def partial_fit(self, X):
"""
Fit the model to the data X which should contain a partial
segment of the data.
Adjust the parameters to maximize the likelihood of v using
Stochastic Maximum Likelihood (SML).
Parameters
----------
X : array-like, shape (n_samples, n_features)
The data to use for training.
"""
v_pos = X
h_pos = self._mean_hiddens(v_pos)
if self.use_pcd:
v_neg = self._sample_visibles(self.h_samples_)
else:
v_neg = self._sample_visibles(h_pos)
h_neg = self._mean_hiddens(v_neg)
lr = float(self.learning_rate) / v_pos.shape[0]
op.add_dot(h_pos, v_pos, self.dW, True, False, alpha=1.0, beta=self.momentum)
op.add_dot(h_neg, v_neg, self.dW, True, False, alpha=-1.0, beta=1.0)
self.W += lr * self.dW
self.dbh *= self.momentum
self.dbv *= self.momentum
self.dbh += (op.sum(h_pos, axis=0) - op.sum(h_neg, axis=0)).reshape(1, self.dbh.shape[1])
self.dbv += (op.sum(v_pos, axis=0) - op.sum(v_neg, axis=0)).reshape(1, self.dbv.shape[1])
self.bh += lr * self.dbh
self.bv += lr * self.dbv
if self.use_pcd:
self.h_samples_ = op.sample_binomial(h_neg)
def _sample_hiddens(self, v):
"""Sample from the distribution P(h|v).
Parameters
----------
v : array-like, shape (n_samples, n_features)
Values of the visible layer to sample from.
Returns
-------
h : array-like, shape (n_samples, n_components)
Values of the hidden layer.
"""
p = self._mean_hiddens(v)
return op.sample_binomial(p)
def _sample_hiddens(self, v):
"""Sample from the distribution P(h|v).
Parameters
----------
v : array-like, shape (n_samples, n_features)
Values of the visible layer to sample from.
Returns
-------
h : array-like, shape (n_samples, n_components)
Values of the hidden layer.
"""
p = self._mean_hiddens(v)
return op.sample_binomial(p)
def partial_fit(self, X):
"""
Fit the model to the data X which should contain a partial
segment of the data.
Adjust the parameters to maximize the likelihood of v using
Stochastic Maximum Likelihood (SML).
Parameters
----------
X : array-like, shape (n_samples, n_features)
The data to use for training.
"""
v_pos = X
h_pos = self._mean_hiddens(v_pos)
if self.use_pcd:
v_neg = self._sample_visibles(self.h_samples_)
else:
v_neg = self._sample_visibles(h_pos)
h_neg = self._mean_hiddens(v_neg)
lr = float(self.learning_rate) / v_pos.shape[0]
op.add_dot(h_pos,
v_pos,
self.dW,
True,
False,
alpha=1.0,
beta=self.momentum)
op.add_dot(h_neg, v_neg, self.dW, True, False, alpha=-1.0, beta=1.0)
self.W += lr * self.dW
self.dbh *= self.momentum
self.dbv *= self.momentum
self.dbh += (op.sum(h_pos, axis=0) - op.sum(h_neg, axis=0)).reshape(
1, self.dbh.shape[1])
self.dbv += (op.sum(v_pos, axis=0) - op.sum(v_neg, axis=0)).reshape(
1, self.dbv.shape[1])
self.bh += lr * self.dbh
self.bv += lr * self.dbv
if self.use_pcd:
self.h_samples_ = op.sample_binomial(h_neg)
