106 lines
32 KiB
Plaintext
106 lines
32 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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"text/plain": [
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"<matplotlib.figure.Figure at 0x10e7ac9b0>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": 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BAwdITk7m0ksvzbWvvzgKYtCgQSxYsIB9+/YB0KZNG5YsWcL+/ftJT0/n3XffpVOnTn6P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\nn3OpFJRtFCJyPXClqg71LN8MtFbVO33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|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x10e7ac588>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"from sklearn.svm import SVR\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"# Generate sample data\n",
|
|
"X = np.sort(5*np.random.rand(40,1), axis=0)\n",
|
|
"y = X**3\n",
|
|
"y=y.ravel()\n",
|
|
"\n",
|
|
"# Add noise to targets\n",
|
|
"X[::4] +=3*(0.5 - np.random.rand(1))\n",
|
|
"y[::5] += 50 * (0.5 - np.random.rand(8))\n",
|
|
"\n",
|
|
"plt.plot(X,y, 'g^')\n",
|
|
"\n",
|
|
"#SVR Fit\n",
|
|
"svr_poly = SVR(kernel='poly', C=1e3, degree=3)\n",
|
|
"y_poly = svr_poly.fit(X, y).predict(X)\n",
|
|
"\n",
|
|
"# Plots\n",
|
|
"z = np.arange(0, 5, 0.1)\n",
|
|
"t = z**3\n",
|
|
"fig = plt.figure()\n",
|
|
"ax = fig.add_subplot(111)\n",
|
|
"plt.plot(z,z**3, 'r--', label='Cubic Function with No Noise')\n",
|
|
"lw = 2\n",
|
|
"plt.scatter(X, y, color='darkorange', label='Gaussian Cubic Noise')\n",
|
|
"plt.plot(X, y_poly, color='green', lw=lw, label='Polynomial model')\n",
|
|
"plt.xlabel('data')\n",
|
|
"plt.ylabel('target')\n",
|
|
"plt.title('Cubic Gaussian Distribution')\n",
|
|
"plt.legend()\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"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": "3.6.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|