{ "cells": [ { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Example linear regression (1st-order polynomial)\n", "*******************\n", "\n", "This is a toy problem meant to demonstrate how one would use the ML Uncertainty toolbox. The problem being solved is a linear regression problem and has an uncertainty that can already be calculated analytically.\n", "\n", "Imports\n", "=======" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T19:47:25.690941Z", "start_time": "2019-06-07T19:47:25.679432Z" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import sklearn.linear_model as sklm\n", "import sklearn.model_selection as skcv\n", "import ml_uncertainty as plu\n", "import plot_utils" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Data and Metadata\n", "=================\n", "\n", "Data for the toy problem\n", "------------------------\n", "\n", "Here's some X data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:40.216871Z", "start_time": "2019-05-31T14:12:40.206247Z" } }, "outputs": [], "source": [ "xdata = np.array([[4.00],\n", "[4.20],\n", "[4.30],\n", "[4.47],\n", "[4.58],\n", "[4.60],\n", "[4.61],\n", "[4.82],\n", "[5.05],\n", "[5.24],\n", "[5.43],\n", "[5.58],\n", "[5.82],\n", "[5.91],\n", "[5.92],\n", "[6.03],\n", "[6.32],\n", "[6.45],\n", "[6.67],\n", "[6.68],\n", "[6.91],\n", "[7.04],\n", "[7.09],\n", "[7.35],\n", "[7.49],\n", "[7.62],\n", "[7.81],\n", "[7.94]])\n" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Here's some Y data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:40.339972Z", "start_time": "2019-05-31T14:12:40.218681Z" } }, "outputs": [], "source": [ "yfull = np.array([[3.70,3.53,4.12,5.03,],\n", "[2.96,4.69,6.44,7.36,],\n", "[4.69,3.68,5.86,5.70,],\n", "[4.28,4.22,5.32,6.33,],\n", "[4.86,5.27,5.61,5.57,],\n", "[4.77,4.61,5.38,4.91,],\n", "[4.74,4.45,6.34,5.71,],\n", "[4.76,4.40,6.51,5.62,],\n", "[5.01,5.52,5.85,6.92,],\n", "[5.59,5.81,5.86,7.98,],\n", "[5.17,4.67,5.58,8.38,],\n", "[6.02,6.04,5.70,6.87,],\n", "[6.68,5.94,5.78,8.10,],\n", "[6.91,5.97,5.68,7.80,],\n", "[6.14,5.74,6.30,7.65,],\n", "[5.67,5.78,4.97,8.98,],\n", "[6.89,6.41,6.40,8.08,],\n", "[6.57,6.46,6.44,8.43,],\n", "[7.34,6.31,6.14,8.55,],\n", "[6.62,5.98,5.78,9.17,],\n", "[7.22,6.59,5.99,8.95,],\n", "[8.05,6.85,6.78,10.92,],\n", "[7.11,7.29,6.19,9.74,],\n", "[6.43,7.40,6.45,9.39,],\n", "[7.02,7.14,7.34,10.13,],\n", "[7.27,7.37,6.12,10.04,],\n", "[7.29,7.07,5.65,10.91,],\n", "[8.29,8.39,6.57,11.78,]])\n", "\n", "ydata = yfull[:,0]" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Sklearn models\n", "--------------\n", "\n", "Make some sklearn models that we'll use for regression." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:40.434371Z", "start_time": "2019-05-31T14:12:40.343343Z" } }, "outputs": [], "source": [ "linear_regressor = sklm.LinearRegression\n", "regr = linear_regressor()\n", "cv = skcv.KFold(n_splits=6,shuffle=True)" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Regression\n", "==========\n", "\n", "Recall the generic for for the linear regression problem and the way to calculate the coefficients. Here, `Y` is the array of dependent variable measurements and `X` is the array of independent variable measurements. To account for the constant term, `X` must be prepended by a column consisting of all ones." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$Y = X\\beta$$\n", "$$\\beta = (X^TX)^{-1}X^TY$$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:40.519322Z", "start_time": "2019-05-31T14:12:40.437740Z" } }, "outputs": [], "source": [ "regr.fit(xdata,ydata)\n", "ypred = regr.predict(xdata)\n", "y_cv = skcv.cross_val_predict(regr,xdata,y=ydata,cv=cv)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:40.697786Z", "start_time": "2019-05-31T14:12:40.522831Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#with plt.xkcd():\n", "fig,ax = plt.subplots(1,1)\n", "\n", "p1 = plt.scatter(xdata,ydata,color='k',label='Measurement')\n", "p2 = plt.plot(xdata,ypred,color='r',lw=2,label='Regression')\n", "p3 = plt.plot(xdata,xdata,color='k',ls='--',label='Underlying truth')\n", "\n", "handles, labels = ax.get_legend_handles_labels()\n", "\n", "plt.legend([handles[i] for i in [2,0,1]],[labels[i] for i in [2,0,1]])\n", "ax.set_xlabel('The X values',size=15)\n", "ax.set_ylabel('The Y values',size=15)\n", "ax.set_xticks([])\n", "ax.set_yticks([])\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "ax.set_ylabel\n", "\n", "t = 1" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T22:14:52.460228Z", "start_time": "2019-06-07T22:14:52.236356Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#with plt.xkcd():\n", "fig,ax = plt.subplots(1,1)\n", "\n", "p1 = plt.scatter(xdata,ydata,color='k',label='Measurement')\n", "p2 = plt.plot(xdata,ypred,color='r',lw=2,label='Regression')\n", "p3 = plt.plot(xdata,xdata,color='k',ls='--',label='Underlying truth')\n", "\n", "for x,y,yp in zip(xdata,ydata,ypred):\n", " plt.plot((x,x),(y,yp),color='k',marker='.')\n", "\n", "handles, labels = ax.get_legend_handles_labels()\n", "\n", "plt.legend([handles[i] for i in [2,0,1]],[labels[i] for i in [2,0,1]])\n", "ax.set_xlabel('The X values',size=15)\n", "ax.set_ylabel('The Y values',size=15)\n", "ax.set_xticks([])\n", "ax.set_yticks([])\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "ax.set_ylabel\n", "\n", "t = 1" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Uncertainty Calculation\n", "=======================\n", "\n", "Traditional uncertainty calculation\n", "-----------------------------------\n", "\n", "This is the equation for the 95% confidence interval for a new prediction :math:`X_{new}` (in linear regression)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$ Y_{new} = X_{new}\\beta + \\epsilon$$\n", "\n", "$$ \\delta Y_{new} = t(0.95,n-2)\\Bigg\\{\n", " \\frac{Y^TY-\\beta^TX^TY}{n-2}\\Bigg[X_{new}(X^TX)^{-1}X_{new}^T+1\\Bigg]\n", " \\Bigg\\}^{1/2}$$" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Here, :math:`t` is the 95th percentile of the one-sided Student's T distribution with :math:`n` - 2 degrees of freedom, with :math:`n` being the number of samples in the regression (i.e. number of rows of :math:`X`). \n", "\n", "Calculate the values for the :math:`t` distribution and the SSE term." