python - 如何从 Python 中的 k-medoids 算法获得 plot 我的答案？

import numpy as np class PAM: def __init__(self, k=2, max_iter=100, tol=0.001): self.k = k self.max_iter = max_iter self.tol = tol self.medoids = [] self.medoids_cost = [] self.hasCoverged = False def isConverged(self, new_medoids): # is old Medoids == new Medoids return set([tuple(x) for x in self.medoids]) == set([tuple(x) for x in new_medoids]) def updateMedoids(self, data, labels, distance): self.hasCoverged = True clusters = [] for i in range(self.k): cluster = [] for j in range(len(data)): if labels[j] == i: cluster.append(data[j]) # data with common labels clusters.append(cluster) new_medoids = [] for i in range(self.k): new_medoid = self.medoids[i] old_medoid_cost = self.medoids_cost[i] for j in range(len(clusters[i])): cur_medoids_cost = 0 for dpoint_index in range(len(clusters[i])): dist = distance(clusters[i][dpoint_index], clusters[i][j]) cur_medoids_cost+= dist if cur_medoids_cost < old_medoid_cost: # if calculated distance is less than the old one we found a new medoid with a minimum cost new_medoid = clusters[i][j] # update medoid old_medoid_cost = cur_medoids_cost new_medoids.append(new_medoid) if not self.isConverged(new_medoids): self.medoids = new_medoids self.hasCoverged = False def fit (self, data, distance) : #init medoids ind = np.random.randint(0, len(data)-1, self.k) self.medoids = data[ind] for i in range(0, self.k): self.medoids_cost.append(0) for i in range(self.max_iter): cur_labels = [] for medoid in range(self.k): self.medoids_cost[medoid] = 0 for k in range(len(data)): d_list = [] for j in range(self.k): dist = distance(data[k], self.medoids[j]) d_list.append(dist) cur_labels.append(d_list.index(min(d_list))) # the cost of each medoid is the sum of minimum distances in k medoids for each data self.medoids_cost[medoid] += min(d_list) self.updateMedoids(data, cur_labels, distance) if self.hasCoverged: break return np.array(self.medoids)

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