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.
See also
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.