Python报错

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一、关于缩进问题报错

>>> import treesTraceback (most recent call last):  File "<pyshell#3>", line 1, in <module>    import trees  File "D:\Python\trees.py", line 9    labelCounts[currentLabel] = 0                                ^TabError: inconsistent use of tabs and spaces in indentation

>>> import treesTraceback (most recent call last):  File "<pyshell#4>", line 1, in <module>    import trees  File "D:\Python\trees.py", line 9    labelCounts[currentLabel] = 0              ^IndentationError: expected an indented block

>>> import treesTraceback (most recent call last):  File "<pyshell#5>", line 1, in <module>    import trees  File "D:\Python\trees.py", line 11    shannonEnt = 0.0                   ^IndentationError: unindent does not match any outer indentation level

以上三种都是有关缩进问题的报错，第二段代码是由于没有缩进导致报错，；第一段和第三段是由于空格和tab混用……

所以要注意缩进问题，空格和tab不能混用。

二、“dict_keys”对象不支持索引

例子来源于《机器学习实战》决策树

def classify(inputTree, featLabels, testVec):    firstStr = inputTree.keys()[0]                      #line 78    secondDict = inputTree[firstStr]    featIndex = featLabels.index(firstStr)    for key in secondDict.keys():        if testVec[featIndex] == key:            if type(secondDict[key]).__name__ == 'dict':                classLabel = classify(secondDict[key], featLabels, testVec)            else:                classLabel = secondDict[key]    return classLabel

如果使用上面的决策树分类函数，报错：

>>> trees.classify(myTree, labels, [1,0])Traceback (most recent call last):  File "<pyshell#9>", line 1, in <module>    trees.classify(myTree, labels, [1,0])  File "D:\Python\trees.py", line 78, in classify    firstStr = inputTree.keys()[0]TypeError: 'dict_keys' object does not support indexing

这是因为Python2.x与3.x的差别导致的。这时我们可以用list(inputTree.keys())或者list（inputTree）来解决。

详细代码如下：

def classify(inputTree, featLabels, testVec):    firstStr = list(inputTree.keys())[0]    secondDict = inputTree[firstStr]    featIndex = featLabels.index(firstStr)    for key in secondDict.keys():        if testVec[featIndex] == key:            if type(secondDict[key]).__name__ == 'dict':                classLabel = classify(secondDict[key], featLabels, testVec)            else:                classLabel = secondDict[key]    return classLabel

>>> trees.classify(myTree, labels, [1,0])'no'

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