first semi working theano version

1 files changed, 16 insertions, 10 deletions

diff --git a/simulation/vi.py b/simulation/vi.py
index 9604c7d..aeccb69 100644
--- a/simulation/vi.py
+++ b/simulation/vi.py
@@ -2,7 +2,10 @@ import time
 import main as mn
 import autograd.numpy as np
 from autograd import grad
+import logging
 
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s',
+                    level=logging.INFO)
 
 def g(m):
     assert (m > 0).all()
@@ -47,29 +50,32 @@ def kl(params1, params0):
 grad_kl = grad(kl)
 
 
-def sgd(mu1, sig1, mu0, sig0, cascades, n_e=100, lr=lambda t: 1e-2):
+def sgd(mu1, sig1, mu0, sig0, cascades, n_e=100, lr=lambda t: 1e-2, n_print=10):
     g_mu1, g_sig1 = grad_kl((mu1, sig1), (mu0, sig0))
     for t in xrange(n_e):
         lrt = lr(t)  # learning rate
         mu1, sig1 = mu1 + lrt * g_mu1, sig1 + lrt * g_sig1
-        for x, s in zip(*cascades):
+        for step, (x, s) in enumerate(zip(*cascades)):
             g_mu1, g_sig1 = grad_ll_full((mu1, sig1), x, s)
             mu1 = np.maximum(mu1 + lrt * g_mu1, 0)
             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))
-        print("Epoch: {}\t LB: {}\t Time: {}".format(t, res, time.time()))
-        print mu1
-        print sig1
+            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 __name__ == '__main__':
-    graph = np.array([[0, 0, 1], [0, 0, 0.5], [0, 0, 0]])
+    #graph = np.array([[0, 0, 1], [0, 0, 0.5], [0, 0, 0]])
+    graph = np.random.binomial(2, p=.2, size=(10, 10))
     p = 0.5
     graph = np.log(1. / (1 - p * graph))
-    cascades = mn.build_cascade_list(mn.simulate_cascades(1000, graph))
+    print(graph[0:2, 0:2])
+    cascades = mn.build_cascade_list(mn.simulate_cascades(500, 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),
                  1 + .2 * np.random.normal(size=graph.shape))
-    sgd(mu1, sig1, mu0, sig0, cascades, n_e=30)
+    sgd(mu1, sig1, mu0, sig0, cascades, n_e=30, n_print=1)