Morten Hjorth-Jensen
85 lines
2.0 KiB
Plaintext
85 lines
2.0 KiB
Plaintext
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"Documents for the Linear, Ridge and Lasso regressions in the sklearn package:\n",
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"\n",
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"Linear: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\n",
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"\n",
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"Ridge: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html\n",
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"\n",
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"Lasso: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html"
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]
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"[0.15631421 2.24887843]\n"
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]
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}
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],
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"source": [
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"from sklearn import linear_model\n",
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"import numpy as np\n",
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"\n",
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"# Form the coefficient matrix of the linear system\n",
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"X_transpose = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])\n",
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"\n",
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"\n",
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"# The underlying linear function is y = 1 * x_1 + 2 * x_2 + epsilon\n",
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"epsilon = np.random.rand(4,)\n",
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"y = np.dot(X_transpose, np.array([1, 2])) + epsilon\n",
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"\n",
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"\n",
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"# Various regression models\n",
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"#clf = linear_model.LinearRegression()\n",
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"#clf = linear_model.Ridge(alpha=0.1)\n",
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"clf = linear_model.Lasso(alpha=0.1)\n",
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"\n",
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"# Fit the model to the data\n",
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"clf.fit(X_transpose, y)\n",
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"\n",
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"\n",
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"# Print the regression coefficients\n",
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"print(clf.coef_)\n"
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]
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},
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