机器学习实战 k-近邻算法 python3
k-近邻算法
贴位大牛的https://blog.csdn.net/c406495762/article/details/75172850
最后的手写数字识别他不是按照原书用python来写的,可以以我的为参考
k-近邻算法概述
简单地说,k-近邻算法就是采用测量不同特征值之间的距离的方法进行分类.
它的工作原理是:存在一个样本数据集合,也称作训练样本集,并且样本集中每一个数据都存在标签,即我们都知道样本集中每一个数据与所属分类的对应关系.输入没有标签的
新数据后,将新数据的每一个特征与样本集中数据对应的特征进行比较,然后算法提取样本集中特征最相似数据(最近邻)的分类标签.一般来说,我们只选择样本数据集中前k个
最相似的数据,这就是k-近邻算法中k的出处,通常k是不大于20的整数.最后,选择k个最相似数据中出现最多的分类标签,作为新数据的分类.
一.电影分类
1.准备数据集
import numpy as np
"""
function:
创建数据集
parameters:
无
returns:
group - 数据集
labels - 分类标签
"""
def createDataSet():
#数据集的二维特征
group = np.array([[1,101],[5,89],[108,5],[115,8]])
#数据集的标签
labels = ['love', 'love', 'action', 'action']
return group, labels
if __name__ == '__main__':
group, labels = createDataSet()
print(group)
print(labels)
2.kNN算法
import numpy as np
import operator
"""
function:
knn算法,分类器
parameters:
inX - 测试集
dataSet - 训练集
labels - 分类标签
k - kNN算法参数,选取距离最小的k个点
returns:
sortedClassCount[0][0] - 分类结果
"""
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
sortedDistIndices = distances.argsort()
#记录标签次数
classCount = {}
for i in range(k):
voteIlabel = labels[sortedDistIndices[i]]
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]
3.预测电影
import numpy as np
import operator
"""
function:
创建数据集
parameters:
无
returns:
group - 数据集
labels - 分类标签
"""
def createDataSet():
#数据集的二维特征
group = np.array([[1,101],[5,89],[108,5],[115,8]])
#数据集的标签
labels = ['love', 'love', 'action', 'action']
return group, labels
"""
function:
knn算法,分类器
parameters:
inX - 测试集
dataSet - 训练集
labels - 分类标签
k - kNN算法参数,选取距离最小的k个点
returns:
sortedClassCount[0][0] - 分类结果
"""
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
sortedDistIndices = distances.argsort()
#记录标签次数
classCount = {}
for i in range(k):
voteIlabel = labels[sortedDistIndices[i]]
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]
if __name__ == '__main__':
group, labels = createDataSet()
test = [101,20]
test_class = classify0(test, group, labels, 3)
print(test_class)
二.约会网站
1.数据解析
import numpy as np
"""
function:
打开并解析文件,对数据进行分类:1不喜欢,2魅力一般,3极具魅力
parameters:
filename - 文件名
returns:
returnMat - 特征矩阵
classLabelVector - 分类Label向量
"""
def file2matrix(filename):
fr = open(filename)
#读取文件内容
arrayOLines = fr.readlines()
numberOfLines = len(arrayOLines)
returnMat = np.zeros((numberOfLines,3))
#分类标签
classLabelVector = []
index = 0
for line in arrayOLines:
#删除空白符
line = line.strip()
#根据\t来切片
listFromLine = line.split('\t')
returnMat[index,:] = listFromLine[0:3]
if listFromLine[-1] == 'didntLike':
classLabelVector.append(1)
elif listFromLine[-1] == 'smallDoses':
classLabelVector.append(2)
elif listFromLine[-1] == 'largeDoses':
classLabelVector.append(3)
index += 1
return returnMat, classLabelVector
if __name__ == '__main__':
filename = "datingTestSet.txt"
datingDataMat, datingLabels = file2matrix(filename)
print(datingDataMat)
print(datingLabels)
2.数据可视化
from matplotlib.font_manager import FontProperties
import matplotlib.lines as mlines
import matplotlib.pyplot as plt
import numpy as np
"""
function:
打开并解析文件,对数据进行分类:1不喜欢,2魅力一般,3极具魅力
parameters:
filename - 文件名
returns:
returnMat - 特征矩阵
classLabelVector - 分类Label向量
"""
def file2matrix(filename):
fr = open(filename)
#读取文件内容
arrayOLines = fr.readlines()
