SDMVC.py

from time import time
import numpy as np
import platform
from sklearn.metrics import log_loss
import tensorflow.keras.backend as K
from tensorflow.keras.layers import Conv1D, Conv2D, Conv2DTranspose, Flatten, Reshape, Conv3D, Conv3DTranspose, MaxPooling2D, Dropout, GlobalMaxPooling2D
from tensorflow.keras.layers import Layer, InputSpec, Input, Dense, Multiply, concatenate
from tensorflow.keras.models import Model
from tensorflow.keras import callbacks
from tensorflow.keras.initializers import VarianceScaling
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.regularizers import Regularizer, l1, l2, l1_l2
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering, SpectralClustering
from sklearn.decomposition import PCA, SparsePCA
from math import log
import Nmetrics
import matplotlib.pyplot as plt


def FAE(dims, act='relu', view=1):
    n_stacks = len(dims) - 1
    init = VarianceScaling(scale=1. / 3., mode='fan_in', distribution='uniform')

    input_name = 'v'+str(view)+'_'
    # input
    x = Input(shape=(dims[0],), name='input' + str(view))

    h = x

    # internal layers in encoder
    for i in range(n_stacks-1):
        h = Dense(dims[i + 1], activation=act, kernel_initializer=init, name=input_name+'encoder_%d' % i)(h)
 
    # hidden layer
    h = Dense(dims[-1], kernel_initializer=init, name='embedding' + str(view))(h)  # hidden layer, features are extracted from here

    y = h
    # internal layers in decoder
    for i in range(n_stacks-1, 0, -1):
        y = Dense(dims[i], activation=act, kernel_initializer=init, name=input_name+'decoder_%d' % i)(y)

    # output
    y = Dense(dims[0], kernel_initializer=init, name=input_name+'decoder_0')(y)

    return Model(inputs=x, outputs=y, name=input_name+'Fae'), Model(inputs=x, outputs=h, name=input_name+'Fencoder')


def MAE(view=2, filters=[32, 64, 128, 10], view_shape = [1, 2, 3]):
    # print(len(view_shape[0]))
    if len(view_shape[0]) == 1:
        typenet = 'f-f'          # Fully connected networks
    else:
        typenet = 'c-c'          # Convolution networks

    if typenet == 'c-c':
        input1_shape = view_shape[0]
        input2_shape = view_shape[1]
        if input1_shape[0] % 8 == 0:
            pad1 = 'same'
        else:
            pad1 = 'valid'
        print("----------------------")
        print(filters)
        input1 = Input(input1_shape, name='input1')
        x = Conv2D(filters[0], 5, strides=2, padding='same', activation='relu', name='conv1_v1')(input1)
        x = Conv2D(filters[1], 5, strides=2, padding='same', activation='relu', name='conv2_v1')(x)
        x = Conv2D(filters[2], 3, strides=2, padding=pad1, activation='relu', name='conv3_v1')(x)
        x = Flatten(name='Flatten1')(x)
        x1 = Dense(units=filters[3], name='embedding1')(x)
        x = Dense(units=filters[2]*int(input1_shape[0]/8)*int(input1_shape[0]/8), activation='relu',
                  name='Dense1')(x1)
        x = Reshape((int(input1_shape[0]/8), int(input1_shape[0]/8), filters[2]), name='Reshape1')(x)
        x = Conv2DTranspose(filters[1], 3, strides=2, padding=pad1, activation='relu', name='deconv3_v1')(x)
        x = Conv2DTranspose(filters[0], 5, strides=2, padding='same', activation='relu', name='deconv2_v1')(x)
        x = Conv2DTranspose(input1_shape[2], 5, strides=2, padding='same', name='deconv1_v1')(x)

        input2 = Input(input2_shape, name='input2')
        xn = Conv2D(filters[0], 5, strides=2, padding='same', activation='relu', name='conv1_v2')(input2)
        xn = Conv2D(filters[1], 5, strides=2, padding='same', activation='relu', name='conv2_v2')(xn)
        xn = Conv2D(filters[2], 3, strides=2, padding=pad1, activation='relu', name='conv3_v2')(xn)
        xn = Flatten(name='Flatten2')(xn)
        x2 = Dense(units=filters[3], name='embedding2')(xn)
        xn = Dense(units=filters[2] * int(input2_shape[0] / 8) * int(input2_shape[0] / 8), activation='relu',
                   name='Dense2')(x2)
        xn = Reshape((int(input2_shape[0] / 8), int(input2_shape[0] / 8), filters[2]), name='Reshape2')(xn)
        xn = Conv2DTranspose(filters[1], 3, strides=2, padding=pad1, activation='relu', name='deconv3_v2')(xn)
        xn = Conv2DTranspose(filters[0], 5, strides=2, padding='same', activation='relu', name='deconv2_v2')(xn)
        xn = Conv2DTranspose(input2_shape[2], 5, strides=2, padding='same', name='deconv1_v2')(xn)
        encoder1 = Model(inputs=input1, outputs=x1)
        encoder2 = Model(inputs=input2, outputs=x2)
        ae1 = Model(inputs=input1, outputs=x)
        ae2 = Model(inputs=input2, outputs=xn)

