sklearn.preprocessing.KernelCenterer

class sklearn.preprocessing.KernelCenterer[source]

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).

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 former 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]