knn algorithm

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knn
In [44]:
import csv
with open(r'C:\Users\huzaifa\Downloads\iris\iris.csv') as csvfile:
    lines = csv.reader(csvfile)
    for row in lines:
        print (', '.join(row))
    

Id, SepalLengthCm, SepalWidthCm, PetalLengthCm, PetalWidthCm, Species
1, 5.1, 3.5, 1.4, 0.2, Iris-setosa
2, 4.9, 3.0, 1.4, 0.2, Iris-setosa
3, 4.7, 3.2, 1.3, 0.2, Iris-setosa
4, 4.6, 3.1, 1.5, 0.2, Iris-setosa
5, 5.0, 3.6, 1.4, 0.2, Iris-setosa
6, 5.4, 3.9, 1.7, 0.4, Iris-setosa
7, 4.6, 3.4, 1.4, 0.3, Iris-setosa
8, 5.0, 3.4, 1.5, 0.2, Iris-setosa
9, 4.4, 2.9, 1.4, 0.2, Iris-setosa
10, 4.9, 3.1, 1.5, 0.1, Iris-setosa
11, 5.4, 3.7, 1.5, 0.2, Iris-setosa
12, 4.8, 3.4, 1.6, 0.2, Iris-setosa
13, 4.8, 3.0, 1.4, 0.1, Iris-setosa
14, 4.3, 3.0, 1.1, 0.1, Iris-setosa
15, 5.8, 4.0, 1.2, 0.2, Iris-setosa
16, 5.7, 4.4, 1.5, 0.4, Iris-setosa
17, 5.4, 3.9, 1.3, 0.4, Iris-setosa
18, 5.1, 3.5, 1.4, 0.3, Iris-setosa
19, 5.7, 3.8, 1.7, 0.3, Iris-setosa
20, 5.1, 3.8, 1.5, 0.3, Iris-setosa
21, 5.4, 3.4, 1.7, 0.2, Iris-setosa
22, 5.1, 3.7, 1.5, 0.4, Iris-setosa
23, 4.6, 3.6, 1.0, 0.2, Iris-setosa
24, 5.1, 3.3, 1.7, 0.5, Iris-setosa
25, 4.8, 3.4, 1.9, 0.2, Iris-setosa
26, 5.0, 3.0, 1.6, 0.2, Iris-setosa
27, 5.0, 3.4, 1.6, 0.4, Iris-setosa
28, 5.2, 3.5, 1.5, 0.2, Iris-setosa
29, 5.2, 3.4, 1.4, 0.2, Iris-setosa
30, 4.7, 3.2, 1.6, 0.2, Iris-setosa
31, 4.8, 3.1, 1.6, 0.2, Iris-setosa
32, 5.4, 3.4, 1.5, 0.4, Iris-setosa
33, 5.2, 4.1, 1.5, 0.1, Iris-setosa
34, 5.5, 4.2, 1.4, 0.2, Iris-setosa
35, 4.9, 3.1, 1.5, 0.1, Iris-setosa
36, 5.0, 3.2, 1.2, 0.2, Iris-setosa
37, 5.5, 3.5, 1.3, 0.2, Iris-setosa
38, 4.9, 3.1, 1.5, 0.1, Iris-setosa
39, 4.4, 3.0, 1.3, 0.2, Iris-setosa
40, 5.1, 3.4, 1.5, 0.2, Iris-setosa
41, 5.0, 3.5, 1.3, 0.3, Iris-setosa
42, 4.5, 2.3, 1.3, 0.3, Iris-setosa
43, 4.4, 3.2, 1.3, 0.2, Iris-setosa
44, 5.0, 3.5, 1.6, 0.6, Iris-setosa
45, 5.1, 3.8, 1.9, 0.4, Iris-setosa
46, 4.8, 3.0, 1.4, 0.3, Iris-setosa
47, 5.1, 3.8, 1.6, 0.2, Iris-setosa
48, 4.6, 3.2, 1.4, 0.2, Iris-setosa
49, 5.3, 3.7, 1.5, 0.2, Iris-setosa
50, 5.0, 3.3, 1.4, 0.2, Iris-setosa
51, 7.0, 3.2, 4.7, 1.4, Iris-versicolor
52, 6.4, 3.2, 4.5, 1.5, Iris-versicolor
53, 6.9, 3.1, 4.9, 1.5, Iris-versicolor
54, 5.5, 2.3, 4.0, 1.3, Iris-versicolor
55, 6.5, 2.8, 4.6, 1.5, Iris-versicolor
56, 5.7, 2.8, 4.5, 1.3, Iris-versicolor
57, 6.3, 3.3, 4.7, 1.6, Iris-versicolor
58, 4.9, 2.4, 3.3, 1.0, Iris-versicolor
59, 6.6, 2.9, 4.6, 1.3, Iris-versicolor
60, 5.2, 2.7, 3.9, 1.4, Iris-versicolor
61, 5.0, 2.0, 3.5, 1.0, Iris-versicolor
62, 5.9, 3.0, 4.2, 1.5, Iris-versicolor
63, 6.0, 2.2, 4.0, 1.0, Iris-versicolor
64, 6.1, 2.9, 4.7, 1.4, Iris-versicolor
65, 5.6, 2.9, 3.6, 1.3, Iris-versicolor
66, 6.7, 3.1, 4.4, 1.4, Iris-versicolor
67, 5.6, 3.0, 4.5, 1.5, Iris-versicolor
68, 5.8, 2.7, 4.1, 1.0, Iris-versicolor
69, 6.2, 2.2, 4.5, 1.5, Iris-versicolor
70, 5.6, 2.5, 3.9, 1.1, Iris-versicolor
71, 5.9, 3.2, 4.8, 1.8, Iris-versicolor
72, 6.1, 2.8, 4.0, 1.3, Iris-versicolor
73, 6.3, 2.5, 4.9, 1.5, Iris-versicolor
74, 6.1, 2.8, 4.7, 1.2, Iris-versicolor
75, 6.4, 2.9, 4.3, 1.3, Iris-versicolor
76, 6.6, 3.0, 4.4, 1.4, Iris-versicolor
77, 6.8, 2.8, 4.8, 1.4, Iris-versicolor
78, 6.7, 3.0, 5.0, 1.7, Iris-versicolor
