correct = 0
N = 0
for fold in range(1,6):
X = data[:,0:4]
Y = data[:,4]
P = dm.get_partitioned_dataset(X,Y,fold)
X_train = P["train"]["X"]
Y_train = P["train"]["Y"]
X_val = P["val"]["X"]
Y_val = P["val"]["Y"]
print("=="*5,"Fold:",fold,"=="*5)
print("Training Started.")
tree = DecisionTree()
tree.NV_name_map = NV_name_map
tree.NA_map = NA_map
tree.create_tree(X_train,Y_train,NV_map)
print("Training Over.")
print("Validating..")
Y_pred = tree.predict(X_val)
print("Actual Y:",Y_val)
print("Predicted Y:",Y_pred)
acc, crct, n = dm.get_accuracy(Y_val,Y_pred)
print("Accuracy:",acc,"%")
correct+=crct
N+=n
trees.append(tree)
print("Overall accuracy =",(correct*100)/N,"%")
print("Press 1 and Enter to print last tree.")
