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machine-learning-in-action-python3's Issues

PCA_Project2里面

def replaceNaNWithMean():
里面应该是这个⑧
meanVal = np.mean(datMat[np.nonzero(~np.isnan(datMat[:, i].A))[0], i])

🐛 Bayes_Project2/Bayes.py 拆分词袋有错误

🐛 正则匹配并不能匹配到正确的词
♐ 这样可以,不知再有没有更好的解决方式

def textParse(bigString):
    # 用特殊符号作为切分标志进行字符串切分,即非字母、非数字
    # \W* 0个或多个非字母数字或下划线字符(等价于[^a-zA-Z0-9_])
    bigString=bigString.split()
    listOfTockens=[]
    for i in range(len(bigString)):
        listOfTockens.append(str("".join(list(filter(str.isalpha, bigString[i])))))
    # 除了单个字母,例如大写I,其他单词变成小写,去掉少于两个字符的字符串
    return [tok.lower() for tok in listOfTockens if len(tok) > 2]

DecisionTree_Project2/DecisionTree.py 方法classify 使用有误

530行里 调用classify方法,给定的第二项输入应该是完整的数据labels,且顺序和数据集顺序应该一致

同时优化了classify方法的写法 更加直观

def classify(inputTree, featLabels, testVec):
    # 获取决策树结点
    # 当前树节点的key首项 表明选择的特征类型
    keyLabel = list(inputTree.keys())[0]
    # 对应类型的特征树
    currDict = inputTree[keyLabel]

    # 获取特征类型在特征中的index
    featIndex = featLabels.index(keyLabel)
    # 获取当前的特征叶子 或者是 特征树
    judgeValue = currDict.get(testVec[featIndex])

    # 如果是树就继续向下走 如果是叶子输出
    if type(judgeValue).__name__ == 'dict':
        return classify(judgeValue, featLabels, testVec)
    else:
        return judgeValue

BUG: Machine-Learning-in-Action-Python3/CART_Project3/CART.py

Machine-Learning-in-Action-Python3/CART_Project3/CART.py
第285行
errorNoMerge = np.sum(np.power(lSet[:, -1] - tree['left'], 2)) + np.sum(np.power(rSet[:, 1] - tree['right'], 2))
中的后半部分rSet的索引似乎应该是[:, -1]。应修改为:
errorNoMerge = np.sum(np.power(lSet[:, -1] - tree['left'], 2)) + np.sum(np.power(rSet[:, -1] - tree['right'], 2))

注释有点问题

Bayes_Project1/Bayes.py中 第106行 numWords应该是模型中总词条数目或者说用户词典中的总词条数目吧?

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