机器学习sklearn之knn拟合iris数据集的实现

knn拟合sklearn中的iris数据集

from sklearn import neighbors
from sklearn import datasets
#导出k近邻算法，并导出数据集
knn=neighbors.KNeighborsClassifier()

iris=datasets.load_iris()
#在数据集中找到iris
#print(iris)

knn.fit(iris.data,iris.target)
#对数据集进行拟合

predictedlabel =knn.predict([[0.1,0.2,0.3,0.4]])
print(predictedlabel)#对数据进行预测


import csv
import random
import math
import operator
 
#导入数据，并分为训练集和测试集
def loadDataset(filename, split, trainingSet = [], testSet = []):
    with open(filename, 'rt') 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])
#求欧拉距离
def euclideanDistance(instance1, instance2, length):
    distance = 0
    for x in range(length):
        distance += pow((instance1[x]-instance2[x]), 2)
    return math.sqrt(distance)
#计算最近邻(K个数据集)，testInstance是实例
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))#distance是一个多个元组的list
        #distances.append(dist)
    distances.sort(key=operator.itemgetter(1))#按照dist排序
    neighbors = []
    for x in range(k):
        neighbors.append(distances[x][0])#要的是数据集
        return neighbors
#投票法找出最近邻的结果哪种最多
def getResponse(neighbors):
    classVotes = {}#key--花名字 value--个数
    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]
#求出精确性
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 = 0.8
    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()