package sklearn

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Module Random_projection.BaseRandomProjectionSource

Sourcetype tag = [
  1. | `BaseRandomProjection
]
Sourcetype t = [ `BaseEstimator | `BaseRandomProjection | `Object | `TransformerMixin ] Obj.t
Sourceval of_pyobject : Py.Object.t -> t
Sourceval to_pyobject : [> tag ] Obj.t -> Py.Object.t
Sourceval as_transformer : t -> [ `TransformerMixin ] Obj.t
Sourceval as_estimator : t -> [ `BaseEstimator ] Obj.t
Sourceval fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> t

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

Returns ------- self

Sourceval fit_transform : ?y:[> `ArrayLike ] Np.Obj.t -> ?fit_params:(string * Py.Object.t) list -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

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 : array-like, sparse matrix, dataframe of shape (n_samples, n_features)

y : ndarray of shape (n_samples,), default=None Target values.

**fit_params : dict Additional fit parameters.

Returns ------- X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.

Sourceval get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

Get 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.

Sourceval set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

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.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

Sourceval transform : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

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.

Returns ------- X_new : numpy array or scipy sparse of shape n_samples, n_components Projected array.

Sourceval to_string : t -> string

Print the object to a human-readable representation.

Sourceval show : t -> string

Print the object to a human-readable representation.

Sourceval pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.