def use_learner(self,example):
output = np.zeros((self.hidden_size))
mllin.product_matrix_vector(self.W,example,self.hidden_act)
self.hidden_act += self.c
mlnonlin.sigmoid(self.hidden_act,output)
return [output]
def use_learner(self, example):
output = np.zeros((self.hidden_size))
mllin.product_matrix_vector(self.W, example, self.hidden_act)
self.hidden_act += self.c
mlnonlin.sigmoid(self.hidden_act, output)
return [output]
def cost(self,outputs,example):
hidden = outputs[0]
mllin.product_matrix_vector(self.W.T,hidden,self.neg_input_act)
self.neg_input_act += self.b
mlnonlin.sigmoid(self.neg_input_act,self.neg_input_prob)
return [ np.sum((example-self.neg_input_prob)**2) ]
def cost(self, outputs, example):
hidden = outputs[0]
mllin.product_matrix_vector(self.W.T, hidden, self.neg_input_act)
self.neg_input_act += self.b
mlnonlin.sigmoid(self.neg_input_act, self.neg_input_prob)
return [np.sum((example - self.neg_input_prob)**2)]
def compute_document_representation(self,word_counts_sparse):
self.input[:] = 0
self.input[word_counts_sparse[1]] = word_counts_sparse[0]
output = np.zeros((self.hidden_size,))
mllin.product_matrix_vector(self.W,self.input,self.hidden_act)
self.hidden_act += self.c*self.input.sum()
mlnonlin.sigmoid(self.hidden_act,output)
return output
def compute_document_representation(self, word_counts_sparse):
self.input[:] = 0
self.input[word_counts_sparse[1]] = word_counts_sparse[0]
output = np.zeros((self.hidden_size,))
mllin.product_matrix_vector(self.W, self.input, self.hidden_act)
self.hidden_act += self.c * self.input.sum()
mlnonlin.sigmoid(self.hidden_act, output)
return output
def use_learner(self,example):
self.input[:] = 0
self.input[example[1]] = example[0]
output = np.zeros((self.hidden_size))
mllin.product_matrix_vector(self.W,self.input,self.hidden_act)
self.hidden_act += self.c*self.input.sum()
mlnonlin.sigmoid(self.hidden_act,output)
return [output]
def cost(self,outputs,example):
hidden = outputs[0]
self.input[:] = 0
self.input[example[1]] = example[0]
mllin.product_matrix_vector(self.W.T,hidden,self.neg_input_act)
self.neg_input_act += self.b
mlnonlin.softmax(self.neg_input_act,self.neg_input_prob)
return [ np.sum((self.input-self.input.sum()*self.neg_input_prob)**2) ]
def use_learner(self, example):
self.input[:] = 0
self.input[example[1]] = example[0]
output = np.zeros((self.hidden_size))
mllin.product_matrix_vector(self.W, self.input, self.hidden_act)
self.hidden_act += self.c * self.input.sum()
mlnonlin.sigmoid(self.hidden_act, output)
return [output]
def bprop(self,target):
"""
Computes the loss derivatives with respect to all parameters
times the current learning rate. It assumes that
``self.fprop(input)`` was called first. All the derivatives are
put in their corresponding object attributes (i.e. ``self.d*``).
"""
self.doutput_act[:] = self.output
self.doutput_act[target] -= 1
self.doutput_act *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.dd[:] = self.doutput_act
for k in range(self.n_k_means):
c = self.cluster_indices[k]
idx = c + k*self.n_clusters
mllin.outer(self.doutput_act,self.layers[k],self.dVs[idx])
mllin.product_matrix_vector(self.Vs[idx].T,self.doutput_act,self.dlayers[k])
#mlnonlin.dsigmoid(self.layers[k],self.dlayers[k],self.dlayer_acts[k])
if self.activation_function == 'sigmoid':
mlnonlin.dsigmoid(self.layers[k],self.dlayers[k],self.dlayer_acts[k])
elif self.activation_function == 'tanh':
mlnonlin.dtanh(self.layers[k],self.dlayers[k],self.dlayer_acts[k])
elif self.activation_function == 'reclin':
mlnonlin.dreclin(self.layers[k],self.dlayers[k],self.dlayer_acts[k])
else:
raise ValueError('activation_function must be either \'sigmoid\', \'tanh\' or \'reclin\'')
self.dcs[idx][:] = self.dlayer_acts[k]
mllin.outer(self.dlayer_acts[k],self.input,self.dWs[idx])
if self.autoencoder_regularization != 0:
self.dae_doutput_act[:] = self.dae_output
self.dae_doutput_act[:] -= self.input
self.dae_doutput_act *= 2*self.autoencoder_regularization*self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.dae_dd[:] = self.dae_doutput_act
for k in range(self.n_k_means):
c = self.cluster_indices[k]
idx = c + k*self.n_clusters
mllin.outer(self.dae_doutput_act,self.dae_layers[k],self.dae_dWsT[idx])
self.dWs[idx] += self.dae_dWsT[idx].T
mllin.product_matrix_vector(self.Ws[idx],self.dae_doutput_act,self.dae_dlayers[k])
#mlnonlin.dsigmoid(self.dae_layers[k],self.dae_dlayers[k],self.dae_dlayer_acts[k])
if self.activation_function == 'sigmoid':
mlnonlin.dsigmoid(self.dae_layers[k],self.dae_dlayers[k],self.dae_dlayer_acts[k])
elif self.activation_function == 'tanh':
mlnonlin.dtanh(self.dae_layers[k],self.dae_dlayers[k],self.dae_dlayer_acts[k])
elif self.activation_function == 'reclin':
mlnonlin.dreclin(self.dae_layers[k],self.dae_dlayers[k],self.dae_dlayer_acts[k])
else:
raise ValueError('activation_function must be either \'sigmoid\', \'tanh\' or \'reclin\'')
self.dcs[idx] += self.dae_dlayer_acts[k]
mllin.outer(self.dae_dlayer_acts[k],self.dae_input,self.dae_dWs[idx])
self.dWs[idx] += self.dae_dWs[idx]
def cost(self, outputs, example):
hidden = outputs[0]
self.input[:] = 0
self.input[example[1]] = example[0]
mllin.product_matrix_vector(self.W.T, hidden, self.neg_input_act)
self.neg_input_act += self.b
mlnonlin.softmax(self.neg_input_act, self.neg_input_prob)
return [np.sum((self.input - self.input.sum() * self.neg_input_prob) ** 2)]
def use_learner(self, example):
self.input[self.input_order] = example
output = np.zeros((self.input_size))
recact = np.zeros((self.input_size))
# fprop
mllin.product_matrix_vector(self.W, self.input, recact)
recact += self.b
mlnonlin.sigmoid(recact, output)
return [output, recact]
def use_learner(self,example):
self.input[self.input_order] = example
output = np.zeros((self.input_size))
recact = np.zeros((self.input_size))
# fprop
mllin.product_matrix_vector(self.W,self.input,recact)
recact += self.b
mlnonlin.sigmoid(recact,output)
return [output,recact]
def update_learner(self,example):
self.layers[0][:] = example[0]
# fprop
for h in range(self.n_hidden_layers):
mllin.product_matrix_vector(self.Ws[h],self.layers[h],self.layer_acts[h+1])
self.layer_acts[h+1] += self.cs[h]
mlnonlin.sigmoid(self.layer_acts[h+1],self.layers[h+1])
mllin.product_matrix_vector(self.U,self.layers[-1],self.output_act)
self.output_act += self.d
mlnonlin.softmax(self.output_act,self.output)
self.doutput_act[:] = self.output
self.doutput_act[example[1]] -= 1
self.doutput_act *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.dd[:] = self.doutput_act
mllin.outer(self.doutput_act,self.layers[-1],self.dU)
mllin.product_matrix_vector(self.U.T,self.doutput_act,self.dlayers[-1])
mlnonlin.dsigmoid(self.layers[-1],self.dlayers[-1],self.dlayer_acts[-1])
for h in range(self.n_hidden_layers-1,-1,-1):
self.dcs[h][:] = self.dlayer_acts[h+1]
mllin.outer(self.dlayer_acts[h+1],self.layers[h],self.dWs[h])
mllin.product_matrix_vector(self.Ws[h].T,self.dlayer_acts[h+1],self.dlayers[h])
mlnonlin.dsigmoid(self.layers[h],self.dlayers[h],self.dlayer_acts[h])
self.U -= self.dU
self.d -= self.dd
for h in range(self.n_hidden_layers-1,-1,-1):
self.Ws[h] -= self.dWs[h]
self.cs[h] -= self.dcs[h]
self.n_updates += 1
def use_learner(self, example):
output = np.zeros((self.n_classes))
self.layers[0][:] = example[0]
# fprop
for h in range(self.n_hidden_layers):
mllin.product_matrix_vector(self.Ws[h], self.layers[h], self.layer_acts[h + 1])
self.layer_acts[h + 1] += self.cs[h]
mlnonlin.sigmoid(self.layer_acts[h + 1], self.layers[h + 1])
mllin.product_matrix_vector(self.U, self.layers[-1], self.output_act)
self.output_act += self.d
mlnonlin.softmax(self.output_act, output)
return [output.argmax(), output]
def use_learner(self, example):
output = np.zeros((self.n_classes))
self.layers[0][:] = example[0]
# fprop
for h in range(self.n_hidden_layers):
mllin.product_matrix_vector(self.Ws[h], self.layers[h],
self.layer_acts[h + 1])
self.layer_acts[h + 1] += self.cs[h]
mlnonlin.sigmoid(self.layer_acts[h + 1], self.layers[h + 1])
mllin.product_matrix_vector(self.U, self.layers[-1], self.output_act)
self.output_act += self.d
mlnonlin.softmax(self.output_act, output)
return [output.argmax(), output]
def update_learner(self,example):
self.layers[0][:] = example[0]
# fprop
for h in range(self.n_hidden_layers):
mllin.product_matrix_vector(self.Ws[h],self.layers[h],self.layer_acts[h+1])
self.layer_acts[h+1] += self.cs[h]
if self.activation_function == 'sigmoid':
mlnonlin.sigmoid(self.layer_acts[h+1],self.layers[h+1])
elif self.activation_function == 'tanh':
mlnonlin.tanh(self.layer_acts[h+1],self.layers[h+1])
elif self.activation_function == 'reclin':
mlnonlin.reclin(self.layer_acts[h+1],self.layers[h+1])
else:
raise ValueError('activation_function must be either \'sigmoid\', \'tanh\' or \'reclin\'')
mllin.product_matrix_vector(self.U,self.layers[-1],self.output_act)
self.output_act += self.d
mlnonlin.softmax(self.output_act,self.output)
self.doutput_act[:] = self.output
self.doutput_act[example[1]] -= 1
self.doutput_act *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.dd[:] = self.doutput_act
mllin.outer(self.doutput_act,self.layers[-1],self.dU)
mllin.product_matrix_vector(self.U.T,self.doutput_act,self.dlayers[-1])
if self.activation_function == 'sigmoid':
mlnonlin.dsigmoid(self.layers[-1],self.dlayers[-1],self.dlayer_acts[-1])
