import math

import numpy as np

from neupy.utils import format_data
from neupy.core.properties import Property
from .utils import bin2sign, hopfield_energy, step_function
from .base import DiscreteMemory


__all__ = ('DiscreteHopfieldNetwork',)


class DiscreteHopfieldNetwork(DiscreteMemory):
    """
    Discrete Hopfield Network. It can memorize binary samples
    and reconstruct them from corrupted samples.

    Notes
    -----
    - Works only with binary data. Input matrix should
      contain only zeros and ones.

    Parameters
    ----------
    {DiscreteMemory.mode}

    {DiscreteMemory.n_times}

    check_limit : bool
        Option enable a limit of patterns control for the
        network using logarithmically proportion rule.
        Defaults to ``True``.

        .. math::

            \\frac{{n_{{features}}}}{{2 \\cdot log_{{e}}(n_{{features}})}}

    Methods
    -------
    energy(input_data)
        Compute Discrete Hopfield Energy.

    train(input_data)
        Save input data pattern into the network memory.

    predict(input_data, n_times=None)
        Recover data from the memory using input pattern.
        For the prediction procedure you can control number
        of iterations. If you set up this value equal to ``None``
        then the value would be equal to the value that you
        set up for the property with the same name - ``n_times``.

    Examples
    --------
    >>> import numpy as np
    >>> from neupy import algorithms
    >>>
    >>> def draw_bin_image(image_matrix):
    ...     for row in image_matrix.tolist():
    ...         print('| ' + ' '.join(' *'[val] for val in row))
    ...
    >>> zero = np.matrix([
    ...     0, 1, 1, 1, 0,
    ...     1, 0, 0, 0, 1,
    ...     1, 0, 0, 0, 1,
    ...     1, 0, 0, 0, 1,
    ...     1, 0, 0, 0, 1,
    ...     0, 1, 1, 1, 0
    ... ])
    >>>
    >>> one = np.matrix([
    ...     0, 1, 1, 0, 0,
    ...     0, 0, 1, 0, 0,
    ...     0, 0, 1, 0, 0,
    ...     0, 0, 1, 0, 0,
    ...     0, 0, 1, 0, 0,
    ...     0, 0, 1, 0, 0
    ... ])
    >>>
    >>> two = np.matrix([
    ...     1, 1, 1, 0, 0,
    ...     0, 0, 0, 1, 0,
    ...     0, 0, 0, 1, 0,
    ...     0, 1, 1, 0, 0,
    ...     1, 0, 0, 0, 0,
    ...     1, 1, 1, 1, 1,
    ... ])
    >>>
    >>> half_zero = np.matrix([
    ...     0, 1, 1, 1, 0,
    ...     1, 0, 0, 0, 1,
    ...     1, 0, 0, 0, 1,
    ...     0, 0, 0, 0, 0,
    ...     0, 0, 0, 0, 0,
    ...     0, 0, 0, 0, 0,
    ... ])
    >>>
    >>> draw_bin_image(zero.reshape((6, 5)))
    |   * * *
    | *       *
    | *       *
    | *       *
    | *       *
    |   * * *
    >>> draw_bin_image(half_zero.reshape((6, 5)))
    |   * * *
    | *       *
    | *       *
    |
    |
    |
    >>> data = np.concatenate([zero, one, two], axis=0)
    >>>
    >>> dhnet = algorithms.DiscreteHopfieldNetwork()
    >>> dhnet.train(data)
    >>>
    >>> result = dhnet.predict(half_zero)
    >>> draw_bin_image(result.reshape((6, 5)))
    |   * * *
    | *       *
    | *       *
    | *       *
    | *       *
    |   * * *

    See Also
    --------
    :ref:`password-recovery`: Password recovery with Discrete Hopfield Network.
    :ref:`discrete-hopfield-network`: Discrete Hopfield Network article.
    """
    check_limit = Property(default=True, expected_type=bool)

    def __init__(self, **options):
        super(DiscreteHopfieldNetwork, self).__init__(**options)
        self.n_memorized_samples = 0

    def train(self, input_data):
        self.discrete_validation(input_data)

        input_data = bin2sign(input_data)
        input_data = format_data(
            input_data, is_feature1d=False, make_float=False)

        n_rows, n_features = input_data.shape
        n_rows_after_update = self.n_memorized_samples + n_rows

        if self.check_limit:
            memory_limit = math.ceil(n_features / (2 * math.log(n_features)))

            if n_rows_after_update > memory_limit:
                raise ValueError("You can't memorize more than {0} "
                                 "samples".format(memory_limit))

        weight_shape = (n_features, n_features)

        if self.weight is None:
            self.weight = np.zeros(weight_shape, dtype=int)

        if self.weight.shape != weight_shape:
            n_features_expected = self.weight.shape[1]
            raise ValueError("Input data has invalid number of features. "
                             "Got {} features instead of {}."
                             "".format(n_features, n_features_expected))

        self.weight += input_data.T.dot(input_data)
        np.fill_diagonal(self.weight, np.zeros(len(self.weight)))
        self.n_memorized_samples = n_rows_after_update

    def predict(self, input_data, n_times=None):
        self.discrete_validation(input_data)
        input_data = format_data(
            bin2sign(input_data), is_feature1d=False, make_float=False)

        if self.mode == 'async':
            if n_times is None:
                n_times = self.n_times

            _, n_features = input_data.shape
            output_data = input_data

            for _ in range(n_times):
                position = np.random.randint(0, n_features - 1)
                raw_new_value = output_data.dot(self.weight[:, position])
                output_data[:, position] = np.sign(raw_new_value)
        else:
            output_data = input_data.dot(self.weight)

        return step_function(output_data).astype(int)

    def energy(self, input_data):
        self.discrete_validation(input_data)

        input_data = bin2sign(input_data)
        input_data = format_data(
            input_data, is_feature1d=False, make_float=False)

        n_rows, n_features = input_data.shape

        if n_rows == 1:
            return hopfield_energy(self.weight, input_data, input_data)

        output = np.zeros(n_rows)
        for i, row in enumerate(input_data):
            output[i] = hopfield_energy(self.weight, row, row)

        return output
