pimaKNN.py 文件源码

def knn(self, itemVector):
        """returns the predicted class of itemVector using k
        Nearest Neighbors"""
        # changed from min to heapq.nsmallest to get the
        # k closest neighbors
        neighbors = heapq.nsmallest(self.k,
                                   [(self.manhattan(itemVector, item[1]), item)
                     for item in self.data])
        # each neighbor gets a vote
        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)
        #get all the classes that have the maximum votes
        maxVotes = resultList[0][0]
        possibleAnswers = [i[1] for i in resultList if i[0] == maxVotes]
        # randomly select one of the classes that received the max votes
        answer = random.choice(possibleAnswers)
        return( answer)