numberOfLines = len(arrayOLines)
returnMat = np.zeros((numberOfLines,3))
#分类标签
classLabelVector = []
index = 0
for line in arrayOLines:
#删除空白符
line = line.strip()
#根据\t来切片
listFromLine = line.split('\t')
returnMat[index,:] = listFromLine[0:3]
if listFromLine[-1] == 'didntLike':
classLabelVector.append(1)
elif listFromLine[-1] == 'smallDoses':
classLabelVector.append(2)
elif listFromLine[-1] == 'largeDoses':
classLabelVector.append(3)
index += 1
return returnMat, classLabelVector
"""
function:
可视化数据
parameters:
datingDataMat - 特征矩阵
datingLabels - 分类标签
returns:
无
"""
def showdatas(datingDataMat, datingLabels):
fig, axs = plt.subplots(nrows=2, ncols=2, sharex=False, sharey=False, figsize=(13,8))
numberOfLines = len(datingLabels)
LabelColors = []
for i in datingLabels:
if i == 1:
LabelColors.append('black')
elif i == 2:
LabelColors.append('orange')
else:
LabelColors.append('red')
#作图
axs[0][0].scatter(x=datingDataMat[:,0], y=datingDataMat[:,1], color=LabelColors, s=15, alpha=.5)
axs0_title_text = axs[0][0].set_title('plane vs game')
axs0_xlabel_text = axs[0][0].set_xlabel('plane')
asx0_ylabel_text = axs[0][0].set_ylabel('game')
axs[0][1].scatter(x=datingDataMat[:,0], y=datingDataMat[:,2], color=LabelColors, s=15, alpha=.5)
axs1_title_text = axs[0][1].set_title('plane vs ice_cream')
axs1_xlabel_text = axs[0][1].set_xlabel('plane')
axs1_ylabel_text = axs[0][1].set_ylabel('ice_cream')
axs[1][0].scatter(x=datingDataMat[:,1], y=datingDataMat[:,2], color=LabelColors, s=15, alpha=.5)
axs2_title_text = axs[1][0].set_title('game vs ice_cream')
axs2_xlabel_text = axs[1][0].set_xlabel('game')
axs2_ylabel_text = axs[1][0].set_ylabel('ice_cream')
#设置图例
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')
#添加图例
axs[0][0].legend(handles=[didntLike,smallDoses,largeDoses])
axs[0][1].legend(handles=[didntLike,smallDoses,largeDoses])
axs[1][0].legend(handles=[didntLike,smallDoses,largeDoses])
plt.show()
if __name__ == '__main__':
filename = 'datingTestSet.txt'
datingDataMat, datingLabels = file2matrix(filename)
showdatas(datingDataMat,datingLabels)
3.数据归一化
from matplotlib.font_manager import FontProperties
import matplotlib.lines as mlines
import matplotlib.pyplot as plt
import numpy as np
"""
function:
打开并解析文件,对数据进行分类:1不喜欢,2魅力一般,3极具魅力
parameters:
filename - 文件名
returns:
returnMat - 特征矩阵
classLabelVector - 分类Label向量
"""
def file2matrix(filename):
fr = open(filename)
#读取文件内容
arrayOLines = fr.readlines()
numberOfLines = len(arrayOLines)
returnMat = np.zeros((numberOfLines,3))
#分类标签
classLabelVector = []
index = 0
for line in arrayOLines:
#删除空白符
line = line.strip()
#根据\t来切片
listFromLine = line.split('\t')
returnMat[index,:] = listFromLine[0:3]
if listFromLine[-1] == 'didntLike':
classLabelVector.append(1)
elif listFromLine[-1] == 'smallDoses':
classLabelVector.append(2)
elif listFromLine[-1] == 'largeDoses':
classLabelVector.append(3)
index += 1
return returnMat, classLabelVector
"""
function:
可视化数据
parameters:
datingDataMat - 特征矩阵
datingLabels - 分类标签
returns:
无
"""
def showdatas(datingDataMat, datingLabels):
fig, axs = plt.subplots(nrows=2, ncols=2, sharex=False, sharey=False, figsize=(13,8))
numberOfLines = len(datingLabels)
LabelColors = []
for i in datingLabels:
if i == 1:
