class sklearn.kernel_approximation.Nystroem(kernel=’rbf’, gamma=None, coef0=None, degree=None, kernel_params=None, n_components=100, random_state=None)
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Approximate a kernel map using a subset of the training data.
Constructs an approximate feature map for an arbitrary kernel using a subset of the data as basis.
Read more in the User Guide.
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See also
RBFSampler
sklearn.metrics.pairwise.kernel_metrics
>>> from sklearn import datasets, svm >>> from sklearn.kernel_approximation import Nystroem >>> digits = datasets.load_digits(n_class=9) >>> data = digits.data / 16. >>> clf = svm.LinearSVC() >>> feature_map_nystroem = Nystroem(gamma=.2, ... random_state=1, ... n_components=300) >>> data_transformed = feature_map_nystroem.fit_transform(data) >>> clf.fit(data_transformed, digits.target) ... LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True, intercept_scaling=1, loss='squared_hinge', max_iter=1000, multi_class='ovr', penalty='l2', random_state=None, tol=0.0001, verbose=0) >>> clf.score(data_transformed, digits.target) 0.9987...
fit (X[, y]) | Fit estimator to data. |
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 feature map to X. |
__init__(kernel=’rbf’, gamma=None, coef0=None, degree=None, kernel_params=None, n_components=100, random_state=None)
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fit(X, y=None)
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Fit estimator to data.
Samples a subset of training points, computes kernel on these and computes normalization matrix.
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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)
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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)
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Apply feature map to X.
Computes an approximate feature map using the kernel between some training points and X.
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sklearn.kernel_approximation.Nystroem
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.kernel_approximation.Nystroem.html