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33 lines
1.3 KiB
33 lines
1.3 KiB
'''
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Author: SJ2050
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Date: 2021-11-07 13:49:04
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LastEditTime: 2021-11-07 15:05:48
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Version: v0.0.1
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Description: Homework3.2.
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Copyright © 2021 SJ2050
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'''
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import numpy as np
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from basic_kMeans import read_csv_data, show_figure, KMeans
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if __name__ == '__main__':
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data_file = 'dataset_circles.csv'
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original_data = read_csv_data(data_file)
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distance_func = lambda p1, p2: abs(np.linalg.norm(p1)-np.linalg.norm(p2))
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def compute_center_point_func(points):
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n = len(points)
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return np.array([0 ,sum(np.linalg.norm(points, axis=1))/n])
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clustered_points, center_points, iter_num, cost = KMeans(original_data[:, 0:2], 2, \
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distance_func, \
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compute_center_point_func, \
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1000, 1e-3).run()
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print(f'迭代次数: {iter_num}, 代价函数值为: {cost:.3f}')
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classified_points = np.array([(clustered_points[i][j][0], clustered_points[i][j][1], i) \
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for i in range(2) \
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for j in range(len(clustered_points[i]))])
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center_points = [(center_points[i][0], center_points[i][1], i) for i in range(2)]
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show_figure(classified_points, center_points)
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