写在之前

本书涉及的源程序和数据都可以在以下网站中找到：http://guidetodatamining.com/
这本书理论比较简单，书中错误较少，动手锻炼较多，如果每个代码都自己写出来，收获不少。总结：适合入门。
欢迎转载，转载请注明出处，如有问题欢迎指正。
合集地址：https://www.zybuluo.com/hainingwyx/note/559139

基于物品属性的过滤

新物品加入，会因为没有被评分过，永远不会被推荐。Pandora是基于基于一种称为音乐基因的项目。
当所用数据挖掘方法基于特征的值来计算 两个对象的距离，且不同特征的尺度不同，就需要使用归一化。一般使用均值和标准差来进行归一化，但这种方法可能会受到离群点的影响，所以引入改进后的归一化：均值用中位数(u​)代替，标准差用绝对标准差(asd)代替。

# 计算中位数和绝对标准差
def getMedian(self, alist):
     """return median of alist"""
    if alist == []:
        return []
    blist = sorted(alist)
    length = len(alist)
    if length % 2 == 1:
        # length of list is odd so return middle element
        return blist[int(((length + 1) / 2) -  1)]
    else:
        # length of list is even so compute midpoint
        v1 = blist[int(length / 2)]
        v2 =blist[(int(length / 2) - 1)]
        return (v1 + v2) / 2.0


def getAbsoluteStandardDeviation(self, alist, median):
    """given alist and median return absolute standard deviation"""
    sum = 0
    for item in alist:
        sum += abs(item - median)
    return sum / len(alist)

def unitTest():
    list1 = [54, 72, 78, 49, 65, 63, 75, 67, 54]
    list2 = [54, 72, 78, 49, 65, 63, 75, 67, 54, 68]
    list3 = [69]
    list4 = [69, 72]
    classifier = Classifier('data/athletesTrainingSet.txt')
    m1 = classifier.getMedian(list1)
    m2 = classifier.getMedian(list2)
    m3 = classifier.getMedian(list3)
    m4 = classifier.getMedian(list4)
    asd1 = classifier.getAbsoluteStandardDeviation(list1, m1)
    asd2 = classifier.getAbsoluteStandardDeviation(list2, m2)
    asd3 = classifier.getAbsoluteStandardDeviation(list3, m3)
    asd4 = classifier.getAbsoluteStandardDeviation(list4, m4)
    assert(round(m1, 3) == 65)
    assert(round(m2, 3) == 66)
    assert(round(m3, 3) == 69)
    assert(round(m4, 3) == 70.5)
    assert(round(asd1, 3) == 8)
    assert(round(asd2, 3) == 7.5)
    assert(round(asd3, 3) == 0)
    assert(round(asd4, 3) == 1.5)
    
    print("getMedian and getAbsoluteStandardDeviation work correctly")

assert语句用于软件组件测试的做法是一种常用的技术。产品每一部分分成一段实现代码加上对实现代码的测试代码，这一点十分重要。

# 归一化
def normalizeColumn(self, columnNumber):
       """given a column number, normalize that column in self.data"""
       # first extract values to list, v is vector, clounm is 0/1,col is a list
       col = [v[1][columnNumber] for v in self.data]
       median = self.getMedian(col)
       asd = self.getAbsoluteStandardDeviation(col, median)
       #print("Median: %f   ASD = %f" % (median, asd))
       self.medianAndDeviation.append((median, asd))
       for v in self.data:
           v[1][columnNumber] = (v[1][columnNumber] - median) / asd