val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?n_components:[ `Int of int | `Auto ] ->
?eps:float ->
?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
unit ->
tReduce dimensionality through Gaussian random projection
The components of the random matrix are drawn from N(0, 1 / n_components).
Read more in the :ref:`User Guide <gaussian_random_matrix>`.
.. versionadded:: 0.13
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`.
Attributes ---------- n_components_ : 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 >>> 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)
See Also -------- SparseRandomProjection
val fit :
?y:Py.Object.t ->
x:[ `Ndarray of Ndarray.t | `PyObject of Py.Object.t ] ->
t ->
tGenerate 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
Returns ------- self
val fit_transform :
?y:Ndarray.t ->
?fit_params:(string * Py.Object.t) list ->
x:Ndarray.t ->
t ->
Ndarray.tFit 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.
**fit_params : dict Additional fit parameters.
Returns ------- X_new : numpy array of shape n_samples, n_features_new Transformed array.
val get_params : ?deep:bool -> t -> Py.Object.tGet parameters for this estimator.
Parameters ---------- deep : bool, default=True 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.
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet 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.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
val transform :
x:[ `Ndarray of Ndarray.t | `PyObject of Py.Object.t ] ->
t ->
Ndarray.tProject 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.
Returns ------- X_new : numpy array or scipy sparse of shape n_samples, n_components Projected array.
val n_components_ : t -> intAttribute n_components_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.