# -*- coding:utf-8 -*-
# -*- author：zzZ_CMing
# -*- 2017/12/28
# -*- python3.5
from
os
import
listdir
from
numpy
import
*
import
numpy
as
np
import
operator
import
datetime
def
KNN
(test_data,train_data,train_label,k)
:
#已知分类的数据集（训练集）的行数
dataSetSize = train_data.shape[
0
]
#求所有距离：先tile函数将输入点拓展成与训练集相同维数的矩阵，计算测试样本与每一个训练样本的距离
all_distances = np.sqrt(np.sum(np.square(tile(test_data,(dataSetSize,
1
))-train_data),axis=
1
))
#print("所有距离：",all_distances)
#按all_distances中元素进行升序排序后得到其对应索引的列表
sort_distance_index = all_distances.argsort()
#print("文件索引排序：",sort_distance_index)
#选择距离最小的k个点
classCount = {}
for
i
in
range(k):
#返回最小距离的训练集的索引(预测值)
voteIlabel = train_label[sort_distance_index[i]]
#print('第',i+1,'次预测值',voteIlabel)
classCount[voteIlabel] = classCount.get(voteIlabel,
0
)+
1
#求众数：按classCount字典的第2个元素（即类别出现的次数）从大到小排序
sortedClassCount = sorted(classCount.items(), key = operator.itemgetter(
1
), reverse =
True
)
return
sortedClassCount[
0
][
0
]
#文本向量化 32x32 -> 1x1024
def
img2vector
(filename)
:
returnVect = []
fr = open(filename)
for
i
in
range(
32
):
lineStr = fr.readline()
for
j
in
range(
32
):
returnVect.append(int(lineStr[j]))
return
returnVect
#从文件名中解析分类数字
def
classnumCut
(fileName)
:
#参考文件名格式为：0_3.txt
fileStr = fileName.split(
'.'
)[
0
]
classNumStr = int(fileStr.split(
'_'
)[
0
])
return
classNumStr
#构建训练集数据向量，及对应分类标签向量
def
trainingDataSet
()
:
train_label = []
trainingFileList = listdir(
'trainingDigits'
)
m = len(trainingFileList)
train_data = zeros((m,
1024
))
#获取训练集的标签
for
i
in
range(m):
# fileNameStr:所有训练集文件名
fileNameStr = trainingFileList[i]
# 得到训练集索引
train_label.append(classnumCut(fileNameStr))
train_data[i,:] = img2vector(
'trainingDigits/%s'
% fileNameStr)
return
train_label,train_data
#测试函数
def
main
()
:
t1 = datetime.datetime.now()
# 计时开始
Nearest_Neighbor_number = int(input(
'选取最邻近的K个值，K='
))
train_label,train_data = trainingDataSet()
testFileList = listdir(
'testDigits'
)
error_sum =
0
test_number = len(testFileList)
for
i
in
range(test_number):
#测试集文件名
fileNameStr = testFileList[i]
#切片后得到测试集索引
classNumStr = classnumCut(fileNameStr)
test_data = img2vector(
'testDigits/%s'
% fileNameStr)
#调用knn算法进行测试
classifierResult = KNN(test_data, train_data, train_label, Nearest_Neighbor_number)
print
(
"第"
,i+
1
,
"组："
,
"预测值:"
,classifierResult,
"真实值:"
,classNumStr)
if
(classifierResult != classNumStr):
error_sum +=
1.0
print
(
"\n测试集总数为:"
,test_number)
print
(
"测试出错总数:"
,error_sum)
print
(
"\n错误率:"
,error_sum/float(test_number)*
100
,
'%'
)
t2 = datetime.datetime.now()
print(
'耗
时 = '
, t2 - t1)
if
__name__ ==
"__main__"
:
main()
