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class sklearn.kernel_approximation.SkewedChi2Sampler(skewedness=1.0, n_components=100, random_state=None)[source] -
Approximates feature map of the ?skewed chi-squared? kernel by Monte Carlo approximation of its Fourier transform.
Read more in the User Guide.
Parameters: skewedness : float
?skewedness? parameter of the kernel. Needs to be cross-validated.
n_components : int
number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.
random_state : {int, RandomState}, optional
If int, random_state is the seed used by the random number generator; if RandomState instance, random_state is the random number generator.
See also
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AdditiveChi2Sampler - A different approach for approximating an additive variant of the chi squared kernel.
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sklearn.metrics.pairwise.chi2_kernel - The exact chi squared kernel.
References
See ?Random Fourier Approximations for Skewed Multiplicative Histogram Kernels? by Fuxin Li, Catalin Ionescu and Cristian Sminchisescu.
Methods
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[, y])Apply the approximate feature map to X. -
__init__(skewedness=1.0, n_components=100, random_state=None)[source]
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fit(X, y=None)[source] -
Fit the model with X.
Samples random projection according to n_features.
Parameters: X : array-like, shape (n_samples, n_features)
Training data, where n_samples in the number of samples and n_features is the number of features.
Returns: self : object
Returns the transformer.
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fit_transform(X, y=None, **fit_params)[source] -
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters: X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns: X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
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get_params(deep=True)[source] -
Get parameters for this estimator.
Parameters: deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
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set_params(**params)[source] -
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.Returns: self :
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transform(X, y=None)[source] -
Apply the approximate feature map to X.
Parameters: X : array-like, shape (n_samples, n_features)
New data, where n_samples in the number of samples and n_features is the number of features.
Returns: X_new : array-like, shape (n_samples, n_components)
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kernel_approximation.SkewedChi2Sampler()
2017-01-15 04:23:04
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