switch to python 3 + blocks version of MLE implemented

1 files changed, 17 insertions, 9 deletions

diff --git a/simulation/vi_theano.py b/simulation/vi_theano.py
index 9e8fdb0..cfd1dcf 100644
--- a/simulation/vi_theano.py
+++ b/simulation/vi_theano.py
@@ -5,11 +5,11 @@ import theano.tensor.shared_randomstreams
 import numpy as np
 
 n_cascades = 1000
-n_nodes = 4
+n_nodes = 3
 n_samples = 100
 srng = tsr.shared_randomstreams.RandomStreams(seed=123)
-lr = 5*1e-3
-n_epochs = 20
+lr = 1e-1
+n_epochs = 1
 
 ###############Variational Inference####################
 
@@ -70,28 +70,36 @@ if __name__ == "__main__":
         graph[k, k] = 0
     p = 0.5
     graph = np.log(1. / (1 - p * graph))
+
+    graph = np.array([[0, 0, 1], [0, 0, 0.5], [0, 0, 0]])
+    p = 0.5
+    graph = np.log(1. / (1 - p * graph))
+
     cascades = mn.build_cascade_list(mn.simulate_cascades(n_cascades, graph),
                                      collapse=True)
     x_obs, s_obs = cascades[0], cascades[1]
 
     #mle
     lkl_plot = []
-    if 0:
+    if 1:
         for i in range(n_epochs):
-            for xt, st in zip(x_obs, s_obs):
+            for k in xrange(len(x_obs)/100):
+                xt = x_obs[k*100:(k+1)*100]
+                st = s_obs[k*100:(k+1)*100]
                 lkl = train_mle(xt, st)
                 lkl_plot.append(lkl)
+                print(params.get_value())
         print(graph)
         w = params.get_value()
         for k in range(len(w)):
             w[k, k] = 0
         print(w)
-        import matplotlib.pyplot as plt
-        plt.plot(lkl_plot)
-        plt.show()
+        #import matplotlib.pyplot as plt
+        #plt.plot(lkl_plot)
+        #plt.show()
 
     #variational inference
-    if 1:
+    if 0:
         for i in range(n_epochs):
             train_kl()
             for k in xrange(len(x_obs)/100):