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.  New in version 0.13.   Parameters 
 
n_componentsint or ‘auto’, 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.  
epsfloat, default=0.1 

Parameter to control the quality of the embedding according to the Johnson-Lindenstrauss lemma when n_components is set to ‘auto’. The value should be strictly positive. Smaller values lead to better embedding and higher number of dimensions (n_components) in the target projection space.  
random_stateint, RandomState instance or None, default=None 

Controls the pseudo random number generator used to generate the projection matrix at fit time. Pass an int for reproducible output across multiple function calls. See Glossary.    Attributes 
 
n_components_int 

Concrete number of components computed when n_components=”auto”.  
components_ndarray 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
>>> rng = np.random.RandomState(42)
>>> X = rng.rand(100, 10000)
>>> transformer = GaussianRandomProjection(random_state=rng)
>>> 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    
fit(X, y=None) [source]
 
Generate a sparse random projection matrix.  Parameters 
 
X{ndarray, sparse matrix} 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 
 
Xarray-like of shape (n_samples, n_features) 

Input samples.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None 

Target values (None for unsupervised transformations).  
**fit_paramsdict 

Additional fit parameters.    Returns 
 
X_newndarray array of shape (n_samples, n_features_new) 

Transformed array.     
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

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 Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
  
transform(X) [source]
 
Project the data by using matrix product with the random matrix  Parameters 
 
X{ndarray, sparse matrix} of shape (n_samples, n_features) 

The input data to project into a smaller dimensional space.    Returns 
 
X_new{ndarray, sparse matrix} of shape (n_samples, n_components) 

Projected array.