KernelCenterer

class cuml.preprocessing.KernelCenterer(*args, **kwargs)

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 cuml.preprocessing.StandardScaler(with_std=False).

Attributes:

K_fit_rows_array, shape (n_samples,)

Average of each column of kernel matrix

K_fit_all_float

Average of kernel matrix

Methods

fit(K[, y])	Fit KernelCenterer
transform(K[, copy])	Center kernel matrix.

Examples

>>> import cupy as cp
>>> from cuml.preprocessing import KernelCenterer
>>> from cuml.metrics import pairwise_kernels
>>> X = cp.array([[ 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.]])

fit(K, y=None) → KernelCenterer

Fit KernelCenterer

Parameters:

Knumpy array of shape [n_samples, n_samples]

Kernel matrix.

Returns:

selfreturns an instance of self.

transform(K, copy=True) → CumlArray

Center kernel matrix.

Parameters:

Knumpy array of shape [n_samples1, n_samples2]

Kernel matrix.

copyboolean, optional, default True

Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.

Returns:

K_newnumpy array of shape [n_samples1, n_samples2]