Program

import csv
import random
import math
import operator
def loadDataset(filename, split, trainingSet=[] , testSet=[]):
with open(filename, 'rb') 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.iteritems(),
reverse=True)
return sortedVotes[0][0]
def getAccuracy(testSet,
predictions): correct = 0
for x in
range(len(testSet)):
key=operator.itemgetter(1
),
if testSet[x][-1] == predictions[x]:
correct += 1
return (correct/float(len(testSet))) * 100.0
def main():
# prepare
data
trainingSet=
[] testSet=[]
split = 0.67
loadDataset('knndat.data', split, trainingSet,
testSet) print('Train set: ' + repr(len(trainingSet)))
print('Test set: ' + repr(len(testSet)))
# generate
predictions
predictions=[]
k=3
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('Accuracy: ' + repr(accuracy) +
'%') main()

OUTPUT
Confusion matrix is as follows
[[11 0 0]
[0 9 1]
[0 1 8]]
Accuracy metrics
0 1.00 1.00 1.00 11
1 0.90 0.90 0.90 10
2 0.89 0.89 0,89 9
Avg/Total 0.93 0.93 0.93 30