LabelColors.append('black')
elif i == 2:
LabelColors.append('orange')
else:
LabelColors.append('red')
#作图
axs[0][0].scatter(x=datingDataMat[:,0], y=datingDataMat[:,1], color=LabelColors, s=15, alpha=.5)
axs0_title_text = axs[0][0].set_title('plane vs game')
axs0_xlabel_text = axs[0][0].set_xlabel('plane')
asx0_ylabel_text = axs[0][0].set_ylabel('game')
axs[0][1].scatter(x=datingDataMat[:,0], y=datingDataMat[:,2], color=LabelColors, s=15, alpha=.5)
axs1_title_text = axs[0][1].set_title('plane vs ice_cream')
axs1_xlabel_text = axs[0][1].set_xlabel('plane')
axs1_ylabel_text = axs[0][1].set_ylabel('ice_cream')
axs[1][0].scatter(x=datingDataMat[:,1], y=datingDataMat[:,2], color=LabelColors, s=15, alpha=.5)
axs2_title_text = axs[1][0].set_title('game vs ice_cream')
axs2_xlabel_text = axs[1][0].set_xlabel('game')
axs2_ylabel_text = axs[1][0].set_ylabel('ice_cream')
#设置图例
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')
#添加图例
axs[0][0].legend(handles=[didntLike,smallDoses,largeDoses])
axs[0][1].legend(handles=[didntLike,smallDoses,largeDoses])
axs[1][0].legend(handles=[didntLike,smallDoses,largeDoses])
plt.show()
"""
function:
对数据进行归一化
parameters:
dataSet - 特征矩阵
returns:
normDataSet - 归一化后的矩阵
ranges - 数据范围
minVals - 数据最小值
"""
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))
return normDataSet, ranges, minVals
if __name__ == '__main__':
filename = 'datingTestSet.txt'
datingDataMat, datingLabels = file2matrix(filename)
normDataSet, ranges, minVals = autoNorm(datingDataMat)
print(normDataSet)
print(ranges)
print(minVals)
4.测试分类器
from matplotlib.font_manager import FontProperties
import matplotlib.lines as mlines
import matplotlib.pyplot as plt
import numpy as np
import operator
"""
function:
knn算法,分类器
parameters:
inX - 测试集
dataSet - 训练集
labels - 分类标签
k - kNN算法参数,选取距离最小的k个点
returns:
sortedClassCount[0][0] - 分类结果
"""
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
sortedDistIndices = distances.argsort()
#记录标签次数
classCount = {}
for i in range(k):
voteIlabel = labels[sortedDistIndices[i]]
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]
"""
function:
打开并解析文件,对数据进行分类:1不喜欢,2魅力一般,3极具魅力
parameters:
filename - 文件名
returns:
returnMat - 特征矩阵
classLabelVector - 分类Label向量
"""
def file2matrix(filename):
fr = open(filename)
#读取文件内容
arrayOLines = fr.readlines()
numberOfLines = len(arrayOLines)
returnMat = np.zeros((numberOfLines,3))
#分类标签
classLabelVector = []
index = 0
for line in arrayOLines:
#删除空白符
line = line.strip()
#根据\t来切片
listFromLine = line.split('\t')
returnMat[index,:] = listFromLine[0:3]
if listFromLine[-1] == 'didntLike':
classLabelVector.append(1)
elif listFromLine[-1] == 'smallDoses':
classLabelVector.append(2)
elif listFromLine[-1] == 'largeDoses':
classLabelVector.append(3)
index += 1
return returnMat, classLabelVector
"""
function:
可视化数据
parameters:
datingDataMat - 特征矩阵
datingLabels - 分类标签
returns:
无
"""
def showdatas(datingDataMat, datingLabels):
fig, axs = plt.subplots(nrows=2, ncols=2, sharex=False, sharey=False, figsize=(13,8))
numberOfLines = len(datingLabels)
LabelColors = []
for i in datingLabels:
if i == 1:
LabelColors.append('black')
elif i == 2:
LabelColors.append('orange')
else:
LabelColors.append('red')
#作图
