Naive Bayes Classifier

data.csv

naive-bayes.py

import pandas as pd
dataFile = pd.read_csv('data.csv')
df = dataFile.copy()
dataArray = df.values

pos = 0
neg = 0
total = 10
for i in range(len(dataArray)):
    if dataArray[i][6] == 1.0:
        pos+=1
    else: 
        neg+=1
        
pPos = pos/total
pNeg = neg/total

prob = []
for i in range(1, 6):
    pos = 0
    neg = 0
    for j in range(len(dataArray)):
        if (dataArray[j][i] == dataArray[j][6]):
            pos += 1
        else:
            neg += 1
    pos = pos/total
    neg = neg/total
    prob.append({'pos': pos, 'neg': neg})

test_pos = 1
test_neg = 1

for i in range(len(prob)):
    test_pos = test_pos * prob[i]['pos']
    test_neg = test_neg * prob[i]['neg']
    
    
 def isInfected(case):
    boolMap = {0: 'pos', 1: 'neg'}
    evidence = 1

    for i in range(len(case)):
        _bool = boolMap[case[i]]
        evidence = evidence * prob[i][_bool]
        if (test_neg / evidence > test_pos / evidence):
            return 0
        else:
            return 1
         
print(isInfected(case = [1, 0, 0, 1, 1]))