adding likelihood to theano implementation

1 files changed, 9 insertions, 9 deletions

diff --git a/simulation/vi.py b/simulation/vi.py
index aeccb69..1e45761 100644
--- a/simulation/vi.py
+++ b/simulation/vi.py
@@ -50,7 +50,7 @@ def kl(params1, params0):
 grad_kl = grad(kl)
 
 
-def sgd(mu1, sig1, mu0, sig0, cascades, n_e=100, lr=lambda t: 1e-2, n_print=10):
+def sgd(mu1, sig1, mu0, sig0, cascades, n_e=100, lr=lambda t: 1e-1, n_print=10):
     g_mu1, g_sig1 = grad_kl((mu1, sig1), (mu0, sig0))
     for t in xrange(n_e):
         lrt = lr(t)  # learning rate
@@ -61,19 +61,19 @@ def sgd(mu1, sig1, mu0, sig0, cascades, n_e=100, lr=lambda t: 1e-2, n_print=10):
             sig1 = np.maximum(sig1 + lrt * g_sig1, 1e-3)
             res = np.sum(ll_full((mu1, sig1), x, s) for x, s in zip(*cascades))\
                     + kl((mu1, sig1), (mu0, sig0))
-            if step % n_print == 0:
-                logging.info("Epoch:{}\tStep:{}\tLB:{}\t".format(t, step, res))
-                print mu1[0:2, 0:2]
-                print sig1[0:2, 0:2]
+            #if step % n_print == 0:
+        logging.info("Epoch:{}\tStep:{}\tLB:{}\t".format(t, step, res))
+        print mu1
+        print sig1
 
 
 if __name__ == '__main__':
-    #graph = np.array([[0, 0, 1], [0, 0, 0.5], [0, 0, 0]])
-    graph = np.random.binomial(2, p=.2, size=(10, 10))
+    graph = np.array([[0, 0, 1], [0, 0, 0.5], [0, 0, 0]])
+    #graph = np.random.binomial(2, p=.2, size=(4, 4))
     p = 0.5
     graph = np.log(1. / (1 - p * graph))
-    print(graph[0:2, 0:2])
-    cascades = mn.build_cascade_list(mn.simulate_cascades(500, graph))
+    print(graph)
+    cascades = mn.build_cascade_list(mn.simulate_cascades(100, graph))
     mu0, sig0 = (1. + .2 * np.random.normal(size=graph.shape),
                  1 + .2 * np.random.normal(size=graph.shape))
     mu1, sig1 = (1. + .2 * np.random.normal(size=graph.shape),