《机器学习实战》pdf下载
http://vdisk.weibo.com/s/d8sdffMJBKv8o?category_id=0&parents_ref=d8sdffMJ22tud
机器学习实战源码和数据集下载
https://blog.csdn.net/sinat_29957455/article/details/79123394
1.import kNN 的时候报错
需要安装numpy,方法是cmd下运行下面这句:
python -m pip install numpy
可能报错
“No module named pip”
上网查询解决方案
windows平台
cmd中敲命令:python -m ensurepip
可能报错
“No module named ensurepip”
卸载重装2.7.9
2.7.9下载:
https://blog.csdn.net/FUCAIHE/article/details/45353283
2.解决pip install 速度慢的问题
https://blog.csdn.net/sinat_29694963/article/details/79514930
3.classLabelVector.append(int(listFromLine[-1]))这句程序执行报错ValueError
https://blog.csdn.net/daniel_0720/article/details/50839783?locationNum=15
4.unindent does not match any outer indentation level
https://www.cnblogs.com/heimanba/p/3783022.html
5.python -m pip install matplotlib出错
https://www.cnblogs.com/-1307/p/6529269.html
6.运行下面这段出错。(NameError: name 'array' is not defined)
import kNN
datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt')
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:,1], datingDataMat[:,2],15.0*array(datingLabels),15.0*array(datingLabels))
plt.show()
解决方案:加上from numpy import array,如下
import kNN
from numpy import array
datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt')
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:,1], datingDataMat[:,2],15.0*array(datingLabels),15.0*array(datingLabels))
plt.show()
http://vdisk.weibo.com/s/d8sdffMJBKv8o?category_id=0&parents_ref=d8sdffMJ22tud
机器学习实战源码和数据集下载
https://blog.csdn.net/sinat_29957455/article/details/79123394
1.import kNN 的时候报错
需要安装numpy,方法是cmd下运行下面这句:
python -m pip install numpy
可能报错
“No module named pip”
上网查询解决方案
windows平台
cmd中敲命令:python -m ensurepip
可能报错
“No module named ensurepip”
查到如果用python 2.X需要python版本大于等于2.7.9,而我装的是2.7.6
https://www.zhihu.com/question/54906859
2.7.9下载:
https://blog.csdn.net/FUCAIHE/article/details/45353283
2.解决pip install 速度慢的问题
https://blog.csdn.net/sinat_29694963/article/details/79514930
3.classLabelVector.append(int(listFromLine[-1]))这句程序执行报错ValueError
https://blog.csdn.net/daniel_0720/article/details/50839783?locationNum=15
4.unindent does not match any outer indentation level
https://www.cnblogs.com/heimanba/p/3783022.html
5.python -m pip install matplotlib出错
https://www.cnblogs.com/-1307/p/6529269.html
6.运行下面这段出错。(NameError: name 'array' is not defined)
import kNN
datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt')
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:,1], datingDataMat[:,2],15.0*array(datingLabels),15.0*array(datingLabels))
plt.show()
解决方案:加上from numpy import array,如下
import kNN
from numpy import array
datingDataMat,datingLabels = kNN.file2matrix('datingTestSet.txt')
import matplotlib
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:,1], datingDataMat[:,2],15.0*array(datingLabels),15.0*array(datingLabels))
plt.show()
py文件中只实现了file2matrix,我认为应该还有一个file1matrix
废话少说,代码在此(建议使用pycharm运行,调试的话:F8单步运行,F7进入,F9运行到下一个断点)
# coding=UTF-8
from numpy import *
import operator
from os import listdir
import matplotlib
import matplotlib.pyplot as plt
from numpy import array
def classify0(inX, dataSet, labels, k):
dataSetSize = dataSet.shape[0]
diffMat = 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.iteritems(), key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]
def createDataSet():
group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
labels = ['A', 'A', 'B', 'B']
return group, labels
def file2matrix(filename):
fr = open(filename)
numberOfLines = len(fr.readlines()) # get the number of lines in the file
returnMat = zeros((numberOfLines, 3)) # prepare matrix to return
classLabelVector = [] # prepare labels return
