sklearn.random_projection.GaussianRandomProjection — scikit-learn 0.20.4 documentation (original) (raw)
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. |
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| 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. |
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 |
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| 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]¶
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 |
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| 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. |
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| 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. |
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| Returns: | params : mapping of string to any Parameter names mapped to their values. |
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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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. |
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| Returns: | X_new : numpy array or scipy sparse of shape [n_samples, n_components] Projected array. |