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:40.719683Z", "start_time": "2019-05-31T14:12:40.699964Z" } }, "outputs": [], "source": [ "#Create the linear regression X matrix\n", "xmatrix_first = np.ones_like(xdata) #Constant term\n", "xmatrix = np.concatenate((xmatrix_first,xdata),axis=1) #Add in the linear term\n", "\n", "cov = np.dot(xmatrix.T,xmatrix) #Covariance matrix\n", "icov = np.linalg.inv(cov) #Inverse covariance matrix\n", "xty = np.dot(xmatrix.T,ydata[:,np.newaxis,]) #XTy\n", "\n", "beta = np.dot(icov,xty) #Coefficients\n", "\n", "dof = len(xmatrix_first) - 2 #Degrees of freedom\n", "tdist = sp.stats.t(dof) #T distribution\n", "#T premultiplier, only one standard deviation because n is large. Squared because of how pred_unc is defined below\n", "tmult = tdist.ppf(0.95) ** 2 \n", "\n", "sse = (np.dot(ydata.T,ydata) - np.dot(ypred.T,ydata))/(dof) #mean squared error\n", "\n", "var_premult = tmult * sse " ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Calculate the uncertainty in the predicted values" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:40.887383Z", "start_time": "2019-05-31T14:12:40.722046Z" }, "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "yl = []\n", "yu = []\n", "for row in xmatrix:\n", "\n", " yp = np.dot(row,beta)\n", " pred_unc = np.sqrt(var_premult*(np.dot(np.dot(row,icov),row.T)))\n", "\n", " yl += [yp - pred_unc]\n", " yu += [yp + pred_unc]\n", "\n", " #print(pred)\n", "\n", " #print(pred,pred_lower,pred_upper)\n", "lower = np.concatenate(yl)\n", "upper = np.concatenate(yu)\n", "\n", "yunc = np.concatenate((yl,yu),axis=1)\n", "\n", "fig,ax = plt.subplots()\n", "\n", "ax.plot(xdata,ypred,color='r',label='Regression')\n", "ax.plot(xdata,yunc,ls=':',color='k',label='95% confidence interval')\n", "ax.scatter(xdata,ydata,color='k',label='Measurement')\n", "\n", "handles, labels = ax.get_legend_handles_labels()\n", "\n", "ax.legend([handles[i] for i in [3,0,1]],[labels[i] for i in [3,0,1]])\n", "\n", "ax.set_xlabel('The X values',size=15)\n", "ax.set_ylabel('The Y values',size=15)\n", "ax.set_xticks([])\n", "ax.set_yticks([])\n", "ax.spines['right'].set_visible(False)\n", "ax.spines['top'].set_visible(False)\n", "#plt.legend()" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Bootstrap uncertainty calculation\n", "---------------------------------\n", "\n", "Use the residual bootstrap of `Almeida `_ This method assumes that the residuals are representative of the uncertainty (which is really the same assumption made by linear regression uncertainty, when you think about it).\n", "\n", "Calculate the mean squared error and the cross-validation error in the regression, then calculate the pseudo-degrees of freedom and the residual weighting that will be bootstrapped. This is a large data set and cross-validation doesnt give us much more information, so the bootstrap weighting is close to 1." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:40.893332Z", "start_time": "2019-05-31T14:12:40.889055Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9594178070226661\n" ] } ], "source": [ "residual,err,mse = plu.get_residual_stats(ydata,ypred)\n", "residual_cv,err_cv,msecv = plu.get_residual_stats(ydata,y_cv)\n", "\n", "num_train = len(xdata)\n", "\n", "pseudo_dof = num_train * (1 - np.sqrt(mse/msecv))\n", "bootstrap_weight = np.sqrt(1 - pseudo_dof/num_train)\n", "\n", "print(bootstrap_weight)\n", "\n", "residual_weighted = residual / bootstrap_weight" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "As an easily demonstrated example, just do bootstraps with 5 samples.\n", "\n", "First generate the bootstrap `Y` values that will be passed to the models." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:40.971653Z", "start_time": "2019-05-31T14:12:40.895024Z" } }, "outputs": [], "source": [ "samples = 5\n", "\n", "residual_boot,boot_indices = plu.bootstrap_data(residual_weighted,samples=samples)\n", "residual_boot = np.squeeze(residual_boot)\n", "\n", "boot_data = (residual_boot + ypred).T #boot_data now has the new Y values with shuffled residuals" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T22:23:23.130789Z", "start_time": "2019-06-07T22:23:23.106482Z" } }, "outputs": [ { "data": { "text/plain": [ "(28, 2)" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "samples = 2\n", "\n", "small_bootstrap = plu.bootstrap_estimator(estimator=linear_regressor,\n", " X=xdata,y=ydata,samples=samples,cv=cv,\n", " )\n", "\n", "small_bootstrap.fit()\n", "ypred = small_bootstrap.predict()\n", "\n", "boot_data = (small_bootstrap.boot_data_+ypred).T\n", "boot_data.shape\n", "# for row in linear_bootstrap.boot_data_:\n", "# plt.scatter(xdata,row+ymodel,s=1,color='k')" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Create some plots, generate and plot five regressions. " ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T22:26:23.748556Z", "start_time": "2019-06-07T22:26:23.443705Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,axes = plot_utils.make_grid_plot(samples,1,plotsize=(3,3),sharey=True,sharex=True,\n", " xlabel='The X values',ylabel='The Y values',\n", " ylabel_buffer=0.2,xlabel_buffer=0.25,\n", " gridspec_kw=dict(wspace=0,hspace=0),\n", " subplot_kw=dict(frameon=True))\n", "\n", "colors = '0.6','b','r','k','c'\n", "for ax in axes.flatten():\n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " ax.spines['bottom'].set_visible(False)\n", "axes.flatten()[-1].spines['bottom'].set_visible(True)\n", "\n", "for plot_num,(color,\n", " yboot,\n", " est,\n", " ax) in enumerate(zip(colors,\n", " boot_data.T[:samples],\n", " small_bootstrap.estimators_,\n", " axes.flatten())):\n", " \n", " yb = yboot[:,np.newaxis]\n", " \n", " ypb = est.predict(xdata)\n", " \n", " ax.scatter(xdata,yb,color=color)\n", " ax.scatter(xdata,ydata,color='k',facecolor='none')\n", " ax.plot(xdata,ypb,color=color)\n", " ax.plot(xdata,ypred,color='r')\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", " \n", " for x,y,yp in zip(xdata,ypb,yb):\n", " ax.plot((x,x),(y,yp),color=color,marker='.')" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T19:52:15.777514Z", "start_time": "2019-06-07T19:52:15.520222Z" }, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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eXl5Z8v7Z3YABA6hevTq9evV6qtfFxMSkzf2eOnUq169fZ/bs2ZkR4lORn62wBK01G3/5hVJ791Jl8WIIDzdmpowda2yqbGubdu358+epXLkyZcqUoVatAJYvr8/Zs1m+p0R6pGtWR55vmWcHNWvWxNHRkc8+++ypX7thwwamTJlCcnIyHh4eLLvXChEij0hOTubo0aNUrlwZ++vXaTZnDlZbthgnGzeGgAB49lm01vy0fj2tWrXCysoKKMM332yndetaREXZ8vrr4OFh0Y/y36S38Pl/feSmzSnEk8nPVmSVmzdv6sX+/vqv4cO1dnAwNoxwc9P6iy+0Npu11lqHhITohg0bakAvXrxYx8VpXbiw1u3aWTj49MkZm1Po+3a3FrmDzqKuO5F3JSQkcPXqVcqUKUOBixfpvmABpqNHjZOdOsHMmVCkCPHx8UyZMoUpU6aQlJSEq2shXFxcsLeHZcsgdVZxrmDRZG5vb8/NmzcpWLCgJPRcQmvNzZs3sbe3t3QoIhcLCgri9O+/U/LUKWzmzsVkNkOpUrBgAbRoAcDOnTvp169f2oyyhg17s3v3NAoXNtZXPGI5R45m0WReokQJrly5krb6UeQO9vb2lChRwtJhiFwmJiYGrTXOzs7UvHmT56ZNw/Tnn2BlZUwEnzDB2DwC+Pnnn2ndujUApUtXZNmyAOrU8eW77+CFFyz5KTKPRWezCCFEepjNZr799lvcTSZe/OknuFc4zsfHmG5Yq9YD1yclJVGvXj2uXXuVZ54ZRnCwXbYohvUvpSvyPL1oSAiRvd0rVGelFM1u3KDR228biTxfPvjkEzh4EGrV4tSpU7Rt25awsDB++w2srGw4cOAA3347mm++ydGJPN0sPgAqhBCPcv36dTZs2ECzcuUoMXEiBe8VhWvSxCiMVaYMCQkJTP3oIyZPnkxiYiJKubNu3XyWLIEePUz4+lr2M2QlSeZCiGwlOTkZa2trCru58UpQEMXffRfi46FgQWOWSteuoBS7du2iX79+nErdFahXr15MnTqRli3hETXtcj3pMxdCZBtBQUGcO3eOdsWLY+rXD44dM068+SZ89hk88wwREREMGzYsbYFc/vwVsbIK4OpVXxwcLBd7JpIVoEKInOHeepPC+fLhvmoVVmvXGnuxlSljdKk0aZJ27enTp1m2bBl2dnaMGjWKZs2GExVll1sTebpJMhdCWExycjK7d++mSJEiVLlwgRJvvw1XroDJBEOGGLv+ODjw119/UaRIEQBKlaqHyfQ5PXo0ZcyYJxexyytkNosQwmJMJhNWf/1FiSFDjNriV64Y0wwPH4Zp00gwmZgwYQIeHh74+xulmUuUgMWLBzBhgiTy+0mfuRAiS0VFRXHw4EHq16uH/fLl6GHDUHfuGLs/TJoE774LJhO7d+/Gz88vbYDTZBrD+fMTKFXKwh8g60mfuRAi+0lKSiL60CHU6NHw229Gpmre3FiK7+HBzZs3GTZsGEuXLgWgQoUKTJzoT0JCI0qWtGjo2ZokcyFEpouIiODGjRtUefZZCs6bx/8+/hiVmAiFC8OcOdChAyjF/v37adOmDREREYAtVap8yOHDIx/YNlE8miRzIUSmCwkJIW7bNiqvWoUKDTVa4z17wvTpcN/G4ikpFTGZTDRq1Ig2bfxp1aoCksfTR5K5ECJTXLlyBWdnZ1yAel9/jWnRIuPEs88aG0Y0bkxCQgL+s2fj5+fH6tX2vPmmG2vXHqBtWw+ppPqUJJkLITJcYmIi27Zto9aff+Li74/p+nWwtoYRI2D0aLC3JzAwkD59/Dh1KoSbN28yYsQEbt+GVq0880QtlYwmyVwIkSG01ty4cQN3d3dsw8PptGoVdhs3Gifr1DGqG1atyq1btxg+YABffPEFAPb25WnUqDGOjjBwoAU/QA4nyVwIkSHOnTvHjm3baH/zJm7TpmEXHW3UF58yBfr3R1tZsWL5cgYOHMytW+HY2try+usf0Lv3SBo3ls1M/itJ5kKIf81sNhMbG4uTkxNl4uIoFhCAQ3CwcbJNG5g3z1jlA2zfto2uXbsC4OXVkO+/96dixYqWCj3XkWQuhPjXduzYwe3r13ktNBSr6dNxSEoCd3eYOxfatkUDKclw4QK89NJLdOrUmeTklwkI6I6bm3SMZyRJ5kKIp5KSkoJSCisrK7xv38Z13DisLlwwTvr5wdSp4OrKnj17ePfddylefAVHj3px+rRixYrllg0+F5PaLEKIdIuLi2Pt2rWE7t8PffpQuEMHbC9cAC8vCAwEf39umc106tSHBg0aEBwcjNk8lTlzQPb4zlzSMhdCPNG9ErX2dnZUPXmS8gMGQHg42NrCqFEwYgTa1pZvV6zgvfcGExERhslkw6hRH/DBBx9IIs8CksyFEI917do19u/fT8uqVbF//328NmwwTjRoYCz+8fLiwoWLdOvWl717twJQtqwvAQH+vPSSlwUjz1ukm0UI8Vj2NjaU27ABuxo1YMMGcHGBhQth1y6jewWYNcvM3r2BuLoWYMmSJZw5s0sSeRaTlrkQ4m9CQ0O5e/cuNa2tKdCnDwUOHTJOtG8Ps2eDuzuBgcFUrOhNoUJWDBxYhoSE1Ywd+xzFihW2bPB5VLpa5kqp15RSve47Lq2U2qeUilRKrVVKuWZeiEKIrHbzyhVcpkxB16wJhw4Zc8XXr4dVq7htb0+vXn74+lanXbtlAJQtC/7+rSSRW1B6u1lGA/nvO/4ceAaYCtQAPs7guIQQWchsNhMcHExkZCRs20a9fv0ot3Ytymw21tifPIlu1YrPP1+Jl5cXS5YsxGSyoWbNm5YOXaRKbzdLGeAYgFLKBWgKtNVab1BK/YmR1N/JnBCFEJktPj6ekMBASo4eDRs2GCVqq1Y16qnUqcP58+f53wvtOXZsMwD169cnICCASpUqWTRu8f+eZgD03v5yDYEUYFvq8RWgUEYGJYTIfElJScaWbFrj8P33vPHRRxTcsAHs7GDyZDhyhASfOvz88wGqVKnCsWObsbd3Y+7cxezevVsSeTaT3pb5UaCLUuoA0BvYqbVOSD1XCgjLjOCEEJnn1KlTHPvxR0pv347tzp1Ga/zFF8HfH559Fq2hUT2wta1J2bJl8fHx4bPPPqNwYekXz47Sm8w/BH4C3gJiMLpZ7vkf8FsGxyWEyATx8fHExsZSIH9+Kv3yC5U//hgVFwdubjBjBrz1FseOR/LFoMF8+OEHDB5cmPz5bahffz9OTk6WDl88RrqSudZ6j1KqFFAeOKe1jrzv9BLgbGYEJ4TIOFprNm7ciPPp07y0ciVW96obdu4MM2eiCxViwoTvGD9+EPAXkZG3WbZsWeqrJZFnd+meZ661jgaOKEMxIExrnay1/iXzwhNC/FcxMTE4ODhgFRtL082bcVi0yJil4uEBCxagmzXnt98u8NFbb7Fp0yYAatd+gWHDhlk4cvE00j0AqpRqoZT6DYgH/gS8U59fqJTqmknxCSH+g8jISFatWsWfAQFQpQqOAQFG3/iQIXDiBEkvv0ytWtOoV68ymzZtwtXVlUWLFnHgwK9UrlzZ0uGLp5DeRUNvAuuBUKDvQ687A/R61OuEEJaRnJwMgEt8PP9btQrPt9+GS5egenU4eJDwEZ+SZOtIcHAwQUEj0TqOTp06ExoaSu/evbGykkofOU16f2KjgOla67eAbx46dwKQOUpCZBOhoaGs/PZbkhYtQlWqRIHNmyFfPpg+HQ4e5JRTJZ591tgEqHbt2kyYMIHNmzezYsVyihQpYunwxb+U3j5zD2DrP5yL58HVoUIIC7hXprZoTAwtZ83CJijIONG0Kfj7c93ek8DvV/Pee+/RqtXXvPLKywCMGTPGglGLjJLeZH4ZqA7seMS5WshsFiEsRmvNnj17sFWKOoGBuE6YAPHx8MwzMHMmdOnCkKEXmTOnJcnJGwFQ6ku8vF62cOQiI6U3mX8BjFNK/QX8kPqcUkq9BAwHJmRGcEKIJ1NK4XzyJOWnT4dz54wn33yT2ImfEe/kwuLp05k/fzzJyXG4uroybdo0evfubdmgRYZLbzKfBpQEvsRYyg+wDzABAVrrOZkQmxDiH8TGxrJv3z5qVaiA6/TpVJs7F6U1lCkDAQHE1nuZChVOExf3Ejdv/gFAp06dmDFjBkWLFrVw9CIzpHfRkAbeUUrNBF4CCgK3gB1a69OZGJ8Q4h+YfvkFp27d4MYNlMkEQ4cS3n8shTwccAB69y7E7Nl/Ubp0aRYsWMArr7xi6ZBFJlJGns58tWrV0ocPH86S9xIiN4qMjOTcuXPULFbMKEu7Zo1xonZtWLSIb09689ZbP3HoUFOqVTM23QwODqZ8+fI4ODhYMHLxH6n0XJSulrlSqsWTrpGVoEJkrovnz5Mwdy563TrUnTvg6Ih54sfc6TaAOzGXWbKkNUlJG/j66zFUq2YMY/n4+Fg4apFV0ttn/jNGCdyHf0Pc36w3ZUhEQog0YWFGQdLCt25RbdAgVGCgcaJFC/S8+TR+qzjhi2Zy6dI4YmNjcXFxoWLFUhaMWFhKepN56Uc8VwCjemJ3oEdGBSSEMJjNZnZt3ozPpk0UXrMGlZgIRYoQM3kOTj3ac/DgQc6ff5UrV44C0LFjR2bOnIm7u7uFIxeWkN4B0EuPePoS8LtSKgWjRG6bjAxMiLzq2rVrFC1aFKt9+3ht0iRMp1PnGPTuzW+vfcJLr7vxSfwRBgyoi9YaT09P5s+fT/PmzS0buLCodFdNfIzfgfEZcB8h8rwbN26wZdUqXj1wALfvvsME6PLlifk0AOfWjagWD926QbNmNWjZsiWVK1dm7NixMsAp/ttsFqWULcaCoue11s8+7lqZzSLEo2mtiY6OJr+zM3rtWlLefhvr8HCwtoaRI3n76ih27v+LihUHM3nyx3h5eQFGN4wUxMoTMnQ2yyEeHOwEsAU8AWekz1yIf+23337j8v79tNuxA9PPP2MNpDxXFxYuRFeuSFyf2Zw/P5bQ0FiSkhL5+eefASSRiwekt5vlBH9P5vHAauAHrfWJDI1KiFzObDZjNpuxVoqqu3dTe+JETHfvgrMzN4dNpeq8fvRYdYRffqlNcOqOQB06dGDWrFkWjlxkV+kdAO2eyXEIkWekpKSwfv16St+9i8+8eTgeOABAUqv/YeM/F2snZwqtGcSUKXPRWuPh4cH8+fNp0eKJyz1EHiZ/pwmRRe6NT5mSkqjz009U69EDDhwAd3d+ePN7PI6sI8q5ODdvRnD69CKsrKwYNmwYJ06ckEQunugfW+ZKqVVPcR+tte6YAfEIkStFRESwc+dOmufLh9P771PszBkAkvv0x3r6FGz33qVzIWNAs0yZMixevJgqVapQrVo1C0cucorHdbMUyrIohMjlHOLjqR0QgNP27QCYK1bizfiFlChQhyJLPmfMmDHMmTMHJ6eeAHTp0sWS4Yoc6B+Tuda6cVYGIkRuc+HCBa5cvkyD69dxGDgQz7AwtK0tatQorEaMwLb/MdaseY5z534HjFktPXv2tHDUIqfKiEVDQohHiA0J4dkxYyB1+7Zwrwa0vLKQJe2Ks2j4cL78ci5msxkPDw/mzZtHy5YtLRyxyMnSncyVUs7Aq0B5wP7h81rr4RkYlxA5jtaakydPUsDFBfc1a6g0ejTq7l20qytq+nTim/Sk+KAzNG3qxfXrVzGZTAwdOpTx48fj6Oho6fBFDpfeRUNlgb2AA+AIhGMU2rIGbgN3MLaPEyLPSklJ4c/16yn11VcQGooCdhftwLd1ZuPfuyglgVWrylCjhhslSxYnICBAStSKDJPeqYkzgcNAEYylpS2AfEBXIAaQmSwiT0pJSeHkyZOYY2KwHjWKZmPG4BwaCiVLwvr1HHx/OZH2qwkLCwfAxsaGzZs3s2/fPknkIkOlt5vlOaA3kJB6bKu1TgFWKKWeAWYD9TIhPiGytcuXL3Nh4UKeXbcOqz//BKUIsB+Iz