merge conflicts 2

1 files changed, 22 insertions, 25 deletions

diff --git a/simulation/vi_blocks.py b/simulation/vi_blocks.py
index 84e637f..1177979 100644
--- a/simulation/vi_blocks.py
+++ b/simulation/vi_blocks.py
@@ -1,17 +1,15 @@
-import main as mn
+import utils
+import utils_blocks as ub
 import theano
 from theano import tensor as tsr
-import blocks
-import blocks.algorithms, blocks.main_loop, blocks.extensions.monitoring
+from blocks import algorithms, main_loop
+import blocks.extensions as be
+import blocks.extensions.monitoring as bm
 import theano.tensor.shared_randomstreams
 import numpy as np
-from six.moves import range
-import fuel
-import fuel.datasets
-import active_blocks as ab
 
 
-class ClippedParams(blocks.algorithms.StepRule):
+class ClippedParams(algorithms.StepRule):
     """A rule to maintain parameters within a specified range"""
     def __init__(self, min_value, max_value):
         self.min_value = min_value
@@ -38,8 +36,8 @@ def create_vi_model(n_nodes, n_samp=100):
     sig0 = theano.shared(value=aux(.5, .1), name='sig0')
 
     srng = tsr.shared_randomstreams.RandomStreams(seed=123)
-    theta = srng.normal((n_samp, n_nodes, n_nodes)) * sig[None, :, :] + mu[None,
-            :, :]
+    theta = (srng.normal((n_samp, n_nodes, n_nodes)) * sig[None, :, :]
+             + mu[None, :, :])
     y = tsr.maximum(tsr.dot(x, theta), 1e-3)
     infect = tsr.log(1. - tsr.exp(-y[0:-1])).dimshuffle(1, 0, 2)
     lkl_pos = tsr.sum(infect * (x[1:] & s[1:])) / n_samp
@@ -56,28 +54,27 @@ if __name__ == "__main__":
     #n_cascades = 10000
     batch_size = 10
     n_samples = 50
-    graph = mn.create_random_graph(n_nodes=4)
+    graph = utils.create_random_graph(n_nodes=4)
     print('GRAPH:\n', graph, '\n-------------\n')
 
     x, s, mu, sig, cost = create_vi_model(len(graph), n_samples)
-    rmse, g_shared = ab.rmse_error(graph, mu)
+    rmse = ub.rmse_error(graph, mu)
 
-    step_rules= blocks.algorithms.CompositeRule([blocks.algorithms.AdaDelta(),
-        ClippedParams(1e-3, 1 - 1e-3)])
+    step_rules = algorithms.CompositeRule([algorithms.AdaDelta(),
+                                           ClippedParams(1e-3, 1 - 1e-3)])
 
-    alg = blocks.algorithms.GradientDescent(cost=cost, parameters=[mu, sig],
-        step_rule=step_rules)
-    #data_stream = ab.create_fixed_data_stream(n_cascades, graph, batch_size,
-    #        shuffle=False)
-    data_stream = ab.create_learned_data_stream(graph, batch_size)
-    loop = blocks.main_loop.MainLoop(
+    alg = algorithms.GradientDescent(cost=cost, parameters=[mu, sig],
+                                     step_rule=step_rules)
+    data_stream = ub.fixed_data_stream(n_cascades, graph, batch_size,
+                                       shuffle=False)
+    # data_stream = ub.dynamic_data_stream(graph, batch_size)
+    loop = main_loop.MainLoop(
         alg, data_stream,
         extensions=[
-            blocks.extensions.FinishAfter(after_n_batches = 10**4),
-            blocks.extensions.monitoring.TrainingDataMonitoring([cost, mu, sig,
-                rmse, g_shared], after_batch=True),
-            blocks.extensions.Printing(every_n_batches = 100,
-                after_epoch=False),
+            be.FinishAfter(after_n_batches=10**4),
+            bm.TrainingDataMonitoring([cost, mu, sig, rmse],
+                                      every_n_batches=10),
+            be.Printing(every_n_batches=100, after_epoch=False),
         ]
     )
     loop.run()