        if view == 2:
            return [ae1, ae2], [encoder1, encoder2]
        else:
            input3_shape = view_shape[2]
            input3 = Input(input3_shape, name='input3')
            xr = Conv2D(filters[0], 5, strides=2, padding='same', activation='relu', name='conv1_v3')(input3)
            xr = Conv2D(filters[1], 5, strides=2, padding='same', activation='relu', name='conv2_v3')(xr)
            xr = Conv2D(filters[2], 3, strides=2, padding=pad1, activation='relu', name='conv3_v3')(xr)
            xr = Flatten(name='Flatten3')(xr)
            x3 = Dense(units=filters[3], name='embedding3')(xr)
            xr = Dense(units=filters[2] * int(input3_shape[0] / 8) * int(input3_shape[0] / 8), activation='relu',
                       name='Dense3')(x3)
            xr = Reshape((int(input3_shape[0] / 8), int(input3_shape[0] / 8), filters[2]), name='Reshape3')(xr)
            xr = Conv2DTranspose(filters[1], 3, strides=2, padding=pad1, activation='relu', name='deconv3_v3')(xr)
            xr = Conv2DTranspose(filters[0], 5, strides=2, padding='same', activation='relu', name='deconv2_v3')(xr)
            xr = Conv2DTranspose(input2_shape[2], 5, strides=2, padding='same', name='deconv1_v3')(xr)

            encoder3 = Model(inputs=input3, outputs=x3)
            ae3 = Model(inputs=input3, outputs=xr)

            return [ae1, ae2, ae3], [encoder1, encoder2, encoder3]

    if typenet == 'f-f':
        ae = []
        encoder = []
        for v in range(view):
            ae_tmp, encoder_tmp = FAE(dims=[view_shape[v][0], 500, 500, 2000, 10], view=v + 1)
            ae.append(ae_tmp)
            encoder.append(encoder_tmp)
        
        return ae, encoder
        

class ClusteringLayer(Layer):
    """
    Clustering layer converts input sample (feature) to soft label, i.e. a vector that represents the probability of the
    sample belonging to each cluster. The probability is calculated with student's t-distribution.

    # Example
    ```
        model.add(ClusteringLayer(n_clusters=10))
    ```
    # Arguments
        n_clusters: number of clusters.
        weights: list of Numpy array with shape `(n_clusters, n_features)` witch represents the initial cluster centers.
        alpha: parameter in Student's t-distribution. Default to 1.0.
    # Input shape
        2D tensor with shape: `(n_samples, n_features)`.
    # Output shape
        2D tensor with shape: `(n_samples, n_clusters)`.
    """

    def __init__(self, n_clusters, weights=None, alpha=1.0, **kwargs):
        if 'input_shape' not in kwargs and 'input_dim' in kwargs:
            kwargs['input_shape'] = (kwargs.pop('input_dim'),)
        super(ClusteringLayer, self).__init__(**kwargs)
        self.n_clusters = n_clusters
        self.alpha = alpha
        self.initial_weights = weights
        self.input_spec = InputSpec(ndim=2)

    def build(self, input_shape):
        assert len(input_shape) == 2    
        input_dim = input_shape.as_list()[1]
        self.input_spec = InputSpec(dtype=K.floatx(), shape=(None, input_dim))
        self.clusters = self.add_weight(shape=(self.n_clusters, input_dim), initializer='glorot_uniform', name='clusters')
        if self.initial_weights is not None:
            self.set_weights(self.initial_weights)
            del self.initial_weights
        self.built = True

