4.2 最邻近规则分类(K-Nearest Neighbor)KNN算法应用

1 数据集介绍：

虹膜

150个实例

萼片长度，萼片宽度，花瓣长度，花瓣宽度

(sepal length, sepal width, petal length and petal width）

类别：

Iris setosa, Iris versicolor, Iris virginica.

2. 利用Python的机器学习库sklearn: SkLearnExample.py

from sklearn import neighbors

from sklearn import datasets

knn = neighbors.KNeighborsClassifier()

iris = datasets.load_iris()

print iris

knn.fit(iris.data, iris.target)

predictedLabel = knn.predict([[0.1, 0.2, 0.3, 0.4]])

print predictedLabel

3. KNN 实现Implementation:

# Example of kNN implemented from Scratch in Python

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(), 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():

# prepare data

trainingSet=[]

testSet=[]

split = 0.67

loadDataset(r‘D:\MaiziEdu\DeepLearningBasics_MachineLearning\Datasets\iris.data.txt‘, 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()