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.
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.
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)
