sklearn.preprocessing.KernelCenterer¶
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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__()¶ x.__init__(...) initializes x; see help(type(x)) for signature
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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.
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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.
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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.
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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 :
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