sklearn 0.22-0.1.3 · OCaml Package

type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?gamma:float ->
  ?n_components:int ->
  ?random_state:[ `Int of int | `RandomState of Py.Object.t | `None ] ->
  unit ->
  t

Approximates feature map of an RBF kernel by Monte Carlo approximation of its Fourier transform.

It implements a variant of Random Kitchen Sinks.1

Read more in the :ref:`User Guide <rbf_kernel_approx>`.

Parameters ---------- gamma : float Parameter of RBF kernel: exp(-gamma * x^2)

n_components : int Number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.

random_state : int, RandomState instance or None, optional (default=None) 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`.

Examples -------- >>> from sklearn.kernel_approximation import RBFSampler >>> from sklearn.linear_model import SGDClassifier >>> X = [0, 0], [1, 1], [1, 0], [0, 1] >>> y = 0, 0, 1, 1 >>> rbf_feature = RBFSampler(gamma=1, random_state=1) >>> X_features = rbf_feature.fit_transform(X) >>> clf = SGDClassifier(max_iter=5, tol=1e-3) >>> clf.fit(X_features, y) SGDClassifier(max_iter=5) >>> clf.score(X_features, y) 1.0

Notes ----- See "Random Features for Large-Scale Kernel Machines" by A. Rahimi and Benjamin Recht.

1 "Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning" by A. Rahimi and Benjamin Recht. (https://people.eecs.berkeley.edu/~brecht/papers/08.rah.rec.nips.pdf)

val fit : 
  ?y:Py.Object.t ->
  x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
  t ->
  t

Fit the model with X.

Samples random projection according to n_features.

Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) Training data, where n_samples in the number of samples and n_features is the number of features.

Returns ------- self : object Returns the transformer.

val fit_transform : 
  ?y:Ndarray.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:Ndarray.t ->
  t ->
  Ndarray.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 : 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.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.

val set_params : ?params:(string * Py.Object.t) list -> 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.

val transform : 
  x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
  t ->
  Ndarray.t

Apply the approximate feature map to X.

Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) New data, where n_samples in the number of samples and n_features is the number of features.

Returns ------- X_new : array-like, shape (n_samples, n_components)

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

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

Pretty-print the object to a formatter.

package sklearn