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sklearn.random_projection.GaussianRandomProjection

class sklearn.random_projection.GaussianRandomProjection(n_components='auto', eps=0.1, random_state=None)

Reduce dimensionality through Gaussian random projection

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

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 : integer, RandomState instance or None (default=None)

Control the pseudo random number generator used to generate the matrix at fit time.

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.

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[, y]) Project the data by using matrix product with the random matrix
__init__(n_components='auto', eps=0.1, random_state=None)
fit(X, y=None)

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 : is not used: placeholder to allow for usage in a Pipeline.

Returns:

self :

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.

get_params(deep=True)

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)

Set the parameters of this 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)

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.

y : is not used: placeholder to allow for usage in a Pipeline.

Returns:

X_new : numpy array or scipy sparse of shape [n_samples, n_components]

Projected array.

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