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KNNClass.py
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# coding:UTF-8
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
class KNNClass():
def __init__(self):
pass
####################################
# 1 训练数据加载
# data_filename： csv 文件地址
# sep：指定分隔符
# return dataset DataFrame格式
####################################
def loadCsv(self,data_filename,sep='\t'):
dataset = pd.read_csv(data_filename, sep=sep)
return dataset
####################################
# 1 测试数据加载
# data_filename： csv 文件地址
# sep：指定分隔符
# return dataset DataFrame格式
####################################
def loadTestCsv(self,data_filename,sep='\t'):
dataset = pd.read_csv(data_filename, sep=sep)
return dataset
####################################
# 3 测试数据加载
# data_filename： csv 文件地址
# sep：指定分隔符
# return dataset DataFrame格式
####################################
def initData(self,dataset):
dataset['no'] = range(len(dataset))
dataset['distance'] = 0.0
return dataset
####################################
# 3.1 欧式距离计算
#
####################################
def euclideanDistance(self,trainSets,testSets):
dataCols=trainSets.columns
for i in range(0,len(trainSets)):
for col in range(0, len(dataCols) - 3):
trainSets['distance'][i]=trainSets['distance'][i]+(trainSets[dataCols[col]][i]-testSets[dataCols[col]])**2
trainSets.loc[i,'distance']=np.sqrt(trainSets.loc[i,'distance'])
return trainSets
####################################
# 4 根据距离排序，列出前k位
#
####################################
def sorfDistance(self,dataset,k=5):
dataset=dataset.sort_values(by=["distance"],ascending=True)
#print(dataset)
topK=dataset[:k]
print(topK)
return topK
####################################
# 5 根据KNN结果，判断测试集属于哪一个类
# 功能：通过统计距离测试样本最近的K个训练样本集合的数量，
# 判断测试样本属于哪一个样本
# 参数：topK 距离测试样本最近的K个训练样本集合
#
####################################
def testClass(self,topK):
categoryTrueNum=topK[(topK['有无']==1)]['有无'].count()
categoryFalseNum =topK[(topK['有无'] == 0)]['有无'].count()
if categoryTrueNum> categoryFalseNum:
return 1
else:
return 0
####################################
# 6 主函数
#
####################################
def main(self,data_filename,dataset,testSets,k):
dataset=self.initData(dataset)
dataset=self.euclideanDistance(dataset, testSets)
topK=self.sorfDistance(dataset,k)
result=self.testClass(topK)
return result
####################################
# 7 画原始数据集的散点图
#
####################################
def drawOrginPic(self,datasets):
cols=datasets.columns
self.drawPicCommon(datasets, cols)
plt.show()
def drawTestPic(self,datasets,testDataset):
cols = datasets.columns
plt.scatter(testDataset[cols[0]], testDataset[cols[1]], c='green', alpha=1, marker='+', label='测试点') # c='green'定义为绿色
self.drawPicCommon(datasets,cols)
plt.show()
def drawPicCommon(self,datasets,cols):
print(datasets)
plt.scatter(datasets[(datasets[cols[2]] == 0)][cols[0]], datasets[(datasets[cols[2]] == 0)][cols[1]],
marker='o', color='red', cmap="RdBu", vmin=-.2, vmax=1.2, edgecolor="white", label='无')
plt.scatter(datasets[(datasets[cols[2]] == 1)][cols[0]], datasets[(datasets[cols[2]] == 0)][cols[1]],
marker='^', color='blue', cmap="RdBu", vmin=-.2, vmax=1.2, edgecolor="white", label='有')
plt.title('是否购买割草机')
plt.xlabel(cols[0])
plt.ylabel(cols[1])
plt.grid(True)
plt.legend(loc='best')
# 1 数据加载
home_folder = os.path.expanduser("E:/pythonWp/pycharmWp/algorithm/")
data_folder = os.path.join(home_folder, "ML", "KNNStudy")
data_filename = os.path.join(data_folder, "KNNdatasets.csv")
knnClass=KNNClass()
dataset = knnClass.loadCsv(data_filename)
knnClass.drawOrginPic(dataset)
data_filename=os.path.join(data_folder, "KNNTestDatasets.csv")
testDatasets = knnClass.loadTestCsv(data_filename)
print(testDatasets)
for i in range(len(testDatasets)):
print("------------%d-------------"%i)
print(testDatasets[i:i+1])
result = knnClass.main(data_filename, dataset, testDatasets[i:i+1],5)
if result == 1:
print("测试集属于第一类样本")
else:
print("测试集属于第二类样本")
print("")
knnClass.drawTestPic(dataset,testDatasets[i:i+1])