adding likelihood to theano implementation

1 files changed, 15 insertions, 12 deletions

diff --git a/simulation/main.py b/simulation/main.py
index c2446d7..decdf8f 100644
--- a/simulation/main.py
+++ b/simulation/main.py
@@ -19,9 +19,9 @@ def simulate_cascade(x, graph):
         - susc: the nodes susceptible at the beginning of this time step
         - x: the subset of susc who became infected
     """
+    yield x, np.zeros(graph.shape[0], dtype=bool)
     susc = np.ones(graph.shape[0], dtype=bool)
-    susc = susc ^ x  # t=0, the source is not susceptible
-    yield x, susc
+    #yield x, susc
     while np.any(x):
         susc = susc ^ x  # nodes infected at previous step are now inactive
         if not np.any(susc):
@@ -60,13 +60,16 @@ if __name__ == "__main__":
     g = np.array([[0, 0, 1], [0, 0, 0.5], [0, 0, 0]])
     p = 0.5
     g = np.log(1. / (1 - p * g))
-    error = []
-    sizes = [10, 10**2, 10**3]
-    for s in sizes:
-        cascades = simulate_cascades(s, g)
-        cascade, y_obs = mn.build_matrix(cascades, 0)
-        conf = mn.bootstrap(cascade, y_obs, n_iter=100)
-        estimand = np.linalg.norm(np.delete(conf - g[0], 0, axis=1), axis=1)
-        error.append(mn.confidence_interval(*np.histogram(estimand, bins=50)))
-        plt.semilogx(sizes, error)
-        plt.show()
+    print(g)
+    sizes = [10**3]
+    for si in sizes:
+        cascades = simulate_cascades(si, g)
+        cascade, y_obs = mn.build_matrix(cascades, 2)
+        print(mn.infer(cascade, y_obs))
+        #conf = mn.bootstrap(cascade, y_obs, n_iter=100)
+        #estimand = np.linalg.norm(np.delete(conf - g[0], 0, axis=1), axis=1)
+        #plt.hist(estimand, bins=40)
+        #plt.show()
+        #error.append(mn.confidence_interval(*np.histogram(estimand, bins=50)))
+    #plt.plot(sizes, error)
+    #plt.show()