elif self.activation_function == 'tanh':
mlnonlin.dtanh(self.layers[-1],self.dlayers[-1],self.dlayer_acts[-1])
elif self.activation_function == 'reclin':
mlnonlin.dreclin(self.layers[-1],self.dlayers[-1],self.dlayer_acts[-1])
else:
raise ValueError('activation_function must be either \'sigmoid\', \'tanh\' or \'reclin\'')
for h in range(self.n_hidden_layers-1,-1,-1):
self.dcs[h][:] = self.dlayer_acts[h+1]
mllin.outer(self.dlayer_acts[h+1],self.layers[h],self.dWs[h])
mllin.product_matrix_vector(self.Ws[h].T,self.dlayer_acts[h+1],self.dlayers[h])
if self.activation_function == 'sigmoid':
mlnonlin.dsigmoid(self.layers[h],self.dlayers[h],self.dlayer_acts[h])
elif self.activation_function == 'tanh':
mlnonlin.dtanh(self.layers[h],self.dlayers[h],self.dlayer_acts[h])
elif self.activation_function == 'reclin':
mlnonlin.dreclin(self.layers[h],self.dlayers[h],self.dlayer_acts[h])
else:
raise ValueError('activation_function must be either \'sigmoid\', \'tanh\' or \'reclin\'')
self.U -= self.dU
self.d -= self.dd
for h in range(self.n_hidden_layers-1,-1,-1):
self.Ws[h] -= self.dWs[h]
self.cs[h] -= self.dcs[h]
self.n_updates += 1
def fprop(self,input):
"""
Computes the output given some input. Puts the result in ``self.output``
"""
self.input[:] = input
self.output_act[:] = self.d
for k in range(self.n_k_means):
if self.n_k_means_inputs == self.input_size:
c = self.clusterings[k].compute_cluster(self.input)
else:
c = self.clusterings[k].compute_cluster(self.input[self.k_means_subset_inputs[k]])
idx = c + k*self.n_clusters
self.cluster_indices[k] = c
mllin.product_matrix_vector(self.Ws[idx],self.input,self.layer_acts[k])
self.layer_acts[k] += self.cs[idx]
#mlnonlin.sigmoid(self.layer_acts[k],self.layers[k])
if self.activation_function == 'sigmoid':
mlnonlin.sigmoid(self.layer_acts[k],self.layers[k])
elif self.activation_function == 'tanh':
mlnonlin.tanh(self.layer_acts[k],self.layers[k])
elif self.activation_function == 'reclin':
mlnonlin.reclin(self.layer_acts[k],self.layers[k])
else:
raise ValueError('activation_function must be either \'sigmoid\', \'tanh\' or \'reclin\'')
mllin.product_matrix_vector(self.Vs[idx],self.layers[k],self.output_acts[k])
self.output_act += self.output_acts[k]
mlnonlin.softmax(self.output_act,self.output)
if self.autoencoder_regularization != 0:
self.dae_input[:] = input
self.rng.shuffle(self.input_idx)
self.dae_input[self.input_idx[:int(self.autoencoder_missing_fraction*self.input_size)]] = 0
self.dae_output_act[:] = self.dae_d
for k in range(self.n_k_means):
idx = self.cluster_indices[k] + k*self.n_clusters
mllin.product_matrix_vector(self.Ws[idx],self.dae_input,self.dae_layer_acts[k])
self.dae_layer_acts[k] += self.cs[idx]
#mlnonlin.sigmoid(self.dae_layer_acts[k],self.dae_layers[k])
if self.activation_function == 'sigmoid':
mlnonlin.sigmoid(self.dae_layer_acts[k],self.dae_layers[k])
elif self.activation_function == 'tanh':
mlnonlin.tanh(self.dae_layer_acts[k],self.dae_layers[k])
elif self.activation_function == 'reclin':
mlnonlin.reclin(self.dae_layer_acts[k],self.dae_layers[k])
else:
raise ValueError('activation_function must be either \'sigmoid\', \'tanh\' or \'reclin\'')
mllin.product_matrix_vector(self.Ws[idx].T,self.dae_layers[k],self.dae_output_acts[k])
self.dae_output_act += self.dae_output_acts[k]
self.dae_output[:] = self.dae_output_act
def update_learner(self,example):
self.input[self.input_order] = example
# fprop
mllin.product_matrix_vector(self.W,self.input,self.recact)
self.recact += self.b
mlnonlin.sigmoid(self.recact,self.rec)
# bprop
np.subtract(self.rec,self.input,self.drec)
self.db[:] = self.drec
mllin.outer(self.drec,self.input,self.dW)
self.dW *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.db *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.W -= self.dW
self.b -= self.db
self.W.ravel()[self.utri_index] = 0 # Setting back upper diagonal to 0
self.n_updates += 1
def use_learner(self,example):
output = np.zeros((self.n_classes))
self.layers[0][:] = example[0]
# fprop
for h in range(self.n_hidden_layers):
mllin.product_matrix_vector(self.Ws[h],self.layers[h],self.layer_acts[h+1])
self.layer_acts[h+1] += self.cs[h]
if self.activation_function == 'sigmoid':
mlnonlin.sigmoid(self.layer_acts[h+1],self.layers[h+1])
elif self.activation_function == 'tanh':
mlnonlin.tanh(self.layer_acts[h+1],self.layers[h+1])
elif self.activation_function == 'reclin':
mlnonlin.reclin(self.layer_acts[h+1],self.layers[h+1])
else:
raise ValueError('activation_function must be either \'sigmoid\', \'tanh\' or \'reclin\'')
mllin.product_matrix_vector(self.U,self.layers[-1],self.output_act)
self.output_act += self.d
mlnonlin.softmax(self.output_act,output)
return [output.argmax(),output]
def update_learner(self, example):
self.input[self.input_order] = example
# fprop
mllin.product_matrix_vector(self.W, self.input, self.recact)
self.recact += self.b
mlnonlin.sigmoid(self.recact, self.rec)
# bprop
np.subtract(self.rec, self.input, self.drec)
self.db[:] = self.drec
mllin.outer(self.drec, self.input, self.dW)
self.dW *= self.learning_rate / (
1. + self.decrease_constant * self.n_updates)
self.db *= self.learning_rate / (
1. + self.decrease_constant * self.n_updates)
self.W -= self.dW
self.b -= self.db
self.W.ravel()[self.utri_index] = 0 # Setting back upper diagonal to 0
self.n_updates += 1
def update_learner(self, example):
self.layers[0][:] = example[0]
# fprop
for h in range(self.n_hidden_layers):
mllin.product_matrix_vector(self.Ws[h], self.layers[h],
self.layer_acts[h + 1])
self.layer_acts[h + 1] += self.cs[h]
mlnonlin.sigmoid(self.layer_acts[h + 1], self.layers[h + 1])
mllin.product_matrix_vector(self.U, self.layers[-1], self.output_act)
self.output_act += self.d
mlnonlin.softmax(self.output_act, self.output)
self.doutput_act[:] = self.output
self.doutput_act[example[1]] -= 1
self.doutput_act *= self.learning_rate / (
1. + self.decrease_constant * self.n_updates)
self.dd[:] = self.doutput_act
mllin.outer(self.doutput_act, self.layers[-1], self.dU)
mllin.product_matrix_vector(self.U.T, self.doutput_act,
self.dlayers[-1])
mlnonlin.dsigmoid(self.layers[-1], self.dlayers[-1],
self.dlayer_acts[-1])
for h in range(self.n_hidden_layers - 1, -1, -1):
self.dcs[h][:] = self.dlayer_acts[h + 1]
mllin.outer(self.dlayer_acts[h + 1], self.layers[h], self.dWs[h])
mllin.product_matrix_vector(self.Ws[h].T, self.dlayer_acts[h + 1],
self.dlayers[h])
mlnonlin.dsigmoid(self.layers[h], self.dlayers[h],
self.dlayer_acts[h])
self.U -= self.dU
self.d -= self.dd
for h in range(self.n_hidden_layers - 1, -1, -1):
self.Ws[h] -= self.dWs[h]
self.cs[h] -= self.dcs[h]
self.n_updates += 1
def update_learner(self, example):
self.input[:] = 0
self.input[example[1]] = example[0]
n_words = int(self.input.sum())
# Performing CD-k
mllin.product_matrix_vector(self.W, self.input, self.hidden_act)
self.hidden_act += self.c * n_words
mlnonlin.sigmoid(self.hidden_act, self.hidden_prob)
self.neg_hidden_prob[:] = self.hidden_prob
for k in range(self.k_contrastive_divergence_steps):
if self.mean_field:
self.hidden[:] = self.neg_hidden_prob
else:
np.less(self.rng.rand(self.hidden_size), self.neg_hidden_prob, self.hidden)
mllin.product_matrix_vector(self.W.T, self.hidden, self.neg_input_act)
self.neg_input_act += self.b
mlnonlin.softmax(self.neg_input_act, self.neg_input_prob)
if self.mean_field:
self.neg_input[:] = n_words * self.neg_input_prob
else:
self.neg_input[:] = self.rng.multinomial(n_words, self.neg_input_prob)
mllin.product_matrix_vector(self.W, self.neg_input, self.neg_hidden_act)
self.neg_hidden_act += self.c * n_words
mlnonlin.sigmoid(self.neg_hidden_act, self.neg_hidden_prob)
mllin.outer(self.hidden_prob, self.input, self.deltaW)
mllin.outer(self.neg_hidden_prob, self.neg_input, self.neg_stats)
self.deltaW -= self.neg_stats
np.subtract(self.input, self.neg_input, self.deltab)
np.subtract(self.hidden_prob, self.neg_hidden_prob, self.deltac)
self.deltaW *= self.learning_rate / (1.0 + self.decrease_constant * self.n_updates)
self.deltab *= self.learning_rate / (1.0 + self.decrease_constant * self.n_updates)
self.deltac *= n_words * self.learning_rate / (1.0 + self.decrease_constant * self.n_updates)
self.W += self.deltaW
self.b += self.deltab
self.c += self.deltac
self.n_updates += 1
def update_learner(self,example):
self.input[:] = 0
self.input[example[1]] = example[0]
n_words = int(self.input.sum())
# Performing CD-k
mllin.product_matrix_vector(self.W,self.input,self.hidden_act)
self.hidden_act += self.c*n_words
mlnonlin.sigmoid(self.hidden_act,self.hidden_prob)
self.neg_hidden_prob[:] = self.hidden_prob
for k in range(self.k_contrastive_divergence_steps):
if self.mean_field:
self.hidden[:] = self.neg_hidden_prob
else:
np.less(self.rng.rand(self.hidden_size),self.neg_hidden_prob,self.hidden)
mllin.product_matrix_vector(self.W.T,self.hidden,self.neg_input_act)
self.neg_input_act += self.b
mlnonlin.softmax(self.neg_input_act,self.neg_input_prob)
if self.mean_field:
self.neg_input[:] = n_words*self.neg_input_prob
else:
self.neg_input[:] = self.rng.multinomial(n_words,self.neg_input_prob)
mllin.product_matrix_vector(self.W,self.neg_input,self.neg_hidden_act)
self.neg_hidden_act += self.c*n_words
mlnonlin.sigmoid(self.neg_hidden_act,self.neg_hidden_prob)
mllin.outer(self.hidden_prob,self.input,self.deltaW)
mllin.outer(self.neg_hidden_prob,self.neg_input,self.neg_stats)
