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@ferrine
Created July 4, 2018 21:53
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Convergence Checks for Mixture model
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING (theano.gof.compilelock): Overriding existing lock by dead process '60385' (I am process '61358')\n",
"/Users/ferres/.pyenv/versions/Q3/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"
]
}
],
"source": [
"import numpy as np\n",
"import pymc3 as pm\n",
"\n",
"data = np.concatenate((np.random.normal(5, 2, 1000), np.random.normal(15, 2, 1000)))\n",
"with pm.Model() as model:\n",
" w = pm.Dirichlet('w', np.ones_like([0, 0]))\n",
"\n",
" mu = pm.Normal('mu', 5., 10., shape=2)\n",
" tau = pm.Gamma('tau', 1., 1., shape=2)\n",
"\n",
" x_obs = pm.NormalMixture('x_obs', w, mu, tau=tau, observed=data)\n",
"#with model:\n",
"# approx = pm.fit(method='advi', n=30000)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"with model:\n",
" inference = pm.ADVI()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[mu, rho]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inference.approx.params"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO (theano.gof.compilelock): Refreshing lock /Users/ferres/.theano/compiledir_Darwin-17.4.0-x86_64-i386-64bit-i386-3.6.3-64/lock_dir/lock\n",
"INFO (theano.gof.compilelock): Refreshing lock /Users/ferres/.theano/compiledir_Darwin-17.4.0-x86_64-i386-64bit-i386-3.6.3-64/lock_dir/lock\n",
"Average Loss = 6,207.5: 100%|██████████| 30000/30000 [00:44<00:00, 675.08it/s]\n",
"Finished [100%]: Average Loss = 6,207.5\n"
]
}
],
"source": [
"tracker = pm.variational.callbacks.Tracker(mu=inference.approx.params[0].get_value, rho=inference.approx.params[1].get_value)\n",
"with model:\n",
" approx = inference.fit(n=30000, callbacks=[tracker])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO (theano.gof.compilelock): Refreshing lock /Users/ferres/.theano/compiledir_Darwin-17.4.0-x86_64-i386-64bit-i386-3.6.3-64/lock_dir/lock\n"
]
},
{
"data": {
"text/plain": [
"array([<matplotlib.axes._subplots.AxesSubplot object at 0x11bf083c8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x11bfc7748>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x11bfdda20>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x11c02bcf8>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x11c00d898>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x11a883080>],\n",
" dtype=object)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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2vJ1YxWfTumjfZyRYAIC4UVRUFOkQopbL5VJhobUHrLckadXVlsp7vd6Qlo+nbXj2FqvogelBl8+YvUBlRsv5ispn07pI7rOsrKyjluE0HwAAAADYhAQLAAAAAGzScq6/Iq5ZfRiuw+MJYTTRy5mUrLT8HEvrtNR9BQAA0BQkWIgLVh+GmzljXgijiV7e0hKVWBgHL7XcfQUAANAUDBEEAAAAAJuQYAEAAACATUiwAAAAAMAm3IMFwFZ1J9LwFOQF9WwZbzuX9qemhTgyAACA0CPBAmCrpkykkTF7gUSCBQAA4gBDBAEAAADAJkFfwcrMzJTDEfv5mNPplMvlinQYIdGS6+YpyLP0foZhWI7B6jrRuI1ojEmSEhMTY+5vtyV/3oBoZ/XZiBLP/ANgn6ATrKKiolDGETYul0uFhdYa3VjRkusWzH0+dXm9XssxWF0nGrcRjTFJUnV1tYpj7G+3JX/eQiUrKyvs20R8svpsRIln/gGwT+xfkgIAAACAKEGCBQAAAAA2YRZBAAAQ1azeU8X9VAAiiQQLAABENav3VHE/FYBIIsECAABA1Kj7wPpg8LB6RBsSLAAAAEQNqw+s52H1iDZMcgEAAAAANiHBAgAAAACbkGABAAAAgE1IsAAAAADAJiRYAAAAAGATZhEEAABAzLI6rbvE1O4ILRIsABHHM08AAE1ldVp3iandEVokWAAijmeeAACAeME9WAAAAABgExIsAAAAALAJQwQBAHEjMzNTDgfnDhvjdDrlcrkiHUaTeAryLJU3DMPyNqyuE+rybCN05SUpMTExaj4PsfzZjJRo32ckWACAuFFUVBTpEKKWy+VSYWFhpMNokrTqakvlvV6v5W1YXSfU5dlG6MpLUnV1tYqj5PMQy5/NSInkPsvKyjpqGU7zAQAAAIBNuIIFAACAFoXHgyCUSLAAAADQovB4EIQSQwQBAAAAwCYkWAAAAABgExIsAAAAALBJ0PdgxcuzRaJ93vzmiJe6eQt2qrZwV71ltYX5aneEaVitTtEajc/xCMc2ojGmpqwTDc8viZfPW2PiuW4AAIRa0AlWvDxbJJ6fNRAvdUvbsV0ls26xtE7mjHmWykfjczzCsY1ojKkp60TD80vi5fPWmEjVLZhniwAAEO1i/5IUAAAAAEQJEiwAAAAAsAnPwQIAAGHTurxMRrG1IagOjydE0QCA/UiwAABA2BjFhSG/zxYAIokhggAAAABgExIsAAAAALAJQwQRclbH2zPWHgAAALGKBAshZ3W8PWPtcTTOpGSl5ecEXd7bzqX9qWmhCwgAAMCPBAtAzPGWlqjkgelBl8+YvUAiwQIAAGHAPVgAAAAAYBOuYAEAAABHYHVousTw9JaMBAsAAAA4AqtD0yWGp7dkDBEEAAAAAJuQYAEAAACATUiwAAAAAMAmJFgAAAAAYBMmuQBs9uy2XXopt1COjVfLnerRNb06SZLmb87XS7mFap/k+9hNH9BV4zpmaF1RmWZ+85MSDUP/m7tdmZJKqms0df1W/e+wfnIYRoNtuD/epJkDu2ms//fcA5W66tMten/MIK3dU6prPtui7qnJqqr16rysdrq9X5bW7inVtWedr+4JtSr31MqVlKgbendSdqe2YdozABA/Hjdf1bMrv5FX0qXdXUdt69d+tUE3f7RRiYahx07spV6tU47a1k+86Q79vlWlTmjbWtLR2/o/SIHlx7RKpq2PsGBnHvQU5CmtuppZB+MICRZgo02l5Xopt1BvjjpWne56UOf84hfK7pihnq1TJEnX9Oqo63t3rrfO09t26flT+iq3vErPLn5Td0p6dMtO3dSnS6MdbjCGtUvXomF9daDGo4mrvlV2R1/HOvKEwXqqbY0k6Zt9B3TtZz8oJcGh0a42Ta80ALQwm0rLtejNd/TmqGOVaBi6fN33R23rF7z8z0Bb/38/FeieY7vb3tb/YtPmessl2vpIsjrzILMOxg8SLMBG35dV6MS2rZWa4JDTmaARmel6d+de3din82HXcToMlXtqVeGpVaLTqZz9lcqvqNJp7dObHU8rZ4KOz2ilnAMVciUl1nttUJtWurVvFz3/YwGdbiNal5fJKC487OsHzzgexJlHoOX4vqxCw44bqFRvgSQF1dYnOp0qr/G19U7DCElb/8P2fKUc8hptPRB+JFiAjQakp+hPm/JUXFWjlIoKfVBQoiEZrQKvP/9jgV7NK9KQjFa6+9huapvo1LQ+nXX7VzlKcRhadNcF+t20Jbqzf9ZRt3Xrl9vU+srr5NmVryqvt9EbKourarR+737d0reLiqpqGrw+OKOVnty6qzlVjglNeUCkw+NR8f23B12eM49AyzEgPUXzv/xaxQNdSklwBNXW/+7ySzXt9juU4jD0lxN6ac5324Nu61Mcvhb+aG39Pb16alsjr7eUth6IFiRYgI36paXqxj6dddl/vleb3/6PjmuTGhj6cXmPDrq1XxcZkv68OV9zvt2uPw/pqUFtWumNkQMlSRvzd6hjcqK8Xmnq+q1KNAzdfWw3dUhObLCtR07opbHzHlfRA9MD4/IPWldcqrNXbZQhQzf27qwB6alau6e0wXt4vaHZD9GmKQ+IzJwxL0TRIJQyMzPlcDB/U2OcTqdcLlekw5CnIM/yOobFIXRWy1tdp19aqu749SRdtnChWiU4gmrrT+jfL9DWf1JUGlRbbxi+tv7Qe7AOOrStH9SnV6MJ1pHa+mjbt+EoH63bSExMjIrPaCyIlvbscEiwAJtd0t2lS7q7lDljnn5/xWXqkuLrMOt2nJd2d9XrJCXJ6/Vq3vP/p7/07aJ7N+ZqxsCu2n6gSs/l7Nb0AV0txVB3/P2RfLPvgPqmHTqgBIhdRUVFkQ4harlcLhUWHn7Ya7jUHVobLK/Fs0FWyzdlnSnnnq1zv/pAkvTgpryjtvUH39/r9WrBlh16fGjvo7b1Rwvp0Lb+cHU4Ulsfjfs2HmJqyjrV1dUqjoLPaCyIZHuWlXX0K88kWLDsaPemHMrh8YQwmuhTWFktV3Kicnfu0ns7i7XYf8ZyV0W1Ovk74CW79mpAemq99V7JK9JZP5uktls+UbmnVg4ZMgyp3FMbkji/3XdAC7bs0IPH9wjJ+wNAPNtdXCynpLzyKstt/dgOGWqb5KStB+IUCRYsM4oLVTLrlqDLt7ShVtd/vlXF1TVK2XS3/jDoGGUk+j5mD3y3XRv3HZBhGOqWmqQ/Dv5vZ1fuqdUr2wv1zs8nq3TeJ7qmV0ddse57JTocenRoL9tiW/PlBp3tn6a9fVKi7juuOzc9A2iWlnrS7bKZ96lg6xYlGobltv7/hveXpJC19QeHDtLWA5ERdIIVL+Pao33MZnOEq25Wx89H4zjnUG7j1dMGSJLaz/yT9sy9M7D8kSN0nqkJDv1jxAAlJfrOep6ama5lYwYdtrw5YkC9mLq3Stb7/vKntU9vdFaq09qna8fSN+vFFIxo2rfhKt+UdeJp7Hw8t5OwX0s96bZs4SMqauTezmDa+oOO1ta/99j8etsItq3/ZsKJQdUBQGgEnWDFy7j2aBmDHgrhqpvV8fPROM65pW4jGmMKxzYYO29NpNrJYMa1t3RWrxYlpGfIU1oiqeGjBYJZJxjxckUKAOzCEEEAAGJEU64WNWUGTSvrxMsVKQCwS+yP+QMAAACAKMEVrBbO6nATieEgAAAAwOGQYLVwVoebSAwHAQAAsJszKVlp+TlBl/e2c2l/alroAkKTkWABAAAAEeYtLbF0/2PG7AUSCVZUIsECABtYPfMocfYRANB09DvRiwQLAGxg9cyjxNlHAEDT0e9EL2YRBAAAAACbkGBFgWeeeUbjxo3T2LFj9fTTTzd4fc2aNRo4cKDGjx+v8ePH6+GHH673usfj0YQJE3TBBRcElj333HMaNWqUunbtGjcPiQZi2e++ytGJ73+p7JXfBJYVlezTJZdcolGjRumSSy7R3r17G123e/fugc//lVdeGVh+2223acSIEYHXNmzYIEnasmWLzjvvPPXq1UtPPPFESOvVEj311FMaO3asxo0bp6lTp6qioqLe63l5ebrooos0YcIEZWdna/ny5ZKk1157LXCsxo8fr27dugWO2RtvvKHs7GyNHTtWc+fODXudAETW777KUc9zf1Gvj9hbVaNffbJZYz7coF99sll7q2saXbfnO59p4kcbNfGjjbr60y2B5T8dqNT5q7/V6R9u0NT1W1VVWytJeuGFF3TmmWdq/PjxuuCCC7R58+bQVq4FIsGKsO+++04vvvii3n77bS1btkzvv/++tm3b1qDc8OHDtWzZMi1btky33357vdeeeeYZ9evXr96yYcOG6eWXX1a3bt1CGj+A4Fzcrb1eGFb/c/rw8/+r0aNHa/Xq1Ro9erQef/zxRtdNSUkJfP4XLVpU77W777478NrgwYMlSW3bttUf/vAHXX/99SGpS0u2Y8cO/e1vf9M777yjFStWyOPx6I033qhX5pFHHtF5552npUuXauHChZoxY4Yk6ec//3ngWC1YsEDHHHOMBg8erKKiIs2ZM0f/+Mc/9MEHH2j37t366KOPIlE9ABFycbf2WvzQH+ste3zrTo1ytdHKnw3WKFcbLfxhZ6PrpiQ49N7px+m904/T307pG1j+x+/ydE2vTvroZ4OV4UzQP3L3SJIuvPBCLV++XMuWLdPUqVM1e/bs0FWshSLBirDvv/9eJ554olJTU+V0OjVixAi9++67Qa+fn5+v5cuX69JLL623fPDgwerevbvd4QJoolMz09U2MaHesnf+/ZEuvvhiSdLFF1+s9957z5ZtuVwuDR06VImJiba8H+qrqalRRUWFampqVF5ers6dOzcoU1ZWJknat2+fOnXq1OD1xYsX6/zzz5ck/fTTT+rVq5fat28vSTr99NP1zjvvhLAGAKLNqZnpatemTb1ly3bt1UVdfe3CRV3ba+muxkc5NMbr9WrNnn2a1Lmdb/1u7bXEv356enqg3IEDB2QYRnPDxyFIsCJs4MCB+uSTT1RUVKTy8nKtWLFC+fn5Dcp99tlnys7O1q9//Wtt2rQpsHzWrFm6++675XBwKIFYU1BcrD6ecqXl56h3zQHt2b1bafk5Df5VVlbqnOxxmjxpUoMk7MEHH1R2drZmzZqlysrKCNWk5ejSpYtuuOEGDR8+XCeeeKLatGmjM844o16Z3/72t3rttdd08skna8qUKZozZ06D93nzzTcDw7p79uypH374Qbm5uaqpqdGSJUsa7QcAtCyFlTXqlOI7UdYx2anCysaHCFbW1uqcVd9q8prvtGSnL4kqrvaoTaJTTocveeqSkqSdFVWBdRYtWqSRI0dqzpw5uv/++0Nck5aHWQQjrF+/fpo2bZp+9atfqVWrVho0aFCDZOn444/Xf/7zH7Vu3VrLly/X1VdfrdWrV2vZsmVyuVwaMmSI1qxZE6EaAGgyr7feg769FeWNPvh77RnHqXNKkoqu/b3OvfkODRw4UD179tRdd92ljh07qqqqStOnT9fChQsbDCGGvfbu3aslS5bo448/Vps2bXT99dfr1Vdf1S9+8YtAmcWLF+viiy/WDTfcoE8//VS33HKLVqxYEWjbP//8c6WmpmrgwIGSfEM6//jHP+rGG2+UYRg65ZRT9OOPP0akfgCi05GuMq0de7w6pyTpxwOVuvSTzRqQnqo2h4yYONSVV16pK6+8Uq+//roeeeQRPfLII3aH3KJx2SMKXHrppXrvvff02muvKSMjQ7179673enp6ulq3bi1JOvPMM1VTU6OioiJ9+umnWrp0qU499VRNnTpVH374oW6++eZIVAFAE3Rs1067KqolSbsqquVKbvycV+eUJElSr25dddpppwUmRujUqZMMw1BycrJ++ctfav369eEJvAX76KOPdMwxx6h9+/ZKTEzU2WefrU8//bRemZdfflnnnXeeJOmUU05RZWVlvcmG3njjDU2ePLneOhMmTNBbb72lN998U3369GnQDwBoeVzJTkt9RI9WyRqRma5v9h1Qu8QE7auuUU2tV5K0o6IqUK6uyZMna8mSJSGqQctFghUFCgsLJflmnnr33Xd14YUX1nt99+7d8np9H5D169ertrZW7dq101133aXPPvtMn3zyiRZoVz+rAAAgAElEQVQuXKif/exn+tu8P9YbWuTw1Kj1zp8aHXbke90T9voC8Jk0eqReyfPddPxK3h6N79S2QZm91TWq9Phmftqzd6/WrVun/v37S5J27dolyTfW/r333gtcEUHodO3aVZ9//rnKy8vl9Xq1atWqBpMMde3aVatWrZLku8+2srIycH9VbW2t3nrrrQYJ1sF+YO/evXr++ecb3FcLoOUZ37GtpT6iqKpGnxaXqV9aigzD0Gnt0/XOzmLf+tv3aEKnDDmTkrXr448C3wNXmy+qT7esw35PbF1eFr4KxxGGCEaBa6+9VsXFxXI6nZo7d64yMjL0wgsvSJKmTJmit99+Wy+88IISEhKUkpKihQsXHvZSsVFcqJJZt+hvObv1xNadKqis1ohzL9C4Dm00b0jPBuUzZ8wLZdUA+N20fqvWFpWquKpGw1d8pTv6ZemOaTN16dJ39Y/cQnVNTdJfT/Rdtfhy7379/acCzRvSU1vKKnTX1z/KYRgyNt+i3/76Ep2UliTl5+iSG2/WnuK98nq9Or5/P91/151Ky8/RrsI9+tkVv1Hp/v0yHAl6+umn9eGHH9a7sRlH1rq8TEZxYYPlYzpn6sIxo3T2mePkTEjQkAH9df240frzvTM1dNhwnXHeZN17772688479fTTT8swDD388MOBNvvjjz9Wly5d1KNHj3rve++992rjxo2SpNtvv119+vQJfSUBRI2b1m/VJ5/crD37KwJ9xNQ+nXXj+q2H7SOeker1EbVer6b26az+6amSpLsGdtNN67fqT5vzNahNqn7ZzSVvaYke/e0tWlW4T4mGoYzEBP1p0DGNDk+XeDBxU5FgRYHXX3+9wbIpU6YEfr7qqqt01VVXHfE9Ro4cqfPPP18VX/mGqlzds6Ou7tnR3kABNNljJzYc8pWZkaGXT+3fYPkJbVvrhLa+YcGntEvTsjGDfOVnzFPRA9NV8vkySdLfOxpSx3b+tfbI8+D/qERSiqSPTzlGkq9zLMvqaXd14t7Bk1WNmSZp2qAO/t8KVTH3d7pJUsbJ16pMUv/+/RtM3X7QyJEj9dZbbzVYvnDhQlviBhCbHjuxd6CNr8tKH3GoHq2S9eaoYxssn30cs0yHGgkWAMQxZ1Ky0vJzgi7vbeeS5ApZPAAAxDsSrCh2uCEqh+MpyOOeKgD1eEtLVHLIGdEjyZi9QOreM3QBxbGmJLP7GXoDIIpZbdck2jaJBCusrCZMDo9Hxfdbm3KZe6oANIczKVmeb79UWnV1UOXpSP+rScks+w5AFLParkm0bRIJVlgdaUx/Y+xMlo5557PGX3jnTOtvZnWdUJdnG6ErHy/biJKYfpp0svU4wsxbWqI9JAkR17VrV3veKEr+9ptVPl62EY0xxcs2ojGmJqxTNsP6JhCdSLAAAIiApgy9AYB4Y3WElyR5y/cprbjo6AUPlg/zaAsSLL8mHVyGxgAAmqgpQ28AIN5YHeElSe1n/kklc+8Muny4R1tEJMGymsw0JZE53DY8BXmN3lvQlPud2v/xSaVZvKcKAAAAQPwyvF5vUAVnz54dXEEAAJpo1qxZjT9FPUj0VQCAUDtqX+X1elvUv/vuu88b6RioG3WjbrH/j7rxL9b+cVzZZ+yz6PzHPou/feYIU6IHAAAAAHGvJSZYsyMdQAhRt9hE3WITdUOs4bhaxz6zjn1mHfvMuqjeZ0HfgwUAAAAAOLKWeAULAAAAAEKCBAsAAAAAbEKCBQAAAAA2iciDhkPF7XbfKulaSYakp03T/Mshr18m6ff+10sl3Wia5pf+13L8yzySakzTPCWMoR9VEHX7maQ3JG3zL3rNNM37/a9NlPSIpARJz5im+f/CFXcwgqjbnZIu8//qlHSspA6maRZF23Fzu91/k3SupN2maQ72L8uU9A9JPSXlSHKbplncyLpXSLrb/+sc0zSf9y8/WdIiSamS3pF0q2maYb95sql1c7vdQyX9VVIb+Y7TXNM0/+F/bZGkMySV+ItfaZrmF6Guy6Gaedw8kr72//qTaZrn+5f3kvSypPaSPpN0uWmaVaGtSUPNOG5jJT1cZ9FASZeYprk4Wo4bGhdEm2rI1ydMknRAvuP3edgDjRLN6V9bklD0b/EuFH1LvDvMPrtY0n3yff8bbprmp4dZN2q+78bNFSy32z1YvgZyuKQTJJ3rdrv7HlJsm6QzTNM8XtIfJD11yOtjTdMcGukv6YcKsm6S9JE//qF1kqsESY9LOlvScZIudbvdx4Up9KMKpm6maf7pYL0k3SXp36ZpFtUpEk3HbZGkiYcs+x9Jy03T7Cdpuf/3evwN7ixJp8q3L2a53e52/pf/Kt8+6uf/d+j7h8uiRrZ91LrJ9wVuimmag/zr/8Xtdret8/qddf5uI/UlfZGaVjdJKq8Tf90O8EFJD5um2VdSsaTf2BxzsBapCXUzTfODOp+7cfIdx6V1ikTDccMhguwvztZ/25Pr5GtjWqTm9K8t0CLZ37/Fu0Wyv2+Jd4vUcJ9tkPRzSSsPt1K0fd+NmwRLvqz2E9M0D5imWSPp3/IdjADTNNfUOUvwsaRuYY6xqY5atyMYLmmLaZpb/WfPX5Y0OURxNoXVul0q6aWwRNYEpmmulFR0yOLJkg6erXte0gWNrHqWpGWmaRb5/0aXSZrodru7SGpjmubH/qtWLxxm/ZBrat1M09xsmub3/p/zJe2W1CGEoVrWjOPWKP8VgnGSXmnK+nayqW4XSXrXNM0DNocH+wXTpk6W9IJpml7TND+W1Nbf1rREzelfWxS7+7eQBRpF7O5bWoLG9plpmt+aprnpKKtG1ffdeBoiuEHSXLfb3V5SuXxDHxq9hOj3G0nv1vndK2mp2+32SnrSNM1Dr25FUrB1O83tdn8pKV/S70zT/EZSV0m5dcpsl+8sUrQI+ri53e5W8jXKN9VZHM3H7aBOpmnu8P+8U1KnRso0dpy6+v9tb2R5tAimbgFut3u4pCRJP9RZPNftdt8r/5k80zQrQxKpdcHWLcXtdn8qqUbS/zNNc7F8wwL3+r+wSTF+3CRdImn+Icui9bi1dMG0qYdrb3ao5WlO/4rm9W8tVXP6FhxeVH3fjZsrWKZpfivfkJylkt6T9IV893s04L+34Dfy3Y910GjTNE+S79LiNLfbPSa0EQcvyLp9LqmHaZonSHpUUkx8EK0cN0nnSVp9yPDAqD1ujfFfhYrLh88drW7+M+T/K+kq0zRr/Yvvku/enmGSMlX/Mxk1jlK3Hv7hqb+Sb/hjn/BF1nxBHrfjJS2pszgmjltLZLFNbfHiuX8Nt3ju30IlnvuWli5uEixJMk3zWdM0TzZNc4x89zxsPrSM2+0eIukZSZNN09xTZ908//+7Jb0u36XGqHG0upmmuc80zTL/z+9ISnS73S5JeZK61ynazb8sagRz3Pwu0SHDA6P9uPntOjj8xv//7kbKHO445an+UNZoO37B1E1ut7uNpLclzfQPSZIkmaa5wz9MqVLSc4qu4xdU3er8DW6V9KGkEyXtkW/Y1cFRAjF53Pzckl43TbP64IIoP24tXhBtatT3C+HUjP4VzevfWqrm9C04vKj6O4urBMvtdnf0/3+MfGOoXzzk9WMkvSbfbF6b6yxv7Xa70w/+LGmCfMMGokYQdevsv+/j4DAsh3xf8tZJ6ud2u3u53e4k+ZKUf4Uz9qM5Wt38r2XIN2vZG3WWRf1x8/uXpCv8P1+hOnWoY4mkCW63u53/5t8Jkpb4hxHsc7vdI/zHd8ph1o+Uo9bN/3f3unz3fLxyyGsHOxlDvnHo0XT8gqlbO7fbnez/2SVplKSN/rOSH8h379Jh14+gYP4mD2pw32OUH7cWL4g29V+SprjdbsPtdo+QVFJnyFKL04z+Fc3o38IUXzRqct8StghjU1R93zW83vi5mut2uz+S796Hakl3mKa53O123yBJpmk+4Xa7n5H0C0k/+lepMU3zFLfb3Vu+L4CS7760F03TnBvm8I8oiLrdJOlG+cbqlvvLrPGvO0nSX+SbtvJvsVY3f5krJU00TfOSOutF3XFzu90vSfqZJJekXfLNnLRYkinpGPn+9tymb4r5UyTdYJrmNf51r5Y0w/9Wc03TfM6//BT9d5r2dyXdbEZmmvYm1c3tdv9avqscde9ZuNI0zS/cbvcK+Sa8MOQbmnPDwTPF4dSMuo2U9KSkWvm+dP3FNM1n/e/ZW76bbDMlrZf060jcp9TMv8meklZL6l5nWKei5bihcUH0F4akx+S7p/WAfMN2j3TPclxrTv/akoSif4t3oehb4t1h9lmRfMNzO0jaK+kL0zTPcrvdWfJNxz7Jv27UfN+NqwQLAAAAACIproYIAgAAAEAkkWABAAAAgE1IsAAAAADAJiRYAAAAAGATEiwAAAAAsAkJFgAAAADYhAQLAAAAAGxCggUAAAAANiHBAgAAAACbkGABAAAAgE1IsAAAAADAJiRYAAAAAGATEiwAAAAAsAkJFhAmhmGcbBjG14ZhbDEMY4FhGEakYwIA4CDDMOYahpFrGEZZpGMBYhkJFhA+f5V0raR+/n8TIxsOAAD1vClpeKSDAGIdCRbQBIZh3GkYxi3+nx82DGOF/+dxhmH8vZHyXSS18Xq9H3u9Xq+kFyRdENagAQAthtV+SpL8fdSOcMYJxCMSLKBpPpJ0uv/nUySlGYaR6F+2spHyXSVtr/P7dv8yAABCwWo/BcAmJFhA03wm6WTDMNpIqpS0Vr4O7HT5OjUAACKJfgqIEGekAwBikdfrrTYMY5ukKyWtkfSVpLGS+kr6tpFV8iR1q/N7N/8yAABs14R+CoBNuIIFNN1Hkn4n31CLjyTdIGm9/x6revxj2vcZhjHCP3vgFElvhDNYAECLE3Q/BcA+JFhA030kqYuktV6vd5ekCh152MVUSc9I2iLpB0nvhjxCAEBLZqmfMgxjnmEY2yW1Mgxju2EY94UnTCC+GJzEAAAAAAB7cAULAAAAAGzCJBeAzQzD+ERS8iGLL/d6vV9HIh4AAOqinwJCiyGCAAAAAGCToK9g5efnhz0Ty8zMVFFRUbg3a5tYjj+WY5eIP9JiOf5Yjl2K7fizsrKM5r5HJPqqxsTycZCIP5JiOXaJ+CON+EMvmL4qqu/BcjiiOryjiuX4Yzl2ifgjLZbjj+XYpdiPP17E+nEg/siJ5dgl4o804o8O8VELAAAAAIgCQd+DVVFR4Q13Vul0OlVTUxPWbdopluOP5dgl4o+0WI4/lmOXYjv+pKSkZg8RjERf1ZhYPg4S8UdSLMcuEX+kEX/oBdNXBX0PViTGQ7pcLhUWFoZ9u3aJ5fhjOXaJ+CMtluOP5dil2I4/Kyur2e8RLWP3Y/k4SMQfSbEcu0T8kUb8oRdMXxX503wAAAAAECd4DhYAW7UuL5Pn2zylVVcHvY63nUv7U9NCGBWAlsRbsFNpO7ZbW4d2CIBNSLAA2MooLlThrFssrZMxe4HEFxsANqkt3KUS2iEAEcIQQQAAAACwCQkWAAAAANiEBAsAAAAAbEKCBQAAAAA2IcECAAAAAJuQYAEAAACATUiwAAAAAMAmJFgAAAAAYBMSLAAAAACwCQkWAAAAANiEBAsAAAAAbEKCBQAAAAA2IcECWjiv16t77rlHo0aNUnZ2tr7++utGy1VVVWn69OkaPXq0xowZo7fffrve62+//ba6du2qzzd+6ytfW6vffpmj8Su/0VkfbdTaPaUhrwsAREqwbelll12m7OxsjR07Vr///e/l8XgkSQ899JBOPvlkDRs2TOPHj9fy5cslSUVFRbrooovUr18/zZw5M2z1AdB0zkgHACCyVqxYoW3btmnVqlX6/PPPddddd+mtt95qUG7BggVq3769Vq1apdraWu3duzfwWllZmZ599lmdeOKJgWUv/VQoSVo2ZpAKK6s1Zd0WvTVqoByG0eC9nUnJSsvPCTpmbzuX9qemWaglgFjWurxMRnFh0OW9Xm8Io2lcsG3pE088ofT0dHm9Xl133XV66623NHnyZEnStddeq7vvvluFhf+ta0pKiqZPn67vvvtOmzZtClt9ADQdCRYQZrm5ubrssst00kkn6dNPP9XQoUPldrv10EMPqbCwUI899phGjhypO+64Q5s2bVJ1dbV++9vf6qyzzlJubq5uueUWHThwQJI0Z84cDRs2TGvWrNH8+fPVrl07bdq0SUOGDNGjjz4qo5Fk5lBLlizRRRddJMMwdPLJJ6ukpES7du1Sp06d6pV7+eWXtXLlSkmSw+FQZmZm4LV58+Zp6tSpeuKJJwLLvi+r0EhXuiTJlZyoNokJ+qrkgIa2bd0gBm9piUoemB70PsyYvUAiwQJaDKO4UCWzbqm3LPdApaas+14ntk3TZ8VlOqFta13crb3mf5+vva3b6uFj0tU/LUX3bszVptJy1Xi9ur1fliZ0aqvcA5W67csclfuvHt0/6BidKYWlLU1P97WLNTU1qqqqOur7tmrVSsOHD9e2bduC3FsAIo0EC4iAnJwcPfnkk5o/f74mTZqkxYsXa/HixVq6dKkeffRRrVy5UqNGjdL8+fNVUlKic845R6effrpcLpdeeuklpaSkaOvWrZo2bZreffddSdKGDRu0YsUKde7cWZMnT9a6des0fPhwzZo1S2vWrGkQw+TJk3XTTTdp586dysrKCizv0qWLdu7cWe9LQUlJiSRfIrV27Vr16NFDc+fOVYcOHfT1119rx44dys7OrpdgHdsmVct27dXkLpnKr6jShpIDyi+vajTBAoCmyDlQqb+e2Ef9h/TQuau/0xv5RXptxAB9fMaFeuzhP6tfWqpGtk/Xn4f0VEl1jc5f/Z1Gt0+XKzlRfx/eTykJDm3bX6Gb1m/Tmf73DGVbetCvfvUrffHFFxo7dqzOPffcwPLnnntOr7/+ugYNGqR7771Xbdu2tX2fAQg9EiwgArp3765jjz1WktS/f3+NHj1ahmFo4MCBys3NVUFBgfbv3x9IWCorK5WXl6dOnTpp5syZ2rhxoxwOh7Zu3Rp4z6FDhwY690GDBik3N1fDhw/X7NmzA2UaDLPJz5GzslyphTsCQ/QSKiuUWpCvtPwM3+/pGarM+1E7duzQ6D499OfrrtRjf39Zf7zr93rivrs1Z8ZdWjhrptLyc5RQWSFHba0k6ZfdXNpSVqFzV3+rrqlJOrldayUEcRY4FKwML/IU5CmtupphiECQLA/fs/Gz1T01WQPbpEqS+qenaFT7dBmGoUG9e2n7gSrtKK/Wsl179dTWXZKkytpa5VVUqVNyku755idt3HdACYahrfsr5ExKVuqenTr52AHqrypp508a2usYFXy1XmndOuqh66+Srr8qsO2E