"""Restricted Boltzmann Machine
"""

# Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca>
#          Vlad Niculae
#          Gabriel Synnaeve
#          Lars Buitinck
# License: BSD 3 clause

import time

import numpy as np
import scipy.sparse as sp

from ..base import BaseEstimator
from ..base import TransformerMixin
from ..externals.six.moves import xrange
from ..utils import atleast2d_or_csr, check_arrays
from ..utils import check_random_state
from ..utils import gen_even_slices
from ..utils import issparse
from ..utils.extmath import safe_sparse_dot
from ..utils.extmath import log_logistic
from ..utils.fixes import expit             # logistic function


class BernoulliRBM(BaseEstimator, TransformerMixin):
    """Bernoulli Restricted Boltzmann Machine (RBM).

    A Restricted Boltzmann Machine with binary visible units and
    binary hiddens. 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.

    Parameters
    ----------
    n_components : int, optional
        Number of binary hidden units.

    learning_rate : float, optional
        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, optional
        Number of examples per minibatch.

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

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

    random_state : integer or numpy.RandomState, optional
        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.

    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(batch_size=10, learning_rate=0.1, n_components=2, n_iter=10,
           random_state=None, verbose=0)

    References
    ----------

    [1] Hinton, G. E., Osindero, S. and Teh, Y. A fast learning algorithm for
        deep belief nets. Neural Computation 18, pp 1527-1554.
        http://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
    """
    def __init__(self, n_components=256, learning_rate=0.1, batch_size=10,
                 n_iter=10, verbose=0, random_state=None):
        self.n_components = n_components
        self.learning_rate = learning_rate
        self.batch_size = batch_size
        self.n_iter = n_iter
        self.verbose = verbose
        self.random_state = random_state

    def transform(self, X):
        """Compute the hidden layer activation probabilities, P(h=1|v=X).

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

        Returns
        -------
        h : array, shape (n_samples, n_components)
            Latent representations of the data.
        """
        X, = check_arrays(X, sparse_format='csr', dtype=np.float)
        return self._mean_hiddens(X)

    def _mean_hiddens(self, v):
        """Computes the probabilities P(h=1|v).

        Parameters
        ----------
        v : array-like, shape (n_samples, n_features)
            Values of the visible layer.

        Returns