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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
epsparameter.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.
See also
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)[source]
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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 : is not used: placeholder to allow for usage in a Pipeline.
Returns: self :
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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.
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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.
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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 :
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transform(X, y=None)[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.
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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random_projection.GaussianRandomProjection()
2017-01-15 04:25:18
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