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).

Read more in the User Guide.

Methods

fit(K[, y]) Fit KernelCenterer
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(K[, y, copy]) Center kernel matrix.
__init__($self, /, *args, **kwargs)

Initialize self. See help(type(self)) for accurate 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=’deprecated’, copy=True)[source]

Center kernel matrix.

Parameters:
K : numpy array of shape [n_samples1, n_samples2]

Kernel matrix.

y : (ignored)

Deprecated since version 0.19: This parameter will be removed in 0.21.

copy : boolean, optional, default True

Set to False to perform inplace computation.

Returns:
K_new : numpy array of shape [n_samples1, n_samples2]