8000 Adding Implementation of SAG · scikit-learn/scikit-learn@4ceffe0 · GitHub
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dsullivan7amueller
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Adding Implementation of SAG
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‎examples/linear_model/plot_sgd_comparison.py

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from sklearn import datasets
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from sklearn.cross_validation import train_test_split
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from sklearn.linear_model import SGDClassifier, Perceptron
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from sklearn.linear_model import SGDClassifier, Perceptron, SAGClassifier
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from sklearn.linear_model import PassiveAggressiveClassifier
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heldout = [0.95, 0.90, 0.75, 0.50, 0.01]
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C=1.0)),
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("Passive-Aggressive II", PassiveAggressiveClassifier(loss='squared_hinge',
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C=1.0)),
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("SAGClassifier", SAGClassifier(random_state=77, tol=.1))
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]
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xx = 1. - np.array(heldout)
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for name, clf in classifiers:
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print("training %s" % name)
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rng = np.random.RandomState(42)
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yy = []
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for i in heldout:

‎sklearn/linear_model/__init__.py

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lasso_path, enet_path, MultiTaskLasso,
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MultiTaskElasticNet, MultiTaskElasticNetCV,
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MultiTaskLassoCV)
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from .sag import SAGClassifier, SAGRegressor
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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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'RidgeCV',
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'RidgeClassifier',
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'RidgeClassifierCV',
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'SAGClassifier',
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'SAGRegressor',
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'SGDClassifier',
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'SGDRegressor',
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'SquaredLoss',

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