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- from scipy.spatial import Voronoi, voronoi_plot_2d
- import matplotlib.pyplot as plt
- import numpy as np
- def get_gray_code(n: int):
- return n ^ (n >> 1)
- def difference(sym0: int, sym1: int):
- return bit_count(sym0 ^ sym1)
- def bit_count(i: int):
- """
- Hamming weight algorithm, just counts number of 1s
- """
- assert 0 <= i < 0x100000000
- i = i - ((i >> 1) & 0x55555555)
- i = (i & 0x33333333) + ((i >> 2) & 0x33333333)
- return (((i + (i >> 4) & 0xF0F0F0F) * 0x1010101) & 0xffffffff) >> 24
- def compute_optimal(points, show_graph=False):
- available = set(range(len(points)))
- map = {}
- vor = Voronoi(points)
- if show_graph:
- voronoi_plot_2d(vor)
- plt.show()
- pass
- if __name__ == '__main__':
- a = np.array([[-1, -1], [-1, 1], [1, 1], [1, -1]])
- # a = basic.load_alphabet('16qam', polar=False)
- compute_optimal(a, show_graph=True)
- pass
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