variational inference sgd function

1 files changed, 8 insertions, 34 deletions

diff --git a/simulation/main.py b/simulation/main.py
index 402aa8d..4fa8f6c 100644
--- a/simulation/main.py
+++ b/simulation/main.py
@@ -19,7 +19,8 @@ def simulate_cascade(x, graph):
         - susc: the nodes susceptible at the beginning of this time step
         - x: the subset of susc who became infected
     """
-    susc = np.ones(graph.shape[0], dtype=bool)  # t=0, everyone is susceptible
+    susc = np.ones(graph.shape[0], dtype=bool)
+    susc = susc ^ x  # t=0, the source is not susceptible
     yield x, susc
     while np.any(x):
         susc = susc ^ x  # nodes infected at previous step are now inactive
@@ -66,41 +67,14 @@ def cascadeLkl(graph, infect, sus):
 
 
 if __name__ == "__main__":
-    # g = np.array([[0, 1, 1, 0], [1, 0, 0, 1], [1, 0, 0, 1], [0, 1, 1, 0]])
     g = np.array([[0, 0, 1], [0, 0, 0.5], [0, 0, 0]])
     p = 0.5
     g = np.log(1. / (1 - p * g))
-    # error = []
+    cascades = simulate_cascades(100, g)
+    cascade, y_obs = mn.build_matrix(cascades, 0)
+    conf = mn.bootstrap(x, y, n_iter=100)
 
-    def source(graph, t):
-        x0 = np.zeros(graph.shape[0], dtype=bool)
-        a = randint(0, 1)
-        x0[a] = True
-        if random() > t:
-            x0[1-a] = True
-        return x0
-
-    thresh = np.arange(0., 1.1, step=0.2)
-    sizes = np.arange(10, 100, step=10)
-    nsimul = 10
-    r = np.zeros(len(sizes), len(thresh))
-    for t in thresh:
-        for i in nsimul:
-            cascades = simulate_cascades(np.max(sizes), g,
-                                         source=lambda graph: source(graph, t))
-            e = np.zeros(g.shape[0])
-            for j, s in enumerate(sizes):
-                x, y = mn.build_matrix(cascades, 2)
-                e += mn.infer(x[:s], y[:s])
-
-    for i, t in enumerate(thresh):
-        plt.plot(sizes, e[:, i], label=str(t))
-    plt.legend()
+    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()
-
-
-        # conf = mn.bootstrap(x, y, 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()