Python实现KNN算法项目 --- 水果分类

1.数据准备

数据下载资源

这里我构造了一个150*5的矩阵，分别代表三类数据。每行的前四个值代表数据的特征，第五个值代表数据的类别

这三类数据分别属于apple、banana、orange

2.原理：

根据首先将数据分为训练数据以及测试数据。然后根据测试数据的每一条数据去计算训练数据中每个数据的距离，然后根据K值找距离最短的 K 个。

就是每次K个最邻近的结果。

3.代码：

# -*- coding:utf-8 -*-import mathimport csvimport randomimport operator__author__ = 'yangxin'class KNearestNeighbor(object):    def __init__(self):        print "Welcome, 水果分类算法!"    @staticmethod    def load_data_set(filename, split, training_set, test_set):        # 对 test_set, train_set 赋值        with open(filename, 'r') as data_file:            lines = csv.reader(data_file)            data_set = list(lines)            for x in range(len(data_set) - 1):                for y in range(4):                    data_set[x][y] = float(data_set[x][y])                if random.random() < split:                    training_set.append(data_set[x])                else:                    test_set.append(data_set[x])    @staticmethod    def calculate_distance(test_data, train_data, length):        # 计算距离每个测试样本对所有训练样本的距离, 欧氏公式        distance = 0        for x in range(length):            distance += pow((test_data[x] - train_data[x]), 2)        return math.sqrt(distance)    def get_neighbors(self, training_set, test_instance, k):        # 返回最近的k个值        distances = []        length = len(test_instance) - 1        for x in xrange(len(training_set)):            dist = self.calculate_distance(test_instance, training_set[x], length)            print "test: %s   -->   train: %s   =    dist: %s" % (test_instance, training_set[x], dist)            distances.append((training_set[x], dist))        distances.sort(key=operator.itemgetter(1))        neighbors = []        for x in range(k):            neighbors.append(distances[x][0])            return neighbors    @staticmethod    def get_label(neighbors):        # 根据少数服从多数，决定归类到哪一类        class_votes = {}        for x in range(len(neighbors)):            response = neighbors[x][-1]            if response in class_votes:                class_votes[response] += 1            else:                class_votes[response] = 1        sorted_votes = sorted(class_votes.items(), key=operator.itemgetter(1), reverse=True)        return sorted_votes[0][0]    @staticmethod    def get_accuracy(test_set, predictions):        # 准确率计算        correct = 0        for x in range(len(test_set)):            if test_set[x][-1] == predictions[x]:                correct += 1        print "correct: %s, total: %s" % (correct, len(test_set))        return (correct / float(len(test_set))) * 100.0    def run(self, k):        training_set = []        test_set = []        split = 0.6   # train / test percentage        self.load_data_set(r'../data/test_data_1.txt', split, training_set, test_set)        print('Total train set: ' + str(len(training_set)))        print('Total test set: ' + str(len(test_set)))        predictions = []        for x in range(len(test_set)):            neighbors = self.get_neighbors(training_set, test_set[x], k)            result = self.get_label(neighbors)            predictions.append(result)        accuracy = self.get_accuracy(test_set, predictions)        print('Accuracy: ' + repr(accuracy) + '%')if __name__ == '__main__':    k = 3    k_nearest_neighbor_obj = KNearestNeighbor()    k_nearest_neighbor_obj.run(k)

4.结果：