5JJWLudZuX4OgQFBWFre4ivvvoKgGLFilk4apEbpTeZ2wNRWmuzUuoWcP//jccBmQwr8ozExEQiIyMpbGWFx9ixeH79tXHC25u42YvY8rkXe74cw4oVn2M2mylVqhTt27e3bNAi10tvN8tpwCP169+Bfkope6WUDdAv865IAAAgAElEQVQLuJYZwQmRHe3etYvTY8eiK1ZEff01iSZ75pecgvngYbbeuc7Bg5X45pvZAAwZMoQTJ07QunVrC0ctcrv0tsxXAj7A18AYYDMQBZhT79E9M4ITIruIjY3FxsYGm8uXaTRlCja7dhknXnyRjS0C+CuqHMdCQ/jf//4HQO3atQkICKB69eqWC1rkKf+qnrlSqiTQDGMQdIfW+viTXiP1zEVOFR8fz6rly2kYFITH0qUQF8dtqwKc6juDOvO6oe4rRTts2DBKlSrF22+/jckk2+KKDJGueubpSuZKKQetdex/iUaSuchpEhMTsbW1hUOHiOvWjXynTgFg7tyFd+JnULP5FQIC/Jg6dSovvfSShaMVuVi6knl6+8wjlFLfKaXaKqXs/kNQQuQIly5dYtWSJcS//TY8/zz5Tp3isrUn8es2EhvgTz6Pqfj51ebw4cNMnjzZ0uEKke4+8+FAe2ANEKOUWo/Rj75Za52cWcEJkdW01iilKHLkCK+NG4d9WBhYWXG5wxCm5vuIF2K3M7JSJS5fvoyVlRWDBw9mwoQJlg5biHRvTjEXmKuUKgZ0SH2sB+4opdYBK7XWWzMvTCEy3+HDh4m9cAHftWux/+47AK4Xq4H7T4uwK1GCG/3fpEuX7wGoWbMmCxcupEaNGpYMWYg0T7U5hdb6mtZ6lta6HlAamIwxELoxM4ITIstoTZGff6Zuz57w3XeQLx9r635K4PTfoEYNbGxs2Lt3L05OTsyePZvffvtNErnIVv5V1USlVDmMJfwdAXfgckYGJURWSExMZN++fXiZTBQZO5aSqdMN4xu9gv2SBZSLiqJChWTAGjc3N1atWkXp0qUpWbKkReMW4lHS3TJXSnkqpYYrpY4Ap4B3gF1AA621x2NfLEQ2ZJWcTAF/fwq9/DLs2kVygULMqLmcC/NWM2TuXGrUqPHA4Kavr68kcpFtpbdq4m9ALeAW8D0wFNil/80kdSEsKCYmhmPHjlFHa0x+fngfT10i0b071p9+Svn9+2nWvAp//vknVlZWJCQkPP6GQmQT6e1mCQHGAVtTC2wJkSOFnT1L/lFjULt2oLTmpltZtrVfSP2xFXjPz4+1a9cCUL16dRYtWkTNmjUtHLEQ6ZPe2SzdMzkOITLNrVu3iImJodTvv1OizzvYhl9FW1vDsGEUHDOGWteu4eXlRXR0NI6OjkyaNIkBAwZgbS0bcYmcQ/5vFbne4fXrqbjAHw7+hi1wKn9tzAsX49XRG4AyZcpQr1497OzsmDt3rvSLixzpqaYmCpFT3Lhxg6SEBAgIoEG/QZQ6+Bva0RFmz6bE5W0sPfINISEhACil+P777/nxxx8lkYscS1rmIteJiopit/8iWvzwPTZHg8kHHPdsRfmt89h66hjveHtz6dIlDh06xM6dOwFwcHCwbNBC/EeSzEWucefOHVzs7TGPnUHb2ZOxJQmKFIHPP6dAvXp0GTSINWvWAMYA5yeffGLhiIXIOP/YzaKU2pK6kbMQ2d6xY8fYPWkyKd7euM7+CFuSuNy8DynHjzMvLAyvSpVYs2YNjo6OfPbZZxw8eJDatWtbOmwhMszjWubFgGNKqanAVK11YhbFJES6aK1JSkrCNjYW1X82bfZ+YZyoUAEWLqSkry+XLl1i6NChxMfH07p1a+bOnUupUqUsG7gQmeBxybwaMBgYC3RRSvXXWu/ImrCEeDytNRt/2Yj7vv1UX7KYKjdukKxsSBr6AXrke9i7umIFeHh4MGvWLAoVKkTbtm1RKl2loYXIcf6xm0VrnaK1/hTwAo4CW5VS3yilfJRSlR5+ZFnEIk+7t+j4bugVnLrNo/rkSXDjBtSrh/XxYHY2qkOlGjVYtmxZ2mv8/Pxo166dJHKRq6V72zil1EiMKokPv0ABWmv92D2yZKch8V9FRUWxeeMmmpw9g8vUqaiYGBLz5cd2xjSut27Ne4MHs3r1agBeeukltm3bZuGIhcgQ6WqFPHE2i1LKBZgC9AG2AdMB6T8XWe73r85Tdsh0XBNTGwXt2mE9ezYLfvqJkZUqERUVhYODAxMnTmTgwIGWDVaILPbYZK6UeguYBqQAXbXW32VJVEKkunr1KiFBv/PS/n34Tp+OSk4muWhxrBfM5XqdOrRr144DBw4A0KpVK+bOnYuHhxTxFHnPPyZzpVQg8DywABiltY7OsqiEAMxmWNjpd/yODEDFXgKl4J13sJ48GfLn55mkJGJiYnB3d+fzzz+XfnGRpz2uZW4H1NFaB2VVMEJorTlz5iy20VF4zpvHR4FLjROVK8OiRWy6c4ca8fEUzp8fGxsb1q5dS5EiRXBxcbFs4EJY2ONqs0giF1nu9yDN5/UCKdL4ZVi6FOzsYNIkrm/YwBuzZ9O8eXOGDh2adn358uUlkQvBY1rmsvGEyCpms5mQkFCetbGm8vBBfH4zdUvZhg0x+/uzcNcuRlarxp07d3BwcMDb2xuttXSpCHEfqc0iLG7MB9E8s+IbvG7OwiouDu3mhvr0U47VqoVfz57s378fgBYtWjBv3jw8PT0tG7AQ2ZAkc2ERSUnJhIeHUeyvvxiwvA/uV48YJzp2RM2ezZWkJGqVLUtiYiLu7u7MmTOH1157TVrjQvwDSeYiy12+DO1bhDPF7l3cg3/EPSUFSpWC+fOhZUsASgC9evVCKcXkyZOlX1yIJ3iqZK6UcgOqACWBjVrr20opeyBRa23OjABF7hEfn4DWZgoHB7L2TD+KJ1wAKysYNIiwd99l0OjR9LSz4+WXXwZg3rx50hIXIp3StdOQUsqklPoEuALsBr4GSqeeXoux2bMQ/2jZMjMvVzvLrZatsWvzipHIq1XDvG8fC728qFCzJt9++y1Dhw5Nq78iiVyI9EvvtnGTMZbzDwDK8GCtgB+B1hkcl8glEhISQWu8Dn7NhvO+FN+5HeztYepUji9dSoP338fPz4/IyEiaNWvGunXrJIkL8S+kN5m/CYzUWi8FLj907hxGghciTXQ0tG4dz7gu35Pg60udBd1xSb4FL71E/OHDfHjnDtWfe459+/ZRtGhRvvvuO3755RdKly795JsLIf4mvX3mrhhJ+1FsgcdWTBR5i9YaJ7tkXg2ZyZsXP8I2JQEKFoQZM6BbN+IiI1myZAkpKSn079+fyZMn4+rqaumwhcjR0pvMjwOvYlRNfFhzQFaKCrZvh/ffj2V6e3+arP6S3uf+ME507UrYyJHkL1sWe6Vwc3Nj6dKluLq6UrduXcsGLUQukd5kPglYq5TKB6zGqGnuo5RqC/gBbTIpPpGD5FfRDL3yIU3GzkNpDZ6emOfP54srVxhevz7vvvsuEyZMAKB58+YWjlaI3CVdyVxr/aNSqjPwCdAz9enFwFWgm9Z6cybFJ7IxsxkGDjQTF3eVCXU2UnvSJGrfugwmE7z/PiEdOtB38GD27NkDwNGjR2UZvhCZJL0DoGitV2mtPYGKQH2gElBKa70qk2IT2ZyVFai//qL7pn4U9/MzVgPVqEF8YCCjbW2pVq8ee/bsoUiRIqxcuZIffvhBErkQmeSpV4BqrU8DpzMhFpEDHD8Ofn4pvD/4CK9GBDFn2weoyEhwcICJE/mrY0deaNiQc+eM8fK+ffsydepU3NzcLBy5ELlbupO5UqoY0ApjpbX9Q6e11npERgYmsidnZ3C4HEr193thffm48WSzZrBgAXh6UlhrypYti729PQsXLqRevXqWDViIPCJdyTx1oPNbjCmIYfx9D1ANSDLPpWbOhGPHEvlw6FnKff89W8ImoRISoFAhzDNnsjQujnpxcXhhrNpcvnw5+fPnx9bW1tKhC5FnpLdlPhnYAnTXWt/KxHhENnTzJtge2UbBJn3h2lVj+W+PHoT27k3fkSMJDAykQYMG7Nq1CysrK5555hlLhyxEnpPeAdCSwBxJ5HnDtWvQqhVs2RJJzLVrTLz1DguOtcLt2lUoV47EjRsZU7w43o0aERgYSJEiRXj77bdlcFMIC0pvy3wfUIFHLxoSuYyzM5w9qzk/Yy4Nj8xGRUSAtTUMH87OF16g77vvcvbsWUAGOIXILv4xmSulHO47fB9YrpSKAbYCkQ9fr7WOzfjwRFZZswZWroRFiyJxi4slpNK7qHXfGyfr1IFFiwgvWpSWHh7ExcVRqVIlFi5cyAsvvGDZwIUQwONb5jEYA5v3KGDpQ8/dT+qz5GBRUXD+bCxH+rzHi1vWYRUdDU5O6MmToX9/lLU1hYCPP/6YuLg4hg4dKgOcQmQjj0vmPbIsCpHlYmJg0CBo2lTz6quJdH/uHN0d+2C1dp9xQevWnB08mF7jx/OWoyM9exoLfwcPHmzBqIUQ/+RxyfwCEKS1jsmqYETWyZcP/vgD7FUoVdeMo+IPP6CSkqBoURI/+4xJISFMfeUVkpKSCAsLo3v37lhZpXvBsBAiiz3uX+dOjCX7IpfYu9fYYjM2VmNlpdk37Vdm7GiF1+rVRiL38+PXgACqjB/PxEmTSEpKok+fPuzdu1cSuRDZ3OP+hco8s1wmIQFCQ80sn7eeO2+8gfWLDbE9fx4qVuTuxo28FRdHw1df5cyZM1SqVInAwEAWLlxIgQIFLB26EOIJnro2i8g5zGYYPx4KFDD6x19srAmdtAbzgP7Y3boFNjbw4YfwwQfYWlkRNGwYdnZ2jBkzhmHDhskApxA5yJOSeQulVMX03Ehr/VUGxCMykJWV0S/u7BzHji9X0Wj1amw2bDBOvvAC5z/4AOfnnqOQnR02wDfffIOjoyPlypWzaNxCiKen7u2E/rcTSpmf4j5aa/3YqYm1atXShw8ffprYxL9w6hQMHQqLF0ORIpAUn0LcZ1PJN2kSNvHxkD8/SR9/zOTwcCZPnUrHjh356iv5PSxENpauLu8ntcwbA5KBc5gjR2DHjhvUst3Ls598gs3Bg8aJ115jb8eO9Bw9mtOnjSrGtra2pKSkYDLJMgEhcrInJfM4rfXdLIlE/GuLFsH58zBlClSoABdD4rjW/31KrVoFKSlQvDhRU6YwcPt2vuzQAYCKFSsSEBCAr6+vhaMXQmQEGQDNBU6ehGPHNCdOnKbMxbPkGzQIz7Nn0UrBgAHcHjqU8rVqERERgZ2dHaNGjWL48OHY2dlZOnQhRAaRZJ4DhYXB4MEwciRUrQrTpkHi9ctc69KLfHv3GhdVqYJatAiefx434LXXXuPMmTMsWLCA8uXLWzR+IUTG+8dkrrWWVSLZlMkEu3dDkyZmXPJfptTevdgOGkT58HC0nR0pH37I1ORk6t69y0upr5k1axZ2dnZSplaIXEpa5jnExo2wbh0EBEDBgnD2LFzctQVaDoUTJ4yLGjXiUK9evDlpEqdOneLZlSs5efIk1tbW2Ns/vNOfECI3kWSeQ5w5YyzH/+uvZBztonFesoQKY8eiYmPBzY2Y8eMZ+PvvLO3WDTAGOP39/bG2lh+xEHnBP84zz2gyz/zpxMbCmDHw8svQvDkkJ4PZrNk3dybV5s3D7fx5AHSnTqyuW5d3JkwgIiICW1tbRo8eLQOcQuQeGTLPXFiItbXRtZI/P7z4YgK2SUlYjx9Pw5kzUWYzlCoFCxYQXb8+A8uXJyIigsaNG+Pv7y8DnELkQZLMs5GgIJgxA5YuBVtbOHwYkpLuEDh6Er7ffovt1asoKyuSBw4kZexY7AoWJD+wYMECoqOj6datmwxwCpFHSTLPRq5ehe3bjf7xihXNOMREoAcP5uUVK4wLfHwI6t+fLjNn0t7FhQkTJgDQtm1bC0YthMgOZPqhBaWkGC3xJUuM41at4Nw5sLU5w+EBA9BeXqgVKyBfPu6OG0dfHx9q+vkRGhrKDz/8QFJSkmU/gBAi25CWuQVZWcEvv4C7O/TsCUqBw7WzlOrZk3J79gCgmzThh1deoe/UqWkDnB9++CEjR47ExsbGwp9ACJFdSMs8i128CG++CbdvG8n7xx/hq680Rw4c4Oq770LVqtju2QMFCxIXEECTlBTaDR1KREQEjRo14o8//mDcuHEyU0UI8QBJ5lksMhLWrzcGNwEcHUEdOkT5Ll0oPncuxMcb2T40FPs+fbC2saFgwYIsW7aMHTt2UKFCBct+ACFEtiTzzLPAd98ZLfIRI4zj6Giws0skaPduaqxbh62/P2gNZcpw7J13sGnRgooVjT1Brly5gr29Pc8884zlPoAQwpLSNUVNWuZZYNs2Yyl+crJx7OwMyT/8QJU33sB2wQKwsiJu4EDe8fXFe8gQ+vbti9ls7A1SokQJSeRCiCeSAdBMcOuWsXpz0CB49lmYNQvs7SEhIZZzew9RYd48HFavBkDXqsXGtm3pPmsW4eHh2NjY0LhxY1JSUrCykt+1Qoj0kWSeCRIS4NtvoUYNI5k7OgJmM2Eff4znrFnGWn1HRyIGDaLrb7+xedQoAHx9fQkICEjrYhFCiPSSZJ5Bfv3VmGY4daox1fDCBXBxgcjISAgNxXXECDx//dW4uHlzYj/7jAr163Pr1i0KFCjAp59+Svfu3WUFpxDiX5G/4zPInj2wciXcvGkcu7iAOT6eP/v0wblBAyPbFy5sXLRhAw5eXgwbNow333yT0NBQevToIYlcCPGvyWyWfyk+HqZPh4YNwdfX6FoxmyFfPrh16xZuJ0+i/PyMPd2AhK5dGaEU1Ro3pkePHgBorSWBCyGeRKomZiazGRYvhrg4I5nfW8MTduYMEX36UGD3bgD0s8+yo0MHOi9aRFhYGEW3bqVz586y648QIkNJN8tTCAmBd981aqo4OMDvv8Pkyca5+Ph4WLeOQo0aUWn3brS1NbfffpvWpUrx8scfExYWRoMGDdixY4es3hRCZDhJ5k8hOBi++QZOnTKOCxQw/nv0l18Ir18f2rVDXbuGuU4dFr/9NsWWLGHD9u24ubnxxRdfsGvXLry8vCz3AYQQuZYk88cwm+GLLyB1SjhvvGFUNaxUyejvNicnw/z5VO3YkZJHjqCdnWHuXJJ37mT29u3Ex8fTtWtXQkND6dmzp8wbF0JkGukzf4LFi6F4cWjf3iiMVaAApKSksHfhQmr4++P0xx9YAYnNm3N32jTcqlbFFliyZAlRUVG8/PLLlv4IQog8QJqKD7l+HQYOhJgYo0Ttzz//f8scgPh4TB99RP2BA3H64w+0uzuBgwZR8vBhBn7ySdplzz33nCRyIUSWkWT+kIsXYeFC2LfPOC5Y0GiRR0ZGsm/KFMze3jBxIlbJyUR17szrXl74zppFWHg4ly5dMgZChRAii0k3C8bGyZcuQb9+ULcu/Pmnsb4nze3bOAweTL0vvwRAV6zI8oYN6fPll8THx+Pq6sr06dOlX1wIYTGSeYCvvgJ/f2PKIfx/Ir929SrnpkwBLy9sv/wSbWtL0ujR1La2pltAAPHx8XTp0oXQ0FB69+4tiVwIYTF5smUeEwNTpkD//lCiBMyfbxTDMpnuu+jPP3Ho3Jlie/cax/XroxYuxMbLi1rh4UTGxbFgwQKaNGlikc8ghBD3y5PJPCICZs6EUqXAzw/c3IzntdZcOHuWwqtW4TRlCq5376JdXDjSoQN3O3WiYeoc8enTp2NtbU2+fPks+CmEEOL/5ZlkHhQEW7cau/14ehrzxd3dH7wm+cgR8nfsiNP58wDcbd6cvvHxrFi0iHI7d3Ls2DHs7e1xdnbO+g8ghBCPkWc6edesMVrjt28bx/cSudaaiyEh6BEjsHn+eZ45fx5dvDjfv/UWhXbtYsXOnbi6ujJ8+HBsbW0t9wGEEOIxcm3VxORkWLAA6tWDmjXh7l1ISgJX1wevu/711zgMGYJLeDgoxfXXX6fdyZMcOHECgM6dOzNjxgyKFCmSZbELIcR98vYeoLGx8PHHxo4/YAxw3kvkKSkpRJ49C9274/7mm7iEh6OrViVx925q7tnDgRMnKF26NJs2bWL58uWSyIUQ2V6u6jO/dAmWLIHx4yF/fjh82FiK/wCtCRk1inLz50N0NNrODj16NFYjRmBrY8OsWbMIDg5m9OjRODg4WOJjCCHEU8tVyXzLFmPDiI4djWJYJUr8/7nExERMly5hGjCAKlu2ABBXrx4DbWxwT0xkgo0NAB06dKBDhw6WCF8IIf61HN1nrjV8/71RW7x5c2PRz/XrDyZxgIS7dznZpw/e33+PKSEB7ebGxiZNeH39euLi4ylcuDDnz5/H0dExQ+MTQogMkPv7zM1mmDDBGOgEY9HP/YncbDbD4cPY1a9P9W+/xZSQQETTprzo7k7LVauIi4/njTfe4OjRo5LIhRA5Wo7rZrl9G2bNgg8+AHt72LABihb9+3VXT50ievBgKmzejDKb0R4eLKhalQEbNqC1pnTp0syfP59mzZpl/YcQQogMluNa5kFBMGkS7NplHJcoAdYP/0rauBH3pk2puHGjcTxkCBw/zvqkJEwmEyNHjuT48eOSyIUQuUaO6DPft88oTdu5s3F84QKULv3360J27aLgxx9TeNs2ABIqVybs448p+eqrAFy8eJHo6GiqVq36r+IQQggLSFefeY5I5q1bG8vvjx17qBjWPVrDsmUkDxqEdVQUOl8+dr/4Im127MCnVi127dolFQ2FEDlVupJ5jugzX7QInJ3/nsiTk5M5vm4dlWbPxnbvXqyByDp16HznDhs3bACgWLFixMbG4uTklPWBCyFEFskRyfxRA5wkJsK0aVSdOBFTUhLmggX5ukYNemzdigY8PT2ZP38+zZs3z+pwhRAiy+W4vof4+HjOfP01umZNrMeONRJ51648nz8/3bduxcpkYsSIEZw4cUISuRAiz8gRLfM00dHE9e9PuRUrUFpDmTIQEIDVyy/TbupUTD/+SEBAAN7e3paOVAghslSOGACNiYkhae1a3EaPhitX0CYTh3x9OdelC5169QKM/nMrKysZ6BRC5Da5ZAD0+nWiXn+dYvv2ARBTqRK9UlJYtXMnbsHBtHj9dVxcXLD+22RzIYTIO7J/BuzalWL79mF2cGBdzZp0DAwkBfDw8GDevHm4uLhYOkIhhLC4bN8noT/5hOu1a1PX2ZnXAwPBZGLo0KGcOHGCli1bWjo8IYTIFrJ9yzylWjWaxsVx/K+/eO655wgICMDHx8fSYQkhRLaS7ZO5tbU1CxcuJCgoiH79+mF65BJQIYTI23LEbBYhhMjDcn89cyGEEAZJ5kIIkQtIMhdCiFxAkrkQQuQCksyFECIXkGQuhBC5gCRzIYTIBSSZCyFELpBli4aUUuHApSx5MyGEyD0itNbNnnRRliVzIYQQmUe6WYQQIheQZC6EELmAJHPxnymldDoejZRS3VO/dsqEGGyVUqFKqQ2PODdEKZWolKqU0e/7iPdappSSinIiy2X7ErgiR6h739f5gB3AJOD+xHoS8MysALTWiUqpd4BtSql2WuvvAZRSJYDxwEyt9cnMen8hLE2SufjPtNYH7n19X6v73P3Pp57L7Di2K6VWArOUUpu11neB2cBtYEKmvrkQFibdLMISSiultiql7qZ2jbR7+AKl1KtKqcNKqXil1A2l1CdKKZt03Pt9wAUYp5RqAbQD3ktN7I+klPoo9T2sHnq+VWq3ULnU4zeVUnuUUreUUreVUjuVUrUeF4xSarxSKuIRz2ul1ICHnuutlDqhlEpQSl1SSg1/6HxlpdSm1Pe/q5QKSf1rRAhJ5sIiVgDrgbbAGWBlancIAEqpDsD3wEGgDfAR0BeY8qQba62vA2OBwcBC4Bet9bonvGwlUARo+NDzHYAjWuuzqceewFdAe6AzcAX4VSlV5klxPYlSahiwAPgBaJX69cSHEv56IAXoivF9+Rxw/q/vLXIJrbU85JFhD8AJ0ED3R5zrnnqu533PFQSSgX6pxwpjcdnSh17bE4gDCqYjBhNwAzADZdMZ91HA/75jO+AOMPQfrrfC6KYMBcbe9/wy4PB9x+MxFn08/HoNDEj9Oj8QA4x76JoJqZ/DBDyT+pqqlv4ZyyN7PqRlLixhy70vtNY3gTDgXsu8PFAKWPV/7dxPiFVlGMfx7w9MHMR/G81ssFoogUQtIhQUDBIGlIoJBgraCCGzEkwCJ2g2LsZUEsUEYxBcOKNgukhbFWikm4oWkYLMgJMwAyKzEnNwHhfPe+l453YbmUXD8feBw+W+55z3Pecunvc57/ueK2lBYyMnVRcBG2ZR/7tkpg2wZZbXNAx0l7YAusis92zjAEmvSvpW0gSZIU8B68s1z8VGYDFwrsU9ryJ/m3vAGHBCUo+klXNs02rGwdz+D5NN3x+SgRoyAwW4RAbLxjZayjvbVSxpMfAVOZRzChiQtHwW1zRU2n67fO8BrkXE7VLvErIT6iTH5TcDb5IZ/aIZtT2dxj3/wZP3/GMp74yIaWAbmakPAuOSrkp6Y45tW014NYvNN/fK5yfAby32j7Yoq/qCnAD9lBy+eQ/YD7SdKIyIkbI+vEfST8AOYF/lkI1khvxORNxoFEpa9h/X8wBYWC2QtKLpmMY9bwcmWtRxs1zjDfLp4TmyMxkAvpP0Ygn29gxzMLf55iZwB3gpIk4+zYnlpaDdwN7IiVAkfQ4clfRNRLTqHKqGgD5yeKMDOFfZ11E+/660t4mcFP2lTZ1/AUskrYmIO6VsW9Mx18j5gBciYsZLT80iYgr4QdJh8glkOf90CPaMcjC3eSUipiXtAU5LWgpcJodhXiGz7A8i4v6/nP41+XLSsUrZCWAncFzSpoho989yZ4Evy3al0SEU18lJypOSDpBZej/Z8bTzPRmoByUdAl4GdjXd86SkfuCIpLXAFXIIdB2wNSLel/QacJAc2x8BVgCfAb9HhAO5eczc5p+IGCYnMV8ns+PzQC/wKxnYZ5D0MTn00BsRjyp1TZdz3yJXxLRrdwz4GVhNZunVfRPkksTngYvkE8Au4BZtRMRdoJsM/hfIZYUftjjuADm01FXqPwN8BFwth4yTQzB9ZAd3HPiTXKJo5r/ANTOrA2fmZlfEY08AAAA0SURBVGY14GBuZlYDDuZmZjXgYG5mVgMO5mZmNeBgbmZWAw7mZmY14GBuZlYDDuZmZjXwGBwP3rljtgfuAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#samples=2\n", "fig,axes = plot_utils.make_grid_plot(samples,1,plotsize=(3,3),sharey=True,sharex=True,\n", " xlabel='The X values',ylabel='The Y values',\n", " gridspec_kw=dict(hspace=0),\n", " subplot_kw=dict(frameon=True))\n", "fig2,ax2=plt.subplots(1,1)\n", "\n", "colors = '0.6','b','r','k','c'\n", "ax2.plot(xdata,ypred,color='r',lw=2,zorder=100,label='Regression')\n", "ax2.plot(xdata,xdata,color='k',ls='--',lw=2,zorder=99,label='Underlying truth')\n", "\n", "ax2.set_xticks([])\n", "ax2.set_yticks([])\n", "\n", "ax2.set_xlabel('The X values',size=15)\n", "ax2.set_ylabel('The Y values',size=15)\n", "\n", "ax2.spines['right'].set_visible(False)\n", "ax2.spines['top'].set_visible(False)\n", "\n", "for ax in axes.flatten():\n", " ax.spines['right'].set_visible(False)\n", " ax.spines['top'].set_visible(False)\n", " ax.spines['bottom'].set_visible(False)\n", "axes.flatten()[-1].spines['bottom'].set_visible(True)\n", "\n", "for plot_num,(color,yboot) in enumerate(zip(colors,boot_data.T[:samples])):\n", " \n", " yb = yboot[:,np.newaxis]\n", " \n", " #fig,ax = plt.subplots(1,1)\n", " regr.fit(xdata,yb)\n", " ypb = regr.predict(xdata)\n", " \n", " #print('{},{}'.format(plot_num-1,plot_num+1))\n", " \n", " for ax in axes.flatten()[[plot_num-1,plot_num]]:\n", " ax.scatter(xdata,yb,color=color)\n", " ax.plot(xdata,ypb,color=color)\n", " ax.set_xticks([])\n", " if color=='k':\n", " ax2.plot(xdata,ypb,color=color,ls=':',label='Bootstrap regression')\n", " else:\n", " ax2.plot(xdata,ypb,color=color,ls=':')\n", "\n", "ax = axes.flatten()[0]\n", "ax.set_yticks([])\n", "#ax.set_ylabel('The Y values')\n", "#ax = axes[2]\n", "#ax.set_xlabel('The X values')\n", "\n", "\n", "ax2.legend()\n" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Using the toolbox\n", "-----------------\n", "\n", "Now do the same thing with 1000 samples. This time we'll just use the :py:func:`.bootstrap` function, and we can just drop the data straight in." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:41.788655Z", "start_time": "2019-05-31T14:12:41.400951Z" } }, "outputs": [], "source": [ "samples = 1000\n", "\n", "linear_bootstrap = plu.bootstrap_estimator(estimator=linear_regressor,\n", " X=xdata,y=ydata,samples=samples,cv=cv,\n", " )\n", "\n", "linear_bootstrap.fit()\n", "\n", "ypred,cpb,bounds,error, = linear_bootstrap.bootstrap_uncertainty_bounds()\n", "\n", "#lbt_out = plu.bootstrap(xdata=xdata,ydata=np.squeeze(ydata),\n", "# PLS_model=regr,cv_object=cv,samples=samples)\n", "#rcv,ecv,msecv,cpb,cpt = lbt_out" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:41.794337Z", "start_time": "2019-05-31T14:12:41.790813Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(28,)\n" ] } ], "source": [ "for est in linear_bootstrap.estimators_:\n", " print(est.predict(linear_bootstrap.data_).shape)\n", " break" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:41.876106Z", "start_time": "2019-05-31T14:12:41.796342Z" } }, "outputs": [ { "data": { "text/plain": [ "(1000, 28)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cpb.shape" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:41.950453Z", "start_time": "2019-05-31T14:12:41.879687Z" } }, "outputs": [ { "data": { "text/plain": [ "(1000, 28)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "linear_bootstrap.boot_data_.shape" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:42.030622Z", "start_time": "2019-05-31T14:12:41.954259Z" } }, "outputs": [ { "data": { "text/plain": [ "(1000, 28)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "linear_bootstrap.boot_indices_.shape" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "Plot the bootstrap results and show that the bootstrap uncertainty exactly reproduces the results of the linear regression uncertainty." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2019-05-31T14:12:44.676094Z", "start_time": "2019-05-31T14:12:42.034581Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig2,ax2=plt.subplots(1,1)\n", "\n", "for ypb in cpb:\n", " ax2.plot(xdata,ypb,color='0.6',ls=':',zorder=0)\n", "\n", "ax2.plot(xdata,ypred,color='r',lw=3,label='Regression',zorder=3)\n", "ax2.plot(xdata,yunc,ls='--',color='r',lw=3,label='95% CI (regression)',zorder=3)\n", "ax2.plot(xdata,ypb,color='0.6',ls=':',label='Bootstrap',zorder=-1)\n", "\n", "ax2.plot(xdata,bounds.T,ls=':',color='k',lw=3,label='95% CI (boot)',zorder=4)\n", "\n", "ax2.scatter(xdata,ydata,color='k',label='Measurement',zorder=5)\n", "\n", "ax2.set_xlabel('The X values',size=15)\n", "ax2.set_ylabel('The Y values',size=15)\n", "\n", "ax2.set_xticks([])\n", "ax2.set_yticks([])\n", "\n", "\n", "ax2.spines['right'].set_visible(False)\n", "ax2.spines['top'].set_visible(False)\n", "\n", "#ax2.legend()\n", "\n", "handles, labels = ax2.get_legend_handles_labels()\n", "\n", "ax2.legend([handles[i] for i in [0,3,1,4,6]],[labels[i] for i in [0,3,1,4,6]])\n", "\n", "t = 1" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "ExecuteTime": { "end_time": "2019-06-07T15:48:41.244914Z", "start_time": "2019-06-07T15:48:37.967210Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig3,ax3=plt.subplots(1,1)\n", "\n", "ax3.plot(xdata,ypred,color='r',lw=3,label='Regression',zorder=3)\n", "\n", "ymodel = linear_bootstrap.base_predict(xdata)\n", "for row in linear_bootstrap.boot_data_:\n", " plt.scatter(xdata,row+ymodel,s=1,color='k')\n", "\n", "ax3.set_xlabel('The X values',size=15)\n", "ax3.set_ylabel('The Y values',size=15)\n", "\n", "ax3.set_xticks([])\n", "ax3.set_yticks([])\n", "\n", "\n", "ax3.spines['right'].set_visible(False)\n", "ax3.spines['top'].set_visible(False)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2019-06-05T20:46:21.845527Z", "start_time": "2019-06-05T20:46:21.840988Z" } }, "outputs": [], "source": [ "bootstrap_x = linear_bootstrap.data_[linear_bootstrap.boot_indices_]\n", "bootstrap_y = linear_bootstrap.ydata_[linear_bootstrap.boot_indices_]" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2019-06-05T20:46:07.650749Z", "start_time": "2019-06-05T20:46:07.644610Z" } }, "outputs": [ { "data": { "text/plain": [ "(1000, 28, 1)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bootstrap_x.shape" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2019-06-05T20:46:29.635104Z", "start_time": "2019-06-05T20:46:29.629754Z" } }, "outputs": [ { "data": { "text/plain": [ "(1000, 28)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bootstrap_y.shape" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2019-06-05T20:47:42.788750Z", "start_time": "2019-06-05T20:47:42.782118Z" } }, "outputs": [ { "data": { "text/plain": [ "array([[6.57, 6.14, 3.7 , ..., 8.29, 8.05, 4.69],\n", " [5.59, 7.22, 6.02, ..., 5.17, 6.43, 5.59],\n", " [4.74, 6.14, 4.69, ..., 7.29, 7.11, 6.57],\n", " ...,\n", " [6.02, 4.69, 7.27, ..., 7.22, 7.29, 4.76],\n", " [8.29, 8.29, 7.11, ..., 7.11, 6.57, 7.02],\n", " [6.62, 6.43, 8.29, ..., 6.14, 6.89, 4.86]])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bootstrap_y" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2019-06-05T20:47:20.357301Z", "start_time": "2019-06-05T20:47:17.213357Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for x,y in