    def call(self, inputs, **kwargs):
        """ student t-distribution, as same as used in t-SNE algorithm.
                 q_ij = 1/(1+dist(x_i, u_j)^2), then normalize it.
        Arguments:
            inputs: the variable containing data, shape=(n_samples, n_features)
        Return:
            q: student's t-distribution, or soft labels for each sample. shape=(n_samples, n_clusters)
        """
        q = 1.0 / (1.0 + (K.sum(K.square(K.expand_dims(inputs, axis=1) - self.clusters), axis=2) / self.alpha))
        q **= (self.alpha + 1.0) / 2.0
        q = K.transpose(K.transpose(q) / K.sum(q, axis=1))
        return q

    def compute_output_shape(self, input_shape):
        assert input_shape and len(input_shape) == 2
        return input_shape[0], self.n_clusters

    def get_config(self):
        config = {'n_clusters': self.n_clusters}
        base_config = super(ClusteringLayer, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class MvDEC(object):
    def __init__(self,
                 filters=[32, 64, 128, 10], 
                 #  view=2,
                 n_clusters=10,
                 alpha=1.0, view_shape = [1, 2, 3, 4, 5, 6]):

        super(MvDEC, self).__init__()

        self.view_shape = view_shape
        self.filters = filters
        self.n_clusters = n_clusters
        self.alpha = alpha
        self.pretrained = False
        # prepare MvDEC model
        self.view = len(view_shape)
        # print(len(view_shape))

        self.AEs, self.encoders = MAE(view=self.view, filters=self.filters, view_shape=self.view_shape)
    
        Input = []
        Output = []
        Input_e = []
        Output_e = []
        clustering_layer = []

        for v in range(self.view):
                Input.append(self.AEs[v].input)
                Output.append(self.AEs[v].output)
                Input_e.append(self.encoders[v].input)
                Output_e.append(self.encoders[v].output)
                clustering_layer.append(ClusteringLayer(self.n_clusters, name='clustering'+str(v+1))(self.encoders[v].output))

        self.autoencoder = Model(inputs=Input, outputs=Output)    # xin _ xout

        self.encoder = Model(inputs=Input_e, outputs=Output_e)   # xin _ q

        Output_m = []
        for v in range(self.view):
            Output_m.append(clustering_layer[v])
            Output_m.append(Output[v])
        self.model = Model(inputs=Input, outputs=Output_m)   # xin _ q _ xout

    def pretrain(self, x, y, optimizer='adam', epochs=200, batch_size=256,
                 save_dir='results/temp', verbose=0):
        print('Begin pretraining: ', '-' * 60)
        multi_loss = []
        for view in range(len(x)):
            multi_loss.append('mse')
        self.autoencoder.compile(optimizer=optimizer, loss=multi_loss)
        csv_logger = callbacks.CSVLogger(save_dir + '/T_pretrain_ae_log.csv')
        save = '/ae_weights.h5'
        cb = [csv_logger]
        if y is not None and verbose > 0:
            class PrintACC(callbacks.Callback):
                def __init__(self, x, y, flag=1):
                    self.x = x
                    self.y = y
                    self.flag = flag
                    super(PrintACC, self).__init__()

                def on_epoch_end(self, epoch, logs=None):
                    time = 1    #  show k-means results on z
                    if int(epochs / time) != 0 and (epoch+1) % int(epochs/time) != 0:
                        # print(epoch)
                        return
                    view_name = 'embedding' + str(self.flag)
                    feature_model = Model(self.model.input[self.flag - 1],
                                              self.model.get_layer(name=view_name).output)
                    
                    features = feature_model.predict(self.x)
                    km = KMeans(n_clusters=len(np.unique(self.y)), n_init=20, n_jobs=4)
                    y_pred = km.fit_predict(features)
                    print('\n' + ' '*8 + '|==>  acc: %.4f,  nmi: %.4f  <==|'
                          % (Nmetrics.acc(self.y, y_pred), Nmetrics.nmi(self.y, y_pred)))

            for view in range(len(x)):
                cb.append(PrintACC(x[view], y, flag=view + 1))

        # begin pretraining
        t0 = time()
        self.autoencoder.fit(x, x, batch_size=batch_size, epochs=epochs, callbacks=cb, verbose=verbose)

        print('Pretraining time: ', time() - t0)
        self.autoencoder.save_weights(save_dir + save)
        print('Pretrained weights are saved to ' + save_dir + save)
        self.pretrained = True
        print('End pretraining: ', '-' * 60)

    def load_weights(self, weights):  # load weights of models
        self.model.load_weights(weights)

    def predict_label(self, x):  # predict cluster labels using the output of clustering layer
        input_dic = {}
        for view in range(len(x)):
            input_dic.update({'input' + str(view+1): x[view]})
        Q_and_X = self.model.predict(input_dic, verbose=0)
        y_pred = []
        for view in range(len(x)):
            # print(view)
            y_pred.append(Q_and_X[view*2].argmax(1))
        
        y_q = Q_and_X[(len(x)-1)*2]
        for view in range(len(x) - 1):
            y_q += Q_and_X[view*2]

