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 fit(self, X, y=None):
"""Fit the model to the data X.
Parameters
----------
X : {array-like, sparse matrix} shape (n_samples, n_features)
Training data.
Returns
-------
self : BernoulliRBM
The fitted model.
"""
self.h_samples_ *= 0
begin = time.time()
for self.current_epoch in xrange(self.n_iter):
for batch_slice in generate_slices(X.shape[0], self.batch_size):
self.partial_fit(X[batch_slice])
if self.verbose:
end = time.time()
H = self.transform(X)
R = self._mean_visibles(H)
d = np.sqrt((op.sum((R - X)**2)) / X.shape[0])
print(
"[%s] Iteration %d, ReconstructionRMSE %.4f time = %.2fs"
%
(type(self).__name__, self.current_epoch, d, end - begin))
begin = end
return self
def fit(self, X, y=None):
"""Fit the model to the data X.
Parameters
----------
X : {array-like, sparse matrix} shape (n_samples, n_features)
Training data.
Returns
-------
self : BernoulliRBM
The fitted model.
"""
self.h_samples_ *= 0
begin = time.time()
for self.current_epoch in xrange(self.n_iter):
for batch_slice in generate_slices(X.shape[0], self.batch_size):
self.partial_fit(X[batch_slice])
if self.verbose:
end = time.time()
H = self.transform(X)
R = self._mean_visibles(H)
d = np.sqrt((op.sum((R-X)**2))/X.shape[0])
print("[%s] Iteration %d, ReconstructionRMSE %.4f time = %.2fs"
% (type(self).__name__, self.current_epoch, d, end - begin))
begin = end
return self
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 calculate_reconstruction_rmse(self, X):
H = self.transform(X)
R = self._mean_visibles(H)
d = (op.sum((R - X)**2)) / X.shape[0]
return np.sqrt(op.to_cpu(d))