79, 6.0, 2.9, 4.5, 1.5, Iris-versicolor
80, 5.7, 2.6, 3.5, 1.0, Iris-versicolor
81, 5.5, 2.4, 3.8, 1.1, Iris-versicolor
82, 5.5, 2.4, 3.7, 1.0, Iris-versicolor
83, 5.8, 2.7, 3.9, 1.2, Iris-versicolor
84, 6.0, 2.7, 5.1, 1.6, Iris-versicolor
85, 5.4, 3.0, 4.5, 1.5, Iris-versicolor
86, 6.0, 3.4, 4.5, 1.6, Iris-versicolor
87, 6.7, 3.1, 4.7, 1.5, Iris-versicolor
88, 6.3, 2.3, 4.4, 1.3, Iris-versicolor
89, 5.6, 3.0, 4.1, 1.3, Iris-versicolor
90, 5.5, 2.5, 4.0, 1.3, Iris-versicolor
91, 5.5, 2.6, 4.4, 1.2, Iris-versicolor
92, 6.1, 3.0, 4.6, 1.4, Iris-versicolor
93, 5.8, 2.6, 4.0, 1.2, Iris-versicolor
94, 5.0, 2.3, 3.3, 1.0, Iris-versicolor
95, 5.6, 2.7, 4.2, 1.3, Iris-versicolor
96, 5.7, 3.0, 4.2, 1.2, Iris-versicolor
97, 5.7, 2.9, 4.2, 1.3, Iris-versicolor
98, 6.2, 2.9, 4.3, 1.3, Iris-versicolor
99, 5.1, 2.5, 3.0, 1.1, Iris-versicolor
100, 5.7, 2.8, 4.1, 1.3, Iris-versicolor
101, 6.3, 3.3, 6.0, 2.5, Iris-virginica
102, 5.8, 2.7, 5.1, 1.9, Iris-virginica
103, 7.1, 3.0, 5.9, 2.1, Iris-virginica
104, 6.3, 2.9, 5.6, 1.8, Iris-virginica
105, 6.5, 3.0, 5.8, 2.2, Iris-virginica
106, 7.6, 3.0, 6.6, 2.1, Iris-virginica
107, 4.9, 2.5, 4.5, 1.7, Iris-virginica
108, 7.3, 2.9, 6.3, 1.8, Iris-virginica
109, 6.7, 2.5, 5.8, 1.8, Iris-virginica
110, 7.2, 3.6, 6.1, 2.5, Iris-virginica
111, 6.5, 3.2, 5.1, 2.0, Iris-virginica
112, 6.4, 2.7, 5.3, 1.9, Iris-virginica
113, 6.8, 3.0, 5.5, 2.1, Iris-virginica
114, 5.7, 2.5, 5.0, 2.0, Iris-virginica
115, 5.8, 2.8, 5.1, 2.4, Iris-virginica
116, 6.4, 3.2, 5.3, 2.3, Iris-virginica
117, 6.5, 3.0, 5.5, 1.8, Iris-virginica
118, 7.7, 3.8, 6.7, 2.2, Iris-virginica
119, 7.7, 2.6, 6.9, 2.3, Iris-virginica
120, 6.0, 2.2, 5.0, 1.5, Iris-virginica
121, 6.9, 3.2, 5.7, 2.3, Iris-virginica
122, 5.6, 2.8, 4.9, 2.0, Iris-virginica
123, 7.7, 2.8, 6.7, 2.0, Iris-virginica
124, 6.3, 2.7, 4.9, 1.8, Iris-virginica
125, 6.7, 3.3, 5.7, 2.1, Iris-virginica
126, 7.2, 3.2, 6.0, 1.8, Iris-virginica
127, 6.2, 2.8, 4.8, 1.8, Iris-virginica
128, 6.1, 3.0, 4.9, 1.8, Iris-virginica
129, 6.4, 2.8, 5.6, 2.1, Iris-virginica
130, 7.2, 3.0, 5.8, 1.6, Iris-virginica
131, 7.4, 2.8, 6.1, 1.9, Iris-virginica
132, 7.9, 3.8, 6.4, 2.0, Iris-virginica
133, 6.4, 2.8, 5.6, 2.2, Iris-virginica
134, 6.3, 2.8, 5.1, 1.5, Iris-virginica
135, 6.1, 2.6, 5.6, 1.4, Iris-virginica
136, 7.7, 3.0, 6.1, 2.3, Iris-virginica
137, 6.3, 3.4, 5.6, 2.4, Iris-virginica
138, 6.4, 3.1, 5.5, 1.8, Iris-virginica
139, 6.0, 3.0, 4.8, 1.8, Iris-virginica
140, 6.9, 3.1, 5.4, 2.1, Iris-virginica
141, 6.7, 3.1, 5.6, 2.4, Iris-virginica
142, 6.9, 3.1, 5.1, 2.3, Iris-virginica
143, 5.8, 2.7, 5.1, 1.9, Iris-virginica
144, 6.8, 3.2, 5.9, 2.3, Iris-virginica
145, 6.7, 3.3, 5.7, 2.5, Iris-virginica
146, 6.7, 3.0, 5.2, 2.3, Iris-virginica
147, 6.3, 2.5, 5.0, 1.9, Iris-virginica
148, 6.5, 3.0, 5.2, 2.0, Iris-virginica
149, 6.2, 3.4, 5.4, 2.3, Iris-virginica
150, 5.9, 3.0, 5.1, 1.8, Iris-virginica
In [63]:
import csv
import random 
def loadDataset(filename,split,trainingSet=[], testSet=[]):
    with open(filename,'r') 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])
In [64]:
trainingSet=[]
testSet=[]
handleDataset(r'iris.', 0.66, trainingSet, testSet)
print ('Train: ' + repr(len(trainingSet)))
print ('Test: ' + repr(len(testSet))

