数据挖掘 --- 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 math
import csv
import random
import 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：