sklearn.kernel_approximation.RBFSampler

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

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://www.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)

fit(X, y=None)

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)

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)

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)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former 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)

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