18CSL76 implement the naïve Bayesian classifier.

 Write a program to implement the naïve Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier, considering a few test data sets.

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 = [] copy = list(dataset); while len(trainSet) < trainSize: index = random.randrange(len(copy)); trainSet.append(copy.pop(index)) return [trainSet, copy] def separateByClass(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) return separated def mean(numbers): return sum(numbers)/float(len(numbers)) def stdev(numbers): avg = mean(numbers) variance = sum([pow(x-avg,2) for x in numbers])/float(len(numbers)-1) return math.sqrt(variance) def summarize(dataset): summaries = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)]; del summaries[-1] return summaries def summarizeByClass(dataset): separated=separateByClass(dataset) summaries={} for classValue, instances in separated.items(): summaries[classValue]=summarize(instances) return summaries def calculateProbability(x,mean,stdev): exponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2)))) return (1 / (math.sqrt(2*math.pi) * stdev)) * exponent def calculateClassProbabilities(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); return probabilities def predict(summaries, inputVector): probabilities = calculateClassProbabilities(summaries, inputVector) 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/float(len(testSet))) * 100.0 def main(): filename="Bayesian_classifier.csv" splitRatio=0.67 dataset=loadCsv(filename) trainingSet,testSet=splitDataset(dataset,splitRatio) print('Split{0} rows into train{1} and test={2}rows'.format(len(dataset),len(trainingSet),len(testSet))) summaries = summarizeByClass(trainingSet); predictions=getPredictions(summaries,testSet) accuracy=getAccuracy(testSet,predictions) print('accuracy of the classifier is:{0}%'.format(accuracy)) main()