axs[0][0].scatter(x=datingDataMat[:,0], y=datingDataMat[:,1], color=LabelColors, s=15, alpha=.5)
axs0_title_text = axs[0][0].set_title('plane vs game')
axs0_xlabel_text = axs[0][0].set_xlabel('plane')
asx0_ylabel_text = axs[0][0].set_ylabel('game')
axs[0][1].scatter(x=datingDataMat[:,0], y=datingDataMat[:,2], color=LabelColors, s=15, alpha=.5)
axs1_title_text = axs[0][1].set_title('plane vs ice_cream')
axs1_xlabel_text = axs[0][1].set_xlabel('plane')
axs1_ylabel_text = axs[0][1].set_ylabel('ice_cream')
axs[1][0].scatter(x=datingDataMat[:,1], y=datingDataMat[:,2], color=LabelColors, s=15, alpha=.5)
axs2_title_text = axs[1][0].set_title('game vs ice_cream')
axs2_xlabel_text = axs[1][0].set_xlabel('game')
axs2_ylabel_text = axs[1][0].set_ylabel('ice_cream')
#设置图例
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')
#添加图例
axs[0][0].legend(handles=[didntLike,smallDoses,largeDoses])
axs[0][1].legend(handles=[didntLike,smallDoses,largeDoses])
axs[1][0].legend(handles=[didntLike,smallDoses,largeDoses])
plt.show()
"""
function:
对数据进行归一化
parameters:
dataSet - 特征矩阵
returns:
normDataSet - 归一化后的矩阵
ranges - 数据范围
minVals - 数据最小值
"""
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))
return normDataSet, ranges, minVals
"""
function:
分类器测试
parameters:
无
returns:
无
"""
def datingClassTest():
filename = "datingTestSet.txt"
datingDataMat, datingLabels = file2matrix(filename)
#取总数据的10%进行测试
hoRatio = 0.1
#数据归一化
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], 6)
print("分类结果:%d\t 实际类别:%d" % (classifierResult, datingLabels[i]))
if classifierResult != datingLabels[i]:
errorCount += 1
print("错误率:%f%%" % (errorCount / float(numTestVecs)*100))
if __name__ == '__main__':
datingClassTest()
5.约会人物分类
from matplotlib.font_manager import FontProperties
import matplotlib.lines as mlines
import matplotlib.pyplot as plt
import numpy as np
import operator
"""
function:
knn算法,分类器
parameters:
inX - 测试集
dataSet - 训练集
labels - 分类标签
k - kNN算法参数,选取距离最小的k个点
returns:
sortedClassCount[0][0] - 分类结果
"""
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
sortedDistIndices = distances.argsort()
#记录标签次数
classCount = {}
for i in range(k):
voteIlabel = labels[sortedDistIndices[i]]
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]
"""
function:
打开并解析文件,对数据进行分类:1不喜欢,2魅力一般,3极具魅力
parameters:
filename - 文件名
returns:
returnMat - 特征矩阵
classLabelVector - 分类Label向量
"""
def file2matrix(filename):
fr = open(filename)
#读取文件内容
arrayOLines = fr.readlines()
numberOfLines = len(arrayOLines)
returnMat = np.zeros((numberOfLines,3))
#分类标签
classLabelVector = []
index = 0
for line in arrayOLines:
#删除空白符
line = line.strip()
#根据\t来切片
listFromLine = line.split('\t')
returnMat[index,:] = listFromLine[0:3]
if listFromLine[-1] == 'didntLike':
classLabelVector.append(1)
elif listFromLine[-1] == 'smallDoses':
classLabelVector.append(2)
elif listFromLine[-1] == 'largeDoses':
classLabelVector.append(3)
index += 1
return returnMat, classLabelVector
"""
function:
可视化数据
parameters:
datingDataMat - 特征矩阵
datingLabels - 分类标签
returns:
无
"""
def showdatas(datingDataMat, datingLabels):
fig, axs = plt.subplots(nrows=2, ncols=2, sharex=False, sharey=False, figsize=(13,8))
numberOfLines = len(datingLabels)
LabelColors = []
for i in datingLabels:
if i == 1:
LabelColors.append('black')
elif i == 2:
LabelColors.append('orange')
else:
LabelColors.append('red')
#作图
axs[0][0].scatter(x=datingDataMat[:,0], y=datingDataMat[:,1], color=LabelColors, s=15, alpha=.5)
axs0_title_text = axs[0][0].set_title('plane vs game')
axs0_xlabel_text = axs[0][0].set_xlabel('plane')
asx0_ylabel_text = axs[0][0].set_ylabel('game')
axs[0][1].scatter(x=datingDataMat[:,0], y=datingDataMat[:,2], color=LabelColors, s=15, alpha=.5)
axs1_title_text = axs[0][1].set_title('plane vs ice_cream')
axs1_xlabel_text = axs[0][1].set_xlabel('plane')
axs1_ylabel_text = axs[0][1].set_ylabel('ice_cream')
axs[1][0].scatter(x=datingDataMat[:,1], y=datingDataMat[:,2], color=LabelColors, s=15, alpha=.5)
axs2_title_text = axs[1][0].set_title('game vs ice_cream')
axs2_xlabel_text = axs[1][0].set_xlabel('game')
axs2_ylabel_text = axs[1][0].set_ylabel('ice_cream')
#设置图例
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')
#添加图例
axs[0][0].legend(handles=[didntLike,smallDoses,largeDoses])
axs[0][1].legend(handles=[didntLike,smallDoses,largeDoses])
axs[1][0].legend(handles=[didntLike,smallDoses,largeDoses])
plt.show()
"""
function:
对数据进行归一化
parameters:
dataSet - 特征矩阵
returns:
normDataSet - 归一化后的矩阵
ranges - 数据范围
minVals - 数据最小值
"""
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))
return normDataSet, ranges, minVals
"""
function:
分类器测试
parameters:
无
returns:
无
"""
def datingClassTest():
filename = "datingTestSet.txt"
datingDataMat, datingLabels = file2matrix(filename)
#取总数据的10%进行测试
hoRatio = 0.1
#数据归一化
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], 6)
print("分类结果:%d\t 实际类别:%d" % (classifierResult, datingLabels[i]))
if classifierResult != datingLabels[i]:
errorCount += 1
print("错误率:%f%%" % (errorCount / float(numTestVecs)*100))
"""
function:
输入特征来进行标签分类
parameters:
无
returns:
无
"""
def classifyPerson():
resultList = ['讨厌', '有些喜欢', '非常喜欢']
precentTats = float(input("game:"))
ffMiles = float(input("plane:"))
iceCream = float(input("ice_cream:"))
filename = "datingTestSet.txt"
#训练集处理
datingDataMat, datingLabels = file2matrix(filename)
#训练集归一化
normMat, ranges, minVals = autoNorm(datingDataMat)
#测试集
inArr = np.array([precentTats, ffMiles, iceCream])
#测试集归一化
norminArr = (inArr - minVals) / ranges
classifierResult = classify0(norminArr, normMat, datingLabels, 4)
print("你可能%s这个人" % (resultList[classifierResult-1]))
if __name__ == "__main__":
classifyPerson()
三.手写数字识别
1.数据处理
import numpy as np
"""
function:
处理训练集数据
parameters:
filename - 文件名
returns:
returnVect - 训练集向量
"""
def img2vector(filename):
returnVect = np.zeros((1,1024))
fr = open(filename)
for i in range(32):
lineStr = fr.readline()
for j in range(32):
returnVect[0, 32*i+j] = int(lineStr[j])
return returnVect
if __name__ == "__main__":
testVector = img2vector("testDigits/0_13.txt")
print(testVector)
2.测试分类器
import numpy as np
"""
function:
处理训练集数据
parameters:
filename - 文件名
returns:
returnVect - 训练集向量
"""
def img2vector(filename):
returnVect = np.zeros((1,1024))
fr = open(filename)
for i in range(32):
lineStr = fr.readline()
for j in range(32):
returnVect[0, 32*i+j] = int(lineStr[j])
return returnVect
if __name__ == "__main__":
testVector = img2vector("testDigits/0_13.txt")
print(testVector)