8.23.5. sklearn.preprocessing.KernelCenterer¶
- class sklearn.preprocessing.KernelCenterer¶
Center a kernel matrix
This is equivalent to centering phi(X) with sklearn.preprocessing.Scaler(with_std=False).
Methods
fit(K) Fit KernelCenterer fit_transform(X[, y]) Fit to data, then transform it set_params(**params) Set the parameters of the estimator. transform(K[, copy]) Center kernel - __init__()¶
x.__init__(...) initializes x; see help(type(x)) for signature
- fit(K)¶
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)¶
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.
Notes
This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.
- set_params(**params)¶
Set the parameters of the 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, copy=True)¶
Center kernel
Parameters : K : numpy array of shape [n_samples1, n_samples2]
Kernel matrix.
Returns : K_new : numpy array of shape [n_samples1, n_samples2]