knn的python代码

import heapq
import random

class Classifier:
    def __init__(self, bucketPrefix, testBucketNumber, dataFormat, k):

　　　　　　""" 一个分类器将建立与bucketprefix文件
　　　　　　除textbucketnumber文件。数据格式是一个字符串，
　　　　　　描述如何解释数据文件的每一行。

　　　　　　"""

        self.medianAndDeviation = []
        self.k = k

        self.format = dataFormat.strip().split('\t')
        self.data = []

        for i in range(1, 11):

            if i != testBucketNumber:
                filename = "%s-%02i" % (bucketPrefix, i)
                f = open(filename)
                lines = f.readlines()
                f.close()
                for line in lines[1:]:
                    fields = line.strip().split('\t')
                    ignore = []
                    vector = []
                    for i in range(len(fields)):

                        if self.format[i] == 'num':
                            vector.append(float(fields[i]))
                        elif self.format[i] == 'comment':
                            ignore.append(fields[i])
                        elif self.format[i] == 'class':
                            classification = fields[i]
                    self.data.append((classification, vector, ignore))
        self.rawData = list(self.data)

        self.vlen = len(self.data[0][1])

        for i in range(self.vlen):
            self.normalizeColumn(i)

    def getMedian(self, alist):
        """返回列表"""
        if alist == []:
            return []
        blist = sorted(alist)
        length = len(alist)
        if length % 2 == 1:

            return blist[int(((length + 1) / 2) -  1)]
        else:

            v1 = blist[int(length / 2)]
            v2 =blist[(int(length / 2) - 1)]
            return (v1 + v2) / 2.0

    def getAbsoluteStandardDeviation(self, alist, median):
        """取绝对标准偏差"""
        sum = 0
        for item in alist:
            sum += abs(item - median)
        return sum / len(alist)

    def normalizeColumn(self, columnNumber):
       """给出一列数，规范self.data列"""
       # 先提取值列表
       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

    def normalizeVector(self, v):
        """We have stored the median and asd for each column.
        We now use them to normalize vector v"""
        vector = list(v)
        for i in range(len(vector)):
            (median, asd) = self.medianAndDeviation[i]
            vector[i] = (vector[i] - median) / asd
        return vector

    def testBucket(self, bucketPrefix, bucketNumber):
        """评估分类bucketPrefix-bucketNumber"""

        filename = "%s-%02i" % (bucketPrefix, bucketNumber)
        f = open(filename)
        lines = f.readlines()
        totals = {}
        f.close()
        for line in lines:
            data = line.strip().split('\t')
            vector = []
            classInColumn = -1
            for i in range(len(self.format)):
                  if self.format[i] == 'num':
                      vector.append(float(data[i]))
                  elif self.format[i] == 'class':
                      classInColumn = i
            theRealClass = data[classInColumn]

            classifiedAs = self.classify(vector)
            totals.setdefault(theRealClass, {})
            totals[theRealClass].setdefault(classifiedAs, 0)
            totals[theRealClass][classifiedAs] += 1
        return totals

    def manhattan(self, vector1, vector2):
        """计算曼哈顿距离"""
        return sum(map(lambda v1, v2: abs(v1 - v2), vector1, vector2))

    def knn(self, itemVector):
        """使用K近邻预测itemVector类"""

        neighbors = heapq.nsmallest(self.k,[(self.manhattan(itemVector, item[1]), item)
                     for item in self.data])

        results = {}
        for neighbor in neighbors:
            theClass = neighbor[1][0]
            results.setdefault(theClass, 0)
            results[theClass] += 1
        resultList = sorted([(i[1], i[0]) for i in results.items()], reverse=True)

        maxVotes = resultList[0][0]
        possibleAnswers = [i[1] for i in resultList if i[0] == maxVotes]

        answer = random.choice(possibleAnswers)
        return( answer)

    def classify(self, itemVector):
        """返回类"""

        return(self.knn(self.normalizeVector(itemVector)))                             

def tenfold(bucketPrefix, dataFormat, k):
    results = {}
    for i in range(1, 11):
        c = Classifier(bucketPrefix, i, dataFormat, k)
        t = c.testBucket(bucketPrefix, i)
        for (key, value) in t.items():
            results.setdefault(key, {})
            for (ckey, cvalue) in value.items():
                results[key].setdefault(ckey, 0)
                results[key][ckey] += cvalue

    categories = list(results.keys())
    categories.sort()
    print(   "\n       Classified as: ")
    header =    "        "
    subheader = "      +"
    for category in categories:
        header += "% 2s   " % category
        subheader += "-----+"
    print (header)
    print (subheader)
    total = 0.0
    correct = 0.0
    for category in categories:
        row = " %s    |" % category
        for c2 in categories:
            if c2 in results[category]:
                count = results[category][c2]
            else:
                count = 0
            row += " %3i |" % count
            total += count
            if c2 == category:
                correct += count
        print(row)
    print(subheader)
    print("\n%5.3f percent correct" %((correct * 100) / total))
    print("total of %i instances" % total)

print("SMALL DATA SET")
tenfold("pimaSmall/pimaSmall",
        "num	num	num	num	num	num	num	num	class", 1)
print("\n\nLARGE DATA SET")

tenfold("pima/pima",
        "num	num	num	num	num	num	num	num	class", 1)