الگوریتم KNN در پایتون

# Importing libraries
import pandas as pd
import numpy as np
import math
import operator
# Importing data
data = pd.read_csv("iris.csv")
#### End of STEP 1
data.head()

# Defining a function which calculates euclidean distance between two data points
def euclideanDistance(data1, data2, length):
distance = 0
for x in range(length):
distance += np.square(data1[x] - data2[x])
return np.sqrt(distance)
# Defining our KNN model
def knn(trainingSet, testInstance, k):
distances = {}
sort = {}
length = testInstance.shape[1]
#### Start of STEP 3
# Calculating euclidean distance between each row of training data and test data
for x in range(len(trainingSet)):
#### Start of STEP 3.1
dist = euclideanDistance(testInstance, trainingSet.iloc[x], length)
distances[x] = dist[0]
#### End of STEP 3.1
#### Start of STEP 3.2
# Sorting them on the basis of distance
sorted_d = sorted(distances.items(), key=operator.itemgetter(1))
#### End of STEP 3.2
neighbors = []
#### Start of STEP 3.3
# Extracting top k neighbors
for x in range(k):
neighbors.append(sorted_d[x][0])
#### End of STEP 3.3
classVotes = {}
#### Start of STEP 3.4
# Calculating the most freq class in the neighbors
for x in range(len(neighbors)):
response = trainingSet.iloc[neighbors[x]][-1]
if response in classVotes:
classVotes[response] += 1
else:
classVotes[response] = 1
#### End of STEP 3.4
#### Start of STEP 3.5
sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)
return(sortedVotes[0][0], neighbors)
#### End of STEP 3.5
# Creating a dummy testset
testSet = [[7.2, 3.6, 5.1, 2.5]]
test = pd.DataFrame(testSet)
#### Start of STEP 2
# Setting number of neighbors = 1
k = 1
#### End of STEP 2
# Running KNN model
result,neigh = knn(data, test, k)
# Predicted class
print(result)
-> Iris-virginica
# Nearest neighbor
print(neigh)
-> [141]

# Setting number of neighbors = 3
k = 3
# Running KNN model
result,neigh = knn(data, test, k)
# Predicted class
print(result) -> Iris-virginica
# 3 nearest neighbors
print(neigh)
-> [141, 139, 120]
# Setting number of neighbors = 5
k = 5
# Running KNN model
result,neigh = knn(data, test, k)
# Predicted class
print(result) -> Iris-virginica
# 5 nearest neighbors
print(neigh)
-> [141, 139, 120, 145, 144]