changes in if __name__ part of files

3 files changed, 18 insertions, 11 deletions

diff --git a/simulation/mle_blocks.py b/simulation/mle_blocks.py
index 0d27869..98bc257 100644
--- a/simulation/mle_blocks.py
+++ b/simulation/mle_blocks.py
@@ -54,8 +54,10 @@ if __name__ == "__main__":
             bm.TrainingDataMonitoring([cost, params,
                                        rmse, error], every_n_batches=freq),
             be.Printing(every_n_batches=freq),
-            ub.JSONDump("logs/active_outdegree_mle.json", every_n_batches=freq),
-            ub.ActiveLearning(data_stream.dataset, graph, every_n_batches=freq),
+            ub.JSONDump("logs/active_estoutdegree_mle.json",
+                every_n_batches=freq),
+            ub.ActiveLearning(data_stream.dataset, params,
+                every_n_batches=freq),
         ],
     )
     loop.run()
diff --git a/simulation/utils_blocks.py b/simulation/utils_blocks.py
index 72a6881..00b429e 100644
--- a/simulation/utils_blocks.py
+++ b/simulation/utils_blocks.py
@@ -37,7 +37,10 @@ class ActiveLearning(be.SimpleExtension):
         self.params = params
 
     def do(self, which_callback, *args):
-        exp_out_par = np.exp(np.sum(self.params, axis=1))
+        try:
+            exp_out_par = np.exp(np.sum(self.params.get_value(), axis=1))
+        except AttributeError:
+            exp_out_par = np.exp(np.sum(self.params, axis=1))
         self.dataset.node_p = exp_out_par / np.sum(exp_out_par)
 
 
diff --git a/simulation/vi_blocks.py b/simulation/vi_blocks.py
index 5deb6f6..50c7fb1 100644
--- a/simulation/vi_blocks.py
+++ b/simulation/vi_blocks.py
@@ -52,16 +52,17 @@ def create_vi_model(n_nodes, n_samp=100):
 
 if __name__ == "__main__":
     batch_size = 100
-    frequency = 10
+    freq = 10
+    graph = utils.create_wheel(1000)
     n_samples = 50
-    graph = utils.create_random_graph(n_nodes=10)
+    #graph = utils.create_random_graph(n_nodes=10)
     print('GRAPH:\n', graph, '\n-------------\n')
 
     x, s, mu, sig, cost = create_vi_model(len(graph), n_samples)
     rmse = ub.rmse_error(graph, mu)
 
     step_rules = algorithms.CompositeRule([algorithms.AdaDelta(),
-                                           ClippedParams(1e-3, 1 - 1e-3)])
+                                           ClippedParams(1e-3, 1000)])
 
     alg = algorithms.GradientDescent(cost=cost, parameters=[mu, sig],
                                      step_rule=step_rules)
@@ -70,11 +71,12 @@ if __name__ == "__main__":
         alg, data_stream,
         log_backend="sqlite",
         extensions=[
-            be.FinishAfter(after_n_batches=10**4),
-            bm.TrainingDataMonitoring([cost, rmse, mu], every_n_batches=frequency),
-            be.Printing(every_n_batches=frequency, after_epoch=False),
-            ub.JSONDump("logs/tmp.json", every_n_batches=10),
-            #ub.ActiveLearning(dataset=data_stream.dataset, params=graph)
+            be.FinishAfter(after_n_batches=10**3),
+            bm.TrainingDataMonitoring([cost, rmse, mu], every_n_batches=freq),
+            be.Printing(every_n_batches=freq, after_epoch=False),
+            ub.JSONDump("logs/nonactive_vi.json", every_n_batches=freq),
+            #ub.ActiveLearning(dataset=data_stream.dataset, params=graph,
+            #every_n_batches=freq)
         ]
     )
     loop.run()