preprocessing.KernelCenterer

Center a kernel matrix

Let K(x, z) be a kernel defined by phi(x)^T phi(z), where phi is a function mapping x to a Hilbert space. KernelCenterer centers (i.e., normalize to have zero mean) the data without explicitly computing phi(x). It is equivalent to centering phi(x) with sklearn.preprocessing.StandardScaler(with_std=False).

Read more in the User Guide.

Methods

__init__()

x.__init__(...) initializes x; see help(type(x)) for signature

fit(K, y=None) [source]

Fit KernelCenterer

Parameters:	K : numpy array of shape [n_samples, n_samples] Kernel matrix.
Returns:	self : returns an instance of self.

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(K, y=None, copy=True) [source]

Center kernel matrix.

Parameters:	K : numpy array of shape [n_samples1, n_samples2] Kernel matrix. copy : boolean, optional, default True Set to False to perform inplace computation.
Returns:	K_new : numpy array of shape [n_samples1, n_samples2]