Algorithm steps:
1. Calculate the distance between the input data and the known label sample set
2. Sort the distance values calculated in step 1.
3. Select the first K values of the sort (the origin of K in the K proximity algorithm)
4. Calculate this The frequency of different tags appearing in the K values, and the tag with the highest frequency is used as the result of this prediction
Here, the distance calculation formula is selected as Euclidean distance, which is also the most common distance calculation formula:
Data set datingTestSet2.txt
40920 8.326976 0.953952 3
14488 7.153469 1.673904 2
26052 1.441871 0.805124 1
75136 13.147394 0.428964 1
38344 1.669788 0.134296 1
72993 10.141740 1.032955 1
35948 6.830792 1.213192 3
42666 13.276369 0.543880 3
67497 8.631577 0.749278 1
35483 12.273169 1.508053 3
50242 3.723498 0.831917 1
63275 8.385879 1.669485 1
5569 4.875435 0.728658 2
51052 4.680098 0.625224 1
77372 15.299570 0.331351 1
43673 1.889461 0.191283 1
61364 7.516754 1.269164 1
69673 14.239195 0.261333 1
15669 0.000000 1.250185 2
28488 10.528555 1.304844 3
6487 3.540265 0.822483 2
37708 2.991551 0.833920 1
22620 5.297865 0.638306 2
28782 6.593803 0.187108 3
19739 2.816760 1.686209 2
36788 12.458258 0.649617 3
sklearn directly adjusts the library to achieve
from sklearn.preprocessing import MinMaxScaler
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
def file2matrix(filename): #打开文件，获取数据和标签
love_dictionary = {
'largeDoses':3, 'smallDoses':2, 'didntLike':1}
fr = open(filename)
arrayOLines = fr.readlines()
numberOfLines = len(arrayOLines) #get the number of lines in the file
returnMat = np.zeros((numberOfLines, 3)) #prepare matrix to return
classLabelVector = [] #prepare labels return
index = 0
for line in arrayOLines:
line = line.strip()
listFromLine = line.split('\t')
returnMat[index, :] = listFromLine[0:3]
if(listFromLine[-1].isdigit()):
classLabelVector.append(int(listFromLine[-1]))
else:
classLabelVector.append(love_dictionary.get(listFromLine[-1]))
index += 1
return returnMat, classLabelVector
if __name__ == '__main__':
hoRatio = 0.50 #取数据的50%作为已知标签的样本集 50%作为未知标签数据
datingDataMat, datingLabels = file2matrix('datingTestSet2.txt') #load data setfrom file
scaler = MinMaxScaler() # 归一化数据
scaler = scaler.fit(datingDataMat) # fit，在这里本质是生成min(x)和max(x)
normMat = scaler.transform(datingDataMat) # 通过接口导出结果
m = normMat.shape[0]
numTestVecs = int(m*hoRatio)
errorCount = 0.0
model =KNeighborsClassifier(n_neighbors=3)
model.fit(normMat[numTestVecs:m, :], datingLabels[numTestVecs:m])
y=model.predict(normMat[0:numTestVecs, :])
#计算准确率
result = y - datingLabels[0:numTestVecs]
error=0
for i in range(len(result)):
if result[i]!=0:
error+=1
print(f'准确率：{
((1-error/len(result))*100)}%')
import numpy as np
from os import listdir
def classify0(inX, dataSet, labels, k):
dataSetSize = dataSet.shape[0]
diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet
sqDiffMat = diffMat**2
sqDistances = sqDiffMat.sum(axis=1)
distances = sqDistances**0.5
sortedDistIndicies = distances.argsort()
classCount = {
}
for i in range(k):
voteIlabel = labels[sortedDistIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]
def file2matrix(filename):
love_dictionary = {
'largeDoses':3, 'smallDoses':2, 'didntLike':1}
fr = open(filename)
arrayOLines = fr.readlines()
numberOfLines = len(arrayOLines) #get the number of lines in the file
returnMat = np.zeros((numberOfLines, 3)) #prepare matrix to return
classLabelVector = [] #prepare labels return
index = 0
for line in arrayOLines:
line = line.strip()
listFromLine = line.split('\t')
returnMat[index, :] = listFromLine[0:3]
if(listFromLine[-1].isdigit()):
classLabelVector.append(int(listFromLine[-1]))
else:
classLabelVector.append(love_dictionary.get(listFromLine[-1]))
index += 1
return returnMat, classLabelVector
def autoNorm(dataSet):
minVals = dataSet.min(0)
maxVals = dataSet.max(0)
ranges = maxVals - minVals
normDataSet = np.zeros(np.shape(dataSet))
m = dataSet.shape[0]
normDataSet = dataSet - np.tile(minVals, (m, 1))
normDataSet = normDataSet/np.tile(ranges, (m, 1)) #element wise divide
return normDataSet, ranges, minVals
def datingClassTest():
hoRatio = 0.50 #hold out 10%
datingDataMat, datingLabels = file2matrix('datingTestSet2.txt') #load data setfrom file
normMat, ranges, minVals = autoNorm(datingDataMat)
m = normMat.shape[0]
numTestVecs = int(m*hoRatio)
errorCount = 0.0
for i in range(numTestVecs):
classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)
print("返回的分类结果是: %d, 真实的分类标签是: %d" % (classifierResult, datingLabels[i]))
if (classifierResult != datingLabels[i]): errorCount += 1.0
print('准确率为{:.2f}%'.format((1-errorCount / float(numTestVecs))*100))
print(errorCount)
if __name__ == '__main__':
datingClassTest()
