{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multi Output GPs in GPflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\n", "\\newcommand{\\GP}{\\mathcal{GP}}\n", "\\newcommand{\\NN}{\\mathcal{N}}\n", "\\newcommand{\\LL}{\\mathcal{L}}\n", "\\newcommand{\\RR}{\\mathbb{R}}\n", "\\newcommand{\\EE}{\\mathbb{E}}\n", "\\newcommand{\\valpha}{\\boldsymbol\\alpha}\n", "\\newcommand{\\vf}{\\mathbf{f}}\n", "\\newcommand{\\vF}{\\mathbf{F}}\n", "\\newcommand{\\vg}{\\mathbf{g}}\n", "\\newcommand{\\vW}{\\mathbf{W}}\n", "\\newcommand{\\vI}{\\mathbf{I}}\n", "\\newcommand{\\vZ}{\\mathbf{Z}}\n", "\\newcommand{\\vu}{\\mathbf{u}}\n", "\\newcommand{\\vU}{\\mathbf{U}}\n", "\\newcommand{\\vX}{\\mathbf{X}}\n", "\\newcommand{\\vY}{\\mathbf{Y}}\n", "\\newcommand{\\identity}{\\mathbb{I}}\n", "$\n", "- $X \\in \\mathbb{R}^{N \\times D}$ the input\n", "- $Y \\in \\RR^{N \\times P}$ the output\n", "- $k_{1..L}$, $L$ kernels on $\\RR^{N \\times D}$\n", "- $g_{1..L}$, $L$ independent $\\GP$s with $g_l \\sim \\GP(0,k_l)$\n", "- $f_{1..P}$, $P$ correlated $\\GP$s with $\\vf = \\vW \\vg$ " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Multi Output Kernels class diagram\n", "![new_multioutput_gp_kernels.png](./new_multioutput_gp_kernels.png)\n", "\n", "\n", "\n", "\n", "### Multi Output Features class diagram\n", "\n", "![new_multioutput_gp_features.png](./new_multioutput_gp_features.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Shape of Kuu and Kuf and the underlying conditional code depends on Mof and Mok classes used\n", "\n", "| Feature | Kernel | Kuu | Kuf | conditional | note |\n", "|------------------------|-------------------------------|---------------|---------------|---------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n", "| InducingPoints | Mok | MxPxMxP | MxPxNxP | fully_correlated_conditional | This is the default. Will be very inefficient for certain kernels. In this case q_mu and q_sqrt are 1 x MP and 1 x MP x MP |\n", "| SharedIndependentMof | SharedIndependentMok | M x M | M x N | base_conditional | These two classes are in a sense redundant as we can achieve the same behaviour using the single output Kernel and InducingFeature classes. They are added for illustrative purposes. But thanks to the conditional dispatch the most efficient code path will be used |\n", "| SeparateIndependentMof | SharedIndependentMok | P x M x M | P x M x N | P x base_conditional | We loop P times over the base_conditional |\n", "| SharedIndependentMof | SeparateIndependentMok | P x M x M | P x M x N | P x base_conditional | We loop P times over the base_conditional |\n", "| SeparateIndependentMof | SeparateIndependentMok | P x M x M | P x M x N | P x base_conditional | We loop P times over the base_conditional |\n", "| SharedIndependentMof | SeparateMixedKernel | L x M x M | M x L x N x P | independent_interdomain_conditional | inducing outputs live in g-space |\n", "| SeparateIndependentMof | SeparateMixedKernel | L x M x M | M x L x N x P | independent_interdomain_conditional | very similar as above |\n", "| MixedKernelSharedMof | SeparateMixedKernel | L x M x M | L x M x N | base_conditional | This is the most efficient implementation for MixedKernels. The inducing outputs live in g-space. Here we use the output of the base conditional and project the mean and covariance with the mixing matrix W. |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Notes:\n", "- MixedKernelSeparateMof is not implemented but can easily be added to the framework" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:58:45.195606Z", "start_time": "2018-06-19T10:58:44.172232Z" }, "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import gpflow as gpf\n", "\n", "import gpflow.multioutput.kernels as mk\n", "import gpflow.multioutput.features as mf\n", "%matplotlib inline\n", "\n", "import logging\n", "gpf.multioutput.conditionals.logger.level = logging.DEBUG\n", "gpf.multioutput.features.logger.level = logging.DEBUG" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:58:46.500202Z", "start_time": "2018-06-19T10:58:46.486870Z" }, "collapsed": true }, "outputs": [], "source": [ "X = np.random.rand(100)[:, None] * 10 - 5 # N x D\n", "G = np.hstack((0.5 * np.sin(3 * X) + X, 3.0 * np.cos(X) - X)) # N x L\n", "Ptrue = np.array([[0.5, -0.3, 1.5], [-0.4, 0.43, 0.0]]) # L x P\n", "Y = np.matmul(G, Ptrue) # N x P\n", "Y += np.random.randn(*Y.shape) * [0.2, 0.2, 0.2]\n", "\n", "D = 1 # number of input dimensions\n", "M = 20 # number of inducing points\n", "L = 2 # number of latent GPs\n", "P = 3 # number of observations = output dimensions\n", "\n", "assert X.shape[1] == D and G.shape[1] == L and Ptrue.shape == (L, P) and X.shape[0] == G.shape[0] == Y.shape[0]\n", "\n", "MAXITER = gpf.test_util.notebook_niter(int(15e1))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:58:48.106040Z", "start_time": "2018-06-19T10:58:48.092379Z" }, "collapsed": true }, "outputs": [], "source": [ "pX = np.linspace(-6, 6, 100)[:, None]\n", "\n", "def plot_model(m):\n", " pY, pYv = m.predict_y(pX)\n", " if pY.ndim == 3:\n", " pY = pY[:, 0, :]\n", " plt.plot(m.X.value, m.Y.value, 'x')\n", " plt.gca().set_prop_cycle(None)\n", " plt.plot(pX, pY)\n", " for i in range(pY.shape[1]):\n", " top = pY[:, i] + 2.0 * pYv[:, i] ** 0.5\n", " bot = pY[:, i] - 2.0 * pYv[:, i] ** 0.5\n", " plt.fill_between(pX[:, 0], top, bot, alpha=0.3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Shared Independent MOK & Shared Independent Features (SLOW CODE)\n", "\n", "We will use the same kernel to model each of the output dimensions.\n", "We will use the same inducing inputs in each of the approximations." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:58:48.863635Z", "start_time": "2018-06-19T10:58:48.766655Z" }, "collapsed": true }, "outputs": [], "source": [ "q_mu = np.zeros((M, P)).reshape(M * P, 1)\n", "q_sqrt = np.eye(M * P).reshape(1, M * P, M * P)\n", "\n", "kernel = mk.SharedIndependentMok(gpf.kernels.RBF(D) + gpf.kernels.Linear(D), P)\n", "feature = gpf.features.InducingPoints(X[:M,...].copy())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:58:49.376514Z", "start_time": "2018-06-19T10:58:49.152460Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:Conditional: InducingPoints -- Mok\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: InducingPoints, kern: SharedIndependentMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: InducingPoints, kern: SharedIndependentMok)\n" ] } ], "source": [ "m = gpf.models.SVGP(X, Y, kernel, gpf.likelihoods.Gaussian(), feature, q_mu=q_mu, q_sqrt=q_sqrt)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:58:51.452121Z", "start_time": "2018-06-19T10:58:50.405419Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 81.063131\n", " Number of iterations: 151\n", " Number of functions evaluations: 171\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 81.063131\n", " Number of iterations: 151\n", " Number of functions evaluations: 171\n" ] } ], "source": [ "opt = gpf.train.ScipyOptimizer()\n", "opt.minimize(m, disp=True, maxiter=MAXITER)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:58:51.794405Z", "start_time": "2018-06-19T10:58:51.454426Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:Conditional: InducingPoints -- Mok\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: InducingPoints, kern: SharedIndependentMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: InducingPoints, kern: SharedIndependentMok)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_model(m)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:58:53.293025Z", "start_time": "2018-06-19T10:58:53.290410Z" }, "collapsed": true }, "outputs": [], "source": [ "def vimshow(K):\n", " vmax = np.abs(K).max()\n", " plt.imshow(K, cmap='RdBu_r', vmin=-vmax, vmax=vmax)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:58:53.773631Z", "start_time": "2018-06-19T10:58:53.549019Z" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "K = (kernel.compute_K_symm(pX))\n", "K_trans = np.transpose(K, [1, 0, 3, 2])\n", "vimshow(np.reshape(K_trans, [100 * 3, 100*3]))\n", "plt.colorbar();\n", "plt.title(\"Kff\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the outputs are uncorrelated, and the same kernel is used for each output. However, during the `conditional` calculations we do not assume this particular block-diagonal structure. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Shared Independent MOK & Shared Independent Features\n", "\n", "We will use the same kernel to model each of the output dimensions.\n", "We will use the same inducing inputs in each of the approximations." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:58:58.849062Z", "start_time": "2018-06-19T10:58:58.840498Z" }, "collapsed": true }, "outputs": [], "source": [ "gpf.reset_default_graph_and_session()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:58:59.599039Z", "start_time": "2018-06-19T10:58:59.546311Z" }, "collapsed": true }, "outputs": [], "source": [ "kernel = mk.SharedIndependentMok(gpf.kernels.RBF(1) + gpf.kernels.Linear(1), P)\n", "feature = mf.SharedIndependentMof(gpf.features.InducingPoints(X[:M,...].copy()))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:00.543712Z", "start_time": "2018-06-19T10:59:00.352130Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:Conditional: SharedIndependentMof - SharedIndepedentMok\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: SharedIndependentMof, kern: SharedIndependentMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: SharedIndependentMof, kern: SharedIndependentMok)\n" ] } ], "source": [ "m = gpf.models.SVGP(X, Y, kernel, gpf.likelihoods.Gaussian(), feat=feature)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:01.859501Z", "start_time": "2018-06-19T10:59:00.959033Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 79.337823\n", " Number of iterations: 151\n", " Number of functions evaluations: 176\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 79.337823\n", " Number of iterations: 151\n", " Number of functions evaluations: 176\n" ] } ], "source": [ "opt = gpf.train.ScipyOptimizer()\n", "opt.minimize(m, disp=True, maxiter=MAXITER)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:02.113396Z", "start_time": "2018-06-19T10:59:01.860960Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:Conditional: SharedIndependentMof - SharedIndepedentMok\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: SharedIndependentMof, kern: SharedIndependentMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: SharedIndependentMof, kern: SharedIndependentMok)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_model(m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, same kernel used for each output dimension and the outputs are uncorrelated. In the `conditional`, however, we explicitly use the block-diagonal structure." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Separate Independent MOK & Shared Independent Features (SLOW CODE)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:09.467172Z", "start_time": "2018-06-19T10:59:09.459302Z" }, "collapsed": true }, "outputs": [], "source": [ "gpf.reset_default_graph_and_session()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:11.113435Z", "start_time": "2018-06-19T10:59:11.001429Z" }, "collapsed": true }, "outputs": [], "source": [ "q_mu = np.zeros((M, P)).reshape(M * P, 1)\n", "q_sqrt = np.eye(M * P).reshape(1, M * P, M * P)\n", "\n", "kern_list = [gpf.kernels.RBF(D) + gpf.kernels.Linear(1) for _ in range(P)]\n", "kernel = mk.SeparateIndependentMok(kern_list)\n", "feature = gpf.features.InducingPoints(X[:M,...].copy())" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:12.048477Z", "start_time": "2018-06-19T10:59:11.606446Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:Conditional: InducingPoints -- Mok\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: InducingPoints, kern: SeparateIndependentMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: InducingPoints, kern: SeparateIndependentMok)\n" ] } ], "source": [ "m = gpf.models.SVGP(X, Y, kernel, gpf.likelihoods.Gaussian(), feature, q_mu=q_mu, q_sqrt=q_sqrt)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:14.851395Z", "start_time": "2018-06-19T10:59:12.771124Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 76.241836\n", " Number of iterations: 151\n", " Number of functions evaluations: 176\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 76.241836\n", " Number of iterations: 151\n", " Number of functions evaluations: 176\n" ] } ], "source": [ "opt = gpf.train.ScipyOptimizer()\n", "opt.minimize(m, disp=True, maxiter=MAXITER)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:15.323015Z", "start_time": "2018-06-19T10:59:14.854599Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:Conditional: InducingPoints -- Mok\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: InducingPoints, kern: SeparateIndependentMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: InducingPoints, kern: SeparateIndependentMok)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_model(m)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:15.624642Z", "start_time": "2018-06-19T10:59:15.324525Z" } }, "outputs": [ { "data": { "image/png": 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W26/842cpI0Mcn0FQ8ZjAkebJ7YOWv1pVn1H0jnox8Juqeg/wm3a8AUWe3HBzk7itOj+2rfyYLa8dkuHgeKjghlt53aat7Nww9qzhMysqLgtiJLfNdkg4D3P1fuC5Nn418FvAD667QaGreDgHrPyRj1U4JEJL+3R+jOhWkOSB/T+uqNgbHnPmKpGffkNEFPhZa2/8FFX9mJ3/S+ApYzeKyAPAAwB333037Yoo4ln4Yqh8ho/Op1MUtXdyMFeOVxHivrDueef4R6CiYhuICH762CO5v6WqHxWR/wx4s4j8+/KkqqoR4BKMEB8EeOaznqWbGm1sY7KlS8aeVd7fWzVhhVIbxdg1u/r/9k2UFRXnBQF5rCk5Vf2o7T8uIr9G7AP1VyJyl6p+TETuAj6++TnQrGC5FHQsld7wxywjlQ5DUhw+Pp0fVXVjCm8VGW1BUjJ2TRiZG7vO5tTPNn5ORcV54xgrHs5MyyJyu4g8Lo2Bvw28B3gDsW0xnKJ9cauat1BsbYjrJZRboL+pam+DSGppA/P7FZ83Or+jKhMNo1v8wNDfdLCFpncsoY2bKlIzgisOAbJd0OEqBR6eAvyaqagJ8Iuq+u9E5PeA14nIC4APAd+66UEKLAqpVvZPE4kE2ItalqpOhLa4Nj5wkIhbePZKBZeIrqf61vjktlZkg/uWn6Pj160aV1QcAOSxZq5am+IvG5n/BPC1p3uWrYuQc88KwjOCarUjsV5Z1ci1UhTEu/hO2aQN9jnDUq2enw6WyK5HcKc0NeP9I8S2geBGSbWi4rIgPCYDD3tDIrrSveZMxeWa0dS9g/41kJSZ9q7rkV1eFFoy0SUMj9cirFFbwzSTTYptE7GVpm5FxSVDHosVD/uCoixCv7tuNFP7hJXN1rJ+VLrz6f6s7rQzfW395qzqSlN1pZobIgzIaV9KTcMoeYpqNVsrDgeP4YqHnaFEAlMLNASFNqQggxLogg8pmJACBilAkTimPAd9c7YMVAxRBiK2e+mOiDIZDbd03WATDUgKNLRNLxCRgw2h7QcjKiouG+aT22bb+CiRu0XkrSLyPhF5r4h8n80/SUTeLCIfsP0Td33tgyA5tDNXy2hpG3SJ7FojtJLsgEx06Zw9tneu95HFxBKxrSsNK0gnq7c1hNYjthRtTcqtJLdEbINIq4QmEmJFxaVDEOe22rZAA/yAqj4deA7wQhF5OmcqC12PAzFX6ZmrthqpjdXM1i4fTqXz06l2q9Un/51Dsn+vDGZkn15hugakV2y/MtI68Iv1CG6o3BiYouX9Rmi9cz3iHFGBFRUHgNgZeD+6yKqiPmbjz4rI+4Gncoay0E04DJLTrnVRJCfNK9GLRhIKUqxmhXRrmIrG1a/sOPnk0kLNgQHRJR8de5CxQxIaI6owJD/dmtwSGWqbkmQqKi4RIrjZ1pRxp4i8szh+0KqcRh4rTyMuNP0OtiwLPQ0Og+RQTpo291RzIngX9ZxIVHLe8uGSustNeEWysksKzru+qlsiOiRHXj2dgtvoUl1FVNCZlGUQYajatJwfV2yiGkkttIRg5FajqxUHATnNQjYPF52JVj9R5A7gV4DvV9XPDBrPriwLPQ0Og+TUXFRo1y/Tfq8nrjNRwVaap+u0m0kKU3VILxcukVpJdP3P1tGysBKjaR3leBgl3Va1aZ8MtW1j0KSZ288gQGjRUJVcxQFAQPz+uh2KyJRIcL+gqr9q06cuC92Egwg8RJ9coFVl0cbgwiIEgipNiOvIt6FbVT70yry6Uq4UoCiDD2Ofla7dz8uPp4GMRVx7gYdBNDWpN23m8TnNAm3maLOoSq7iICDIPqOrArwSeL+q/lhx6kxloetwIEpOOWlCNleDCk6EBcrUSz72Lqk0LSKi0fQkmCJTwEU/XTJdSzUX7+h8dkMbdasV6wdKTIYKLkVJNfQDEENf22IelVto+6otjY34KslVHAQE3P7WXf1K4NuBPxaRd9vcDwE/yinLQjfhMEiOGF0NYoGBNi7z4AVCEALK1JNNWHElYZHNV2emZ1qZvjRdS18c9IMQqoNSi/xiK8zUAj3fGn0TtR+AaPL5rNoKAlsyUdsWmoV9dDVXKw4D+6pdVdXfZrUb/FRloZtwECQXFG41AScxwDCNy8jH4nsfya5VwYuYz87hs4MtqjpRQS0ogUVfWzSuUI8RphGdSlcGJtJFajdWPCQVNkZ45bmk4MpgRNPYsCO3UWJL50IkuUpwFYcCEcFND4IyToWDeGO1FkukVU5bcFa/FXLyWippEILokqpLJqYmv12R/6ZKJrb8mZyt63D3gMJkDSMENwwqlAEEU2iJ3CCarj1Vl4iumqsVhwKRx1YXkn1j0QaCSCS3tCS9EwgBpxKda0Z0riwTJfrhVNR8d3arJfqKdEnAYOpNZSnSuqpIf6dOIGXUtDRNV/jdSlLLSs7OH1/FYMWVw2Ot1dI+kcxV7wQXhOAi2fkgTF0yURMJxnsWqAUqgvnWIqEpcTX65KNTjcovEZtAL0k4+u82LAMYwpI52pmk/Whpp+4aaJpOwSXTc+BzK9Vdj+TaItJaSa7iQFC7kJwRMZ+NnPDWKrkkwdk4r0AfYEFnniZVlxRdoB+MEMuhy488L7YYM1XpVFuP4Bbz4tyIilvMq0+u4uAgcqpk4IPBYZCc5cW5ogVSV8blMsE5F6OmU1z204Ug7DE/8fQoqxoGcz2C6pmn603TMWKsqLh0nK6s62BwEG8clJwn50RyJHXqozkaJGozp2rj6KcLIjiJgYpktnojvxRxda7wzVmNq9DlzUk2e9dj1FQdi6gO/XCFGZqCCzmwkPYD0zQmAEdC1DagIaw3pysqLghVyZ0RSmyrlHI5crVCEjAuqbpIYE7j3ot03YBzX6X+ikIpCJF8c37EuzWWFNw9oG9+djcNKxwGpVplVNSILN7WV2m9hN+s4iLBhUVDaAPa1uhqxeVDRHCXajadDQdBciHAZ08ark1i/lurrlByLjfSnFpAolN30XGX/7jYzz9oTAj2LpFb9M156dJJcuulnGayIiG4xLCKoZgrt+RTK9VZT9HZfLh1c1S9JXILi4Ywb9CaQlJxIKjR1TMjKrlbTWDizOSM2bzRHCUqtkWAKRjBRUXnilBpiLINF5SJlYAJ/Ry6VWoOWC7pWlfxUCq8QcWDpvPQmZ1lzltWcJ1608U8m6atEVsiuFCVXMUhoKaQnB1BYd60zCZRis2bwGziwNlShR6cRVtb7UzXEDQHJXKbc+uIqZAzflOCcFJqJZmdqU5/VScSLfLgoEd06Vh7uXGF384ILik4bQOt7au5WnEYqNHVM0NVuTlvaYNa4GDSjWfR/GTibO0Ha7kkKVEEpj6SoXNxahElHd6JdS0RK+uyO4o2TN5U4trgw5DIBnlzw4BDF1Togg3ZXLXuIvGaRVZwzckcDaEbZ7JraRe1/XnF5UNcja6eGYm85pYQ7F2ASSSwFJDoFqRJaSaKU8Up2Wx1pECE7a3+1YmVeZnfbV2gISu7sV5xSxf3z5UKrTsORb1q2zNT43FUcIngQqHe2vkiz1dUHAKqktsB2UQF2hCYN8DE5flFa51IoCOyEFNKktma6lyzb26E0JKpulVLpRGkoEPKjSuPU9ukpfy20I+gZj9c22YTNRFcKBRcqOZqxSFBBHE1unomtEF5dN7SWMAAwLtAGxzJdGUWO5FMncOLWkDC4ZwpNqtzneJoXazVWhDwzucmm6l8K1h3klRk4YtW6Gsxpu5SRLWX6xYyqZVzXdQ1RlLDoqGdN4RF0zNRm5NbUcmdzGsKScVhoZLc2RA7/0ZzlYnLfrdovvbnvHQdS4KmLiWdinO2Dit0C1OnTsExhWTZVt0UfBhtrzQMPgxUW1mUr4PjnCrSdmZqt0UTNsybXhCiouLyIXAVzVUReRXwd4CPq+p/ZXNPAn4JeBrwQeBbVfWT1tL4J4DnAY8C36Wqv7/pMzTEwIN3ksls4oTZxOFdS2tRymtmzjoRgjMlJ9E350WYEutap+azC0FyAEPpcuZitBVOSx1jVQ7pOAwIjWaxHGyw45wLZ3lwrY3b+SLuTcE1f31iKSW1rKviALDnNR4uCtvQ8s8D9w3mVi0A+43APbY9APzMNi+hRNWW1Ny8CTQ2jgtMd9u8iWs/tCGuyBVUc4VEm5YsDHGFr7ZQdVBUUpSfvUnGrQk4DIvxGfjihsGGrsrBUkUsDy4ruPLcoskEF9ozJbpUVOwXIjCZbbcdEDaSnKq+DfhPg+n7iQu/YvtvKuZfoxFvB55gK+5s+oxMYCWhDYmuSecyuWlWfq2RXQjakZ3tU8VEiXC2DLleKVd+/5LE7JperlwRcY1Bh9DLixvmxHXR1UhwVclVHALE8uS22Q4JZ/XJrVoA9qnAh4vrPmJzH2MAEXmAqPa4++67ef33POuMr3JOcPFHo352VjrMEPpewLMI/k/80+9DvEecY3JjhjiHvz5DvMNPJ7jr18E5xHlkdh2ZzmAyRSZTSHPe29wMmUyR6Qx1HsShfgbi4ngyXZ7zU3AOFQf2M1mE+AcqAE3oVlFbBC32y+fnbVyZ7VYTaO0P00kbCEFZBGVRnA8a8x7TKm5J6d9qAvOmzX8cX3HfPTv+X6rYCOEoAw+7LyIfPfmn5gFVfVBV71XVe5985527vsaVRyI48a7be4crloAT5+OX0L6IUuzFd/PJeaxbdF/ZBWXvPiloPi0g5ERyGk/uDyip9T2xiaqkXMfYmcZb2V+sc44J3yn1qOK8Id33a9N2QDirklu1AOxHgbuL6z7f5ip2RCI2P5v0Cc45IzBnX7Dy2C194XKeU9qbUkvjRHwqpyOOki6dWONTsGUiNc8nY9/bkpOQ0n+6fZBY0RKsqCUoMdAUhJkvIu0ut0c91btWnB2HZopug7O+8aoFYN8AfIdEPAf4dGHWVuyAyY0Zk+sz3HSCm0668czM0cksEpzt019UcdFEjddM85x4n8lNS6KDpXFP8RXnhl2WnXSNXKQYu3TOFheKCi12mYl5jzB1jqmLc05g6uPxxHXXTX0czyaO2cRx28xz28znmueKc4a4vQUeRORVIvJxEXlPMfckEXmziHzA9k/cx2tvJDkR+TfA7wBfIiIfsUVffxT4ehH5APB1dgzwRuDPgYeAnwP+0T5esoKeiepnk0LBdaqtVGmSCC795S3/Ao+pOMx8XUV2Z0Qkvj4bChRLSpo56iTXD8fmqeQ5Z8dpPDUztTRdKy4AlkKyzbYFfp7tszZ2wkZzVVW/bcWppQVgzT/3wl1fqmIZKcjgvMNNJ10QISm2ySybqZ16i4pOprPCXxfJsWeWLpGdWya4FHQYQCQmYJfxXyEtEiS2yHdUdY7YbEEkJipOnYvLS7axBRZOYmt76/wMIeY9xheIPQDNMvUu9hyctyGbrxXnjf0lA6vq20TkaYPp+4Hn2vjVwG8BP7jrZx1ExUPFZiz74PwScZV+OSBe4wsfXKn2Vqm4M36JY9v61WSTFhZKiwqVqX/Okbs9x6oVW7+j8MvlztCuy3d0QvTRuUpyF4LTRVfvFJF3FscPquqDG+5ZlbWxEyrJHQn8NAUcShO1IK6k2kpfHPRJz46XoqprzNIlE3YNsoIr2tKvvNZ8cylmkFdjI63jEcfRqpXcKBWka6RqwYiKi8KpCvQfVtV7z/pJqqoispe/XpXkjgTu+nUbuF6QIZuoRcAhBximsx4JpoBDMkd1EFnFFabrmMl6SohgCw5pNmtjK/pIVLFFfYqsdoTlLGA69VHVhWAlfKlW2UzXVNZXrdULxPlGV1dlbeyESnLHgkxWfcJaMlFX3JPvLbEnMluHRHRa9PSLa2x0vjnvJDZGNaJL/rm+CduZq1M6c9cFa6pacf4Q+2N6fkhZGz9KP2tjJ1SSOxL0CC590QYmanndWIQrBxzOkdiEFGRIXZlXX5utVSUnCOO055OLiGNvPLiAnE9n0Yxz+bdUDCDsTclZ1sZzib67jwA/TCS311kGx4eAb93HZ1WSOxLI7HpHXj1yK0xU6Jupk85clemsR3BlVFVFsqm6NL8FGYpIt5Zt4YtLPrqQxhDXwEXxAm1IXZsLs9VWYQsh7pO5Gs1SzVHXtFpbq+QehBXnC0H21oXkNFkbu6JzTEXoAAAgAElEQVSS3JFApiPqzYitp+T8IBixlw9f/Rwn40IqxwkGcw7r75fm6Pvn2mDrbRSKrlcqkRWeMvWln67i3HGktauV5I4FOSduWcUBWb0BPQWX1N9aFVfOr8BojhzLRctOOjPVWZQ15cwBvSAELjU0FbyL94gTmoGiSwGLLggRmCJx8SKlMGsrzhdSSa7i/NAzR0uy8/1gRLxmJE9u26jpDr66oky1V786di7YWpFJ3bWFYBsqui5NpEsYblVzFPYsa3VUnAEi3ffwiFBJ7lhQ5r4lM7UwYZeCD5AJbjTYMKxJLXGGwERZJl8qvKTmoIu0tiR1Z/46SyvRYr7MoRsjuhh4ULwXak/RC8Q5RuLPC5XkjgS5HxwMkoA79Sal6cqA3AZm6jA/brSU6yzvWTBcUm/JhIUuCBHPCx5bd8NZGZjSM10VwQWN5WFK33x1nhCU49MWx4rtE8MPCZXkjgRlP7jSdM1+uKH/LU6O+9uGCcC9D9quQL/UgMk0Hao56Iiup+4Qgqm2oOR2TGV9a4q6MlB13gNtLNYnhKrkLhinbcF1CKgkdywYiaQOgwswKMMaKrZiv6p90lpseV0ZcCgDn2VKiSuK90NBkEkJpqjrGNE5q1V1WpiwFecPoSq5ivNDLttiOeFXRbq/sAWxLZVtlQRXlnENzq9UcxqWvuS5olQE1UhaLQOig7w8ZG/OiM5b9FW0M10h5tF5IVZLmPkK4J23wES3PGXFRUDgCCPZleSOBL3o6irVZnsdU29DgivOj5mya1ujG9mty5Er6/MTqY3NOZW8qFCKujpbFyLl+CbzlfR52k8e3tQMoGI/UED98VHG8b3xYxS5YoERhbZibomw0nxZ3ZDmd0kdoSMoLSOpRQVEqeiGcx6xdXAHqo7uWSnPTojKLt4vRpzHpy6OEqfoSHNIqCR3JNCyD5ztlxTYJr+bG5DguvEZkbuN2HGptFaZrtCpOqDz1dl1KZoKXWAiPq/7nIoLQiW5inPDJnO02C8pN1giuFX+utH8uTXlYatM1rXX05muZdS1NHNTpBU6Exa6xptglRLSKb6K80ZVchXnCPVFi5sxQlpDaDDud1u5T+N1X2gNiLgevXSqTAbqq+83Gyo6gJxJZ4nBUJKfZnPVg63fqoUPvJqrF4WaQlJxftjGzza4dpTYVo23CTisQFJzS3WsdGkhpYma77HrUsJweQ7KggfrMdd7bpFIXHFxqCRXcW7YRG6bVNsW41FVeJpXpFRfXUpJ6Tdb8tEpvcoIYPS8KxpjdmkngEhtJ3dRkFqgX3GO0LIw+jRKbYvjtYnBY/64Il8uEVsmJPpmaya64iNKVRd9amSLM2isZuh8dp3zLhPowJytBfoXh2quVpwfzmKCrjpmxCzd4p544+qE4DGkNVdz4IC+cguq2aPWI8vCn5cwNGdz3lzFBUHWBqEOFZXkjgS9wAOsJaW13UVWYezcui+0Fkao3esKNVYeQz8dJN5jOXIp969X/tUdlMs3yGAtB9Wq4i4Utayr4lyxRqmdZonBrc6f4a/1mJobnZMuYbhLEemU29A3V/7Lhl3Oa47cRaOmkFScJ9YptbEv3mm+jBtIbaMfxkzY0j8HnY+unINO1a0juzi/LNPKVJTsz6u4MKg7Pso4vjd+jEL9SNe0XclpV5T+uYLoYJnsSnQ+NemdH8btxpJ8vUhVcJeFIy3r2vjGIvIqEfm4iLynmHuZiHxURN5t2/OKcy8RkYdE5E9E5BvO68Ufc3BuaVNZv41i31/S0jenIR8PzcxSycmK8+UG0bQd2xwsbRUXBJHttq0eJfcZTzwkIi8+r1feRsn9PPBTwGsG8z+uqv+8nBCRpwPPB74U+DzgLSLyxara7uFdH9NYGVU9C7a5X0+hl0YirtAnsiWTVVfXKQyvXX5wXwFWXBT2p+RExAM/DXw98BHg90TkDar6vr18QIGNJKeqbxORp235vPuB16rqLeAvROQh4NnA75z5DSsihtHVXbGOxFaQ1pmfB8jgeWVU9KxcVSOrF489ukCeDTykqn8OICKvJfLH3klulzd+kYj8kZmzT7S5pwIfLq75iM0tQUQeEJF3isg7/+PDD+/wGo8N6B62HlJt6liN6o6tl0qzdWlu5JzsYau4IAy/N6s2uDP9ftv2wOBJW3PFrjhr4OFngB8h/u78CPAvgO85zQNU9UHgQYD/+lnPqobHBiwKXth2wfh1lw2fMVRaSxhTauvUm7jNanFXlM/Yt9KtWIIiOTK+BR5W1XvP8322xZlITlX/Ko1F5OeAX7fDjwJ3F5d+vs1V7IiyED0t3CJrHLxuTXqF0PdnDa8dfeom0hpim2tP+8x028g99a/kRUD32YX5wrjiTDaJiNxVHH4zkCKvbwCeLyLXROQLgHuA393tFSsg5pANN1Ud3SCS2HBLGJqvY+eBZbP1PCOzBUTD2o0wslVcCM7kGhnH7wH3iMgXiMiMGLB8w/7feAslJyL/Bngu0cb+CPDDwHNF5BnEf88Hgb8PoKrvFZHXEZ2HDfDCGlndDxpjoVJlpQVjhpDB1yxR01ABLlUQFOVYuRge+oprjfoaU1j9D9hRCQ59eYWqqEru/KHsL6Ktqo2IvAh4EzFF8lWq+t79PL2PbaKr3zYy/co1178cePkuL1WxjKF5mdAOSp9g0IxSir5sts9qr7guEVtpypZzm0zLUYLbRGqbSHENqW11f8Xesc/+far6RuCNe3vgCtSKhyNB+d0a/moPE227bh1Fy6IVZDf062VS2zC3FmPkdoYgRI/UxqK1FReKfSq5i0QluSPBIhRthrpGHkC/lrXkrLzaFd09ToS2uM6tUHUw0k2k9MmtIplEcBtIaUmVbbpnxXijiVyxP2jn8jgmVJI7EiROaMtea5mMum9eMlXFeq0N/Wtxhayo8HrqLi8lOK7ZRtXcKhN2JSFtMDe3JLlMbJXgLhzH2G6+ktyRYBG65fuygtNlBadoVm5OpAg2xH1sSpm6f3T3p1WwVLXr/Sadj67nn4NxghuaqamONf1ibKHMVqq0NB4oxVFFWHEuUI6zvVUluSOBqpqKEyMw+ou7qPTUGyz75sbOl9HZuIJW31d3an9cfuEVBHcacivPryK3quYuFMf4N6WS3JEg/yprt/BymbUWRLvFmW1uSGbpCxoYIUIkm6/DbLjkcN620iJhJcGNkNha1VbMieo4YVZcCGrgoeLc0ARFUrNJAWzd0URIIoJKP5dOTd1BYcZi66Ay7Aqi3QIzYqvWF2ZrgmIlYKsIZlibWhJSMV5Sa6sIbfiM3vmRGtmKc4NqTFk6NlSSOxLEv6BqPjUjH7pVsHqkROGj09JfVxIgS0S3zTusvH7gj1tSXJsIbixAMXY/A3KrJHehOEKOqyR3LOibmql0qzNZk/qK14gRoJmiKj0TtjRRh6ZrXl+B+Lx19bHbvfgaH9wmP9s6YiwbdB7jb94RIrotju9nXUnuSDBvQyYg76LhKtI1CBeVvLKVoKbgBCfQmqk6Smor5jolGBViirSeCUZImajaJs/3iG2dOdojvHiPti2EtpZ0XSCO8WddSe5I0KrSqsYl/EIkupScKRLVXTJjvXRKLBEUQECzquv52UZMV10RVo2LP7OhhnU58jmmxFYRnIyotVFyC1YWXQv0Lww18FBxbrjVRCW3QJl6YRFgagvZeJdSRKLPrs2OOkD7qi5FYROpjZmuyWwtU0oQ2ZwQvEXAIfnfss8uqbbQjF5fEpsmYkukVhJdxYXgCK3VSnLHgtYCDwAhpCho56hLyi757IR+VYPHAgcsE11JXEmphaQaN2Fbx/+qwMKQGAe+tlHVZmNtFjZXie4ioGZNHBsqyR0JQtBISk4gmH+uLVYlHCG6FJgQ6RJ8M4mh+JSSYo9IpFgqtqTmysjq2gThVYGG+I9YnSNXbFm9LeZ26Yh6K8murSR3UajmasW54aQN0RdmZqpzhSITcM5ITSSbr95ZlYTmmi3bdeSWqEYoEoZH0k/ODC0aXQ5M2J6JGpplciuUWkl06Tx2vpqsFwOlmqsV54hSyblepCCZrAKub752NaeaE4MDIKkszAIRSb2VKSTldzmZu1tjTZJuL3+uuHboe4sfbEqtUG7JfCUEm68Ed5EIRxhfrSR3JMgF+iHyWVBlihslOidCGxRxZTCBnukakLyk3zCS2jNNd1Fz6/x1AxMVuqhpfInQ88Nl9TYkuPK6inNHVXIV54ZFG3AumqY5lYRg5qr0zFcIeBGaQM90DWqSzcX0ElXJwQtRyZFVb767ZMKWGPuOr+vpJkNfW5kzV8zrYt6RVbOI42bR+dvSXDkufXUV546aDFxxrmhVaVsliDD1DkiENqy61xh9LRRdZ7p29a/ZbGVZqa1Sb2vLuvZVXpVVWugpuyXlNrhmV9dhxWaowuIIu2ZWkjsS3GpCJCsBiKoOAkEisU29ZFWXFF5SdAQK01Vpg2RfXSoBS0GI5cVtYgWEskWJ1yq1FrZQcc2iCzAkBbdCvenCAg7NPPvqKsldBGoKScU5IuR+R5YzF2CBueGEnqpL41AEI5QuvcSvYYRsphLLxM6FPEqCG0n/6OXGjSi5YRpJ9cldDI7VXN3zQpoV54VFCCyCsmiVJiiLoIQQ/7IGxcbxS5jGrSohmKkbNKcAKHYPFoTQGDU71+/vIMiQUZCZrvKxpZw4M0+zOdskBVj9chcChTZst+0CEfkfReS9IhJE5N7BuZeIyEMi8ici8g3bPK8quSPBorU2Sy5GDmInEhfrU0V6Yyeao7AABAsw9FJLUuChH4Rwu8q3YdR0kB9Xmqo9wkr7xbwfeEgEeOukR25lSom24XQpLhVnwgUqufcA/wPws+WkiDyduAj1lwKfB7xFRL5409rOleSOBNkXEozEEPvCRdPU2TiVZMHQhLUHmX9uHZGlL3OKuG6FsuB+xfkSoto3M8sKhjAwVwsSLE3WMF+gIRDawHTL16w4O5Ru1bhz/RzV98Pookr3A69V1VvAX4jIQ8Czgd9Z97xKckeCuQUeZt51SwWmmiuEYP46p6VPTnsRV1/Ineh76xpvrlNCW9exLt0YlsmvzIsbK88a1qjmCGroEV9UgZHgdFf7qGI7KLTbk9ydIvLO4vhBVX1wxzd4KvD24vgjNrcWleSOBInk2qC06nIHkmR6gmNKDEZ0UdeQE4adqtW6Kt55UNCivrUs3h9TcAFryjkgu1yyVaJsl2TH5VxXeN8Rly7mnUozM1UXXcS1NFGTgmtO5mgluQtD9OVuTXIPq+q9q06KyFuAzx059VJVff1Z3m8VKskdCW41gWuTSGzxr2nA5+r8aLqmqGtOK0GimWv+uaTGUu5ciWEVxJkxILdVKo6hqWp7DWNR1dD3wYVAO28ywWkNPFwY9pUmp6pfd4bbPgrcXRx/vs2tRSW5I8G8aXvk5J3gU484C0YAOY9u6l0MVqRKCOchxPw5b0EIZ0GKYPWrnhR9BYTcTLMkvtw0E3qENlqTWozLRphdwKHN+XH9KoZ+gCHulxVcWDSZ7CrOHweQQvIG4BdF5MeIgYd7gN/ddNPGFBIRuVtE3ioi77Ow7vfZ/JNE5M0i8gHbP9HmRUR+0sK8fyQiz9rt31UBUX21Idg+bvM2RAVXpIkE6/mVthBSD7ruy5mCGKr0WueUq6P35k/5rqVZOuw8Eh9eqLQShWorzdkudaTzwWlbqLmq5C4GSu/7t27bBSLyzSLyEeArgP9bRN4EoKrvBV4HvA/4d8ALN0VWYbs8uQb4AVV9OvAc4IUWyn0x8Juqeg/wm3YM8I1Ehr0HeAD4mVP8+ypWYN6ETHS3moLsNBJbsHy3tiC19F1ri336S1z+RVbGye08kM3Xsj9c0Xmk14GkSPTVtk9wGsp9TQa+CKTo6jbbTp+j+muq+vmqek1Vn6Kq31Cce7mqfqGqfomq/tttnrfRXFXVjwEfs/FnReT9xIjG/cBz7bJXA78F/KDNv0ajLHi7iDxBRO6y51ScEa+4757LfoVl+FnXcHPPjxb6WS4e1qaJfP9t/yVTgRve8TlTxw3vePzEccMLMyfccccMP/NMb0y49vgZbuq58cTr+Jlncn3K7PG34aYTrj3hcbjZhMn1GbPH3YabTZHZddztj0dm15HbHodMZza+I/4M3ASd3UD9DJ3eQCczcBPm6mjsD9HNRdyfNDGZuwnKp281LNr4R+uz8ybOnTTM28DNecunHp0zb9J4wc15yyM3F7RNoFm0zG82NIuWtg3cutnQzFsWj36GZn6TsFiwePTTNLdu0s5vsnj0Mzv/PzkAc/VMOJVPTkSeBjwTeAfwlIK4/hJ4io2fCny4uC2FeXskJyIPEJUed99d+hIrKk6PRHAzJ6Ob8w4/c7ipw009fuYR7/JenMPNJoh3OO/inHfgvG3R6BFvY+fi+hYQ97LeKJKil0IKUHvBOshE/2pjXV+8G99mE4efODQofuJwE8GprfPhBZ042smMCdH8cpMZzpRwaOYWed8BhfvjmLB1WZeI3AH8CvD9qtr7s2Cq7VT/elV9UFXvVdV7n3znnae5taJiCYngbnhZIrvJ1ONnDj/1OCMxP4tz4hxuOokE5yKxuWkaF4RWEp3rsgp1A7kNkYLa3km3xKRYM1QRvItR8EhqPpPbbOK6YNPExYWJnOC9w03iFsez3jaZ3cBN43hXKFi54ObtkLCVkhORKZHgfkFVf9Wm/yqZoSJyF/Bxmz9TmLeiYhd8zjSRnON2I7rbp47J9Uk0U2+fMr0xwU3jOJqpM9w0mqZxPM1zfjaByRSZzGyLYyZTcD4SXaHkNKm5AeklBeekU3DOItdehKkXWhWmTghOLP8x9mKeNwFMl902i8Q6bwK+EeZOUFWcD8gCNExoXEvQKaHxmdRCM4+E5zyP7uHnfIzm6jbRVQFeCbxfVX+sOPUG4Dtt/J3A64v577Ao63OAT1d/XMV544Z3hZqL+0RwfhaVWzJT01wis2ia+myidmouqjdJpSIpLzEpuy3M1CFEyD390uNS/mJUcym1pzRTo6K7ZopuNnFR7flovnrfbZOpqbqJx5ua89eu70fJaWzeus12SNhGyX0l8O3AH4vIu23uh4AfBV4nIi8APgR8q517I/A84CHgUeC79/rGFRUjePykM1GTgpvdMcUbsc1un+FncT4quqjg3CyO/fUZftrNy3SWlVzeOx9VnfOI9wQjOU0SbQXhpQCKQ3BobGFlydlJucVGqN0e4MasM4tvzCLRpb6C3sVEb2kk13hKyqGcBNomAHfQNi2hme/lZ5zM1WPDNtHV32Z1OffXjlyvwAt3fK+KilMhRVF7Cm7qCyUXAw5JpSU/XFRz0xxs8NNJ9sUlMltWdL4jtoRNgYfBcVJsyZWdgxDSdYrxEv1xADeN2Mqql5l3JPpqW4dXpW2793CTNI6+uX3gGM3VWvFQcSXQDzJ0JmpUb9PCVO2bqc4XwYfpJEdUJUVVJ9Mi8NAFH4DOXN3SZBWJnTUEzWE652JDBWfrdEy9I2jAiS6ZrEAv0poIcA74xqKuviOhydQRRGJX6GYfgYfaGbii4tJwxx2znCaSAgvJRHVTb6brJOfAxf21THjZTL12vQs0XLueAw4yLYMPE3CTzlR1BdkVY1vqFpWu3XwZdIgcZWZqsL0TmLi8wpoP8dpU0tcGZdYEC0rEQMS8CTwC+IkzQgu0bUCc0Obx7bv/kE/XheRgUEmu4kogq7epz1HURHDRdO3M07wvCC4HIJJaK9XcRf47JDYyTaarK0zYyYr8udSCKym6hFCouuB3DwYoleQqKi4Nkdii+ZmIbXJ9Ygm/rvO/TQcJv67c+0xupV+uzJUT71emi2yLZLJK9r9JLzHYqcYcOPPNBaeFidqR7rVJ9/mJfNpizrWx4aBYusmuUCUryGNCJbmKK4FUquV8Z66mKGoZSRXvmN52ozNbk5q7bqbp7HokuDKy6n02YVWky4uDrf1yInEJyJw3R5cvN/WOqfXJajW3MmVhCcKuhXYS1/m4zaKs3kWySaard9KZsJNACJEo2ybW/A7Xzz0LlN2L7y8DleQqrgT6OXBdgCGTWDJRvak673rmaqnUmEz7EdXChC1JLfvjDEsR17H3RFCJvf8EQSQuIJQCENFc7dbXXbSRwKY+kt/clX0Fu89ujHxSZcS8CQQfKyNaJ/spx6o+uYqKy8ONJ17PpmlZyZDUWwoyxHF5fhpJbXa9yInrgg2J8GQyy6Zqz1wtj1dAbNlIlUhqQjRZnXT9X1K+XCiUXFJ1XlKaiQITFiEUfQUDs0mn5NI+j9uo6rzffWG+6pOrqLhEJAWX0kGW1FsKLvTOdyVa/Zy48eBDMlWXCE1KNTcs6zKbdIBU5hVbzScS61JJkqrDxZb2zhYw8ikiOyn8cEGKsT3LdRHZeRPQyX58cpXkKiouCZPr05zoO7k+y2M/myLOZbM0qj0zUZNZOplG1WYBhiHRJbNVh744t5rsxpBrVxHatIqaxCoC7yQuGWnmKt7FxNuWvID4NC9iFAMK7aQjtjb4PIZovt6Y+azo9oVKchUVl4TZ42/L7ZIiyXn89VlWcT0ld/16JK7Z9ajgpoWZOrseFdx0ZqVcDpnM0NIf5yb9AERZ2jUCR1HaJan8PqaKtKiNNea4+WINXRfX13VJpZkinKqwsPqqmXfMzW93q3FZwc1tTZDUYHU22d1cDarcqtHViorLQdkuyU2nRYlWX93FSOm0q0NNqSI2R5E+UpZ2LaWNDPfbvOPIcrdpScik6KK5qnF9XWPEIBqThANMnbAIMPXQqpiyK1NJ/NJaIKmr9D5QlVxFxSXh2hMelzuI9MxVIzq5dj2bob00kWSOJgVXXpeCDabiNFU52D3DAMSQ+EpCcxIL6p1IXNoP8C7601xa19YpaTXcYMtMLiTgnMdJwIVYMeFCbF7pRFi0gamDqY8JwbOJy6Q2m/i8AFJZ7H9WVJ9cRcUlokwLKaOoOcE3mZ5lmkjZG24k8BAVXKSq5IvrmaWnbbNU7DvlBsGWRkspJck/55zGdXODraPrIATbi9Bqa366tFpb9NWVOXPJfI29gndHrV2tqLgkZHO0DDJMC2JLhFYouER8yQfXCzSkTiNjam1d6/MB8SUyy6eLNujOfHHO5l13CqwSAiO6VkM0V73gNOa9td7FfXcTEHBB8FMzV/PqWbv/qtdk4IqKS8Tscbdln1svappUWjZDXafqprOO1IogQ1ZwuQjf9czU0dy4sWhrOmVlXI7OZA0ay7qcRqLzZsamlprOzFUXNJuurVNcMIJzsaIh2EI5i6BMXGBq6SRBIyG2IQYM5ntoZFnLuioqLhEp522MsJZMUyvVKkmwH2ToFNxYnepyMvD2KRopuurofHNOYxVEIJEhBMufEyfE9OEQXXZYoELjXDn24jlp2kyi4GzxcCiDE2dFTAY+f5ITkX8G/PfELlJ/Bny3qn7Kzr0EeAHQAt+rqm/a9LxKchVXAilwsERuJfFBr1SrR3BlkAGWCG5lIvCOSKSX6hxUjcSwJQDB1oiIRBoyn8bSrzTGTFcmPi8qHgkumpgp4Xgn6IWZq28GXqKqjYi8AngJ8IO23vPzgS8FPg94i4h88aYFpivJVVwJuNsf3/nXJnGF1iWfGywpPSCTm46pt0RwPv6qjJ4zU3XVyl2pED9Ykb5Li8qGqLjieclMJ0o2Xb31lRO7JpFVUGXhusXFW40Lii9CyOM2LTiuupd1Fy6qrEtVf6M4fDvwLTa+H3itqt4C/kJEHgKeDfzOuudVkqu4EpDZ9TgoSKzLeeuIj0EQQkX6is3Zr0Tywdk473dss5RMSR0cp+qvFIAICD6Zs1hAQuJ/UtHD1Mesk2BpKFHlxURinLBIqSYKXvaTQtJsT3J3isg7i+MHVfXBM3zs9wC/ZOOnEkkvIa3pvBaV5CquBOS2x3VrMJRKDpZV21K1wmp1Fm8az4kbNWGHtatEghLpiMgRic27qNKcXaVWyxqvV1QFsdS5ZG3alUyc0JrtmhRWUnfAksLbR+rHKZXcw6p676qTIvIW4HNHTr1UVV9v17yUmPvyC6d81R4qyVVcCZTpImUgIXf2nSybm70IqSm4rN7KKCqsTfrNzx0gtTAfQ5cc3AUiQnHsrb7V5/p+a3zpotkaFLyL5qhADlAkP1yp8JKq2xWqurfoqqp+3brzIvJdwN8Bvla7jp9nWtO5klzFlUAMPFilQZHcmyOfpRk6QlZr1Zsdl/eMLkO4odQrdSTJpilRHY0RHZifDnIuXcBISzofXQ49WBQ1kd2UuEbq1NJW/CkiwOtwET45EbkP+MfAV6lquSb2G4BfFJEfIwYe7gF+d9PzKslVXAnIbXcsEVIYS9gtSSqruJK8ViT6jt1X3r/u3ehUXboyma1AR2zJXE2mqYu+O09UbqppRVYbF3yTzqV7g8LE3ivduysusKzrp4BrwJttTdm3q+o/UNX3isjrgPcRzdgXboqsQiW5iqsCH31uS+aljZfKscqqBRhVd+V+SbltSXAlUgmXqubCfOgUXHdNNw/LJAhkvx0YgYkUUdlO6QE5QrsP6MVEV79ozbmXAy8/zfMqyVVcCaTieWCcmIbrpTKi2kb2K8kN8jPXdQYe88uJFERHiqh2hOalyJEj9piLyq07NyS8+FztiI2k4DrS2xVqQYxjQyW5iisBnd3ok1fCGDmNBg7W+NigR5JLQYbhsYbeXEl0uX+lWPAAI6L08akhgEYFBrZqvWCVD2mOfN3SHOWcLF13duheVv26aFSSq7gSUF+sEL+O2NL1q7qJnJXYRuaKWvzRuUR+HbFpz2eXkJQckM3aUtHlZ2t3kK6Ppqr2rjszFNqrWLsqIncDrwGeQvy5PqiqPyEiLwP+Z+A/2qU/pKpvtHtOXV9WUbELdHqjOxgjoBW+s1VVCiuTfU+ZBFxGUIGc8pGQ1FokrO5MMjd7D7LBKjU1RrQBE8cAABPhSURBVD86ZMIdoESRemzYRsk1wA+o6u+LyOOAd4nIm+3cj6vqPy8vPmt9WUXFLtDJbP0F25LTWa8rf/vXKLrhcguJyMZoyBeTPVpb4WAbLU/dD79173EVzVVV/RjwMRt/VkTez/pSijPVl1VU7AS34at8xjKsrbHh+UUpfQ97XGNm33y2jMdC4EFEngY8E3gH8JXAi0TkO4B3EtXeJ9myvkxEHgAeALj77ruHpysqToW5rieZbSluG6JYRUyj0wOFJ6vObcIW1/pztyX1QlJI9o2t/7yJyB3ArwDfr6qfAX4G+ELgGUSl9y9O88Gq+qCq3quq9z75zjtPc2tFxRKaoHlrdWQL41uwpNq0tbYp4xuwdE/C0q//kHQ09Ld15zRAaLqtmJfQjG/tyNbM87YrVKFtw1bbIWErJSciUyLB/YKq/iqAqv5Vcf7ngF+3wzPVl1VU7IJeAfqIn2ussWVsRW5JuEvP66Ke0Km3sVhA0P75tWpwldoamZfh3LBh5RhR9u7fv+o6RiW3TXRVgFcC71fVHyvm7zJ/HcA3A++x8ZnqyyoqdsHNhXaLN5dpFYzMrb1OirnuFzqXWo0QH/Tz4EaJLhHQEhEVxyWJ6fK4R1pjzxuZkxWfe1ZcSZIj+t6+HfhjEXm3zf0Q8G0i8gzi/9MPAn8f4Kz1ZRUVu6BV7RRcUS6VjrUgrBSFTOquJL1Wu5Tb0oOWS7ByBYH0FNwoxI2TEFuQ25DYtiA1GZrBYc8EZx2Hjw3bRFd/m3EF/sY195y6vqyiYhecNF3tJ6RSqY6QhkrO2fJ/CWnhZ1cQX2qFBFhHX+mSdLXrDFLen4ivp+YK8hEN4+RTktqQ0Ib3p7kwQogj9/Tu2xFXMoWkouIY0AQ1YjLCUSEVAAjaNzcVVPqKLXf3SMVTidTSNVY5UKpDiL/0uWKBLaKzK/xqPcW2itwGxLZkvo5dP3zmjriqycAVFQeP1Jbbu1QCZUv+WSZu6qeWVsNKgQUnxOaUqUCesjqhIzVS0bwtKdhbcCb1Ld8AGSq3kqjGCEnDsnIb3jO8dmnOOpW0u3uM9KqWdVVUHAM+favBS1RfXgTnIrEl83OSylklmZa25F8iNNt7Z2oQ6fvy7IJ4Xm3NGclEN+qfG5M9QwIaJaYVqq1MI1lBaNq2EFo0tBBC3APaLM7+w83vfnUDDxUVB49FG2yJPmXqJS704hygVrYaWS4pPRFbD0s7NeeANsTjIBoXfqYwVYtgQ6/TB12taerdNiS8oYrrkZbNAUho7KED1WbzknLmyjkjNw1tR2YF0RHavSg50Nwg4JhQSa7iSuBWE3AuLuHXqph5GskgLb4MsfXQ1Lvso3MSF3vxSZVpXOQ5NrUcITqWj2ODSu2ll4xiVZ5baZZuq94K5aalcktJvyGgizRu96LkYoF+JbmKikvBZ+eNmanC1GTU1Ltswk6DKTmBaRvV3dR16i5IjKT6Yh0GL5IDFKpipu040SXX3VLwYej0X0deISCh7Sk1KVWbBmiS0msJzWJZsS3mnZlamqzVXK2oOG40QWlQXBCCi2QH0YSNXGZKzhReUndeomkrTmgVcBr9dUR/XFJ1RO5DR4gtYW0AYqjixs4vKbVinHxrQ9VWmqNJsQ3Op3P7wJXMk6uoOAZ8+qSJQQdTaE6ikkv7qZOs9JI5G1RyoCL4FKSQ7K+L4QlLP3GRv5IPL6j2xyKbC8HHVFxo+gouqbc8jiZpSGZos+hIbjFfJrT5yRLh7UvJqSrhwOpSt0EluYorgXkbotlpCs2ZYotzASYu+9zAESTunaMYx713Kd2EvOCMaLeSfTJV0wpZfoV4G8VQqQ3me+qtiJZmxZaIq20LwrP5ZoEu5qNKL8z3YK5SlVxFxaXh5rzFOzGC8r1OvM78bUH6Zmw2W72L5Beiby6xWFyouW+6inQ5dQkKOW/Or3rBsYTcIvetd41t2radQkv7MfVmSk0X8yX1pm1LWDS082YvP2fdk9m7DiLyI8S+lAH4OPBdqvofrI7+J4DnAY/a/O9vel4luYorgU89Osc7YTbxzJuo4G7MfM6VS2kd3gkLFyOsndmqBOeMAKOii4rQ4QrT1RNXw2ptL64LQviCVHvKboViW0ruTWZqYaLq/CQTlt46Wfa5DUxTbeZLxKYhoG2gOdm91RKqF0JywD9T1f8VQES+F/jfgH8AfCOx4cc9wJcT2719+aaHVZKruBJIxBYXP55kk3M2ieanDxRkJyzaALhog7ZR7RFgIYEpDpzigiKuI7FANFt76zKcAmWlQiK+5KOTgYkaVVrhT0sma2miDpVesyDMF7SLBm0DYdHErQ2EPSg55WJIzvpVJtxO96O+H3iNxgLat4vIEwbdkEZRSa7iSiCZq7OJAxpmk85w9CnoIBCc4to0pyyw8qygMY3EeQiR6IJYJas1xyyjrMHM1rgWardfiUHS7+ichp4PrjQ5x8zQrNxsLhFcMJJrTuZom5Tcrd1/yKqExdaK8E4ReWdx/KCqPrjtzSLycuA7gE8DX23TTwU+XFyWuo5Xkqu4+vjUo4tMcrfNPN613Jh5bpoJ2wbNPrt2QqyKAFzoOpRMneAk+utaNaVnQQxJnUUUSMnC2gUgKPfppcZ8cAPVlvPfQoMEi6KaMitN1Gy63jrpAgs2F+aLSGgh0J7Ms3ILi07VtRdvrj6sqveuOikibwE+d+TUS1X19ar6UuCltvLfi4AfPvX7GirJVVwJ3Jy3zCbOzFW4lsxUl2pOhYmR4CJEAvOiOFHzz0Vz1SXJFqB1iksWphYJv1YVkUgtaO5dMo4w0jGkCDgs94HroqndfqDuLPFX29BTb+08maqLrOTCotmPT479BR5U9eu2vPQXiG3dfpgzdh2vJFdxJfDIzQV+4vAizJvAbOK41YRMdknJzSae22aeuQtE3x25q29UchCCMPWCs6L8IOazQyKVuVT+FfPmeivdryO7pOKKxF8pKhkygZWR0kRopuD05NEl9RbmTSa05uRWJrzFX58U5/dR1nUxPjkRuUdVP2CH9wP/3sZvIC6e9VpiwOHTm/xxUEmu4oqgbQIalDBx+KZTb2k/s8BEOr5WKDoITFVYFErOqRCC9layV+wXvVB1aT6PB1UPq5pVjnUg6aoZuq1UbikAEeaLSF5JvWUlF6OqYd7kuRR4aOd7ICe9mBQS4EdF5EuIsZ4PESOrEBXd84CHiCkk373NwyrJVVwJNIsWP3GEoMwL5dYGZTZxOfoK4F2wvdhvgGPRKlOPEVskuGiuxs4mwczZiYsRWkllXmAm64ak4GEuHMMuv8k0TcGHMCjZiqZqIrhklraJ1NJ88skZwbXzlnbe0pzsI09OCRcTXf27K+YVeOFpn1dJruJKYH6zwU1isq+q4s3PNvPOIq6dggNoQ9or7SRqsdbKvFptab3rJQ4vXCTBNgjeRTUXrGWTWAvilSklPTIblHZZXlywPDddWMS0mec8uDxezGlO5j1zNAUX2pvzuLfz7bxlcbMhLFraediPuaralZcdESrJVVwJNIsWpw7vHc6bUmqE9CtZKrlZk5RcZ0rOvCNo7Es39VERluZqq10QIuiy522TO25VpBVYLt1KQYYiTSQqupCTe6M5uuiUXGGeJvWWCC4R3s5IZWZHhkpyFVcC5YLGsogtgVJ/tzl9kpsbyc0mgTZEk3ZuycFTFwMIcZHpvrkagiy1UioWCduMkdIusbSMsjUSg+NkpvYIrdjnxN95w+JmMmNDT8mFxX4K6y/IJ7dXVJKruBK4dbPBe8FNHBomBG8maOvwjeMRolpLvrqm8Nm1wWcCjPlz0ZRdBCvH9y62RFeltSoIFetOssXaDksY1qcOlFuOqhalWslMbbI5usgmalg0LP76hHYeWPz1PKu3+V/PCYs9K7lKchUVl4Nm3qIThw/QuDYqOSd4VTQofuJ6pmu5B7jVRGKbeUuSwzFxAS++Z7qmnLigXZmXiEYT1roIi5mzfpguUrRaSuOy4WWvdGsx75VqJTM1bm0MMiQTdd5kYsv7RZsJLvrk9hN4qCRXUXFJWDz6GdrJDDeZEXTKZGqBhdbhvRFe42gt8DAr8ue6SKy3IIWllQThpGlh4nO6ycLFBGKxtSJySsk2GO0y0gUcUmVDmSOXzNT2ZE47b3IeXHsyz0GGxc0mK7hbn7mVSW7+yIKwCDSLlkf2sMpWbH9e+8lVVFwKmvnN/GUOjacB/KT7hQzFL/l8Ev1zpZJLZJd8d858dTHaqtlchfEKh41+udE2S4UqKtJFygL8tsh1K31vKU2knbdW6RBywKFddOpt0SrzoNxs99AHrkZXKyouD2GxoAFcaHGTGdBfI7QMTAQjs0RoTUFumeimZpKqZnMVJznKOoWcL5daAWy1kFXRgWRpHYailEtDm1NFtKhFLZN/O1LrTNQxgpsH5WQfzS71YvLk9o1KchVXAotHP40zcxXAT2bAHbiJYzJ1sWWSV1wbV/UKvjNXb8x8zp8ru5ekIERc0UtZhC7KumgDE+dyvtw6dMsK9s3VRGapEWYKOiRfXDtvimBDDD7E3Lgmm6lhEYMKKcgwf2SRCe7Ti5aFwjzE8a5QOMoUko1t6UXkuoj8roj8oYi8V0T+d5v/AhF5h4g8JCK/JCIzm79mxw/Z+aed7z+hogKaWzdp5jcJzZzQzGmbOW3TEppAaJS2CbRtNw6WNpKU260mjVsaM13bENNIgsbAQghdakkJtfNxPHixYXWDIXYgMfUGvQqHHGkdlG6lPLmk4rr0kC7IEBYhK7iFws022LYfJZfSXTZth4SNJAfcAr5GVb8MeAZwn4g8B3gF8OOq+kXAJ4EX2PUvAD5p8z9u11VUnCva+U3CYk4zv0l766Qgu5a2jQTXNiESlh0ns7X0x7WZ4BK52bHGLa1x0BbElrBx4eWxnnJFjWrpj0vkFtouqlr643qR1GLfLNpsog633XGcJLfRXLV6sUfscGqbAl8D/D2bfzXwMmI74vttDPDLwE+JiNhzKirOBYtHP0No5tFkdT47yCezG4RmhrjbCT6gGoMJIcTSL7WSrtSm6cbMAw0wyQnCTpRFG9NJktnqJUZXyzrWbSoeZBhZTQ0xU/ffFFG1ziLBIqrtSSzpak4WuRa1OVmYyRoTf5uThkeaqNpOzES9aaruM3uIrl7pwIOIeOBdwBcBPw38GfApVU3JN6lDJxTdO1W1EZFPA38DeHjwzAeAB+zwkRu33faJ4TWXjDup77MOh/Y+LOydHr3sF+lwaD+jL9nlZr35iTct3v1/3Lnl5Qfz796K5FS1BZ4hIk8Afg34m7t+sLVCzu2QReSd6zqJXjTq+6zHob0PHN47HeL77HK/qt63r3e5SGzjk8tQ1U8BbwW+AniCiCSSLDt05u6ddv5zgE/s5W0rKioqToltoqtPNgWHiNwAvh54P5HsvsUu+07g9TZ+gx1j5/+f6o+rqKi4LGxjrt4FvNr8cg54nar+uoi8D3itiPwT4A+AV9r1rwT+TxF5CPhPwPO3fJetV/K5INT3WY9Dex84vHeq73MAkCqyKioqrjJO5ZOrqKioODZUkquoqLjSuHSSE5H7RORPrAzsxZf0Dh8UkT8WkXenMLuIPElE3iwiH7D9E8/5HV4lIh8XkfcUc6PvIBE/aT+zPxKRZ13Q+7xMRD5qP6d3i8jzinMvsff5ExH5hnN4n7tF5K0i8j4rL/w+m7+Un9Ga97mUn1Etv1wDVb20jdjA4c+A/wKYAX8IPP0S3uODwJ2DuX8KvNjGLwZecc7v8N8CzwLes+kdiMuy/Vtijv1zgHdc0Pu8DPhfRq59uv2/uwZ8gf0/9Xt+n7uAZ9n4ccCf2udeys9ozftcys/I/p132HgKvMP+3a8Dnm/z/wr4hzb+R8C/svHzgV86z+/3ZW6XreSeDTykqn+uqnPgtcSysEPA/cRyNWz/Tef5Yar6NmI0ept3uB94jUa8nZizeNcFvM8q3A+8VlVvqepfENfFfPae3+djqvr7Nv4sMY3pqVzSz2jN+6zCuf6M7N+5qvzyl21++PNJP7dfBr5W5Cy93A8fl01yuQTMUJaHXSQU+A0ReZeVmwE8RbvVuf8SeMolvNeqd7jMn9uLzPx7VWHCX+j7mGn1TKJaufSf0eB94JJ+RiLiReTdwMeBN/P/t3P2LHVEQRh+psgHxDTpUkaxDSJBUkhKQbt0VjYp09gL+QfxD1iKpAhJYZ38AdMkVyXEpBXRTltJJsWcTU7EKwTcHTm8Dyx7756F8zLsDnfO3Pf8h/0S6OyXzZGd5G4K8+4+CywCL83sWT3o8Zs+9b82N0EDsQHDFLEbzRHwemgBZjYBvANW3f2sHsuI0SV60mLk7j/dfYZwIM1xDfbLFshOcn8sYIXaHjYY7n5YzieEN3cOOO7Km3I+GVrXFRpS4ubux+VF+gVs8LfcGkSPmd0iEsqWu78vl9NidJme7BgVDbJfVmQnuU/AdOkA3SYWQLeHFGBm98zsfvcZWAD2+NeeVtvWhmSchm1gpXQQnwKnVcnWGxfWtJ4Tcer0LJeO3SNgGti55rmNcNN8dff1aiglRuP0ZMXIZL8cT3bng+iCHRDrB2sJ808SXa8vwH6ngVif+Ah8Bz4AD3rW8YYob86JtZMX4zQQnbRuy6td4MlAejbLfCPiJXlY3b9W9HwDFnvQM0+UoiPgczmWsmJ0hZ6UGAGPCXvliEisr6rne4dodLwF7pTrd8v3H2V8ss/nO/OQrUsI0TTZ5aoQQvSKkpwQommU5IQQTaMkJ4RoGiU5IUTTKMkJIZpGSU4I0TS/ATv+PCzXn73DAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "K = (kernel.compute_K_symm(pX))\n", "K_trans = np.transpose(K, [1, 0, 3, 2])\n", "vimshow(np.reshape(K_trans, [100 * 3, 100*3]));\n", "plt.colorbar();\n", "plt.title(\"Kff\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the outputs are uncorrelated, *but a different kernel* is used for each output. However, during the `conditional` calculations we do not assume this particular block-diagonal structure. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Separate Independent MOK & Shared Independent Features" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:21.702858Z", "start_time": "2018-06-19T10:59:21.673859Z" }, "collapsed": true }, "outputs": [], "source": [ "gpf.reset_default_graph_and_session()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:22.210502Z", "start_time": "2018-06-19T10:59:22.089982Z" }, "collapsed": true }, "outputs": [], "source": [ "kern_list = [gpf.kernels.RBF(D) + gpf.kernels.Linear(1) for _ in range(P)]\n", "kernel = mk.SeparateIndependentMok(kern_list)\n", "feature = mf.SharedIndependentMof(gpf.features.InducingPoints(X[:M,...].copy()))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:23.046491Z", "start_time": "2018-06-19T10:59:22.556308Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:conditional: object, SharedIndependentMof, SeparateIndependentMok, object\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: SharedIndependentMof, kern: SeparateIndependentMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: SharedIndependentMof, kern: SeparateIndependentMok)\n" ] } ], "source": [ "m = gpf.models.SVGP(X, Y, kernel, gpf.likelihoods.Gaussian(), feat=feature)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:27.955259Z", "start_time": "2018-06-19T10:59:25.816610Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 75.998447\n", " Number of iterations: 151\n", " Number of functions evaluations: 172\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 75.998447\n", " Number of iterations: 151\n", " Number of functions evaluations: 172\n" ] } ], "source": [ "opt = gpf.train.ScipyOptimizer()\n", "opt.minimize(m, disp=True, maxiter=MAXITER)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:28.559474Z", "start_time": "2018-06-19T10:59:27.956308Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:conditional: object, SharedIndependentMof, SeparateIndependentMok, object\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: SharedIndependentMof, kern: SeparateIndependentMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: SharedIndependentMof, kern: SeparateIndependentMok)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_model(m)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:28.719575Z", "start_time": "2018-06-19T10:59:28.560739Z" } }, "outputs": [ { "data": { "text/plain": [ "-75.998446937421292" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m.compute_log_likelihood()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Separate Independent Kernel & Separate Independent Features" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:34.470299Z", "start_time": "2018-06-19T10:59:34.439405Z" }, "collapsed": true }, "outputs": [], "source": [ "gpf.reset_default_graph_and_session()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:35.106186Z", "start_time": "2018-06-19T10:59:34.971615Z" }, "collapsed": true }, "outputs": [], "source": [ "kern_list = [gpf.kernels.RBF(D) + gpf.kernels.Linear(1) for _ in range(P)]\n", "kernel = mk.SeparateIndependentMok(kern_list)\n", "feature_list = [gpf.features.InducingPoints(X[np.random.permutation(len(X))[:M],...].copy()) for _ in range(P)]\n", "feature = mf.SeparateIndependentMof(feature_list)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:35.944347Z", "start_time": "2018-06-19T10:59:35.397366Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:conditional: object, SharedIndependentMof, SeparateIndependentMok, object\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: SeparateIndependentMof, kern: SeparateIndependentMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: SeparateIndependentMof, kern: SeparateIndependentMok)\n" ] } ], "source": [ "m = gpf.models.SVGP(X, Y, kernel, gpf.likelihoods.Gaussian(), feat=feature)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:38.607599Z", "start_time": "2018-06-19T10:59:36.428011Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 78.204454\n", " Number of iterations: 151\n", " Number of functions evaluations: 180\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 78.204454\n", " Number of iterations: 151\n", " Number of functions evaluations: 180\n" ] } ], "source": [ "opt = gpf.train.ScipyOptimizer()\n", "opt.minimize(m, disp=True, maxiter=MAXITER)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:39.328923Z", "start_time": "2018-06-19T10:59:38.608677Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:conditional: object, SharedIndependentMof, SeparateIndependentMok, object\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: SeparateIndependentMof, kern: SeparateIndependentMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: SeparateIndependentMof, kern: SeparateIndependentMok)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_model(m)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:39.927604Z", "start_time": "2018-06-19T10:59:39.330061Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:conditional: object, SharedIndependentMof, SeparateIndependentMok, object\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: SeparateIndependentMof, kern: SeparateIndependentMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: SeparateIndependentMof, kern: SeparateIndependentMok)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(len(m.feature.feat_list)):\n", " q_mu_unwhitenede, q_var_unwhitened = m.predict_f(m.feature.feat_list[i].Z.value)\n", " plt.plot(m.feature.feat_list[i].Z.value, q_mu_unwhitenede[:, [i]], \"o\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This plot shows that we use different inducing *inputs* in each output dimension." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Mixed Kernel\n", "\n", "## 1. Mixed Kernel & Correlated features (SLOW)\n", "\n", "Remember: $f(x) = W g(x)$, where $g(x) \\in \\mathbb{R}^L$, $f(x) \\in \\mathbb{R}^P$ and $W \\in \\mathbb{R}^{P \\times L}$.\n", "In this scenario we ignore the fact that observations are produced by mixing uncorrelated latent GPs. We directly model the correlated observations. This means that we place our inducing outputs in the $f$ space and end up with the following (large) correlation matrices.\n", "\n", "- $ K_{uu} = M \\times P \\times M \\times P $\n", "- $ K_{uf} = M \\times P \\times N \\times P $\n", "\n", "We'll have to use `fully_correlated_conditional` or `base_conditional` depending on the `full_cov`/`full_output_cov` args." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T10:59:48.838797Z", "start_time": "2018-06-19T10:59:48.808721Z" }, "collapsed": true }, "outputs": [], "source": [ "gpf.reset_default_graph_and_session()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:14.728947Z", "start_time": "2018-06-19T11:00:14.543249Z" }, "collapsed": true }, "outputs": [], "source": [ "q_mu = np.zeros((M, P)).reshape(M * P, 1)\n", "q_sqrt = np.eye(M * P).reshape(1, M * P, M * P)\n", "\n", "kern_list = [gpf.kernels.RBF(D) + gpf.kernels.Linear(D) for _ in range(L)]\n", "kernel = mk.SeparateMixedMok(kern_list, W=np.random.randn(P, L))\n", "feature = gpf.features.InducingPoints(X[:M,...].copy())" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:15.696320Z", "start_time": "2018-06-19T11:00:15.384185Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:Conditional: InducingPoints -- Mok\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: InducingPoints, kern: SeparateMixedMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: InducingPoints, kern: SeparateMixedMok)\n" ] } ], "source": [ "m = gpf.models.SVGP(X, Y, kernel, gpf.likelihoods.Gaussian(), feat=feature, q_mu=q_mu, q_sqrt=q_sqrt)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:17.545198Z", "start_time": "2018-06-19T11:00:15.837612Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 181.615405\n", " Number of iterations: 151\n", " Number of functions evaluations: 184\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 181.615405\n", " Number of iterations: 151\n", " Number of functions evaluations: 184\n" ] } ], "source": [ "opt = gpf.train.ScipyOptimizer()\n", "opt.minimize(m, disp=True, maxiter=MAXITER);" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:18.064483Z", "start_time": "2018-06-19T11:00:17.547218Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:Conditional: InducingPoints -- Mok\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: InducingPoints, kern: SeparateMixedMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: InducingPoints, kern: SeparateMixedMok)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_model(m)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:18.207308Z", "start_time": "2018-06-19T11:00:18.065983Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vimshow(m.kern.W.value.T)\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Mixed Kernel & Uncorrelated features (BETTER)\n", "\n", "Remember: $f(x) = W g(x)$, where $g(x) \\in \\mathbb{R}^L$, $f(x) \\in \\mathbb{R}^P$ and $W \\in \\mathbb{R}^{P \\times L}$.\n", "We assume that the outputs of $g$ are uncorrelated, and by *mixing* them with $W$ they become correlated.\n", "In this scenario we assume that are inducing outputs live in the $g$ (i.e. $\\mathbb{R}^L$) space.\n", "\n", "\n", "- $ K_{uu} = L \\times M \\times M $\n", "- $ K_{uf} = M \\times L \\times N \\times P $\n", "\n", "We'll use `independent_latents_conditional`" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:24.912718Z", "start_time": "2018-06-19T11:00:24.896649Z" }, "collapsed": true }, "outputs": [], "source": [ "gpf.reset_default_graph_and_session()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:25.585881Z", "start_time": "2018-06-19T11:00:25.489128Z" }, "collapsed": true }, "outputs": [], "source": [ "q_mu = np.zeros((M, L))\n", "q_sqrt = np.repeat(np.eye(M)[None, ...], L, axis=0) * 1.0\n", "\n", "kern_list = [gpf.kernels.RBF(D) + gpf.kernels.Linear(D) for _ in range(L)]\n", "kernel = mk.SeparateMixedMok(kern_list, W=Ptrue.T)\n", "feature = mf.SharedIndependentMof(gpf.features.InducingPoints(X[:M,...].copy()))" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:26.471044Z", "start_time": "2018-06-19T11:00:26.161522Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:Conditional: (SharedIndependentMof, SeparateIndepedentMof) - SeparateMixedMok\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: SharedIndependentMof, kern: SeparateMixedMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: SharedIndependentMof, kern: SeparateMixedMok)\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: SharedIndependentMof, kern: SeparateMixedMok)\n", "DEBUG:gpflow.multioutput.conditionals:independent_interdomain_conditional\n" ] } ], "source": [ "m = gpf.models.SVGP(X, Y, kernel, gpf.likelihoods.Gaussian(), feat=feature, q_mu=q_mu, q_sqrt=q_sqrt)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:29.932345Z", "start_time": "2018-06-19T11:00:28.146716Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 158.436419\n", " Number of iterations: 151\n", " Number of functions evaluations: 170\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 158.436419\n", " Number of iterations: 151\n", " Number of functions evaluations: 170\n" ] } ], "source": [ "opt = gpf.train.ScipyOptimizer()\n", "opt.minimize(m, disp=True, maxiter=MAXITER)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:30.325112Z", "start_time": "2018-06-19T11:00:29.935059Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:Conditional: (SharedIndependentMof, SeparateIndepedentMof) - SeparateMixedMok\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: SharedIndependentMof, kern: SeparateMixedMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: SharedIndependentMof, kern: SeparateMixedMok)\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: SharedIndependentMof, kern: SeparateMixedMok)\n", "DEBUG:gpflow.multioutput.conditionals:independent_interdomain_conditional\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_model(m)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:30.615110Z", "start_time": "2018-06-19T11:00:30.459494Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(m.kern.W.value.T)\n", "plt.colorbar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Mixed Kernel & Uncorrelated features (OPTIMAL)\n", "\n", "Remember: $f(x) = W g(x)$, where $g(x) \\in \\mathbb{R}^L$, $f(x) \\in \\mathbb{R}^P$ and $W \\in \\mathbb{R}^{P \\times L}$.\n", "We assume that the outputs of $g$ are uncorrelated, and by *mixing* them with $W$ they become correlated.