self.deltaW -= self.neg_stats
np.subtract(self.input,self.neg_input,self.deltab)
np.subtract(self.hidden_prob,self.neg_hidden_prob,self.deltac)
self.deltaW *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.deltab *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.deltac *= n_words*self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.W += self.deltaW
self.b += self.deltab
self.c += self.deltac
self.n_updates += 1
def update_learner(self, example):
self.input[:] = example
# Performing CD-1
mllin.product_matrix_vector(self.W, self.input, self.hidden_act)
self.hidden_act += self.c
mlnonlin.sigmoid(self.hidden_act, self.hidden_prob)
np.less(self.rng.rand(self.hidden_size), self.hidden_prob, self.hidden)
mllin.product_matrix_vector(self.W.T, self.hidden, self.neg_input_act)
self.neg_input_act += self.b
mlnonlin.sigmoid(self.neg_input_act, self.neg_input_prob)
np.less(self.rng.rand(self.input_size), self.neg_input_prob,
self.neg_input)
mllin.product_matrix_vector(self.W, self.neg_input,
self.neg_hidden_act)
self.neg_hidden_act += self.c
mlnonlin.sigmoid(self.neg_hidden_act, self.neg_hidden_prob)
mllin.outer(self.hidden_prob, self.input, self.deltaW)
mllin.outer(self.neg_hidden_prob, self.neg_input, self.neg_stats)
self.deltaW -= self.neg_stats
np.subtract(self.input, self.neg_input, self.deltab)
np.subtract(self.hidden_prob, self.neg_hidden_prob, self.deltac)
self.deltaW *= self.learning_rate / (
1. + self.decrease_constant * self.n_updates)
self.deltab *= self.learning_rate / (
1. + self.decrease_constant * self.n_updates)
self.deltac *= self.learning_rate / (
1. + self.decrease_constant * self.n_updates)
self.W += self.deltaW
self.b += self.deltab
self.c += self.deltac
if self.l1_regularization > 0:
self.W *= (np.abs(self.W) >
(self.l1_regularization * self.learning_rate /
(1. + self.decrease_constant * self.n_updates)))
self.n_updates += 1
def update_learner(self,example):
self.input[:] = example
# Performing CD-1
mllin.product_matrix_vector(self.W,self.input,self.hidden_act)
self.hidden_act += self.c
mlnonlin.sigmoid(self.hidden_act,self.hidden_prob)
np.less(self.rng.rand(self.hidden_size),self.hidden_prob,self.hidden)
mllin.product_matrix_vector(self.W.T,self.hidden,self.neg_input_act)
self.neg_input_act += self.b
mlnonlin.sigmoid(self.neg_input_act,self.neg_input_prob)
np.less(self.rng.rand(self.input_size),self.neg_input_prob,self.neg_input)
mllin.product_matrix_vector(self.W,self.neg_input,self.neg_hidden_act)
self.neg_hidden_act += self.c
mlnonlin.sigmoid(self.neg_hidden_act,self.neg_hidden_prob)
mllin.outer(self.hidden_prob,self.input,self.deltaW)
mllin.outer(self.neg_hidden_prob,self.neg_input,self.neg_stats)
self.deltaW -= self.neg_stats
np.subtract(self.input,self.neg_input,self.deltab)
np.subtract(self.hidden_prob,self.neg_hidden_prob,self.deltac)
self.deltaW *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.deltab *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.deltac *= self.learning_rate/(1.+self.decrease_constant*self.n_updates)
self.W += self.deltaW
self.b += self.deltab
self.c += self.deltac
if self.l1_regularization > 0:
self.W *= (np.abs(self.W) > (self.l1_regularization * self.learning_rate/(1.+self.decrease_constant*self.n_updates)))
self.n_updates += 1
def cond_probs(self, y_set, gamma_set):
"""
Given the set of gamma variables, outputs the set of
probabilities p(y_t | y_{t-1}, ... , y_1, gamma_{t-1}, ... , gamma_1)
"""
# Note (HUGO): this function should probably be implemented in C
# to make it much faster, since it requires for loops.
# Setting variables with friendlier name
d_y = self.input_size
d_z = self.latent_size
A = self.A
C = self.C
Sigma = self.Sigma
E = self.E
cond_probs = []
map_probs = []
laplace_probs = []
y_pred = []
z_n_z_n_post_sum = zeros((d_z, d_z))
# Temporary variable, to avoid memory allocation
vec_d_z = zeros(d_z)
vec_d_z2 = zeros(d_z)
vec_d_y = zeros(d_y)
mat_d_z_d_z = zeros((d_z, d_z))
mat_d_z_d_z2 = zeros((d_z, d_z))
eye_d_z = eye(d_z)
mat_times_C_trans = zeros((d_z, d_y))
pred = zeros(d_y)
cov_pred = zeros((d_y, d_y))
A_gamma = zeros((d_z, d_z))
E_gamma = zeros((d_z, d_z))
K = zeros((d_z, d_y))
KC = zeros((d_z, d_z))
J = zeros((d_z, d_z))
A_times_prev_mu = zeros(d_z)
Af_d_y_d_y = zeros((d_y, d_y), order='fortran') # Temporary variables
Bf_d_y_d_z = zeros((d_y, d_z), order='fortran') # for calls to
Af_d_z_d_z = zeros((d_z, d_z),
order='fortran') # math.linalg.solve(...)
Bf_d_z_d_z = zeros((d_z, d_z), order='fortran')
pivots_d_y = zeros((d_y), dtype='i', order='fortran')
pivots_d_z = zeros((d_z), dtype='i', order='fortran')
z_n_z_n_post = zeros((d_z, d_z))
next_z_n_z_n_post = zeros((d_z, d_z))
log_det_diff2_log_gamma = 0
for y_t, gamma_t in zip(y_set, gamma_set):
T = len(y_t)
cond_probs_t = zeros(T)
map_probs_t = zeros(T)
laplace_probs_t = zeros(T)
y_pred_t = zeros((T, d_y))
mu_kalman_t = zeros((T, d_z)) # Filtering mus
E_kalman_t = zeros((T, d_z, d_z)) # Filtering Es
mu_post_t = zeros((T, d_z))
E_post_t = zeros((T, d_z, d_z))
P_t = zeros((T - 1, d_z, d_z))
# Forward pass
# Initialization at n = 0
A_times_prev_mu[:] = 0
multiply(C.T, reshape(gamma_t[0], (-1, 1)), mat_times_C_trans)
pred[:] = 0
product_matrix_matrix(C, mat_times_C_trans, cov_pred)
cov_pred += Sigma
solve(cov_pred, mat_times_C_trans.T, K.T, Af_d_y_d_y, Bf_d_y_d_z,
pivots_d_y)
vec_d_y[:] = y_t[0]
vec_d_y -= pred
product_matrix_vector(K, vec_d_y, mu_kalman_t[0])
product_matrix_matrix(K, C, KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
multiply(mat_d_z_d_z, gamma_t[0], E_kalman_t[0])
cond_probs_t[0] = self.multivariate_norm_log_pdf(
y_t[0], pred, cov_pred)
y_pred_t[0] = pred
# from n=1 to T-1
for n in xrange(T - 1):
divide(1., E, vec_d_z)
divide(1., gamma_t[n + 1], vec_d_z2)
vec_d_z += vec_d_z2
divide(1., vec_d_z, vec_d_z2)
setdiag(E_gamma, vec_d_z2)
divide(E, gamma_t[n + 1], vec_d_z)
vec_d_z += 1
divide(A, reshape(vec_d_z, (-1, 1)), A_gamma)
P_tn = P_t[n]
product_matrix_matrix(E_kalman_t[n], A_gamma.T, mat_d_z_d_z)
product_matrix_matrix(A_gamma, mat_d_z_d_z, P_tn)
P_tn += E_gamma
product_matrix_vector(A_gamma, mu_kalman_t[n], A_times_prev_mu)
product_matrix_matrix(P_tn, C.T, mat_times_C_trans)
product_matrix_vector(C, A_times_prev_mu, pred)
product_matrix_matrix(C, mat_times_C_trans, cov_pred)
cov_pred += Sigma
solve(cov_pred, mat_times_C_trans.T, K.T, Af_d_y_d_y,
Bf_d_y_d_z, pivots_d_y)
vec_d_y[:] = y_t[n + 1]
vec_d_y -= pred
product_matrix_vector(K, vec_d_y, mu_kalman_t[n + 1])
mu_kalman_t[n + 1] += A_times_prev_mu
product_matrix_matrix(K, C, KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
product_matrix_matrix(mat_d_z_d_z, P_tn, mat_d_z_d_z2)
# To ensure symmetry
E_kalman_t[n + 1] = mat_d_z_d_z2
E_kalman_t[n + 1] += mat_d_z_d_z2.T
E_kalman_t[n + 1] /= 2
mu_post_t[-1] = mu_kalman_t[-1]
E_post_t[-1] = E_kalman_t[-1]
# Compute last step statistics
outer(mu_post_t[-1], mu_post_t[-1], z_n_z_n_post)
z_n_z_n_post += E_post_t[-1]
# Update cumulative statistics
z_n_z_n_post_sum += z_n_z_n_post
cond_probs_t[n + 1] = self.multivariate_norm_log_pdf(
y_t[n + 1], pred, cov_pred)
y_pred_t[n + 1] = pred
#print y_t, y_pred_t
# Backward pass
pred[:] = 0
cov_pred[:] = 0
for n in xrange(T - 2, -1, -1):
next_z_n_z_n_post[:] = z_n_z_n_post
divide(E, gamma_t[n + 1], vec_d_z)
vec_d_z += 1
divide(A, reshape(vec_d_z, (-1, 1)), A_gamma)
P_tn = P_t[n]
solve(P_tn.T, A_gamma, mat_d_z_d_z, Af_d_z_d_z, Bf_d_z_d_z,
pivots_d_z)
product_matrix_matrix(E_kalman_t[n], mat_d_z_d_z.T, J)
product_matrix_vector(A_gamma, mu_kalman_t[n], vec_d_z)
vec_d_z *= -1
vec_d_z += mu_post_t[n + 1]
product_matrix_vector(J, vec_d_z, mu_post_t[n])
mu_post_t[n] += mu_kalman_t[n]
mat_d_z_d_z[:] = E_post_t[n + 1]
mat_d_z_d_z -= P_tn
product_matrix_matrix(mat_d_z_d_z, J.T, mat_d_z_d_z2)
product_matrix_matrix(J, mat_d_z_d_z2, mat_d_z_d_z)
# To ensure symmetry
E_post_t[n] = E_kalman_t[n]
E_post_t[n] += mat_d_z_d_z
E_post_t[n] += E_kalman_t[n].T
E_post_t[n] += mat_d_z_d_z.T
E_post_t[n] /= 2
outer(mu_post_t[n], mu_post_t[n], z_n_z_n_post)
z_n_z_n_post += E_post_t[n]
dummy = self.compute_gamma(A, E, z_n_z_n_post,
next_z_n_z_n_post, gamma_t[n + 1])
log_prior_gamma = self.log_prior_gamma(gamma_t[n + 1])
#print log_prior_gamma
log_prior_log_gamma = self.log_prior_log_gamma(gamma_t[n + 1])
log_det_diff2_log_gamma = self.log_det_diff2_log_gamma(
A, E, z_n_z_n_post, next_z_n_z_n_post, gamma_t[n + 1])
map_probs_t[n + 1] = cond_probs_t[n + 1] + log_prior_gamma
laplace_probs_t[
n +
1] = cond_probs_t[n + 1] + log_prior_log_gamma + d_z * log(
2 * pi) / 2 - 0.5 * log_det_diff2_log_gamma
gamma_t[0] = (diag(z_n_z_n_post) + 2 * self.gamma_prior_beta) / (
2 * self.gamma_prior_alpha + 3)
log_prior_gamma = self.log_prior_gamma(gamma_t[0])
log_prior_log_gamma = self.log_prior_log_gamma(gamma_t[0])
log_det_diff2_log_gamma = sum(
(z_n_z_n_post / 2 + self.gamma_prior_beta) / gamma_t[0])
map_probs_t[0] = cond_probs_t[0] + log_prior_gamma
laplace_probs_t[
0] = cond_probs_t[0] + log_prior_log_gamma + d_z * log(
2 * pi) / 2 - 0.5 * log_det_diff2_log_gamma
cond_probs += [cond_probs_t]
map_probs += [map_probs_t]
laplace_probs += [laplace_probs_t]
y_pred += [y_pred_t]
return cond_probs, map_probs, laplace_probs, y_pred
def EM_step(self, y_set, gamma_set, training=False, return_mu_post=False):
"""
Computes the posterior statistics and outputs the M step
estimates of the parameters.
