from keras.layers import Input, Dense

from keras.models import Model

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

from sklearn.cluster import k_means

from sklearn.metrics import silhouette_score, davies_bouldin_score

from sklearn.preprocessing import normalize

import time

from sklearn import metrics

from myUtils import *

from AEclass import AE

import os

if __name__ == '__main__':

    datapath = 'data/single-cell/'

    resultpath = 'result/single-cell/'

    # groundtruth = np.loadtxt('{}/c.txt'.format(datapath))

    # groundtruth = list(np.int_(groundtruth))

    omics = np.loadtxt('{}/omics.txt'.format(datapath))

    omics = np.transpose(omics)

    omics1=omics[0:206]

    omics2=omics[206:412]

    omics1 = normalize(omics1, axis=0, norm='max')

    omics2 = normalize(omics2, axis=0, norm='max')

    omics = np.concatenate((omics1, omics2), axis=1)

    # omics1 = np.loadtxt('{}/ata.txt'.format(datapath))

    # omics1 = np.transpose(omics1)

    # omics1 = normalize(omics1, axis=0, norm='max')

    #

    # omics2 = np.loadtxt('{}/rna.txt'.format(datapath))

    # omics2 = np.transpose(omics2)

    # omics2 = normalize(omics2, axis=0, norm='max')

    #

    # omics = np.concatenate((omics1, omics2), axis=1)

    print(omics1.shape)

    print(omics2.shape)

    # data = omics

    # input_dim = data.shape[1]

    # encoding1_dim = 4096

    # encoding2_dim = 1024

    # middle_dim = 2

    # dims = [encoding1_dim, encoding2_dim, middle_dim]

    # ae = AE(data, dims)

    # ae.autoencoder.summary()

    # ae.train()

    # encoded_factors = ae.predict(data)

    # if not os.path.exists("{}/AE_FCTAE_EM.txt".format(resultpath)):

    #     os.mknod("{}/AE_FCTAE_EM.txt".format(resultpath))

    # np.savetxt("{}/AE_FCTAE_EM.txt".format(resultpath), encoded_factors)

    #

    # # if not os.path.exists("AE_FCTAE_Kmeans.txt"):

    # #     os.mknod("AE_FCTAE_Kmeans.txt")

    # # fo = open("AE_FCTAE_Kmeans.txt", "a")

    # clf = KMeans(n_clusters=typenum)

    # t0 = time.time()

    # clf.fit(encoded_factors)  # 模型训练

    # km_batch = time.time() - t0  # 使用kmeans训练数据消耗的时间

    #

    # print(datatype, typenum)

    # print("K-Means算法模型训练消耗时间:%.4fs" % km_batch)

    #

    # # 效果评估

    # score_funcs = [

    #     metrics.adjusted_rand_score,  # ARI（调整兰德指数）

    #     metrics.v_measure_score,  # 均一性与完整性的加权平均

    #     metrics.adjusted_mutual_info_score,  # AMI（调整互信息）

    #     metrics.mutual_info_score,  # 互信息

    # ]

    # centers = clf.cluster_centers_

    # # print("centers:")

    # # print(centers)

    # print("zlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzly")

    # labels = clf.labels_

    # print("labels:")

    # print(labels)

    # labels = list(np.int_(labels))

    # if not os.path.exists("{}/AE_FCTAE_CL.txt".format(resultpath)):

    #     os.mknod("{}/AE_FCTAE_CL.txt".format(resultpath))

    # np.savetxt("{}/AE_FCTAE_CL.txt".format(resultpath), labels, fmt='%d')

    # print("zlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzly")

    # # 2. 迭代对每个评估函数进行评估操作

    # for score_func in score_funcs:

    #     t0 = time.time()

    #     km_scores = score_func(groundtruth, labels)

    #     print("K-Means算法:%s评估函数计算结果值:%.5f；计算消耗时间:%0.3fs" % (score_func.__name__, km_scores, time.time() - t0))

    # t0 = time.time()

    # jaccard_score = jaccard_coefficient(groundtruth, labels)

    # print("K-Means算法:%s评估函数计算结果值:%.5f；计算消耗时间:%0.3fs" % (

    #     jaccard_coefficient.__name__, jaccard_score, time.time() - t0))

    # silhouetteScore = silhouette_score(encoded_factors, labels, metric='euclidean')

    # davies_bouldinScore = davies_bouldin_score(encoded_factors, labels)

    # print("silhouetteScore:", silhouetteScore)

    # print("davies_bouldinScore:", davies_bouldinScore)

    # print("zlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzly")