#color_data = [(norm_s0(s[0]), norm_s1(s[1])) for s in board_raw]
color_data = [(s[0], s[1]) for s in board_raw]
+ init_x = sum(c[0] for c in color_data) / float(len(color_data))
+
clusters, score = k_means.cluster(3, 2,zip(color_data, range(len(color_data))),
- [[0., 0.5], [0.5, 0.5], [1., 0.5]])
+ [[0., 0.5], [init_x, 0.5], [1., 0.5]])
# clusters1, score1 = k_means.cluster(1, 2,zip(color_data, range(len(color_data))),
# [[0.5, 0.5]])
# clusters2, score2 = k_means.cluster(2, 2,zip(color_data, range(len(color_data))),