Also outputs the non-parametric, sparsity inducing variances gamma_t.
Optionally, can output the posterior means of the latent state variables.
"""
# Setting variables with friendlier name
d_y = self.input_size
d_z = self.latent_size
#V_zero = self.V_zero
A = self.A
C = self.C
Sigma = self.Sigma
E = self.E
# Variables for estimating new parameters
A_new = zeros((d_z, d_z))
C_new = zeros((d_y, d_z))
z_n_z_n_1_post_sum = zeros((d_z, d_z))
z_n_z_n_post_sum = zeros((d_z, d_z))
A_new_denums = zeros((d_z, d_z, d_z))
y_n_z_n_post_sum = zeros((d_y, d_z))
# Temporary variable, to avoid memory allocation
vec_d_z = zeros(d_z)
vec_d_z2 = zeros(d_z)
vec_d_y = zeros(d_y)
mat_d_z_d_z = zeros((d_z, d_z))
mat_d_z_d_z2 = zeros((d_z, d_z))
eye_d_z = eye(d_z)
mat_times_C_trans = zeros((d_z, d_y))
pred = zeros(d_y)
cov_pred = zeros((d_y, d_y))
A_gamma = zeros((d_z, d_z))
E_gamma = zeros((d_z, d_z))
K = zeros((d_z, d_y))
KC = zeros((d_z, d_z))
J = zeros((d_z, d_z))
A_times_prev_mu = zeros(d_z)
Af_d_y_d_y = zeros((d_y, d_y), order='fortran') # Temporary variables
Bf_d_y_d_z = zeros((d_y, d_z), order='fortran') # for calls to
Af_d_z_d_z = zeros((d_z, d_z),
order='fortran') # math.linalg.solve(...)
Bf_d_z_d_z = zeros((d_z, d_z), order='fortran')
pivots_d_y = zeros((d_y), dtype='i', order='fortran')
pivots_d_z = zeros((d_z), dtype='i', order='fortran')
z_n_z_n_1_post = zeros((d_z, d_z))
z_n_z_n_post = zeros((d_z, d_z))
weighted_z_n_z_n_post = zeros((d_z, d_z, d_z))
next_z_n_z_n_post = zeros((d_z, d_z))
y_n_z_n_post = zeros((d_y, d_z))
if training == True:
max_Esteps = self.max_Esteps
last_Esteps = self.last_Esteps
else:
max_Esteps = self.max_test_Esteps
last_Esteps = self.max_test_Esteps
Esteps = 0
have_A_denum = False
get_A_denum = False
finished = False
while not finished:
T_sum = 0
gamma_mean_diff = 0
z_n_z_n_1_post_sum[:] = 0
z_n_z_n_post_sum[:] = 0
y_n_z_n_post_sum[:] = 0
A_new_denums[:] = 0
Esteps += 1
if Esteps == max_Esteps:
get_A_denum = True
finished = True
elif Esteps >= last_Esteps:
get_A_denum = True
if return_mu_post:
mu_post = []
for y_t, gamma_t in zip(y_set, gamma_set):
T = len(y_t)
T_sum += T
mu_kalman_t = zeros((T, d_z)) # Filtering mus
E_kalman_t = zeros((T, d_z, d_z)) # Filtering Es
mu_post_t = zeros((T, d_z))
E_post_t = zeros((T, d_z, d_z))
P_t = zeros((T - 1, d_z, d_z))
# Forward pass
# Initialization at n = 0
A_times_prev_mu[:] = 0
multiply(C.T, reshape(gamma_t[0], (-1, 1)), mat_times_C_trans)
pred[:] = 0
product_matrix_matrix(C, mat_times_C_trans, cov_pred)
cov_pred += Sigma
solve(cov_pred, mat_times_C_trans.T, K.T, Af_d_y_d_y,
Bf_d_y_d_z, pivots_d_y)
vec_d_y[:] = y_t[0]
vec_d_y -= pred
product_matrix_vector(K, vec_d_y, mu_kalman_t[0])
product_matrix_matrix(K, C, KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
multiply(mat_d_z_d_z, gamma_t[0], E_kalman_t[0])
# from n=1 to T-1
for n in xrange(T - 1):
divide(1., E, vec_d_z)
divide(1., gamma_t[n + 1], vec_d_z2)
vec_d_z += vec_d_z2
divide(1., vec_d_z, vec_d_z2)
setdiag(E_gamma, vec_d_z2)
divide(E, gamma_t[n + 1], vec_d_z)
vec_d_z += 1
divide(A, reshape(vec_d_z, (-1, 1)), A_gamma)
P_tn = P_t[n]
product_matrix_matrix(E_kalman_t[n], A_gamma.T,
mat_d_z_d_z)
product_matrix_matrix(A_gamma, mat_d_z_d_z, P_tn)
P_tn += E_gamma
product_matrix_vector(A_gamma, mu_kalman_t[n],
A_times_prev_mu)
product_matrix_matrix(P_tn, C.T, mat_times_C_trans)
product_matrix_vector(C, A_times_prev_mu, pred)
product_matrix_matrix(C, mat_times_C_trans, cov_pred)
cov_pred += Sigma
solve(cov_pred, mat_times_C_trans.T, K.T, Af_d_y_d_y,
Bf_d_y_d_z, pivots_d_y)
vec_d_y[:] = y_t[n + 1]
vec_d_y -= pred
product_matrix_vector(K, vec_d_y, mu_kalman_t[n + 1])
mu_kalman_t[n + 1] += A_times_prev_mu
product_matrix_matrix(K, C, KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
product_matrix_matrix(mat_d_z_d_z, P_tn, mat_d_z_d_z2)
# To ensure symmetry
E_kalman_t[n + 1] = mat_d_z_d_z2
E_kalman_t[n + 1] += mat_d_z_d_z2.T
E_kalman_t[n + 1] /= 2
mu_post_t[-1] = mu_kalman_t[-1]
E_post_t[-1] = E_kalman_t[-1]
# Compute last step statistics
outer(mu_post_t[-1], mu_post_t[-1], z_n_z_n_post)
z_n_z_n_post += E_post_t[-1]
outer(y_t[-1], mu_post_t[-1], y_n_z_n_post)
# Update cumulative statistics
z_n_z_n_post_sum += z_n_z_n_post
y_n_z_n_post_sum += y_n_z_n_post
# Backward pass
pred[:] = 0
cov_pred[:] = 0
for n in xrange(T - 2, -1, -1):
next_z_n_z_n_post[:] = z_n_z_n_post
divide(E, gamma_t[n + 1], vec_d_z)
vec_d_z += 1
divide(A, reshape(vec_d_z, (-1, 1)), A_gamma)
P_tn = P_t[n]
solve(P_tn.T, A_gamma, mat_d_z_d_z, Af_d_z_d_z, Bf_d_z_d_z,
pivots_d_z)
product_matrix_matrix(E_kalman_t[n], mat_d_z_d_z.T, J)
product_matrix_vector(A_gamma, mu_kalman_t[n], vec_d_z)
vec_d_z *= -1
vec_d_z += mu_post_t[n + 1]
product_matrix_vector(J, vec_d_z, mu_post_t[n])
mu_post_t[n] += mu_kalman_t[n]
mat_d_z_d_z[:] = E_post_t[n + 1]
mat_d_z_d_z -= P_tn
product_matrix_matrix(mat_d_z_d_z, J.T, mat_d_z_d_z2)
product_matrix_matrix(J, mat_d_z_d_z2, mat_d_z_d_z)
# To ensure symmetry
E_post_t[n] = E_kalman_t[n]
E_post_t[n] += mat_d_z_d_z
E_post_t[n] += E_kalman_t[n].T
E_post_t[n] += mat_d_z_d_z.T
E_post_t[n] /= 2
# Compute posterior statistics
product_matrix_matrix(J, E_post_t[n + 1], z_n_z_n_1_post)
outer(mu_post_t[n + 1], mu_post_t[n], mat_d_z_d_z)
z_n_z_n_1_post += mat_d_z_d_z
outer(mu_post_t[n], mu_post_t[n], z_n_z_n_post)
z_n_z_n_post += E_post_t[n]
outer(y_t[n], mu_post_t[n], y_n_z_n_post)
# Update cumulative statistics
z_n_z_n_1_post_sum += z_n_z_n_1_post
z_n_z_n_post_sum += z_n_z_n_post
y_n_z_n_post_sum += y_n_z_n_post
gamma_mean_diff += self.compute_gamma(
A, E, z_n_z_n_post, next_z_n_z_n_post, gamma_t[n + 1])
#print gamma_t[n+1]
if get_A_denum == True:
# Compute the denominator of the A update,
# which requires d_z matrices of size (d_z,d_z)
# (i.e. d_z different weighted sums of the z_n_z_n_post matrices)
add(gamma_t[n + 1], E, vec_d_z)
divide(gamma_t[n + 1], vec_d_z, vec_d_z2)
multiply(reshape(z_n_z_n_post, (1, d_z, d_z)),
reshape(vec_d_z2, (d_z, 1, 1)),
weighted_z_n_z_n_post)
A_new_denums += weighted_z_n_z_n_post
have_A_denum = True
new_gamma = (diag(z_n_z_n_post) + 2 * self.gamma_prior_beta
) / (2 * self.gamma_prior_alpha + 3)
gamma_mean_diff += sum((gamma_t[0] - new_gamma)**2) / d_z
gamma_t[0] = new_gamma
gamma_mean_diff /= T_sum
if gamma_mean_diff < self.gamma_change_tolerance:
if training == True:
if have_A_denum == True:
finished = True
self.last_Esteps = Esteps
else:
get_A_denum = True
else:
finished = True
elif gamma_mean_diff <= 10 * self.gamma_change_tolerance and training == True:
get_A_denum = True
if self.verbose:
print gamma_mean_diff, max_Esteps, Esteps
if return_mu_post:
mu_post += [mu_post_t]
# Compute the M step estimates of the parameters
if training == True:
for i in xrange(d_z):
solve(
A_new_denums[i] +
eye(d_z) * self.latent_transition_matrix_regularizer,
z_n_z_n_1_post_sum[i:(i + 1)].T, A_new[i:(i + 1)].T)
solve(
z_n_z_n_post_sum + eye_d_z * self.emission_matrix_regularizer,
y_n_z_n_post_sum.T, C_new.T)
if return_mu_post:
return (A_new, C_new), gamma_set, mu_post
else:
return (A_new, C_new), gamma_set
def EM_step(self, y_set, return_mu_post=False):
"""
Computes the posterior statistics and outputs the M step
estimates of the parameters.
