68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
"""
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The :mod:`sklearn.linear_model` module implements genelarized linear models. It
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includes Ridge regression, Bayesian Regression, Lasso and Elastic Net
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estimators computed with Least Angle Regression and coordinate descent. It also
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implements Stochastic Gradient Descent related algorithms.
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"""
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# See http://scikit-learn.sourceforge.net/modules/sgd.html and
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# http://scikit-learn.sourceforge.net/modules/linear_model.html for
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# complete documentation.
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from .base import LinearRegression
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from .bayes import BayesianRidge, ARDRegression
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from .least_angle import Lars, LassoLars, lars_path, LarsCV, LassoLarsCV, \
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LassoLarsIC
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from .coordinate_descent import Lasso, ElasticNet, LassoCV, ElasticNetCV, \
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lasso_path, enet_path, MultiTaskLasso, \
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MultiTaskElasticNet
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from .sgd_fast import Hinge, Log, ModifiedHuber, SquaredLoss, Huber
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from .stochastic_gradient import SGDClassifier, SGDRegressor
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from .ridge import Ridge, RidgeCV, RidgeClassifier, RidgeClassifierCV, \
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ridge_regression
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from .logistic import LogisticRegression
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from .omp import orthogonal_mp, orthogonal_mp_gram, OrthogonalMatchingPursuit
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from .perceptron import Perceptron
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from .randomized_l1 import RandomizedLasso, RandomizedLogisticRegression, \
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lasso_stability_path
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from .isotonic_regression_ import IsotonicRegression, isotonic_regression
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__all__ = ['ARDRegression',
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'BayesianRidge',
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'ElasticNet',
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'ElasticNetCV',
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'Hinge',
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'Huber',
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'Lars',
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'LarsCV',
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'Lasso',
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'LassoCV',
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'LassoLars',
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'LassoLarsCV',
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'LassoLarsIC',
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'LinearRegression',
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'Log',
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'LogisticRegression',
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'ModifiedHuber',
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'MultiTaskElasticNet',
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'MultiTaskLasso',
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'OrthogonalMatchingPursuit',
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'Perceptron',
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'RandomizedLasso',
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'RandomizedLogisticRegression',
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'Ridge',
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'RidgeCV',
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'RidgeClassifier',
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'RidgeClassifierCV',
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'SGDClassifier',
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'SGDRegressor',
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'SquaredLoss',
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'enet_path',
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'lars_path',
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'lasso_path',
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'lasso_stability_path',
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'orthogonal_mp',
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'orthogonal_mp_gram',
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'ridge_regression']
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