sklearn.random_projection.GaussianRandomProjection

class sklearn.random_projection.GaussianRandomProjection(n_components=’auto’, eps=0.1, random_state=None) [source]

Reduce dimensionality through Gaussian random projection

The components of the random matrix are drawn from N(0, 1 / n_components).

Read more in the User Guide.

Parameters:

n_components : int or ‘auto’, optional (default = ‘auto’) Dimensionality of the target projection space. n_components can be automatically adjusted according to the number of samples in the dataset and the bound given by the Johnson-Lindenstrauss lemma. In that case the quality of the embedding is controlled by the eps parameter. It should be noted that Johnson-Lindenstrauss lemma can yield very conservative estimated of the required number of components as it makes no assumption on the structure of the dataset. eps : strictly positive float, optional (default=0.1) Parameter to control the quality of the embedding according to the Johnson-Lindenstrauss lemma when n_components is set to ‘auto’. Smaller values lead to better embedding and higher number of dimensions (n_components) in the target projection space. random_state : int, RandomState instance or None, optional (default=None) Control the pseudo random number generator used to generate the matrix at fit time. 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.

Attributes:

n_component_ : int Concrete number of components computed when n_components=”auto”. components_ : numpy array of shape [n_components, n_features] Random matrix used for the projection.

See also

SparseRandomProjection

Examples

>>> import numpy as np
>>> from sklearn.random_projection import GaussianRandomProjection
>>> X = np.random.rand(100, 10000)
>>> transformer = GaussianRandomProjection()
>>> X_new = transformer.fit_transform(X)
>>> X_new.shape
(100, 3947)

Methods

fit(X[, y])	Generate a sparse random projection matrix
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)	Project the data by using matrix product with the random matrix

__init__(n_components=’auto’, eps=0.1, random_state=None) [source]

fit(X, y=None) [source]

Generate a sparse random projection matrix

Parameters:	X : numpy array or scipy.sparse of shape [n_samples, n_features] Training set: only the shape is used to find optimal random matrix dimensions based on the theory referenced in the afore mentioned papers. y Ignored
Returns:	self

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]

Project the data by using matrix product with the random matrix

Parameters:	X : numpy array or scipy.sparse of shape [n_samples, n_features] The input data to project into a smaller dimensional space.
Returns:	X_new : numpy array or scipy sparse of shape [n_samples, n_components] Projected array.