Files
FYS-STK4155/doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/Ridge Regression Sklearn Tutorial.ipynb
T
2018-05-06 22:19:59 -04:00

87 lines
37 KiB
Plaintext

{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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nPXsWGTvDPXPv2S7FA6A4WsyNh9xIdaKaX7zxi44xIsp1qf7lL5FolDG/+PkQ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\nJECEEEKMiQSIEEKIMfn/oQ9pO37ByZsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a1a1c0a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import linear_model\n",
"\n",
"# X is the 10x10 Hilbert matrix, a square matrix with elements being the unit fractions\n",
"X = 1. / (np.arange(1, 11) + np.arange(0, 10)[:, np.newaxis])\n",
"y = np.ones(10)\n",
"\n",
"# #############################################################################\n",
"# Compute paths\n",
"\n",
"n_alphas = 200\n",
"alphas = np.logspace(-10, -2, n_alphas)\n",
"\n",
"coefs = []\n",
"for a in alphas:\n",
" ridge = linear_model.Ridge(alpha=a, fit_intercept=False)\n",
" ridge.fit(X, y)\n",
" coefs.append(ridge.coef_)\n",
"\n",
"# #############################################################################\n",
"# Display results\n",
"\n",
"ax = plt.gca()\n",
"\n",
"ax.plot(alphas, coefs)\n",
"ax.set_xscale('log')\n",
"ax.set_xlim(ax.get_xlim()[::-1]) # reverse axis\n",
"plt.xlabel('alpha')\n",
"plt.ylabel('weights')\n",
"plt.title('Ridge coefficients as a function of the regularization')\n",
"plt.axis('tight')\n",
"plt.show()"
]
},
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