zip(bootstrap_x,bootstrap_y):\n", " plt.scatter(x,y,s=1,color='k')" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2019-06-05T20:48:33.874610Z", "start_time": "2019-06-05T20:48:33.842447Z" } }, "outputs": [], "source": [ "import sklearn.ensemble as skens" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2019-06-05T20:49:18.166103Z", "start_time": "2019-06-05T20:49:18.161941Z" } }, "outputs": [], "source": [ "bagger = skens.BaggingRegressor(base_estimator=linear_regressor(),n_estimators=1000)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2019-06-05T20:49:19.851934Z", "start_time": "2019-06-05T20:49:18.962154Z" } }, "outputs": [ { "data": { "text/plain": [ "BaggingRegressor(base_estimator=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False),\n", " bootstrap=True, bootstrap_features=False, max_features=1.0,\n", " max_samples=1.0, n_estimators=1000, n_jobs=1, oob_score=False,\n", " random_state=None, verbose=0, warm_start=False)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bagger.fit(xdata,ydata)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2019-06-06T15:29:38.395667Z", "start_time": "2019-06-06T15:29:38.329141Z" } }, "outputs": [], "source": [ "y = np.stack([est.predict(xdata) for est in bagger.estimators_])\n", "bounds_boot = np.array([np.percentile(y,2.5,axis=0),np.percentile(y,97.5,axis=0),])" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2019-06-06T15:29:41.940153Z", "start_time": "2019-06-06T15:29:39.196449Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig2,ax2=plt.subplots(1,1)\n", "\n", "for ypb in y:\n", " ax2.plot(xdata,ypb,color='0.6',ls=':',zorder=0)\n", "\n", "ax2.plot(xdata,ypred,color='r',lw=3,label='Regression',zorder=3)\n", "ax2.plot(xdata,yunc,ls='--',color='r',lw=3,label='95% CI (regression)',zorder=3)\n", "ax2.plot(xdata,ypb,color='0.6',ls=':',label='Bootstrap',zorder=-1)\n", "\n", "ax2.plot(xdata,bounds_boot.T,ls=':',color='k',lw=3,label='95% CI (boot)',zorder=4)\n", "\n", "ax2.scatter(xdata,ydata,color='k',label='Measurement',zorder=5)\n", "\n", "ax2.set_xlabel('The X values',size=15)\n", "ax2.set_ylabel('The Y values',size=15)\n", "\n", "ax2.set_xticks([])\n", "ax2.set_yticks([])\n", "\n", "\n", "ax2.spines['right'].set_visible(False)\n", "ax2.spines['top'].set_visible(False)\n", "\n", "#ax2.legend()\n", "\n", "handles, labels = ax2.get_legend_handles_labels()\n", "\n", "ax2.legend([handles[i] for i in [0,3,1,4,6]],[labels[i] for i in [0,3,1,4,6]])\n", "\n", "t = 1" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "ExecuteTime": { "end_time": "2019-06-06T15:29:42.043375Z", "start_time": "2019-06-06T15:29:41.941854Z" } }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(xdata,bounds_boot.T,color='r')\n", "plt.plot(xdata,bounds.T,color='b')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2019-06-06T15:28:52.947710Z", "start_time": "2019-06-06T15:28:52.783610Z" } }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LlJFbyIdLk3lncTIHsgvoFVeH5y7txNk6xzHgqbhFzoS1sGGqMyxyMBmaD3LOeWzU2bVIaVn5vL1oGx8t287h/CLOaVOPO89pSc/YOq5lkvKl4hY5XSnL4IdHIPUXqN8Brv0SWg5xbX/sHQdymLIgic9W7KCw2MMFnRpxx6AWdGhc05U84j0qbpFTlZ4IPz4Om6Y5G0GNfBW6XuPsle2CbenZvDI3gW9W7yLIwGXdo7nt7BbERfnHPidS/lTcImWVnQ4//RNWvAMh4XDOI9DnTtfeeEzcd5jX5iXyzeqdVAkJ4sY+sdw6MI5GNau6kkd8R8UtcjKFubBsEiwa76x+7HEjDHoIqtV3Jc7mPVm8MjeB6Wt3Ex4SzM3947h1YHPqV9ey9MpCxS1yPB4PrPkU5j4NmanQ5gIYOg7qtXYlzvpdGbw6N5Hv1+0hskowt5/dglv6a1l6ZaTiFjmWrfOcg3n3rHVWPF76BsT2dyXKmtRDTJyTyI8b91I9LIS7B7fkT/3iqK2jwSotFbfI0fZugNmPQeJsZy+Ry9529hZxYcXjqpSDvDIngXmb06gRHsI9Q1szpp/2EREVt4gjczfMewZWfwxh1WHY09BrLIT4fhjil+QDTJyTwMKEdGpHhHLf8Dbc0KcZ1cNV2OJQcUvlln8YlkyEJa9AcSGcdQcMvBcifLtYxVrLsiSnsJcm7SeqWhUeOr8t1/VuRmSY/jeV/6XvCKmcigth1Qcw/3nI3ucMhwx5DOrE+TSGtZbFifuZOCeBn5MPUK96GI+OaM81vbRTnxyfilsqF2udhTM/PgH7EyGmL4z+N0TH+ziGsxf2xDkJrEo5RKOa4Ywb2YGrejYlPFSFLSem4pbKI2W5M1Nkx3KIagOj/wOtz/PpEnVrLXM37WPinAR+S82gSa2qPH1xR66IjyYsRIUtZaPiloovPcF5hb1pGlRrCBdNgK7XQbDvvv2ttczesJeJcxNYtzOT6NpVef7STlzaPVp7YcspU3FLxZW111mivvI9CK3qyhJ1j8fyw4Y9TJiTyMbdmTSrG8ELl3fmkm5NCA1WYcvpUXFLxZNzwJkpsvwNKC6A+D/B2Q9AtXo+i+DxWL5ft4dX5iawaU8WcVGRvHRFF0Z1bUyIClvOkIpbKo7cQ86eIksnQcFh6HgZnPMPqNvCZxGKPZYZa3czcU4CCfsO06JeJBOu7sqIzo112oyUGxW3BL78LFj2Oix9BfIyoN1IZxOoBu19FqF0YbeqX42Jo7txYadGKmwpdypuCVwFOfDzFFg8AXIPOJtADXrIp6fPHKuwXxndjQtU2OJFKm4JPEUFsOp9WPAvOLwXWg51hkSa9PBZhGKPZXpJYSeWFPar13Tjgo6NCFJhi5eVqbiNMbWAt4COgAX+ZK1d6s1gIn/gKXa2WZ3/HBxKgWb94MoPIKa3zyKosMUflPUV9wRgprX2cmNMFSDCi5lE/pe1sPE7Z1/s9M3QqCuMGA8tBvts8YwKW/zJSYvbGFMDGAiMAbDWFgAF3o0lglPYW+fCnCdh92qIau28wm430qeFPW3NLibOSWBrWjatG1TjtWu6c37HhipscU1ZXnE3B9KAd40xXYCVwF+ttdleTSaVW8oymPMUbF8ENWPg4snQ+SqfHcirwhZ/VpbiDgG6A3dba5cbYyYADwKPHn2RMWYsMBYgJiamvHNKZbF7jTMkkjALIuvDBS9C9xt8ti+2ClsCQVmKOxVItdYuL/n8C5zi/h/W2inAFID4+HhbbgmlckhPdA4yWP8VhNeCoU84Bxn4aHl66cJu06A6k67tznkdVNjif05a3NbaPcaYHcaYNtbazcAQYIP3o0mlkJHq7Cfy68cQEg4D74M+f4aqtXzy9EcKe8KcBJJU2BIgyjqr5G7g45IZJUnATd6LJJXC4TRY9DL88pbz+Vm3Qf+/+Ww/kSN7ibw8e7NeYUvAKVNxW2tXA77daV4qptyDsORVWDYZivKg6zXOBlC1mvrk6a21zN+SxouzNrN+Vyat6ldTYUvA0cpJ8Y28DKesl74G+ZnQ4RI452GIauWzCL8kH+CFmZv4JfkgTetU5eUruzCqaxMtTZeAo+IW78rPguWvO4fx5mVAu4vg7AehYUefRVi3M4MXf9jM/M1p1KsexlMXd+Sq+KY6wEAClopbvCP/MPzyJiyeeNQGUA9Coy4+i5CUdpiXZm9h+prd1KwayoPnt+XGPrE6hFcCnopbyldBDqx4GxaNh5x0aDXMKWwfbgC181Aur8xJ4POVqYSFBHH34JbcMqA5NauG+iyDiDepuKV8FObByndh4cuQvc/ZR2TQP6BpT59FSMvK57V5iXyyPAWA63s3465zWlKvum8W74j4iopbzkxRPqz6ABa+BFm7IW4gDPoAmvXxWYRDOQW8sSCJ9xYnU1Ds4Yoe0dw9pBVNalX1WQYRX1Jxy+kpKoDVH8GClyAzFWL6wKVvQtwAn0XIzi/inUXbmLIgicMFRVzUuTH3nNuauCjfHQYs4gYVt5ya4kL47d/w078gIwWie8GoV6H5IJ/t2OfxWL5clcq/Zm1mX1Y+57ZvwN+HtaZtwxo+eX4Rt6m4pWyKi2Dt587y9IPboHF3GPF/0HKIzwobYHnSfp6avoF1OzPp0rQWk6/rTo9mdXz2/CL+QMUtJ+YphnVfwU/Pw/5EaNgZRn8KrYf7tLBT9ufw3Pcb+X7dHhrVDGf8VV0Z2aWxVjtKpaTilmPzeGDDVJj/vHPqTIOOcNXH0PZCnxZ2Zl4hr81N5N3FyQQHGf52bmtuHdBcc7GlUlNxy/86ckzY/Odg3wao1xaueN85dSbIdysNi4o9fLpiBy//sIX92QVc1j2a+4a3oWHNcJ9lEPFXKm5xWAtbZsK8Z2HPGqjbEi5729lTxEenzhyxMCGNp6dtZPPeLHrF1uG9m9rTKbqmTzOI+DMVd2XnKYaN3zrT+vauhdpxcMkb0PFyCPbtt0fivsM8O2Mjczfto2mdqky+tjvndWyI8eHQjEggUHFXVsVFsO4LZ+FM+hao28o517HTlT4v7EM5BYz/MYGPlm0nPDSYB89vy5i+sYSHahxb5FhU3JVNUT6s/tjZS+TQdudNx8vfhfajfD4kUljs4cOl25kwJ4GsvEKu7hXD385tTVQ1LVEXOREVd2VRkAOr3nd268va5Wz6dP4/ofV5Pp0lAs5hBnM37eOZ6RtJSs+mf8soHhnRTgtoRMpIxV3R5WU6x4Mtfc3Zra9Zf7h4kk9XOh5t4+5Mnpm+kUWJ6TSvF8nbN8YzuG19jWOLnAIVd0WVc8A5wGD5684BBi2HwoB7fbr509H2Zebx