9Ax5Sn0nn4JpS+vW+8UXX1RFRYVuvvlmrV69WmPGjNGUKVN02223qUOHDpo+fbruv/9+zZ8/35b9BCC8SLCACEhOTg787HA4lJSUFPjZ4/HI6/XqqaeeUt++feut99BDD6lDhw5atmyZamtr1bt378BrB99DkhISElRTUyNJ9c66JlRXybMzT5J0XlampvXprPY//qjvH/+TjsvyDfnb/u0GpT2/QCUpiZKkzBnzlDD/HqUmOHTGv19TycrXdWZ5lRat+155d+/SN19/rUmX/EqSVFBZrV/+7i49PaCDTmjbWrOO6x6I6cI136lX6//WO5waG150NAxDBIJj9fNl52cryfHfkzYOGUpy+ObucjgcqvF65TAMPXlSH/VJS6m33vzN+eqQnKglpx+nWq/Ub8nn8paWaP9zj8qxbVegPp5vflLp9+tV8snbmr0xt95kPQmdsuTZlR90W3povVNSUjRhwgQtWbJEY8aMUYcOHQKxX3bZZbriiits2UcAwo8EC4hC48eP13PPPac5c+bIMAxt2LBBgwcP1r59+9SlSxc5HA7985//DMw+dSR1r2Cl5ec0+CI0vlNbPf/jbp3fpZ3W792vdGeCOvm/EBxkGIayO2Zo7Z5SjXK10eo9+9QvLUVtEhP05fihgXLujzdp3oN/UM/XnlW5p1Zer1etnAlaWbBPCYah/umpzdwzABC8Mzq00aIfd+v+47r72tKSAxqc0UqlNR51SUmSwzD0yvZCeYKYE6PuCSPJd/KpqM69o8G0pfv371dZWZk6deqkmpoaLV++XKeeeqok1btf691339WAAQOaWXsAkUKCBUShGTNmaNq0acrOzlZtba26d++uF154QVdccYWuu+46vfLKKxo7dqxatWrV7G2N69BGH+wu0en/3qBUh0N/HtIz8NrEjzbqP/6f7xrYVbd9kaPZ325XZpJTD9Up15jCympdvu57OWSoU0qi/jL0yOUBwG639u2i+zbmasJHG1Ur35DCRcP6akqPDrr+8616NW+PzujQRq0Smv/UmqO1pWslHThwQFdddZWqqqpUW1urkSNH6vLLL5fkm7Ro48aNcjqd6tKlix588MHA+qeeeqrKyspUVVWl9957Ty+99JL69+/f7JgBhAYJFhBm3bt314oVKwK//+Uvf2nwWmpqqubNm9dg3d69e+v9998P/H7wmSgjR47UyJEjA8vnzp0bdDyGYWjO4GMafe29048L/NwtNVmvnHbkM6rmiAHKHDhARZK6t0rWh2cMDjoOALCie6tkvT9mUOD3+Sf0DPzco0vnwGv/7/geDdbt1TpFS+u0bzMGdpMkndY+Xae1Tw8s/8OgxtvGxgTTlnbo0EHvvPNOo2UeffRRSZLL5ao3TbskffLJJ0HHASDyeNAwAAAAANiEBAsAAAAAbMIQQQAxx5mUHJgKORiOICYDARAeVj+/Ep9hALGFBAtAzPGWlqikzuxdR5M5o+H9bAAiw+rnV+IzDCC2MEQQAAAAAGxCggUAAAAANiHBAgAAAACbBH0PVmZmphyO8OZjTqdTLpcrrNu0UyzHH8uxS7Ebv7dgp2oLd6m2MF/tvN6jlne4Osno0Dno9/cU5FmOyTCMkJYPxzaaElNiYmJM/g3F6t++XSLRVzUm1o+DlfittivhaCNkfRMhb1eCbVPq7vuDfUKwrPYJodCS/vajEfFHh6ATrKKiolDG0ajGHrYXS2I5/liOXYrd+NN2bFfJrFuCLp8xe4HKjODnqkmrrrYckzeIRK855cOxjabEVF1dreIY/BuK1b99ScrKymr2e0Sir2pMLB8HyVr8VtuVcLQRsr6JkLcrwbYpdfd9qPuEUGhJf/vRiPhDL5i+KvKn+QAAAAAgTpBgAQAAAIBNSLAAAAAAwCYkWAAAAABgExIsAAAAALBJZKeaAeJY6/IyGcXWZsJxeDwhigYAEO+cSclKy8+xtI63nUv7U9NCExDQQpFgASFiFBdaml5XkjJnzLNU3mpnSgIHAJERbHvtKcgLTH1vtc32lpao5IHpltbJmL1AIsECbEWCBcQwq52p1QQOAGCPpiQ/tNlAbOIeLAAAAACwCVewgCBZvaeK4XgAgGhndag592wBR0eCBQTJ6j1VDO0AAEQ7q0MXuWcLODqGCAIAAACATUiwAAAAAMAmDBEEgEZwXwJaqtblZfJ8+9+pwo+G+00BoD4SLABoBPcloKUyigtVyP2mANBkDBEEAAAAAJuQYAEAAACATUiwAAAAAMAmJFgAAAAAYBMSLAAAAACwCQkWAAAAANiEBAsAAAAAbEKCBQAAAAA24UHDaJFal5fJKC60tI7D4wlRNAAAxAZnUrLS8nMO+7qnIE9p1dX1lnnbubSfB7GjBSHBQotkFBeqZNYtltbJnDEvRNEAABAbvKUlKnlguqV1MmYvkEiw0IIwRBAAAAAAbEKCBQAAAAA2IcECAAAAAJtwDxbiwqGTVjR2k21dTFgBAEB4HG1ijEMxKQZiHQkW4oLVSSuYsAIAgPCwOjEGk2Ig1jFEEAAAAABsEvQVrMzMTDkc4c3HnE6nXC5XWLdpp1iOP9Zi9xTkWSpvGIblbVhdJxq3EY0xhWMb4YgpMTExKj4zsfbZtVsk+qrGxPJxCHV7Go7Po6xvIi7aoXjZRiTb01j+7ErEHy2CTrCKiopCGUejXC6XCgutPQw2msRy/LEW+5Hut2qM1+u1vA2r60TjNqIxpnBsIxwxVVdXqzgKPjOx9tmtKysrq9nvEYm+qjGxfBxC3Z6G4/Mo65uIi3YoXrYRyfY0lj+7EvGHQzB9VeRP8wEAAABAnCDBAgAAAACbkGABAAAAgE2Yph0AbGD1OS8Sz3pB6B36jMBg8JxAAGgeEiwAsIHV57xIPOsFoWf1GYESzwkEgOYiwQKACLF61YsrXrB6RYqrUQAQfiRYABAhVq96ccULVq9IcTUKAMKPSS4AAAAAwCYkWAAAAABgExIsAAAAALAJCRYAAAAA2IQECwAAAABsQoIFAAAAADYhwQIAAAAAm5BgAQAAAIBNSLAAAAAAwCYkWAAAAABgExIsAAAAALAJCRYAAAAA2IQECwAAAABsQoIFAAAAADZxRjoAAEBwnEnJSsvPOWIZT0Ge0qqrA79727m0PzUtxJGhKVqXl8koLrS0jsPjCVE0AAC7kGABQIzwlpao5IHpltbJmL1AIsGKSkZxoUpm3WJpncwZ80IUDQDALiRYsMzqWVerZ9A5qwsAQMsVzNX6uppypf5w3zUOHQXQnG2g5SLBgmVWz7paPYPOWV0AAFouq1frm3KlPtTfZdCyMckFAAAAANiEBAsAAAAAbEKCBQAAAAA2IcECAAAAAJswyQVCzupsQMwICAAAgmX1e4bEdw2EFgkWQs7qbEAteUbAx81X9ezKb+SVdGl3l67p1UmSNH9zvl7KLVT7JN9HdvqArhrXMUNrv9qgmz/aqETD0GMn9lKv1ikqqa7R1PVb9b/D+slhGA224f54k2YO7KYT2raWJOUeqNRVn27R+2MGae2eUl3z2RZ1T01WVa1X52W10x+kwPJjWiWr3FMrV1KibujdSdmd2oZr1wBAszy7bZdeyi08bPvacdLU41kAACAASURBVPN18uzKD7Sv64rKNPObnw7bvjbmSO3rys+/0C+Xrq/Xvt7eL4v21QZNeUZgS/6ugdALOsHKzMyUwxHeEYVOp1Mulyus27RTLMd/pNg9BXmW3sto5Eu+neXjZRubSsu16M139OaoY5VoGLp83ffK7pihnq1TJEnX9Oqo63t3rrfOoy//U8+f0le55VX6v58KdM+x3fXolp26qU+XRpOrYGIa1i5di4b11YEajyau+la/2Px9veWS9M2+A7r2sx+UkuDQaFebZtW7KevEw/EO1zYSExNjth1qikj0VY0Jpv232pZK8fF3GY6YdEjxTaXleim38Ijt64xnn9aeuXcG1nl6264jtq9Nqfeh7Wt2R18Sdbj2dXIU7tto3EY4YoqVtjSWv3tKsR//QUEnWEVFRaGMo1Eul0uFhdYeOBtNYjn+I8Xe2AP4jsTr9Ya0fLxs4/uyCg07bqBSvQWSpBGZ6Xp3517d2KfzYddxOp0qr6lVhadWTsNQzv5K5VdU6bT26c2OqZUzQcdntNIPuXlKOeS1QW1a6da+XfT8jwUNEqxo3LfRGFO4tlFdXa3iGGmHsrKymv0ekeirGhNM+2+1LZXi4+8yHDHpkOLfl1XoxLatlZrgS74ba18P3YbTYajcc/j2tTn1Pti+5hyokCspsV65uu3r+VG4b6NxG+GIKVba0lj+7inFRvzB9FUMEQSixID0FM3/8msVD3QpJcGhDwpKNCSjVeD1538s0Kt5RRqS0Up3H9tNbROd+t3ll2ra7XcoxWHoLyf00pzvtuvO/kf/4N/65Tal+M/yV3m9jc52U1xVo/V79+ueXj21rZHXB2e00pNbdzWtsgAQRgPSU/SnTXkqrqo5bPu6+IprNKhyX6B9ndans27/Kiek7estfbuoqKqmweu0r0BsI8ECokS/tFTd/uuzddnCv6pVgkPHtUkNDPO7vEcH3dqviwxJf96crznfbtefh/TUkH599cbIgZKkT4pK1TE5UV6vNHX9ViUahu4+tps6JCc22NYjJ/RqcI/AQeuKS3X2qo0yZOjG3p11XO/GE6wmnDAEgIjol5aqG/v8//buPT6K6v7/+Gs2myuBkLBAEgwX5SYgguKtCBhAvNVL1e+I99rWVmt/VOSrLeBXQCkqKihq6631Uutl1FZtvQAqVq13RSyggEIEcgFCQgiQQLLZ3x8bFgIhmU129vp+Ph48SHbPzHzO7OSc/cycM5PLpZ+uOWT72mXqnUy76vJA+zq4U0aL7evdFRUc3LoG174O6JjOR1urD1qH2leR2KYESySKXPnjMzn763cBuHNVMXlp/u57/yTp4gJPkw4b/EMdFnxXyoPDDueWlRuYNrAHG3ft4fGizdw0oEdQMew/F6AlK7bvom/mgYMHRUSi08QCDxML/HM7mmtfXS5XUO3rn174B5OCjEHtq0hiiPxMYBEJ2FxZCUBxzR7eLKvk3PwcADbV7pursXDTNgZ0TG+y3IvFFRR2zaJzipsabwMuDAwDarwNjsT5zfZdLPiulCt6dXVk/SIioVa+29+Ohqx93b3bkTjVvorEPl3BEokil06fyZa135FsGNw2uCdZyf4/0TnfbmTl9l0YhsFh6SncPqRXYJkabwMvbizn6eP7A/67YV352RqSXS7uH9YnZLHtHdpS422gS0oyMwcVHHSDCxGRaPWrL9dSWVd/yPbVveoX5FVX225fn5o0DZ6+PySxqX0ViS9KsESiyOI/3kdFM8/yuK+FRCk9ycXzJw4I/H5CTkcWjx58yPLWfmUBCjJSeaux/EldOjZ7B8KTunRkxYThrcYvIhKtXjppQLOv721fc6bNPaj9bal9zelZwIH3rGypfR19zDCGNDM8UO2rSPzREEEREREREZEQUYIlIiIiIiISIkqwREREREREQkQJloiIiIiISIgowRIREREREQkRJVgiIiIiIiIhogRLREREREQkRJRgiYiIiIiIhIgSrCi0ZMkSRo0axcknncQjc24js6Soyb/n75/PqaNHcfopY7jgzDPY+MESMkuKSF3/HVN+cx3jxo1jzJgx3H//vifMV1VVcfXVVzN69GjGjBnD559/HsEaioiT3t1SxSn/Xs6od5cz74m/HvR+cXExF154IRMmTGD8+PG8/fbbAOzZs4fJkyczbtw4xo8fz4cffhhY5uWXXw68fumll1JRceAjVmPD3vZ15MiRPPDAAwe9//HHH3PaaafRs2dP/vWvfzV5z7IsRo4cyciRI7EsC4Camhouv/xyRo8eTWFhIXPmzAlLPUQkMlprQwBeffVVTjnlFAoLC7nuuusCr1966aUceeSRXHHFFc0uN2vq7+nft+9B3/sO9a9DzQ5H6ijt5450ANKU1+tl+vTpPPvss/RjD6NPO4NRX79H/47pgTKn1Xm5cEA2AIs2beOm637NX4/vx8vFFezpdjhvv/02NTU1nHLKKZx33nkUFBRwyy23UFhYyKOPPsqePXuoqamJVBVFxEFen4+bV6znb8f3Jy8tmfMWvcUpF15E//79A2Xuu+8+zj77bK688kpWr17N5ZdfzieffMIzzzwDwNtvv015eTmXXXYZr7/+Og0NDdxyyy28++675OTkMHv2bB5//HGmTJkSqWq2yf7ta15eHmeeeSYTJkxosm969OjB/Pnzeeihh5osW1lZyfz583n99dcxDIMzzjiDCRMmkJqayjXXXMPIkSPZs2cPF110Ee+88w6maYa7eiLiMDttyNq1a3nggQd4+eWX6dy5M+Xl5YH3rrnmGmpqanj66acPWveyZcuo2rIZ357dVM2YZCuerFkLID2z/RWTkFOCFUYdanZgVJa3WObTr5dzRF53Bif7cHldnJ2XzaJN25okWB2TkwI/13gbMAz/z4YBu2pqqa+vp6amhuTkZDIzM9m+fTuffPIJ9957LwApKSmkpKS0GJN3SzGZdXXNxujyeoOqt4iEz1fbdtI7I41eGakAnH/qOBYuXNjkCwDAjh3+M5/bt2+ne/fuAKxevZqRI0cC4PF46NSpE8uWLWPIkCH4fD527dpFdnY21dXV9O7dO3yVCpGlS5fSu3dvevXqBcC555570L4pKCgAwOVqOsDj3//+N6NGjSI7239ya9SoUbz77rucd955gX2WkpLCUUcdRWlpaTiqIyJh5E5JZdXiNwLf0Sgv4X8KR7Pkhec45qp9V6ReeORP/Oq8H3PYrm34Ut3g8QTeGzVqVJORAXt5vV5uu+02nrjl9/xz0eKgYsosKWq6rha+vwH4sj3sVFLmOCVYYWRUlrd6VuK70kq6bamiasYkcqbNJS89ha+27Tyo3JNFm3m0aBN1DT6eO8H/5eDM3GyWeNMYPnw4NTU1zJw5k+zsbJYvX06XLl2YPHkyK1euZOjQodx6661kZGTYiulAOdPmBlVeRMKnrLaO/LTkwO89unfjo3UbmpSZMmUKl1xyCX/5y1+oqanhueeeA2DQoEEsWrSI8847j5KSEv773/9SUlLC8OHDuf322xk3bhwZGRn06dMnJofClZWVkZ+fH/g9Ly+PpUuXtnnZsrKyJmWqqqpYvHgxP//5z0MTsIhEDV91Fd89cGfgOxpAdvFWvtq2k6qifdMuvv3iO3Z3SGPsHx/A6NGL638/jcLCwhbX/fjjjzNhwgRy90vG7MZUNeemoJbRVa/w0BysGHVl7258cMpRTB1wGAu+858t/WrbTpJcLr788ks+/vhjHn74YX744Qe8Xi///e9/ueKKK1i0aBEZGRmHHDcsIvHFleQmeef2JuP233jqcS47bTyr/vkSL86by/W/vpaMjWv5xegT6dnVwxlnnMGMGTMYMWIESUlJ1NXV8dRTT7Fw4UK+/PJLjjzyyCZzPAXq6+uZdM2vuPaC8xic7MP7zbJW509oNIBI/KlvgKKdu7FOGMCfZ8/ixhtvpKqq6pDly8rK+Ne//sXPfvazMEYpTtMVrCiTm5ZMSe2+S7ulNXvonpp8yPLn5GczfcUPALxSUsGEH19C9pZisoEfDT6SVe++xcjhw+jRrSujc3OgpIgLTziG+U8+rQ5eJA4d2IYUb1hP9rKPm1ypfuK9Ffz1uH5UzfiIAcCushLWTbsOT2oyd8xawI4/+K9OnXPOORx++OGsWLECIDAs8Oyzz+bBBx8MW51CJTc3l5KSksDvpaWl5Obm2l52/6E9paWl/OhHPwr8ftNNN3FEXncuXfUfqmb8x9Y6NRpAJLbY+Y6Wl5bM8M4dSHYZ9O3Th3498in79EN6DD4SgPStZbh31wSG9n3/wYf88P33jDrxBPD5p36Menc5758yJGz1ktBTghVljs7qwLqdtazftZvMujr+WVrJgmF9mpRZt7OWPh3SAHh7cxW9M/w/56en8O8PP+S0D19lV72Xjz/8lssr15L2wSt0r93Bl1N+yRGZaSxcXUIfb0NgGKKIxI/925DctGRefGsJ8/M7NynTIz2F/2zdzv8c5mHNjhp2N/jokuKmxtuAu/EGOO+99x5ut5v+/ftTVlbGmjVr2Lp1K126dOG9996jb9++kaheuwwbNox169axfv16cnNzeeWVV2wnimPGjOGOO+5g27ZtgH//TJ06FYA777yT6upqHpoxlepZ1zsWv4hE1oHta3Pf0U7L7cwrJRWYBR62bFjPmuVf0+Xp+6lK8X/l3rm1mrq1mwInvU4EPj/Rv46caXPpNuZUJVdxQAlWlHG7DG4b3JPLP10Dl17FhXnZDOiYzj2rSzgqK4MJ3TvzxA9b+KB8O8mGQVZyEvOO7g3Alb26MnVXDeM+X4EPMA/rwpGdMgC4dXABk75aR53PR8+MFO4e2jtidRQR5+zfhnjx8dNLL2NA8bImbcjNAw/jd8t/4LF1mzGAeUN7YxgG5bv3cNZlV0FKKrm5uSxYsADwX72ZPHky559/PsnJyYE77cUat9vN7NmzueSSS2hoaOCiiy5iwIAB3HXXXRx99NFMmDCBr776ip///OeB+VT33HMPS5YsITs7m+uvv56zzjoLgMmTJ5OdnU1JSQkLFiygb9++jL7sKrxlxVzZuxsXFwQ3l0JEot+B7etFh3kO+o42xtOJ97ZsZ+x7K0j5dgrTBx5GdmNydcFHq/h+Zy07670c/87X3HVUL8Z0zYpwrcQJSrCi0NhuWYztlkXOtLlUNE5enNJ/3+TqWYMKml2ugzuJp2+ZEVhmf4M7ZfDayUc6E7CIRJW9bQhAzpWXUjFnWZM2pH/HdP5x0sCDlivISOWLl55jR37vg9674oorDvnsllgybtw4xo0b1+S1G2+8MfDzsGHD+OKLL5pdduLEiUycOLHJa/n5+RQXFwOQWVIU9E2DRCS27N++7rV/+2oYBrcMKuAWaPI9DuClkwa0uv5vTxseslglcnSTCxERERERkRDRFSwREQlo7rkqLYnXZ6rYeW7hgXTTIBERASVYIiKyn2CfqxKvz1TRMwJFRKSt4jbBCvbsY1LHLLzVh35OQXNi+cxtz9cb5xi8Pq7lgs0Jdhmny2sbzpWPl21EY0wObmP9mccGH4uETKB9bU0UHTNh3UZYYhobhm1EY70TdBthjimR2ti2XM0P9jt1LH+fPpS4TbCCPfuYM22unoYtIiIiItKorVfzE30kRNwmWE7r+D9XkrS7tsW5Ct4txWTW7Xsgncbni4iIiEikBDvPVt9d2yYiCZbdy417E5S2DN9z+oAwklObvR16SzQ+X0REREQiJdh5tuH47rp/0nfgxYlDifZhiBFJsMIxfE/JTMv2jh8+8BkNdgS7jNPltY3Yjikc24jGmMK1DQk/u/MzovWYiYe/xy7T72LrH25svWA7thGN9U7UbURjTGJfsEkfRP8wRMPn89kqOGvWLHsFRURE2mjGjBlGe5ZXXyUiIk5rta/y+XxR+2/mzJm+SMeQqPHHcuyKP/L/Yjn+WI49HuKPl3+x/jkofsWu+GPzn+KPjn+uMCV6IiIiIiIicS/aE6xZkQ6gnWI5/liOHRR/pMVy/LEcO8R+/PEi1j8HxR85sRw7KP5IU/xRwPYcLBEREREREWlZtF/BEhERERERiRlKsEREREREREIkIs/BOpBpmqcD9wFJwGOWZd1xwPs3AL8A6oEtwM8sy/oh7IE2w0bs1wDXAV5gB/BLy7JWhj3QQ2gt/v3KXQC8CBxnWdbnYQyxRTb2/0+Bu4DixpcesCzrsbAG2QI7+980TROYCfiAZZZlXRLWIA/Bxr6fDxQ2/poBdLMsq3N4ozw0G/H3BJ4EOjeW+b1lWa+HPdBDsBF/L+AvQFegArjMsqyNYQ80ztn4HEYD9wJDgYmWZb0Y/igPTf1v5Kj/jSz1v5ET6/2vHRG/gmWaZhLwIHAGMAi42DTNQQcUWwqMsCxrKP5GJiqeImwz9mcsyzrKsqxh+OOeF+YwD8lm/Jim2RH4LfBJeCNsmd34gectyxrW+C+aGvdW4zdNsx8wFRhpWdZg4PqwB9oMO7FbljV5734H7gf+Hv5Im2fz2LkZsCzLGg5MBP4Y3igPzWb8dwNPNbabtwK3hzfK+Gfzc1gP/BR4JrzRtU79b+So/40s9b+RE+v9r10RT7CA44HvLMtaa1nWHuA54Nz9C1iWtcSyrF2Nv34MHBbmGA/FTuzb9/u1A/6zINGi1fgb3QbcCdSGMzgb7MYfrezEfzXwoGVZlQCWZW0Oc4yHEuy+vxh4NiyR2WMnfh/QqfHnLKAkjPG1xk78g4B3Gn9e0sz70n52+oAiy7K+BhoiEWAr1P9GjvrfyFL/Gzmx3v/aEg0JVg9gw36/b2x87VB+DrzhaET22YrdNM3rTNP8Hv8ZtElhis2OVuM3TfMYoMCyrNfCGZhNdo+dC0zT/No0zRdN0ywIT2i22Im/P9DfNM3/mKb5ceNl9Whg+++2cahaH/Z92Y8GduKfCVxmmuZG4HXg/4UnNFvsxL8MOL/x558AHU3T7BKG2BJJsP1XtFH/GznqfyNL/W/kxHr/a0s0JFi2maZ5GTAC/5jemGFZ1oOWZR0B/A7/Zc+YYJqmC/+QiimRjqUd/gn0bhzeshj/mN5Y4gb6AafgPwv1qGmaUTOO2qaJwIuWZXkjHUiQLgaesCzrMOBM4K+NfxOx4n+BMaZpLgXG4J8HEWufgUQJ9b/hpf43Kqj/jZxY73+jIsEqBvY/q3EY+yZEBpimOR6YDpxjWdbuMMXWGlux7+c54DxHIwpOa/F3BIYA75qmWQScCLxqmuaIsEXYslb3v2VZW/c7Xh4Djg1TbHbYOX42Aq9allVnWdY6YDX+Bj/Sgjn2JxJdwxPAXvw/BywAy7I+AtIAT1iia52dY7/EsqzzG8ewT298bVv4QkwIwfYB0Ub9b+So/40s9b+RE+v9ry3RcBfBz4B+pmn2wb+DJwJN7tJimuZw4GHg9CgaAwv2Yu9nWdaaxl/PAtYQPVqM37KsKvY7oE3TfBf43yi6i5Gd/Z9nWVZp46/nAN+EN8QWtRo/8DL+MzmPm6bpwT9kYW1Yo2yendgxTXMgkA18FN7wWmUn/vXAOOAJ0zSPxN/AbwlrlIdm59j3ABWWZTXgn6j9l7BHGf9s/R1EMfW/kaP+N7LU/0ZOrPe/tkT8CpZlWfXAb4CF+P/4LMuyVpimeatpmuc0FrsLyAReME3zK9M0X41QuE3YjP03pmmuME3zK+AG4MoIhXsQm/FHLZvxT2rc/8vwj7//aWSiPZjN+BcCW03TXIn/RgU3Wpa1NTIR7xPEsTMReM6yrGiaXG43/inA1Y3HzrPAT6OlHjbjPwVYZZrmaqA78IeIBBvH7HwOpmke1ziP4H+Ah03TXBG5iJtS/xs56n8jS/1v5MR6/2uX4fPFVLwiIiIiIiJRK+JXsEREREREROKFEiwREREREZEQUYIlIiIiIiISIkqwREREREREQkQJloiIiIiISIgowRIREREREQkRJVgiIiIiIiIhogRLREREREQkRJRgiYiIiIiIhIgSLBERERERkRBRgiUiIiIiIhIiSrBERERERERCRAmWiIiIiIhIiCjBErHBMIzOhmH8OsTrzDEMY7FhGGsa/88O5fpFRCRxONRP/Y9hGCsMw2gwDGNEKNctEs+UYInY0xkIaccF/B542+fz9QPebvxdRESkLZzop5YD5wPvhXi9InFNCZaIPXcARxiG8ZVhGPMNw3jbMIwvDcP4r2EY5wIYhtHbMIzlexcwDON/DcOY2cI6zwWebPz5SeA8p4IXEZG4F/J+yufzfePz+VY5H7pIfHFHOgCRGPF7YIjP5xtmGIYbyPD5fNsNw/AAHxuG8Wob1tnd5/OVNv5cBnQPVbAiIpJwnOinRKQNlGCJBM8A5hiGMRpoAHrQzuTI5/P5DMPwhSI4ERFJeCHvp0TEPiVYIsG7FOgKHOvz+eoMwygC0oB6mg67TWtlPZsMw8jz+XylhmHkAZsdiVZERBJNqPopEWkDzcESsaca6Nj4cxawubHTKgR6Nb6+CehmGEYXwzBSgR+3ss5XgSsbf74SeCXEMYuISOJwop8SkTbQFSwRG3w+31bDMP7TODn4M2CgYRj/BT4Hvm0sU2cYxq3Ap0Dx3tdbcAdgGYbxc+AHwHSsAiIiEtec6KcMw/gJcD/+q2GvGYbxlc/nO83JeojEA8Pn07QPERERERGRUNAQQRERERERkRDREEERhxmG8SAw8oCX7/P5fI9HIh4REZH9qZ8SCS0NERQREREREQkR21ewSkpKojYTy8nJoaKiItJhREQi1x1Uf9U/cesfj3XPz8832ruOaO6rYk08HmPRRPvXWdq/zkvUfWynr4qLOVguV1xUo00Sue6g+qv+iVv/RK67hIeOMWdp/zpL+9d52seHpj0jIiIiIiISIkqwREREREREQkQJloiIiIiISIjoNu0iIhI3cnJyNC8gRNxuNx6PJ9JhxC3tX2dp/zpP+/jQlGCJiEjcSMQ7WjnF4/FQXl4e6TDilvavs7R/nZeo+zg/P7/VMkqwRGzqULMDo9J+Q+LL9rAzPdPBiEREJFYc2Id4txSTWVfX4jLqR0RikxIsEZuMynKqZkyyXT5r1gJQxygiIgTfh4D6EZFYpYHqIiIiIiIiIaIES0REREREJEQ0RFBEREQkSMHOy3V5vQ5GIyLRRAmWiIiISJCCnVOVM22ug9GISDSxnWBF87NFEvk+/Ilcdwhv/b1bioMqn5yc7Hhs+vwTt/6JXHeRROFOSSWzpMh2ed11UCQ62E6wovnZIol6H35I7LpDeOvf2u10D1RXV0elw7Hp80/c+sdj3e08W0Qkkfiqq6iac5Pt8rrroEh0iM5LUiIiIiIiIjFICZaIiIiIiEiI6CYXIlEk2LtS+Xz1YOjPWERERCRa6JuZSBQJ9q5Unjl/gq49HIxIRERERIKhIYIiIiIiIiIhoitYIiHm8/mYsXID//6JSVrHTsyfP5+jjjrqoHJff/01kydPpra2lrFjx3LrrbcCcPfqYhZtqsIFdEl1c8/Q3uSmpfDR1mp+8cV3FKSnAnB6bmdmh7NiIiISFnv7kfcuupyU7ZXcM7Q3R2VlHFTu66qdTFlWRG2Dj8KunbjX5wNg7ty5LFq0CMMw8Hg8zJ8/n9zcXD788EN+9rOfUVBQAMCZZ57J5MmTw1o3kUSgK1giIbZky3aKdu1m6d+f584772Tq1KnNlps6dSpz587lgw8+YN26dSxZsgSAX/XJZdGoQbw5ahDjunXmvjWlgWWOy+7Im43vXd9Pt7QWEYlHe/uRZc89xR1DejJ9+Q/Nlpu+fD13HtWL98YMpmjXbt768GMArr32Wt566y0WL17M+PHjmT9/fmCZ448/nsWLF7N48WIlVyIO0RUsiXkbNmzg0ksv5ZhjjuHzzz9n2LBhmKbJPffcQ3l5OQ888AADBgzg5ptvZtWqVdTV1TFlyhROO+00NmzYwKRJk9i1axcAs2fP5rjjjuPDDz9k3rx5ZGdns2rVKoYOHcrjU6fYimfRpm1c0KMLhmFw7LHHUlVVxaZNm+jevXugzKZNm6iurubYY48F4MILL+TNN9/knIG/pmNyUqDcrnovhhHCnSUiIgdpSz8y/arLORnYsGs31y8rosbrBeDWwT0ZkZ3JR1urmb+mhOwUN6urazi2bg53uX0YNhr1/fuRY7Iz2V7vZVNtHd3TkgNlNtXWsaPeyzHZ/udeXdCjC6+//x9OHXkSmQDVWwGoLysmpWYHmSVFpG8tw727psnDi/VwYpHQU4IlcaGoqIiHH36YefPmceaZZ/Lyyy/z8ssvs2jRIu6//3769evHyJEjmTdvHlVVVZx11lmMGjUKj8fDs88+S1paGmvXruW6667jjTfeAGD58uW888475Obmcu655/Lxsq8ZBMxauYGPtlYfFMPZ+Tlcd0QuZbV15KWlBF7Py8ujrKysSYJVVlZGXl7eQWX2mruqmJeKt9LRncTzJ/QPvP7lth2c9v5KuqclM33gYXhCuRNFRBJYsP3I2aefxmsDu+BJTeZvx/cjLcnFup21/GbpOl47+UgAVmzfxVujBtM9LRlzfQmfdWzg+JzMoPuR3LQUymr3NEmwymr3kNukTDIlpSWBGyUd2I9UzZjEzq3VfPLl95w4dnygHzl+3qN6OLFIiCnBkrhQUFDAkUf6O7T+/ftz8sknYxgGAwcOZMOGDZSWlrJ48WIeeughAHbv3k1xcTHdu3dn+vTprFy5EpfLxdq1awPrHDZsGPn5/mF4gwcPZn1JGYOAGYMKHK/PTQN6cNOAHjzwXSlP/LCFKf3zGdIpg48Kj6KDO4l3Nldx9Rffs9LxSEREEkPw/cgeimv30D01hf9bsZ6V23eRZBis3VkbWOfRWR3IS/cnQUf168vGdf/leDKjqh9Z5ngkIolHCZbEhdTU1MDPLpeLlJSUwM9er5ekpCQeeeQR+vbt22S5e+65h65du7J48WIapsK4dwAAHZJJREFUGho4/PDDA+/tXQdAUlIS9d56oPUrWLlpyZTW7gm8XlpaSm5ubpOyubm5lJaWtlgG4Cc9unDlZ2uY0j+/ydDBsd2yuHnFesort5Gk27SLiLRbsP1IZkkRVTMmMW91CV1Tk1k4ahANPui38MvAelJc+6a6J7lceBtvQhFsP3Lg1SrYd1VrX5k68np6oL6sSbnW+pGt27aRqim9IiGlBEsSwpgxY3j88ceZPXs2hmGwfPlyhgwZwvbt28nLy8PlcvHCCy/gbRxD35LWzjye2r0zT/6wmct9Pr744gs6derUZHggQPfu3enYsSNffPEFxxxzDC+++CJXXXUVAOt21tKnQxrgH4d/RKb/58276+ia4sYwDL7atpMGn48unbPY1pYdIiIiQTmwH1m2ajW9gep6L3lpKbgMgxc3luP1tb4uu/3IT30+vqzcQUd3UpPhgQDd05LJdCfxZeUOhnfuwEvFW/l/l46EJS8F1Y/kZGWxsy07REQOSQmWJITrr7+eGTNmMH78eBoaGigoKOCpp57iyiuv5Je//CUvvvgihYWFZGQcfBvcYI3t2oklm6sY1nib9nnz5gXeO/XUU1m8eDEAc+bMCdymvbCwkLFjx0LpD9zxbTHf76zFZRj0SE/h9iE9AXi9tJK/rt+C2zBIS3LxwPDDbU2WFhFJNB1qdmBUltsun7GpFFeDN3Dzh+RdO0ir3EJmSZH/vbo93HzR+fx+3n1MGDOahoYGeuXn8WgWXNGrK7/6ci0vFW9lTNdOZCS1/wbNe/uRoY23ab97aO/Ae6e/v5I3Rw0CYPbgnkz5uojahgYKu2Yx4cTjqVzykvoRkQgzfD4bp1qAkpISewUjwOPxUF5uvyGNJ4lcdwhv/fcOB7Era9YCduT3dnQbnjl/ojKBhwgm8vEfj3XPz89v9ze9aO6rYk0sH2PBtqUAOdPmUjHnpqgpH65ttKWvigWxfPzGikTdx3b6Kj0HS0REREREJEQ0RFASUrDDRwBcNuZniYiIiEhiU4IlCcmoLG/T8BERiW45OTm4XBqcEQputxuPJzaftufdUhz0MsHORXK6fLi2kZycHLOfc0ti+fiNFdrHh6YES0RE4kZFRUWkQ4gbsTy/IrOuLuhl7M5JD1f5cG2jrq6Oyhj9nFsSy8dvrEjUfbz3Gakt0Wk+ERERERGRENEVLBEREZEE5U5JDdye3g5ftoed6ZnOBSQSB5RgiYiIiCQoX3UVVUHc2j1r1gJQgiXSIiVYEheCvStgot4RsC13T9TZShERERH7lGBJXAj2roCJekfAttw9UWcrRUREROzTTS5ERERERERCRAmWiIiIiIhIiGiIoESdYOYJebcUk1lXl7BzqkREREQkuijBkqjTlnlCiTqnSkRERESiixIskRhmuN1BPb9EV/pEREREnGU7wcrJycHlis4pW263G4/HE+kwIiIe6+7dUhz0MoZhOFq+LcskJycH/dkEW/eG7duomn2j7fJdpt8V1PqhbfUIl3g8/u1K5LqLiIhEM9sJVkVFhZNxtIvH46G8PLhn+8SLeKx7Zl1d0Mv4fD5Hy7dlmbq6OiqD/GyCrnuQ1WhLvdtSj3CJx+Pfrnise35+fqRDEBERaTcNERRxiDslNajhe6AhfCIiIiKxTgmWiEN81VVUzbkpqGV0sw4RERGR2KYES0RERERsacvoDF+2h53pmc4EJBKFlGCJiIiIiC1tGZ2RNWsBKMGSBBKdtwUUERERERGJQUqwREREREREQkQJloiIiIiISIgowRIREREREQkRJVgiIiIiIiIhorsIiuM61OzAqCy3XV4P2xURERGRWKUESxxnVJZTNWOS7fJ62K6IiIiIxColWBI0XZESEREREWmeEiwJmq5IiUi0ysnJweXS9OJQcLvdeDyeSIfRJt4txUEvYxhGVJWPp20kJyeH/ViK5eM3VmgfH5oSLBERiRsVFRWRDiFueDweysvtj1aIJpl1dUEv4/P5oqp8PG2jrq6OyjAfS7F8/MaKRN3H+fn5rZbRaT4REREREZEQ0RUsEWmROyWVzJIi2+V92R52pmc6F5CIiIhIFFOCJSIt8lVXUTXnJtvls2YtACVYIiIikqA0RFBERERERCRElGCJiIiIiIiEiIYIioiIiIhjNJdXEo0SLBERERFxjObySqLREEEREREREZEQUYIlIiIiIiISIkqwREREREREQkQJloiIiIiISIgowRIREREREQkRJVgiIiIiIiIhogRLREREREQkRPQcLBEREYlqHWp2YFSW2y7v8nodjEZEpGW2E6ycnBxcrui84OV2u/F4PJEOIyIiUXfvluKgyhuG4Wj5RN4GwRYPQ0zJyclhOyb1t5+YdZfEY1SWUzVjku3yOdPmOhiNiEjLbCdYFRUVTsbRLh6Ph/Jy+2e24kkk6p5ZVxdUeZ/P52j5RN4GwRYPQ0x1dXVUhumY1N9+fNU9Pz8/0iGIiIi0W3RekhIREREREYlBmoMlIiHlTkkls6QoqGV82R52pmc6E5CIiIhIGCnBEpGQ8lVXUTXnpqCWyZq1AJRgiYiISBzQEEEREREREZEQUYIlIiIiIiISIkqwREREREREQkQJloiIiIiISIjoJhciIhI3cnJycLl07jAUoulh1k4/4L4ty0Tjw+fjZRuheGB9NB2/8Ur7+NCUYImISNyoqKiIdAhxI5oeZu30A+7bskw0Pnw+XrYRigfWR9PxG68SdR/n5+e3WkYJlojEnA41OzAqy/FuKbb1xUvP2RIREZFwUYIlIhEX7MOJXV4vlbdOtl1ez9kSiR57T5AEw+X1OhSNiEjoKcESkYgL9uHEOdPmOhiNiDjJqCynasakoJbR37yIxBIlWAlOZxIlEQR7hQw0rFBERETaRglWnAk2YQp2qBXoTKLEnmCvkIGGFYqIiEjbKMGKM8EOvVCyJCIiIiISOkqwRERERCRqBDusW0O6JdoowRIRERGRqBHssG4N6ZZoo8fdi4iIiIiIhIgSLBERERERkRBRgiUiIiIiIhIiSrBERERERERCRAmWiIiIiIhIiOgugiIiItJmbXnAvYhIPFOCFcXsdFreLcVk1tUFflfHJSIi4aQH3IuINKUEK4oF22mBOi4RERERkUjSHCyJK39et4nx761g3HsreGzdpsDr81aXcNzbX3P6+ys5/f2VLPzoEwA+q9jBhPdXctYH37BuZy0AVXX1XPrpahp8vma3YX68imXbdgZ+37BrN+PfWwHAR1urGbxoKae/v5JjLr2K+WtKmrx+xgcrOeXfy7nwo1W8tWmbI/tARETar7X+5KSf/pLT31/JO5urAGf6k/zTzuH091cy9t8r1J+IxBBdwZK4saq6hmc3lPPPkUeSbBhc/tkaxnfLoneHNAB+0acbvzo8F4Cck06gYslLPLpuE0+O6MuGmj08vX4L/3dkAfd/V8ZvjsjDZRhtiuO47I48cVxfUifP4oRzL2B8t85NXgdYsX0XV3/xPWlJLk72dApB7UVEJFTs9CdTH3uEijk3BZZxoj/50dFDeKRzPbvqvZz+wTet9ifntLPeIhIaSrAkbqzZUcvwzh1IT/JfmD0xpyNvlG3j2iNyD7mM22VQ422g1tuA2zAo2rmbkto9nNSlY7vj6ZCezlFZGRTtqsWTktzkvcGdMvht3zye/GGLEiwRkSgTbf1JhjvJVn+SqAmWOyWVzJKiJq8dOEf9QL5sDzvTMx2OTBKVEiyJGwM6pnHXqmIq99STluRiyZYqhmZlBN5/8octvFRcwdCsDO7ZXg3AdUfkMvnrItJcBvce3YfZ327kxv75rW7rt8vWkebyd7x7fL5mx9purapi6badTOqbR8We+oPeH5KVwcNrNzWzpIiIRJKd/uTlK3/B4N3bufnIw+ic7Ha0P6ncU6/+pAW+6iqq9ruaaEfWrAWgBEscYjvBysnJweWKzilbbrcbj8cT6TBCzrulOOhljCCHIQRbPhzbaGtM/TLTufaIXC79dA0ZSS4GdUoPDMu4vFdXftsvDwO4e3UJ0x58iD/gP/P3yo8GAvBJRTXdUpPx+eDXS9eSbBjcfORhdE1NPiiu+47uw9GdOwD+MfNXff5d4L3PKqs544OVJH//O649PJcBHdP5aGv1QTE3NyQ/6LoHWzwKP+9wbKMtMSUnJ0d1uxKv7Z5INLDTn3SZeifTrrqc2d9s5O6hvVvtT+6uqCC5mW211J98uGw5ZyQ1YGAE3Z+ISOTYTrAqKiqcjKNdPB4P5eX2n8ERK1q6tH0oviBb2WDLh2Mb7YlpYoGHiQX+L513riomL83fne1NkgAuLvDwi5XfwpFdmiy/4LtSHhx2OLes3MC0gT3YuGsPjxdt5qYBPYKKa+/Y+Jxpc5uMzz/Qiu276JuZ1mw9bAu2eBR+3uHYRltiqqurozKK25V4bPfy81s/2y8SLq31Jy6Xi4sLPE0SIjh0f/KnF/5BcPcF3jcHqzXN9SciEjnReUlKpI3Kd/uT0uKaPbxZVsm5+TkAbKrdl6wu3LSNQYf3brLci8UVFHbNonOKmxpvAy4MDANqvA2OxPnN9l0s+K6UK3p1dWT9IiLSPnb7kwEd05ssd8j+ZPduR+JUfyISfTQHS+LKr75cS2VdPcmGwW2De5KV7D/E53y7kZXbd2EYBoelp/DQ7b+GR+4E/EnUixvLefr4/oD/7lBXfraGZJeL+4f1CVlse4cO1ngb6JKSzMxBBbrBhYhIlGqtP3Gv+gV51dXcPqRXYJmW+pOnJk2Dp+8PSWzqT0SimxIsiSsvnTSg2dfvOyBRyvF0Ye+g1/QkF8+fuG+5E3I6snj04ENuwzqx6TYKMlJ5q7H8SV06NnvHqJO6dGTFhOF2qiAi7RDN84Vjjd15fsHOF47GeZrNLdNaf9Jl+l1s/cONTd5rqT/p0qsnWw9YV2v9yY+n337QNlrqT2Jl30a6PET/PNtYoLnAh6YES0RE4kY0zxeONXbn+QU7Xzga52mGYxvRGFO8bCMe59nGgnicC2yHnfnCSrBERJrR3HNVWqJnqki86FCzA6OyvNXnCO3l8nrDEJWISOxQgiUi0oxgn6uiZ6pIvDAqy6maYf9+dznT5joYjYhI7NFAdRERERERkRBRgiUiIiIiIhIiGiLYDnvHqduV1DELb3WV7fIa1y4iIiIiEluUYLVDW8apBzOnQ+PaRUREREJPNzISJynBEhEREZGEohsZiZM0B0tERERERCRE4ibBWrJkCaNGjWLkyJE88MADB72/e/durrnmGkaOHMmPf/xjNmzYAMDSpUs59dRTOfXUUxk/fjxvvPFGYJkbbriBoUOHMnbs2LDVQ0Siy7tbqjjl38sZ9e5yHvy+7KD3d3sb+PXStQz7iWm7bXnssccYO3YshYWFPProo2Gri0SftvZd7733Hqeffjrjxo3j9NNP54MPPggs88orrzB+/HgKCwv5wx/+ELa6iMSitrbxexUXF9OvXz8eeuihcIUsMSAuEiyv18v06dN5+umnWbJkCS+//DKrV69uUubZZ58lKyuL//znP1x99dWBTmfgwIG88cYbLF68mL/97W/87ne/o76+HgDTNPnb3/4W9vqISHTw+nzcvGI9Tx7Xj7dHD+LVkgpWV9c0KfP8xnKy3El89Q/LVtvy7bff8swzz/Daa6/x4av/YMlr/2LTJx+QWVLU6r8ONTsisRvEIe3pu3JycnjiiSd4++23uffee/ntb38LQEVFBbNnz+b5559nyZIlbN68mffffz/sdROJBe1p4/eaOXMmhYWF4QxbYkBczMH67LPP6N27N7169QLg3HPPZeHChfTv3z9QZtGiRdxwww0AnHXWWUyfPh2fz0d6enqgzO7duzEMI/D7iSeeeNCZChFJHF9t20nvjDR6ZaQCcHZeNos2baN/x33txqJNVUzulwfYa1vWrFnD8OHDSU9PJ7lkEyO2lfDC1Bu49ojcVuPRHID4snTp0jb3XUOGDAmUGTBgALW1tezevZv169fTp08funTpAsCoUaN4/fXXGTVqVBhrJhIb2tPGG4bBm2++Sc+ePcnIyIhI/BK94uIKVklJCfn5+YHf8/LyKCtrepm3rKwsUMbtdtOpUycqKysB+PLLLyksLGTcuHHccccduN1xkXeKSDuV1daRn5Yc+D0vPYVNu+sOKLOH/LQUwF7bMnDgQD755BMqKirYVVvLki1VlNbuCV+lJGrs3y9B8H3XXq+99hpDhgwhNTWV3r178/3337Nhwwbq6+tZuHAhJSUlzldGJAYF08a7U1LpvHkjWRnp7PlmGXy3kofuncf/XXwBKdXbSKmq0KgDCVAmARxzzDEsWbKENWvWcP3111NYWEhaWlqkwxKRGNdc29KvXz+uu+46LrnkEjq6XQzqlI5rvyvnIsFYtWoVc+bM4ZlnngGgc+fO3H777Vx77bUYhsGIESP44YcfIhylSOzbe9fBhspytt85jT9+X8ZPO2fgvfP31K4uIcntouq7j5sso1EHiSsuEqz8/PwmZ+hKS0vJzW063CY3Nzdwpau+vp7t27eTnZ3dpEy/fv3IyMhg1apVHH300WGJXUSiV25aMiW1+85mltbsoXtq8gFlUiip3cNAsN22XHzxxVx88cVklhQxbeIF5KU1Xackhr390l52+q7q7VUU1FRhlGyneNNmrv71JB65ZRpDUoDGZ/qcP6Q/5z/iv2HG4wvfJikpKVxVEokpwbTxg4H6Bh/VdV6yk5NYum0nr5dVcvu3xWyv82IYkOpy8dPe3cJcC4lGcZFgjRgxgnXr1rF+/Xpyc3N55ZVXePDBB5uUmTBhAi+88AIjRozgtddeY+TIkRiGwfr168nPzyerrpYNK5ezdvUqBib7Ag+fy9hUiqtuT7MPo3N5vWGonYhEytFZHVi3s5b1u3aTm5bMP0srWTCsT5Myp3bL4sWNW5mQkspbTz/BmGOH07H0B4qKSzisezfcbjfrS8uatC1bKirpmpNNcXEJb5ZV8vKPBkaohhJJw4YNC7rvGn3sMWyf+Vuq6uoxP17NTf3yOPLvj1H1933LlO+uw5OazLa6ev5ctJMH//yXMNdMJDYE08afCrxeVsmPunTCMAxeOmlAoMy81SV0cCu5kn3iIsFyu93Mnj2bSy65hIaGBi666CIGDBjAXXfdxdFHH82ECROYOHEikyZNYuTIkXTu3Jk//vGPAHz66ac8+OCDpPoa8G0q5ra++bjn30IV8Jula/mooprKPfUMGHUKN/TLZ2KBJ7DdnGlzI1RjEQkHt8vgtsE9ufzTNXjxcdFhHgZ0TOee1SUclZXBhO6duajAw/XL1nHUmefQccc2Hhh+OFUzJvFO8Vb++H0ZyYaBy6BJ23LxR6uorKsnrXs+tw3uSVZyXDTFEqS29F1PzJgGj97Jkz9soWjXbu5bU8p9a0oBePr4fnhSk5m5cgMrG++EdvPN/8fR6UmBq1t26OShJIpg2vihF10eaONFWhM3vfq4ceMYN25ck9duvPHGwM9paWk88sgjBy134YUXcuGFF5JZUkTVjElN3tMfkYiM7ZbF2G5ZTV6b0n/fjQnSklw8dMwR5EybS8WcmwKvX9CjCxf06NLsOvee+TxwGUk8wfZdmSVFVAGT+uYxqW9es+vcv+/KOflEKg7o21qjk4eSSNraxu/vhv3Ki0AcJVgiItJ+HWp2YFSW2y7vy/awU5O4RUTCLtj2GtRmh0vcJljBHnQaEiEi0c6dkhqYD+rdUkxmXV3LCxB8Z2pUlh90Nb8lukuWiEjz9m+z7UjqmIW3usp2eZfXS+Wtk4OKSW12eMRtghXslwQNiRCRaLf3NsHBUGcavdpy9lknA0ViR7Btds60uUGXl+gUkQQrVoeg9Hz9i6YvvD6u+YKH4nT5eNlGNMYUtdsY6+z627JMNG6jnTGtP/PY4JcXaUWwJwKhbV+oDuq7WhPlf48xu41ojClettGOmNS+ixMikmAF26l0uf1hMltIyJobKqOzfCIiIiIiEm4xMUSwLcNidNlURERERETCLSYSLBERaZtgJ1nr6r+f7s4lIvEo2D4hWtu1aJ9uZPh8PlsFZ82aZa+giIhIG82YMcNoz/Lqq0RExGmt9lU+ny/m/82cOdMX6RhUd9Vf9Vf9VXf9i6d/Osa0f2P5n/av9nEk/7nClOiJiIiIiIjEvXhJsGZFOoAISuS6g+qv+ieuRK67hIeOMWdp/zpL+9d52seHYHsOloiIiIiIiLQsXq5giYiIiIiIRJwSLBERERERkRBRgiUiIiIiIhIiUfegYdM0TwfuA5KAxyzLuuOA91OBp4Bjga3ARZZlFZmmeTzwSGMxA5hpWdY/7KwzmoS6/qZpFjSW7w74gEcsy7ovPLUJnhOff+NyScDnQLFlWT92vibBc+jY7ww8BgzB//n/zLKsj8JRn2A5VP/JwC/w1/2/wFWWZdWGoz7Bamv993u/J7ASf/3vtrNOSRyJ3rc6zaH9WwRUA16g3rKsEeGoS7RSG+ksh/ZvEQl6DEfVFazGL8EPAmcAg4CLTdMcdECxnwOVlmX1BeYDdza+vhwYYVnWMOB04GHTNN021xkVnKg/UA9MsSxrEHAicF2C1X+v3wLfOBl/ezhY9/uANy3LGggcTZTuA4f+9nsAkxrfG4K/05jofG2C18767zUPeCPIdUoCSPS+1WkO912FlmUNS6Qvps1RG+ksJ/bvfhLyGI62K1jHA99ZlrUWwDTN54Bz8WfEe50LzGz8+UXgAdM0Dcuydu1XJg3/GWu764wWIa+/ZVmlQGnjz9WmaX4D9CBB6t+4nsOAs4A/ADc4Fn37hLzupmlmAaOBnwJYlrUH2ONcFdrFkc8efxuXbppmHZABlDgTfru1p/4+0zTPA9YBO4NcpySGRO9bneZU+yX7qI10lhP7N6FF1RUs/F/8N+z3+8bG15otY1lWPVAFdAEwTfME0zRX4B8KdE3j+3bWGS2cqH+AaZq9geHAJ04EHwJO1f9e4CagwbnQ282JuvcBtgCPm6a51DTNx0zT7OBsNdos5PW3LKsYuBtYj/8kQ5VlWYscrUXbtbn+pmlmAr/j4OeRxFLbJ85K9L7VaU71XT5gkWmaX5im+UsH448FaiOd5cT+hQQ+hqMtwWoXy7I+sSxrMHAcMNU0zbRIxxROLdW/8Q/gJeB6y7K2RypGJzVXf9M0fwxstizriwiH56hDfPZu4BjgT5ZlDcd/Zun3EQzTMYf47LPxn3HrA+QDHUzTvCyScTpkJjDfsqwdkQ5E4lOi961Oa2H/nmxZ1jH4h21dZ5rm6IgFGdtmojbSSTM59P5N2GM42hKsYqBgv98Pa3yt2TKN45Sz8E+2C7As6xtgB/6J/XbWGS2cqD+maSbjT67+ZlnW3x2JPDScqP9I4JzGiZbPAWNN03zaieDbyYm6bwQ2Wpa194rli/gTrmjkRP3HA+ssy9piWVYd8HfgR45E337tqf8JwNzGY/x6YJppmr+xuU5JDInetzrNkb678So8lmVtBv6BfxhXolIb6Swn9m9CH8PRNgfrM6CfaZp98H+QE4FLDijzKnAl8BFwIfBO4/jPPsAGy7LqTdPsBQwEioBtNtYZLUJef9M0DeDPwDeWZc0LV0XaKOT1tyxrKjAVwDTNU4D/tSwrGq9iOFH3ctM0N5imOcCyrFXAOKJ3bLkTf/tJwImmaWYANfjr/3k4KtMGba4/MGpvAdM0ZwI7LMt6oLEDjJW2T5yV6H2r05zouzsArsa50x2ACcCtYapPNFIb6Swn9m9CH8NRdQWrcUznb4CF+O92ZlmWtcI0zVtN0zynsdif8Y/5/A7/DQv2Dnk6GVhmmuZX+LPkX1uWVX6odYavVvY5UX/8V3Aux3/l5qvGf2eGsVq2OVT/mOBg3f8f8DfTNL8GhgFzwlOj4Dj0t/8J/qt2X+Kf2+Bi3+2Qo0o76x/UOp2qg0SvRO9bneZQ+90d+MA0zWXAp8BrlmW9Gb5aRRe1kc5yYv+S4Mew4fPphjUiIiIiIiKhEFVXsERERERERGKZEiwREREREZEQUYIlIiIiIiISIkqwREREREREQkQJloiIiIiISIgowRIREREREQkRJVgiIiIiIiIh8v8BrO3thVTOLjUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bf0fcf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.plot_posterior(approx.sample(1000))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['mu', 'rho'])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tracker.hist.keys()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"mu_hist = np.asarray(tracker.hist['mu'])\n",
"rho_hist = np.asarray(tracker.hist['rho'])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"mu_trace = mu_hist.T[approx.ordering.by_name['mu'].slc]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'μ convergence trace')"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11cc2a358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(mu_trace[0])\n",
"plt.plot(mu_trace[1])\n",
"plt.title('μ convergence trace')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Average Loss = 5,657.3: 100%|██████████| 30000/30000 [00:47<00:00, 625.78it/s]\n",
"Finished [100%]: Average Loss = 5,657\n"
]
}
],
"source": [
"with model:\n",
" inference2 = pm.ADVI(start={'mu': np.array([4., 6.])})\n",
"tracker2 = pm.variational.callbacks.Tracker(\n",
" mu=inference2.approx.params[0].get_value, \n",
" rho=inference2.approx.params[1].get_value\n",
")\n",
"with model:\n",
" approx2 = inference2.fit(n=30000, callbacks=[tracker2])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"mu_hist2 = np.asarray(tracker2.hist['mu'])\n",
"rho_hist2 = np.asarray(tracker2.hist['rho'])\n",
"mu_trace2 = mu_hist2.T[approx2.ordering.by_name['mu'].slc]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'μ convergence trace')"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11e2aef60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(mu_trace2[0])\n",
"plt.plot(mu_trace2[1])\n",
"plt.title('μ convergence trace')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Q3",
"language": "python",
"name": "q3"
},
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
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}
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