checked in some scripts I hadn't been tracking

mean_field_experiments/hypercube1.py

@@ -0,0 +1,79 @@
+n = 2
+eps = 1e-6
+tol = 1e-3 #stop when variational parameters change by less than this amount in 2 norm
+
+#True distribution:
+#   with probability 1-eps, exactly one bit is on. uniform over which is on
+#   with probability, eps, some other number of bits is on
+#Mean field distribution:
+#   All bits are independent
+#
+
+
+# q( h_i ) propto exp( E_[h_-i \sim q] log P(h) )
+# Suppose h_i is 0.
+# Then we have
+# sum_{j \neq i} q_j \Pi_{k \neq i,j} (1-q_k)
+# chance of log P(h) being log(1-eps)
+# and a one minus that chance of it being eps
+# Suppose h_i is 1
+# Then we have
+# \Pi_{j \neq i} (1-q_j)
+# chance of log P(h) being log(1-eps)
+
+
+import numpy as np
+rng = np.random.RandomState([1,2,5])
+
+#q = rng.uniform(0.,1.,(n,))
+q = np.zeros(n)
+q[0] = 1.
+
+print q
+
+while True:
+    prev_q = q.copy()
+
+    order = range(n)
+    rng.shuffle(order)
+
+    for var_to_update in order:
+        high_prob = 0.
+
+        for i in xrange(n):
+            if i == var_to_update:
+                continue
+
+            rest_off_prob = 1
+
+            for j in xrange(n):
+                if j in [var_to_update, i]:
+                    continue
+                rest_off_prob *= (1.-q[j])
+
+            high_prob += q[i] * rest_off_prob
+        #end for i
+
+        zero_mass = high_prob * np.log(1.-eps) + (1.-high_prob)*np.log(eps)
+
+
+        high_prob = 1.
+
+        for i in xrange(n):
+            if i == var_to_update:
+                continue
+
+            high_prob *= (1.-q[i])
+
+        one_mass = high_prob * np.log(1.-eps) + (1.-high_prob)*np.log(eps)
+
+        prob = one_mass / (zero_mass + one_mass)
+
+        q[var_to_update] = prob
+
+    print q
+
+    if np.sqrt(np.sum(np.square(prev_q-q))) < tol:
+        break
+
+