        # y_q = y_q/len(x)
        y_mean_pred = y_q.argmax(1)
        return y_pred, y_mean_pred

    @staticmethod    
    def target_distribution(q):
        weight = q ** 2 / q.sum(0)
        # return q
        return (weight.T / weight.sum(1)).T

    def compile(self, optimizer='sgd', loss=['kld', 'mse'], loss_weights=[0.1, 1.0]):
        self.model.compile(optimizer=optimizer, loss=loss, loss_weights=loss_weights)

    def train_on_batch(self, xin, yout, sample_weight=None):
        return self.model.train_on_batch(xin, yout, sample_weight)

    # DEMVC
    def fit(self, arg, x, y, maxiter=2e4, batch_size=256, tol=1e-3,
            UpdateCoo=200, save_dir='./results/tmp'):
        print('Begin clustering:', '-' * 60)
        print('Update Coo:', UpdateCoo)
        save_interval = int(maxiter)  # only save the initial and final model
        print('Save interval', save_interval)
        # Step 1: initialize cluster centers using k-means
        t1 = time()
        ting = time() - t1
        print(ting)
        
        time_record = []
        time_record.append(int(ting))
        print(time_record)
        print('Initializing cluster centers with k-means.')
        kmeans = KMeans(n_clusters=self.n_clusters, n_init=100)
        
        input_dic = {}
        for view in range(len(x)):
            input_dic.update({'input' + str(view+1): x[view]})
        features = self.encoder.predict(input_dic)

        y_pred = []
        center = []

        # --------------------------------------------
        c = 1
        if c == 1:
            for view in range(len(x)):
                y_pred.append(kmeans.fit_predict(features[view]))
                np.save('TC'+str(view+1)+'.npy', [kmeans.cluster_centers_])
                center.append(np.load('TC'+str(view+1)+'.npy'))
        else:
            from numpy import hstack
            from sklearn import preprocessing
            min_max_scaler = preprocessing.MinMaxScaler()
            # n_features = []
            # for view in range(len(x)):
            #     n_features.append(min_max_scaler.fit_transform(features[view]))
            #     # n_features.append(features[view])
            # z = hstack(n_features)
            z = hstack(features)
            print(features[0].shape, len(x), z.shape)
            y_pred.append(kmeans.fit_predict(z))
            for view in range(len(x)-1):
                y_pred.append(y_pred[0])
            print(kmeans.cluster_centers_.shape)
            centers = kmeans.cluster_centers_
            for view in range(len(x)):
                b = 10*view
                e = b + 10
                np.save('TC'+str(view+1)+'.npy', [centers[:, b:e]])
                center.append(np.load('TC'+str(view+1)+'.npy'))
        # --------------------------------------------

        for view in range(len(x)):
            acc = np.round(Nmetrics.acc(y, y_pred[view]), 5)
            nmi = np.round(Nmetrics.nmi(y, y_pred[view]), 5)
            vmea = np.round(Nmetrics.vmeasure(y, y_pred[view]), 5)
            ari = np.round(Nmetrics.ari(y, y_pred[view]), 5)
            print('Start-'+str(view+1)+': acc=%.5f, nmi=%.5f, v-measure=%.5f, ari=%.5f' % (acc, nmi, vmea, ari))

        y_pred_last = []
        y_pred_sp = []
        for view in range(len(x)):
            y_pred_last.append(y_pred[view])
            y_pred_sp.append(y_pred[view])

        for view in range(len(x)):
            if arg.K12q == 0:
                self.model.get_layer(name='clustering'+str(view+1)).set_weights(center[view])
            else:
                self.model.get_layer(name='clustering'+str(view+1)).set_weights(center[arg.K12q - 1])

        # Step 2: deep clustering
        # logging file
        import csv
        import os
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)
        logfile = open(save_dir + '/log.csv', 'w')
        logwriter = csv.DictWriter(logfile, fieldnames=['iter', 'nmi', 'vmea', 'ari', 'loss'])
        logwriter.writeheader()

        index_array = np.arange(x[0].shape[0])
        index = 0
        
        Loss = []
        avg_loss = []
        for view in range(len(x)):
            Loss.append(0)
            avg_loss.append(0)

        flag = 1
        
        vf = arg.view_first

        update_interval = arg.UpdateCoo
        
        for ite in range(int(maxiter)):  # fine-turing

            if ite % update_interval == 0:

