n_clusters = 32
X = X_train

cluster = AgglomerativeClustering(n_clusters=n_clusters).fit(X)
costMatrix = cost_matrix(cluster.labels_,y_train)
predicted = HungarianAlgorithm(costMatrix,cluster.labels_)
#find centers
clusterCenters = np.zeros((n_clusters,2576))
covs = np.zeros((n_clusters,2576))
weights = np.zeros(n_clusters)
for i in range(n_clusters):
    indices = findIndices(i,cluster.labels_)
    print(indices)
    faces = np.zeros((len(indices),2576))

    for ind, index in enumerate(indices):
        faces[ind] = X[index]
    clusterCenters[i] = np.mean(faces,axis = 0)
    covs[i] = 1/(np.diag(np.cov(faces.T))+1e-6)
    weights[i] = faces.shape[0]/320