The set of probabilities p(y_t | y_{t-1}, ... , y_1) are also given.
"""
# Setting variables with friendlier name
d_y = self.input_size
d_z = self.latent_size
mu_zero = self.mu_zero
V_zero = self.V_zero
A = self.A
C = self.C
Sigma = self.Sigma
E = self.E
# Variables for estimating new parameters
A_new = zeros((d_z, d_z))
C_new = zeros((d_y, d_z))
E_new = zeros((d_z, d_z))
Sigma_new = zeros((d_y, d_y))
mu_zero_new = zeros((d_z))
V_zero_new = zeros((d_z, d_z))
z_n_z_n_1_post_sum = zeros((d_z, d_z))
z_n_z_n_post_sum = zeros((d_z, d_z))
z_n_z_n_post_sum_no_last = zeros((d_z, d_z))
z_n_z_n_post_sum_no_first = zeros((d_z, d_z))
z_n_z_n_post_sum_first = zeros((d_z, d_z))
outer_z_n_z_n_post_sum_first = zeros((d_z, d_z))
z_n_post_sum_first = zeros((d_z))
y_n_z_n_post_sum = zeros((d_y, d_z))
y_n_y_n_sum = zeros((d_y, d_y))
cond_probs = []
# Temporary variable, to avoid memory allocation
vec_d_z = zeros(d_z)
vec_d_y = zeros(d_y)
mat_d_z_d_z = zeros((d_z, d_z))
mat_d_z_d_z2 = zeros((d_z, d_z))
eye_d_z = eye(d_z)
mat_times_C_trans = zeros((d_z, d_y))
pred = zeros(d_y)
cov_pred = zeros((d_y, d_y))
K = zeros((d_z, d_y))
KC = zeros((d_z, d_z))
J = zeros((d_z, d_z))
A_times_prev_mu = zeros(d_z)
Af_d_y_d_y = zeros((d_y, d_y), order='fortran') # Temporary variables
Bf_d_y_d_z = zeros((d_y, d_z), order='fortran') # for calls to
Af_d_z_d_z = zeros((d_z, d_z),
order='fortran') # math.linalg.solve(...)
Bf_d_z_d_z = zeros((d_z, d_z), order='fortran')
pivots_d_y = zeros((d_y), dtype='i', order='fortran')
pivots_d_z = zeros((d_z), dtype='i', order='fortran')
z_n_z_n_1_post = zeros((d_z, d_z))
z_n_z_n_post = zeros((d_z, d_z))
y_n_z_n_post = zeros((d_y, d_z))
y_n_y_n = zeros((d_y, d_y))
T_sum = 0
if return_mu_post:
mu_post = []
for y_t in y_set:
T = len(y_t)
T_sum += T
mu_kalman_t = zeros((T, d_z)) # Filtering mus
E_kalman_t = zeros((T, d_z, d_z)) # Filtering Es
mu_post_t = zeros(
(T, d_z)) # Posterior mus (could be removed and computed once)
E_post_t = zeros(
(T, d_z,
d_z)) # Posterior Es (could be removed and computed once)
P_t = zeros((T - 1, d_z, d_z))
cond_probs_t = zeros(T)
# Forward pass
# Initialization at n = 0
A_times_prev_mu[:] = 0
product_matrix_matrix(V_zero, C.T, mat_times_C_trans)
product_matrix_vector(C, mu_zero, pred)
product_matrix_matrix(C, mat_times_C_trans, cov_pred)
cov_pred += Sigma
solve(cov_pred, mat_times_C_trans.T, K.T, Af_d_y_d_y, Bf_d_y_d_z,
pivots_d_y)
vec_d_y[:] = y_t[0]
vec_d_y -= pred
product_matrix_vector(K, vec_d_y, mu_kalman_t[0])
mu_kalman_t[0] += mu_zero
product_matrix_matrix(K, C, KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
product_matrix_matrix(mat_d_z_d_z, V_zero, E_kalman_t[0])
cond_probs_t[0] = self.multivariate_norm_log_pdf(
y_t[0], pred, cov_pred)
# from n=1 to T-1
for n in xrange(T - 1):
P_tn = P_t[n]
product_matrix_matrix(E_kalman_t[n], A.T, mat_d_z_d_z)
product_matrix_matrix(A, mat_d_z_d_z, P_tn)
P_tn += E
product_matrix_vector(A, mu_kalman_t[n], A_times_prev_mu)
product_matrix_matrix(P_tn, C.T, mat_times_C_trans)
product_matrix_vector(C, A_times_prev_mu, pred)
product_matrix_matrix(C, mat_times_C_trans, cov_pred)
cov_pred += Sigma
solve(cov_pred, mat_times_C_trans.T, K.T, Af_d_y_d_y,
Bf_d_y_d_z, pivots_d_y)
vec_d_y[:] = y_t[n + 1]
vec_d_y -= pred
product_matrix_vector(K, vec_d_y, mu_kalman_t[n + 1])
mu_kalman_t[n + 1] += A_times_prev_mu
product_matrix_matrix(K, C, KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
product_matrix_matrix(mat_d_z_d_z, P_tn, mat_d_z_d_z2)
# To ensure symmetry
E_kalman_t[n + 1] = mat_d_z_d_z2
E_kalman_t[n + 1] += mat_d_z_d_z2.T
E_kalman_t[n + 1] /= 2
cond_probs_t[n + 1] = self.multivariate_norm_log_pdf(
y_t[n + 1], pred, cov_pred)
mu_post_t[-1] = mu_kalman_t[-1]
E_post_t[-1] = E_kalman_t[-1]
# Compute last step statistics
outer(mu_post_t[-1], mu_post_t[-1], z_n_z_n_post)
z_n_z_n_post += E_post_t[-1]
outer(y_t[-1], mu_post_t[-1], y_n_z_n_post)
outer(y_t[-1], y_t[-1], y_n_y_n)
# Update cumulative statistics
z_n_z_n_post_sum += z_n_z_n_post
z_n_z_n_post_sum_no_first += z_n_z_n_post
y_n_z_n_post_sum += y_n_z_n_post
y_n_y_n_sum += y_n_y_n
# Backward pass
pred[:] = 0
cov_pred[:] = 0
for n in xrange(T - 2, -1, -1):
P_tn = P_t[n]
solve(P_tn.T, A, mat_d_z_d_z, Af_d_z_d_z, Bf_d_z_d_z,
pivots_d_z)
product_matrix_matrix(E_kalman_t[n], mat_d_z_d_z.T, J)
product_matrix_vector(A, mu_kalman_t[n], vec_d_z)
vec_d_z *= -1
vec_d_z += mu_post_t[n + 1]
product_matrix_vector(J, vec_d_z, mu_post_t[n])
mu_post_t[n] += mu_kalman_t[n]
mat_d_z_d_z[:] = E_post_t[n + 1]
mat_d_z_d_z -= P_tn
product_matrix_matrix(mat_d_z_d_z, J.T, mat_d_z_d_z2)
product_matrix_matrix(J, mat_d_z_d_z2, mat_d_z_d_z)
# To ensure symmetry
E_post_t[n] = E_kalman_t[n]
E_post_t[n] += mat_d_z_d_z
E_post_t[n] += E_kalman_t[n].T
E_post_t[n] += mat_d_z_d_z.T
E_post_t[n] /= 2
# Compute posterior statistics
product_matrix_matrix(J, E_post_t[n + 1], z_n_z_n_1_post)
outer(mu_post_t[n + 1], mu_post_t[n], mat_d_z_d_z)
z_n_z_n_1_post += mat_d_z_d_z
outer(mu_post_t[n], mu_post_t[n], z_n_z_n_post)
z_n_z_n_post += E_post_t[n]
outer(y_t[n], mu_post_t[n], y_n_z_n_post)
outer(y_t[n], y_t[n], y_n_y_n)
# Update cumulative statistics
z_n_z_n_1_post_sum += z_n_z_n_1_post
z_n_z_n_post_sum += z_n_z_n_post
if n > 0:
z_n_z_n_post_sum_no_first += z_n_z_n_post
else:
z_n_z_n_post_sum_first += z_n_z_n_post
z_n_post_sum_first += mu_post_t[n]
outer(mu_post_t[n], mu_post_t[n], mat_d_z_d_z)
outer_z_n_z_n_post_sum_first += mat_d_z_d_z
z_n_z_n_post_sum_no_last += z_n_z_n_post
y_n_z_n_post_sum += y_n_z_n_post
y_n_y_n_sum += y_n_y_n
cond_probs += [cond_probs_t]
if return_mu_post:
mu_post += [mu_post_t]
# Compute the M step estimates of the parameters
#A_new = dot(z_n_z_n_1_post_sum,inv(z_n_z_n_post_sum_no_last+
# eye_d_z*self.latent_transition_matrix_regularizer))
solve(
z_n_z_n_post_sum_no_last +
eye_d_z * self.latent_transition_matrix_regularizer,
z_n_z_n_1_post_sum.T, A_new.T)
#C_new = dot(y_n_z_n_post_sum, inv(z_n_z_n_post_sum+
# eye_d_z*self.input_transition_matrix_regularizer))
solve(
z_n_z_n_post_sum +
eye_d_z * self.input_transition_matrix_regularizer,
y_n_z_n_post_sum.T, C_new.T)
E_new[:] = z_n_z_n_post_sum_no_first
z_n_z_n_1_A_T = dot(z_n_z_n_1_post_sum, A_new.T)
E_new -= z_n_z_n_1_A_T.T
E_new -= z_n_z_n_1_A_T # There is an error in Bishop's equation: the transpose on A is missing
E_new += dot(A_new, dot(z_n_z_n_post_sum_no_last, A_new.T))
E_new += eye_d_z * self.latent_covariance_matrix_regularizer
E_new /= T_sum - len(y_set)
Sigma_new[:] = y_n_y_n_sum
C_z_n_y_n = dot(C_new, y_n_z_n_post_sum.T)
Sigma_new -= C_z_n_y_n
Sigma_new -= C_z_n_y_n.T # There is an error in Bishop's equation: the transpose on C is missing
Sigma_new += dot(C_new, dot(z_n_z_n_post_sum, C_new.T)) # ... idem
Sigma_new += eye(d_y) * self.input_covariance_matrix_regularizer
Sigma_new /= T_sum
mu_zero_new[:] = z_n_post_sum_first
mu_zero_new /= len(y_set)
V_zero_new[:] = z_n_z_n_post_sum_first
V_zero_new -= outer_z_n_z_n_post_sum_first
V_zero_new /= len(y_set)
if return_mu_post:
return (A_new, C_new, E_new, Sigma_new, mu_zero_new,
V_zero_new), cond_probs, mu_post
else:
return (A_new, C_new, E_new, Sigma_new, mu_zero_new,
V_zero_new), cond_probs
def EM_step(self,y_set,gamma_set,training = False, return_mu_post = False):
"""
Computes the posterior statistics and outputs the M step
estimates of the parameters.
