Added root functions. More work on LW

main.py

@@ -3,12 +3,59 @@
 #Professor: Dr. Sen, Fall 2021
 #Noah Schrick - 1492657
 
-def main():
-    print("Hello")
-    print("Now Hello")
+import json
 
-def likelihood_weighting():
-    print("Hello")
+def main():
+    bayes_net = import_bayes()
+    print(len(bayes_net))
+    #print(is_root("3", bayes_net))
+
+#Import the BN from the json
+def import_bayes():
+    with open ("gen-bn/bn.json") as json_file:
+        bayes_json = json.load(json_file)
+    json_file.close()
+    return bayes_json
+
+
+
+#Checks if node has parents
+def is_root(node, BN):
+    return (BN[node]["parents"]) == []
+
+#Return a list of the root nodes
+def get_root(node, BN):
+    roots = []
+    for i in range(len(BN)):
+        if ((BN[node]["parents"]) == []):
+            roots.append(node)
+    return roots
+
+
+#print(bayes_json["x"]): prints the information about node x (an int)
+#print(bayes_json["x"]["parents"] prints the information about node x's parents
+#class BayesianNetwork:
+
+
+#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, samples):
+    #Vector of weighted counts for each value of X, initialized to zero
+    W = {}
+    #Init True and False probabilities
+    T = 0
+    F = 0
+
+    for i in range(samples):
+        x,w = weighted_sample(bayes_net, e)
+        print("Hello")
+
+#Returns an event and a weight
+def weighted_sample(bayes_net, e):
+    w = 1
+    #x = get_variable_nodes
+
+
+ #   for
 
 def gibbs_sampling():
     print("Hello")