'''
Author: SJ2050
Date: 2021-11-05 23:21:46
LastEditTime: 2021-11-07 15:14:50
Version: v0.0.1
Description: Homework3.1.
Copyright © 2021 SJ2050
'''
import numpy as np
from basic_kMeans import read_csv_data, show_figure, KMeans

if __name__ == '__main__':
    data_file = 'dataset_circles.csv'
    original_data = read_csv_data(data_file)

    distance_func = lambda p1, p2: np.linalg.norm(p1 - p2)
    def compute_center_point_func(points):
        n = len(points)
        return sum(points)/n

    clustered_points, center_points, iter_num, cost = KMeans(original_data[:, 0:2], 2, \
                                                            distance_func, \
                                                            compute_center_point_func, \
                                                            1000, 1e-3).run()
    print(f'迭代次数: {iter_num}, 代价函数值为: {cost:.3f}')

    classified_points = np.array([(clustered_points[i][j][0], clustered_points[i][j][1], i) \
                                        for i in range(2) \
                                            for j in range(len(clustered_points[i]))])
    center_points = [(center_points[i][0], center_points[i][1], i) for i in range(2)]
    show_figure(classified_points, center_points)