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use raw strings in docstrings for __init__ (#169)
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+21
-21
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10 files changed

+21
-21
lines changed

lightning/impl/adagrad.py

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@@ -48,7 +48,7 @@ def _fit(self, X, Y):
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class AdaGradClassifier(BaseClassifier, _BaseAdagrad):
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"""
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r"""
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Estimator for learning linear classifiers by AdaGrad.
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Solves the following objective:
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class AdaGradRegressor(BaseRegressor, _BaseAdagrad):
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"""
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r"""
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Estimator for learning linear regressors by AdaGrad.
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Solves the following objective:

lightning/impl/dual_cd.py

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class LinearSVC(BaseClassifier):
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"""Estimator for learning linear support vector machine by coordinate
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r"""Estimator for learning linear support vector machine by coordinate
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descent in the dual.
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Parameters
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class LinearSVR(BaseRegressor):
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"""Estimator for learning a linear support vector regressor by coordinate
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r"""Estimator for learning a linear support vector regressor by coordinate
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descent in the dual.
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Parameters

lightning/impl/fista.py

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@@ -131,7 +131,7 @@ def _fit(self, X, y, n_vectors):
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class FistaClassifier(BaseClassifier, _BaseFista):
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"""Estimator for learning linear classifiers by FISTA.
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r"""Estimator for learning linear classifiers by FISTA.
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The objective functions considered take the form
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class FistaRegressor(BaseRegressor, _BaseFista):
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"""Estimator for learning linear classifiers by FISTA.
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r"""Estimator for learning linear classifiers by FISTA.
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The objective functions considered take the form
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lightning/impl/prank.py

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@@ -26,7 +26,7 @@ def classes_(self):
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class PRank(_BasePRank):
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"""Online algorithm for learning an ordinal regression model.
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r"""Online algorithm for learning an ordinal regression model.
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Parameters
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----------
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class KernelPRank(_BasePRank):
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"""Kernelized online algorithm for learning an ordinal regression model.
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r"""Kernelized online algorithm for learning an ordinal regression model.
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Parameters
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----------

lightning/impl/primal_cd.py

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class CDClassifier(_BaseCD, BaseClassifier):
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"""Estimator for learning linear classifiers by (block) coordinate descent.
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r"""Estimator for learning linear classifiers by (block) coordinate descent.
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The objective functions considered take the form
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class CDRegressor(_BaseCD, BaseRegressor):
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"""Estimator for learning linear regressors by (block) coordinate descent.
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r"""Estimator for learning linear regressors by (block) coordinate descent.
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The objective functions considered take the form
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lightning/impl/primal_newton.py

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class KernelSVC(BaseClassifier):
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"""Estimator for learning kernel SVMs by Newton's method.
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r"""Estimator for learning kernel SVMs by Newton's method.
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Parameters
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----------

lightning/impl/sag.py

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class SAGClassifier(BaseClassifier, _BaseSAG):
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"""
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r"""
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Estimator for learning linear classifiers by SAG.
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Solves the following objective:
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class SAGAClassifier(SAGClassifier):
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"""
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r"""
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Estimator for learning linear classifiers by SAGA.
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Solves the following objective:
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class SAGRegressor(BaseRegressor, _BaseSAG):
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"""
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r"""
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Estimator for learning linear regressors by SAG.
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Solves the following objective:
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class SAGARegressor(SAGRegressor):
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"""
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r"""
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Estimator for learning linear regressors by SAG.
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Solves the following objective:

lightning/impl/sdca.py

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class SDCAClassifier(BaseClassifier, _BaseSDCA):
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"""
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r"""
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Estimator for learning linear classifiers by (proximal) SDCA.
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Solves the following objective:
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class SDCARegressor(BaseRegressor, _BaseSDCA):
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"""
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r"""
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Estimator for learning linear regressors by (proximal) SDCA.
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Solves the following objective:

lightning/impl/sgd.py

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class SGDClassifier(BaseClassifier, _BaseSGD):
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"""Estimator for learning linear classifiers by SGD.
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r"""Estimator for learning linear classifiers by SGD.
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Parameters
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class SGDRegressor(BaseRegressor, _BaseSGD):
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"""Estimator for learning linear regressors by SGD.
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r"""Estimator for learning linear regressors by SGD.
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Parameters
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lightning/impl/svrg.py

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class SVRGClassifier(BaseClassifier, _BaseSVRG):
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"""
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r"""
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Estimator for learning linear classifiers by SVRG.
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Solves the following objective:
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class SVRGRegressor(BaseRegressor, _BaseSVRG):
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"""
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r"""
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Estimator for learning linear regressors by SVRG.
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Solves the following objective:

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