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package sklearn

sklearn
- Sklearn

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type tag = [

| `BernoulliRBM

]

type t = [ `BaseEstimator | `BernoulliRBM | `Object | `TransformerMixin ] Obj.t

val of_pyobject : Py.Object.t -> t

val to_pyobject : [> tag ] Obj.t -> Py.Object.t

val as_transformer : t -> [ `TransformerMixin ] Obj.t

val as_estimator : t -> [ `BaseEstimator ] Obj.t

val create : 
  ?n_components:int ->
  ?learning_rate:float ->
  ?batch_size:int ->
  ?n_iter:int ->
  ?verbose:int ->
  ?random_state:int ->
  unit ->
  t

Bernoulli Restricted Boltzmann Machine (RBM).

A Restricted Boltzmann Machine with binary visible units and binary hidden units. Parameters are estimated using Stochastic Maximum Likelihood (SML), also known as Persistent Contrastive Divergence (PCD) 2.

The time complexity of this implementation is ``O(d ** 2)`` assuming d ~ n_features ~ n_components.

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

Parameters ---------- n_components : int, default=256 Number of binary hidden units.

learning_rate : float, default=0.1 The learning rate for weight updates. It is *highly* recommended to tune this hyper-parameter. Reasonable values are in the 10**0., -3. range.

batch_size : int, default=10 Number of examples per minibatch.

n_iter : int, default=10 Number of iterations/sweeps over the training dataset to perform during training.

verbose : int, default=0 The verbosity level. The default, zero, means silent mode.

random_state : integer or RandomState, default=None Determines random number generation for:

Gibbs sampling from visible and hidden layers.

Initializing components, sampling from layers during fit.

Corrupting the data when scoring samples.

Pass an int for reproducible results across multiple function calls. See :term:`Glossary <random_state>`.

Attributes ---------- intercept_hidden_ : array-like, shape (n_components,) Biases of the hidden units.

intercept_visible_ : array-like, shape (n_features,) Biases of the visible units.

components_ : array-like, shape (n_components, n_features) Weight matrix, where n_features in the number of visible units and n_components is the number of hidden units.

h_samples_ : array-like, shape (batch_size, n_components) Hidden Activation sampled from the model distribution, where batch_size in the number of examples per minibatch and n_components is the number of hidden units.

Examples --------

>>> import numpy as np >>> from sklearn.neural_network import BernoulliRBM >>> X = np.array([0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]) >>> model = BernoulliRBM(n_components=2) >>> model.fit(X) BernoulliRBM(n_components=2)

References ----------

1 Hinton, G. E., Osindero, S. and Teh, Y. A fast learning algorithm for deep belief nets. Neural Computation 18, pp 1527-1554. https://www.cs.toronto.edu/~hinton/absps/fastnc.pdf

2 Tieleman, T. Training Restricted Boltzmann Machines using Approximations to the Likelihood Gradient. International Conference on Machine Learning (ICML) 2008

val fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> t

Fit the model to the data X.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Training data.

Returns ------- self : BernoulliRBM The fitted model.

val 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.

val 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.

val gibbs : 
  v:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Perform one Gibbs sampling step.

Parameters ---------- v : ndarray of shape (n_samples, n_features) Values of the visible layer to start from.

Returns ------- v_new : ndarray of shape (n_samples, n_features) Values of the visible layer after one Gibbs step.

val partial_fit : 
  ?y:Py.Object.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  t

Fit the model to the data X which should contain a partial segment of the data.

Parameters ---------- X : ndarray of shape (n_samples, n_features) Training data.

Returns ------- self : BernoulliRBM The fitted model.

val score_samples : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Compute the pseudo-likelihood of X.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Values of the visible layer. Must be all-boolean (not checked).

Returns ------- pseudo_likelihood : ndarray of shape (n_samples,) Value of the pseudo-likelihood (proxy for likelihood).

Notes ----- This method is not deterministic: it computes a quantity called the free energy on X, then on a randomly corrupted version of X, and returns the log of the logistic function of the difference.

val 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.

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

Compute the hidden layer activation probabilities, P(h=1|v=X).

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The data to be transformed.

Returns ------- h : ndarray of shape (n_samples, n_components) Latent representations of the data.

val intercept_hidden_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute intercept_hidden_: get value or raise Not_found if None.

val intercept_hidden_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute intercept_hidden_: get value as an option.

val intercept_visible_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute intercept_visible_: get value or raise Not_found if None.

val intercept_visible_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute intercept_visible_: get value as an option.

val components_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute components_: get value or raise Not_found if None.

val components_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute components_: get value as an option.

val h_samples_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute h_samples_: get value or raise Not_found if None.

val h_samples_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute h_samples_: get value as an option.

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.