Davies-Bouldin Index in Java

Question

我正在寫一個遺傳算法，試圖選擇一組數據點以最大化集羣間距離，同時保持兩簇之間的簇內距離很小。

我認爲像Davies-Bouldin指數這樣的羣集有效性度量是一個很好的適應度函數，但我正在努力尋找僞代碼或java代碼中算法的實現。

有人可以幫我嗎？

謝謝。

Answer 1

快速Google search給了一個鏈接到這個implemention。這實現的tinatool（穩定狀態下的生物網絡T-不變的相關計算庫）的一部分。

Answer 2

我已經在Python基於https://en.wikipedia.org/wiki/Davies%E2%80%93Bouldin_index

def davies_bouldin(X, labels, cluster_ctr): 
    #get the cluster assignemnts 
    clusters = set(labels) 
    #get the number of clusters 
    num_clusters = len(clusters) 
    #array to hold the number of items for each cluster, indexed by cluster number 
    num_items_in_clusters = [0]*num_clusters 
    #get the number of items for each cluster 
    for i in range(len(labels)): 
     num_items_in_clusters[labels[i]] += 1 
    max_num = -9999 
    for i in range(num_clusters): 
     s_i = intra_cluster_dist(X, labels, clusters[i], num_items_in_clusters[i], cluster_ctr[i]) 
    for j in range(num_clusters): 
     if(i != j): 
      s_j = intra_cluster_dist(X, labels, clusters[j], num_items_in_clusters[j], cluster_ctr[j]) 
      m_ij = np.linalg.norm(cluster_ctr[clusters[i]]-cluster_ctr[clusters[j]]) 
      r_ij = (s_i + s_j)/m_ij 
      if(r_ij > max_num): 
       max_num = r_ij 
return max_num 

def intra_cluster_dist(X, labels, cluster, num_items_in_cluster, centroid): 
    total_dist = 0 
    #for every item in cluster j, compute the distance the the center of cluster j, take average 
    for k in range(num_items_in_cluster): 
     dist = np.linalg.norm(X[labels==cluster]-centroid) 
     total_dist = dist + total_dist 
return total_dist/num_items_in_cluster 

希望實現它這有助於