# Compute transition matrix past
mc_past = MarkovChain(past_step, states)
mc_past = mc_past.csr_sparse_matrix()
print('matrix done', datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'))
print('start clustering')
# DBSCAN
dbscan_next = Clustering(mc_next)
dbscan_past = Clustering(mc_past)
print("End clustering", datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'), '\n')
"""
BACKPROB HERE
"""
unique_next, counts_next, labels_next = dbscan_next.unique_labels()
unique_past, counts_past, labels_past = dbscan_past.unique_labels()
cluster_dict_next = dbscan_next.cluster_dict(labels_next, mc_next)
cluster_dict_past = dbscan_past.cluster_dict(labels_past, mc_past)
first_list = dbscan_next.list_cluster(cluster_dict_next, labels_next, labels_past)
second_list = dbscan_past.list_cluster(cluster_dict_past, labels_next, labels_past)
cluster_mean_hist = ca(first_list, second_list).cluster_backprob()
#print(cluster_mean_hist)
end_time = datetime.now()
print('Duration: {}'.format(end_time - start_time))
