import csv
import random
import math
import operator
def loadDataset(filename,split,trainingSet=[],testSet=[]):
    with open(filename) as csvfile:
        lines = csv.reader(csvfile)
        dataset = list(lines)
        for x in range(len(dataset)-1):
            for y in range(4):
                dataset[x][y] = float(dataset[x][y])
            if random.random()<split:
                trainingSet.append(dataset[x])
            else:
                testSet.append(dataset[x])
                
def euclideanDistance(instance1, instance2, length):
    distance = 0
    for x in range(length):
        distance += pow((instance1[x]-instance2[x]),2)
    return math.sqrt(distance)

def getNeighbors(trainingSet, testInstance, k):
    distances=[]
    length = len(testInstance)-1
    for x in range(len(trainingSet)):
        dist = euclideanDistance(testInstance, trainingSet[x], length)
        distances.append((trainingSet[x],dist))
    distances.sort(key=operator.itemgetter(1))
    neighbors=[]
    for x in range(k):
        neighbors.append(distances[x][0])
    return neighbors

def getResponse(neighbors):
    classVotes = {}
    for x in range (len(neighbors)):
        response = neighbors[x][-1]
        if response in classVotes:
            classVotes[response]+=1
        else:
            classVotes[response]=1
    sortedVotes = sorted(classVotes.items(), key = operator.itemgetter(1),reverse=True)
    return sortedVotes[0][0]

def getAccuracy(testSet, predictions):
    correct = 0
    for x in range(len(testSet)):
        if testSet[x][-1]==predictions[x]:
            correct+=1
    return (correct/float(len(testSet)))*100.0
    
def main():
    trainingSet=[]
    testSet=[]
    split=0.67
    loadDataset('iris_data.csv',split,trainingSet,testSet)
    print('\nNumber of trainingdata :'+(repr(len(trainingSet))))
    print('Number of test data :'+(repr(len(testSet))))
    predictions=[]
    k=3
    print('\nThe predictions are :')
    for x in range (len(testSet)):
        neighbors=getNeighbors(trainingSet, testSet[x], k)
        result = getResponse(neighbors)
        predictions.append(result)
        print('Predicted ='+repr(result)+',actual ='+repr(testSet[x][-1]))
    accuracy = getAccuracy(testSet, predictions)
    print('\nThe Accuracy is :'+repr(accuracy)+'%')
main()