROmOCyYw5eM/TekyJy1tDrL4jI\nfpnmoxma+GLFwpgJGhpD4yXgTVWtUdWTge8BBRP8iE3ABeO890XgXQBV3UGgj5+rwwrYmAg6Hrso\nN+ZYLQQGVPWJ4Rmq+sFEF9axB69BRE4CPlVVf9DsJuCUYw/VmMiyYmHMxJ0KvDfWGyLyeyB9jLe+\no6pvhPjcRcDro+Y9CCSLSIWq7p90pMZEmBULYyJAVb8cxuLnEzSAkIhcQGAo3/8kcHRhxcJEnV2z\nMGbiPgL+z1hviMjvReSDMX7OPdoHikgqkKWqh4amXcA64CZgB4GjGWOizo4sjJm4rcADInK9qj4F\nICJ/A6SGcWSxEPhd0PS/AP+uqvtEZAdwcVgRGxMhdmRhzARpYPCX/wecN3Tr7EfAWiY4jKWIPA+8\nA8wVkXoRWUHQ9QoRmQucBzw8tIgdWZiYYYMfGRNFIvI+8EVVHYh2LMYcjRULY4wxIdlpKGOMMSFZ\nsTDGGBOSFQtjjDEhWbEwxhgTkhULY4wxIVmxMMYYE5IVC2OMMSFZsTDGGBPS/wJsfitu1eoDWgAA\nAABJRU5ErkJggg==\n", "text/plain": [ - "<matplotlib.figure.Figure at 0x7fc629cb3a20>" + "<matplotlib.figure.Figure at 0x7f5d3bca4828>" ] }, "metadata": {}, @@ -217,7 +223,7 @@ "if cv_type==2:\n", " rs = KFold(n_splits=10)\n", "\n", - "\n", + "# make the estimate pipeline: \n", "for tt1, index in enumerate(hprange):\n", " if class_method == 1:\n", " estimator = make_pipeline(scaler, linear_model.LogisticRegression(C=index))\n", @@ -255,8 +261,8 @@ "source": [ "## Learning Curve\n", "\n", - "A learning curve shows the validation and training score of an estimator for varying numbers of training samples. It is a tool to find out how much we benefit from adding more training data and whether the estimator suffers more from a variance error or a bias error. If both the validation score and the training score converge to a value that is too low with increasing size of the training set, we will not benefit much from more training data.\n", - "If the training score is much greater than the validation score for the maximum number of training samples, adding more training samples will most likely increase generalization." + "A learning curve shows the validation and training score of an estimator for varying numbers of training samples. It is a technique to find out how much we benefit from adding more training data and whether the estimator suffers more from a variance error or a bias error. If both the validation score and the training score converge to a value that is too low with increasing size of the training set, we will not benefit much from more training data.\n", + "If the training score is much greater than the validation score for the maximum number of training samples, the estimator might benefit from more training data." ] }, { @@ -266,9 +272,9 @@ "outputs": [ { "data": { - "image/png": 