sklearn.kernel_approximation
.SkewedChi2Sampler¶

class
sklearn.kernel_approximation.
SkewedChi2Sampler
(skewedness=1.0, n_components=100, random_state=None)[source]¶ Approximates feature map of the “skewed chisquared” kernel by Monte Carlo approximation of its Fourier transform.
Read more in the User Guide.
Parameters: skewedness : float
“skewedness” parameter of the kernel. Needs to be crossvalidated.
n_components : int
number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
See also
AdditiveChi2Sampler
 A different approach for approximating an additive variant of the chi squared kernel.
sklearn.metrics.pairwise.chi2_kernel
 The exact chi squared kernel.
References
See “Random Fourier Approximations for Skewed Multiplicative Histogram Kernels” by Fuxin Li, Catalin Ionescu and Cristian Sminchisescu.
Methods
fit
(X[, y])Fit the model with X. 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
(X)Apply the approximate feature map to X. 
fit
(X, y=None)[source]¶ Fit the model with X.
Samples random projection according to n_features.
Parameters: X : arraylike, shape (n_samples, n_features)
Training data, where n_samples in the number of samples and n_features is the number of features.
Returns: self : object
Returns the transformer.

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
(X)[source]¶ Apply the approximate feature map to X.
Parameters: X : arraylike, shape (n_samples, n_features)
New data, where n_samples in the number of samples and n_features is the number of features. All values of X must be strictly greater than “skewedness”.
Returns: X_new : arraylike, shape (n_samples, n_components)