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1、TypeError: 'dict_keys' object does not support indexing

????機(jī)器學(xué)習(xí)實(shí)戰(zhàn)第三章決策樹(shù)中遇到的，主要是Python的版本問(wèn)題，下面這段是Python2的寫(xiě)法：

firstStr = myTree.keys()[0]

? ? Python3：先轉(zhuǎn)換成list

firstStr = list(myTree.keys())[0]

2、TypeError: write() argument must be str, not bytes

????使用pickle存儲(chǔ)的時(shí)候出現(xiàn)錯(cuò)誤

? ? 錯(cuò)誤代碼：

try:with open(fileName, 'w') as fw:pickle.dump(inputTree, fw) except IOError as e:print("File Error : " + str(e))

????錯(cuò)誤原因：pickle的存儲(chǔ)方式默認(rèn)是二進(jìn)制

????修正：

try:with open(fileName, 'wb') as fw:pickle.dump(inputTree, fw) except IOError as e:print("File Error : " + str(e))

3、UnicodeDecodeError: 'gbk' codec can't decode byte 0xae in position 199: illegal multibyte sequence

• 文件中包含了非法字符，gbk無(wú)法解析

def spamTest():docList = []classList = []fullList = []for i in range(1, 26):wordList = textParse(open('email/spam/%d.txt' % i).read())docList.append(wordList)fullList.extend(wordList)classList.append(1)wordList = textParse(open('email/ham/%d.txt' % i).read()) # 出錯(cuò)部分docList.append(wordList)fullList.extend(wordList)classList.append(0)vocabList = bayes.createVocabList(docList)trainingSet = list(range(50))testSet = []for i in range(10):randIndex = int(random.uniform(0, len(trainingSet)))testSet.append(trainingSet[randIndex])del trainingSet[randIndex]trainMat = []trainClasses = []for docIndex in trainingSet:trainMat.append(bayes.setOfWords2Vec(vocabList, docList[docIndex]))trainClasses.append(classList[docIndex])p0V, p1V, pSpam = bayes.trainNB0(array(trainMat), array(trainClasses))errorCount = 0for docIndex in testSet:wordVector = bayes.setOfWords2Vec(vocabList, docList[docIndex])if bayes.classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:errorCount += 1print('the error rate is:', float(errorCount) / len(testSet))

1、嘗試使用比gbk包含字符更多的gb18030,卒

wordList = textParse(open('email/ham/%d.txt' % i, encoding='gb18030').read())

2、忽略錯(cuò)誤，通過(guò)

wordList = textParse(open('email/ham/%d.txt' % i, encoding='gb18030', errors='ignore').read())

3、打開(kāi)文件看看哪個(gè)是非法字符，我選擇放棄
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4、TypeError: 'range' object doesn't support item deletion

# spamTest(): def spamTest():docList = []classList = []fullList = []for i in range(1, 26):wordList = textParse(open('email/spam/%d.txt' % i, encoding='gb18030', errors='ignore').read())docList.append(wordList)fullList.extend(wordList)classList.append(1)wordList = textParse(open('email/ham/%d.txt' % i, encoding='gb18030', errors='ignore').read())docList.append(wordList)fullList.extend(wordList)classList.append(0)vocabList = bayes.createVocabList(docList)trainingSet = range(50) # 需要修改部分testSet = []for i in range(10):randIndex = int(random.uniform(0, len(trainingSet)))testSet.append(trainingSet[randIndex])del trainingSet[randIndex] # 出錯(cuò)代碼部分trainMat = []trainClasses = []for docIndex in trainingSet:trainMat.append(bayes.setOfWords2Vec(vocabList, docList[docIndex]))trainClasses.append(classList[docList])p0V, p1V, pSpam = bayes.trainNB0(array(trainMat), array(trainClasses))errorCount = 0for docIndex in testSet:wordVector = bayes.setOfWords2Vec(vocabList, docList[docIndex])if bayes.classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:errorCount += 1print('the error rate is:', float(errorCount) / len(testSet))

python3.x , 出現(xiàn)錯(cuò)誤 'range' object doesn't support item deletion

原因：python3.x ? range返回的是range對(duì)象，不返回?cái)?shù)組對(duì)象

解決方法：

把 trainingSet = range(50) 改為?trainingSet = list(range(50))

5、TypeError: 'numpy.float64' object cannot be interpreted as an integer

出錯(cuò)代碼：隨機(jī)梯度上升算法

# 隨機(jī)梯度上升算法 def stocGradAscent0(dataMatrix, classLabels):m, n = shape(dataMatrix)alpha = 0.01weights = ones(n)for i in range(m):h = sigmoid(sum(dataMatrix[i] * weights))error = classLabels[i] - hweights = weights + alpha * error * dataMatrix[i]return weights

出錯(cuò)原因：error 是一個(gè)float64，

weights ：<class 'numpy.ndarray'>

dataMatrix[i] ：<class 'list'>

在Python中，如果是一個(gè)整型n乘以一個(gè)列表L， 列表長(zhǎng)度會(huì)變成n*len(L)，而當(dāng)你用一個(gè)浮點(diǎn)數(shù)乘以一個(gè)列表，自然而然也就出錯(cuò)了，而且我們要的也不是這個(gè)結(jié)果，而是對(duì)于當(dāng)前向量的每一位乘上一個(gè)error。

其實(shí)這地方就是Python 中的list和numpy的array混用的問(wèn)題，對(duì)dataMatrix進(jìn)行強(qiáng)制類型轉(zhuǎn)換就行了(也可以在參數(shù)傳遞之前進(jìn)行轉(zhuǎn)換，吐槽Python的類型機(jī)制)

# 隨機(jī)梯度上升算法 def stocGradAscent0(dataMatrix, classLabels):# 強(qiáng)制類型轉(zhuǎn)換，避免array和list混用dataMatrix = array(dataMatrix)m, n = shape(dataMatrix)alpha = 0.01weights = ones(n)for i in range(m):h = sigmoid(sum(dataMatrix[i] * weights))error = classLabels[i] - hweights = weights + alpha * error * dataMatrix[i]return weights

6. copy和copy.deepcopy

copy對(duì)于一個(gè)復(fù)雜對(duì)象的子對(duì)象并不會(huì)完全復(fù)制，什么是復(fù)雜對(duì)象的子對(duì)象呢？就比如序列里的嵌套序列，字典里的嵌套序列等都是復(fù)雜對(duì)象的子對(duì)象。對(duì)于子對(duì)象，python會(huì)把它當(dāng)作一個(gè)公共鏡像存儲(chǔ)起來(lái)，所有對(duì)他的復(fù)制都被當(dāng)成一個(gè)引用，所以說(shuō)當(dāng)其中一個(gè)引用將鏡像改變了之后另一個(gè)引用使用鏡像的時(shí)候鏡像已經(jīng)被改變了。

deepcopy的時(shí)候會(huì)將復(fù)雜對(duì)象的每一層復(fù)制一個(gè)單獨(dú)的個(gè)體出來(lái)。?

7. /和//

python3

/ 保留小數(shù)位， 3/2 = 1.5; 2/2 = 1.0

// floor(), 3/2 = 1 2//2 = 1

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