\n", "With this setup we perform the optimal routine to calculate the conditional. Namely, calculate the conditional of the uncorrelated latent $g$ and afterwards project the mean and variance using the mixing matrix: $\\mu_f = W \\mu_g$ and $\\Sigma_f = W~\\Sigma_g W^\\top$\n", "\n", "\n", "- $ K_{uu} = L \\times M \\times M $\n", "- $ K_{uf} = L \\times M \\times N $\n", "\n", "We'll use `base_conditional`" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:35.438985Z", "start_time": "2018-06-19T11:00:35.422006Z" }, "collapsed": true }, "outputs": [], "source": [ "gpf.reset_default_graph_and_session()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:35.866898Z", "start_time": "2018-06-19T11:00:35.769898Z" }, "collapsed": true }, "outputs": [], "source": [ "q_mu = np.zeros((M, L))\n", "q_sqrt = np.repeat(np.eye(M)[None, ...], L, axis=0) * 1.0\n", "\n", "kern_list = [gpf.kernels.RBF(D) + gpf.kernels.Linear(D) for _ in range(L)]\n", "kernel = mk.SeparateMixedMok(kern_list, W=np.random.randn(P, L))\n", "feature = mf.MixedKernelSharedMof(gpf.features.InducingPoints(X[:M,...].copy()))" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:36.651216Z", "start_time": "2018-06-19T11:00:36.271414Z" }, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:conditional: MixedKernelSharedMof, SeparateMixedMok\n", "DEBUG:gpflow.multioutput.conditionals:conditional: object, SharedIndependentMof, SeparateIndependentMok, object\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: MixedKernelSharedMof, kern: SeparateMixedMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: MixedKernelSharedMof, kern: SeparateMixedMok)\n" ] } ], "source": [ "m = gpf.models.SVGP(X, Y, kernel, gpf.likelihoods.Gaussian(), feat=feature, q_mu=q_mu, q_sqrt=q_sqrt)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:39.472947Z", "start_time": "2018-06-19T11:00:37.552120Z" }, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 163.438162\n", " Number of iterations: 151\n", " Number of functions evaluations: 176\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Optimization terminated with:\n", " Message: b'STOP: TOTAL NO. of ITERATIONS EXCEEDS LIMIT'\n", " Objective function value: 163.438162\n", " Number of iterations: 151\n", " Number of functions evaluations: 176\n" ] } ], "source": [ "opt = gpf.train.ScipyOptimizer()\n", "opt.minimize(m, disp=True, maxiter=MAXITER)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "ExecuteTime": { "end_time": "2018-06-19T11:00:39.947168Z", "start_time": "2018-06-19T11:00:39.474197Z" }, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "DEBUG:gpflow.multioutput.conditionals:conditional: MixedKernelSharedMof, SeparateMixedMok\n", "DEBUG:gpflow.multioutput.conditionals:conditional: object, SharedIndependentMof, SeparateIndependentMok, object\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuu(feat: MixedKernelSharedMof, kern: SeparateMixedMok) with jitter=1e-06\n", "DEBUG:gpflow.multioutput.features:Dispatch to Kuf(feat: MixedKernelSharedMof, kern: SeparateMixedMok)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_model(m)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Mixed Kernel & Separate independent features (OPTIMAL)\n", "\n", "Remember: $f(x) = W g(x)$, where $g(x) \\in \\mathbb{R}^L$, $f(x) \\in \\mathbb{R}^P$ and $W \\in \\mathbb{R}^{P \\times L}$.\n", "We assume that the outputs of $g$ are uncorrelated, and by *mixing* them with $W$ they become correlated.\n", "With this setup we perform the optimal routine to calculate the conditional. Namely, calculate the conditional of the uncorrelated latent $g$ and afterwards project the mean and variance using the mixing matrix: $\\mu_f = W \\mu_g$ and $\\Sigma_f = W~\\Sigma_g W^\\top$\n", "\n", "\n", "- $ K_{uu} = L \\times M \\times M $\n", "- $ K_{uf} = L \\times M \\times N $\n", "\n", "We'll use `base_conditional`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "gpf.reset_default_graph_and_session()" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/net/lofar1/data1/albert/miniconda3/envs/kerastf/lib/python3.6/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", "/net/lofar1/data1/albert/miniconda3/envs/kerastf/lib/python3.6/site-packages/multipledispatch-0.4.9-py3.6.egg/multipledispatch/dispatcher.py:24: AmbiguityWarning: \n", "Ambiguities exist in dispatched function _expectation\n", "\n", "The following signatures may result in ambiguous behavior:\n", "\t[Gaussian, Identity, NoneType, Kernel, InducingPoints], [Gaussian, Linear, NoneType, Sum, InducingPoints]\n", "\n", "\n", "Consider making the following additions:\n", "\n", "@dispatch(Gaussian, Identity, NoneType, Sum, InducingPoints)\n", "def _expectation(...)\n", " warn(warning_text(dispatcher.name, ambiguities), AmbiguityWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "-3.4041344381903564\n", "0.13859998443104302\n" ] } ], "source": [ "import numpy as np\n", "import gpflow as gp\n", "\n", "import gpflow.multioutput.kernels as mk\n", "import gpflow.multioutput.features as mf\n", "\n", "\n", "D = 1 # number of input dimensions\n", "M = 20 # number of inducing points\n", "L = 2 # number of latent GPs\n", "P = 5 # number of observations = output dimensions\n", "\n", "noise = 0.1\n", "\n", "X = np.tile(np.linspace(0,1,M*2)[:,None],(1,D))\n", "Y = np.concatenate([p*np.sin(10*X) + noise*np.random.normal(size=X.shape) for p in range(1,P+1)],axis=1)\n", "\n", "def _kern():\n", " return gp.kernels.Matern32(D)\n", " \n", "with gp.defer_build():\n", "\n", " W = np.random.normal(size=(P,L))\n", "\n", " kern = mk.SeparateMixedMok([_kern() for _ in range(L)], W)\n", "\n", " feature_list = [gp.features.InducingPoints(X[:M,:]) for l in range(L)]\n", " feature = mf.MixedKernelSeparateMof(feature_list)\n", "\n", " q_mu = np.zeros((M, L))\n", " q_sqrt = np.repeat(np.eye(M)[None, ...], L, axis=0) * 1.0\n", "\n", " likelihood = gp.likelihoods.Gaussian()\n", " likelihood.variance = noise\n", " likelihood.variance.trainable = False\n", "\n", " model = gp.models.SVGP(X, Y, kern, likelihood, \n", " feat = feature,\n", " minibatch_size=None,\n", " num_data = X.shape[0],\n", " whiten=False,\n", " q_mu = q_mu,\n", " q_sqrt = q_sqrt)\n", " model.compile()\n", "\n", "opt = gp.train.AdamOptimizer(1e-2)\n", "print(model.predict_density(X,Y).mean())\n", "opt.minimize(model, maxiter=10000)\n", "print(model.predict_density(X,Y).mean())" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ystar,varstar = model.predict_f(X)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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ZMOPxccYT40wkJsgV5+orrviuUGevo8PdQYO9AY344HGTgihiffZZNA4HibPn\nMK1fT7a3l+SZM+hqa9E3NOCpr2aqr4Cz0szMeILadlcpp/3uEPrlwrIU9my6gG8wSjyUobbdiSAI\nlDfYkHI54qdOAZC6eBE5n8fy9NNoHXasTz/9lfuN5+JMJiZLYh7OhClIBQLpAL6kDxkZr8mL1+zF\noFFy1UVBxK634za52V65HZfx4QJseeZpcqMjGFevJnPrFqYNG9A4nVCUSF26RNWap+i7nMFbb2Oy\nP8r0UAxnhZmqFU51dJ4KANNDMQKjMZAprVZrV7rI3O4uzectJpMkzpxBW1GBaeNGZabAcwe/slaj\nKBUZi48xFB1iLD5GIj/fFVKQCiTyCQwaAyatidHYKKOxUcxaM23uNjrcHTiNCwfa3MW0YQOCyUz8\nxAmse/cS/ed/JnXhAhqLGffhI0wJAt46G+GpFOHpFGXVFoLjCVXYlwN3K00FQanY1Ju0OCvMSvFF\nPEEhGCRz+zbGtWvRlpVh3b9/XurVlylKRS5MXeBG4AapQgpf0lfaAukARXlhwy+73k65uRyPyVMS\n+7HYGLtrd9PufnD1nqjXY9uzh4Jvmkx3N6nLl7EdPAhApqcH58aNmGx60vEcZTVWguNxkpEsEX9q\nXpdKleVJMS8RHIsTGEvg8JowmnV4aq0YTSKhK4q1fneAjFwsYjtwQInxvHjogUMyJFliPD5Of6Sf\noegQ8Vy8JOrxXJxEPkEilyCej5MpKBWhWkHLtqptrPWsRRREUoUUnf5OOv2dVFur2Ve/D7v+/uOT\nje1tZG4qqZjGdevI3LiBoa0NzWencDTsRpYk9CYNwbE4ZdUWQpMJ6le70SyjxmDLTthlSWZ6KEpw\nPKn4og0avPU25FSKTFeXEpQ5exZBr8e8dSvGNavR19Y+cH+RTITjw8c5PX6arkAX0ZzinxQFEa/J\nyxrPGirNlVRaKhEEgUAqQCCtbNPJafojcz3d21xtJPNJJhIT7KrdhU7U3feY+sZGTJs3Ybpxg/QX\nX2DatEnJUJBk0pcuUbX2GQavBWb7ZsTxj8Qob7Srwq6i5K1PJslninhXW5W6jTYnmVu3ShWmma4u\n8iMjWHbtKlnqWtf8laQkS0zEJxQxjw2RKWSQZZmh2BBnJ86SzCsph1pRi01nw6q34jV5seqtWHVW\nBiIDfD75OYORQfbU7Zm3Up1MTPJO/zu80vIKNv39p22at+8gevQo5u3byQ0OkvjkE7ReL1bbAFGh\nBk+djcneCNlUHoNZR8SXWlb9k5adsEf8KXyDUYoFCW+9DUEU8NbbSF0+j5wvkBsaIj8xgWX3bnQV\n5Vh37nzgvrpnuvl45GM+GvmJHOUhAAAgAElEQVSIodgQVZYqVpWtotJcidfsRSsu/PPW2+upt89V\n6aULaQKpAOOJcW4EbjCdmiaWizGdmuYHjT/Abbx/b3Xrrl1ke3rJ3LxJ6uJF7IcOAZAdGMSxYRMG\ns/Kl4Cg34R+JE5pMkM95HjhdXmXpI8uyEjSdLchzlJvxNtjR6wVCV5Usq0IgQPLcOXSNjRjXrsWy\nfRuGpqZ5+4nn4rw78C6RbGTeY2cnzjIcG6bMWMa+un14TV70Gv193TdtrjZ6w72cmzzHm71vsrVy\nK+u960vxplguxtv9bz9Q3PW1NejqasmPjWPZvZv4Bx+QvnYNk0aLpsqBp9bKZG+EwJjiaw+OxZeV\nsC+ftcksd6fEGK06bG4DzgozmkKWzM2bc+mNbjfGNWuw7ru/CyZbzHJi+AS/6vkVv+z5JSOxEXZW\n7+SVFa+wsXwjVdaq+4r6/TBpTdTb69lZvZOXV7xMvpjnrb63ODtxljd73qR7pvu+7xONRuyHXsC0\ncaPyZeSb7ZMty6QvX6SyWeljU9lkp5CTCI4nmFmmqV8qCtFAmlggTdSfxlNrRavXUN3iJN3VhZRM\nIuVyxI4fRzSZsO3bh7G1BfPWrfP2EclEONp3tCTqkixxPXCdX/X8ivHEOE9VPcWP2n5Era0Wg9bw\nQJ+8IAi0u9v5SftPqLPVcWHqAkf7jxLKzM1QvSvuidz9r1vLbMGgoakJ/YoVpK5cQYqEsQZ6Mcwm\nRQTHE8iSvOxy2peVsGfTBSb7IiQjOcrrbaWgafrqFeRCkfT160ixGJZnnsG8fh362oWl0r6kj1/f\n+TW/6fsNv+3/LbIs80rLK6z3rn/oRVxhrsBr9iLw4OBTtbWa19peo9pSzZnxMxwbPsbx4eOcHDlJ\nvphf8HpDczOOI0cQTKZS7xhQBgE7xBhagwZbmRGTTacEzJZxJZ6KkuJ4N6/bU2+lstmBVpRKNRHJ\nM2eUBl8HD6KrrcG2f/+89wfTQY72Hy0FRKdT0/ym9zd8Pvk5NZYa3mh/gw3lG9AIj74qtOgsPN/4\nPAfqDxDLxniz902uTl9FkpU+L7FcjLcH7i/uuqoq9I1K+rF11y4EjYbE6dNYM9MUoxG89VbymSLR\nQHrZ5bQvK1fM3TJ7URQoq7VgsOiwGItEbt9GSiZJX7mi+K/XrMby1FML3t/p7+STsU84NXqK4dgw\njfZG9tbtxahdGHG36W3U2eqos9VRY60pvSaVTzEaVzIBxuJjZIvZee8z68y82Pwi1/zXuOS7RCAV\nIJqNEkgHeLH5xQXLUvvBA8SPHyNxSpnkdDfHOHPlIpXr9jF+J0xFo53hrhkCI3GS0SwWhzrwd7mR\nSeYJT6eULqYeI1ankapmB+muTqRUmsydO2R7ejBv3Yquvg77wYPzVqu+pI/3Bt8jV8yRK+a4OHWR\nmzM3sWgtPNfwHM2O5pJh0+ZqY2vlVtKFNLFcTAmc5uLKz7MB1YI0Zz0LgkCrq5Uaaw1nJ85yyXeJ\n4dgwzzU8h01vI5qN8vbA27za8ioW3fw4kXn7dnIjo4gWC5adO0mcPo2h7zZGbR1C/Wq0elEpQqww\nExyfq9Be6iwrYZ8eijIzmcRdbUGr01BebyN99SpyoUjy88+Ri0UszzyD7T5ZMLeCtzjad5SPRj4i\nWUiys3on6zzrShezgECDvYF6ez11tjochvs3+jfrzKx0r2SleyWSLDGdnGY0PspIbIRgWpmmJAgC\nmyo2UWWp4qPRj3ir/y2ern6aTCHD4RWHKTPNlXKLZjOef/WvyNy6TeLTT9FWVaGxWslPTuFYF2JK\np8FdZWH0VojgeJzgWEIV9mWIfzhGzJ8ily5St9KNq9KMICsVzIVwWLl2qqsxbdmCecuWUhk/wFhs\njA+HP6QgFRiKDvHZxGck80nWetayrXIbeo1yr+g1enbX7qbN1QaAw+Cg0lJ53/MJpoP0hnrpi/SV\nAq1mnZnnGp+jP9zP6fHTvNn7Jvvq99FobySajfLb/t8uEHddeTmG5iayA4MYOjrI9PSQPHcOW3Ub\nmWQDnlorvqFYqUhvueS0LxtXTCqWY7wnXJoSI4gCLhdkbt8mPzlJtrcX08aNWHfvWtCtbjg6zN93\n/T2/7f8tCPBqy6vzXC8WnYWXVrzEoeZDrPGseaCofxlREKmyVrG9ajuvt7/O4ebDeExzpdpV1ipe\na3uNWmstn018xvmp8xztP8pkYnLefkyrVuH+oz9ELhZJnDxZ6tWeu3oZ72wPHFeVmdBkEv9IDOkB\nI8lUlibFgkRgNsVRqxdxVppxV1vIXL9OMZ4gfuIEglaruGAqKkqtAwAGIgO8P/Q+0WyUY8PHODZ8\nDIPGwJHWIzxT80xJ1KssVfyk/SclUf8qPCYPO2t28rOOn/FS80u0udpKcakWVwuvtb2GTWfjw6EP\n+Xzyc4pyUbHc+98ufRHcxbx9OwiC0m7g2WeRs1m0gzeQ/ZN462wgU4ovLRd3zLIR9uCYEjQ1O5Qp\nMa5KC/nr15DzBRJnziBardj27VvggvGn/Pzna/+Zk6MnqbRW8lrra1SY5yauNzubSwGgx6XOXsdr\nba+xv35/yeVi0po41HSIJnsTF6YuMJmY5N2BdxmMDM57r/NHP8K651ny4+Nkrl8HoBAI4pQCiBoB\nT52VYkEmOBYnMr1825kuR2YmEmTiOSLTKTy1VvRGLVarSKqzk+Tnn1MMBrHt34/GYcd2YL8yuxQl\n6+v48HG6gl386s6vGI2Nsr1yOz9u+3HpHhAFke1V23m15dUHpiY+DFEQqbfXc6DhAH+0+o/YV7+P\nGmsNDoODH7b+kNVlq7keuM47/e+QyCWIZCO83f82qfzcNawtKyu5ILVlZWgrKsj19GBL+9AJOWxu\nA4GxBLIsE5pMIC2DPu3LQthlWWasO6RMialTUp7KygQyd7qVfjAzM8oAjb175g2ejmaj/KfL/4mP\nRj6iylrFoaZDGLSKG0Mn6thbt5fnG5+/r4/9m3I3W+D3Vv4eO6t3YtAomQXP1j2LUWPk49GPyRaz\nHB8+zs3gzdL7NDYb7t//ffRNTSTPn6cwMwNA4doVymos2NxGDGYtgbHEsu2fsVyZHlKCprKsTBpy\nVpjJdF4jc1u5/o3r16NvbMSydWupuVdPqIejfUc52n+UM+Nn8Jg9vN7+OpsqNpWCow6DgyOtR9hc\nsfkrK1IfBZ1Gx0r3Sl5peYXDzYdxGV3srt3NgfoDzGRmeLP3TUZiI0SyEd4deLdU7ARg3ra1NLvA\n0N5OcWYGe3KMwtQUnjob2VSBeChDMS8RXgaGzbIQ9ngow/RQDARwVVow2fRoB24gxROkLl1CV1eH\neceOeaO9MoUM/+HSf+DD4Q+ptlZzqOlQqWCowlzB6+2vs6ps1RM7Z62oZUP5Bn7a8VM2lm/EqrOy\nt24v4UyYi1MXkZE5M36GC1NzfbPNmzbhePllBINBGZ9XKFCMRHAVphE1Ip5aK/GZDP7hmNoYbJkQ\nC6ZJxbIExhLY3AaMVh1Op0jis89InDqF1uvF8tRTaMvLMc0OjYlmo/zF1b/g172/JpwJs7duLy83\nv4zTMBd4bHe183rb65Sbn0w/9jp7HW+0v8E67zraXG38uO3HWHQWPhj6gAtTFwikA6VgLoDW5Sq1\n0ja0toIoIvd3o4/7cbo0aLRCqZ1veEoV9iVBcDxBaCqJvcyIzqChzC2QuXOH5PnzyIUCll27sO7Y\nXnp9QSrw5xf/nHcH36XGWsMLTS+gE3UIgsCWii38sPWHj+xHf1wMGgNPVT/F6+2v0+ZuY61nLTeC\nNxiLjwHwxfQXnBo9hSRLSgfKF57Htm8fxZmZUgpksesqjjIDZbXKaiUwqkx0V1n6TA8rw82zqQLe\nehtavQaxr5P48RMgSdieew7RoMd28ACCKCLLMj//4uecnzxPo72RN9rfYKV75TyLfGP5RvY37Een\nuX9l9GKh0+h4puYZjrQeYYVjBUdaj9Dh7uCa/xqnx04znZzm/cH3yUtKKrB561bQiIhGI/qGBjK9\nvTj1KYp+H2U1VsK+JIV8kWggteTjTEte2CVJZvxOmGyqgLvKgqgRMU91k5+cUoYHbNiAqWNVqXOj\nLMv8x8v/kbf635on6hpBw+Hmw2yr2vbI3RgXE5fRxaGmQzxd/TQug4tPRj8pLUXvhO5weuw0oLQb\nsO7di3HNGtKdneTGxpDiCawZHwaTFrvHSHA8oTSBUlnSZNMFItMpAqNxJYBeacZulUie+pj8xATm\nHTvQOJ2Yt21D61YqnM9PnefdwXfxmrwcbDiIWWeet8+d1Tt5qnphKvCTpMJSwY/bfsxT1U+xt34v\nWyq20BPuoTPQyVRyimNDxyhIBTR2O8aODgAMK1cip1KYA33IoQBlVUZkCWbGkxTzErHg0h5As+SF\nPepPERhRGn65Ks047DK53m4Sn36KaLVi3rx5XivSv7zyl/zyzi/nW+oIHGw4SK3twT1jvg0qLZU8\n36QUc6SLaT4d/7SUAXMndIeByAAA1l3PYN29C43TSeLkSaRMBv1wFxqtEkTNpYv4h2MkI9mHHU7l\ne45/OEYuXSDsS+GpUYwa42QP6ctXEEwmjB0daCuV7o2gtNj9+Rc/J1PI8Gzts/MMGEEQ2Fu3lw3l\nG76Tz6IRNWyt3Mrr7a/zUvNLrHCs4MLUBYajw4zFxzgxfAJJljBv2Yqg06JvaEAwGMj39mDXZdAn\nZzA79ATG4siyvOTnoS55YVfcMCnsHhNavQZrsI9M102KwSCWnTvRNzeXmnz9dedf819u/xdqrbUl\nUQfYXbubZmfzww7zrbHCuYJXWl9hW+U2BqOD9IR7Ss99Ov4pyXxSGUSwfQe2gweVCTiffooUi2Et\nRnBVmNFoRQJjCbUSdQkjS/Ksy202aFpvQ8ikEDrPkR8fx7RhA6LRoAzQEEUkWeIfbv4Dt2Zusc67\nDq95Lo9dI2j4QeMPnmhM6VFxG9283PIyr7a8itfk5ePRj5lJzzAcG+bjkY8RLWaMq9cgaDQYWlvJ\nDg3h1EQp+AN4a8yk43mS0Rzh6VTJKFqKLGlhL+YlJnrC5NIF3FVmDNoCYt8NUhcvoqupQd/SgmX7\nNgDOjZ/jr67/FbXWWp5ver4k6psrNrPas/q7/BgLWO9dz886fka1pZqzE2eJZRW3SqaQ4dToKWRZ\nxrx5E4bZXh+5/n4lT9/Xg6ARKauxEPal8A/HlkXq13IkGkyTzxZKM01NNj3GmSEyX1xFMBgwrlmD\nefuOUtfGi1MXOdp/FJvextaKuf4weo2eF5tfpNnxu2HYgJKR9kLTCxxqPoRe1PPh0Iek8in6I/2c\nGjuFaeMGBJ0WQ3s7FApoRnvRCzlsYhxRIxAci5PPFJb0ivWxhV0QhDpBED4RBOG2IAi3BEH408U4\nscUg5EsSHE8gCOCsNGONjysVpvk8lt27MTQ2oKuupiAV+LOLf4ZFq/StuCvqq9yr2F61/SuOskhI\nX09gd9Xs4g/X/CECAidHT5Z6a4zFx+gKdiFotVie2aW09K2qInnmDPrwBJpkBE+tFVmS8Y/El0Xq\n13IkPJUkEcqSSRbw1lkpRiKYRrvIDQ1hXLcOfXUVpo2KWyWYDvJ3XX9HJBthd83uUlDUqDXy8oqX\nF88FWchBdAImrylbKvTV73kAFZYKdtfs5vmm50kX0hwfPk5RKtIT6uFSrEtxM1VUIDocZHt6cJky\nyMEArkoTMxNJikWJ8BJ2xyxGS4EC8L/LsvyFIAg24KogCB/Jsnx7Efb9WMyMxwlPJbF7TWgEGd2N\nMyS7uzFt2IDW7VYq1oBfXPsFE4kJ9tfPRfob7A08W/fs1z+oLEM2BqkZ5cKNjsHgpzB6QXlMlma3\nIkgFKBaUf+UirHsDXv45aL+65F8QBF5re43eUC//3PfPXPNfY3OFkq52fvI8tbZa3M1NGJqbsO7a\nReSf/ols1w3MliaKtesx2XSlFgNl1cunnelyQJZkwj6lL4xGK+CstlDsuYXQdRG0Wkzr1mFcq7TD\