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example of sampling via edward (#108)
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 67, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import tensorflow as tf\n", | ||
"from edward.models import Poisson, Normal" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 78, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"nuispar = tf.constant([3.])\n", | ||
"\n", | ||
"x = Poisson(rate = tf.ones(1)*nuispar)\n", | ||
"n = Normal(loc = nuispar, scale = tf.ones(1))\n", | ||
"joined = tf.concat([x,n], axis=0) # p(n, x | nuispar) = Pois(n|nuispar) * Normal(x |mu = nuispar, sigma = 1)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 79, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"results = []\n", | ||
"for i in range(1000): #thee is probably a batched evaluation version .. this is stupid\n", | ||
" with tf.Session() as sess:\n", | ||
" r = sess.run(joined)\n", | ||
" results.append(r)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 80, | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"Populating the interactive namespace from numpy and matplotlib\n" | ||
] | ||
}, | ||
{ | ||
"name": "stderr", | ||
"output_type": "stream", | ||
"text": [ | ||
"/Users/lukas/.local/share/virtualenvs/pyhf-EFAVEj2h/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['f']\n", | ||
"`%matplotlib` prevents importing * from pylab and numpy\n", | ||
" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" | ||
] | ||
}, | ||
{ | ||
"data": { | ||
"text/plain": [ | ||
"(array([ 6., 32., 62., 140., 188., 243., 191., 87., 41., 10.]),\n", | ||
" array([-0.08035064, 0.51699646, 1.11434355, 1.71169064, 2.30903773,\n", | ||
" 2.90638483, 3.50373192, 4.10107901, 4.6984261 , 5.2957732 ,\n", | ||
" 5.89312029]),\n", | ||
" <a list of 10 Patch objects>)" | ||
] | ||
}, | ||
"execution_count": 80, | ||
"metadata": {}, | ||
"output_type": "execute_result" | ||
}, | ||
{ | ||
"data": { | ||
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\n", | ||
"text/plain": [ | ||
"<Figure size 864x288 with 3 Axes>" | ||
] | ||
}, | ||
"metadata": {}, | ||
"output_type": "display_data" | ||
} | ||
], | ||
"source": [ | ||
"%pylab inline\n", | ||
"r = np.array(results)\n", | ||
"f, axarr = plt.subplots(1,3)\n", | ||
"f.set_size_inches(12,4)\n", | ||
"axarr[0].scatter(r[:,0],r[:,1])\n", | ||
"axarr[1].hist(r[:,0], bins = np.linspace(0,10,11))\n", | ||
"axarr[2].hist(r[:,1])" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
} | ||
], | ||
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