Fix the mess and use sqlite backend

1 files changed, 0 insertions, 31 deletions

diff --git a/simulation/utils_blocks.py b/simulation/utils_blocks.py
index 0d30786..3b29972 100644
--- a/simulation/utils_blocks.py
+++ b/simulation/utils_blocks.py
@@ -121,34 +121,3 @@ def dynamic_data_stream(graph, batch_size):
     data_set = LearnedDataset(node_p, graph)
     scheme = fuel.schemes.ConstantScheme(batch_size)
     return fuel.streams.DataStream(dataset=data_set, iteration_scheme=scheme)
-
-
-if __name__ == "__main__":
-    batch_size = 100
-    n_obs = 1000
-    frequency = 1
-    graph = utils.create_wheel(1000)
-    print('GRAPH:\n', graph, '\n-------------\n')
-
-    g_shared = theano.shared(value=graph, name='graph')
-    x, s, params, cost = create_mle_model(graph)
-    rmse = rmse_error(g_shared, params)
-    error = relative_error(g_shared, params)
-
-    alg = algorithms.GradientDescent(
-        cost=-cost, parameters=[params], step_rule=blocks.algorithms.AdaDelta()
-    )
-    data_stream = create_learned_data_stream(graph, batch_size)
-    #data_stream = create_fixed_data_stream(n_obs, graph, batch_size)
-    loop = main_loop.MainLoop(
-        alg, data_stream,
-        extensions=[
-            be.FinishAfter(after_n_batches=10**4),
-            bm.TrainingDataMonitoring([cost, rmse, error],
-                                       every_n_batches=frequency),
-            be.Printing(every_n_batches=frequency),
-            JSONDump("tmpactive_log.json", every_n_batches=frequency),
-            ActiveLearning(data_stream.dataset, every_n_batches=frequency)
-        ],
-    )
-    loop.run()