KNN最鄰近規則分類算法實踐實現【Python實現】

     KNN算法實踐【Python實現】  分類算法

本博客按照理論思想實現KNN，算法理論分析見上一篇博客。

【點我】KNN算法介紹（最鄰近規則分類算法）

1.例子：根據花的四個特徵預測某種花屬於某種

數據集介紹

數據特徵：

萼片長度，萼片寬度，花瓣長度，花瓣寬度
(sepal length, sepal width, petal length and petal width）
類別lable：
Iris setosa, Iris versicolor, Iris virginica.

總共有150個樣例，選擇部分作爲訓練集，部分作爲測試集。

代碼實踐：調用Python庫sklearn實現

1.安裝Python和機器學習庫，和一些依賴包；

本人是直接安裝了包含了衆多包的Anaconda3 ，下載後再window7 64bit上雙擊安裝即可；

Anaconda3較大，如果網速不好，可以從百度雲下載地址：http://pan.baidu.com/s/1dFIfoYX

2.打開cmd 輸入：pip list 可以查看到已經安裝的包；

3. 在cmd中運行如下的Python程序：

from sklearn import neighbors
from sklearn import datasets

knn = neighbors.KNeighborsClassifier()

iris = datasets.load_iris()
# save data
# f = open("iris.data.csv", 'wb')
# f.write(str(iris))
# f.close()

print (iris)

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

predictedLabel = knn.predict([[0.1, 0.2, 0.3, 0.4]])
#print ("hello")
print ("predictedLabel is :" + predictedLabel)
#print (predictedLabel)

2.自己用Python實現該算法：

import csv
import random
import math
import operator


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])


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)):
        #testinstance
        dist = euclideanDistance(testInstance, trainingSet[x], length)
        distances.append((trainingSet[x], dist))
        #distances.append(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)#.iteritems()
    return sortedVotes[0][0]


def getAccuracy(testSet, predictions):
    correct = 0
    #print (len(testSet))
    #print (len(predictions))
    for x in range(len(testSet)):
        print (testSet[x][-1])
        print (predictions[x])
        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('irisdata.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)):
        # trainingsettrainingSet[x]
        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) + '%')

if __name__ == '__main__':
    main()

運行截圖：

附錄：

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