val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?n_components:int ->
?learning_rate:float ->
?batch_size:int ->
?n_iter:int ->
?verbose:int ->
?random_state:[ `Int of int | `RandomState of Py.Object.t ] ->
unit ->
tBernoulli 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 A random number generator instance to define the state of the random permutations generator. If an integer is given, it fixes the seed. Defaults to the global numpy random number generator.
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:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
tFit 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: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.
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:Ndarray.t -> t -> tFit 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:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tCompute 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 -> 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 | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tCompute 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.
Attribute intercept_hidden_: see constructor for documentation
Attribute intercept_visible_: 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 : Format.formatter -> t -> unitPretty-print the object to a formatter.