kernel_approximation.RBFSampler()

class sklearn.kernel_approximation.RBFSampler(gamma=1.0, n_components=100, random_state=None) [source]

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.

Parameters:	gamma : float Parameter of RBF kernel: exp(-gamma * x^2) 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.

Notes

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)

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__(gamma=1.0, n_components=100, random_state=None) [source]

fit(X, y=None) [source]

Fit the model with X.

Samples random projection according to n_features.

Parameters:	X : {array-like, sparse matrix}, 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.

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.

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.

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 :

transform(X, y=None) [source]

Apply the approximate feature map to X.

Parameters:	X : {array-like, sparse matrix}, 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)

Examples using sklearn.kernel_approximation.RBFSampler

Explicit feature map approximation for RBF kernels