8.12.3. sklearn.kernel_approximation.SkewedChi2Sampler¶
- class sklearn.kernel_approximation.SkewedChi2Sampler(skewedness=1.0, n_components=100, random_state=None)¶
Approximates feature map of the “skewed chi-squared” kernel by Monte Carlo approximation of its Fourier transform.
Parameters : skewedness: float :
“skewedness” parameter of the kernel. Needs to be cross-validated.
n_components: int :
number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.
random_state : {int, RandomState}, optional
If int, random_state is the seed used by the random number generator; if RandomState instance, random_state is the random number generator.
Notes
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 set_params(**params) Set the parameters of the estimator. transform(X[, y]) Apply the approximate feature map to X. - __init__(skewedness=1.0, n_components=100, random_state=None)¶
- fit(X, y=None)¶
Fit the model with X.
Samples random projection according to n_features.
Parameters : X: array-like, 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)¶
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(X, y=None)¶
Apply the approximate feature map to X.
Parameters : X: array-like, shape (n_samples, n_features) :
New data, where n_samples in the number of samples and n_features is the number of features.
Returns : X_new: array-like, shape (n_samples, n_components) :