Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

example of sampling via edward #108

Merged
merged 1 commit into from
Apr 11, 2018
Merged
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
124 changes: 124 additions & 0 deletions examples/experiments/edwardpyhf.ipynb
Original file line number Diff line number Diff line change
@@ -0,0 +1,124 @@
{
"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": {
"image/png": "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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": []
}
],
"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
}