0g9b+GzlDmqEh/LYiPZc36cZocE6zEDkVKm4K5qsvbD0VVjxDhQchrYjYMDfoUl3V+LkFhTz5sIkXv9pK4XFHv7UL467B7ekVkQVV/KIVAQq7ooibbNT2L99Cp5C6HCpU9gN2rsSx+OxfP3rTv41azN7MvM4v2NDHjivLbHauU/kjKm4A5m1sH2xc57jlpkQEg7droU+f4a6LVyLtXTrfp6ZUbIRVHRNJo7uRq84bQQlUl5U3IGouMjZR2TJK7B7NUREwaCHoOctEBnlWqyktMM89/0mZm/YS2NtBCXiNSruQJKfBas+hGWTnb2w67aEEeOhy9UQ6t5pLwezC5gwx1lAExYSxH3D23Bz/zgtoBHxEhV3IMjcBcvfgBXvQn4GxPT97xxsH278VFpRsYePl6fw8uwtvy+guWdoa53xKOJlKm5/tmed84bj2i/AFjurG/vcDdG+OzH9eJYkpjPuuw1s3ptF3xZ1eeyi9lpAI+IjKm5/Yy0kzXPGr7fOhdBI6Hkz9L4Dase6nY4dB3J4evoGZq3fS9M6VXn9uh4M79BAC2hEfEjF7S+KCmD9V05h710H1RrCkMch/iaoWtvtdGTnFzFpfiJvLtxGsDEaxxZxkYrbbXmZsPI95w3HrF1Qrx2MmgSdLocQ98eKrbVMXb2T57/fxN7MfC7u2pgHz2+nAw1EXKTidkvmblg+ueQNx0yIGwgjJzpL0/1k2OG3HYcY9916VqUconN0TSZdq4N5RfyBitvX9m10hkPWfFbyhuPF0O8v0Lib28l+ty8rj3/N3MznK1OJqhbGC5d35vLu0ZqPLeInVNy+cGSF4+KJkDALQiMg/k/Q506/eMPxiPyiYt5bnMwrcxPJLyrmtoHN+fPgllQPD3U7mogcpUzFbYxJBrKAYqDIWhvvzVAVhqfYOXh3yUTYudJZ4XjOw84Kxwj/GXI4sj/209M3si09myFt6/Pwhe1oXq+a29FE5BhO5RX3OdbadK8lqUgKc51TZpa8Cge3QZ3mcOHL0PUaV1c4HsvWtMM8+d0GftqSRvN6kbx3U08GtanvdiwROQENlZSn7P3wy5vw8xTI2Q9N4uHccc7Wqj4+FuxkMvMKmfhjAu8tSaZqaDCPXNiOG/vGan9skQBQ1uK2wA/GGAu8Ya2d4sVMgSdjJyyeAKs+gKJcZyl6v79CTB+/mSFyhMdj+WJlKi/M2sT+7AKu7NGU+85ro3MeRQJIWYu7n7V2lzGmPjDbGLPJWrvg6AuMMWOBsQAxMTHlHNNPpSc649erPwEsdL4a+t4N9du6neyYVm4/yLjv1rMmNYMezWrz7phedIqu6XYsETlFZSpua+2ukr/vM8Z8DfQCFpS6ZgowBSA+Pt6Wc07/YS3sWO5M6ds0HYKrQI8bnVfYtfzzB9bezDye/34TX/+6kwY1whh/VVdGdW2sZeoiAeqkxW2MiQSCrLVZJR8PA570ejJ/4ymGTdOcwk79xVmGPvA+6HUrVPPPN/PyCot5e9E2XpuXSFGx5a5zWnDnoJZEhumtDZFAVpb/gxsAX5e8OgsBPrHWzvRqKn9SkO0MhSx9FQ4mQ+04uOBFZ4ZIFf88hstay+wNe3l6+kZSDuQwrH0DHrmwPTF1I9yOJiLl4KTFba1NArr4IIt/ObzPmR3yy1uQexCie8K5T0HbC/1uhsjREvdlMe67DSxMSKdV/Wp8dPNZ9G/l3qk4IlL+9DtzaUcfultc4BR1379AzFluJzuhnIIiJs5J5K2FSURUCebxi9pzXe9mmt4nUgGpuOE4h+5eB33ucvXQ3bKw1jJr/V6e/G49uzLyuDI+mgfOa0tdTe8TqbAqd3Ef69Ddcx6G+Jshsq7b6U4qZX8Oj3+7jnmb02jbsDoTR3cjPtZ/ltKLiHdUzuL+w6G7reCiCdD5Kr9bkn4seYXFvPFTEq/NTyQ0yPDIhe0Y0zeWEA2LiFQKlau4Sx+626wfXPACtBru6qG7p2LBljQe+2YdyftzGNG5EY9c2F6HGohUMpWjuPducIZD1n7ud4fultXujFyenraR6Wt3ExcVyYc392JAq3puxxIRF1Tc4rYWkhc5e4gkzvbbPbBPprDYw3uLk/m/H7dQ7LH8/dzWjD27OWEh/jslUUS8q+IVd3ERbPzW2UNk168QWQ8GP+K84ehHe2CXxc/bDvDo1HVs3pvF4Lb1GTeyA03raBGNSGVXcYq7IMfZA/vICsc6LWDEeOgyGkIDaww4/XA+z83YxJerUmlSqypTru/Bue0baG8REQEqQnH/vsLxbcg94KxwHPY0tLnAr1c4Hkuxx/Lvn1N4YeYmcguLuWNQC+4e3JKIKoH/n0lEyk/gNsK+jc6r6zWfQXGhs8Kxz58hprff7YFdFmtTM3hk6lp+S82gT/O6PHVxB1rWr+52LBHxQ4FV3NbCtp+cI8ESZ0NIVeh2fUCscDyezLxCXpq1mQ+XbadOZBgTru7KyC7aclVEji8wiru4ENZ9BUtfgT1rnTccz3nEmSUSACscj8Vay3drdvPUtA2kH87nht7N+PvwNtTQieoichL+Xdy5h2Dle86imaxdUK8tjHwFOl0ZcG84Hm1bejaPTl3HosR0OjWpyds3xtM5upbbsUQkQPhncR/c7ixH//VDKDgMcQNh5ERoMSRgVjgeS15hMZPnb2Xy/K2EhQTx5KgOXHtWM4KDNCwiImXnX8WdutIZDtnwDZgg6HiZM37dKPC3Az96qfrILo155MJ21K8RuL81iIh7/Ke48zLh/REQFOrMDjnrdqjZxO1UZ2xvZh5PTdslYeDYAAAJmUlEQVTAtDXOUnUdbCAiZ8p/iju8BlzzKTTuBmGBPw2u2GP5YGkyL/2whYJiD/cMbc1tZzcnPDSw5paLiP/xn+IGZyy7AvhtxyEenrqWdTszGdAqiqdGdSQ2yj/PpxSRwONfxR3gMnILeXHWZj5avp161cJ4ZXQ3RnRupDnZIlKuVNzlwFrLt7/t4qlpGzmQnc+NfWL527DWmpMtIl6h4j5DqQdzeGTqOuZvTqNLdE3eu6knHZvUdDuWiFRgKu7TVFDk4e1F25g4JwFj4LER7bmxb6zmZIuI16m4T8OihHQe+3YdSWnZDG3XgCdGtie6tvbJFhHfUHGfgl2HcnlmunN8WLO6Ebw7pifntK3vdiwRqWRU3GVw9LCIxzrHh906UHOyRcQdKu6TWJiQxuPfricpLZth7Rvw6Ij2Oj5MRFyl4j6OtKx8xn23nmlrSoZFburJOW00LCIi7lNxl2Kt5atVO3lq+gZy8ou1VF1E/I6K+yg7DuTwj6/XsjAhnR7NavPPyzrp+DAR8TsqbpwNod5fksyLP2zGAONGduD63s0I0pxsEfFDlb64t+zN4oEv1/BryiEGtanHM5d0okmtqm7HEhE5rjIXtzEmGFgB7LTWjvBeJN8oKPIwaX4ir81LpFpYCOOv6sqorjqkV0T836m84v4rsBGo4aUsPvNrykEe+HINW/YeZlTXxjw2oj11q4W5HUtEpEzKVNzGmGjgQuAZ4G9eTeRFOQVFvDhrC+8u2UbDGuG8MyaewW0buB1LROSUlPUV93jgfiBgp1gs2JLGP75eS+rBXK7v3Yz7z2tDdW27KiIB6KTFbYwZAeyz1q40xgw6wXVjgbEAMTEx5RbwTKUfzuepaRv4ZvUumteL5PPb+9Azto7bsURETltZXnH3A0YaYy4AwoEaxpiPrLXXHX2RtXYKMAUgPj7elnvSU+TxWD5fuYNnZ2wit6CYvw5pxR2DWmghjYgEvJMWt7X2IeAhgJJX3PeWLm1/k7gvi398tY6fkw/QK64Oz17SiZb1q7kdS0SkXFSoedx5hcVMmr+VyfMTiagSwguXdebyHtFaSCMiFcopFbe1dj4w3ytJztDSrft5+Ou1JKVnc3HXxjwyoj1RmuInIhVQwL/iPphdwLMzNvL5ylRi6kTw4c29GNCqntuxRES8JmCL21rLN6t38eS0DWTmFnLHoBb8ZXArqlbRm48iUrEFZHGn7M/h4anOLn5dm9biuUs70a5RwC/oFBEpk4Aq7sJiD28t3MaEOVsICQriyVEduPasZjpZXUQqlYAp7tU7DvHgl2vYtCeL4R0aMG5kRxrWDHc7loiIz/l9cWflFfLSD1t4f2kyDaqH88b1PRjeoaHbsUREXOPXxT1r/R4e/2Y9e7PyuLFPLH8f1lr7i4hIpeeXxb0nI4/Hv13HrPV7aduwOpOv6063mNpuxxIR8Qt+VdzFHsvHy7fzwszNFHk8PHh+W27uH0docJDb0URE/IbfFHdGTiE3vvszq3ccYkCrKJ65uBMxdSPcjiUi4nf8prhrVA0htm4EY/rG6ggxEZET8JviNsYw/upubscQEfF7GjwWEQkwKm4RkQCj4hYRCTAqbhGRAKPiFhEJMCpuEZEAo+IWEQkwKm4RkQBjrLXl/4cakwZsP81/PApIL8c45cVfc4GynQ5/zQXKdjr8NReUPVsza22ZDsz1SnGfCWPMCmttvNs5SvPXXKBsp8Nfc4GynQ5/zQXeyaahEhGRAKPiFhEJMP5Y3FPcDnAc/poLlO10+GsuULbT4a+5wAvZ/G6MW0RETswfX3GLiMgJuFbcxphgY8yvxphpx3gszBjzqTEm0Riz3BgT60fZxhhj0owxq0v+usWHuZKNMWtLnnfFMR43xpiJJfdtjTGmu5/kGmSMyTjqnj3mi1wlz13LGPOFMWaTMWajMaZPqcdduWdlzObz+2aMaXPU8602xmQaY/5fqWvc+j4rSzY3v9fuMcasN8asM8b82xgTXurx8us1a60rfwF/Az4Bph3jsTuB10s+vhr41I+yjQFedemeJQNRJ3j8AuB7wAC9geV+kmvQse6lj7K9D9xS8nEVoJY/3LMyZnPtvpU8fzCwB2d+sV/cszJkc+WeAU2AbUDVks8/A8aUuqbces2VV9zGmGjgQuCt41wyCuebGuALYIjx0VlmZcjmz0YBH1jHMqCWMaaR26HcYoypAQwE3gaw1hZYaw+VusyVe1bGbG4bAmy11pZeTOcP32fHy+amEKCqMSYEiAB2lXq83HrNraGS8cD9gOc4jzcBdgBYa4uADKCub6KdNBvAZSW/In5hjGnqo1wAFvjBGLPSGDP2GI//ft9KpJZ8ze1cAH2MMb8ZY743xnTwQSaA5kAa8G7J0NdbxpjIUte4dc/Kkg3cuW9HXA38+xhfd+ueHe142cCFe2at3Qm8CKQAu4EMa+0PpS4rt17zeXEbY0YA+6y1K0902TG+5vXpL2XM9h0Qa63tDPzIf3+C+kI/a2134HzgLmPMwFKPu3LfOHmuVTi/0nYBXgGm+iATOK+AugOTrbXdgGzgwVLXuHXPypLNrfuGMaYKMBL4/FgPH+NrPpuedpJsrtwzY0xtnFfUcUBjINIYc13py47xj57WfXPjFXc/YKQxJhn4DzDYGPNRqWtSgaYAJb921AQO+EM2a+1+a21+yadvAj18kOvIc+8q+fs+4GugV6lLfr9vJaL5469rPs9lrc201h4u+XgGEGqMifJ2Lpz7kWqtXV7y+Rc4ZVn6Gp/fs7Jkc/G+gfNDeJW1du8xHnPrnh1x3Gwu3rOhwDZrbZq1thD4Cuhb6ppy6zWfF7e19iFrbbS1Nhbn15251trSP5m+BW4s+fjykmu8/hO9LNlKjeWNBDZ6O1fJ80YaY6of+RgYBqwrddm3wA0l7/r3xvl1bbfbuYwxDY+M5RljeuF83+33Zi4Aa+0eYIcxpk3Jl4YAG0pd5vN7VtZsbt23EqM5/lCEK/fsKMfN5uI9SwF6G2MiSp5/CH/shnLrtZDTjlnOjDFPAiustd/ivGHzoTEmEecn0tV+lO0vxpiRQFFJtjE+itEA+LrkezIE+MRaO9MYczuAtfZ1YAbOO/6JQA5wk5/kuhy4wxhTBOQCV/viB3GJu4GPS369TgJu8oN7VtZsrtw3Y0wEcC5w21Ff84t7VoZsrtwza+1yY8wXOEM1RcCvwBRv9ZpWToqIBBitnBQRCTAqbhGRAKPiFhEJMCpuEZEAo+IWEQkwKm4RkQCj4hYRCTAqbhGRAPP/AZCCheUBuT54AAAAAElFTkSuQmCC\n", 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