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4vkQMkezWLe2sFJfXhcvrotpZTbW7OiAmVosVh9WRtJtMa1gnbYHwteZNMYiw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\n8suGNRFtxnFjOD3ORickunwuZm2YxZw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tJxIJWVRURG5uLitWrIjYt2fPHrZt2xYIbEgm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\nG0ldCjwiqZ+ZbW8wZ+DkqC53Az8BPgt8BdhHCLUOgKTPAr+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cxgB+YHlooYg0KKW+CNQnyy+BXGCeiPjitPkKyAF8SqnlwO0i8nYrrrVvMG9+iWD+8/n9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\nR5jc+r6yxHsVZ206gATgbtzK3C9xY6iPAI3jXTc7KtyX/4f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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fc68af34a90>" + "<matplotlib.figure.Figure at 0x7f5d9539cc18>" ] }, "metadata": {}, @@ -276,9 +282,9 @@ }, { "data": { - "image/png": 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rozkCMV25FFcVR3Tl8uvuXxk9ezQ7SncEhTdNbsobQ9/glKNOqdV9PV6PsahR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\nj+boo4/mlVde4bbbbqNPnz6Ul5cDcMEFF/Dqq68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Au8rxCADOow23MspWqIDPror5fBbEaFj9PrvLCtZQu78bSYI35GZl9gmftX4Wz//wPLuK\ndtEspRmjzx/NgA4DAn4XpzhxiCOo0IX6Xd33ZHVY2VawDYc4sDlsOMWJXew4nU4cTgd2sRtnpx2H\nOHA4HcZZHNgdRpggOJ1Oz7N0iMPz+7rTOpxG/bzLcIrzaHmus91p9wmzO+2eOviEOe3kH8lnzb41\nnuecdySPYV8OA6gRgdHiotEcpzicDqwOK1aHlWJrMWX2MhxOx9GG3KuB9v7s39j7v0G78W+svdNW\n9hZeG7h7CKW2UkqsJZTaSym1ur7bSo6eXWG/7PmFRXmLcIixzt7Oop2M/GYkb694m8ZJjX0afXej\n7m6Y7RIkzKvRdguFR9h/qLWfocYpt5czdsFYLS4aTX1FRLA6rNicNkptpRRbi7E5bJ5G3RxjJsGc\ncFwMrXhjd9qrFIAyW5kR7xXuEQ5bCSVWVxqvPOX2Y9+fxylO1h1Yx7oD66Jwp/WX/CP1cFVkpVQM\nMBK4G8gB9gOfAU+KSEkVeZ8CxlWSxC4inoW2qkj/qIi8FHbFNZpjxO60Y3VYKbeXG42rvcwzbGSO\nMWMxWYg3x1ddUJhYHdYqG3u3QLjjvAWg1OYSDr/4CkcYe4Zojmuy0+rnqsivAn8BZgEvAx1c37sq\npfqJSGXrmMwENgcJ7wI8CnwVIt8o4IBf2M+RVFqjiQSnOLE6rFTYKzyNtdNpGKZNMSZiY2JJik2q\ntFfy6dpPeXbJsxwoPUBaXBq9snuR0yAn+DBREIGwOW21eMf1G5MyYY4xE6NiMMeYMcUY383K+GyK\nMXnSuONNyuRJ547zCfMqIyYmxohzxZtiTMQQ41NOjIoJzK+O1sl9jjXFEhsTiznGzMrdK5n26zSs\nDqvnXhLMCTxzUT1bFVkp1RF4AJgpItd5hW8FJgI3Ax+Fyi8iq4HVQcp1b5r9Xoiss0VkWzWrrdFU\niohgc9qwOqyeHoLVYUUQo0GIMRFvjvd4IYXCKU7W7V/HkrwlzFg3gw0HN3jijlQc4etNX9f0rdQ5\nCeYEkixJJMYmkhSbREJsAkmxft8tSSSaE9l6eCvzNs3zEVGLycLQM4fSO7u3T4Pvacj9BUIFCfNL\nt2HFBjqd1Slknd1OAG6nCG/vNne4v8MDBH53e7q5hcS7Du6z2wPO7eXm7fHm7R3nTe+c3ieFt9gt\nGD+p/2by7wLPA4OpRFyCoZRKxBClncB/KkmXCpSKiD2S8jUaf+xOOzaHzWd4yylOlFKYlIlYUyzJ\n5vAWudx+ZDtL8pewOG8xS/OXUlBeUMO1P3YUyiMA7iOUAIRKl2gJnq8qAfZn5vqZPL/Uy1us52gG\ndghvQVRv7zi3IHh7zTmchreXIBRVFKFQHs83bweKGOUrChaTxaeX4X7B8BcBf4GoKdvaoM6DakxM\n/KlLcekBOIFl3oEiUq6U+sUVHyk3AqnARBEJtT3jaiAFcCillgFPi8i/q3EtzUmGU5we99M9RXs8\nw03uBiHWFEtibGLYDUNBWQE/bP+BJflLWJq3lG1HttVY3U3K5NO4VyYA/j0EjwB4CYQ7PN4cX+dO\nBm5BuLrd1VzZ9kofUSixlvi6E0PwXoPg6Q0E6yW4RWFHzA5OSTslQBDc3+v6tzieqEtxaQYcEJFg\nFsGdwHlKKYuIWIPEh+IujD+ZfwaJOwxMBn4ECoB2wIPAXKXUnSIyLVShSqnhwHCArKwscnNzASgu\nLvZ81tQ/vN9k3W+zABWlFaxettp3bkcYWJ1W1h5Zy8rDK1l1eBWbijcdbfiqQZIpidtb3k68KZ74\nmHgSTAnEm1xnr+/xpnhiVWxkDZ8TqHAdXjhwUOT6F3UEn98j4Lfx/6m8F9sO5lLtej7B5s5454mE\n0pJSlv2wrOqEGpT35KFavbBSfwCxIhLgqqCU+gC4DWgoIofDLK8dsAFYICL9wszTCPgNiAdOEZEq\nd/Dq3r27rFixAoDc3Fz69OkTzqU0xzmh5pQAnnH3WJPhfLh2+Vo69uhYZZlOcbJ231qW5C9hSf4S\nlu1YRrkjfBfb1LhUzj/lfJItyXy58Usfz6wEcwL/uPgfYQ/71AbedoZgkzJ92nLx/ig+xmpvY7h3\nz8HdQ/DMqvfqOdQW+v88KKV+FpHuVaWry55LKdA4RFy8V5pwuct1nhJuBhE5qJR6B3gKOA/4NoLr\naU5QanJOSf6RfJbkGWISqd0kNiaW7s2606tlL3pn96ZzVmfMMcZ/0d4te1fbnhAu3kbnoAZprxnt\n7t/GY2/Ad1jJbWvwFgkfYfASCj2cVD+pS3HZBZyulIoLMjTWHGPILKwhMaWUGbgdOITh1hwJ21zn\njAjzaU4QanJOyaGyQ4bdJM8Qk7wjeRHlPz3zdHpn96ZXy16c1fwsEmMTg6Yb2GFgWGLib5D2tj+4\nPyvf8SSvzPi4zppNZh9xcBukvdfg8hYJjcabuhSX5cAlwFnAEnegUioeOBNYHEFZVwFZwGshbDiV\n0cZ13hthPs1xSDTmlFRGub2clQUrmbNkDkvyl7Bm75qI7CbNU5rTu2VvemX34vzs88lIDP+dxu60\nU2GvCDRQc7Q34e2t5J7f4N2DCDW0VNvDS5r6T12Ky6fA4xhG9SVe4X8GEoHp7gClVGsM+8wGguMe\nEgs6t8XVs0kSkSN+4acAI4CDGIZ+zQlEVXNKzDHmsOaUVIZTnPy27zfPUNfyncsjspukxaVx/inn\n07NlT3pl9+LUBqdG1IhbHVasduOe4kxxZCRmeIac/IeWtEBojifqTFxEZI1S6k3gfqXUTGAeR2fo\nL8J3jssCoCVBfHOUUs2AS4FlIrImxOWSga1KqdnAeo56iw1zxd0iImVRuTFNjRHNOSWVkXc4z2OE\nX5q/lMPlYfmUAMbEve7NutMruxe9snvRJasLphhT2Pm97UEA8eZ4miQ3ISE2weNQoNGcCNT18i8P\nYtg8hgNXYCzL8jrG2mKVLf3izVDAROWG/DLgC+BsYACGoBwA5gP/EBHtW3ic4Z5T4h7eOtY5JZXh\nbTdZkr8k4oX8OmZ2pFd2L3q37M1Zzc8iITYhovwiQoWjwuNUkGRJIjMpk3hzvMegr9GcaNTpX65r\nouPLrqOydDmVxD0LPFtF/gqMXormOMbqsFJYXkiJreSo262A2WS4AcfHRmchxzJbGct3LfeIyW/7\nfovIbpIVl8VFbS6iZ8ue9DylJ40SG0VcB6c4qbBXYHfaUUqRakklJSmFeHN8RD0djeZ4Rb8WaY4L\nSqwl7Cjc4TFEJ1uOfXjLjcPpMOwm+UftJpGs5psWl8b52ed7hrpKNpVUur5UZfWocFTgcDowxZhI\njUsl2ZJ8zHYhjeZ4RIuLps4pKCtgb/FeEi2JURsG2nZ4myEmeUv4YfsPEdtNejTrQa+Whph0btzZ\npzexVq0NuyyPh5cYbs8N4hqQHJdMnClOG9819RotLpo6wylO9pfsp6C8gOS45GN6ez9Udoil+UtZ\nmr+0WnaTTo07eXom1bGbeGNzGB5sTnFiMVnISMwgMTYRi8miBUVz0hCxuCilUjD2RLkEY27J7SLy\nX6VUBnAv8FklLsMaDWAMEe0u3k2prZQUS0rEja7bbrI4b7HHbhIJLVJb0Du79zHZTbypsFdgc9oQ\nERJiE2ic1JiE2AQsJssxlavRnKhEJC5KqUxgKdAKY6OuVkACgIgcUEoNARoAD0W5npp6hNVhZUfh\nDkQkbNuK226yOH8xS/KWsGLXiojsJg3iGnBe9nmeCYwt01oeUy/C38MrMTaRRgmNojq0p9GcyET6\nv2A80ATDpTcf2OcXPwe4KAr10tRTymxl7CjcYUxwrMT7S0TIO5Ln6Zn8mP8jhyvCt5vEmeLo0byH\nZ6irU+NOx+yF5fbwcoqTUlspSZYkspKytIeXRhOESMXlSuAtEVnpWlHYny0Y8040mgAKywvZVbyL\nxFjft3vvTZ4aJjSkXaN27CjcwfbC7RGV36lxJ886XT2a9Tgmu4kbb5fhGBVDalwqsTGxtE5vrT28\nNJpKiFRcMgi+b70bJ0dXNNZoAKMXcrD0IAdKDwQY7meun8mj3z1Kud1YUuVQ2SH+u+O/YZV7Suop\n9G7Zm57ZPemZ3ZP0hPSo1NfhdFBuL8cpTswxZtLi0kiyJHk2xlqv1mth0WiqIFJx2QO0riS+K8Zw\nmUYDGA31nuI9FFmLSIkLNNw/vfhpj7BURYO4BsZ8E9eS9C0btIxaPb09vGJNsTRKbERSbJL28NJo\nqkmk4jIPuEsp9Trgsxy+UupsjGXvJ0SpbpoTHJvDxs7CndicNlLjUgPiv9/6PftK/M12R/G2m/Ru\n2ZuOmR2jatvwXxRSe3hpNNEjUnH5G3A1sAr4EmPR7yFKqT8DAzH2aHkhqjXUnJCU28vZcWQHShlr\nZXkjIkxZNYW/L/p7yPwZCRn8NOynqNhNvK/r3m0SIDE2US8KqdHUEBGJi4jsUUqdA7wB3ImxSvFt\nGCIzDxghIoeiXkvNCUVRRRG7inYRb44PaLRtDhtjvx/L9DXTQ+Q2tvAd12dcVIRFRCi3lxtreKFI\njkvWi0JqNLVAxP+7RGQ7cI1SKhVj2XoFbNaiohERCsoL2Feyj6TYpIAhrIKyAoZ/PZwftwdunZMW\nl0ZhRWFUtvD19/BKsaSQEpdCQmyCNsRrNLVE2OKilEoGJgL/FpHPRaQQYzdJjQanONlXso/DZYeD\nGu43H9rMkNlD2HZ4m094siWZt694m76n9j2m67sXhXSKE5M6uihknDlOC4pGUweELS4iUqyUuhn4\noQbrozkBsTvt7CraRbmtnNT4QMP94rzF3P313RRWFPqEZ6dlM+2aabTLaFft67onNcbGxNIwviFJ\nliS9KKRGcxwQ6bDYOiCnBuqhOUGpsFews3AngpAcF7iUy7RfpvHkwidxiMMn/OzmZzPl6ikRz00J\ntu1vYmwicea4Y7oPjUYTXSIVl38AbymlPhSR32uiQpoThxJrCTsLd2IxWwLcd+1OO+MWjmPar9MC\n8t3U8Saeu+i5sAQh2La/WclZJMYmag8vjeY4JlJxaQ9sB9Yopb4GNgGlfmlERJ6ORuU0xy+V7cFy\npPwI98y9h8V5i33CFYqxvcZyT/d7Kh22ci8KaXfYAfS2vxrNCUik/1Of8vp8bYg0AmhxqaeICPtL\n93Oo7FDQPVi2Fmxl6JyhbD7ku0pQYmwib17+Jpe0viRk2VaHlQp7BUopUiwppCal6kUhNZoTlEjF\n5dQaqYXmhKCqPVh+3P4jf/7qzwG7PjZPac60AdM4PfP0kGWX2kqJIYZT0k7R2/5qNPWASCdR5tVU\nRTTHN1aHlZ2FO3GKM+geLB+t+YgxC8Zgd9p9wrs17cZ7V79HZlJmyLKLrcUkmBNomtJUD3tpNPWE\nav9Pdi257+7JbBWRg9GpkuZ4w70HiynGFDBr3uF08PTip3l35bsB+Qa2H8iLl7xIvDn4QtlOcVJs\nLaZhfEMykzJ1b0WjqUdUZ5vjMzAmU/b0C18C/EVEVkepbprjAPceLAnmwPW3iiqKuHfevXy/9fuA\nfI+d/xgPnPVASMO93Wmn1FpKVnIWDRMa1kjdNRpN3RHpNsedMLY5jsdYuNK9cXlH4CpgiVLqPBFZ\nG9VaamqdyvZgAcg/ks/Q2UPZeHCjT3iCOYGJl03k8jaXhyy7wm5sD5zdIJvE2MQaqb9Go6lbIu25\n/B2wAeeJyBrvCJfwLHaluS461dPUBQ6ng30l+yisKAy6lMuyncu468u7OFTmu5xck+QmTLtmGp2z\nOocs2224z2mYo5e212jqMZEOcvcG3vQXFgAR+Q14C7ggGhXT1A02h43tR7ZTbC0OKiyfrf2Mm2bc\nFCAsZ2Sdwdxb54YUFhGh2FpMnCmOlg1aamHRaOo5kYpLEsZulKHY7UoTNkqpGKXUKKXUBqVUuVJq\nu1LqZaVUWOUopSTEURwifTul1GylVIFSqkQptUQpdWyrJtYTyu3l5B3OwyGOgD1YnOLk2SXPMuqb\nUZ79UNxc1fYqvrjxC5okNwlarlOcFFmLaBDfgBapLfS8FY3mJCDSYbEtwJXAmyHir3SliYRXgb8A\ns4CXgQ6u712VUv1ExBlGGUuAyX5hNv9ESqnWwI+AHWMpmyPAn4FvlFKXicj8COteb3DvwRJnjgvo\nVZRYS3j5LUa0AAAgAElEQVTg3w/wzR/fBOR76JyHeOjch6o03DdJaUKD+AY1UneNRnP8Eam4fAA8\np5T6CHgG2OAK7wCMAS4BRodbmFKqI/AAMFNErvMK34rhkXYz8FEYRW0RkX+Fke45oAHQTUR+cV3r\nA2At8KZSqr2ISLj1rw9UtQfLzsKdDJ0zlHX71/mEx5vieaX/K1zT/pqQZWvDvUZz8hLpsNhLwOcY\njf5qoNx1/Arc4op7OYLybsHYbGyCX/i7GGuWDQ63IKWUxbXnTKj4JIwtmnPdwgLGVgLAFKAt0CP8\nqp/4uPdg2Ve8jxRLSoCw/LzrZ6746IoAYWmc1JgZN86oVFjKbGUIQk7DHC0sGs1JSKQz9B3ATUqp\nKcAAjEmUCvgDmF2NYaUegBNY5nedcqXUL4Tf2F+PIUQmpdR+4FPgCRE54pWmCxAH/DdI/p+86rMs\nSHy9w+60s7toN2W2sqB7sMxaP4uHv32YCkeFT3inxp2Yes1UmqU0C1qu23CfZEmiaXJTbV/RaE5S\nqjVDX0S+A76LwvWbAQdEpCJI3E7gPKWURUSsQeLdLMPoMW0GUoHLgfuBC1xzbtyGfXdruDPEtQCa\nR3oDJyKV7cHiFCcv/fgSr/3vtYB8l512GRMvmxiyJ+Kecd8ooREZiRl6wy6N5iQm0kmU6UCLULPw\nlVJdgO0iUhBmkYlAMGEBY7jNnSakuIjI2X5BHyilVmPYhEa6zu5yCHG9cr80PiilhgPDAbKyssjN\nzQWguLjY8/lEwSlObA4bSqmAxr/cUc6Lv7/IkgNLAvLdcsotDGkyhK2/bA1aroggIphNZvaoyhwK\nT3xOxOeuiQ762YdPdTYL+5PrCMZUYDlwT5jllQKNQ8TFe6WJlBeBccAVHBUXdznBdqiq9FoiMhmX\nN1r37t2lT58+AOTm5uL+fCJwuPwwe4r2BN2DZXfRbu788k5WH/B9b7CYLLx48Ytcf/r1IcutsFdg\nd9ppntr8pLCvnGjPXRM99LMPn0jF5UKgMq+sL4HbIihvF3C6UiouyNBYc4whs8qGxIIiIjal1C4g\nw+9a7nL9cYcFGzI74RERDpQe4GDZwaBLufy651fumHMHe0v2+oQ3SmjEe9e8R49moU1fZbYyYlSM\nnhip0Wh8iNRbrBmQX0n8Do7aNsJhuasOZ3kHKqXigTOBFRHWzzt/C8C7tVyDMSR2bpAs57jO1bre\n8YzD6WBX0S4OlR0ixZISICxf/f4VAz8bGCAsHTI6MG/QvJDCIiIUVRQRb44nOy1bC4tGo/EhUnEp\nAVpWEt+S0DaUYHyKsXPlg37hf8awf0x3ByilWiul2nsnci37H4ynMXplX7kDXIb9r4A+rpWd3WUk\nA8MwtmyuV55iVoeV/CP5lNnKApZyERFe/elV7vn6Hsrt5T75+rXqx+ybZ9MitUXQcp3ipLCikPSE\ndJqlNNMeYRqNJoBIh8X+BwxRSr0oIkXeEUqpFOB2ImigRWSNUupN4H6l1ExgHkdn6C/CdwLlAgzx\n8rZCP6GUOgdYiNGjSsbwFrvQVdfX/S45BrgI+FYp9SpQiCFkzYEr6tMESu89WBItiQFxD3/7MHM2\nzgnIN6L7CMb0HBNSMOxOO6W2UpqlNCMtPq1G6q7RaE58IhWXl4D5wI9Kqb8Bv2D0PLpiGNBbYPQC\nIuFBYBuGN9YVwAEMUXgyjKVfcoHTgSFAI8CB0QMZC7wiIj6v5CKyWSl1PvA8xkoCFmAlcGl9Wvql\nsj1Y9pXs4845d7Jqzyqf8NiYWJ7v9zw3d7o5ZLnl9nIcTgct01oGbBqm0Wg03kQ6iXKhUupe4DWM\nIS03CsNd+P5IG2nXxMyXqWJmv4jkBAmbAwS+fldeznog9NTyE5iq9mD5bd9v3DHnDnYV7fIJbxjf\nkHevepdzTwlmjjIotZZiijFpw71GowmLiCdRisgkpdTXwI3AaRjCshGYISL10tvqRMApTvYW7w25\nB8t/Nv+H++fdT5m9zCe8TXobpg2YRk6DnKDlumfcJ1uSaZLcRNtXNBpNWFR3hv5O4FWllBnD06s5\nxoKQWlzqAJvDxq6iXVgdVlLiUnziRIS3lr/Fc0ufQ/A1KfVp2Ye3r3yb1LjA5V/ANeO+ophGiXrG\nvUajiYwqxUUp1QcYCDwrInu8wnMwhqQ6eYW9LyJ3Rr2WmpCU28vZWWhouv8eLBX2Cv46/6/MWDcj\nIN9dXe/iyQueDJhM6cbmsFFmL6NZSrOga49pNBpNZYTTcxkKXCgif/EL/wDoDPyA4ZnVH8OTbJGI\nvB/VWmqCUlRRxO7i3VhMlgA7yIHSAwz7chjLdy33CTfHmBnfdzy3dQk911Ub7jUazbESjrj0wGu+\nCIBrvklPYLGI9HGF/R+wCsMdWYtLDVLVHiwbDmxg6OyhbC/c7hPeIK4Bk66aRM/sniHLLrWWYo4x\n06JBC22412g01SYccWkK/O4X1gfDBXmKO0BEylybiD0QtdppAnCKk/0l+ykoKwhquJ+/ZT73zr2X\nEluJT3irhq2YNmAaLZNbUnywGFu5DXEctcEIxsKTSilMysQfe/6olfs5EUlLS2P9+vV1XQ1NHVAf\nn73JZCIlJYX09HTi4oItvVg9whGXOKDML8y9Jsgiv/DtgJ5ZV0O492Apt5cH2EFEhMkrJ/P0oqcD\nDPc9s3sy6cpJJJuSKdpXRKP0RqQ0ScFsNnvEyeF0YI4xY44xa8N9FRQVFZGSklJ1Qk29o749exHB\nZrNRWFhIfn4+2dnZUROYcMQlH+joF9YT2Cci2/3CE4HD0aiYxhfvPVj8DfdWh5WxC8by0W+BO0Lf\n1uU2nr7waWJNsRQfLKZReiPSG6V74kUEpzixmCzazVijOclQSmGxWMjIMNb4PXToEE2bNo1K2eGs\nLbYEuF0p1dlVmWuBNsC/g6TtjHZHjjqltlLyDuehlAowsB8qO8StX9waICwxKobxF47nuYue88zS\nt5XbSEk9+tYlIghCnClOC4tGc5KTmppKUVFR1QnDJJyey3PAIOAXpdRBjGVWrPjNqFdKmTD2qP8i\narXTcKT8CLuLdgfdg2XTwU0MnT2UbUe2+YSnWFJ458p36JPTxydcHILZbJThFCcKhcVkCZjJr9Fo\nTj5iY2NxOBxRK6/KVkVEtgIXYCwqeRCjx9JHRNb6Jb3QFR/Rciya4IgI+0v2s7t4N8lxyQHCkrst\nl6s+vipAWFqmteSrW74KEBY3SimcTicxKkYLi0aj8RBtW2tYM/RFZAVwVRVp5mMMi2mOEYfTwZ7i\nPRRbi0mxBHqETV01lSdzn8Tpt67nuS3OZfJVk0lPSCcYgmjDvUajqRWqtfyLpuawOqzsKtyF3WkP\nWMrF5rAxLncc7/8aOI3olk638OxFz4acm2Jz2BARbbjXaDS1gh4TOY4os5WRdzgPJ86APVgOlx/m\ntlm3BQiLQvHkBU/y4sUvhhSWcns5NocNc4xZC0sNM3r0aJRS7Nmzp+rEQSgvL0cpxT333BPlmmk0\ntYsWl+OEwvJC8o7kYTFZiDfH+8RtKdjCVR9fxZL8JT7hSbFJTB0wlbu73R1yiKvEWkIMxh73J8sw\nmFIq7GPbtm11XV2Npl6ih8XqmKr2YFmav5S7v7qbwxW+04dapLZg2jXT6JDZIWS5bptNVnLWSdVj\n+fDDD32+L1myhMmTJzN8+HB69erlE5eZmRnVa48fP56nnnqK+Pj4qhMHIT4+nrKyMo9Xn0ZzoqL/\nguuQqvZg+XD1hzzx/RPYnXaf8O7NuvPe1e+RkZgRtFyH00GxtZjMxEwaJTY6aXosbgYPHuzz3W63\nM3nyZM4999yAuFCICKWlpSQlJVWd2Auz2XzMwlBdYaqvVPdZaOoWPSxWR9gcNrYf2W70LvyExe60\n8+TCJxk9f3SAsFx/+vV8dv1nIYXF5rBRaiuleUpzMpJqcQ+W6dMhJwdiYozz9Om1c90o8J///Ael\nFB9//DGvvfYa7du3Jy4ujtdffx2AH3/8kdtvv502bdqQmJhI8+bN6d27N19//XVAWcFsLu6wrVu3\n8uijj9K8eXPi4+P505/+xHfffeeTP5jNxTts8eLF9OzZk8TERDIzM7nnnnsoLS0NqMf8+fM5++yz\niY+Pp2nTpjzyyCOsWrUKpRTPP/98lb/J/v37eeCBB2jVqhXx8fFkZGTQvXt3XnvttYC0n3zyCb17\n9yYtLY3ExETat2/Pgw8+6DNnoqioiL/+9a+0atUKi8VC06ZNueOOO9ixY0dEzwJg/fr13HrrrWRl\nZWGxWGjVqhWjR4+mrMx/lSpNXaJ7LnVAZXuwFFYUcu/ce1m4bWFAvjE9x3Bfj/tCCkaZrQwRoWWD\nlgF2m5DUhPjk5cHgwcZxrIhUnSZKvPDCCxw5coQ777yTxo0b06pVKwA+//xz/vjjD26++Ways7PZ\nsWMHH3/8MVdddRVffPEFAwcODKv8W265hYSEBP76179SVlbGq6++ytVXX83mzZtp3rx5lfmXLVvG\n559/zrBhwxg8eDALFixg0qRJWCwWJk6c6Em3YMECLrvsMho3bszjjz9OSkoKn3zyCbm5uWH/FgMG\nDGDFihXcc889dO7cmZKSEtatW0dubi4jR470pHv44Yd55ZVX6Ny5Mw8//DBZWVls3ryZGTNm8Pzz\nz2MymbBarVx00UUsX76cm2++mUceeYQNGzbwzjvv8O233/Lzzz/TpEkTn+uHehY//fQTF198MZmZ\nmdx33300adKEVatW8corr/DTTz+xYMECTKaTZwj4uEZE9BHB0a1bN3GzcOFCiZSi8iLZeGCjbC3Y\nKjsLd/ocP+b/KG1fbys8hc+RMD5Bpvw8JSC99/H7gd9l66GtYrVbQ1573bp1gYFG8338HlFg6tSp\nAsjUqVODxv/73/8WQDIzM+XgwYMB8cXFxT7fCwsLpaioSE499VTp2rWrT9xjjz0mgOzevTsgbODA\ngeJ0Oj3hixcvFkCeeuopT1hZWZkAcvfddweEmUwmWblypc/1+vbtK3FxcVJeXu4J69KliyQmJkp+\nfr4nrKKiQrp16yaAPPfcc0F/Bzd79+4VQEaNGlVpukWLFgkg/fv3l4qKCp847/ucOHGiAPJ///d/\nPmlmzJghgAwbNswTVtmzcDgc0r59e+nUqVPAM/noo48EkI8//rjSOh8rhYWFNVp+XRO0jfADWCFh\ntJV6WKwWKSgrYHvhdhLMCQFuwz/t+IkrPrqC3w/67m7QNLkps2+ezWVtLgtapohQVFFEsiWZ7AbZ\nnnXENJFz5513kp4eOAHVe6y/tLSUgwcPUl5ezgUXXMAvv/xCRUVFWOU/+OCDPr3Onj17YrFY2LRp\nU1j5L7jgArp27eoT1rdvXyoqKti+3VhDNi8vj9WrV3P99ddzyimneNJZLBb+8hf//f6Ck5SUhNls\n5scffyQ/Pz9kuumuoc8XXngBi8X379n7PmfNmoXFYuHRRx/1SXPdddfRvn17Zs2aFVB2sGfx888/\ns2HDBgYPHkxZWRkHDhzwHH379sVisfDtt9+GdY+amkeLSy1hdVjZW7KX1LjUAM+tT3/7lJtn3ExB\neYFPeNcmXZl761w6Ne5EMBxOB0XWIjISM2iS3EQv5XKMtG3bNmj47t27ufPOO8nMzCQpKYlTTz2V\nzMxMpk2bhohw5MiRsMp3D+24UUrRsGFDDh48WK38AI0aNQLwlLF161YA2rVrF5A2WFgwkpKSeOml\nl1i5ciU5OTl07tyZkSNHsmiR7w4bmzZtIjY2lk6dgv99utm6dSvZ2dlBl6rv2LEjBw8epLCw0Cc8\n2LNw76MyevRoMjMzfY4mTZpgtVrZu3dvWPeoqXm0zaUWiSHG543O4XTw7JJneefndwLSXtPuGl6+\n5OWQ2wzbHDbK7eU0T2keMJM/IiQKNo3p02H4cPA2LCcmwuTJMGjQsZdfSyQmJgaEORwOLrroIrZu\n3crIkSPp1q0bsbGxJCcnM2nSJGbMmIHT6QxSWiChbAES5jOozJbgLiPcsqpi5MiRXHfddcydO5fF\nixfzySefMHHiRG6//Xbef//9iK5VnToFexbucsaMGUPfvn2D5nMvHa+pe7S41BHF1mLun3c/3235\nLiDukfMe4cGzH6zUcA9EZrivSdwCMnYs5OdDdjY888wJJSyhWLFiBevXr+fZZ59lzJgxwNENo954\n4406rl0gp556KgAbN24MiAsWVhktWrTg7rvv5u6778Zut3PTTTfxwQcf8Mgjj9C5c2fatWtHbm4u\na9eupUuXLiHLad26NUuXLqW4