Python 存储已添加到集群的行和列的索引

我的问题是关于层次聚类的。我正试图从给定的距离矩阵出发，用Python从头开始创建一个层次聚类算法。我希望从scipy.cluster.hierarchy.linkage获得与linkage函数相同的输出。但是，当我想要将文件的标记（距离矩阵中的行/列）添加到算法中较早形成的集群中时，我遇到了一个问题

def cluster(distance_matrix):

    n = len(distance_matrix)

    cluster_dictionary = {i:i for i in range(n)}
    linkage_matrix = np.zeros((n - 1, 4))

    for i in range(n - 1):
        # Create a mask with True on all values that are not on the diagonal
        mask = np.ones(distance_matrix.shape, dtype=bool)
        np.fill_diagonal(mask, 0)

        # Find the index of the minimum distance between two texts disregarding the diagonal
        min_distance = np.min(distance_matrix[mask])
        ind = np.where(distance_matrix == min_distance)[0]

        # Gather the indices of our clusters from the indices of our distance matrix
        cluster_ind = [cluster_dictionary[idx] for idx in ind]

        # Update our cluster_dictionary so each index is properly assigned to a cluster
        for text_idx in cluster_ind:
            if text_idx > n - 1:
                for k, v in cluster_dictionary.items():
                    if v == text_idx:
                        cluster_dictionary[k] = i + n
            else:
                cluster_dictionary[text_idx] = i + n

        linkage_matrix[i, 0] = cluster_ind[0]
        linkage_matrix[i, 1] = cluster_ind[1]
        linkage_matrix[i, 2] = min_distance
        linkage_matrix[i, 3] = 0

        # Calculate the new distances our texts are from our newly found cluster
        distance_vector = distance_from_cluster(ind, distance_matrix)

        # Remove the row and column that we just clustered, also remove it from our custom indices
        distance_matrix = delete_row_column(distance_matrix, ind)

        # Insert our new distance vector for the clustered group into the distance matrix
        distance_matrix = np.insert(distance_matrix, 0, distance_vector[1:], axis=0) 
        distance_matrix = np.insert(distance_matrix, 0, distance_vector, axis=1) 

    return np.array(linkage_matrix)

当我跑步时：

import scipy.spatial.distance as ssd

distance_matrix = np.array([[0, 9, 3, 6, 11],
                            [9, 0, 7, 5, 10],
                            [3, 7, 0, 9, 2],
                            [6, 5, 9, 0, 8],
                            [11, 10, 2, 8, 0]])


own_linkage = cluster(distance_matrix)
print(own_linkage)
print()
print(hierarchy.linkage(ssd.squareform(distance_matrix), method='complete', metric='cosine'))

我得到：

[[ 2.  4.  2.  0.]
 [ 5.  3.  5.  0.]
 [ 0.  6.  9.  0.]
 [ 7.  1. 11.  0.]]

[[ 2.  4.  2.  2.]
 [ 1.  3.  5.  2.]
 [ 0.  6.  9.  3.]
 [ 5.  7. 11.  5.]]

忽略最后一列，我的算法在链接矩阵的第二行出错

我认为这与这样一个事实有关，因为当我将函数中新发现的簇添加到距离矩阵中时，我稍微更改了索引，这使得我的

cluster\u字典

出错。我认为存储某种自定义索引可能会解决这个问题，但我找不到一个好的编程方法来解决这个问题。我认为在计算距离矩阵中从新添加的集群到另一个文件的距离时也可能出错，但是这个问题可能源于前面提到的问题

以下是我在上述程序中使用的函数：

def delete_row_column(distance_matrix, indices):
    """Returns the distance matrix with the rows and columns specified in indices
    deleted"""
    remain_ind = [i for i in range(len(distance_matrix)) if not i in indices]
    distance_matrix = distance_matrix[np.ix_(remain_ind, remain_ind)]
    return distance_matrix


def distance_from_cluster(cluster, distance_matrix):
    """Finds all distances that our texts are from the cluster defined as the longest distance
    from a point within our cluster to a given text"""
    distance_vector = [0]
    for i, distance_row in enumerate(distance_matrix):
        distances = [distance_row[i] for i in cluster]
        # Disregard texts already in the cluster
        if not 0 in distances:
            distance_vector.append(np.max(distances))
    return np.array(distance_vector)