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@sharanry
Last active July 26, 2018 10:29
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Half Cauchy Distribution
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sharan/anaconda3/envs/pymc3/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 tensorflow as tf\n",
"import tensorflow_probability as tfp\n",
"import pymc4 as pm\n",
"import tensorflow_probability.python.edward2 as ed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = pm.Model()\n",
"@model.define\n",
"def simple(cfg):\n",
" pm.HalfCauchy(scale=5., name=\"HalfCauchy\")\n",
" ed.Cauchy(loc=0., scale=5., name=\"Cauchy\")\n",
" ed.HalfNormal(scale=5., name=\"HalfNormal\")"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Acceptance rate: 0.9048\n"
]
}
],
"source": [
"trace = pm.sample(model)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f60f87740f0>"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"sns.distplot(trace[\"HalfCauchy\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## HalfCauchy is scaled up and behaves like an actual Half-Cauchy distribution. However, it continues to give negative samples."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f60fa057eb8>"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(trace[\"Cauchy\"])"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sharan/anaconda3/envs/pymc3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
" warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f60f7b34208>"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(trace[\"HalfNormal\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (pymc3)",
"language": "python",
"name": "pymc3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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