uJjk5GSfuHXr1pGRkUFqamqI3Edp06YNYKze269fv4juRVP76HGU\nOmBH4Q4GfDIgQFjizfG8c+U7jDpnVKUz7s0xZrLTso8PYXEzaBBs2wZOp3GuB8ICR3sL/m/fK1eu\nZO7cuXVRpUrJycmhU6dOzJgxw2OHAbBarT4eZZVRUlIS4NZrNpvp3NlYl/bQoUMA3HrrrYAxTGWz\n2XzSe/9eAwYMwGq18tJLL/mkmTVrFuvXr2fAgAFh1evss8+mbdu2vPHGGz735sZms1FQUBAkp6Yu\n0D2XWmb5ruUM+3IYB0oP+IRnJWUx9ZqpnNHkjKD53Ib7tPg0spKztH2llujSpQtt27Zl/PjxHD58\nmDZt2vDrr7/y/vvv06VLF1auXFnXVQzglVde4bLLLuOcc87hnnvuISUlhY8//tjzwlLV3Kc1a9Zw\n6aWXMnDgQDp27EiDBg347bffeOedd2jbti3nnHMOAL1792bkyJG89tprdO/enRtuuIGsrCy2bNnC\nZ599xtq1a4mPj2f48OF8+OGH/O1vf2Pz5s2cf/75bNy4kbfffptmzZrx9NNPh3VfJpOJf/3rX/Tr\n14+OHTty55130qFDB0pKSti0aRNffPEFEydO5Oabbz62H1ATFbS41CJzNs7hiYVPYHVYfcI7N+7M\n1Gum0jQl+PaiDqeDElsJjZMb0zC+4Uk3474usVgszJs3j0cffZR//vOflJWVcfrpp/Pxxx+zdOnS\n41JcLr74YubOncsTTzzBM888Q8OGDbn11lsZMGAAvXv3JiEhuB3PTatWrbj99tvJzc1l5syZWK1W\nmjdvzr333stjjz3ms8f6hAkT6NatG2+99RbPP/88IkJ2djbXXHMNsbGG52JcXBwLFizg73//O59/\n/jmfffYZ6enp3HLLLYwfPz5gjktl9OjRg1WrVvHcc88xa9Ys3nrrLVJTUzn11FMZPnw4vXv3rt6P\npok6KloGwGpdXKkYYCRwN5AD7Ac+A54UkZIq8rYFBgOXAK2BeOAP4HNggn9+pdRTwLgQxT0qIi+F\niPOhe/fusmLFCgByc3Pp06dPlXn+tfpf3DfvPgorCgPiLm9zOa9d+hqJsYEGTDC8zCrsFTRLaXZs\nhnsMb5sOHYKvRaYJH7fN5URj+vTpDB48mFmzZoU9FKXx5UR99uESThuhlPpZRLpXVVZd91xeBf4C\nzMLYNrmD63tXpVQ/EanMDedO4D7gS2A6YMPYDXM8cKNS6hwRCbYexCjggF/Yz8d0F5Xwz1X/ZPhX\nw3FI4PahI88eySPnPRJyiOu4M9xrTgicTid2u91n7klFRQUTJkwgLi4uYPFOjaYmqDNxUUp1BB4A\nZorIdV7hW4GJwM3AR5UUMQN4TkS8Jxm8o5TaBIwF7gKCufPMFpFtx1j9sBAR7p17b1BhaRjfkL+e\n/9eQeUusJVhMFpqlNNMTIzURUVhYSIcOHRg0aBBt27Zl//79fPzxx6xdu5Zx48Z55sZoNDVJXfZc\nbgEUMMEv/F3geYwhr5DiIsbWy8H4FENcQs7sUkqlAqUiYg+VJhoopQLsK24Olx8OGq4N95pjJSEh\ngUsuuYSZM2d6FtBs3749kyZNYvjw4XVcO83JQl2KSw/ACSzzDhSRcqXUL6746tDCdQ41VXc1kAI4\nlFLLgKdF5N/VvFaVZKdlk3ckLyC8WUqzgDBtuNdEg7i4OM9ER42mrqjL1+JmwAERCbYw004gQykV\nfN/eECilTMCTgJ3AXs9hYDLGUNw1wBigJTBXKTU0sqqHzzMXPRNgrE8wJzC652ifMKvDSpm9jOYp\nzUlPSNfCotFoTmjqzFtMKfUHECsi2UHiPgBuAxqKSPDxo+Blvg7cDzwuIs+Fkb4R8BuGp9kpIlIc\nIt1wYDhAVlZWt08++QQg6IzjYMzfO593t77L/or9ZMZlckfOHVzU+CJPvNPlt2AxWVDUnKikpaVx\n2mmn1Vj5JwsOh0Mv636SUt+f/ebNm6tcK+/CCy8My1usLsVlDdBYRLKCxH0G3ADEiUhwo0VgnqeB\nJ4DJInJ3BPUYBzwF9BeRKpdUrY4rMhg9k20F20iO8xWj4opi4sxxNE9tjjmmZkcptStydKjv7qia\n0NT3Zx9NV+S6HBbbhTH0FRckrjnGkFm4wvIUhrBMBe6pPHUA21znWl3xTkQoLC8kNT6VU9JOqXFh\n0Wg0mtqkLsVluev6Z3kHKqXigTOBUN5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AcBFh2tJp3PHRHSHCcnPnm5l64VQVFkVRYkZltznugLXNcRLW6scrPEHtgX7A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\njo2LMcaYjmPjYowxpuP8H1P15R5mWd8AAAAAAElFTkSuQmCC\n", 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+JSC8sKyQWz65hQUbFvjYE+wJvNzrZXq26RmQRoVFUZQDIdJa419AGXCGiKzw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\nUukz+tsARwOj8ZljM4Ajzey5ii8tCIKgz9Jy4xI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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fc62b6e7d30>" + "<matplotlib.figure.Figure