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2017-03-18_添加交流的课程注释

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jiangzhonglian
2017-03-18 20:55:22 +08:00
parent 096dd4c516
commit fc481871c4
4 changed files with 74 additions and 35 deletions
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18
src/python/02.kNN/kNN.py
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@@ -23,6 +23,8 @@ def createDataSet():
def classify0(inX, dataSet, labels, k):
"""
inx[1,2,3]
DS=[[1,2,3],[1,2,0]]
inX: 用于分类的输入向量
dataSet: 输入的训练样本集
labels: 标签向量
@@ -36,6 +38,10 @@ def classify0(inX, dataSet, labels, k):
dataSetSize = dataSet.shape[0]
# tile生成和训练样本对应的矩阵，并与训练样本求差
diffMat = tile(inX, (dataSetSize, 1)) - dataSet
"""
[[1,2,3],[1,2,3]]-[[1,2,3],[1,2,0]]
(A1-A2)^2+(B1-B2)^2+(c1-c2)^2
"""
# 取平方
sqDiffMat = diffMat ** 2
# 将矩阵的每一行相加
@@ -64,7 +70,7 @@ def test1():
group, labels = createDataSet()
print str(group)
print str(labels)
print classify0([0, 0], group, labels, 3)
print classify0([0.1, 0.1], group, labels, 3)
# ----------------------------------------------------------------------------------------
@@ -119,7 +125,7 @@ def datingClassTest():
"""
hoRatio = 0.9 # 测试范围,一部分测试一部分作为样本
# 从文件中加载数据
datingDataMat, datingLabels = file2matrix('../../../testData/datingTestSet2.txt') # load data setfrom file
datingDataMat, datingLabels = file2matrix('testData/datingTestSet2.txt') # load data setfrom file
# 归一化数据
normMat, ranges, minVals = autoNorm(datingDataMat)
m = normMat.shape[0]
@@ -153,7 +159,7 @@ def img2vector(filename):
def handwritingClassTest():
# 1. 导入数据
hwLabels = []
trainingFileList = listdir('../../../testData/trainingDigits') # load the training set
trainingFileList = listdir('testData/trainingDigits') # load the training set
m = len(trainingFileList)
trainingMat = zeros((m, 1024))
for i in range(m):
@@ -161,17 +167,17 @@ def handwritingClassTest():
fileStr = fileNameStr.split('.')[0] # take off .txt
classNumStr = int(fileStr.split('_')[0])
hwLabels.append(classNumStr)
trainingMat[i, :] = img2vector('../../../testData/trainingDigits/%s' % fileNameStr)
trainingMat[i, :] = img2vector('testData/trainingDigits/%s' % fileNameStr)
# 2. 导入测试数据
testFileList = listdir('../../../testData/testDigits') # iterate through the test set
testFileList = listdir('testData/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('../../../testData/testDigits/%s' % fileNameStr)
vectorUnderTest = img2vector('testData/testDigits/%s' % fileNameStr)
classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
print "the classifier came back with: %d, the real answer is: %d" % (classifierResult, classNumStr)
if (classifierResult != classNumStr): errorCount += 1.0