fr = open(filename)
index = 0
for line in fr.readlines():
line = line.strip()
listFromLine = line.split('\t')
returnMat[index, :] = listFromLine[0:3]
classLabelVector.append(int(listFromLine[-1]))
index += 1
return returnMat, classLabelVector
def file1matrix(filename):
fr = open(filename)
numberOfLines = len(fr.readlines()) # get the number of lines in the file
returnMat = zeros((numberOfLines, 3)) # prepare matrix to return
classLabelVector = [] # prepare labels return
fr = open(filename)
index = 0
for line in fr.readlines():
line = line.strip()
listFromLine = line.split('\t')
returnMat[index, :] = listFromLine[0:3]
if listFromLine[-1] == 'largeDoses':
classLabelVector.append(3)
elif listFromLine[-1] == 'smallDoses':
classLabelVector.append(2)
elif listFromLine[-1] == 'didntLike':
classLabelVector.append(1)
index += 1
return returnMat, classLabelVector
def autoNorm(dataSet):
minVals = dataSet.min(0)
maxVals = dataSet.max(0)
ranges = maxVals - minVals
normDataSet = zeros(shape(dataSet))
m = dataSet.shape[0]
normDataSet = dataSet - tile(minVals, (m, 1))
normDataSet = normDataSet / 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 "the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i])
if (classifierResult != datingLabels[i]): errorCount += 1.0
print "the total error rate is: %f" % (errorCount / float(numTestVecs))
print errorCount
def img2vector(filename):
returnVect = 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
def handwritingClassTest():
hwLabels = []
trainingFileList = listdir('trainingDigits') # load the training set
m = len(trainingFileList)
trainingMat = zeros((m, 1024))
for i in range(m):
fileNameStr = trainingFileList[i]
fileStr = fileNameStr.split('.')[0] # take off .txt
classNumStr = int(fileStr.split('_')[0])
hwLabels.append(classNumStr)
trainingMat[i, :] = img2vector('trainingDigits/%s' % fileNameStr)
testFileList = listdir('testDigits') # iterate through the test set
errorCount = 0.0
mTest = len(testFileList)
for i in range(mTest):
fileNameStr = testFileList[i]
fileStr = fileNameStr.split('.')[0] # take off .txt
classNumStr = int(fileStr.split('_')[0])
vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)
classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
if (classifierResult != classNumStr):
print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr)
print "fileNameStr: %s" % fileNameStr
if (classifierResult != classNumStr): errorCount += 1.0
print "\nthe total number of errors is: %d" % errorCount
print "\nthe total error rate is: %f" % (errorCount / float(mTest))
def classifyPerson():
resultList = ["not at all", "in small doses", "in large doses"]
percentTats = float(raw_input("percentage of time spent playing video games? "))
ffMiles = float(raw_input("frequent flier miles earned per year? "))
iceCream = float(raw_input("liters of ice cream consumed per year? "))
inArr = array([ffMiles, percentTats, iceCream])
datingDataMat, datingLabels = file2matrix('datingTestSet.txt')
normMat, ranges, minVals = autoNorm(datingDataMat) # 需要对新来的测试集也做归一化,故需要用到ranges和minVals两个变量
classifierResult = classify0((inArr - minVals) / ranges, normMat, datingLabels, 3)
print "You will probably like this person: ", resultList[classifierResult - 1]
datingDataMat1,datingLabels1 =file1matrix('datingTestSet.txt')
fig = plt.figure()
ax = fig.add_subplot(111)
#ax.scatter(datingDataMat1[:,1], datingDataMat1[:,2])
ax.scatter(datingDataMat1[:,1], datingDataMat1[:,2],15.0*array(datingLabels1),15.0*array(datingLabels1))
plt.show()
normal ,ranges ,minVals = autoNorm(datingDataMat1)
#datingClassTest()
# classifyPerson()
handwritingClassTest()