at 0x7f5d3bc61cf8>" ] }, "metadata": {}, @@ -296,9 +302,9 @@ }, { "data": { - "image/png": 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BGA/0BcaFUpAxpiPwN2ChiFxdJj0T191oNwDvhlBUgYi8E0K+EUAPXHe0zXCn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\n", 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C4Emgs+t1D2PMuSKVFm/37xtgdqW0ssqZjDHtgO+AcpxT2RwEbgQ+M8ZcKCJf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\ncZdB1bxpQ2r7tDndSOChdF9kuRe/f+I+aR2OAIb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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fc62b7696d8>" + "<matplotlib.figure.Figure at 0x7f5d3a0bfeb8>" ] }, "metadata": {}, @@ -306,9 +312,9 @@ }, { "data": { - "image/png": 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1G+0S4I0ImioUkdcjqDcG6IM1o+0JT9lzxpj3gLuNMS/V9308eSVW6pY4W1xE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\nBsc9smkEYDcKVuhH29cX4sslCIIgaDgx5hIEQRA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MZZFpXt1XnaVbfBWWFjLu23F8sPKDgLxTm5/Ki71fJDUhtcp6CkoLiDbRpCel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\n+6Rv376Smpoqf/31l1/e4sWLxWaz+Z2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"text/plain": [ - "<matplotlib.figure.Figure at 0x7fc629db1908>" + "<matplotlib.figure.Figure at 0x7f5d3a06dba8>" ] }, "metadata": {}, @@ -321,26 +327,29 @@ "from sklearn.model_selection import learning_curve\n", "import plot_learn_curve as plc\n", "\n", - "\n", "# read mnist data set\n", "X, y = ld.read_data('mnist')\n", "\n", "# samples from the nmist data\n", - "num_of_samples=600\n", + "num_of_samples = 600\n", "X=X[0:num_of_samples,:].astype(float)\n", "y=y[0:num_of_samples].astype(float)\n", "\n", - "\n", "# fix random seed for reproducibility\n", - "seed = 7\n", - "np.random.seed(seed)\n", + "np.random.seed(RANDOM_STATE)\n", "train_sizes=np.linspace(0.04, 1, 5)\n", + "\n", "cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)\n", "hyperpara_range=np.logspace(-3.,1.,5)\n", + "\n", + "\n", + "# loop of hyperparameter range \n", "for hyperpara in hyperpara_range: \n", " estimator = make_pipeline(scaler,linear_model.LogisticRegression(C=hyperpara))\n", - " train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, n_jobs=4, \n", - " train_sizes=train_sizes)\n", + " train_sizes, train_scores, test_scores = learning_curve(estimator, X, y, cv=cv, \n", + " n_jobs=4, train_sizes=train_sizes)\n", + " \n", + " # plot the learning curves for the current hyperparameter \n", " plc.plot_learn_curve(train_sizes, train_scores, test_scores, r'C =' + str(hyperpara))\n", " \n", " "