# Path: mla/rbm.py
# class RBM(BaseEstimator):
#     y_required = False
# 
#     def __init__(self, n_hidden=128, learning_rate=0.1, batch_size=10, max_epochs=100):
#         """Bernoulli Restricted Boltzmann Machine (RBM)
# 
#         Parameters
#         ----------
# 
#         n_hidden : int, default 128
#             The number of hidden units.
#         learning_rate : float, default 0.1
#         batch_size : int, default 10
#         max_epochs : int, default 100
#         """
#         self.max_epochs = max_epochs
#         self.batch_size = batch_size
#         self.lr = learning_rate
#         self.n_hidden = n_hidden
# 
#     def fit(self, X, y=None):
#         self.n_visible = X.shape[1]
#         self._init_weights()
#         self._setup_input(X, y)
#         self._train()
# 
#     def _init_weights(self):
# 
#         self.W = np.random.randn(self.n_visible, self.n_hidden) * 0.1
# 
#         # Bias for visible and hidden units
#         self.bias_v = np.zeros(self.n_visible, dtype=np.float32)
#         self.bias_h = np.zeros(self.n_hidden, dtype=np.float32)
# 
#         self.errors = []
# 
#     def _train(self):
#         """Use CD-1 training procedure, basically an exact inference for `positive_associations`,
#         followed by a "non burn-in" block Gibbs Sampling for the `negative_associations`."""
# 
#         for i in range(self.max_epochs):
#             error = 0
#             for batch in batch_iterator(self.X, batch_size=self.batch_size):
#                 positive_hidden = sigmoid(np.dot(batch, self.W) + self.bias_h)
#                 hidden_states = self._sample(positive_hidden)  # sample hidden state h1
#                 positive_associations = np.dot(batch.T, positive_hidden)
# 
#                 negative_visible = sigmoid(np.dot(hidden_states, self.W.T) + self.bias_v)
#                 negative_visible = self._sample(negative_visible)  # use the sampled hidden state h1 to sample v1
#                 negative_hidden = sigmoid(np.dot(negative_visible, self.W) + self.bias_h)
#                 negative_associations = np.dot(negative_visible.T, negative_hidden)
# 
#                 lr = self.lr / float(batch.shape[0])
#                 self.W += lr * ((positive_associations - negative_associations) / float(self.batch_size))
#                 self.bias_h += lr * (negative_hidden.sum(axis=0) - negative_associations.sum(axis=0))
#                 self.bias_v += lr * (np.asarray(batch.sum(axis=0)).squeeze() - negative_visible.sum(axis=0))
# 
#                 error += np.sum((batch - negative_visible) ** 2)
# 
#             self.errors.append(error)
#             logging.info("Iteration %s, error %s" % (i, error))
#         logging.debug("Weights: %s" % self.W)
#         logging.debug("Hidden bias: %s" % self.bias_h)
#         logging.debug("Visible bias: %s" % self.bias_v)
# 
#     def _sample(self, X):
#         return X > np.random.random_sample(size=X.shape)
# 
#     def _predict(self, X=None):
#         return sigmoid(np.dot(X, self.W) + self.bias_h)


import logging
import numpy as np
from mla.rbm import RBM
    from matplotlib import pyplot as plt



logging.basicConfig(level=logging.DEBUG)


def print_curve(rbm):

    def moving_average(a, n=25):
        ret = np.cumsum(a, dtype=float)
        ret[n:] = ret[n:] - ret[:-n]
        return ret[n - 1:] / n

    plt.plot(moving_average(rbm.errors))
    plt.show()


X = np.random.uniform(0, 1, (1500, 10))

# Path: mla/rbm.py
# class RBM(BaseEstimator):
#     y_required = False
# 
#     def __init__(self, n_hidden=128, learning_rate=0.1, batch_size=10, max_epochs=100):
#         """Bernoulli Restricted Boltzmann Machine (RBM)
# 
#         Parameters
#         ----------
# 
#         n_hidden : int, default 128
#             The number of hidden units.
#         learning_rate : float, default 0.1
#         batch_size : int, default 10
#         max_epochs : int, default 100
#         """
#         self.max_epochs = max_epochs
#         self.batch_size = batch_size
#         self.lr = learning_rate
#         self.n_hidden = n_hidden
# 
#     def fit(self, X, y=None):
#         self.n_visible = X.shape[1]
#         self._init_weights()
#         self._setup_input(X, y)
#         self._train()
# 
#     def _init_weights(self):
# 
#         self.W = np.random.randn(self.n_visible, self.n_hidden) * 0.1
# 
#         # Bias for visible and hidden units
#         self.bias_v = np.zeros(self.n_visible, dtype=np.float32)
#         self.bias_h = np.zeros(self.n_hidden, dtype=np.float32)
# 
#         self.errors = []
# 
#     def _train(self):
#         """Use CD-1 training procedure, basically an exact inference for `positive_associations`,
#         followed by a "non burn-in" block Gibbs Sampling for the `negative_associations`."""
# 
#         for i in range(self.max_epochs):
#             error = 0
#             for batch in batch_iterator(self.X, batch_size=self.batch_size):
#                 positive_hidden = sigmoid(np.dot(batch, self.W) + self.bias_h)
#                 hidden_states = self._sample(positive_hidden)  # sample hidden state h1
#                 positive_associations = np.dot(batch.T, positive_hidden)
# 
#                 negative_visible = sigmoid(np.dot(hidden_states, self.W.T) + self.bias_v)
#                 negative_visible = self._sample(negative_visible)  # use the sampled hidden state h1 to sample v1
#                 negative_hidden = sigmoid(np.dot(negative_visible, self.W) + self.bias_h)
#                 negative_associations = np.dot(negative_visible.T, negative_hidden)
# 
#                 lr = self.lr / float(batch.shape[0])
#                 self.W += lr * ((positive_associations - negative_associations) / float(self.batch_size))
#                 self.bias_h += lr * (negative_hidden.sum(axis=0) - negative_associations.sum(axis=0))
#                 self.bias_v += lr * (np.asarray(batch.sum(axis=0)).squeeze() - negative_visible.sum(axis=0))
# 
#                 error += np.sum((batch - negative_visible) ** 2)
# 
#             self.errors.append(error)
#             logging.info("Iteration %s, error %s" % (i, error))
#         logging.debug("Weights: %s" % self.W)
#         logging.debug("Hidden bias: %s" % self.bias_h)
#         logging.debug("Visible bias: %s" % self.bias_v)
# 
#     def _sample(self, X):
#         return X > np.random.random_sample(size=X.shape)
# 
#     def _predict(self, X=None):
#         return sigmoid(np.dot(X, self.W) + self.bias_h)


# Path: examples/rbm.py
import logging
import numpy as np
from mla.rbm import RBM
    from matplotlib import pyplot as plt




logging.basicConfig(level=logging.DEBUG)


def print_curve(rbm):

    def moving_average(a, n=25):
        ret = np.cumsum(a, dtype=float)
        ret[n:] = ret[n:] - ret[:-n]
        return ret[n - 1:] / n

    plt.plot(moving_average(rbm.errors))
    plt.show()


X = np.random.uniform(0, 1, (1500, 10))

rbm = RBM(n_hidden=10, max_epochs=200, batch_size=10, learning_rate=0.1)