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.
- Attributes
- K_fit_rows_array, shape (n_samples,)
Average of each column of kernel matrix
- K_fit_all_float
Average of kernel matrix
Examples
>>> from sklearn.preprocessing import KernelCenterer >>> from sklearn.metrics.pairwise import pairwise_kernels >>> X = [[ 1., -2., 2.], ... [ -2., 1., 3.], ... [ 4., 1., -2.]] >>> K = pairwise_kernels(X, metric='linear') >>> K array([[ 9., 2., -2.], [ 2., 14., -13.], [ -2., -13., 21.]]) >>> transformer = KernelCenterer().fit(K) >>> transformer KernelCenterer() >>> transformer.transform(K) array([[ 5., 0., -5.], [ 0., 14., -14.], [ -5., -14., 19.]])
Methods
fit(self, K[, y])Fit KernelCenterer
fit_transform(self, X[, y])Fit to data, then transform it.
get_params(self[, deep])Get parameters for this estimator.
set_params(self, \*\*params)Set the parameters of this estimator.
transform(self, K[, copy])Center kernel matrix.
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fit(self, K, y=None)[source]¶ Fit KernelCenterer
- Parameters
- Knumpy array of shape [n_samples, n_samples]
Kernel matrix.
- Returns
- selfreturns an instance of self.
-
fit_transform(self, 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
- Xnumpy array of shape [n_samples, n_features]
Training set.
- ynumpy array of shape [n_samples]
Target values.
- Returns
- X_newnumpy array of shape [n_samples, n_features_new]
Transformed array.
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get_params(self, deep=True)[source]¶ Get parameters for this estimator.
- Parameters
- deepboolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsmapping of string to any
Parameter names mapped to their values.
-
set_params(self, **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