ランダムよりはマシな精度で高速なKMEANS初期化処理を作りました - Qiita

def Kmeans_Predict(means, x): distances = [] for m in means: distances.append(np.linalg.norm(m - x)) predict = np.argmin(distances) return distances[predict], predict # 中心値同士の平均距離を計算 def KmeansInit_CalcRadiusAverageDistance(means): length = len(means) avrDistance = 0 cnt = 0 for i in range(length): for j in range(i): if j == i: continue avrDistance += np.linalg.norm(means[i] - means[j]) cnt += 1 r