# import math def kmeans points cl_num len points len points clusters cl

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85``` ```import math def kmeans(points, cl_num): n = len(points) k = len(points[0]) clusters = [] cl_centers = [] for i in range(n): clusters.append(0) for i in range(cl_num): cl_centers.append(points[i]) iters = 0 while True: new_cl_acc = [] new_cl_cnt = [] for i in range(cl_num): new_cl_acc.append([]) new_cl_cnt.append(0) for j in range(k): new_cl_acc[i].append(0) # Rearranging points into clusters, adding point to new cluster center accumulation points for i in range(n): min_ind = -1 min_dist = 1000000 for j in range(cl_num): dist = calc_dist(points[i], cl_centers[j]) if dist < min_dist: min_dist = dist min_ind = j clusters[i] = min_ind sum_to_point(new_cl_acc[min_ind], points[i]) new_cl_cnt[min_ind] += 1 # Recalculate cluster centers cl_centers = [] for i in range(cl_num): cl_centers.append(div_point(new_cl_acc[i], new_cl_cnt[i])) # iters += 1 if iters > 100: break return clusters def calc_dist(p1, p2): buf = 0 for i in range(len(p1)): buf += pow(p1[i] - p2[i], 2) return math.sqrt(buf) def sum_to_point(p, p2): for i in range(len(p)): p[i] += p2[i] def div_point(p, k): p2 = [] for i in range(len(p)): p2.append(p[i] / k) return p2 ```