Naïve Bayesian Classifier.py

import csv 
import random 
import math 
 
def loadcsv(filename): 
    lines = csv.reader(open(filename, "r")) 
    dataset = list(lines) 
    for i in range(len(dataset)): 
        dataset[i] = [float(x) for x in dataset[i]]  
    return dataset 
 
def splitDataset(dataset, splitRatio): 
    trainSize = int(len(dataset) * splitRatio) 
    trainSet = [] 
    trainSet,testSet = dataset[:trainSize],dataset[trainSize:] 
    return [trainSet, testSet] 
 
def mean(numbers): 
    return sum(numbers)/(len(numbers)) 
 
def stdev(numbers): 
    avg = mean(numbers) 
    v = 0 
    for x in numbers: 
        v += (x-avg)**2 
    return math.sqrt(v/(len(numbers)-1)) 
 
def summarizeByClass(dataset): 
    separated = {} 
    for i in range(len(dataset)): 
        vector = dataset[i] 
        if (vector[-1] not in separated): 
            separated[vector[-1]] = [] 
        separated[vector[-1]].append(vector) 
    summaries = {} 
    for classValue, instances in separated.items(): 
        summaries[classValue] = [(mean(attribute), stdev(attribute)) for attribute in zip(*instances)][:-1] 
    return summaries 
 
def calculateProbability(x, mean, stdev): 
    exponent = math.exp(
        (-(x-mean)**2)/(2*(stdev**2))
    ) 
    return (1 / ((2*math.pi)**(1/2)*stdev)) * exponent 
 
def predict(summaries, inputVector): 
    probabilities = {} 
    for classValue, classSummaries in summaries.items(): 
        probabilities[classValue] = 1 
        for i in range(len(classSummaries)): 
            mean, stdev = classSummaries[i] 
            x = inputVector[i] 
            probabilities[classValue] *= calculateProbability(x, mean, stdev) 
    bestLabel, bestProb = None, -1 
    for classValue, probability in probabilities.items(): 
        if bestLabel is None or probability > bestProb: 
            bestProb = probability 
            bestLabel = classValue 
    return bestLabel 
 
def getPredictions(summaries, testSet): 
    predictions = [] 
    for i in range(len(testSet)): 
        result = predict(summaries, testSet[i]) 
        predictions.append(result) 
    return predictions 
 
def getAccuracy(testSet, predictions): 
    correct = 0 
    for i in range(len(testSet)): 
        if testSet[i][-1] == predictions[i]: 
            correct += 1 
    return (correct/(len(testSet))) * 100.0 
 
filename = 'pima-indians-diabetes.csv' 
splitRatio = 0.67 
dataset = loadcsv(filename) 
trainingSet, testSet = splitDataset(dataset, splitRatio)     
summaries = summarizeByClass(trainingSet)     
predictions = getPredictions(summaries, testSet) 
print("\nPredictions:\n",predictions)     
accuracy = getAccuracy(testSet, predictions) 
print('Accuracy ',accuracy)