  File "<ipython-input-64-4a726438412d>", line 5
    print ('Test: ' + repr(len(testSet))
                                        ^
SyntaxError: unexpected EOF while parsing
In [65]:
import math
def euclideanDistance(instance1, instance2, length):
    distance = 0
    for x in range(length):
        distance += pow((instance1[x] - instance2[x]), 2)
    return math.sqrt(distance)
In [66]:
data1 = [2, 2, 2, 'a']
data2 = [4, 4, 4, 'b']
distance = euclideanDistance(data1, data2, 3)
print ('Distance: ' + repr(distance))

Distance: 3.4641016151377544
In [67]:
import operator 
def getKNeighbors(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
In [68]:
trainSet = [[2, 2, 2, 'a'], [4, 4, 4, 'b']]
testInstance = [5, 5, 5]
k = 1
neighbors = getKNeighbors(trainSet, testInstance, 1)
print(neighbors)

[[4, 4, 4, 'b']]
In [69]:
import operator
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]
In [70]:
neighbors = [[1,1,1,'a'], [2,2,2,'a'], [3,3,3,'b']]
print(getResponse(neighbors))

a
In [71]:
def getAccuracy(testSet, predictions):
    correct = 0
    for x in range(len(testSet)):
        if testSet[x][-1] is predictions[x]:
            correct += 1
    return (correct/float(len(testSet))) * 100.0
In [72]:
testSet = [[1,1,1,'a'], [2,2,2,'a'], [3,3,3,'b']]
predictions = ['a', 'a', 'a']
accuracy = getAccuracy(testSet, predictions)
print(accuracy)

66.66666666666666
In [73]:
testSet = [[1,1,1,'a'], [2,2,2,'a'], [3,3,3,'b']]
predictions = ['a', 'a', 'a']
accuracy = getAccuracy(testSet, predictions)
print(accuracy)

66.66666666666666
In [ ]:

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