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groundrace/univariate_density.ipynb

Last active January 27, 2018 11:21
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Univariate histogram and kernel density estimation
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univariate_density.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0a9a727780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as pp\n",
"val = 0 #flatten on y=0\n",
"ar = np.random.uniform(low=0, high=10, size=(80,))\n",
"pp.plot(ar, np.zeros_like(ar) + val, '.')\n",
"pp.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0aa2bbdda0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pp.hist(ar, bins=8)\n",
"pp.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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V8MynB7l2ajKTUtx3ymRHmjsugfBgf16y+GItLX03Ul3fzLt7yrlyQqLX7/ko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\nIVr6SinlQ7T0lVLKh2jpK6WUD/n/x9+suzfkB9sAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0a9a6b0ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from scipy.stats import gaussian_kde\n",
"density = gaussian_kde(ar)\n",
"xs = np.linspace(0,10,200)\n",
"density.covariance_factor = lambda : .25\n",
"density._compute_covariance()\n",
"pp.plot(xs,density(xs))\n",
"pp.show()"
]
},
{
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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