Added a random query generation

main.py

@@ -27,8 +27,11 @@ def main():
     #Generate random evidence
     E = gen_ev(bayes_net)
     
+    #Generate a random query that is not an evidence variable in the form of {var : val}
+    X = gen_query(E, bayes_net)
+
     #Get W from LW
-    W = likelihood_weighting(1, E, bayes_net, 10)
+    W = likelihood_weighting(X, E, bayes_net, 10)
     
     #Print if desired
     print()
@@ -41,6 +44,7 @@ def main():
 def gen_json(type, num_nodes):
     os.chdir("./gen_bn")
     os.system('python gen_bn.py' + ' ' + type + ' ' + str(num_nodes))
+    os.chdir("..")
  
 #Import the BN from the json
 def import_bayes():
@@ -53,11 +57,11 @@ def import_bayes():
 def gen_ev(bayes_net):
     total_nodes = len(bayes_net)
     #Arbitrarily, let's only generate total_nodes/2 (rounded up) evidence variables at most, but at least 1
-    num_ev = random.randint(1, int(math.ceil(total_nodes/2)))
+    num_ev = random.randint(0, int(math.ceil(total_nodes/2)))
     fixed_ev = []
     #Go through and generate nodes that will be fixed
     for i in range(num_ev):
-        fixed_var = random.randint(0, total_nodes-1)
+        fixed_var = random.randint(1, total_nodes-1)
         if fixed_var not in fixed_ev:
             fixed_ev.append(fixed_var)
     #Now generate random values for the ev
@@ -69,9 +73,23 @@ def gen_ev(bayes_net):
             E[str(i)] = True
         else:
             E[str(i)] = False
-    
     return E
 
+#Given the evidence variables and the bayes net, generate a random variable to query and its value
+def gen_query(ev, bayes_net):
+    possible_vars = list(range(len(bayes_net)))
+    ev_vars = [*ev]
+    for e in ev_vars:
+        if int(e) in possible_vars:
+            possible_vars.remove(int(e))
+    query = random.choice(possible_vars)
+    rand_prob = random.random()
+    if rand_prob >= 0.5:
+        val = True
+    else:
+        val = False
+    
+    return {query : val}
 
 #Checks if node has parents
 def is_root(node, BN):
@@ -99,8 +117,6 @@ def get_parents(node, BN):
 #Compute the estimate of P(X|e), where X is the query variable, and e is the observed value for variables E
 def likelihood_weighting(X, e, bayes_net, num_samples):
     W = {}
-    T = 0
-    F = 0
 
     for i in range(num_samples):
         w = 1