import monkdata as m

import math

import dtree

# Assignment 1 #

################

#Setting up lists

monk_entropy = []

data_sets = [m.monk1, m.monk2, m.monk3]

#calculating entropy of the monk sets

for set in data_sets:

monk_entropy.append(dtree.entropy(set))

#print(monk_entropy)

# Assignment 2 #

################

#Setting up lists

info_gain_m1 = []

info_gain_m2 = []

info_gain_m3 = []

attribute = []

#starting counter

i = 0;

#iterating over all the test sets

for sets in [info_gain_m1, info_gain_m2, info_gain_m3]:

#for all attributes in the sets, the average information gain is added to the list

for k in range(6):

attribute.append(dtree.averageGain(data_sets[i], m.attributes[k]));

sets.append(attribute)

attribute = []

i += 1;

#print(info_gain_m1)

#print(info_gain_m2)

#print(info_gain_m3)

# Assignment 3 #

################

selected = dtree.select(m.monk1, m.attributes[4], 1)

t=dtree.buildTree(m.monk1, m.attributes);

print(dtree.check(t, m.monk1test))

print(t)