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()