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@jakevdp
Created January 8, 2015 00:01
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Simple KDE example with scikit-learn
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{
"metadata": {
"name": "",
"signature": "sha256:77165790a6d969fe3d3f19f41d04ec38176b032bba531bfe8176e09c63113f6f"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.neighbors import KernelDensity"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# create some data\n",
"x = np.random.randn(1000)\n",
"plt.hist(x, 50);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Udiciuv3WnTm8I2L3mKt+TAn3TEe9Ptu/IOmt6h3vns4E719bfFXSxonznertfSMB2y+T\n9ElJfxkRB+qupyoR8ZztT0v6IQ24kEvVR5u8bsPd2yUdqfL55s32HknvkXR7RHyr7noq1pavHHpc\n0utsX2/7Kkk/J+nhmmvCGNz7DrIPSToWER+su56y2b7O9tZi+eWSdmtIZlZ9tMknJL1e0guS/kPS\nnRFxqrInnDPbX1ZvYuHipMI/RcSv1FhSqWz/jKQ/kXSdpOckHYmIt9Rb1exsv0XSB/XiyWVDD8nK\nxPbHJf2YpG+TdErS70bER+qtqhy2b5X0GfW+B/FicN0dEX9XX1XlsX2jpFX1dqq3SHogIt4/cH1O\n0gGAfPgaNABIiPAGgIQIbwBIiPAGgIQIbwBIiPAGgIQIbwBIiPAGgIT+H4bDw+mLTBu+AAAAAElF\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1070b9ad0>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# perform kernel density\n",
"xfit = np.linspace(-5, 5, 1000)\n",
"\n",
"# make data of shape [n_samples, n_features]\n",
"# this is required by scikit-learn, as it usually uses multi-dimensional data\n",
"X = x[:, np.newaxis]\n",
"Xfit = xfit[:, np.newaxis]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"kde = KernelDensity(bandwidth=0.2)\n",
"kde.fit(X)\n",
"\n",
"# really unfortunate notation... but this is how you get the density\n",
"density = np.exp(kde.score_samples(Xfit))\n",
"plt.hist(x, 50, normed=True, alpha=0.5)\n",
"plt.plot(xfit, density, '-k', lw=2);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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s0CZMmADAgQM7qaoqdziNCSZW4E2L27z5LwBMmTLF4SThKTk5mUsvvZSqqsqa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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10851af90>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
}
],
"metadata": {}
}
]
}
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