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{
"cells": [
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import quantile_transform, QuantileTransformer"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"x = np.random.gamma(10, size=10000) - 5"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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zYAgsD/N6nMZKV1Xnuukl4K8ZHCZr1cUkawG66aUJ17PkqupiVV2pqueAP6OBz0OSVzII\ngI9W1Se75rF+FgyB5aH5x2kkuTHJdz8/D/wM8NTLv2pFOwzs7OZ3Ag9PsJaJeP6Lr/MLrPDPQ5IA\nHwKOV9X7h1aN9bPgzWLLRHf52wd44XEa+yZc0pJK8kYG//3D4E72j7WyD5J8HLiDwdMiLwL3A38D\nHAK+DzgN3FNVK/bE6Rz74A4Gh4IKOAX86tCx8RUnyVuBfwaeBJ7rmt/H4LzA2D4LhoAkNczDQZLU\nMENAkhpmCEhSwwwBSWqYISBJDTMEJKlhhoAkNcwQkKSG/R82Fd1Fz2zhYQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f281b909e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(x, bins=30);"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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kzkkuTvJgkv1J/m2haxy1Af62fz7JPyR5qJvzOxejzlFJcluSY0kePcX28eZXVb3oH8x8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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f281be1ccc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qt = QuantileTransformer()\n",
"qx = qt.fit_transform(x.reshape(-1, 1)).ravel()\n",
"plt.hist(qx, bins=30);"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f281baaf588>]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f281c56f390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(qt.quantiles_, 'o')"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"y = np.hstack([x, np.zeros(10000)])"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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rRzuqkfqFqrp5Ht3CuBNYP6W2FdhbVauAve311Wwn/TkA+Gh7L9zcrldezc4CH6yq1cBa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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f281c1eabe0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(y, bins=30);"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f281c24ba20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"qt = QuantileTransformer()\n",
"qy = qt.fit_transform(y.reshape(-1, 1)).ravel()\n",
"plt.hist(qy, bins=30);"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f281bc79fd0>]"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f281c80f518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(qt.quantiles_, 'o')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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