nKEpFfn3n11ydvkqLs6XUUtqis/Dyipe/cqLXfYn7YLITnA2QnoGEX9kyEeX+uBejA1yNs1sD6B99\nZsCWyi2MxEeI5+KcGDnB6fHT7KvbR2egk+ZVL6C5dQtjezupS5ew5/1MFy04bXlmijJRfxqLI0Xd\nqvu3xf6+89jCLsvyFDA1+3NcEIRuoAb4ToU9lykw2R8llylS025BH/eTPfMJotmMaetW9E1N6Coq\nGI2N8t/v/HcqzBW0OpXqtQpzBc81Pvfo2S/FAviuw9QNRbxzSZjph8AdmBlQRFtvA3sViFoQNCDO\nbnd/zqfhxq8g2AM/fQvMX33BaUUt/3bbv+V26DaXfZfxmrzU2+spykU+HvmYH7X+COuuXeTHx9E3\nNZG+fh1Lx0aisSY8tVbGusP4hqI0rfegNy6rtkFLmthMhkwiR2gqqZTUh0NYw8Pk+vswrV+PxuHA\n2K6U/l/0XeTtgbfRiTqerlbGP9611B9Z1IsFSPhg+Cx88f8qRoxcBJMb6p+C8g7lGr8fmShMXVc2\nQQCLV3lf87NfeQ+Igsj++v2E0iHCmTCXpy/jNrrZWL6RT0OXeH7lSgqhMKlLlyj09WBvrUFOBNDq\n3YSnkrirLKQTOUzWB09H+76yqHezIAiNwEbg4n2e+2PgjwHq6+u//PSiE5pMEppIIIjg9BoxX3yb\ngt+Pde9eRIMBy47tSLLEv7/w70kVUrzQ9AKCIOAwODjUfOiB04vmUcwrlsnYBUhHINiniHloQLHC\n9Vao3gDeVWCvUS7ch+FsgDvvwS+2wZG/gxV7v/IUjFojf/nsX/KzD3/G8ZHjHG4+TKWlkmA6yEXf\nRXZW78S0cSP5ySkib74J3Z1ojC2U1bYzfidMYCSudL1rWR5d75YD4akkMxNJZAk8dVbyw3cw3TmH\nJAgYN2zAuGolgk6HL+njl3d+yVRyij21ezDrzGhFLYeaDn21qIeGIDwEkTFFyEcvQHgQRB1UrQdb\nFUxchp73YeQs1O2AyrWKYXM/sjEI9Cj3T2wCzGXww7+B1oMPPQ230c2O6h0UpALhbJgLUxdKhVT9\n9dXUdTvRVleT7enBsWYH0ZgBh0Mk7E8jzbpjTC2qsD8QQRCswG+A/02W5QVJ0rIs/y3wtwBbtmx5\n4uHo4Hic0FQKh9cMsTDijc+RTSYMbW0YWlag9Xh4b+A9Lvou0u5qp9xcjkVn4fCKw5i0pofvvJBT\nfIRjFxXrPDoGPR9AOqwsJSvXKWLuqP1qMb+X8lVgsMHN38Cvfg+2/8+w6Q/A3fTQt9XYavjF/l/w\nL0/8S94fep9XV7xKmamM6/7rNNgbqN68mcydO+gaGkh3dmJt2U40V4ej3MzMRAL/SFwV9iWCLMmE\nfElmJhOY7Xr06TByxI/UewvjqlVorFZMa9eSl/K8M/AOn09+TrWlulSE9FzDcw/syAhAJgZ9J8B/\nBwLdMHYJkn7QWaBxN1RvBN3s/VOxRjFyRj6HvuMwcg7qtkHVBtDoIRtXhPyumANYyqHuKZi6Bv/0\nB7D7Xyv3gd78wFNa51nHSGyEvXV7iWVjfDz6Ma+1vcYV8TY1K+oxtreT+OQTLJEJdFo7NhLMFI1E\n/GlsU8klee0vSlaMIAg6FFH/R1mW31qMfT4OmUSe6aEY+WwRV5UZw/ANCiPDGFevRtDpMG/bRiAV\n4G9u/A0aQcP2qu0ICBxoOPDAAboAFLLKRXrhr2DglCLk/R9B5z8qfvM1P4Yd/yu0PgfOOkXUDTYo\na4GGp6B+hyLSD7lIcdTCxp+B1gjnfg4f/5/wxX+D8MhDP/OqslX82TN/hk7Q8d7ge8SyMWRkTo6c\nJCdKmDdswLxlC3I2i7H/CoWpSbx1Vgo5ienB6JLOEFhOxEMZ0rEcyUgOZ7lRad88cA5kGdPGjejr\n69A4nZybOMeHQ0or3t21u5V+RLXP0uhovP+OZRnGr8Llv4M7H8Cl/1tZXcpFaHsBdvwv0LBTEXVR\no1zHtnLFet/0B0r8yFym3DcX/xqu/Te48AsYOKmsfBt3w9Y/hi3/k+KG2fBTZT+n/xw+/DcQ6H3g\nZ76bY2/WmXm+6XlERD4b/4x8Mc/ligT61hbQaMj1KkFUUzGOVicQ9qVIRnNLsr3GY1vsglJr/P8A\n3bIs/+Xjn9LjMzORIDSZRBQFbMY85pufgChiWrMGQ1srOO387aX/wHBsmB1VO7DoLKzzrqPGunBi\nUonwMNx+R7HQASIj0POhEhCq3gTNe5SL2Voxu3kV6+NBIp6JQWJaCTTd/Tc7m1dudivifvMtuP1b\nxc0TGYOKVbBiPxjv/+Wzt24vf7rpT/mLq3/Bu4Pv8sOWHwJwZvwMBzqeRd/QgK6+ntyNaxgadqKp\nqkFn0Cirm8kkFqfap/37TmgqScSvZHtYhCQkY2j7rmFobVV862vWMhgZ5NjQMfoj/Wyt2IrL6GJb\n5TY6yjruv9NEAHo/VNwvA6dgqlO5tlufB3fz3KpU1CpCXr9dCYreRZahkIFcSjGMLv2Ncr13vKLc\nM/frCmnxKPdA15uK4ZSagQ3/A7QcBN39B9vsqtnFydGTbKvaxtmJswxGBxGcAi1NZeibmsj29eHY\nsYuAYMZulYlMp5AKRcK+FBWNDzHovocshivmaeBnQJcgCJ2zj/0fsix/sAj7/kYEx+OEfEkc5SaE\nyRHou4mhpQXRZsWybRufT57n+PBx7Ho76zzrcBvdD05rlGUYPQ9DnylWeSELQ6cVV4zRCRv+R2g5\noFgrtocsYb+M0a5snta5x2JTMH0Tpm8pv69/A+68rxwvE1GOPzMAjbugdiuICxdcr7e/TiAd4B9u\n/QPvDb7HKy2v0Bfuo9nRTOWa1eSGh4m+9Rb2ocsEXHWU1bjxDcXwDUWp61iaGQLLBVlWsmGi/jQ6\ngwZd1I+1/wIUCpg2b0Zjt5Gr8fBxzy/5bOIznAYnG8s3ssazhi2VWxbusFhQ3CdjFxXD4s67yiq1\nbrtyDd71l2v1igumdhsY7pNhJQiKJa8zQcdhZbuLJCmB18gYREYVt2ZhdvVosCn3162jiqszm4DQ\nMKx6UflC+RLt7naGYkNIskT3TDfnJs9Rb6vnUmWCZ9tbyfX3w/gQFrcTWyFGqGgnGkgT8SVVYf8y\nsiyfhYcM8vyWSYSzBEbjFLISTrcW68VPIJ/HuG4dhtZWAroM/9j9j4SzYSVnXaNjf/3++w+fzqeh\n+z0lwwUUq73nAyXQU7sNtv+JYqlbPAvf+02wVynbin2KgE93Kb7IASeMnVeO37Rb8fH7bkDbD8A5\nPxCtETX8i7X/gnAmzFv9b/HB4Ae8tOIlPhv/jNdXH0bX2YmutpbCzctIdU9T1laJbxAm+yIkwhms\nrqU/XWapEg9lyCbzRANpXC4RIZfC3H8RfXMzWrcbw5o1fDR2kuuB68RyMQ41HaLV3cquml0Ldxab\nhO53IRlUDJvhs4rQrv89JcgPiuVcswVqt8z51b8uogj2amWr364YUolpxcCZUuYKsPY15b4bPqPc\ne9m44qtv3gOa+ffts7XP4kv42FW7i9/2/5ar/qvsqNrB2IZKPKdMZHt6cO5dRTyrR6OF0FQKd3WG\nQr6IVvfos1p/11lyOW4zk0onR1EjYMmFMPReRltZia6iAt261bw78C6XfZeps9bRaG9kc8XmeeXT\nJeI+xVJIhRRhn/gCIsOKn/Dgn8Gm3wfTEwq6iBrwtilbPg2tB+D6r+HWW9D9jmJBNe9VbrrKNcoX\nwT35vxadhT9Z/yfE83GODx/nxPAJnm98novRG2xubcW8dSvRo0dxjl4iVlGNxWkkNJlkZjKpCvv3\nmPBUingog1SUsRLHPHAFIZfFtGkTglbDHU+W0ZlRrk5fpdJcyY6qHRyoP7Cwl3pkDG78Wsk9v/Oe\nEtgs71BiR1ojaHTKCvX/Z++9w+M6y/z9+0zvfSTNjLpkdbnLdmzHLXaK0xPjECCEsIS6LPBbWGCB\n3aUs3y0ssBvaQkgghXRIAqQ5hTjuslxkS5Zl9TIqUzW9z++P49h4gThx3GTmvq65xpLmnHmPrzOf\ned6nuhaL1vrZRBDEna++RPS7T3bC2F5ouE78YhndBakIZFNiVk7TjaA72TpYLVOzumw1sUyMenM9\nBz0HqTfXc6RCxaqaClLdvZjyM8gkOgyqDMHpGNl0lpnpOFbXpVPPcUkJez6XxzcaITAZw2hToujZ\nCuEZ1JctRe50cCA3wssjL5POpVnuWk6JtuREUc8puPdD1zPg3if+Oxk6bq28DzZ8Q/Sfny/kanAt\nEv34DRthz70w+LqY826uhPAaMc1yzpWiyB+nRFvCXc13kcgkeH3sdV4bfQ2JIKFmzirUPU5x/F/3\nTjIVyzDZ6xg/Fmaif4byJstZGZpQ4PwiumGiBKdiCBJQZ4Noju1EVlqGvLiYWJmdPcGDdPu6iaaj\nXF99PRurN/7pTjU4Ap2Pw3gH9L0MCNBwPRQfb6thrRVTEM+VUfPHyBTibsC1SNzBWqpAaYC+l2D3\nj8TPRHBMdM2Utp3w9VcZq5hjnkMsHWNwZpBt49u4rvo6hhc4KTvcQ7q/D2OFjZlkhEDGxIwnQWAy\nVhD2i5UZTxyfO0ImlcOoSqHv24lEp0NRXU26oYpXRp7jiO8ILbYWijRFXFF+xalFSJmUmPFy6HEx\nnSufFbedDRvh8r8H29ub53hOEITjAdoycScxvEN87PuFmFoZmYLWzVB68ouq2dbMprpNxDNx9kzu\noURbgkll4poyF+rFi0k/8wzagQ6yZgcgjg8M+xMYrGe4rS5wwYgEkiRjaYLTcXTqHNqh/UgTUbSL\nN5DNZ9lp8pDKpNg3vQ+XzsXtDbejlP6fYHlgWAxWDm8Xg6TGMtFSVhlF33ntejEl93wjCGCrFR+1\n6+HgI7D/IdFFNLpHjEE13wxtfyMaYMBK10rGwmOnBFIVKypx/VYvthion48vpkYqFQPOtuM92iWS\nS8OouaSE/c2BGhKpgCdCORAAACAASURBVH6yC+nkMKply5CajGyTD/PG2BsopUraittY5liGSfVH\nVod/EB64Ucx2kSrE6L5zIbgWQOMNfzET5bxjrYEld4t+/ZJW8cYe2yNWrI7vgzVfhtp1J15+uety\nPDEPk9FJdrh3UKIt4ZhrHlUuFzKHA93RbUSr21ConMxMxfC7owVhn4X4J6LEI2lS8QxWXQRt324E\newkyp5M+uR+fzs7h6cPEM3GuqbzmT7uVBoZEUR/bK4q6rQ6abhLdgs4FolHxNlpdnHN0dljxd6Il\n3/W0eP9PdoqZOgcfFfPeWzehlqm53HU50XT0ZCC1/namW52UvHEUzeQgaq0evSJBcEogncgQ8sQx\nFb9FKvIs4pLp7phJZ/FPHHfDGAV0vdtBJkPV3Iyv0sK2iR24o27aStqoMdfQams9eXBoEu6/BsIT\nYjrVsk+JgcnG60T3y8Ui6m+iMoqpYBUrxGDqko+LxSBje+Cx98OLX4FcFhCDqRurN3Jt1bUopUq2\nDG9ht3SYpEmNpq0NIRZGPbgPgy5PyJfAMxYmf4lPl7nUyOfzBCaizBxv6GaODCAL+9C0NuFL+Djm\nhGQ2yf7p/ZTry7m98fZTT/CmqLsPQu8LYsZJ442in3vBHeJn4WIQ9T+mYjm03CqubdknoXy56HN/\n6m/gR8shMk2tuZZacy2Xl4oC3zHdwcDmpWQMGiKvvopJGsKgEGMSIW/ikmqId8kIu29cvLGz6RyG\ntAfVyCFUdXWgVbNDP8nuid0YlUbmFc1jXdm6k37keBB+sVF0ZTTfIvrz1CYxEl+z9s+mFF4USKRQ\ndyU03QAaM9RvhIUfEnPgd/4A7lkoZjIgBlPXV65nfcV6gskgb4y9wYHiOLJSF7LiYnQ9b6DJBsll\n8wTcUUK+xFu/d4GLimgwKQ7UmI6jkmcwDuwmL1dCVSlDKTexUiudnk6S2SSb6jadOqfUPyiK+lS3\nGCg1lkHzrVCzBhbdBca3qO240JQvFf39Cq1o4Cz7pFjn4emBJ+8CYJVrFVXGqhOB1El9jpErW8lF\nIij2v4pOnkIqyYv5/5OXTo/2i1S13jm+4wM1pFKwDb6BkM2gmjuXkWIJe4OdBJIBljmWsaZ0DTrF\n8SBJMgq/vF4se264XgxGGl2w+C7R5TEbKG6GRR8Ss3X0JTDv/aK1FfPDL66Fx+6AwBB15jqWlCxh\nUfEiegI9vCLrY1oIo2lrQxqbwTqyC4kEZqbj+N2RC31VBd4B/oko6VSWaCCJXhJFNdaNtLaeoego\nwQoL8XyKg56D1Jhq2FS36Y8OHIDDT4LnGHQ/I/Z3WfQhWHgHVK48Y6NmLBDj6f3j50ckSxeLBo4g\niC7U0jaxwntoG+y4B41cwwrnCpY5liETZGwb34Z7bSOx+jJSXYcxB3vRK8TgaTKWJhK4NCqwLwlh\nj0dShHxxApMx9Ko0uv5dyEpLyZi07LWFaZ9sp0RTwtrStdRb6sWDUnF45DbRP1dzhRgUci0US5lV\n52dQ9VlDaxPTLy1V4g1e1Ahtd4tFJL0vwg+WwOv/wSrX5SwpWUKJtoSt7jd4wx4g5ypGYitC37sd\nrVJM//JPRC/5Yb+XEoEJsSgJwObtRMhlyM4pJpKJEq4u5sD0AdK5NB9s+iD6N6s8ff1w6CnwDULX\nU6JhsOrzYmsAc8UZr2XXgI/r79nGZx87wNeeOXx+7iPXIqi7+mQFbPlloDbDG9+F4R3UW+pptDay\nxLGEscgYPdJphlfNAYMe5e7fY5BFj7tj4pfMyLxLQti9oxFC3jjZTA6rvxtpPIxm3jz6tBH2Jo4S\ny8RY4VrBytKV4gHphOiLG3oDSpeK3/JlS0R/3cXqejkdcpWYFeNaKP4slYs++LaPiB0mX/tXzAce\nYUHRAjaUb0AiSHhS6KAvMYqmtRlZ2Isx4SYVzxLxJwh5Lu1hv5cK0aCYDRNwh5FJslj7XiVrKcat\nihFzmAkpshzyHqLR0sj11ccrPqe6xUZzM6Nw+AkxhfC674t9Wt5BP/T/yyN7RvjAvbsxaxW8f2k5\nD+0aOX/i7pwvuiMFQayIrb0S4n547dsw0cnq0tUsKFqAVWVlu3s7Uy0leBdXQziEo+c5JEIO/0SU\nwFT03K/1PDBLVewk+Vwe71iEwEQMiSRPce+LCAYToWId3c4c+6f3U2Ws4uqqqzEqjaKo/+6zYjvR\nomYx2l++DGqvuNCX8u6RSMQvpzkb4M00TqUeqtaIX14vfZW2SIgSbQlry9biSXh5yDlAxGUmL5VR\nNLYTgOB0HL/70rjBL3X8bnF3FfIlMeQCKGamSdZWkMqmCdUUs296H9l8lo/N/Zg4RMa9XyxyC0+K\nRUhyDdz+qJgLfob1C5lsjn95tosv//oQy2tt/OaTK/jWTS18fHUND+8e4avnS9wdc8X0TEEi7l7t\nDWJK8IFfoZsZZ6VrJatKVxFNR9mePYqnzka+aQ7avt0Y8z6Ck1FioRSxUOrcr/UcM+uFfcYbFy2W\nySjGfACVbxjV3GaGBD/b8r1kchnWlq1lUdEiUdRf+qqYFmWuEr/hK1e8rb7ns4rSxdC66WRVoCCI\nbhljGbKn7matupQqYxWttlb2MMSL8h6kVdUYhnajkqYJToqWS2Ee6sWPfzJK2BsjlwPb9D5yMjme\nEkjrVXjNErp8Xcyzz2Nd+ToY2Q1HX4CoT/wMyJRw1/NiEPIMmYmluesX7fxixxB/s7KK++5cjFEt\nRxAEvnh1PZ9cU8Ovdo/wlafPk7iXtEDj9aK411whJhn0vghdT9MkqGgraaPJ0kSnp5PeGg0TDTYE\ngwFn7/NksxDyxvFeAkPeZ72we0cjhL1xspk8RRO7ycsV+MuM9FbK6fYdodHayMaqjcgRYOt/Qsf9\nYvfFppvE9qDVZzD+7gw5rxF3aw0s+ODJeIFUIX6RAWW//wI1SivLHMuwqWz8qniQqWodknQSU2KM\nSDB1oudIgYuX6EySZDSNf9iPQJ7iY1uIlVaSk0NoTgkdnn0AfGbRZxAGtx5vNR0UC3wkUvibLVD8\nFzo6vg0GPBFu/tF2dg34+PdbW/nadU3IpCclRRAEvnBVPZ9aW8Mje0b44lOd+CJJxoNxxoNxIsnM\nuflMFDeJRYUqg2jQBAZgWnQ/rTbUstK1EpVMxZZEB0Grksxl87FP7EWaS+EfC+Mdj8z6GNOsLlDK\npLMEp6L4xsJIyFF8bAtCXR0TBHlN5kEikbCxaiN1pjmioO/6kTjVqPU9oruicsW7ev98Po8nnGTA\nE6FzfIapUBKtQopaIUUll6KQScjm8sRTWRLpHHnyyKUSlDIJcqkEhUyC4s1nmQS7XkmpWY1dpzw7\nZf06uxhUPfyU2NRJZRRz8zsfY92ehxmeezXrK9bzVO+T/KB+kH/erqdobBeTVVUEPXF841HMJWfu\ncy1wbvFPRMnl88z40ugzXuTpGBPlBnIKKeMOBT3HeljiWMLiGb/YIiARgs5HxIrqD70I9vozfu9t\nx7x88uEOZFIJv7hrCQ0leoZ9UcKJDOFEhkgyQyyVIZbKYtYoWFpl4YmOMbonQlzRUHTi/pZJBAxq\nOUa1HINahvH4v10mDWrFu2jKVdIq1nLkcmIzvb5XwFyFoed51lQspt/Zzysjr7C1sgx9UEpZQwv2\n6X14pG2k4hmCUzEsjtl7789qYfe7o2TSOYLTcUypCWTZJIFyDUcqZfSHBllcvFgcede3RRxakU2J\nBUcN14qDL94BsVSGZw64GfBEGPRGGfBEGQ/GSWbe2l2hlEnQKmVolVKK9SpaXUYM6rceu6eSS3GZ\n1ZQef7wroVfqTrY+9fWJecq1G5D3Ps8Neju/rl7EguKF7MnvYbLaScmhHUirNxMcDRKsMJDN5pBK\nZ/3G7pIkMBEjNhkgnZVQMdlBQm8hY5UQrrSxbWqnaDFb20RRT0ZEUc8k4QO/AefcM3rPSCLN914+\nxv3bB7Hpldw838WeQT97Bv1vedzSKgv5POwZEl/3prhncnn80RT+6Kl+bYkgUGpWU1esp7ZIh1oh\nJZPN4Y+lCMXTVNl0SE9X/u+cL36JhSdg/wNiCmTtFbSMHWJVyTKO+o+yLXaIxab52OaXY3u9l8mS\nZYQmQ3hHtQVhv1B4RyOEpsJks1Aytp2MyUrAAFtU/agzajbXbabIOwC7fix2ZpxzNczd/I59islM\nljvv20P7UOAUC6PRYcCklmPUyDFrFNh1CjL5PKl0nkQ6SzSVIZLIMJNI44uk2DcSYN9IgMUVFtY1\n2qm26Yins0STWWKpzPGtKSTSWfqnI/RPR4gmM7iDcfyxFCUGFU1OA3OK9JRZ1JSaNajeTqtRqQya\nb4IDvxItd8c8iHoo6XySBSo1iaJm9k128LvWKB89lMMUHyMYKCebyhKcimF1XjrNkS4VEtE0iUiK\nwEgAkFI0uo3AnHIEqcALplGGg8PcaltIXej4cPXOR8TnzQ9C5fJ39F6BaIp+T4QjEyEe3DVM71SE\nGruWK5tKUMhOfukrZBJKDCqcJjU2nYJMLk8mmyeVzZHJ5lhy3Gp/5oCbaDKDVinjTU+MVCKc8shk\nc0RT4ufizR1vNJnhTQfJkkoLP3j/Aor0p+lG6lokWu6TnTC+F0paEIB1MQ3HytbwSM8jPOOcoKgr\nh62hAmkqzkz7CDOlJlKJzKwd8j47V42Yux4JJPAN+pGQxT62nUBrDfvLsrgTU6wtW8tqZZE4Vm7w\ndbH3xfK/fUeiHk9l6XLP8E/PdNE9EWJDYzGNDj2CICCXChQbVLhMalxmNSVGFUrZW4usOxjnV7tH\neLR9hD3P+6myafnAsgo2LSzFqJGTzGSZDiXZ2e9l6zEv+0cCjAfFKlCVXEI6m+fpA+5TzlmkV1Ju\n0VBbpOMjl1dTW/QXRFgqF11Q+x8Sp9HUrIOYl2V7H2FixUeot9Tzh/wR3mezUDS+C19NBaGJIH63\nriDsFyHBqRi5ZJLQTBZNKogiHSZabmBHeZB9wR7m6sr5grJGbPvc+SgkZuDGH0D91W/r/L5Ikp7J\nMP2eCL5IimAsxe86J/BFUyyvsbK4woxWKcNpUuM0qXGZ1BTpladtorVyjp0Wp5FnDo6TyuRIZnIk\n0zmSmeyJnzO5PHKpgEYhQy2XYlDJKTFI0Shk6FQy9EoZL3RNct3/bOMndyxiYflpBm+XLYG1X4En\n7hTntc7/AKbQJOtNRRwtWkj7VDuHLDqWy3OYj43hV7koDYVm9ZB34UKU0C5evDi/d+/ed3WO0SN+\nxro9HHx5FFN0mNa932Xwmnn858JhslKB7y/8EovHDkD7z8WZitd+R5xIdBpyuTzD/hhd7hn6pyO8\n1D1FlzvEmno7Ny9wnRDyIr3q9FvBv0Ayk+WFw5M8sHOYjuEAKrmEG+Y5SaRzbD3mIRhLIxFgUYWZ\n1XV22iotmDRyeqfCHB4P4Y+mCCXSzMTTxFNZ4ukso/44qWyO9y8t57Pr67Bo/0Kf7HhQnDeZjIgf\n+v0PkMxl+PFlH+Dnw7/nE9vNXL49zNbLv4Pdkqd6ZS0LNpQjlRfcMRcTPTsnmGw/Sld3lvKRLVgS\nh9izTsH9Zf2Uqe18Q9/KIplRFPWIBzZ8HZZ/+rTnnQ4l2D3op98TOWFND3qjvNA1iUSA9y8t55oW\nB1U2LVbduekf86YmCYJAMJZiYibBZCjB5EwCTzhJNifGtn5/aIJIMsOn19XwqTW1yE9jWPHyv8C2\n74nFTI755IBHdRp+3P8k+XSaf+itQ+lfRl+2gRpZP85br2bu2rJzco1niiAIHfl8/s+MuzqVWWmx\n5/N5fOMRgv2TZPMSSkZeJ1zsYFdlHH8mxC2l17Bg8ggc2yIWKaz4jGitvgXxVJb9owG6xkNEkuJw\n2219XrrcITYvLuXrN7S8u2DOH6GUSblxvosb57s4PD7DQ7uGefrAODqljCsaillTb2fVHDtGzam+\n+PoSA+sbSzgyGaLbHcITPln+HEtlODgmnus3+8f59Lpa7lxe+ae7CLVJLGQ68JD4c/OtKPY/wHsP\n/JZXXOU8sWCCVduzGBIThGaKyMTF5ki20oLVfrGQSWcJ++IE3WFAS5HnAH0LrTzs6sKiMLJJWcp8\nmVHsAROZFu//04j65EyC3YM+Bjwn6xfy+TztQwF2Dfiosmn58QcWUl9y7hvi/XE8yaRRYNIoaHSI\n75vJ5pgKJ+kcDWJUy3nu0ATff7mPV3o8fHxVNS0uI2VmzZ/fOVzxz6LFPvAaqC1ITOVsiITpdizj\nmaEXeKl4mptyXlTuAL6IFEskTdifQG+ZfcNnZqWwz3jiJCNJApNRJHkZtukDDC+rY4txiBJVER/O\n6ZF6u8RWntVrYc0/iuldf4ZYKkPHcIDOsRlSxwOhWqWU/ukoHcMB7lhWwTdubD5nwydaXEb+7da5\nfOPGFmQS4bRbWbVCysJyMwvLzUyHEnRNhOiZEPNuL6u2UlekY/egn28/18ODO4f50jWNbGwtOXX9\n+mKxM17n46C1Icy5kpKe33Gto5YfarIMVRixTbQzUHUjsbEp/OWmgrBfRMxMx0n7g4TjUuTpCNJ8\nkJ8s9iCTyNiodrJebkF66EkxnrLoQ7D2q3/xXOPBOLsHfAz7TpbSy6UCTpOKx/eOsWvAzy0LXPzr\nza1nzbB5N8ikEnHXbFKzrNrKvFIj9+8YYvegn2/8rptrWx04TWqWVllpdhpO/TwJAtz2MNy/UUz5\nrF6DvXQJ1+bU9JjqeINjLPT2YM5amNA1Ee/uwlOmn5XCPiv3197RCOmpKUJxBabQMbIKOc/ODRDL\nJ9mkraYiGYejz4PeCbf89M9ONY8mM7ze6+G+bYPsHQqQzuaoLdJx0wIXeqWcx/aOcv08J1+/4dyJ\n+h+jkEnecZP/IoOKtfVFfHRVNdfOdVBqVmPVKdnY6uCm+U6SmRyf+tU+bv3xDg6MBk892Fx5vEpP\ngKJmcsZS7jy2gyKJnt8vTGCfFueSB8fDzExFyZ4m+6fA+SM4HSPt8RBJyrF5O9myIEtUlmGjvpqF\nOTllh58VM0Hmvheu/OafzAUFGPXHeLJjjMfbRxn2xRAEKDWr2dBUzLwyE//9Sh97hwJ8/YZm/mvz\nvItC1P8vZq2Cq1sd/OzOxXxqbQ2BaJpH9oxyZCLEy0emeGDnEEcnw6fmyluq4FO7Rb/7wGvQ/Rva\n0nC1thK5VMFvnEPkzTOQzxHoGsI/MTvv/VlnsWfSWQITYYIjfrJ5Hc7RbYxX2tlpGqdFXcJ7FU4x\ntS+fhU33njIPESCSzNA+5KdrfIZ0No9EEGh06GmrtGDVKXn+0ARfefoQq+vs/Nd75s2KiSpSiUBd\nsZ66Yj3eSJLOsSAKmYQyi4Zud4hdAz5u/uF2PnJ5FZ+/qv6ke6a4ScyU6HsZyZyrUO29j1tiMf63\nNsOHJTmUyQDhhIb0tJcZT8msTv+6VMjn8gTHZgh7YuQwYvYf5rdXxbkxXkGdOsHawR1iSuO898K6\nr52YKASia+XYdIS9QwGmQmJQ/s3srqbjro5vP3eEx/aOUm7R8MhHl9FWabkg1/lOMKjkfOGqBq5q\nLuFjD3bw1L5xzBo5dp2Sl7qnmFOk44b5LhZVHA+yqgxw1wvw7KfgwCPIoh5urL+GTnMjr2UPsL/4\nEPXdDrxyJ0UeL363FXu5/q0XcZEx64Td746SmvIQikoRchms/i5+fq0GpSDjw5pajO4D4hSktV8V\nm2AdJ5HOsmvAx6GxGTK5PFKJQLPTwJIqCyaNGGjcdszLZx49wIJyMz/5wKJTUrlmCzadknUNxayo\ntdHtDmHTKZlTrGPbMS8/e2OQl7qn+MHtC2ktPV6RWtYmznQd3QOli7l7rJ2HKirZ3SSlzLOfceUa\nkhPTs75g41IhHEiQmPQQjEkQcmn67T2sCjqYW5Lmur52pIJMrFtY9gkwiCMPM9kc3RMhOoYDBGNp\nAKrtWhZVmHGZxGlZv+uc4Ou/7SYQS/Gx1dV89oq6i9JKfyvmlpp46XOruPeNQbb1eTk2FaZ3OsKO\nfh+/3DmMXiWj2WlkdZ2duy+vQnbDD8XRlzv+B3vnE3yicilHVXZeLOmhobeFpLKSyK4OPDXOgrCf\na3xjYdLuSWaSWiyBLjw2NfudUa5Ul7E6K4WhrWIZ8arPA5DN5TkwGmD3oJ9kOocEcJhUmNQKPJEk\nP/5DPyP+GMO+GH3TEartWu67s23W3dT/F6VMyoJyMwvKzYz4YtQV6/nDUQ8vH5nixh9u445lFXz1\n2kYxk6BmHSRmEDJJJNPdvCcS5TeLsnzjsS7GStcRmknh65+maq4NYRbsYC5lZqbipD1eZpJS7N5D\ndJXb2Zia4bKxPmQKneh+abkF7PUk0lk6x2Y4MBogmhQnapk0clbX2am2izGT8WCcrz19mFd7pml1\nGfnFXW20uGZZ2+o/Qq+S87kNdXxuQ90Jd+sfjk4zMZPAG05ybDrMrgEf7UN+fvyBhShXfQG0dtj2\nPRoHtvGl4mr+Xp3n13O3cVXXPAKJPPphD4l5dlS6ty4svJiYdcIeH5tiJpwml5fimNzDs4viOHIq\nPiqxo+j6DXm1Gc/GexkZDtA+5Gf3oB9POEkokSGRzuKPpsj+UR8IhVRCmUVNuUXD8horH11d/SfZ\nKLOdcquGcquGRRVmWlwGntg7xi93DvNKzzTfvLGFtQ1For897kdWs54PHf0tD5WVElKNIckmCcQl\nWNxThAPVhXmoFxhf3xTeQBwEO5pwB9cIGZbQS1JlQT7vfVC5gmzpUjoG/bQP+U8kBMilAm2VFhZV\nmJFJxVYXv9wxxHdeOgrA165r4s7LKk7p9TLb0SplbGx1sKrOTvuQn8PHd+sHRoO82jPNbf+7i199\nZCmahXeCTAUdv2Dt2B4+ai3mh9Zxrkt349fOp6ivD89oGWWNF79b6k1mnbCn3eP4jm9DdTPdbG3K\n8WmpgbojzxHIa7kj9kUOf7fjlGP0KhmVVi2VZSbKLWoqLFrKLBoqrBpKDKpZ4Uc/G1RYtdx9eTUr\nam3c+8YAzx2a5O4H9rKx1cHnr6yjvOVWSITRuvdxXTTK8wvyrBw+gt9WT9LjITAeLgj7BSQRTRMZ\nmWQsEUad1iK1DLHM2M2U3k5R6/uhqIFh60pe2zlE4LjLBaDSpqHMrGE8GOf7Lx+jdypMlzvEeDDO\nmno737qphVLzpTHE+c+hU8pYW1/E4goz7UN+pBKxwPCVI9Ns/J83uO+uNqrnbgbyoLHy0d7neV1W\nySu1f2C5u43QsVGmj0xSWm+eNTvWWSfsoWiQRKYIq+8wOxtSXJZRcMfgPsYkTt4T+yK1tfWskQoY\nVXJKzWo2znXQ7Jy9W8uzjSAINDuN/MemeVzVXMK/Pd/DswfdHBgN8rHV1Vxfez2G8AR3HLif2xs1\nXHuwC499PoHoNN7uESrmFp3+TQqcEwLuMMEpN7KUC5t3L+sbexhS2JG3bCKhdfBqbhm9+ydIZ3P0\nTISZCMWJJjOMBxMndqlSiUCFVUOry8iXNzZwbavjvGR9XQzoVXLWNRSzqMLC7gEfCqmUF7omuP2n\nu/in65q4sulW5LkMaX8//+Ud4EaXwNr+UQKCDfPQKDOeSkzFs+MLcNYJ+3QkRR45xdP7eG69hHu8\nQxyV1vOeyOdZXF/B3FITSrmEpVVW5peZzrg69FJHLpVwdYuDy+fY+Pfnj/KrPSN863dHaG8u5sOV\nVzKnaB+L00MMm7sBCCVkBIfdxCMp1Lq/UNVa4Jzi7Rpmf2YIi+DAKuxgWlLMkabLaU4aeVBYwnQg\nTufYDIfGZ4ins5RbNDQ6DNw438WcYj11xTqqbNrTtr641DGq5VzZXEJbpYUKq5qfvjHIPz3TRd9l\nldzYch2V84NY//D/+HtvmKO6ndiymwn1djF1pBZT8eyYhTzrhD0Z0yDkUqRzh9mk8XEouYgPRf6O\npbUlzC8TfciX1VjRKGbdpV0QtEo537iphevmOfnMo/t5+oCbAa+Rjxfdyubh/8dP5oW5obufmNbK\njMeNr2+a0vmlF3rZf3Vk0lnGe/tRzRhQJgK0lHTxsnYerpCJB5WXsW0wQu/kJLl8nnWNRXx8dQ2L\nK8x/Ndb4mWDWKvjiNY20lpr47GMHuH/HIMF4iiVFV7C4Zh+3HX2GT5cdwj5wM8Fomsn9x6heXol8\nFiRWnJVIiSAIVwuCcFQQhD5BEL50Ns7558jl8iRTBmy+Lo41xolHF/H+yOdYVFXEDfOc3L60jCsa\niwuifgYsqbLw2udXc9N8J51jM3ztaA1J5dVkitPoZvaQlpkIJ9KM7eu50Ev9qyQw7OP15Btosg3Y\nZ9rZL1vCYXUT/zK+hp/tj9I3HeGWhS5e+/wafn5nG22VloKov002tjp45O6lZLJ5nt4/zs5JgRfV\nm+lXlvMvyW6kyYNE1HOYHu1l+sjEhV7u2+JdC7sgCFLgh8A1QBNwuyAIZz6W5S2YPDRADjV2zz7i\nlib+IfEJLqu28rXrmtjcVnb6Fp4F3hKVXMb337uAe+9cTCYHfzt5A2vCatrL9yPJpcnG1bh7e0nF\n06c/WYGzytFd7SSiaRCkFMu7+L5mHQ9Nr2I6IePDKypp/8p6/vM986i0FWoNzoRFFRYe/egyAB7e\nPcK9x7T8SP1pZBkdDu1WsjItY9EBere2X+CVvj3OhsW+BOjL5/MD+Xw+BTwK3HgWzvsn7H729wi5\nFAHVAPdI7ubKpiLuv6uN5lmcd3sxsr6xmFf/fjXLqu087vskB5qjWHydpCQlRIIeBnbOjpv7UiGf\ny/P70Ueo8S5AG3XznaJr6JJUsHlxKbv+8Qr+6frmE0V2Bc6cFpeRZz61krsvr0YulfDrSRcfyXyW\njZbtKJI+NMk5tI9tIzg+faGXelrOhrC7gNE/+nns+O9OQRCEjwqCsFcQhL0ej+eM3sitGqK+93Ge\ndFzDTa12/veOxajkBbfLucCqU/LQ3yzh7us2IITX4VfsJivVoJMF2fvi74gf74BZ4Nzzym8fo1s9\ngiCrIZ8YYK+u5kOCfwAAIABJREFUgq9ubOA/Ns1Dqyzc/2eTcquGL13TwJb/bzVbv7CWRQvaeCK7\nnqr0VjLKel629PKZnz1NKJ46/ckuIOetGiGfz/80n88vzufzi+12+xmdo/lwHG3gAPLaJr57e1vB\nh3iOEQSBO5dXcc8Hv0lHeRxFKkQq6aBRsYVv/ejH7B1663FoBd4dkWSG3+3tZcfRe1h0bCEAz5oc\n/MN8Ex9ZNTuyM2Yz5VYNX968Cr1qOdXmXQCsHaxnl+EgV/z7CzzVMXZ+B9S/A86GsI8Df9yNvvT4\n7846oU99gYeX38F3PrD6r6ao6GKgqtTC+xo+gia0F58wHwc+/iH4bR6697u8/2e76BguCPzZJJHO\nsr3Pyy+3D5De/z1eM/txRhejjIyyoETLx97zzub1Fnh31CxbSY9kOdZID7bUUpTGbSRyE3z+iYPc\ndX87Q97o6U9ynjkbwt4OzBEEoUoQBAXwXuDZs3DeP+GGJdV88LL52Cps5+L0Bd6C1stXImhnQCLj\nlzO3Es3q+L7sHjaNfJM7f/wKX/51J/FU9kIvc1aTzIiN6u7bPsieQT81oT1MRF6mdLyMpMqJgJdN\nSxsKw8XPM2WLmkhpWrEJXaQUVpYO1VBjfoQ2wzh/6PWw6Sc7+Onr/YQTF09Swbu+Q/L5fAb4W+BF\n4AjweD6f73q35/1LuBY1zpqy3ksJS5WV0rlXoo2MYYg28sv0SnpMddwk3c4WzVfY2b6Hzz9xkB39\nXhLpgsC/E9LZHO1Dfu7fPsTOfh/pTJ51mn5qJ37J4wY5K/qXIuSyOMrsOBfXXejl/tWhtWqwl1Xg\nNZYhS0e5fGQx/RYf863P8LmyY8RTGf7thR4+/mAHbxzzXBT3/1mJvOTz+eeA587GuU6HrbpgrV8I\npFIJJW1N+Pc8SlR3Ge3WJykJl2NqaaHk6Av8p/ZhbjvsQC4VODg6w8JyEwvKzbOy9fH5IpPN0Tk+\nw94h/4nuixatgsrcMAMH/kAw2UMCE1LZfLSJMaz1DRiLCumM5xtBEChuqcI7NEjOP0hYO5fy8MN0\nyob4Unk/K0oVfHWgie39Po5NR1hbX8Q1rSUsrDBjUF2YhoKzLqSuUM+6JV8yFNU7cFeV4g5kWTow\nl5fn7EYvNbCpbBltA69ym/koTx2WoJZLSaSz7B8N0lZpZm6pCXnBfXCCXC5P94Q4ACUUT+OJJHEH\nE4QTaXonZjCkPTyuepj3lpu4eWcLKaWZImmYkvnVhd3qBcJW70S/rYhoYIiMVMMdu1r51w1dDPX9\nnqvrNfxmbRH/7W7mlzuHeWzvKC91TzK31MQN8xysqivCrj83g7//EgWVLPC2MdrVaBfMw/TMHmSq\nJRy2/J7KKRkLK+dT7d7HN+W/YLv2v3j+8CTvXSLG07f2etk3HKStykKry3hK755Rf4xv/b6b3qkI\nD31k6YmhD5cyfdMRdvR7GfHF2HrMw7AvRvJ4a91qs4xbit18NvwdHtLImJFAlX8uPksKU0sltkrT\nBV79Xy8Gmwp1STGaoJeZYApFpJmSaC8/1ynYsP+XaDNxvtRi44PLV/OT1/vZ0j3F670etvV5mVOk\nY0NTMZsWllJxngrICmZUgbeNIBGwN5Vi1qZJK0xcc3geWxRetiTcZKpWI5sZ5smmbSDA84cmKbdo\nEAQxbe+1nml+sWOILvcMsWSG77/cy/rvvs4bx7x4wknuun8PM5dwRet4MM5j7SP89qCbw+MzPNI+\nwoAnyuVzbHx38zx23V3Oq0v283XJfcSz0/zCqGdjdzEBUyumvBdTbRla0/m1+gqcRCKVYKkvxaDU\nocz58JubuG1XJUMKOb/WqsgfeAjaf44z3ss3bmzh2b9dyZeubqDZYWDAE+WeV/u45cc7+PqzXYTO\nQ5C1IOwF3hFWlw7d4nnIMjFa3W2khRy/j02wW2sAQylFh3/K/VcpmQoneGDnENfOdWA7vg2diaX4\nyR/6WfHvr/L9l4+xoamYV/5+Nf97xyIGPFE+/mDHicEQlwreSJJnDozzePso44E4nWNBnuwYQ6uQ\n8dQnlnPvnW3cUh6nZOx5GNuDMN3FV5zlkJewsqeRjFyDscyGvWb2DHm4VLGUmZBbLGj1MpJKM7Uj\nCspDen5oNDKmtcCRZ+H5L4F7P3a9ko+vqeHeOxfzrZuauaKhCKlE4P4dQ+wZOPfpwQVhL/CO0JmV\n6KtLMacnCOnquOFYM4NpD0/lfPgqV0A6xoKuf+OejXYOjc/wrd8d4bbFpSwoN/Fi1yS/7ZxAEARu\nWeDishoriXSOFbU2/v3Wuewc8PHFpzov2qKPd8J0OMHzhyZ4aNcwA54omWyOV49O89pRDytrbWz5\n3GrmlZnAPwBdT4NvgHz/qzxjddAhzbJhpJy4vApZNo65vhSLU3ehL+mvHmORGpndjkUjQD5HRF3G\nzV0NRCU5/tlRRrioEUZ2wLOfgWGxoKnIoGJzWznfurmFL13TwDdvbGFN/ZkVaL4TCj72Au8IQRCw\nlhow15TgcSuZ3zeHbVV9dETH2GJZyOaiJiSju9ng+g3/uuEmvvzSNJv/dxfd7hAKmYS/XVtLuUWc\n5jMdSvL0/nGKDSrmlhr57Po5fP/lY7hMaj5/Vf2FvtQzYtQfo33Iz7AvduJ3iXSWl7qnGPRG+bt1\ntXxmfZ0YawiOwOGnIOYjf+Rpgiod37WYMSfSXLHfRH9RCxZNAmOZBZX20hrXOBtRqGQYKopIDY+g\nlqXw2ObT1PMwLU029hv9/MrRzN1KE5LRnfDK12HNF6F6DQAOo5pbFpaSSGfPy/jBgrAXeMdYXTr0\nzXNQDXURlbpYM1HB4xU97JXnaSxfyDxvL/S/yu3NJqZXrOJ7273cvMDFl69poMggduCcCiXYM+in\n3xNhKpRgS3cChUxgVZ2NH7zWh9Ok5n1Lyy/wlb498vk8fdMR2ocCTIUSJ36vlEuQSwQe2DlEJpvn\nZx9czIamYvGP4Sk49ASk4tD1a3K5DN+oW4w/Nswd43VIwmpyJXIs9RZspQVr/WLBXKIh6CjB4J9i\nKutAMRPhqoFGuufv5JXUFEucc1mQjomW+44fQDoBdVfB8fYnKvn56eVeEPYC7xiNQYHWqsOiTeFW\n1LK0v4dXnIN0THdSU3EVc1yL0IzuBtdiPmPeyp1/dwsm56m9TYoNKq6f58QXSbJ/JMjRqTCpTI65\nLhPDvhhfffoQuXye25eUX7RTsBLpLN3uENv7vEyFEiQyOVKZLGqFDKNKjj+a4uHdw9TYdfzkjkXU\n2I8LdMwPnY9COgm9L0BkiqfrVrI1MUFtwsjCIxq8lmYUJDGWVWJxFHLXLxZMxVpkNhsG7QRTUfDa\n5lJ5dIpllXZ2Ch5eVHuoqFmNJR2Fvi2g0EImDo03gOT8DegoCHuBM8JaqsO/sBH3bj/JkIF1PhdP\nyAcYSgd5qWENN04eRuh/BeZ/AFP/06C7HQzOPz2PTsn6pmJW19s5NhXhsHuGjS0Ontw3xtd/20W/\nJ8KKGhsOkwqnUY1Ze+Ha08ZSGQY9UV7pmWZHv5cjE+G3zOQRBLh+rpNv39KK7s0ujMkwHHwUUjEY\n3wvTXRwtncdjWhWZYJob3LUYh6fobWnEXqLGVKxBNgsm9vy1oDEo0BiUZEqLUPnDTDuXsbDjP1jr\nbqPD5Gdb2k+V3MitDdcjO/QEHPktyNSQSULzLSA7P/dvQdgLnBFWp5YxuxF99hgefT1rJqK8bBuj\nY3Iv5XNuYbThSsoP/Qa8R8HeAJ2Pwfz3g+7PD8OWSyU0OQ00OQ0EYymaXQb+8TeHeKx9FPJgUIs+\nZrVCisOowmFU4zCqKDaozkl1az6fJxhLMxlKcHh8hq29HrrcIUb8MTK5PDKJQLlFw6o5NmqLdFTb\ntZg0CoxqOSa1AqNGjl4pO7VZXTouinrUA4NbYXwvYUslj1Qv5sjoFlaGHbjcasIKDXmJDGuVBaur\n4Ia52LCV6ogF7ehVfjwZB1lBib0/xhpHCS8Ko3Ram3GkY6xquRUOPAxdvwa5CrIpaH0PyM99vUZB\n2AucEUqNHJ1ZiaXazvBwDgbjXOFy8qRsiPHIOC9WL+HDgzuRDrwG1lrR13jwUVjwAdC8deqeSaPg\nurlO5th13PKTHTzSPkJDiYFGh54ivYoBT5QBj9hRTyIIWLRybDoldr3yxPM76VOez+cJxNJMhxNM\nhZJMhxL0eyJ0js3QOxXGGxF7b+tVMpqcBtoqLWxsLaHFZXz7g6GzadGnPrZXdL8kgqQd83hqzmVs\n83WgEZRsmCrC2j9Nb/G1yGV5DEUazMWat30dBc4P1lIdY0cDmJ16PL1ZJstXUTy4jZX1deyw+tg5\n3YGzciOlsRjVrZvhwINw6HGQKsFUfiKgei4pCHuBM8bq0jHTUMroYD8ewcnVXgUvWcbpmNxL6ZxS\ndi96L8tf/z6Md0DZUkhF4eAjorirTj/1qt5h4MmPL+enWwd47tAEB0aDlFs0NJToqbZpUcql5PJ5\nvJEU3kiKnsnwiWM1Cik2nRKrToFEEMjm8mRzeTInnnPk8nlSmRzeSIpUJkcml2PQE6VrIsSIL0Ye\ncBhVrKixsqDcxKo6O40OA/p32v8jlxUtt733wcRBUJth3vt4zepgf8bHVGyKW4K1GEJSZNNTBObU\nU+TQYHHokBRaMVx0KFQyDDY12WQJ8r4Rpp1LKe9/Du14mqvsLp609NEXHec1QzV2iRp9621w4CE4\n9BhUnJ+WywVhL3DGWJxaRrqkWKwSpvMLqe5+mCuKnPxGNow74gazk1bXQvTDO8BUAfoSSITgwHFx\nV57ezdDoMPC92+bzL9c388zBcR7ZM8pL3VMoZBIWV5ipseuQSgQiyQzRZIbI8Uc0IT7HUlk0Cikm\njQKTRo75+LNJLT+RdjYdTtDtDnF0Mkwik0OnlLGi1sbKWhvNLgP1Jfozn6ebz8Or34Q990IqAqVL\noPJyRg12uoU4u3p/T4nUQtu0HstgFK99PnlBgrXCVAiaXsTYSnXMTMcwmqX4fFZSWhvW/ikWlZax\n1Wpk9+Ruqo3VvGy2c6MgRdKySQyYv/otqF3/tu79d0NB2AucMXKFFINNTVGzC++2SaYSJm4O2HjJ\n7GbfVAdOnZPnm69kU2AEyf4HoeYKcC6AeEC03Oe/HxRvz9Vg1Mj54GWV3LGsgsPjIR5tH+GZA252\n9Pv+5LUKmQSLRoFRLaPEqGImnmEsEKN74tRxfmaNHIVMwlQoiUwicPkcO+9ZVMqGpmLkZ8NvH/XB\nEx+EoW2gsUHzzWBwkjFXsFWeoWN4B7FMjDt8c1BlJSgHjjGx8HMoFAI6iwqDrTCc/WLFVKJBKpdg\nqbDg9fmZaNhIRccDqINFXDdZwn3FRznkPYS0aD57nY0sIQ+NN8KRZ8T4SsPGc7q+grAXeFdYXaLl\nolVmcDtXUHH416y3unhGOsRUbAo0xbx63b+yfuf90PcSBIeh/hqIeo8HVN8HsrffA0UQBFpLjbSW\ntvKVaxvZ0j1FMp2j2KiixCA+DGrZnx2bGE6kGfbFGPRGGfRGGfJG8UZTfGptETfMc57dgdAju+GR\n2yAxAxUroHy5mO5mrmSvrYyhsdfp9HbSoqyi2ifFPpoli5wZTRklLh2mIk3BDXMRI5VKsDi0ZJJZ\npBIfXksLZVI5tsEo9SYr1cV2OqY7aLA00BEewlWzGlc+D/VXn3NRh4KwF3iXmEtEASqqsTDYLcPX\nl+W2UCMvmcbomOxgY/VGelN+Km++h9r2X8KuH0LHJDTdJJ7g0BMw9zaQvvPKSo1Cxo3z/2Ru+l9E\nr5LT4jLS4jq9f/+Myeeh/V544Yug0MPCO0F3vCjJXIF/zhXsP/YUr4+9jlwiZ+N4EUpBQHb0EGNV\nawEBi0uHuaQQNL3YsZbq8IyEMRWpCUzliVYuQDfYgb61mOvH7Px3qYd90/tY7lzOy4EuNjdehzrq\nPS9rK5gEBd4VUpkEU7EGS7kZmZBl3HE56s4h1s+UMhwexhsXb+Rt7h0krvga3HqfKH77H4SxdgiM\niIHFZPg07zQLSCfg6U/Cc58HUyUs+tBJUTeVk2/ZxOvu7XT7upmITrBaOw+rP02xDySRGaYcS1Gq\nJGhNSoxFBWG/2NFbVCg1cswuPbm8BG/1WshlKRqO4YqrmZdzcch7iFAyRDQd5bXwgJhEcB4oCHuB\nd43VpUUiFbA61HitLYQG3Lwv0oIyJ6VjqgOAWCbGDvcOaLkFPvQ7MQWy/xUxx9c3CB2/hPDkBb6S\nd0FwFO67Eg7+SnS9tGwC2XEfuakM5m6mZ6af/mA/Oyd24tA6WD6sRi1TIe0eJG5wEpaYMbv06C0q\n5IWipIseQRCwleow2NQIEpiR2Ui6Gsh2H8GpsLFx2IwEgd2TuwEYCg1xJNh3XtZWEPYC7xqjXY1E\nJqGozgaCgLvkMrSdA1wRLmNgZgB/QmxT2uPvYTQ8Co558P6nYM6V4O+HjvtgfJ9oxU/3XOCrOQMG\nXoefrgbPUWi+FSovP9EbBFMZtG4mlkuzw72DHe4dpHNpNujaUHvCOBJqmBrHXX8tIGB1aDAVF7Jh\nZgvWUh1SuRSjXU04pSZStxIhlcQw6MeZN3JZopS+YB/TsWkAgsngeVlXQdgLvGskUglGuxqVVoFB\nL+AuXU2su4c7onNR5CTsm9p34rVbx7aSzqXBXA7X/w8s/ogYPO16Cg4+BvseELNIZgP5POy4Bx68\nCSQyWHAH2Oac/LulClo3g0zBTvdO+oJ9HAseY0HRAqoGomjlGiRdQ+RkCrzGBpQaKWqDouBfn0Wo\ntHL0FiWmYg3prEDYWEnSXkni4CEqdOVsGDOhlijZ6d55XttRF4S9wFnhzZzromozaakGj6UF3cEB\n1kXL6Qv2iRkywExyhvbJdvEggwNW/wMs+xRUrYHAIOz5KWz9Lzj0lFitebGSy8GzfwsvfRWKm2He\n+0BjFf8mCFC5QgwKyxSMhcc47DvM1rGtGJVGlqoa0biDlEuLyAwcI1SzjGhGicWpQ2NQFlr0zjJs\npXpMx2MikbyWaOPlEIsgGxijUuVi3Uwp7qib4fDweVtTQdgLnBVMRRoEiYDJZUAuyzNadQ3xw4f5\ncGQBpqySFwZfIJKKAHDQcxBPzCMeqLPDko9A6yZo+4hYcj3wKrz4JXjt2xdnUDWfFwOk+x+CmnVQ\nt/FkyqZcJfYDqVoFgkA2l2Xr2FY6pjoIpUKsLl2Npc+DU+uAngHI5ZgsXw0IWBxaTIUWArMOi0OL\nUiNa7uG0ilRRNWmTg2jHfhwaB1d4bJglOna5d5HNZc/LmgrCXuCsIJVLxCCSIGB3KgmpnUQVNtSd\n/XzRv4xMNsXzQ8+TzqbJ5/O8NvoaufzxMXhKvVisVLtBDDo23Sz2Kd/2XXjwFjEnPHeRjMzL5+HZ\nT8Pen0PZMnC1nfSn64th0V1gPdmieN/0PvqD/RycPki9uZ5yqZ1id4wShY1EVxdJZwMzghWVVoZa\nLy8I+yxEKpdgdmgwFWtIxHJk5BoiDSvJh4JkBoeoMdRwjcdFIBlgu3v7eVlTQdgLnDXe9A3b64oQ\nyDNSfyPxzk4W5cr4UGIxvriPV0ZfIZ/P4417Oeg5ePJgiQTmrIfmm6CkRbTeXYvFlMgHrhf92Edf\ngEzqwlxcPg+eXnjkvWKQ17kQqlafFHXHPFjwQVCbThwSSATomOzg9bHXUUgVXOa8DFPfFFX6SpKH\nuyARJ1i3imhKjsWhRaESG6sVmH3YSnWYjt//EZmFZGkjGZ2FaMc+dHId6+OVuCQWnu1/llg6dpqz\nvXsKwl7grGEu0YIgoFDKMJkkTBmbyeSlJA52cnOsgTWmNgZnBtkzuQeA9sl2ZpIzp56kuEnM/zY4\nxZ4aiz4k5oQPboVH3yemFO574Py5aPJ5MVNn733w/BfFzozFreLuQhDEoGnDRvEhPVnvl81l2TK8\nhUPeQ0zFpljuXI42J6d+Wo4qJyO+bx/Jklr8umrx/86pxVSs/rMVswUufgw2NQarGr1FideTRVCq\nidavIOf1kB4bo0xfxq2TZYRTYXa6d57z9RSEvcBZQ66UoreIFmdRrZkcUiYbryXR2Yk8necTsSU0\nWhrZN72P3kDv/9/emQbHdWX3/Xdf7zsa3dh3gATBnRQxFCUxFClSIqURNdKMxnHicWZsxzO2q/Il\ncdlx5ksq+RKXs1S5bJejpFJOvEkjcTQjeSRRoiiJEkWCBEWBFDdwwb5vve/dNx8e2CQEbiKBxsL7\nq+pCL6/vOxfd/e/T5557Dplcho/7Pp49kMMPW34IpS36Bp+1L8HWH0PFBhg+q4dCXtkFR/4rhEfn\nZzKZpF6V8uT/hnNvQuf7euy/pEUviSCEXqXxkd/UvfWvcXzoOD2hHo4PHafKWUWzt5nqgSQVJj+J\nL79EJpOE1z1FOGPH6jRhc6owzFJGCIGvykl5o4dUIkvE4idet5GszUXs1CkMwsAusZr/UvK77K7b\nPe/2KGFXzCnecj07xlXqwmrO0VvyOLl0mnhHB/7xNL9d8T0qHZV83Pcxw9FhBiIDnBk7M3sgo0UX\n9BW79RorNi+s3AuP/oG+ASjYr1dNfOVJ+NUf6iKcTswe55sSGoKL7+hpjJ3v6zVtRr6Cy+9BcRO0\n7Aeh6Zkwrb+lV6z8Gn2hPjpGOzg6cJSszLKjegcGKdgwaoVkiviXX5KqbiHhqSKaMFBcYcdgNOAu\nmf8GDIr5w1/jxFNqw+owMjYOWO1EVz5GZmCA9MgIHouHzbaVdx1nLlDCrphTrsfZhRCUVNtJSBvh\nVTtInDlDLpHgW6MOvrvyuzhMDt7rfo9wKszRwaP0h/tvPWDNVj0cc11AzXZ9A9C2P4AVz0A6Bif/\nF/z99+HAv4azb8DE1W+22JpJweBpPdxy6m/0munXUy3HL8HFX+nZOmte1LNeVj8Pa164ZfGyWDrG\nh70fcmnqEleDV9lStoUiSxHfCvuxpQTx06eR6TShNU8RSuk7U4srHHhKbBhU0a8ljc1pxum1Utbo\nIRZKkXSWEG/cQs5kJf6FvpfDbXEXxJYHKgImhPgzYD+QAq4CvyWlLMzWKsWixGI3YfdYiAWT+FeU\nMNDVS3f102y4dIR4RwcOm43nNuxiomGCA5cP8G7Xu7y04iXe73mfl5tfxm2+xRvfWaoX0+pr0zcv\n5TJ60bCqR6ByE4xdhN7jcPFt6Ppkuub543qJ4NI1ehw8k9DDK5kkZJM3rifDunjfvCgrcxAahMlr\n0Hdcj/evexmKqnVxv0MHqI/6PmIoOsSnA59S4ahgc+lm/FYf9WeTZKJR4mfOkGtcQ8ZTSjjqxOY0\nYXOZVRhmmeCvcRGeiDNwaYrxKY1qu5PYiq1oF46QmZwsmB0PWt3xA+BPpJQZIcSfAn8C/PGDm6VY\nyhRX2IkFkxhNBrzFGhMTXnJNa0mcOYNt40aKLg2xc/VOwukwv7r2Kw71HmJf/T7evfYu3135XUy3\nqvSoaXr3mZJVugcdnPbwhaaLd8nqaSE+BlcPQe9RPaumcovuZd+NZFjfIDV5Tf+bSQICvPW6mNc/\nAY277thp/szYGa4Fr3Go9xBCCHbX7saoGdmRaSQXaCN26hRkswRbdpHOGYjGoHKlvuCshH154Kt0\n0HfeQGmdm8HLASpWlxJd+SiOzs+Jnz6N++k9BbHjgYRdSvn+TTePAy8/mDmK5YC33EH/xSkASpqK\nmZiYYGTlXiquniPe0YHmsNPa+i/pL+1nKqHn9p4ePc0jZY/wYe+H7K3fe/vsEHux3n1p4Au49tGN\nkIkQev64r0kX/d5j0P2p7snbikAzgcGsd4k3TF83mPWQTaBbbzANYHaCvxm8jbqo24uh5Xnwr7jj\nnMfj4xwbPMbJ4ZOMxkZ5pu4ZXGYXrWWtWI5cIxkOkzh3Dq15LUlHCWGhe/2+KgdOrwWTRRX9Wg4Y\nzQaKyhwkYxmGrgYZD5soc3iINzyC6GwvmNc+l/XYfxt47XYPCiF+DPwYoLa2dg5Pq1hs2FxmrE4z\niUgKp9+J0TDOMJXUNTbmvfZUxxn2bN3DVGKKkdgIJ4ZPUObQS9y2j7TzrfJv3f4EQkD1Fl3EOw/q\nXvbNeKr13Z+RURj6Um9Jl03pXwLxqH49k9L/IsFdrZc0KG4ARymYbXorP2+9/gvBfOeiXJlchkM9\nh+gN93J69DQtxS00FTVR4ahgXbqU8MhxYu3tAMTW/jMkgkDUiKPIgNWhsmGWGyW1LiYHI/irHYz3\nRyhbU0qk+XFsV08SPvg+nuefn3cb7irsQohDwOylf/iplPKX08f8FMgAf3+7caSUrwCvALS2thau\nGo5iQfCW2xm6kkIIgdtvJTgqYeM25LV/IH7mDAaXk+LWVh6vfJx4Js54fJwPej7g+83fp324HZ/N\nR6On8c4nsRXBxn+uZ8NER3WvOzJ247qzVK8geSek1D14T7Uu5N56faH2G+STfz74OYORQT7s/RCP\nxcP2yu14LB72Newj+c4HZAMBkhcuYFm3nlFjKRmHn8RYhrp17vz/SrF8cPv1Ou1lDR7GeiNMxm0U\nu30ka9cT+eQTMlNTGL3eebXhrsIupbxjUEgI8SPgeWC3LGT5MsWixlvuYOiKvo5eVOFiciTJoKmJ\nysZGEh0d2DZuJP7ll6x//HH6I/3srd/LgcsH+KDnA15oeoHDvYfxrPDgs/nufjKTVc9aKfraL8F4\nAGIT+mIrAGJasKdFWwhd1F0V99XBCaAr2MXZsbN83P8x8Uyc7674Lm6Lm+cbn8c4GSbS00vs5Ekw\nGEivfZxcTiOYcSC0KMUVjukc9jlsyadYcK7XaU/G0hSV2RjrjeBvKSWyajvWwYvEOzpw7dw5rzY8\nUH6VEGIf8EfAC1LK+d8nq1gyOL0WzDbdb7ienz0cdWJt/RYylSLe0UH87Ffkkkn21O2hpbiFJ6uf\nZCg6xImhE6SyKd7rfo9E5gFy021FerimZNX0pVkvq+tfoV98TfqXwX2K+nB0mI/6PuL85Hm6gl08\nWv4oFc6LFyB4AAAeGklEQVQKnm14Fo/FQ+zYMTITEyQ7O7Ft2EBQK8bg8zM5Eqeo1D4dj1Xe+nLE\nX+sCIShv8JBJ5whmnWSLKzD88Z/Nu6jDg+ex/wXgAj4QQnwphPjrObBJsUy4vlnJZDbgKDITyTmI\nuaowT3vt2XBY35WqmXiu4Tm2lG1hjW8Np8dO0xXsIpgM8n7P+wWriHevSCk5NXKKN6+8yWBkkM8H\nPqfGWcOmkk3sqd1DuaOcVF8fqd4+YidOIMxmjBu2EMuaiVu8ZFI5fNVOACXsyxSLzYjHb8VZbMHh\nMTPSE8FQ4kdYC7MJ7YGEXUq5QkpZI6XcNH35vbkyTLH0uTl27CmxEYsLxuN27K2tN7z2jg5kKoXd\nZOf5xud5quYpSmwlHO47TCgZoj/cz4HLB/K9UxeaaDrKW1ffom2ojXQ2zaGeQxgNRp6qfYonqp+g\nsagRKSXRz4+RGR0lde0ato0bCVGEwVfC5EgSo0lvTGI0G3B57yEVU7Ek8de4EEJQ1ughGc0QNRSB\noTDZT2qrm2LecBVbMU737vRMh2PGtQpyxWV5rz0TCBI7/SUAXquX55ueZ1/9PgAO9hwkk8swHh/n\njc43ODl88kap3wWgK9jFa5deYyAyQCAZ4IOeD5hITPBUzVNsq9zGxhK9ZkzqyhUyo6NE29oQFgvW\nTZsIpKyIkjKmRmIUVzrQNIG3XK9hr1ieeMv1cFtxuR2zzcBITxRzXV1Bzq2EXTFvCO3GxhtHkQWD\nSSOatRNMO2Z67V+cIhvQF1qrnFW8uPJF9tTsYTw+nq9fnZM5Tg6f5I3ON5iITxR0HplchiP9R3i3\n612Go8N82Pshr158lf5wP9sqtrGrZhdPVD4BgMzliB5vIz00RLq3F9sjj5AQdrJF5QQnM8gc+TBM\nSa2roPNQFBbNoOGrciI0QVmDm8hUkmCwMI6JEnbFvHK9ZZ4QAk+JjeBkgnBRHQZ/yY1YezRG5MiR\n/HOavc38WsuvsblkM+cnznNy+CRDkSFi6RhjsTFe73yd9uH2gnjvk4lJDnQe4OjAUQ73HubVi69y\nLXCNDSUb+I3Vv8G++n3srtud31CVOHeObCBArK0NYbNhW7+eQMqGqbKCiYEIVocJh8eM3a3XFVEs\nb/w101/iNS4MRsHVL8YKct653KCkUMzC7bdiMGlk0zk8JTYmB6PEzKXEErrXHrh2jURHB5rFQvLq\nVSxNevehLWVb+OHaHzLWPkb7SDvtI/oGH7NmxmPx8H73+1Q6K9lRvYO99XvxWDxzancml+H06GkO\n9x7m5PBJOqc6MQgDG0o2sKlkE3aTHY/Fw7MNz2LS9KwamUoRO3GSVH8/6YEBHNu3I40mYtYaMlmN\n8GSSqlVFejqc8tYfChweS752Ukmti8ErAULjcdz++V1EVcKumFc0g4anxM7kYASPX/dQg5Mp4tWr\ncBDF3NhIvKMD67p1RD79FHNNDcKs53Xvqt1FJB3h/MR5AskAwWSQYDJIIBlgODbM5cBlPun/hL84\n/Rf8+VN/zqbSTXNi87XgNT4f+JxP+j+hbagNTWgzBB3AYXKwv2l//jZAvKODbDRK7MQJNIcD69q1\nhDNWRGUFE71RAHxVTjSDwF/lnBNbFYufkhoXPcEkZfV6OCYylVTCrlj6XN9ibbIasbvNBMfiRFaU\n47PasW/bRuDVV4keO4Zr926iJ0/ifEKPV2tC49mGZ8nkMozERmaNm8llGI2N8mHvh/zovR/xkw0/\n4ccbfozhDoW67kQgEeCzwc/oCfbkfyU0eZrYXrV9hoBbjVb2N+2fUYkyF48T++I06b4+MkNDOJ58\nEmE0EnU1IsxmxvvHcPmsWGxGvBWO/KKyYvnjq3bQd2ECs83Irt9soXJl0d2f9ICoGLti3nH7rVin\nd1d6SmxEppKk05Jkw0aMXi+2TZtIXrxIemiIeEfHjEJJZoOZ/U37qXBUzBrXqBmpdFbyvZXfo9Re\nyl91/BU/+eAnXJi8wDfZBJ3OpTk+dJzXLr1GT7CHtuE22kfaafG2sKduzwxRN2kmvt3wbYqtM0v3\nxtpPkUsmibW1oblcWFevJiMMJNyVRANJkrEM/ip9vUEtmj5cGE2G/J6OQrU+VMKumHeEEJTW6WLm\nKbWBhNB4nLClDIPbhX3LFjSnk8iRI8h0hsjHn8x4vtlg5vmm56lx1dxyfLvJzv7G/azxraFtuI0/\n/PgP+bsLf8dAZOCWx6dzaYLJIMPRYS5MXOAfL/wjX4x8QSaX4djQMU6PnmaNbw07a3aiiRsfEU1o\n7GvYly9Wdp1sKETiq7OkurvJjI5ib21FGAzEa9aDycx4f1RPb6xwYHWacftUp6SHDX9NYb/MVShG\nURD8NU76L03paY9GoYdjKhxo61sRRz/C8cQThA8eJHHuHELTSFy6hHXVqvzzTZqJZxue5WD3QXpC\nPbPGN2gGnqx+Er/Vz2cDn/HKmVfoDfWyzr8Ok2YilokRz8SJZ+Jk8rVjbiCl5LOBz/hq4ivW+9fz\nROUTM7wrIQRP1z19yy+XaFsbuXRGj617PFhWrUIr8hKy15CLZpgcilJUbsdg1JS3/pByvTBYoVAe\nu6IgGE0GfeFQE7j9NoJjcaSUhK3lGHzFmJuaMFVXEzt+nFwsRvSzo+SSyZljaEb21e+jsej2VR/X\n+teyv2k/iWyCn1/+OZ8Pfk53qJvR2CjhVPi2on6k/whfTXzFxpKNs0Qd4MnqJ2kqapr13Mz4OMlL\nnaSuXiU7Po79W99CGI3kNj1BKpElMBojm87hr9bzmf3VatH0YUQIkU99LARK2BUFIx+OKbGRTmSJ\nR9JMDEZx7tyJ0DQcO3YgMxmix46Ri8WItbXNGsOgGXim7hmavc23PU+ls5KXV76M2+Lmna53OHD5\nAJ8NfEbnVCeBZGBG/D0nc/lCXptLN/NYxWOzRP2xysdY41tzy3NFjx1HZrPETpzA4PViWbkS24b1\nTEb1fqgTA1FMFgNunxVvuUM11HiI8de48oVF5xsVilEUDIfHgrPYSique83B0Th2l5mo5sO2YT3x\njjPYNm0i/sUXWNesAU1gaWnBVFo6YxxNaOyu3Y1BGLgweeGW53KZXby44kVOj55mKDLEhckLnB0/\nC+gx+1JbKaX2UoLJIFeDV2kta6W1rHVm+AXBI2WPsLl08y3PET1+nFR3N8krV8hOTeF65hmM3iK0\ndVsIfj5KOpUlOBqjrMGN0AQltcpbf5jRC4MVZn1FCbuioJTVu4lMJrC5TATH4lQ0eRi6GmT1tm2k\nuruxb9lCsrOTyJEjFH3/+0Q++YSil1+e5UULIdhZsxOTwcTZsbNIZmfBmDQTW8u3ArpnPpWYYjQ2\nmr+cHj2NRLK1fCtbyrbkn+c0OVntW01LcQsu861j4pGjR4l/cRqZy+neus+HecUKnLt2MTgQBymZ\nHIwipZ67brGb5j13WbH4cfkKs9tYCbuioHgrHBgtBjwlNka6QmQzOSKTCcKhLM5duwj+4pezFlLD\nBw/i2rMHYZz5dhVCsL1qO1vKtjAQHqA/0s9AZIBgMjjrvJrQ8Nl8+Gw+VvtWA3p2TDKTxGl2IoSg\nwd3Aat9qal21d0xLixw5QrzjDADJS5fIBYO4nn0W29o1GMqrmDjXC8BEfwSb24Tdbaak1lWwVDfF\n4qVQ7wEl7IqComlC37A0EGH4WojQRAJvmZ3BKwFattVgXbMaKWV+IdXS1ETy8hVysTjubz+HZrHM\nGtNmtLHCu4IVXr3hdCgVygv9YGSQdE5veC0QCCEQiHwao8VuodnbTEtxy4x89VshpSTy8cckvjqn\n385miZ08ibG0FOu6dTi2b2e0P0w2nSMylSQaTFG7plhfNC3gwplCoYRdUXBK69wMXg6gGQTB0Tje\nMjuhsTiRqSSO7dtJ9fTi2LFjxo7U9MAAwZ//HPf+FzA479xc2m124/a58575XCClJHL4MInzN2L6\niQsXyIXDOJ98EteunQizmdFuvcjTSHcIg1EX9KJSO2ar+qgpCofKilEUHIvNSHG5Q29yPZ32CDB4\nZQrNYsG588lZO1IBMuMTBA+8QWZqqqD2ylyO8PsfzBD1bCBA7NgxjBUVOHfvxtLYSGg8TiKSIhnX\nc9f9NS6Vu65YEJSwKxaE0noXnhIbqXiGRFTPkgmMxImFUlgaG7GsXJHfkRo+dIhsOAxANhQmeOAA\n6eHhgtgps1nCBw+S7Oy8cV86Tei990DT8Ox/HteTOwDdSwcY7QmB1BeKzTZjvsmIQlEolLArFgS3\n30ZpnV5EKzg23QddSoau6A03nDt2YPC4ce3di0wkCL75JtmgviiaiycI/uIXJLu65tVGmUoRevc9\nkleu3rhvOs6enZjA9fTTuL/9bTS7nUQ0TWAkTjaTY6w3grfCjsVu1NujqS5JigKjhF2xIAghqF3r\nw+owEhyL5++fHIqSiKbR7HYc27djKi/H853vIFMpgm++mQ/DyHSG0DvvED93bl7sywYCTL3xBqmv\nfXkkzp0j2dmJfetWXLt25sse6F66ZGIgQjado6zeDUKoMIxiQVDCrlgw/DVOisochCcSJKJ65orM\n3fDarS0tmOvrMJaW4nnxRWQup4v7xHRrvJwkcvgjwoc/QqbTc2ZXqqeHqZ+9TnZicsb96ZERop9+\niqmuDseOHTh37QIgm80x3hdBSslIVwhHkRmn14K33I7FphZNFYVHCbtiwTCaDKzZXoGmCbrPTuQX\nUcf7IySnd6c6d+5EWCwY/X48L72E0DRd3MdutBhLnDvH1Ouvzyj3e7/E2tsJvv1PyK/VqcnF44Tf\new/N4cC1Zw+up55Cs+mx88mBKJlUluBonEQ0Q1m9G6FpVK/yPrA9CsX9oIRdsaDUrvVR3VJMeCLB\nRH8E0L324at6PN3gcuHasxuEwOj16uJuNhP8xS9mLKBmJyYJ/Oxn9x2a0ePp7xI9dhy+Vstd5nKE\nP/iAXDyO+9lnsW95BEtjQ/7xkS7d1pHuECarAW+FA1+VA5vLfF+2KBQPihJ2xYLi8Fho2OTH6bXQ\ne2GKdDILwFhvOH/d0tiI/RG9XovB49HF3WYj+Mtfkhq4UXNdpjNEDn9E6OD75FKpe7YhMzXF1Ouv\nz1gkvZnYyZOk+/pw7tiBZUUTju3b84+FJvRMnlgoRWg8QVmdnuJYtVJ564qFQwm7YsGpXlVM/Xof\nuWyO3vN6OCWXzTF87UZpAPu2bZiqqwHdiy966SUMLheht98m1ds7Y7xkZyeBV18jPTJ623PKbJZs\nMEjiUieB198gO3nr3PhUdzfx9nYsq1djXbsW1549aOYbnvhot56GOdIdQjPoi6W+KidWZ+FqbysU\nX0et7CgWHE+JjdJ6D5NDMQYvB/BVOSgqtTPaE6JihQejyYDQNNx7n2HqtZ+Ri0TQHA48L71E8Je/\nJPTOO7iffRZzXV1+zGwwSODAG9i3tKJZzGTDEXKRMNlwmFw4Qi4enxVyuRmZy5EeHCR86BAGvx/n\njh3YNm/CVFmZPyY0EWdqOEo6mWViIIK/2oXJaqSqef57WioUd0IJu2JRUN3iJTgaY3IoSs9XE7h2\n6FXwRrpCVDXrYQ3Nbse9by+BN9+EbA7NZsPz4osE33rrhrjX198YNKtXXrxXZCpFqq+PVHc3qe5u\nZCKBsFpx79uHqbwMx7Zt+WOT8QxXTo0ic5Kx3jAyB2UNbkpqXQXtlKNQ3Aol7IpFgavYirfCQf16\nHxePDTPQGaB2TTEjXSHK6t0YzXqDClNFBc7HHyfy6WcAaFYrnhdeIPT224TefRfXvn1YGhrudKoZ\nZCMRXci7ukj390Muh7BYMNfVYW5owFRbi2az4nr6aYRBtyGbzXH55AiZZJZcVjLSE8JTYsPhMRek\nA71CcTfmRNiFEP8O+K9AiZRyfC7GVDx8VDXrXntJrYuRrhDFlQ6cRRY6T47Qsq0czaAvCdk2bSI9\nPELy8mVAF3f3Cy8Qeustwu+9B3v3Ymm8ffs80EM1sba2G2O43VjXr9fFvLw8L+IAjm3bMPr9+dtd\nX44TC+rpkJNDUTLJHGUNbkrr3KrYl2JR8MDvQiFEDfAM0Hu3YxWKO+H0Wigqt1OdzhEYidF9dpw1\nT1QSmUxw7ctxmh4pydezdj21i8zEeH7RU7NYdHF/+23CBw/CM89gaZrdozQXixFrbydx7hxoGrbN\nm7G0tGDwem9ZK9tUWYlt840OSoOXA0wOTqdlTm9IsjlNFJXbqVihvHXF4mAusmL+B/BHcIsWNgrF\nN6Sq2YvRbKB2bTHxUJqRLr2w1uRghL7zNzYgCbMZ93PPIUw34tnXxd1YWqoX7rpyJf9YLpkk2tbG\n5N/+LYmvvsK6ejXeH/wAx+OPYywuni3qQmBpbsa1d2/+scBIjP5LN7JnwpMJYqEUZQ1uyhs9qp+p\nYtHwQB67EOI7wICUsuNunUGEED8GfgxQW1v7IKdVLGMcHgvFFQ697kpZlIHOAN5yO1aHieFrQcx2\nI+UNHgCMXi+up/cQPnwYmdBDI5rZjHv/fkL/9E+E338fmckgEwlip04hEwnMK1bgePRRDEW3967N\n9XV6+KWkJH9fPJzi6unRfCZNLicZuBTAaNYorXdR0eiZx/+KQvHNEPIOKV8AQohDQPktHvop8B+A\nZ6SUQSFEN9B6LzH21tZW2d7efh/mKh4G4uEUZz8ZIBVPc/aTAZxFFpq3lumesxCs2FKqi/80Mpsl\n1dNLsrOTVHcXMp3Ri4b96ldkBgcBMNXU6GL9tcbYN2OqrMDx2GMzUhoBMuks5z8bJBGZrmcjJd1n\nJxjvi9C42c+GnTVUqfIBigIghDglpWy923F39dillHtuc4L1QANw3VuvBr4QQmyVUhamWLZiWWJz\nmfFVOZjoj1Dd7KX3/CRTw7G8J3/t9CgmSwWuYj0lUhgMWBobsDQ2IFMpkl1dJDs78Rj2EzvZjqmq\nCnNNzW3PZyzx49i2bWaq5DQyJ7n6xVhe1EHfjDTeF6FyhYeyeg/lyltXLDLuOxQjpTwL5N2fb+Kx\nKxR3o6rZy+RglNI6F2N9YfrOT+IpsWEwauSykssnR1jzROWsHZ7CbMa6ahXWVavIxeMkn34amUzp\nNdHF9Ys2/VfPjTfX19+2yXDfxUmCo7H87cBojL7zU3jL7VQ2F1He5MFgUhu4FYsLlZulWJRYHSb8\n1U7GesPUrdNz24euBKlu0UMemVSWSyeGWfNE5W0XLTWbDdvatfdtw0DnVL4YGeghomunx7C7zTRs\n9GO2mShrcN/3+ArFfDFnroaUsl5564q5pHJlEUITuIqt+KocDHcF83XbAZLRNJ0nhsmks3N+7sHL\nUwzclAGTSWW53D6KMAhWtJbqhb6aizAYlLeuWHyod6Vi0WKxm/IdiKpbvAhN0HtukpsX/KOBJBeP\nDecrQc4Fg1cC9F+8Ieq5nOTKqTFSiQwrt5RisRmxucyU1KjuSIrFiRJ2xaKmcmURmkHDbDVStbKI\n4FicwEh8xjGxYJILnw/mm3M8CENXAvRfuJEvL6Wk99wE4ckE9ev9OL36gu31LxqFYjGihF2xqDFb\njfn6K6X1bmxOE73nJ8llczOOS0TSXDg6SDxy73XYv87Q1SB9F2Z2YRrtCTPWG6G8yYO/2gmAy2fF\nW+641RAKxaJACbti0VPe5MHqNKNpgtp1xaTiGYZuWtS8Tiqe4cLRIaKB5C1GuTPD14L0nZ+YcV9w\nNEbv+UmKymxUr7qxoalmdfE3n4RCUUCUsCsWPZomqF/vA8Dts1Fc6WDo6syF1OtkUlkuHh8iNB6f\n9djtGL4WpPfcTFEPTya4cmoMu8tM46YbNWq8FY58OEahWKwoYVcsCdx+G77pUEjNar1g1821Y24m\nm87ReWKYqeHobcdLJ7OEJxP0X5qaJerRYJLLJ0cw2ww0by3DYNQ/JkITyltXLAlUHrtiyVCzupjA\nSAwzety9/+IUgZEYRWX2WcfmspIrp0apXePDaNZIRNP6JaL/zaZztziDnqveeWIEg0mj+dHyGTny\nJbUurA7VREOx+FHCrlgymK1GqluK6Tk7TlmDm/G+CL3nJ3H7rfla7Tcjc5Ker+59a0UyluZS2wgI\nWPVoORbbjY+HwaTlOzkpFIsdFYpRLClK61w4iixomqBuXTHJWIbL7aNMDkXJZe+/cnQqkeFS2wi5\nnGTV1vJZnnlFkyrLq1g6KGFXLCmEENSv94MQuP02qlu8xMIprn4xxpeH+ug+M054MsHdqpbeTDqV\n5VLbCOlkluatZdjd5hmPm6xGylShL8USQoViFEsOR5GF0joXo90hKpo8lDe4CU0kmBiIMDEYZawv\ngtlmwFfpxFflwOo03bbIl77QOkIylqF5aynOIsusY1TpAMVSQwm7YklS3eJlaihGOplBaAJPiQ1P\niY1sRm+rNz4QYehqUM93F2A0aZgsBoxmAyazhtFswGgxEBqLEw+lWLGlFLfPNus8qnSAYimihF2x\nJDGaDNSsKeba6dEZ9xuMGr4qJ74qJ+lEhqmROKlEhkwySzqVI5PKEgulSCdzZDM5hICGTSW3zKzR\nDBq1a32qdIBiyaGEXbFk8Vc7Ge8L33YzkslqpLTu9t52LieRUt4yzOLyWWnYWKLSGxVLEhU4VCxp\n6jf4cftnh1DuBU0Ts0RdM2jUrvPR8liFEnXFkkV57IoljdVhouWxCqLBJCPXQkwMRpC5+0t7VF66\nYrmghF2xLHB4LDRuLqF6tZeR7hBjPWEyqXur0a4ZNKpbvJQ1uG+bPaNQLCWUsCuWFWarkZqWYipX\nFjHRH2G0O0Q6mZtuc6qL9s3XzTYjdWt9s3qnKhRLGSXsimWJwaBRWuemtE71JFU8fKjFU4VCoVhm\nKGFXKBSKZYYSdoVCoVhmKGFXKBSKZYYSdoVCoVhmKGFXKBSKZYYSdoVCoVhmKGFXKBSKZYYSdoVC\noVhmiG/SQmzOTirEGNBzn0/3A/feoXh5oOb8cKDm/HDwIHOuk1KW3O2gBRH2B0EI0S6lbF1oOwqJ\nmvPDgZrzw0Eh5qxCMQqFQrHMUMKuUCgUy4ylKOyvLLQBC4Ca88OBmvPDwbzPecnF2BUKhUJxZ5ai\nx65QKBSKO7BohV0IsU8IcUkIcUUI8e9v8bhFCPHa9ONtQoj6wls5t9zDnP+tEOK8EOKMEOJDIUTd\nQtg5l9xtzjcd9z0hhBRCLOkMinuZrxDi16Zf53NCiH8otI1zzT28r2uFEB8JIU5Pv7efWwg75xIh\nxP8RQowKIb66zeNCCPHn0/+TM0KIR+bUACnlorsABuAq0AiYgQ5gzdeO+QPgr6ev/zrw2kLbXYA5\n7wLs09d//2GY8/RxLuAIcBxoXWi75/k1XgmcBrzTt0sX2u4CzPkV4Penr68Buhfa7jmY9w7gEeCr\n2zz+HPAuIIBtQNtcnn+xeuxbgStSymtSyhTwKvCdrx3zHeD/Tl9/A9gtlnYn4rvOWUr5kZQyNn3z\nOFBdYBvnmnt5nQH+M/CnQKKQxs0D9zLf3wX+Uko5BSClHC2wjXPNvcxZAtd7GHqAwQLaNy9IKY8A\nk3c45DvA/5M6x4EiIUTFXJ1/sQp7FdB30+3+6ftueYyUMgMEAV9BrJsf7mXON/M76N/4S5m7znn6\nJ2qNlPJXhTRsnriX17gZaBZCHBVCHBdC7CuYdfPDvcz5PwI/EEL0A+8A/6Ywpi0o3/Tz/o1QzayX\nIEKIHwCtwJMLbct8IoTQgP8O/GiBTSkkRvRwzE70X2RHhBDrpZSBBbVqfvkXwN9IKf+bEOIx4G+F\nEOuklLmFNmypslg99gGg5qbb1dP33fIYIYQR/SfcREGsmx/uZc4IIfYAPwVekFImC2TbfHG3ObuA\ndcDHQohu9FjkW0t4AfVeXuN+4C0pZVpK2QV0ogv9UuVe5vw7wM8ApJTHACt6PZXlzD193u+XxSrs\nJ4GVQogGIYQZfXH0ra8d8xbww+nrLwOH5fSqxBLlrnMWQmwG/ie6qC/12CvcZc5SyqCU0i+lrJdS\n1qOvK7wgpWxfGHMfmHt5X/8C3VtHCOFHD81cK6SRc8y9zLkX2A0ghFiNLuxjBbWy8LwF/Kvp7Jht\nQFBKOTRnoy/06vEdVpWfQ/dWrgI/nb7vP6F/sEF/8V8HrgAngMaFtrkAcz4EjABfTl/eWmib53vO\nXzv2Y5ZwVsw9vsYCPfx0HjgL/PpC21yAOa8BjqJnzHwJPLPQNs/BnP8RGALS6L/Cfgf4PeD3bnqd\n/3L6f3J2rt/XauepQqFQLDMWayhGoVAoFPeJEnaFQqFYZihhVygUimWGEnaFQqFYZihhVygUimWG\nEnaFQqFYZihhVygUimWGEnaFQqFYZvx/gb4AWr8KgesAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pylab as plt\n", "[plt.plot(X[:,0],Y[:,p]) for p in range(P)]\n", "[plt.fill_between(X[:,0],ystar[:,p]-2*np.sqrt(varstar[:,p]),ystar[:,p]+2*np.sqrt(varstar[:,p]),alpha=0.5) for p in range(P)]\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.imshow(model.kern.W.value)\n", "plt.colorbar()\n", "plt.show()" ] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }