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@dsuess
Created April 21, 2017 06:56
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Liu West Resampler Fail
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import qinfer as qi\n",
"import matplotlib.pyplot as pl\n",
"import numpy as np\n",
"\n",
"from tools.helpers import Progress"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class CircularModel(qi.Model):\n",
" def __init__(self, sigma=.01, ):\n",
" self._sigma = sigma\n",
" super().__init__()\n",
" \n",
" @property\n",
" def n_modelparams(self):\n",
" return 1\n",
" \n",
" @property\n",
" def expparams_dtype(self):\n",
" return []\n",
" \n",
" @property\n",
" def is_n_outcomes_constant(self):\n",
" return True\n",
" \n",
" @property\n",
" def n_outcomes(self):\n",
" return 1\n",
" \n",
" def domain(self, _):\n",
" return qi.RealDomain(min=0, max=2 * np.pi)\n",
" \n",
" def are_models_valid(self, modelparams):\n",
" return (modelparams[..., 0] >= 0) * (modelparams[..., 0] < 2 * np.pi)\n",
" \n",
" def canonicalize(self, modelparams):\n",
" return np.mod(modelparams, 2 * np.pi)\n",
" \n",
" def simulate_experiment(self, modelparams, expparams, repeat=1):\n",
" x = modelparams[None, :, None] + self._sigma * np.random.randn(repeat)\n",
" return self.canonicalize(x)\n",
" \n",
" def likelihood(self, outcomes, modelparams, expparams):\n",
" super().likelihood(outcomes, modelparams, expparams)\n",
" nr_summands = np.ceil(self._sigma / np.pi) + 3\n",
" n = np.arange(-nr_summands, nr_summands + 1)[:, None, None]\n",
" x = outcomes[None, :, None] - modelparams[None, None, :, 0] + 2 * np.pi * n\n",
" x /= self._sigma\n",
" p = 1 / np.sqrt(2 * np.pi * self._sigma**2) * np.exp(- x * x / 2)\n",
" return np.sum(p, axis=0)[..., None]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def weights_to_markersize(weights, maxsize=30):\n",
" return maxsize * weights / np.max(weights)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
" 0% ( 0 of 20) | | Elapsed Time: 0:00:00 ETA: --:--:--\r",
" 5% ( 1 of 20) |# | Elapsed Time: 0:00:00 ETA: 0:00:00\r",
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" 15% ( 3 of 20) |### | Elapsed Time: 0:00:00 ETA: 0:00:00\r",
" 20% ( 4 of 20) |##### | Elapsed Time: 0:00:00 ETA: 0:00:00\r",
" 25% ( 5 of 20) |###### | Elapsed Time: 0:00:00 ETA: 0:00:00\r",
" 30% ( 6 of 20) |####### | Elapsed Time: 0:00:00 ETA: 0:00:00\r",
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" 95% (19 of 20) |######################## | Elapsed Time: 0:00:00 ETA: 0:00:00"
]
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x1211a5588>"
]
},
"metadata": {
"image/png": {
"height": 576,
"width": 1077
}
},
"output_type": "display_data"
}
],
"source": [
"from warnings import filterwarnings\n",
"\n",
"filterwarnings('ignore', category=qi.ApproximationWarning)\n",
"PARTICLES = 50\n",
"NR_OUTCOMES = 10\n",
"\n",
"modelparams_true = np.array([4])\n",
"model = CircularModel(sigma=1.)\n",
"pl.figure(0, figsize=(18, 10))\n",
"outcomes = model.simulate_experiment(modelparams_true, [], repeat=NR_OUTCOMES).ravel()\n",
"\n",
"for i in Progress(range(20)):\n",
" prior = qi.UniformDistribution(ranges=np.array([0, 2 * np.pi]))\n",
" updater = qi.SMCUpdater(model, PARTICLES, prior, resampler=qi.LiuWestResampler(a=0.0))\n",
" updater.batch_update(outcomes, np.empty((NR_OUTCOMES)), resample_interval=1)\n",
"\n",
" pl.scatter(updater.particle_locations, i * np.ones(PARTICLES),\n",
" s=weights_to_markersize(updater.particle_weights))\n",
" \n",
"pl.gca().twinx().hist(model.simulate_experiment(modelparams_true, [], repeat=1000000).ravel(),\n",
" bins=500, normed=True, alpha=.2);"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\r",
" 0% ( 0 of 20) | | Elapsed Time: 0:00:00 ETA: --:--:--\r",
" 5% ( 1 of 20) |# | Elapsed Time: 0:00:00 ETA: 0:00:00\r",
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]
},
{
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+fOELX8CBAwfmPv/2t7+NV155Bb/5m7+Jo0ePrvo3MljDIgZr\n5F+ugzUAs5M8OGUgHNIhFAFf1fXlS6SUmBrXEQoaiIQN2GwCdqe5FIXdoUBKicCUgakJHRJARYUC\nb5Watw52KSWmJw0M9ceRiEk4XArqm2zweK0PqOhJiWtDCVwbSCKpSzhdCppa7PBWpn7DyzAkxkeS\n6O9NIBoxoKkCNY02NDRpsNkLGwwQChro645hdDgJXZ9Z17zRhuYWO1xlay8wYTWmJnX0XIxhelK/\nPhOMMKe/bGmzQbOt3fMRjRg493YUgRTLFFVWq9hmYQaVsZEkui/EEA3PBGLNTHVeXauhbYtjVW8F\nR8IGBnokhvojgGI2j9IwA57a2u2ozCCASRoSA70JXO2OQ9fl3IC0ogo0tdrR3Lr6t0yNmWP0dccx\nUb4RQgCGYc5OsLXTicaWwqyZXqoCUzounY9ioDcBXb/+9niqwepkUmKgN45LXTGEwwYUYS5x0b7d\niSr/9brf0CUG+xPmdkGzbamp17B5mwPeyvU5o0Yqs+crOG1A1cyBeW+lhkTcwMDVBKIRcxC7odmW\nskxKKTE2optBEoo5C0SqWQUSCYnR4QSSSXNZEl+adtgwJMZHk5ie1KHMzHbhrbTeZkfCBuIxA06X\nsqolO3TdQCwKOJyAqqbej5XB+lLqECSiGxfrIiIqNtZDRFRsxayHGJxBRLMYrJE7DNYorNUGawwP\nD+P3fu/3UFNTg+9+97tQlOv9rZFIZC5I44c//OHcDDSZOnHiBL7zne9g3759+KM/+qO5z8+cOYOv\nfvWr6OjowFe+8pW06fre97636jGYG2dUgW5IQgh4fCo8Kd6ONacu1+CrTv/dCp+a81k00hFCwFup\nwluZ/VvZqiYyWs9SUQT8ddktw5BrZeUKtu10YdvOYqek+Lw+FbsPuBGPGYhFJRRFwOVeH1McO10K\n9t7iRjxuYPxaErGYhNMl4K+1WV4bvbpGQ3WNhnDQQCRqDqqXe5ScBBi53Ar23+ZDPGZgoG8chjQ/\nc7kz37dQBJo22tHYakMoYCARl9BsAuUVuZseW1EEmjfa0dRqQ6jSg2RCwu4UKCsv3FT6pczjVbHn\nYBn2HFx5gFzTBFo2OdCyafkp0xRVoKnFjqaWG3st2NnztZjNrqB188rLNAkh4K/V4K9d/lbUZhOW\n2jRFMesRf2127Vm25XwxVVXgLlt+mxs5gIqIiIiIiIiIiIjIijNnzgAAbrrppgWBGgDgcrmwfft2\nnD59GhcuXMCuXbuyOsbsMiaLlzOZPfaePXuWfKeurg4NDQ0YHBzE8PAw6uvrszr2LAZrWDQbRUv5\no2kaDFWdixIkovWttnZ1389XVaGqKlxuFRs3567er6zM2a7Sqt1cl/+DEFFBzE7Hx/tPIiom1kVE\nVGysh4io2IpZDyWmOKsQEZlmB5E5drZ6Nt5XFsUXv/jFlJ9/85vfXPZ7s6teNDQ0pPx7fX09Tp8+\njcHBwayDNZ577jkAS4MyVjr2bLDG4ODgqoM11u5c+kRERERERERERERERERERLSuhMNhAIDb7U75\n99nPQ6FQVvt/8skn8dZbb2Hjxo245557Cnrs+TizhkVcHzP//H4/dF3nultEVFRrdQ1AwXaKaN3g\n+uxEVApYFxFRsbEeIqJiK2Y9JNdYvxQR5c9a7a8uRexDL6zGxkYAK8+gkS0pJYDslp1+5ZVX8A//\n8A/w+Xz4/Oc/PzebViGOvRiDNYiIiNY52XNxwf9Fa3uRUkJERERERERERERERLS82dkrZme5WCwS\niSzYzqpXX30Vf/7nfw6v14s//dM/RV3d0iXm83XsVBisQUREtA4wIIOIiIiIiIiIiIiIiNaD2Zk5\nBgcHU/59aGgIANDQ0GB5nydPnsR3vvMd+Hw+fPnLX0773ZWOPft5JsdOR1n1HoiIiIiIiIiIiIiI\niMgy2XNx7h8REREttGPHDgDA6dOnYRjGgr9FIhF0dXXBbrdjy5YtlvZ34sQJ/MVf/AUqKytx7Nix\nZQMtdu7cCQB46623lvxteHgYg4ODqKmpSTkrR6YYrEFEREREREREREREREREREQlob6+HjfddBNG\nRkbwy1/+csHfHnnkEcRiMdx1111wOp1zn/f396O/v3/Jvo4fP47vfve78Pv9+MpXvrJikEVnZyea\nmppw7tw5vPbaa3OfG4aBf/7nfwYA3HvvvRBCrOYnAuAyKEREROsS38ogIiIiIiIiIlob2I9DRES0\n1Kc//Wl86Utfwt///d/jnXfeQXNzMy5cuIB3330XDQ0N+OQnP7lg+8997nMAzGCOWWfOnMFf/uVf\nQkqJHTt24LnnnltynLKyMjzwwANz/1cUBZ/97Gfx1a9+Fd/61rdw6623wu/348yZM7h06RK2bdu2\nYPvVYLAGERERERERERERERERERERlYz6+np84xvfwCOPPIK33noLb775JiorK3H//ffjE5/4BMrL\ny1fcx+joKKSUAJAyUAMAampqlgRfbNmyZe7Yb7/9NiKRCGpqavDxj38cR48ehc1mW/0PBCDkbOpo\nWQMDA8VOwrrn9/uRuNSFycnJYieFiG5gPp8PANZ1XSRa24udBCJaht/vB2A+SBARFQvrIiIqNtZD\nRFRs+a6HOJsGEVlxI/RXFwr7xQursbGx2ElYE5RiJ4CIiIiIiIiIiIiIiIiIiIjoRsJlUIiIiG4w\ni9/cYEQxERERERERERERERFRYXFmDSIiIiIiIiIiIiIiIiIiIqICYrAGERERERERERERERERERER\nUQExWIOIiIiIiIiIiIiIiIiIiIiogBisQURERERERERERERERERERFRADNYgIiIiIiIiIiIiIiIi\nIiIiKiAGaxAREREREREREREREREREREVEIM1iIiIiIiIiIiIiIiIiIiIiAqIwRpERERERERERERE\nREREREREBcRgDSIiIiIiIiIiIiIiIiIiIqIC0oqdACIiIiodsufigv+L1vYipYSIiIiIiIiIaO1i\nHwsRERGthDNrEBERERERERERERERERERERUQgzWIiIiIiIiIiIiIiIiIiIiICojLoBARERERERER\nEREREeXR4mVRiIiIiDizBhEREREREREREREREREREVEBMViDiIiIiIiIiIiIiIiIiIiIqIAYrEFE\nRERERERERERERERERERUQAzWICIiIiIiIiIiIiIiIiIiIiogBmsQERERERERERERERERERERFRCD\nNYiIiIiIiIiIiIiIiIiIiIgKiMEaRERERERERERERERERERERAXEYA0iIiIiIiIiIiIiIiIiIiKi\nAmKwBhEREREREREREREREREREVEBMViDiIiIiIiIiIiIiIiIiIiIqIAYrEFERERERERERERERERE\nRERUQFqxE0BERETFJXsuZrWtaG3PR3KIiIiIiIiIiIiIiIjWPc6sQURERERERERERERERERERFRA\nDNYgIiIiIiIiIiIiIiIiIiIiKiAug0KUZ0ndwOmhMI5fmcZ4JAlFAK0+B+5p82JTlQNCiJTfiyYN\nnOoL4sWeAKbjOjQBdNS4cFebF00V9gL/CqLcCscNvNoXwIu9AQRiOnQpAQA2VaC+3IZ72nzYUeuC\noqQuH6XAMCTOjUTw3JUpDAUTAICGchvubvOioyZ92uNJA28OhvBv3dOYiupQBNBe7cTdbV60eO1p\n64T1Jq4bONETwGPnxjESSkBRBDprXHioowrba1xFPw+6IfFafxCPnhvH1akYAGCjz4GHOquxt6EM\nagnlzZFQAj9/bwLHr0wjmjTgtik4vMmLD27xodptK3bySlosaeD/fXsUT16YQDRp1kNlNgUPdVbh\noc4q6AbwfPc0fto1jrFwEqoisKvOjaMdVdhS7YQhgVP9QfxkXj6pdmsABEZDcQghsMHrwEMdVTjQ\nVF5S+QYA+qfjeLxrHC9fDSCmS3jsKj6wxYd7N3tR4SzsY4JuSLw5GMKj58bRPREFgKKcOynNuv3R\nc+M4NxKBYUjUltvw0e1VONTqgV1dGOs+Hkniyfcm8KvLUwgnDDg1BXdtrMADWytRW56b8hdNGni+\nexqPz8uHu+vcONpZhS3VrpwcI51L41H85Ow43hoKIWlIVLk0PLitEu9rq4Dbpub12Jm6PB7FT7vG\n8cZACAlDotKl4YGtPtyzyVtyaU1FNyReHwji0bPj6J2pT1p9DjzUUY19jcVvd5KGxCt9Afzk7Diu\nTseR1CWklFAVAZ9TxQe3VOLedh88jsKea0NKvDUYwqNnx3F5pu5oqrDjwa2ViCYlHj9/vdzsrHXh\naGc1tlY7Ld1nJA2JV64G8JNz4+ifjkMRwOYqJx7qrMbuejcUi/cqk5EknrwwiV9dnkQwbkBVBDQh\nMRk1IGG+QbOpyoGbm8pxqj+EwUB85v7QhY91VmFXnXsuvboh8WpfED85N4a+6TgAoK3SiY91VmFP\nQxkUIRCI6Xjm0iSevDCJ6ZgOuypwS7MHH9leiWavY0n64rqBE93TeOTdMVwLJqBLQMD8BwAGAE0B\nfE4NH9ziw33tPnjz0EZIKfHeWBQ/OTuGM9ci0A2JareGj3ZU4c7WCji0tfeu0fxy0x+Iz1xrMw/d\nlEEeyiUpJc5ei+DH58ZwfjQKw5CoK7fhox1VONTigU0t3HlO6BIvz5SxdPleSonTA9P4x5N9ODsS\nRlKX5ueQUIRAg8eGox3VuG1DeUHTvhqhuI7nLk/hifcmMBnVYVME9jeW4aMdVdhY6bS8n9FwAj8/\nP4Hj3dOIJMxngLvbKnD/1kr4b7BngLlzcWUakeSNfS5obQgnDJzqD+KlnmkE4gY0BeisceN9bRVo\n8NgRSxp4YyCIF3oCc/1GW2b7jXxL2/L1IqlLvDMcxvErUxgNm33ozV4HDrdVoN3i/RvRetE/Fcfx\nK1M4PxpBUgKVrkG8f6sfnVUqnGvwvpiIlieknBkho2UNDAwUOwnrnt/vR+JSFyYnJ4udlJwZCyfw\n/VeGEEkYiOnXi5oAYFcFNlY68am9NbAvamB7JmP4q1PDkFIu+J4iAFUIHGwux0OdVUXp3CFarYtj\nEfzt6yMwpETCSN0E2RWg0mXDZw/Ww+MsbKe/z+cDgGXrolBMx1+eGsZYOLGgjAKAQxXwu234zwfr\nUWZfWLaHAnF8/5UhJA25pE6wKQI76lz4d7trSipIRbS2L/i/7LmY9m9W9U/H8fDTPYgkjbkBcmCm\nbtQEbqovwxfuaIJNLc55mIom8SfP9OJaKLEgfQDg1ASaKhz46uENKC/wgFQqv7wwgb95/RoMKZE0\nrn9uUwSEAH7vlnrc1eYtXgJLWPd4FJ9/shvJNHfCdhWwqwqShlyaT1WBPQ1uDEwnMBJemk8Wc2oC\ntWU2fO1Ii6UBLr/fDwAYHR21/Hsy9aN3RvEv745BNyTmV2N21cw7f3RnE/Y1luft+PMFYzq+/OxV\n9E/HUpa5TM7daiR0A998YQBvD4cQT0rMT4lTE3DbVHz93hY0eMyg2Re6p/Cdl4cACcTntWeaAihC\n4P/YV4v7t1auKk3dE1F86VdXEdcX1peKMMv5rRs8+L9ua8j5QL5uSPzg1SG80D2NhCExv7l2aAI2\nReCr72/B5irrg0r5YkiJvzo1jGcvTyG5OK2qgKoIfOXwBmz15zewZTWmYzq+9EwvhoLxlGWgwWPH\nV9/fgooCtzuzddH53kE8/EwvJiLJtPWdTQFUReCP39eMm+rLCpK+cELHsWevomdyad0BmPW1XPR/\nuypwc1M5PneoEdoy5WYsnMAfP92LyejS3+zUBDZVOvHlezbAZVu+o/TlqwH8Py8OwIA5MJ0ppyaw\npdqFP7m7GdGEgYef6cVoOIno/EZ/ZrsWrwOf2FmNb704AEMC8XnHU4V5fY52VOE3d/vnBjsGA3H8\n16d6MBXTkea2fAFtZj9fuLMJB5py10YkDYlvvzSAU31BxPWl9a9DVfC1Iy1raoBqPJLEw0/3YDxF\nuXFqAht9Dvzp4Q0FDSaL6wa+8W/9ODsSRixFO1dmV/H1Iy2o9+T/5ZCRUAIPP92LqVjq87Ol2oUv\n3tmIH7w2hjf6pxBNGGn2BLhm035vC+rKS/vFlvOjERx79ip0KRFb1K5risC9m734TwfqVhyQfObS\n5FyfUWLBMwAghMBnDtbh8CZfvn5GSeG5oHxb7bPZ/D4UALgyHsUPX78Gw5ALniFm+3x317nRNRpZ\n0m+kCEAT5ssDn9ztL6l+o1yYiCTx/VeGEIrrKfvZNngd+PT+2jUZvEmUCUNK/Mu7Y3i9PwRdLn6+\nVaAI4P+8uW5N3ReXmmz7syk7jY2NxU7CmqAeO3bsWLETsRYEAoFiJ2Hdc7vdMCZGEY1Gi52UnAjH\nDXz7xQEE4gaSKfoVdAlMR3T0TccXDIZcCybwvZcHEdMXDp4AZmejIYHhQAKxpFHSnc5EqfRPx/E/\nXh1GfNFgymK6NN8kPnMtgluaC/s2utNpDjylq4uSusR3Xh7EtVACqfoMdWkOHpwbMdM+G1Q1FU3i\n2ycHEU4YS8r27PfGw0mMRRLYWVeYQQ4rhK9q4QdT4+n/ZsFUNInf/0U3pmN66rrRMDtve6ZiuKO1\nIuP9r1ZCN/CHv+zBQCCe8vomDSAQS+LNgRCObPYVNWjuxZ5p/OWrw4jrS8uTIc089fpACO1VzrnB\nZTJNR5P4zOOXU5bFWfrMQFe6Nrx/OoFAmny8WNIAgnEdr/YFcV+7d8U6ze12AwDC4fDKO8/Cz86P\n43+9PbpkQAwwf5tuACevBrCnvizvs7PohsQfP92DnqkY4vrSv2d67lbjv5/ox5uDoQUDnPPTEU0a\neKF7Gu/f5MW5kQj+7xcHEE9xvzZb/t4eDqO+3IZWX3YBDaPhBP7gyR4EU9xLSpjHGArEMRJK4GCz\nJ6tjpPM3rw3j+JVpxFLlEcMsG8/3TOPOVg/K7cUNXPuHN6/h6YtTafNzwpA40RPA7S2egs/6YEXS\nkPjiUz3om4qlbXeCcR2v94dwb3th2x23241IQsdnfnwOo2nue2YZ0kzrS70B7GssR5Urv8FVhpT4\nk2d6cXk8mrLuSEeXwHAwgcFAHLduSF1uokkDn/9FN0bDybTXZDKq48xwGO/f5E07qPrOcAj/7fn+\nlO20VUnDHLx491oYj5+fwHAwkTLYOWmY91jPdweQNJDyOVKXwMWxKDRVoKPGjemYjt//RTcmo/qS\nspOOMbOfl68GsKvOjZqy3LQR3z05iJevBpcMzgDmb5utc+4uwVl9UoklDfzBk91pnxfM66XjneEw\nDi+Th3LtG8/3453hcNrzHE0YeKFnGkc2+/I6GBZO6Pj8L7oxFklfxiYiSTx1cQq9E5GU6V28fTRp\nztp3ZLN3ySxYpaJvOob/+lQPIkkJPU273jsZQzRp4KaG9M+DL1+dxndfHlr2GeDNwRA2+hxoqljf\nAzk8F1QIq342m9eHMhSM4wevDC3b5zs409ana8vHwkmMR5PYWefOLj0lKJo08O2XBjCVrp9IAtNR\nHT2TMexvLOMMG7SuPXZuAqf6gkgYqZ5vJZKGxJuDYeytd8Nd5GfxtSqb/mzKnseT2/6q9ao0n2CI\n1oGXeqcRSSzfMZeQEhfHouidjM199osLEykHCeaLGxLPdwcQimXQM0lUAn7WNb7gzYHl6BKYiiTx\n1mB+Biuz9c5wGBOR5IqDvGPhBM5cu572Zy9PIb7CqG7ckHhjMIyxUCJXyS05P+0aRySx/KBAXDeX\nIJlfNxbKi70BjIYTy17fpAH0B+J4rT9YuIQtYkiJH742vGLndVyXc2+a0XV/+8bwstfYqkx2oUtz\n8P3F3uIGAMd1A//zrRFLeefv3riW9/S81h9EfyC+bNBLIc5d90QUrw+kDtSYJWFOWfx41zj+6tTQ\nyvdrupyb+SYb/3JmbMnb84vFdInj3dMYDsazOkYqY+EEnro4tWIeiSUN/OidsZwdNxuT0SSeOD+5\nclp1A//f2yMFSlVmTvYGMByMr9juDAXjeKWv8PXHk+eGMRVd/r5nvpgu8fcFqDveGgyhZzJ1gMtK\nYrrEid4ABgOpy82zlycxFdWX/c0JQ+LyRBTvDKe/T/2b166tWE9YkTAkukYjGAsvfx2ScuV2KaZL\n/K+3RxFNGnji/ETWz5NxXeJvX8/NdR4MxPFCb2DZciwBRBIGHj07nnabUvLc5akVnxcShsSViSje\nHirMs87FsSjeHlq5nQvFDfziwkRe0/LMpSkEVpjNJWFITMd0y8+OhjQD2566ULoztf7zWyMLZtNI\nJaZL/LRrAtNpyqaUEj+0ULfEdYm/XufPAFJK/DXPBa0xPz8/uep7g7gh8Xp/CGPh9dNv9MrVIEJx\nY9l2ISkleiZjuDJR+H4iokKZjup4qQINPzMAACAASURBVDew4v1PXDfwy4ule89DRJljsAZRHhhS\n4vnuaSQsPAwmpcS/dU8BMDtG3h2OWBr8EQJ4ua94A4VEmZqKJnFpPLOHqpgh8eyVqTylKDvPXl55\nAAswO9qevWSmPaEbeLUvaGmgQ0qJF3pKZzYn2XNxwb/V0A2JX7w3aWlgJWlI/LSr8B3yPz47tuKS\nFgAQTUr869niDVK+PRRGxMqUDgAmokmcH10fs1blygvdxSlj0aTEj98t7uD2ixnULxfHo2kHM3Ml\nozKXx3P3065xJC0MCCUMicfPT2AsnLS033hS4s2BUMbpiSUNPHtlynK78cR7uRtYe9LiQJchgRd6\nphFOFC94+OmLk7DyYp0hgZNXgwiWYKBzqZSBdP759X5L9z3zdY1EMJLnwNMfnx23dN7SMQyJn51P\nfZ/x6NkJS785mpRpgwe6J3Jbf87OLpELQgDHL0/hZ+fH0y4FZkXPZAx906sfMPnZ+XEYFupfXQJP\nX5pEYvGUBCXo0XPjGeShwpTrx86Np12Ccr6EIfF410TWgYYrkVLiJxbPT6biusRjXeMlOSgfiOk4\n1R+y3NfzdJoBmDPXwghanE4oENdx9lokg1SuLWeuhRHiuaA1JBjT0TVirc93JRKy6C8B5IqUEsev\nTFlqo+K6xHNXpguQKqLiOHk1ACvzxkgAp4fCK77cQURrB4M1iPIgkpCIWOz5MiTQO2l25F0LxqGp\n1qZym30Th2itGJi2nr/nGwklSqrDbShoffBhdtvxSBKwdLttdkSv17I9EU1aegAHzLqxa6TwHWp9\nU9YHVnqKMPPHrIvjUcuDNlICl9dpnspGUk+9FFGh9E3nN/hhJedHI5YHODVF5L0+6s6gHPXn8dx1\njUQsL1OQyZIG0aSBS1mcw+FQAorFdiNpAOdyOABx9lrYcl2tKQJDgeK91Xf2WsRyXWhTBfrzHHyU\njasZtDuZbJsLSd3AUBaD8TZVoDvPbz1eHl9d3aRL4FyK+4ykITMKNLmUJh2XxqOWAomKIZqUaZfC\nyIQqBK5kGIidyrmRSEbt8kjIWrBcseiGxHAGzwuXCvSG8Pkx6+1cJGHkLbgtrktMRPJ3DQNxHeFs\nptzJs6tTMdgsPgvHdYmzI6lnXLk8HkPCYoFJGjKre5C1ItNzwechKrbBQHZ9Yqmsp36juC4RsBh4\nJQH0TXFmDVq/Lo9HLb38CwCqInAtg3tOIipt+V1IlugGJaW02L1uMmbiqjNdyzjbtY+JiiHb/FpC\ncRozrCdoYdqtf2+9lm3DsBqyMrN93lKSXianvph5U0pp+fhSlmI5Kp5in4piH1/PJDNkkM+ylVFy\n8peMjOqbTLtYszmHmX4nl+1GpvvKKE/lWKZvflt5e7/Qlq5EvMy2BU6+BMwMn8Vx8/VW/qxc7D1V\ndpBSZvSb09UdpZfTFtIzfF5NRSI319nIoAIWEHPPzutFoYLSMzqOyN99uCEzb0czISBK8r430+Yn\n3W/ItM0txXORK5meixK8BaA1bPGso6K1feXv5DoN6yRPS2TWLpTSy1xEuZbJs6G5PRGtF5xZgygP\n3DbFcrS0ANBQbgcA1JTZkLT4ZoAmgOYKW7ZJJCq4unIb9Cx6SHwuFaKEXk2sdlmPc6x2m9v6nJrl\nziFFAE1eezZJK7hMl0ipdKmWn8IFgI0+x+oSmIWaMuv1ar2neNdpg9cBp2btZGoK0LxG8lQh2FSr\n8xXkRyZ5LB82VTrhsHiPYkig2ZvfctiQQTnK57lr8zks5wshANXiU5RTU7IqfzVlmuVBCEUAm6py\nd502Vzlh9aW/hC5RX168+mVzlROaxWuR0GVG+a1Q6jLI13Xlha0/bKqCSlfmx0waMu91R1PF6q6l\nIoBNlUvTaFMVeB2q5f00p0nHhjz//tWwqwLb/C4oq2wMpZQ5+Z2bqpyW06JLCb+7tJ+BVUXA57Se\nh1abl61q9Tktt3OaIuCxW/8NmXBqAm5bfvYNAA5VwG0vva7Oxgq75VkgbIrZvqXS4nXAbrGRtiki\nbR21HvBc0FpTW26ztOyiFYoAmipK914jEw5VwGn1hh5AbRGfPYjyrdnrsPwsntQl/Bn0URNRaSu9\nJxiidUBRBG7bUG6pcbWrAu/bWAEAqHCq2Fxt9WZb4LaWiuwTSVRg/jIbGj2Zda7aFYG7N3rzlKLs\nvK+twtJAp0MVuHuTWUYdmoKb6t2WOqJVIXBX6/os2zZVweE2r+W68aMdVflP1CJHOyotXV+nJvBQ\nEdI360BTORSLQUxOTcGuOneeU7S27G8sK8pxHarA0Y7Kohx71vvaKiy/fVHvsaE1z0FTRzuqLAUe\n5fvcfWR7FRwW0qEJ4L52r+WBJiGAg02ejNPjtqm4tdljqd3QFIEPb8tdffTAtkqoFg4sAOxvKoMn\ng4HtXPvglkoIC8OPAsCuejd8JdiZdbSz2lIZcGoCD3UWvt35xJ5Gy4Nhs1q8jrwPQFutO9LRFIEP\nb099Ph/cVmnpN7uWuRfYWu1EhSN3+U0V5mBjLkgARzb7cO9mn+XO4FRqymzYlGZAORMPbqu09NsU\nAdzRWpHRgE6xfHh7laU8ZN5PVhcgRcBHOyqttXMK8IF2n6V2IBtCCDxg8ZpnyqYA92/1Wb5HLqQq\nl4YdtS6LWwt8YIsv5V/2NpRBsxgxalMV7