1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
|
from numpy import * # 导入科学计算包
from matplotlib.font_manager import FontProperties
import matplotlib.lines as mlines
import matplotlib.pyplot as plt # 导入绘图工具包
import operator # 导入运算符模块
def file2matrix(filename): #读取文本数据
fr = open(filename) # 打开文件
arrarOLines = fr.readlines() # 读取内容
numberOfLines = len(arrarOLines) # 解析有多少行
returnMat = zeros((numberOfLines, 3)) # 创建行数*3的矩阵，以0填充
classLabelVector = []
index = 0
for line in arrarOLines:
line = line.strip() # 删除空白字符
listFromLine = line.split('\t') # 以空格来分割
returnMat[index, :] = listFromLine[0:3] # 前三位放入矩阵
classLabelVector.append(listFromLine[-1]) # 最后一位存入标签
index += 1
return returnMat, classLabelVector
def showData(datingDataMat, datingLabels): #绘制散点图
font = FontProperties('SimHei') #设置字体为黑体
fig, ax = plt.subplots(nrows=2, ncols=2, sharex=False, sharey=False, figsize=(15, 10)) #将fig画布分为2*2四个区域，x轴和y轴不共享，每个画布13*8大小
LabelsColors = []
for i in datingLabels:
if i == 'didntLike': #区分三种标签的颜色
LabelsColors.append('black')
if i == 'smallDoses':
LabelsColors.append('orange')
if i == 'largeDoses':
LabelsColors.append('red')
"""左上角[0][0]"""
ax[0][0].scatter(x=datingDataMat[:, 0], y=datingDataMat[:, 1], color=LabelsColors, s=15, alpha=.5) #设置x轴数据和y轴数据，颜色，圆点大小，透明度
ax00_title = ax[0][0].set_title(u'每年获得的飞行常客里程数与玩视频游戏所消耗时间占比', FontProperties=font) #设置标题和字体
ax00_xlabel = ax[0][0].set_xlabel(u'每年获得的飞行常客里程数', FontProperties=font)
ax00_ylabel = ax[0][0].set_ylabel(u'玩视频游戏所消耗时间占比', FontProperties=font)
plt.setp(ax00_title, size=10, weight='bold', color='red') #设置图例
plt.setp(ax00_xlabel, size=8, weight='bold', color='black')
plt.setp(ax00_ylabel, size=8, weight='bold', color='black')
"""右上角[0][1]"""
ax[0][1].scatter(x=datingDataMat[:, 0], y=datingDataMat[:, 2], color=LabelsColors, s=15, alpha=.5)
ax01_title = ax[0][1].set_title(u'每年获得的飞行常客里程数与每周消费的冰激淋公升数', FontProperties=font)
ax01_xlabel = ax[0][1].set_xlabel(u'每年获得的飞行常客里程数', FontProperties=font)
ax01_ylabel = ax[0][1].set_ylabel(u'每周消费的冰激淋公升数', FontProperties=font)
plt.setp(ax01_title, size=10, weight='bold', color='red')
plt.setp(ax01_xlabel, size=8, weight='bold', color='black')
plt.setp(ax01_ylabel, size=8, weight='bold', color='black')
"""左下角[1][0]"""
ax[1][0].scatter(x=datingDataMat[:, 1], y=datingDataMat[:, 2], color=LabelsColors, s=15, alpha=.5)
ax10_title = ax[1][0].set_title(u'玩视频游戏所消耗时间占比与每周消费的冰激淋公升数', FontProperties=font)
ax10_xlabel = ax[1][0].set_xlabel(u'玩视频游戏所消耗时间占比', FontProperties=font)
ax10_ylabel = ax[1][0].set_ylabel(u'每周消费的冰激淋公升数', FontProperties=font)
plt.setp(ax10_title, size=10, weight='bold', color='red')
plt.setp(ax10_xlabel, size=8, weight='bold', color='black')
plt.setp(ax10_ylabel, size=8, weight='bold', color='black')
didntLike = mlines.Line2D([], [], color='black', marker='.', markersize=6, label='didntLike')
smallDoses = mlines.Line2D([], [], color='orange', marker='.', markersize=6, label='smallDoses')
largeDoses = mlines.Line2D([], [], color='red', marker='.', markersize=6, label='largeDoses')
ax[0][0].legend(handles=[didntLike, smallDoses, largeDoses]) #添加图例
ax[0][1].legend(handles=[didntLike, smallDoses, largeDoses])
ax[1][0].legend(handles=[didntLike, smallDoses, largeDoses])
plt.show() #显示图表
def autonorm(dataSet): #归一化特征值 newvalue=(oldvue-minvals)/(maxvals-minvals) [0,1]
minvals=dataSet.min(0) #从列中选取最小值
maxvals=dataSet.max(0)
ranges=maxvals-minvals
normdata=dataSet-tile(minvals,(dataSet.shape[0],1))
normdata=normdata/tile(ranges,(dataSet.shape[0],1))
return normdata
def classify0(inx,dataSet,labels,k): #原始分类器
diffmat=tile(inx,(dataSet.shape[0],1))-dataSet
diffmat=diffmat**2
dismat=diffmat.sum(axis=1) #axis=0(默认)矩阵按列相加 axis=1 矩阵按行相加
dismat=dismat**0.5 #计算距离
sorteddismat=dismat.argsort() #argsort()默认从小到大排序，返回下标
count_class={}
for i in range(k):
label=labels[sorteddismat[i]]
count_class[label]=count_class.get(label,0)+1
sortedclasscount=sorted(count_class.items(),key=operator.itemgetter(1),reverse=True) #按label数量从小到大排序
return sortedclasscount[0][0]
def datatest(): #测试错误率
datamat,labels=file2matrix("datingTestSet.txt")
normdata=autonorm(datamat) #归一特征值
m=normdata.shape[0]
num_test=int(m*0.1) #测试数据占10%
error_count=0
for i in range(num_test): #计算错误率
res_classify=classify0(normdata[i,:],normdata[num_test:m,:],labels[num_test:m],3)
print("the classifier came back with %s,the real answer is %s"%(res_classify,labels[i]))
if(res_classify!=labels[i]):
error_count=error_count+1
error_rate=error_count/num_test
print("error_count is ",error_count)
print("the total error rate is ",error_rate)
return error_rate
def classifperson(): # 最终预测器
games=float(input("please input the time of playing video games weekly:"))
fly=float(input("please input the flier miles yearly:"))
icecream=float(input("please input the icecream consumed yearly:"))
dataSet,labels=file2matrix("datingTestSet.txt")
res=classify0(array([games,fly,icecream]),dataSet,labels,3)
print("You will probably like this person:",res)
return res
classifperson()
|