class sklearn.kernel_approximation.RBFSampler(gamma=1.0, n_components=100, random_state=None)
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Approximates feature map of an RBF kernel by Monte Carlo approximation of its Fourier transform.
It implements a variant of Random Kitchen Sinks.[1]
Read more in the User Guide.
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See “Random Features for Large-Scale Kernel Machines” by A. Rahimi and Benjamin Recht.
[1] “Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning” by A. Rahimi and Benjamin Recht. (http://people.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)
>>> from sklearn.kernel_approximation import RBFSampler >>> from sklearn.linear_model import SGDClassifier >>> X = [[0, 0], [1, 1], [1, 0], [0, 1]] >>> y = [0, 0, 1, 1] >>> rbf_feature = RBFSampler(gamma=1, random_state=1) >>> X_features = rbf_feature.fit_transform(X) >>> clf = SGDClassifier(max_iter=5) >>> clf.fit(X_features, y) ... SGDClassifier(alpha=0.0001, average=False, class_weight=None, early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True, l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=5, n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2', power_t=0.5, random_state=None, shuffle=True, tol=None, validation_fraction=0.1, verbose=0, warm_start=False) >>> clf.score(X_features, y) 1.0
fit (X[, y]) | Fit the model with X. |
fit_transform (X[, y]) | Fit to data, then transform it. |
get_params ([deep]) | Get parameters for this estimator. |
set_params (**params) | Set the parameters of this estimator. |
transform (X) | Apply the approximate feature map to X. |
__init__(gamma=1.0, n_components=100, random_state=None)
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fit(X, y=None)
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Fit the model with X.
Samples random projection according to n_features.
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fit_transform(X, y=None, **fit_params)
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Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
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get_params(deep=True)
[source]
Get parameters for this estimator.
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set_params(**params)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
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transform(X)
[source]
Apply the approximate feature map to X.
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sklearn.kernel_approximation.RBFSampler
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.RBFSampler.html