demo5.py

# -*- coding: UTF-8 -*-
from collections import defaultdict
from random import uniform
from math import sqrt


def read_points():
    dataset = []
    with open('D:\debt.csv', 'r', encoding='utf8') as file:
        for line in file:
            if line == '\n':
                continue
            result = line.strip().split(',')
            fltline = [float(i) for i in result]
            dataset.append(fltline)
        file.close()
        return dataset


def write_results(listResult, dataset, k):
    with open('D:\demo1.csv', 'a') as file:
        for kind in range(k):
            file.write("CLASSINFO:%d\n" % (kind + 1))
            for j in listResult[kind]:
                file.write('%d\n' % j)
            file.write('\n')
        file.write('\n\n')
        file.close()


def point_avg(points):
    dimensions = len(points[0])
    new_center = []
    for dimension in range(dimensions):
        sum = 0
        for p in points:
            sum += p[dimension]
        new_center.append(float("%.8f" % (sum / float(len(points)))))
    return new_center

def update_centers(data_set, assignments, k):
    new_means = defaultdict(list)
    centers = []
    for assignment, point in zip(assignments, data_set):
        new_means[assignment].append(point)
    for i in range(k):
        points = new_means[i]
        if len(points) != 0:
            centers.append(point_avg(points))
    return centers


def assign_points(data_points, centers):
    assignments = []
    for point in data_points:
        shortest = float('inf')
        shortest_index = 0
        for i in range(len(centers)):
            value = distance(point, centers[i])
            if value < shortest:
                shortest = value
                shortest_index = i
        assignments.append(shortest_index)
    # if len(set(assignments)) < len(centers):
    #     print("\n--!!!产生随机数错误，请重新运行程序！!!!--\n")
    #     exit()
    return assignments


def distance(a, b):
    dimention = len(a)
    sum = 0
    for i in range(dimention):
        sq = (a[i] - b[i]) ** 2
        sum += sq
    return sqrt(sum)


def generate_k(data_set, k):
    centers = []
    dimentions = len(data_set[0])
    min_max = defaultdict(int)
    for point in data_set:
        for i in range(dimentions):
            value = point[i]
            min_key = 'min_%d' % i
            max_key = 'max_%d' % i
            if min_key not in min_max or value < min_max[min_key]:
                min_max[min_key] = value
            if max_key not in min_max or value > min_max[max_key]:
                min_max[max_key] = value
    for j in range(k):
        rand_point = []
        for i in range(dimentions):
            min_val = min_max['min_%d' % i]
            max_val = min_max['max_%d' % i]
            tmp = float("%.8f" % (uniform(min_val, max_val)))
            rand_point.append(tmp)
        centers.append(rand_point)
    return centers


def k_means(dataset, k):
    k_points = generate_k(dataset, k)
    assignments = assign_points(dataset, k_points)
    old_assignments = None
    while assignments != old_assignments:
        new_centers = update_centers(dataset, assignments, k)
        old_assignments = assignments
        assignments = assign_points(dataset, new_centers)
    result = list(zip(assignments, dataset))
    # print(len(result))
    result_len=len(result)
    print(result_len)
    print('\n\n---------------------------------分类结果---------------------------------------\n\n')
    for out in result:
        print(out, end='\n')
    print('\n\n---------------------------------标号简记---------------------------------------\n\n')
    listResult = [[] for i in range(k)]
    count = 0
    for i in assignments:
        listResult[i].append(count)
        count = count + 1
    listResult_len0=len(listResult[0])
    listResult_len1=len(listResult[1])
    listResult_len2=len(listResult[2])
    print(listResult_len0)
    write_results(listResult, dataset, k)
    for kind in range(k):
        # kind.to_bytes()
        print("第%d类数据对应的行数为:" % (kind + 1))
        count = 0
        for j in listResult[kind]:
            print(j, end=' ')
            count = count + 1
            if count % 25 == 0:
                print('\n')
        print('\n')
    print('\n\n--------------------------------------------------------------------------------\n\n')
    precent0=round(listResult_len0*100/result_len,2)
    precent1=round(listResult_len1*100/result_len,2)
    precent2=round(listResult_len2*100/result_len,2)
    print ("第一类数据占总数的:" +str(precent0)+"%")
    print("第二类数据占总数的:" + str(precent1)+"%")
    print("第三类数据占总数的:" + str(precent2)+"%")

def main():
    dataset = read_points()
    k_means(dataset, 3)


if __name__ == "__main__":
    main()
    pass