!pip install jovian --upgrade --quiet

import csv
a = []
print("\n The Given Training Data Set \n")

with open('ws.csv', 'r') as csvFile:
    reader = csv.reader(csvFile)
    for row in reader:
        a.append (row)
        print(row)
num_attributes = len(a[0])-1 # we don't want last col which is target concet ( yes/no)

print("\n The initial value of hypothesis: ")
# S = ['0'] * num_attributes
G = ['?'] * num_attributes
print ("\n The most specific hypothesis S0 : [0,0,0,0,0,0]\n")
print (" \n The most general hypothesis G0 : [?,?,?,?,?,?]\n")

S = a[0];

# Comparing with Remaining Training Examples of Given Data Set

print("\n Candidate Elimination algorithm  Hypotheses Version Space Computation\n")
temp=[]

for i in range(0,len(a)):
    if a[i][-1]=='Yes':
        for j in range(0,num_attributes):
            if a[i][j]!=S[j]:
                S[j]='?'

        for j in range(0,num_attributes):
            for k  in range(0,len(temp)):
                if temp[k][j] != '?' and temp[k][j] != S[j]:
                    del temp[k] #remove it if it's not matching with the specific hypothesis

        print(" For Training Example No :{0} the hypothesis is S{0}  ".format(i+1),S)

        if (len(temp)==0):
            print(" For Training Example No :{0} the hypothesis is G{0} ".format(i+1),G)
        else:
            print(" For Training Example No :{0} the hypothesis is G{0}".format(i+1),temp)

    if a[i][num_attributes]=='No': 
        for j in range(0,num_attributes):
             if S[j] != a[i][j] and S[j]!= '?':  #if not  matching with the specific Hypothesis take it seperately and store it 
                 G[j]=S[j]
                 temp.append(G) # this is the version space to store all Hypotheses
                 G = ['?'] * num_attributes

        print(" For Training Example No :{0} the hypothesis is S{0} ".format(i+1),S)
        print(" For Training Example No :{0} the hypothesis is G{0}".format(i+1),temp)

jovian.commit(project='ml-lab-program-2')