Gkozj1vIextKIOm8FzQ2uFzamjN0UsUqhC4szXzZ4xS\nJITAoRYPNAt1t0MVuKdtffaXEQHAoQ0ey8+322pcKCviszgR5VbpP+USrVF3bayAbYWHaFUAtWU2\ntFdf7+S6f8vKHRc2ReCmBjcqS7DDmWg5D26rstwxJwA4NIGbm8vzm6gM7Wssh1Nb/s18RQAum4K9\n9dc7hI5s9q342zUhsKnKgcZ1/NbPxzqrYV+hbrQp5luWW6tXPwCQqXvafHDbVr6+HoeKQ0XsHNEU\ngd/c7V8xsMShCvz7PTUl2WldTL9zc13BZ9cQMGfeurutuAFobpuKB7etHJRkVwX+w97avKfnUKsH\nFQ512aCEQpy77TUubPQ5VpylwaYqeKjTj/+wt8ZS+fvfdvktr1G/2G/sqrZ0T7irzo2WHAbVNHjs\n2N9YtuJAo10V+OQuf86Om43achtub/FYSuu/211ToFRl5n0bK1BmV1dsd8rtKu4sQjDn0V31cGQw\nOG5XBf73vfk/17du8KDSqWUVbGBTBDprXGirTH2f8YEtlSv+ZkUA1W4bDjSlvk8VQuBT+2oyDnRJ\nRRXmW/kr3Z9YOZJDFfhguw8eh4qPdlTBnmXgg10V+NS+3LQRm6qc6KhxrVjf2VWBT+woTGDDan2g\n3WfpeaHKZcPBAj3r7Kx1o7li5XbOrir4yPb8BpZ+aKtvxcARVZjXPJOYLLuq4MEcBi/m2m/tWblO\nsKsCd7R60s4goyoCv3WTtWeA376pJm9BN6VAVQR+aw/PBa0tD2TQJ5aOTQi0VzuLOtNnrh1qrYDN\nQrtQ5bZhW43VwDeitae6zIaddSvfF9sU856eiNYP9dixY8eKnYi1IBAIFDsJ657b7YYxMYpoNFrs\npOSEU1OwtdqF00MhAEvXx7SrAjVldvzng/ULOsm8Tg0NHjveHQ4DWLr2mEMV2FztxG/dVAOFD5u0\nxlS5NVS5NHSNRACkX1vPJgC3XcF/uaUBXmdhg5KcTrPjPl1dpM0MjJ0eCkNKYPFstnZFoMyh4r/c\nUg/PvDcqy+wq2nxOvD0UgsDSdaAdqkBThR3/cX8dtDVatoVv5c7RMruKnXVuvNgbMM/DovPn1AQ2\neJ04dnhD1gMIq2FTBW7Z4MGLPdMpr69DFfA5Nfy3+1pz+sZsNrZUOxHVDVwejy45j4owf8uv76zG\ng2neHL6RldtVtFc78UL3dNptBMy2WhGp82mL1zH3+UqzWjtUAY9DxTfua0WVhSUF3G5zJpRwOLzi\nttnYXe/GYDCBgUAc+qLKaDbv/KcDdbijAAPDqiJw2wYPXuwNQDdkyjKXybnLlhACt7V48MZACJGk\ngeSi82JXBByagq+8fwNafA5srHRCFQJdIxFILGzPxEy6P7S1Er+xqzrrpbwqnBq2+V04eTV9fdle\n5cSf3N2c83bjYHM5zo5EMBVNLjkXmmJ2Dv3RXU3oqC3+rD0HmspxfjSCiVRpncnPf3BHI3bVl+Yb\ntZoicOuGcrzYG4AhU5cBr1PDN+5tKfg9kdvthkNT0Vmp4ESP+Tycrr5TYNaZnzlYh1s35L/uUGbK\n7MneIJIp6o50nJrApkoH/uTuDWkDqRyaggON5TjRm/o3O1SBmjIbvnFvK9zLLBVhLlmm4N1r4SX1\nhFUOTaCu3I6v39uCuzZ6caJnGjJNmqrcNnxydzXevZb6PtupCdyyoRyfOVgPRQiU2VXsrnPjRM/0\nkrYgndm28dP7anFXDgPobt3gwdtDIQTj+pJybFMAh6rgS3dvwOYiBPJmw64puLmpHCd6Ammvl99t\nw9fva0VZnpYbWUwIgdtbPHitP4RoinbOpgq4NAVfff+GvC9j5NQU7G8sw4ke8z4s1fmpLbfjG/e1\noGs8gXBcX3bpALsq4LQp+NqRlpIOeq9229DqdeDVvmDK50GnJrCr3o3fv71p2cCC9moXErrExbGl\nzwCz9yAPdVbhaOfaCG5aDZ4LKoSUz2ZT4wu2WbYvZN62PpeG2nIbzqVpqx2qQIvPgXBcT/v3Zq8d\nn95Xu64CkByagg6/C28t04de7bLhswczCyImWot21LpwZSKGYFxfco+kCgGbIvDv99asmfviUmSl\n/5pyx+NZHzNB5ZuQ0uJiyDe4gYGBYidh3fP7/Uhc6sLk5GSxk5JTgZiOl3oDONEzjXDCfByvLbfh\ncJsXexvcaaewHAsl8HxPAK/2BRGf6T3bWOnAPW1edNa6+JY0rWnDwThe6A7gtf4g4ouewrwOBXe3\n+XCwuRwuW+Efwnw+MzJ5pboomjRwqi+I565MYSpqPkj7nCrubvPi5ubytFM0T0WTONE9jZeuBhGd\n6SVt9Njx/s1e7K5zr+kgLNHabnnbiUgSv3hvAj9/bwLBuHkeWn0OfKyzCre3VGT9JnquhOI6fnV5\nCo+dG8dYOAnAnO77oc5K3NPmK0reTKdrJIJHz43hVF8Qhpwd/C7H0Y7qBTM30VLDwTj++tQwXh8I\nzXWEqQI41OLBfzxQh6Qh8cT5CTx5YXKuDW+rdODXdlTjtg0eRJMGnrk0hce6xjE+k0/K7QqkBIIJ\nAwJAtVvDRzuq8P5NXssDMn6/OVvB6Ohorn/yHCkl3h4O49GzY3PBZ7aZpdk+0lGFljwP1CwWSRh4\n7sokHj07gZFQAkB25261ErrEi73T+PHZMfROxgEA5Q4VD2z14f4tlUuW0bg0HsVPzo7j5NUAkoaE\nIszggYc6qnIWyDAaTuCJ8xP45bx8uKnKiV/bUYVbmz1566jVDYlX+4L417NjuDhmBjC6bArubffi\nwa1VqC3PX/BMpgwpcaoviB+fHcP5UTOtTk3Bkc1efHh7JerWwNrW4YSOZy9N4dF57Y7freFoRxUO\nb7a+9E4uza+LAjEdT1+cxE/Pj2Mioi/Yzq4Ad2/y4qPbq/I+yLtYNGng+JUpPHp2HMNBs+6odGk4\nsqkCUV3i2ctTCMUX1t+3bvBYCnAKxHQ8dXESj3eNY3LmXq/eY8PHOqvxvo0VlgcLrkxE8di5cZzo\nCcwNOGuKQGLePbDPqWKb34WukTCmYzP3hxXmse5svX6sYFzHMxcn8VjXBCYiZj6pLbfhYx1VuHuT\nF05NQd90DD89N4HjV6YQ1yWEMGdVeKizCnsbypYEkE1GknjivQk83jWOSDJ995CmmG3ERzuq8rJE\nlm5InLwawL++O4YrEzEAQJldwQe3+PChrZWoTjPTQCkLzuShn54fx+RMuakvt+Ghzirc3eYtyoBT\nQjdwoieAfz07hr4ps53zOFQ8sLUSH9zqg6+AQWHTMR1PXZjA4+cn5p6nGjw2/NqO6/neW1mFZy+M\n4h9e7kbvTHpnA2kFgAqHige3V+ID7b6CB7RlaygQx+Pnx/GrS9Nzz4Pba1x4qLMKN2ew1OH50Qh+\ncnYMr/YHoRuAqgC3NHtwtKMKW/031pvnPBeUT6mezWTPxQXbLNcXsnhbABgNJfBv3dNmn9jMaGzb\nTJ9vR60L0zEdJ7qncXJev1FThdlvtKt2bfcbLScUN/By7zSe75me6yeqKdNwzyYv9taXFeWFHqJi\nMAyJM9cieO7K5Fy/hNOm4M5N1bi53r4m74tLSSb917R6jY2NxU7CmsBgDYsYrJF/6zVYYz7dMDvL\nMg20SOoSqoKs38wkKmWz+Xu2NSr2Q6fVYI35jJnO9kzTnm2dUKqyvdmdHeAs1fOgz1zfUn9zRUqJ\npCGhKYLtRRbiM51g6TqAVsqni/PJavJNIYI15jPzDooeJDWrVMqcISUMCUuDuoUqf4mZNrPQ9aUh\nJfQSyiPLKbX8nI1SKQPp6qLZ9CkCJXWu0523XNxn5OqaJHQJbea5TkqJSELCZVtYb1hNr5U0zT+e\nFWYwiQQgoM5cX1VI6FIU9DpnUv+uFaVSrucrpfOcNCQElp6f+fXQ/PSWUtpXI9Mymspsu7fa/awH\nPBeUD/kI1phvpT7fpC6hFOH+v9jWW38ZUbbkzLO4v9pcpm49j50VCoM1CovBGtasjbBzonUi244Z\nrUQ6QInyYTZ/r+Xnr2wDTEqps7aYSr2Tda1cJyEKO5Cy3qz0ls5K+XRxPlkr+QaYzTvFTsV1pXLu\nFGEug2NFocpfscq4IgSUEsojyym1/JyNUikD6cxPXymd63TnLRf3Gbm6JvPLsBACbvvS/VpNr5U0\nZVpnmMdefH0FCv0eayb171pRiuW6lM6zlXw/P72llPbVyEW7vh7avVzhuaC1aKU+3xu1T7gU202i\nYhBCQGPbRrTuce4oIiIiIiIiIiIiIiIiIiIiogJisAYRERERERERERERERERERFRATFYg4iIiIiI\niIiIiIiIiIiIiKiAGKxBREREREREREREREREREREVEAM1iAiIiIiIiIiIiIiIiIiIiIqIAZrEBER\nERERERERERERERERERUQgzWIiIiIiIiIiIiIiIiIiIiICkgrdgKIiIiIiIiIiIiIiIjWOtlzsdhJ\nICIiojWEM2sQERERERERERERERERERERFRCDNYiIiIiIiIiIiIiIiIiIiIgKiMEaRERERERERERE\nRERERERERAWkFTsBREREtP4tXrNVtLYXKSVERERERERERERERETFV5RgjePHj+MHP/jBstsIIfCj\nH/1oxX397u/+LkZGRlL+zev14oc//GFWaSQiIiIiIiIiIiIiIiIiIiLKh6IEa2zcuBEf//jHU/6t\nq6sLZ86cwd69ey3vz+1240Mf+tCSz51OZ9ZpJCIiIiIiIiIiIiKiG8vs7KCJqVHzA6+/iKkhIiKi\n9axowRobN25M+beHH34YAHDkyBHL+ysrK8Ov//qv5yJpRERERERERERERERERERERHlVlGCNdHp7\ne3HhwgVUVVVh3759xU4OERERERERERERERFRSrOzcBARERFlo6SCNZ555hkAwOHDh6EoiuXvJRIJ\nPP/88xgdHYXT6URLSws6Ozsz2gcRERERERERERERERERERFRIZRMsEY8HscLL7wAIQQOHz6c0Xcn\nJyfxve99b8FntbW1+OxnP4vOzk5L+/jiF7+Y8vNvfvObAAC/n+vS5ZumaTBUFT6fr9hJIaIbmKqq\nAMC6KAu2ZdrKuXVeLWxLdKPTNPMWnfefRFRMrIuIqNhYDxFRoSQudS38YKZPaLaPyDu/T4P9RURU\nYOyvzh32SVMpKplgjZdeegmhUAj79u3L6CHs7rvvRkdHB5qbm+FyuTA8PIwnn3wSv/rVr/D1r38d\nX/va17Bx48b8JZyIiIiIiIiIiIiIiNaMJQEaREREREUgpJSy2IkAgC996Us4f/48vvCFL+DAgQOr\n3t8//dM/4Wc/+xluvvlm/OEf/uGq9zcwMLDqfdDy/H4/Epe6MDk5WeykENENbDZCmXVR5kRr+4L/\nL7du6+Jtiei62cDl0dHRFbYkIsof1kVEVGysh4gon5brs5jFPiIiKgWsi3KHfdKF1djYWOwkrAlK\nsRMAAH19fTh//jyqq6uxb9++nOzzvvvuAwCcO3cuJ/sjIiIiIiIiIiIiIiIiIiIiyoWSWAbl6aef\nBgDcc889UJTcxI9UVFQAAGKxWE72R0RERMuz8lYKERERERERERERERERlcDMGvF4HM8//zyEEDh8\n+HDO9vvee+8BAGpra3O2TyIiIiIiIiIiIiIiIiIiIqLVKnqwxssvv4xQKIS9e/fOrUW5WDKZRH9/\nP4aGhhZ8fvXqVQSDwSXbj4yM4O/+7u8AAHfeeWfuE01ERERERERERERERERERESUpaIvg/LMM88A\nAI4cOZJ2m/HxcXzuc59DTU0Nvv/97899fvLkSTz22GPYsWMHamtr4XQ6MTw8jDfeeAOJRAJ79+7F\nRz7ykbz/BiIiIiIiIiIiIiIiIiIiIiKrihqs0dfXh66uLlRXV2Pfvn0Zf3/nzp0YGBhAd3c33nvv\nPcRiMbjdbmzfvh133XUX7rrrLggh8pByIiIiIiIiIiIiIiIiIiIiouwUNVijubkZjzzyyIrb1dbW\nptyus7MTnZ2d+UgaERERERERERERERGtA7LnYrGTQERERLSEUuwEEBEREREREREREREREREREd1I\nijqzBhEREd2YFr/RIlrbi5QSIiIiIiIiIiIiIiKiwmOwBhERERXd/OANBm4QEREREREREREREdF6\nx2VQiIiIiIiIiIiIiIiIiIiIiAqIwRpEREREREREREREREREREREBcRgDSIiIiIiIiIiIiIiIiIi\nIqICYrAGERERERERERERERERERERUQFpxU4AERER0XJkz8UF/xet7UVKCRERERERERERERERUW5w\nZg0iIiIiIiIiIiIiIiIiIiKiAuLMGkRERFRSFs+kQUREREREREREREREtN4wWIMoBRkNA4FpQNUA\nXxWEsnYmoZGhIBAKAnY74K2EEGL57Q0DmJoAkgmgvALC5S5QSvNPJpPmb5MS8HghHI5iJ2lNkfE4\nMD1p/sfrA+IJIBSwnLeW7C+pA1PjgDRmrofT2vfyWB5lNAIEpwFFAbzVEOrCfReqfMhI2EyHZjPP\n7Rqqc1aDZdQamUwCY9cAQwcqqyGc+aun5x9LVlRChINAIm7myzKPtX0kEuY+pASq/JbL+motOG5l\nNYTTdf1v4SAwOW6WMX8thKLm/viBKWB6CnA4gOrajOvInKYlj79XGoZ5nhNxoMIHUV6RervxUSAS\nBso9EN5KAIAx3AcMDQDuMoi2rRCa7fr2UgLjI0A0CngqICp8mactMG22WxlcA2nowOg1s573VUG4\nyzM+bjbk5BgQCgFlZRC+6oIcsxTIcAiYHAM0Daiug1DT580FdZ+vOqs2uFjXN2Va5sqlBvjr8lIP\n5YsMBcz22mY3y9ai+5S5/Owug6hcmp+lrgNjw0AyaV5Ld1nGaTCCAeDqZUDXgaZWKCmOM3e8+fVU\nBu1XoS1MZxVEWW7y51x75HQCVTUFaY9kLAqMjwJCmHnEZlv5SzcgOT0BBAKA02XeI2X6PGXokJff\nM58bahuhNG7IU0oLT8aikANXgcCUmYfqm5ZtI1bcn5TA6DAQn7lf8aS+X8lqvxOjQCQCeDwQFZU5\n2S/lv+7O5j6RiIiIiKgQGKxBNI/s64E88RRw5QKgqoBhADY75ME7IQ7eVbABp2zIi11m2gf7ZtKu\nA+5yyFvvgdh3G4S2sLjLeBzy1Ang1eNALGYOVus6ZOtmiDvuhWjZVJwfkgMyOA158jjw5klz0E4I\nc+Bx+27zt/nrip3EkiYnxiBf/BVw5nXz3EnD7FwHAJvNPKeucshb74bYf/uSvLVkf6EQ5CvPAa+/\nZJYpIcy8tnUnxB1HIOoaU3+vvxfyhaeAK+8tLY833wXhzL48ysE+s7xc7DL3LSWgqpAH7oBx30cA\nRYVx4lfAq/8GxOeXj3aIO++F2NCW9bEXpKPnkvkbr165/hudTshb3gex/w4Iuz0nxyk1MjgN+fJx\n4A2W0eXIwDSMpx8Fjv/ietlJJoE9t0B54Nchmjfm7ljBaRhPPwY893NAT5ptSDIJKRSz3BsGsKUT\nyoO/AbF1Z+p9TI7BePLHwImnZz4xrytuvhPKhz6RtqyvOu1TEzB++WPg+acAyLk6Bvtvh9h1M+TL\nzwHnTpsDpIYB2B0Q7/+w+S8HAVjy3Tdh/OxHQPd7ZnCErpvBBvd9DOJ9H1gQkJBv8sp7MJ54BHj3\nzXm/1w5x+MMQRz6yqt8r4zHI4z+HfOoxIBIy68VkEmjvMPPFtl2QhgF58jnIX/yLGXihqkDCDHZD\nJGTWp7P7EwLYuR/4rc9AvP0a5C9/bA4Gq6q535ZNZj7//9m78zg5rvre+99TXb1Mz6KZ0WiXJdmW\nZFnGlo0l7/u+QSAYA1lYsjgXTJIbCCG5NzyXxBBCSO7zeiUhD7m5hMtNAsYkBIxtbLAdvBFbeJFs\nyZItybbWmdGMZp/pteo8f9TMaJZeqmfrkfR58+IlT3fVqVPbqeo6v/qd87aUr9trL8t/8H7prdeP\n74O6Bpmb3ytz1S0FOw1tekj28QdlH3vgeDufz0tnbwrW54yzprytitbTWtmtTwXbp/1IsI/yeWnx\nMpnb3h/ca56kHQd2/97gPNnx0vCxaaWoK3PtHTI3vntcEMW49sj3jrd9m7bIuf0Doa7B1di/Revy\n1h75D31n/HkZjclcf4fM9e+eUuDCXMm+tl2D3/2G/B1j6p6okbnhF6Rrb5Ne+bnsw/8qtR8efzzf\neqfMxVdLqUHZx34o+8QPpVxecob35TnvlHPHXTJr1pWtg//GTtlvfU06vH/c515js8z7Pipz8dWj\n543NpGWfeEj2sR9I6dTx/X7WRcOzTAAAIABJREFUO4JjZ93G2dhMFbPp1PF6ZtJj6nlucHyuPXtq\n5e54KTjW3t4b7A/PC4Jhb3rvrF2PbNth+Q9/V3rhGcmJSLLBF1fcKOeWXzylgtFKsdu3Bvvm4NvH\n901js8wt75O5/IayQQl+alD2m38b/L71/dHPvXgi2L/v+uAJe/2wbYfk3/+PwW9Pa49/7kala26T\n/5GPyykSGFqwvFz2+P3K0MDx8+vMDcF9xdmbplZP35N99nHZR/5N6j52/H5lxWo5t98lnX/xCbsP\nqs1mMrI/fUj2x98f33aX+e0Ruvwp3CcCAAAAc8lYO+bXEIo6cuRItatw0mtpaVFu32719PRUZfn+\ntuelR74XvHk3ketKdQtkPva7M/bG00zyf/oj6bmfFql7VFqyXOZXPi4TDTp+bWpI9v/8dZDlYKQT\nfuI8N7xLzuYrZrfis8B2HpX95t8EDz59b/yXxkgRV+auX5vTh/QnEntof/BAPJcd97CsIDcqLVoq\n86v3FA0qsN3HgmMtnQo6gAuUYX7xIzLrxz8897dtlR751yLH5/D5+NHfkamr/G0bf+fL0g/vK3y+\nRFwpWScTi8r2dpc4P35BzubLKl72uHo896T004eLtzlNLTIf+e1x2QFOBvZYR3BMTOMcNavXznIt\nq892tsv/0h8ED5knHiPGSNGYnLs/I7Ppoukv69hR+V/6TJCVqdDxOFYsLvPeX5Fzwy+ML+PwAfl/\n8YdSJhU8AB1XX0eKxeT8zv8z7Yetk+reekj+lz9bvI0pJhqTGpvl/NFXZOoXTHn5/g/+JXiwPCYI\nYVQsLq1cI+dTX6g4a0xLS4skqbOzM3xdfva47L98LXiDVBPa72hUWtAs54/+YkpvgNrUkPy/+KOg\nQzaXnTxBLC7d8UFp72vS7lcKb49S3GjhYy8Wl669TZE7P1Z0Vv+B+2Qf/bfi+2D5ajm//4VxAbe2\nvzc45nu6CqyPCQJcPni3nCtvrGw9SrC+L/9//5X0ytYgSLZQXc/bIuc3f/+ky67kP/+U7P/9m8L3\nFm5UalgQnIuNC0u3R8Ntn/mNT8u54JKiy6vG/i3Gf+4/ZP/p7wqfl8PZtJw/+spo9pn5xH/iIdl/\n+z+Fzy03Gvzf94qfexvOkw69HbzFXGhfulGZD39SziXXFK2D98SD0n3/UPye1BjpkmvlfOx3paHB\n4HrQ2V60nTLv+6ic624vury5YAf75f/5H0jHOorX8/2/JueaWysqd7auR6XY11+V/zf3BusxJoBA\nUtCRHa+R89k/l1m+asaWeaKx1sq//+vSU48W3zdnnCXnd/9H0WAav7db9r//VnDvXMyadUFbcoJd\nP+yu7fL/+k8K/+YaZhqbtfAvv6FuGyJbVnpI/lf+m9R2aLjdnSAWl7njA3JuvbOyeubz8v/mT6W9\nu4rvx8uul/NLv0XARoXs0ID8L/+h1NFWvE1876/KueHdUyp/KveJOPHN9LCqjY1Bxr9qPa8GAIm2\naCadCs+V55Ply2fn5b2TTeTzn//856tdiRNBf39/tatw0ksmk/K7O5VOl3gIMUvsobel7/1T8U4q\n3w9+7L/1hnTBJfPqB7i/c5v0xIOl654alDqPymwM3iKx//K14MfwxA61sfO8vVdaueaEehvK5vOy\n//svg87NYg91fV/a/ar0jneedJ3g02UHB2W/8f+G72TzfSk9JB09InPOBZPL83zZr//PYAgT6xco\nYLiM11+VNmwafbM0OB//b/GHdr4f1PGtPRWfj7btiHT/14ufL9aXsukgdf/Eh85jl//2HmnlGTKN\nzaGXPW4x+16XHr6/9HmbSUuH3w71VvmJIjhH/yrcOXrOBUWzAEx1u58orOfJ/8LvBVkGJga0jPA8\n2W3PB9ltKnjbcNKyfE/+Fz4dBO8VuyZMWK727JRZs05m8bKgjExG/r3/NRgmqeB+tZKXl33xZzKX\nXTdjw7jYXFb+n/7XIBV4sTamGN+TMinZXdtkrrx5Std1+8Izsv/2zeJtpudJA/2yrQcrDn5MJoNt\nNDQ0FK4ub70h+/d/Ubwuvi+l07KvvRy8QVjh+vpf/YK0f2/xNsvzgra882jhB+1lF1Bk/3lekHmo\ncWHBjF/2xZ/J/us/lt4Hg32yhw/I2XJlMI+18v/yvwWZLUqtz67tMmedK9O8qPL1KcD+8D7p2cdK\n17WzXfI8mQ3nzsgy5wN7YJ/s3/1Z6WMzk5F99QXpqptlv/jpIMiiWHvkedL2rTIXXFIw0Kpa+7cQ\n+9Ye2a/9eZl1T8vufEnm6srPy9lkd22X/eZfl657Pld6P7W3BveJxa5jviftfFHmHe8seF23b+yQ\n/Ye/LB88fPjtIJvgA98KgkNK7fc3dsicuaGqGbz8//k5qfVg2fbUrN0o07I4XJk/D3k9ajskZ/Pl\nU6z5eLb7WBB0kkkX3kfWSrmc7IvPylx7u0zk1Ezqap/5sfTQd0vvm95u2a5OOedfPHl+a2X/+L8E\nAWyl9HTJdnfIOb94INt8Y491BMdQuUDhdEqZF56VvfrWsu2k/3dfCp7Z5EqcX/t2y6xcI7N0Rei6\n+t/6e2n71uL3OJ4nHTkQBP2fXj5jEI7z//pPgyGuSrWJe3bKnL5OZtGyisqeyn0iThK9XTNaXGI4\nq2s1nlcDwAjaoplzsj9Xnm/q6+fnsKTzzYkVdg/MEvvUo+UfEvjDD9GPHJybSoVgrS3+Zv5Y+bz0\nxg7Zvt6gs7rtYPEHp6Pz5GSffHTmKjsH7O5XCr+tOpHnyT7/5OxX6ARjX/7Pyt5Ml4Jja99u2Z7u\nyd/t3SkNDWrSm6QTeflgmIKRejz94xDnox+cjxNSYpdjn/lJ+bLDyOdkn3pkyrPbJ0Oct15eOvS2\nbGf7lJcz3wTnaIgfFZ4nu/Wp2a/QfPXqz6WB/vIBCPmc/Ef/fXrL2vFSMDZ4sc7yQrJZ+Q98a/RP\nOzJcUDleXv5/PDyFShZmX3w2yORRro0pWh9PajscPNSvdNnWyv/3fy6/3rmstH2rbPexqdUxJP+H\n9xV+g3TcRJ50tFXat7uism3bYemNneHa5ZloXyfKZmQf+JYKJQP0vx9mH+SkHS/IHusI/n7rjeBe\nrtz1LpsJhu2YATaXk/1JkTfeJyzT/uQHslMJeJmn/Ie+Wz6Ax/eCYXMe/XepL0R7lM8FQy4VUoX9\nW4z/0HfCnZcdbcHb2vOI/8C3Ks+QM4ktH2iRywVDNxWqw7//c7hrk7WyP/jnIJA2zH7/wbdKTzOL\n7P59QQBaiSwCkoJ6/vDb4cq0Vvb7/xTuerTt+Rm7HvlPPBgiyNMG7drPn5mRZZ5orO/L/uDb4fbN\n8z+VHeibXMbe14Lg3TD+8z9kiwUpzEP+Ez8sHlQxgdfRHmTuKsEebZV2by9/L5LNBPcPIdmhAeln\nj5e/lmUzsg/eJ1vJPfUpzh4+ENyXhmkTHwjXJo5V0X1iV0fF5QMAAAAzhWANnPLs4KC0f1+4ifM5\n2Z8/PbsVqkT7kaCTLST7ylbZF58J9/a0JB05INsXvvyqe/7JcG/0+l7wsJJRoMZ74ZnyD0oKsVZ2\n+/OTP37+qXD7w1ppx4uyeU92aDDI6hJGheejzWalPTtDT1/Wkf2y/ZMfqpatR09X0GEahucFHdIn\ni60VnqOn6MNO/7EHhoMQyk3oS88/KTuNDvLQy5ro4Fuyx45KkuxjD4QLwsnnpJ/+qPJlFWF/EnK5\npWQzQYdTpQ69LfWE7/Cyz/yk8mWELXtwQNq1XaGCVqawvv5Tj5YP8Jxtg/3Sm6+P+8ge3h908Idh\nreyzwT7w/+Oh8Nk/Xn91Su38JK9sDT+tUfD27knApoekV35evsNeCjJM/OQH4du+F54p2ClZlf1b\ngB0akHa+rFDnZSYj//Efzko9psJ2dQaZdOZkYVZ69UXZ1PgsQra3e9I5X1I6FT5YbP+eWQ+gK8YP\nE2Q/4s3dwT1jOQffCt+ZL8k++1joaUt68pFw65JJB8Fqp6K9u4JjMxQT/HaawP57+KAC+f4J80KC\ntTY4hsIG3OayZdtJ/+kfS37I8o62yrYdCjWp3fp0MORSGNmM9MaOcNNC/pM/Cv+yyIE3jwfehmAP\nvV3ZfeIs3qsDAAAA5RCsAfT3SJWkZR3unJoXeo5JTiTctF4+yERwrCPcQ3NJcl2pL/zDv6qr4EGl\nvHy4LBynksEpDvc0krp9ogo6MiVJ6cEg+CgS8piWguM5rIE+aSbHcY64lR1zI3q6w7c51kqdJ9Fb\nPoUysBTj5cu/kXyy6qgwm0q51Ngll9U2tflc9/j1sLsz/HypQdmpBIUV0jUD12Nrg+walepsD3/9\nzedk26ewjLB6jgX7IwxrpUrr0nowfJDnbDFmcpahzvbw14t8XrZ1uFOm7XAF90HRyo7vImzn0dBv\nDyubPXkyKvX2VHZNTw2Gn9aYwgHLVdi/BfV0hT8vZYMA7Pmi62iwbeaK6wbba1wdOsJ3jo6oZL9X\n6/dc2+HwmazCHp+d7ZIJeX+bz4XuoC7F5nPBEDdhzdZ5Ns8FQa3hgxEK3itUej049FZl01dLNlt5\n9p6jZdrJtkPhO/4jkWDothDs0daKhgk9aa7hc6HtUAVtoltZ233s6NTuEwEAAIAqIFgDiETCP9yT\nKgvsmG2V1sWNVfDgWMF2mU/rW04lHfG+X9m2OBWE7XgspNBD/UrK84ePtYhb2flYyT6MRMoPK1EJ\na6d2DLkVbueT6TitpNPOt5Vvq5NFJft8um3ZVNt4O2beSvarNHNBUzN1fYpOoVOy0m0ejVW+jLAq\nbjcrXN+pbJ8ZZ2Qm1iPiVjYCTmx4H1Sy/lNt5ydy3fAdz44ztx3ls8mt8NgM2+EsBW1foWOzGvu3\nENetbHip+XStj0SnPLrUlBS6js3m7w9rq9euzcbvsEqPnVi8sukLqfQ3Q6X3CSeLStp+6fh1aqxK\nt91M7N+5UOkzGKl8+z5b9zeVtBfGnDzX8LlQ0TVbFf72r7RtnMV7dQAAAKAMgjWApkWSE/IhiutK\n6zbObn0qsXy15Id8eyQalzljvbT27PA/iq2VFi2Zev3m2unrwz8Qa1ksM58ejM8Hy1d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BEREREREREREREREREREdUQZ9YgIiIiWmYWvjUlNmypU06IiIiIiIio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\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zn/0Mly5dwte+9jW0t7fv+J7f78eHP/zhHX/3er0lOT6AwRpEREREREREREREe9r21deI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\n1Bv3ZuxxrRTualP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"text/plain": [
"<matplotlib.figure.Figure at 0x120f0ee80>"
]
},
"metadata": {
"image/png": {
"height": 576,
"width": 1077
}
},
"output_type": "display_data"
}
],
"source": [
"from warnings import filterwarnings\n",
"\n",
"filterwarnings('ignore', category=qi.ApproximationWarning)\n",
"PARTICLES = 50\n",
"NR_OUTCOMES = 10\n",
"\n",
"modelparams_true = np.array([0.001])\n",
"model = CircularModel(sigma=1.)\n",
"pl.figure(0, figsize=(18, 10))\n",
"outcomes = model.simulate_experiment(modelparams_true, [], repeat=NR_OUTCOMES).ravel()\n",
"\n",
"for i in Progress(range(20)):\n",
" prior = qi.UniformDistribution(ranges=np.array([0, 2 * np.pi]))\n",
" updater = qi.SMCUpdater(model, PARTICLES, prior, resampler=qi.LiuWestResampler(a=0.0))\n",
" updater.batch_update(outcomes, np.empty((NR_OUTCOMES)), resample_interval=1)\n",
"\n",
" pl.scatter(updater.particle_locations, i * np.ones(PARTICLES),\n",
" s=weights_to_markersize(updater.particle_weights))\n",
" \n",
"pl.gca().twinx().hist(model.simulate_experiment(modelparams_true, [], repeat=1000000).ravel(),\n",
" bins=500, normed=True, alpha=.2);"
]
}
],
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