Also outputs the non-parametric, sparsity inducing variances gamma_t.
Optionally, can output the posterior means of the latent state variables.
"""
# Setting variables with friendlier name
d_y = self.input_size
d_z = self.latent_size
#V_zero = self.V_zero
A = self.A
C = self.C
Sigma = self.Sigma
E = self.E
# Variables for estimating new parameters
A_new = zeros((d_z,d_z))
C_new = zeros((d_y,d_z))
z_n_z_n_1_post_sum = zeros((d_z,d_z))
z_n_z_n_post_sum = zeros((d_z,d_z))
A_new_denums = zeros((d_z,d_z,d_z))
y_n_z_n_post_sum = zeros((d_y,d_z))
# Temporary variable, to avoid memory allocation
vec_d_z = zeros(d_z)
vec_d_z2 = zeros(d_z)
vec_d_y = zeros(d_y)
mat_d_z_d_z = zeros((d_z,d_z))
mat_d_z_d_z2 = zeros((d_z,d_z))
eye_d_z = eye(d_z)
mat_times_C_trans = zeros((d_z,d_y))
pred = zeros(d_y)
cov_pred = zeros((d_y,d_y))
A_gamma = zeros((d_z,d_z))
E_gamma = zeros((d_z,d_z))
K = zeros((d_z,d_y))
KC = zeros((d_z,d_z))
J = zeros((d_z,d_z))
A_times_prev_mu = zeros(d_z)
Af_d_y_d_y = zeros((d_y,d_y),order='fortran') # Temporary variables
Bf_d_y_d_z = zeros((d_y,d_z),order='fortran') # for calls to
Af_d_z_d_z = zeros((d_z,d_z),order='fortran') # math.linalg.solve(...)
Bf_d_z_d_z = zeros((d_z,d_z),order='fortran')
pivots_d_y = zeros((d_y),dtype='i',order='fortran')
pivots_d_z = zeros((d_z),dtype='i',order='fortran')
z_n_z_n_1_post = zeros((d_z,d_z))
z_n_z_n_post = zeros((d_z,d_z))
weighted_z_n_z_n_post = zeros((d_z,d_z,d_z))
next_z_n_z_n_post = zeros((d_z,d_z))
y_n_z_n_post = zeros((d_y,d_z))
if training == True:
max_Esteps = self.max_Esteps
last_Esteps = self.last_Esteps
else:
max_Esteps = self.max_test_Esteps
last_Esteps = self.max_test_Esteps
Esteps = 0
have_A_denum = False
get_A_denum = False
finished = False
while not finished:
T_sum = 0
gamma_mean_diff = 0
z_n_z_n_1_post_sum[:] = 0
z_n_z_n_post_sum[:] = 0
y_n_z_n_post_sum[:] = 0
A_new_denums[:] = 0
Esteps += 1
if Esteps == max_Esteps:
get_A_denum = True
finished = True
elif Esteps >= last_Esteps:
get_A_denum = True
if return_mu_post:
mu_post = []
for y_t,gamma_t in zip(y_set,gamma_set):
T = len(y_t)
T_sum += T
mu_kalman_t = zeros((T,d_z)) # Filtering mus
E_kalman_t = zeros((T,d_z,d_z)) # Filtering Es
mu_post_t = zeros((T,d_z))
E_post_t = zeros((T,d_z,d_z))
P_t = zeros((T-1,d_z,d_z))
# Forward pass
# Initialization at n = 0
A_times_prev_mu[:] = 0
multiply(C.T,reshape(gamma_t[0],(-1,1)),mat_times_C_trans)
pred[:] = 0
product_matrix_matrix(C,mat_times_C_trans,cov_pred)
cov_pred += Sigma
solve(cov_pred,mat_times_C_trans.T,K.T,Af_d_y_d_y,Bf_d_y_d_z,pivots_d_y)
vec_d_y[:] = y_t[0]
vec_d_y -= pred
product_matrix_vector(K,vec_d_y,mu_kalman_t[0])
product_matrix_matrix(K,C,KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
multiply(mat_d_z_d_z,gamma_t[0],E_kalman_t[0])
# from n=1 to T-1
for n in xrange(T-1):
divide(1.,E,vec_d_z)
divide(1.,gamma_t[n+1],vec_d_z2)
vec_d_z += vec_d_z2
divide(1.,vec_d_z,vec_d_z2)
setdiag(E_gamma,vec_d_z2)
divide(E,gamma_t[n+1],vec_d_z)
vec_d_z += 1
divide(A,reshape(vec_d_z,(-1,1)),A_gamma)
P_tn = P_t[n]
product_matrix_matrix(E_kalman_t[n],A_gamma.T,mat_d_z_d_z)
product_matrix_matrix(A_gamma,mat_d_z_d_z,P_tn)
P_tn += E_gamma
product_matrix_vector(A_gamma,mu_kalman_t[n],A_times_prev_mu)
product_matrix_matrix(P_tn,C.T,mat_times_C_trans)
product_matrix_vector(C,A_times_prev_mu,pred)
product_matrix_matrix(C,mat_times_C_trans,cov_pred)
cov_pred += Sigma
solve(cov_pred,mat_times_C_trans.T,K.T,Af_d_y_d_y,Bf_d_y_d_z,pivots_d_y)
vec_d_y[:] = y_t[n+1]
vec_d_y -= pred
product_matrix_vector(K,vec_d_y,mu_kalman_t[n+1])
mu_kalman_t[n+1] += A_times_prev_mu
product_matrix_matrix(K,C,KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
product_matrix_matrix(mat_d_z_d_z,P_tn,mat_d_z_d_z2)
# To ensure symmetry
E_kalman_t[n+1] = mat_d_z_d_z2
E_kalman_t[n+1] += mat_d_z_d_z2.T
E_kalman_t[n+1] /= 2
mu_post_t[-1] = mu_kalman_t[-1]
E_post_t[-1] = E_kalman_t[-1]
# Compute last step statistics
outer(mu_post_t[-1],mu_post_t[-1],z_n_z_n_post)
z_n_z_n_post += E_post_t[-1]
outer(y_t[-1],mu_post_t[-1],y_n_z_n_post)
# Update cumulative statistics
z_n_z_n_post_sum += z_n_z_n_post
y_n_z_n_post_sum += y_n_z_n_post
# Backward pass
pred[:] = 0
cov_pred[:] = 0
for n in xrange(T-2,-1,-1):
next_z_n_z_n_post[:] = z_n_z_n_post
divide(E,gamma_t[n+1],vec_d_z)
vec_d_z += 1
divide(A,reshape(vec_d_z,(-1,1)),A_gamma)
P_tn = P_t[n]
solve(P_tn.T,A_gamma,mat_d_z_d_z,Af_d_z_d_z,Bf_d_z_d_z,pivots_d_z)
product_matrix_matrix(E_kalman_t[n],mat_d_z_d_z.T,J)
product_matrix_vector(A_gamma,mu_kalman_t[n],vec_d_z)
vec_d_z *= -1
vec_d_z += mu_post_t[n+1]
product_matrix_vector(J,vec_d_z,mu_post_t[n])
mu_post_t[n] += mu_kalman_t[n]
mat_d_z_d_z[:] = E_post_t[n+1]
mat_d_z_d_z -= P_tn
product_matrix_matrix(mat_d_z_d_z,J.T,mat_d_z_d_z2)
product_matrix_matrix(J,mat_d_z_d_z2,mat_d_z_d_z)
# To ensure symmetry
E_post_t[n] = E_kalman_t[n]
E_post_t[n] += mat_d_z_d_z
E_post_t[n] += E_kalman_t[n].T
E_post_t[n] += mat_d_z_d_z.T
E_post_t[n] /= 2
# Compute posterior statistics
product_matrix_matrix(J,E_post_t[n+1],z_n_z_n_1_post)
outer(mu_post_t[n+1],mu_post_t[n],mat_d_z_d_z)
z_n_z_n_1_post += mat_d_z_d_z
outer(mu_post_t[n],mu_post_t[n],z_n_z_n_post)
z_n_z_n_post += E_post_t[n]
outer(y_t[n],mu_post_t[n],y_n_z_n_post)
# Update cumulative statistics
z_n_z_n_1_post_sum += z_n_z_n_1_post
z_n_z_n_post_sum += z_n_z_n_post
y_n_z_n_post_sum += y_n_z_n_post
gamma_mean_diff += self.compute_gamma(A,E,z_n_z_n_post,next_z_n_z_n_post,gamma_t[n+1])
#print gamma_t[n+1]
if get_A_denum == True:
# Compute the denominator of the A update,
# which requires d_z matrices of size (d_z,d_z)
# (i.e. d_z different weighted sums of the z_n_z_n_post matrices)
add(gamma_t[n+1],E,vec_d_z)
divide(gamma_t[n+1],vec_d_z,vec_d_z2)
multiply(reshape(z_n_z_n_post,(1,d_z,d_z)),reshape(vec_d_z2,(d_z,1,1)),weighted_z_n_z_n_post)
A_new_denums += weighted_z_n_z_n_post
have_A_denum = True
new_gamma = (diag(z_n_z_n_post)+2*self.gamma_prior_beta)/(2*self.gamma_prior_alpha+3)
gamma_mean_diff += sum((gamma_t[0]-new_gamma)**2)/d_z
gamma_t[0] = new_gamma
gamma_mean_diff /= T_sum
if gamma_mean_diff < self.gamma_change_tolerance:
if training == True:
if have_A_denum == True:
finished = True
self.last_Esteps = Esteps
else:
get_A_denum = True
else:
finished = True
elif gamma_mean_diff <= 10*self.gamma_change_tolerance and training == True:
get_A_denum = True
if self.verbose:
print gamma_mean_diff, max_Esteps, Esteps
if return_mu_post:
mu_post += [mu_post_t]
# Compute the M step estimates of the parameters
if training == True:
for i in xrange(d_z):
solve(A_new_denums[i]+eye(d_z)*self.latent_transition_matrix_regularizer,z_n_z_n_1_post_sum[i:(i+1)].T,A_new[i:(i+1)].T)
solve(z_n_z_n_post_sum+eye_d_z*self.emission_matrix_regularizer,y_n_z_n_post_sum.T,C_new.T)
if return_mu_post:
return (A_new,C_new),gamma_set,mu_post
else:
return (A_new,C_new),gamma_set
def EM_step(self,y_set,return_mu_post = False):
"""
Computes the posterior statistics and outputs the M step
estimates of the parameters.
The set of probabilities p(y_t | y_{t-1}, ... , y_1) are also given.
