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 DAEclass import DAE

import os

if __name__ == '__main__':

    datatypes=["equal","heterogeneous"]

    typenums=[5,10,15]

    noise_factor=0.5

    for datatype in datatypes:

        for typenum in typenums:

            datapath='data/simulations/{}/{}'.format(datatype, typenum)

            resultpath='result/simulations/{}/{}'.format(datatype, typenum)

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

            groundtruth = list(np.int_(groundtruth))

            omics1 = np.loadtxt('{}/o1.txt'.format(datapath))

            omics1 = np.transpose(omics1)

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

            omics2 = np.loadtxt('{}/o2.txt'.format(datapath))

            omics2 = np.transpose(omics2)

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

            omics3 = np.loadtxt('{}/o3.txt'.format(datapath))

            omics3 = np.transpose(omics3)

            omics3 = normalize(omics3, axis=0, norm='max')

            omics = np.concatenate((omics1, omics2, omics3), axis=1)

            # ae = AE(data, dims)

            # ae.train()

            # encoded_factors = ae.predict(data)

            noise_factor = 0.1

            encoding1_dim1 = 100

            encoding2_dim1 = 50

            if typenum==15:

                middle_dim1 = 5

            elif typenum==10:

                middle_dim1 = 4

            elif typenum==5:

                middle_dim1 = 3

            dims1 = [encoding1_dim1, encoding2_dim1, middle_dim1]

            ae1 = DAE(omics1, dims1,noise_factor)

            ae1.train()

            ae1.autoencoder.summary()

            encoded_factor1 = ae1.predict(omics1)

            encoding1_dim2 = 80

            encoding2_dim2 = 50

            if typenum==15:

                middle_dim2 = 5

            elif typenum==10:

                middle_dim2 = 3

            elif typenum==5:

                middle_dim2 = 1

            dims2 = [encoding1_dim2, encoding2_dim2, middle_dim2]

            ae2 = DAE(omics2, dims2,noise_factor)

            ae2.train()

            ae2.autoencoder.summary()

            encoded_factor2 = ae2.predict(omics2)

            encoding1_dim3 = 80

            encoding2_dim3 = 50

            if typenum==15:

                middle_dim3 = 5

            elif typenum==10:

                middle_dim3 = 3

            elif typenum==5:

                middle_dim3 = 1

            dims3 = [encoding1_dim3, encoding2_dim3, middle_dim3]

            ae3 = DAE(omics3, dims3,noise_factor)

            ae3.autoencoder.summary()

            ae3.train()

            encoded_factor3 = ae3.predict(omics3)

            encoded_factors = np.concatenate((encoded_factor1, encoded_factor2, encoded_factor3), axis=1)

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

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

            np.savetxt("{}/DAE_FAETC_EM_{}.txt".format(resultpath,typenum), 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("zlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzly")

            # #print("centers:")

            # #print(centers)

            # print("zlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzlyzly")

            # labels = clf.labels_

            # print("labels:")

            # print(labels)

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

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

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

            # np.savetxt("{}/DAE_FAETC_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")