"""
# Setting variables with friendlier name
d_y = self.input_size
d_z = self.latent_size
mu_zero = self.mu_zero
V_zero = self.V_zero
A = self.A
C = self.C
Sigma = self.Sigma
E = self.E
# Variables for estimating new parameters
A_new = zeros((d_z,d_z))
C_new = zeros((d_y,d_z))
E_new = zeros((d_z,d_z))
Sigma_new = zeros((d_y,d_y))
mu_zero_new = zeros((d_z))
V_zero_new = zeros((d_z,d_z))
z_n_z_n_1_post_sum = zeros((d_z,d_z))
z_n_z_n_post_sum = zeros((d_z,d_z))
z_n_z_n_post_sum_no_last = zeros((d_z,d_z))
z_n_z_n_post_sum_no_first = zeros((d_z,d_z))
z_n_z_n_post_sum_first = zeros((d_z,d_z))
outer_z_n_z_n_post_sum_first = zeros((d_z,d_z))
z_n_post_sum_first = zeros((d_z))
y_n_z_n_post_sum = zeros((d_y,d_z))
y_n_y_n_sum = zeros((d_y,d_y))
cond_probs = []
# Temporary variable, to avoid memory allocation
vec_d_z = zeros(d_z)
vec_d_y = zeros(d_y)
mat_d_z_d_z = zeros((d_z,d_z))
mat_d_z_d_z2 = zeros((d_z,d_z))
eye_d_z = eye(d_z)
mat_times_C_trans = zeros((d_z,d_y))
pred = zeros(d_y)
cov_pred = zeros((d_y,d_y))
K = zeros((d_z,d_y))
KC = zeros((d_z,d_z))
J = zeros((d_z,d_z))
A_times_prev_mu = zeros(d_z)
Af_d_y_d_y = zeros((d_y,d_y),order='fortran') # Temporary variables
Bf_d_y_d_z = zeros((d_y,d_z),order='fortran') # for calls to
Af_d_z_d_z = zeros((d_z,d_z),order='fortran') # math.linalg.solve(...)
Bf_d_z_d_z = zeros((d_z,d_z),order='fortran')
pivots_d_y = zeros((d_y),dtype='i',order='fortran')
pivots_d_z = zeros((d_z),dtype='i',order='fortran')
z_n_z_n_1_post = zeros((d_z,d_z))
z_n_z_n_post = zeros((d_z,d_z))
y_n_z_n_post = zeros((d_y,d_z))
y_n_y_n = zeros((d_y,d_y))
T_sum = 0
if return_mu_post:
mu_post = []
for y_t in y_set:
T = len(y_t)
T_sum += T
mu_kalman_t = zeros((T,d_z)) # Filtering mus
E_kalman_t = zeros((T,d_z,d_z)) # Filtering Es
mu_post_t = zeros((T,d_z)) # Posterior mus (could be removed and computed once)
E_post_t = zeros((T,d_z,d_z)) # Posterior Es (could be removed and computed once)
P_t = zeros((T-1,d_z,d_z))
cond_probs_t = zeros(T)
# Forward pass
# Initialization at n = 0
A_times_prev_mu[:] = 0
product_matrix_matrix(V_zero,C.T,mat_times_C_trans)
product_matrix_vector(C,mu_zero,pred)
product_matrix_matrix(C,mat_times_C_trans,cov_pred)
cov_pred += Sigma
solve(cov_pred,mat_times_C_trans.T,K.T,Af_d_y_d_y,Bf_d_y_d_z,pivots_d_y)
vec_d_y[:] = y_t[0]
vec_d_y -= pred
product_matrix_vector(K,vec_d_y,mu_kalman_t[0])
mu_kalman_t[0] += mu_zero
product_matrix_matrix(K,C,KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
product_matrix_matrix(mat_d_z_d_z,V_zero,E_kalman_t[0])
cond_probs_t[0] = self.multivariate_norm_log_pdf(y_t[0],pred,cov_pred)
# from n=1 to T-1
for n in xrange(T-1):
P_tn = P_t[n]
product_matrix_matrix(E_kalman_t[n],A.T,mat_d_z_d_z)
product_matrix_matrix(A,mat_d_z_d_z,P_tn)
P_tn += E
product_matrix_vector(A,mu_kalman_t[n],A_times_prev_mu)
product_matrix_matrix(P_tn,C.T,mat_times_C_trans)
product_matrix_vector(C,A_times_prev_mu,pred)
product_matrix_matrix(C,mat_times_C_trans,cov_pred)
cov_pred += Sigma
solve(cov_pred,mat_times_C_trans.T,K.T,Af_d_y_d_y,Bf_d_y_d_z,pivots_d_y)
vec_d_y[:] = y_t[n+1]
vec_d_y -= pred
product_matrix_vector(K,vec_d_y,mu_kalman_t[n+1])
mu_kalman_t[n+1] += A_times_prev_mu
product_matrix_matrix(K,C,KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
product_matrix_matrix(mat_d_z_d_z,P_tn,mat_d_z_d_z2)
# To ensure symmetry
E_kalman_t[n+1] = mat_d_z_d_z2
E_kalman_t[n+1] += mat_d_z_d_z2.T
E_kalman_t[n+1] /= 2
cond_probs_t[n+1] = self.multivariate_norm_log_pdf(y_t[n+1],pred,cov_pred)
mu_post_t[-1] = mu_kalman_t[-1]
E_post_t[-1] = E_kalman_t[-1]
# Compute last step statistics
outer(mu_post_t[-1],mu_post_t[-1],z_n_z_n_post)
z_n_z_n_post += E_post_t[-1]
outer(y_t[-1],mu_post_t[-1],y_n_z_n_post)
outer(y_t[-1],y_t[-1],y_n_y_n)
# Update cumulative statistics
z_n_z_n_post_sum += z_n_z_n_post
z_n_z_n_post_sum_no_first += z_n_z_n_post
y_n_z_n_post_sum += y_n_z_n_post
y_n_y_n_sum += y_n_y_n
# Backward pass
pred[:] = 0
cov_pred[:] = 0
for n in xrange(T-2,-1,-1):
P_tn = P_t[n]
solve(P_tn.T,A,mat_d_z_d_z,Af_d_z_d_z,Bf_d_z_d_z,pivots_d_z)
product_matrix_matrix(E_kalman_t[n],mat_d_z_d_z.T,J)
product_matrix_vector(A,mu_kalman_t[n],vec_d_z)
vec_d_z *= -1
vec_d_z += mu_post_t[n+1]
product_matrix_vector(J,vec_d_z,mu_post_t[n])
mu_post_t[n] += mu_kalman_t[n]
mat_d_z_d_z[:] = E_post_t[n+1]
mat_d_z_d_z -= P_tn
product_matrix_matrix(mat_d_z_d_z,J.T,mat_d_z_d_z2)
product_matrix_matrix(J,mat_d_z_d_z2,mat_d_z_d_z)
# To ensure symmetry
E_post_t[n] = E_kalman_t[n]
E_post_t[n] += mat_d_z_d_z
E_post_t[n] += E_kalman_t[n].T
E_post_t[n] += mat_d_z_d_z.T
E_post_t[n] /= 2
# Compute posterior statistics
product_matrix_matrix(J,E_post_t[n+1],z_n_z_n_1_post)
outer(mu_post_t[n+1],mu_post_t[n],mat_d_z_d_z)
z_n_z_n_1_post += mat_d_z_d_z
outer(mu_post_t[n],mu_post_t[n],z_n_z_n_post)
z_n_z_n_post += E_post_t[n]
outer(y_t[n],mu_post_t[n],y_n_z_n_post)
outer(y_t[n],y_t[n],y_n_y_n)
# Update cumulative statistics
z_n_z_n_1_post_sum += z_n_z_n_1_post
z_n_z_n_post_sum += z_n_z_n_post
if n > 0:
z_n_z_n_post_sum_no_first += z_n_z_n_post
else:
z_n_z_n_post_sum_first += z_n_z_n_post
z_n_post_sum_first += mu_post_t[n]
outer(mu_post_t[n],mu_post_t[n],mat_d_z_d_z)
outer_z_n_z_n_post_sum_first += mat_d_z_d_z
z_n_z_n_post_sum_no_last += z_n_z_n_post
y_n_z_n_post_sum += y_n_z_n_post
y_n_y_n_sum += y_n_y_n
cond_probs += [cond_probs_t]
if return_mu_post:
mu_post += [mu_post_t]
# Compute the M step estimates of the parameters
#A_new = dot(z_n_z_n_1_post_sum,inv(z_n_z_n_post_sum_no_last+
# eye_d_z*self.latent_transition_matrix_regularizer))
solve(z_n_z_n_post_sum_no_last+eye_d_z*self.latent_transition_matrix_regularizer,
z_n_z_n_1_post_sum.T,A_new.T)
#C_new = dot(y_n_z_n_post_sum, inv(z_n_z_n_post_sum+
# eye_d_z*self.input_transition_matrix_regularizer))
solve(z_n_z_n_post_sum+eye_d_z*self.input_transition_matrix_regularizer,
y_n_z_n_post_sum.T,C_new.T)
E_new[:] = z_n_z_n_post_sum_no_first
z_n_z_n_1_A_T = dot(z_n_z_n_1_post_sum,A_new.T)
E_new -= z_n_z_n_1_A_T.T
E_new -= z_n_z_n_1_A_T # There is an error in Bishop's equation: the transpose on A is missing
E_new += dot(A_new,dot(z_n_z_n_post_sum_no_last,A_new.T))
E_new += eye_d_z*self.latent_covariance_matrix_regularizer
E_new /= T_sum - len(y_set)
Sigma_new[:] = y_n_y_n_sum
C_z_n_y_n = dot(C_new,y_n_z_n_post_sum.T)
Sigma_new -= C_z_n_y_n
Sigma_new -= C_z_n_y_n.T # There is an error in Bishop's equation: the transpose on C is missing
Sigma_new += dot(C_new,dot(z_n_z_n_post_sum,C_new.T)) # ... idem
Sigma_new += eye(d_y)*self.input_covariance_matrix_regularizer
Sigma_new /= T_sum
mu_zero_new[:] = z_n_post_sum_first
mu_zero_new /= len(y_set)
V_zero_new[:] = z_n_z_n_post_sum_first
V_zero_new -= outer_z_n_z_n_post_sum_first
V_zero_new /= len(y_set)
if return_mu_post:
return (A_new,C_new,E_new,Sigma_new,mu_zero_new,V_zero_new),cond_probs,mu_post
else:
return (A_new,C_new,E_new,Sigma_new,mu_zero_new,V_zero_new),cond_probs
def cond_probs(self,y_set,gamma_set):
"""
Given the set of gamma variables, outputs the set of
probabilities p(y_t | y_{t-1}, ... , y_1, gamma_{t-1}, ... , gamma_1)
"""
# Note (HUGO): this function should probably be implemented in C
# to make it much faster, since it requires for loops.
# Setting variables with friendlier name
d_y = self.input_size
d_z = self.latent_size
A = self.A
C = self.C
Sigma = self.Sigma
E = self.E
cond_probs = []
map_probs = []
laplace_probs = []
y_pred = []
z_n_z_n_post_sum = zeros((d_z,d_z))
# Temporary variable, to avoid memory allocation
vec_d_z = zeros(d_z)
vec_d_z2 = zeros(d_z)
vec_d_y = zeros(d_y)
mat_d_z_d_z = zeros((d_z,d_z))
mat_d_z_d_z2 = zeros((d_z,d_z))
eye_d_z = eye(d_z)
mat_times_C_trans = zeros((d_z,d_y))
pred = zeros(d_y)
cov_pred = zeros((d_y,d_y))
A_gamma = zeros((d_z,d_z))
E_gamma = zeros((d_z,d_z))
K = zeros((d_z,d_y))
KC = zeros((d_z,d_z))
J = zeros((d_z,d_z))
A_times_prev_mu = zeros(d_z)
Af_d_y_d_y = zeros((d_y,d_y),order='fortran') # Temporary variables
Bf_d_y_d_z = zeros((d_y,d_z),order='fortran') # for calls to
Af_d_z_d_z = zeros((d_z,d_z),order='fortran') # math.linalg.solve(...)
Bf_d_z_d_z = zeros((d_z,d_z),order='fortran')
pivots_d_y = zeros((d_y),dtype='i',order='fortran')
pivots_d_z = zeros((d_z),dtype='i',order='fortran')
z_n_z_n_post = zeros((d_z,d_z))
next_z_n_z_n_post = zeros((d_z,d_z))
log_det_diff2_log_gamma = 0
for y_t,gamma_t in zip(y_set,gamma_set):
T = len(y_t)
cond_probs_t = zeros(T)
map_probs_t = zeros(T)
laplace_probs_t = zeros(T)
y_pred_t = zeros((T,d_y))
mu_kalman_t = zeros((T,d_z)) # Filtering mus
E_kalman_t = zeros((T,d_z,d_z)) # Filtering Es
mu_post_t = zeros((T,d_z))
E_post_t = zeros((T,d_z,d_z))
P_t = zeros((T-1,d_z,d_z))
# Forward pass
# Initialization at n = 0
A_times_prev_mu[:] = 0
multiply(C.T,reshape(gamma_t[0],(-1,1)),mat_times_C_trans)
pred[:] = 0
product_matrix_matrix(C,mat_times_C_trans,cov_pred)
cov_pred += Sigma
solve(cov_pred,mat_times_C_trans.T,K.T,Af_d_y_d_y,Bf_d_y_d_z,pivots_d_y)
vec_d_y[:] = y_t[0]
vec_d_y -= pred
product_matrix_vector(K,vec_d_y,mu_kalman_t[0])
product_matrix_matrix(K,C,KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
multiply(mat_d_z_d_z,gamma_t[0],E_kalman_t[0])
cond_probs_t[0] = self.multivariate_norm_log_pdf(y_t[0],pred,cov_pred)
y_pred_t[0] = pred
# from n=1 to T-1
for n in xrange(T-1):
divide(1.,E,vec_d_z)
divide(1.,gamma_t[n+1],vec_d_z2)
vec_d_z += vec_d_z2
divide(1.,vec_d_z,vec_d_z2)
setdiag(E_gamma,vec_d_z2)
divide(E,gamma_t[n+1],vec_d_z)
vec_d_z += 1
divide(A,reshape(vec_d_z,(-1,1)),A_gamma)
P_tn = P_t[n]
product_matrix_matrix(E_kalman_t[n],A_gamma.T,mat_d_z_d_z)
product_matrix_matrix(A_gamma,mat_d_z_d_z,P_tn)
P_tn += E_gamma
product_matrix_vector(A_gamma,mu_kalman_t[n],A_times_prev_mu)
product_matrix_matrix(P_tn,C.T,mat_times_C_trans)
product_matrix_vector(C,A_times_prev_mu,pred)
product_matrix_matrix(C,mat_times_C_trans,cov_pred)
cov_pred += Sigma
solve(cov_pred,mat_times_C_trans.T,K.T,Af_d_y_d_y,Bf_d_y_d_z,pivots_d_y)
vec_d_y[:] = y_t[n+1]
vec_d_y -= pred
product_matrix_vector(K,vec_d_y,mu_kalman_t[n+1])
mu_kalman_t[n+1] += A_times_prev_mu
product_matrix_matrix(K,C,KC)
mat_d_z_d_z[:] = eye_d_z
mat_d_z_d_z -= KC
product_matrix_matrix(mat_d_z_d_z,P_tn,mat_d_z_d_z2)
# To ensure symmetry
E_kalman_t[n+1] = mat_d_z_d_z2
E_kalman_t[n+1] += mat_d_z_d_z2.T
E_kalman_t[n+1] /= 2
mu_post_t[-1] = mu_kalman_t[-1]
E_post_t[-1] = E_kalman_t[-1]
# Compute last step statistics
outer(mu_post_t[-1],mu_post_t[-1],z_n_z_n_post)
z_n_z_n_post += E_post_t[-1]
# Update cumulative statistics
z_n_z_n_post_sum += z_n_z_n_post
cond_probs_t[n+1] = self.multivariate_norm_log_pdf(y_t[n+1],pred,cov_pred)
y_pred_t[n+1] = pred
#print y_t, y_pred_t
# Backward pass
pred[:] = 0
cov_pred[:] = 0
for n in xrange(T-2,-1,-1):
next_z_n_z_n_post[:] = z_n_z_n_post
divide(E,gamma_t[n+1],vec_d_z)
vec_d_z += 1
divide(A,reshape(vec_d_z,(-1,1)),A_gamma)
P_tn = P_t[n]
solve(P_tn.T,A_gamma,mat_d_z_d_z,Af_d_z_d_z,Bf_d_z_d_z,pivots_d_z)
product_matrix_matrix(E_kalman_t[n],mat_d_z_d_z.T,J)
product_matrix_vector(A_gamma,mu_kalman_t[n],vec_d_z)
vec_d_z *= -1
vec_d_z += mu_post_t[n+1]
product_matrix_vector(J,vec_d_z,mu_post_t[n])
mu_post_t[n] += mu_kalman_t[n]
mat_d_z_d_z[:] = E_post_t[n+1]
mat_d_z_d_z -= P_tn
product_matrix_matrix(mat_d_z_d_z,J.T,mat_d_z_d_z2)
product_matrix_matrix(J,mat_d_z_d_z2,mat_d_z_d_z)
# To ensure symmetry
E_post_t[n] = E_kalman_t[n]
E_post_t[n] += mat_d_z_d_z
E_post_t[n] += E_kalman_t[n].T
E_post_t[n] += mat_d_z_d_z.T
E_post_t[n] /= 2
outer(mu_post_t[n],mu_post_t[n],z_n_z_n_post)
z_n_z_n_post += E_post_t[n]
dummy = self.compute_gamma(A,E,z_n_z_n_post,next_z_n_z_n_post,gamma_t[n+1])
log_prior_gamma = self.log_prior_gamma(gamma_t[n+1])
#print log_prior_gamma
log_prior_log_gamma = self.log_prior_log_gamma(gamma_t[n+1])
log_det_diff2_log_gamma = self.log_det_diff2_log_gamma(A,E,z_n_z_n_post,next_z_n_z_n_post,gamma_t[n+1])
map_probs_t[n+1] = cond_probs_t[n+1]+log_prior_gamma
laplace_probs_t[n+1] = cond_probs_t[n+1]+log_prior_log_gamma+d_z*log(2*pi)/2-0.5*log_det_diff2_log_gamma
gamma_t[0] = (diag(z_n_z_n_post)+2*self.gamma_prior_beta)/(2*self.gamma_prior_alpha+3)
log_prior_gamma = self.log_prior_gamma(gamma_t[0])
log_prior_log_gamma = self.log_prior_log_gamma(gamma_t[0])
log_det_diff2_log_gamma = sum((z_n_z_n_post/2+self.gamma_prior_beta)/gamma_t[0])
map_probs_t[0] = cond_probs_t[0]+log_prior_gamma
laplace_probs_t[0] = cond_probs_t[0]+log_prior_log_gamma+d_z*log(2*pi)/2-0.5*log_det_diff2_log_gamma
cond_probs += [cond_probs_t]
map_probs += [map_probs_t]
laplace_probs += [laplace_probs_t]
y_pred += [y_pred_t]
return cond_probs, map_probs, laplace_probs, y_pred
def update_learner(self, example):
# apply example to the inputs
self.layers[0][:] = example[0]
# forward propagation: compute activation values of all units
# hidden layers
for h in range(self.n_hidden_layers):
mllin.product_matrix_vector(self.Ws[h], self.layers[h], self.layer_acts[h + 1])
self.layer_acts[h + 1] += self.cs[h]
mlnonlin.sigmoid(self.layer_acts[h + 1], self.layers[h + 1])
# output layer
mllin.product_matrix_vector(self.U, self.layers[-1], self.output_act)
self.output_act += self.d
mlnonlin.softmax(self.output_act, self.output)
# back propagation: compute delta errors and updates to weights and
# biases
# TA:begin
if self.cost_function == 'CE':
self.doutput_act[:] = self.output
self.doutput_act[example[1]] -= 1
elif self.cost_function == 'SSE':
y = self.output.copy()
t = np.zeros(np.shape(y))
t[example[1]] = 1
# nr of classes
c = np.size(y)
T2 = (y-t)*y
T2 = np.array([T2])
T2 = T2.T
T2 = np.tile(T2,[1,c])
T3 = np.eye(c,c)
T3 = T3 - np.tile(y,[c,1])
# delta error at output layer
self.doutput_act = np.sum(T2*T3,axis=0)
elif self.cost_function == 'EXP':
y = self.output.copy()
t = np.zeros(np.shape(y))
t[example[1]] = 1
# nr of classes
c = np.size(y)
T1 = y-t
T1 = np.square(T1)
T1 = np.sum(T1)
T1 = T1/self.tau
T1 = np.exp(T1)
T1 = 2*T1
T2 = (y-t)*y
T2 = np.array([T2])
T2 = T2.T
T2 = np.tile(T2,[1,c])
T3 = np.eye(c,c)
T3 = T3 - np.tile(y,[c,1])
# delta error at output layer
self.doutput_act = T1 * np.sum(T2*T3,axis=0)
# TA:end
self.doutput_act *= self.learning_rate / (1. + self.decrease_constant * self.n_updates)
self.dd[:] = self.doutput_act
mllin.outer(self.doutput_act, self.layers[-1], self.dU)
mllin.product_matrix_vector(self.U.T, self.doutput_act, self.dlayers[-1])
"""
The description and argument names of dsigmoid() are unclear. In
practice, dsigmoid(s,dx,ds) computes s*(1-s)*dx element-wise and puts
the result in ds. [TA]
"""
mlnonlin.dsigmoid(self.layers[-1], self.dlayers[-1], self.dlayer_acts[-1])
for h in range(self.n_hidden_layers - 1, -1, -1):
self.dcs[h][:] = self.dlayer_acts[h + 1]
mllin.outer(self.dlayer_acts[h + 1], self.layers[h], self.dWs[h])
mllin.product_matrix_vector(self.Ws[h].T, self.dlayer_acts[h + 1], self.dlayers[h])
mlnonlin.dsigmoid(self.layers[h], self.dlayers[h], self.dlayer_acts[h])
#TA:
if not self.freeze_Ws_cs:
# update output weights and biases
self.U -= self.dU
self.d -= self.dd
# update all hidden weights and biases
for h in range(self.n_hidden_layers - 1, -1, -1):
self.Ws[h] -= self.dWs[h]
self.cs[h] -= self.dcs[h]
else:
# update output weights and biases
self.U -= self.dU
self.d -= self.dd
# # update only highest hidden layer
# h = self.n_hidden_layers - 1
# self.Ws[h] -= self.dWs[h]
# self.cs[h] -= self.dcs[h]
self.n_updates += 1