                Q_and_X = self.model.predict(input_dic)
                # Coo
                for view in range(len(x)):
                    y_pred_sp[view] = Q_and_X[view*2].argmax(1)

                # print(flag, (flag % len(x)))
                # view_num = len(x)
                q_index = (flag + vf - 1) % len(x)
                if q_index == 0:
                    q_index = len(x)
                p = self.target_distribution(Q_and_X[(q_index-1) * 2])  # q->p
                # print(q_index)
                flag += 1
                print('Next corresponding: p' + str(q_index))

                P = []
                if arg.Coo == 1:
                    for view in range(len(x)):
                        P.append(p)
                else:
                    for view in range(len(x)):
                        P.append(self.target_distribution(Q_and_X[view*2]))

                ge = np.random.randint(0, x[0].shape[0], 1, dtype=int)
                ge = int(ge)
                print('Number of sample:' + str(ge))
                for view in range(len(x)):
                    for i in Q_and_X[view*2][ge]:
                        print("%.3f  " % i, end="")
                    print("\n")

                # evaluate the clustering performance
                for view in range(len(x)):
                    avg_loss[view] = Loss[view] / update_interval
                
                for view in range(len(x)):
                    Loss[view] = 0.

                if y is not None:
                    for num in range(self.n_clusters):
                        same = np.where(y == num)   
                        same = np.array(same)[0]
                        Out = y_pred_sp[len(x)-1][same]
                        for view in range(len(x)-1):
                            Out += y_pred_sp[view][same]

                        out = Out
                        comp = y_pred_sp[0][same]
                        
                        for i in range(len(out)):
                            if Out[i]/len(x) == comp[i]:
                                out[i] = 0
                            else:
                                out[i] = 1
                        if (len(out) != 0):        # Simply calculate the scale of the alignment
                            print('%d, %.2f%%, %d' % (num,len(np.array(np.where(out == 0))[0]) * 100/len(out),len(same)))
                        else:
                            print('%d, %.2f%%. %d' % (num, 0, len(same)))

                    for view in range(len(x)):
                        acc = np.round(Nmetrics.acc(y, y_pred_sp[view]), 5)
                        nmi = np.round(Nmetrics.nmi(y, y_pred_sp[view]), 5)
                        vme = np.round(Nmetrics.vmeasure(y, y_pred_sp[view]), 5)
                        ari = np.round(Nmetrics.ari(y, y_pred_sp[view]), 5)
                        logdict = dict(iter=ite, nmi=nmi, vmea=vme, ari=ari, loss=avg_loss[view])
                        logwriter.writerow(logdict)
                        logfile.flush()
                        print('V'+str(view+1)+'-Iter %d: acc=%.5f, nmi=%.5f, v-measure=%.5f, ari=%.5f; loss=%.5f' % (
                        ite, acc, nmi, vme, ari, avg_loss[view]))

                    ting = time() - t1

            # train on batch
            idx = index_array[index * batch_size: min((index + 1) * batch_size, x[0].shape[0])]
            x_batch = []
            y_batch = []
            for view in range(len(x)):
                x_batch.append(x[view][idx])
                y_batch.append(P[view][idx])
                y_batch.append(x[view][idx])

            tmp = self.train_on_batch(xin=x_batch, yout=y_batch)   # [y, xn, y, x]
            
            for view in range(len(x)):
                Loss[view] += tmp[2*view+1]
            
            index = index + 1 if (index + 1) * batch_size <= x[0].shape[0] else 0
            # ite += 1

        # save the trained model
        logfile.close()
        # print('saving model to:', save_dir + '/model_final.h5')
        # self.model.save_weights(save_dir + '/model_final.h5')
        # self.autoencoder.save_weights(save_dir + '/pre_model.h5')
        print('Clustering time: %ds' % (time() - t1))
        print('End clustering:', '-' * 60)
        
        Q_and_X = self.model.predict(input_dic)
        y_pred = []
        for view in range(len(x)):
            y_pred.append(Q_and_X[view*2].argmax(1))
        
        y_q = Q_and_X[(len(x)-1)*2]
        for view in range(len(x) - 1):
            y_q += Q_and_X[view*2]
        # y_q = y_q/len(x)
        y_mean_pred = y_q.argmax(1)
        return y_pred, y_mean_pred

    # SDMVC
    def new_fit(self, arg, x, y, maxiter=2e4, batch_size=256, tol=1e-3,
            UpdateCoo=200, save_dir='./results/tmp'):
        print('Begin clustering:', '-' * 60)
        print('Update Coo:', UpdateCoo)
        save_interval = int(maxiter)  # only save the initial and final model
        print('Save interval', save_interval)
        # Step 1: initialize cluster centers using k-means
        t1 = time()
        ting = time() - t1
        # print(ting)
        time_record = []
        time_record.append(int(ting))
        # print(time_record)
        print('Initializing cluster centers with k-means.')
        kmeans = KMeans(n_clusters=self.n_clusters, n_init=100)

        input_dic = {}
        for view in range(len(x)):
            input_dic.update({'input' + str(view + 1): x[view]})
        features = self.encoder.predict(input_dic)

        if len(x) <= 3:      # small trick: less view, more times to over arg.AR, so as to get high Aligned Rate
            arg.ARtime = 3
        else:
            arg.ARtime = 2

        y_pred = []
        center = []

        from numpy import hstack
        from sklearn import preprocessing
        min_max_scaler = preprocessing.MinMaxScaler()
        # --------------------------------------------
        c = 1
        if c == 1:
            for view in range(len(x)):
                y_pred.append(kmeans.fit_predict(features[view]))
                # np.save('TC' + str(view + 1) + '.npy', [kmeans.cluster_centers_])
                # center.append(np.load('TC' + str(view + 1) + '.npy'))
                center.append([kmeans.cluster_centers_])

        elif c == 2:
            n_features = []
            for view in range(len(x)):
                # n_features.append(min_max_scaler.fit_transform(features[view]))
                n_features.append(features[view])
            z = hstack(n_features)
            print(features[0].shape, len(x), z.shape)
            y_pred.append(kmeans.fit_predict(z))
            for view in range(len(x) - 1):
                y_pred.append(y_pred[0])
            print(kmeans.cluster_centers_.shape)
            centers = kmeans.cluster_centers_
            # print(self.new_P(z, centers))
            new_P = self.new_P(z, centers)
            print(new_P.argmax(1))
            print(y_pred[0])
            for view in range(len(x)):
                b = 10 * view
                e = b + 10
                np.save('TC' + str(view + 1) + '.npy', [centers[:, b:e]])
                center.append(np.load('TC' + str(view + 1) + '.npy'))
        else:
            for view in range(len(x)):
                y_pred.append(kmeans.fit_predict(features[view]))
            print("random")
        # --------------------------------------------

        for view in range(len(x)):
            acc = np.round(Nmetrics.acc(y, y_pred[view]), 5)
            nmi = np.round(Nmetrics.nmi(y, y_pred[view]), 5)
            vmea = np.round(Nmetrics.vmeasure(y, y_pred[view]), 5)
            ari = np.round(Nmetrics.ari(y, y_pred[view]), 5)
            print('Start-' + str(view + 1) + ': acc=%.5f, nmi=%.5f, v-measure=%.5f, ari=%.5f' % (acc, nmi, vmea, ari))

        y_pred_last = []
        y_pred_sp = []
        for view in range(len(x)):
            y_pred_last.append(y_pred[view])
            y_pred_sp.append(y_pred[view])

        for view in range(len(x)):
            # break
            if arg.K12q == 0:
                self.model.get_layer(name='clustering' + str(view + 1)).set_weights(center[view])
            else:
                self.model.get_layer(name='clustering' + str(view + 1)).set_weights(center[arg.K12q - 1])

        # Step 2: deep clustering
        # logging file
        import csv
        import os
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)
        logfile = open(save_dir + '/log.csv', 'w')
        logwriter = csv.DictWriter(logfile, fieldnames=['iter', 'nmi', 'vmea', 'ari', 'loss'])
        logwriter.writeheader()

        index_array = np.arange(x[0].shape[0])
        index = 0

        Loss = []
        avg_loss = []
        kl_loss = []
        for view in range(len(x)):
            Loss.append(0)
            avg_loss.append(0)
            kl_loss.append(100000)

        update_interval = arg.UpdateCoo
        center_init = 0
        alignment = 0
        alignment_large = 0

        ACC = []
        NMI = []
        ARI = []
        vACC = []
        vNMI = []
        vARI = []
        Rate = []
        MVKLLoss = []
        ite = 0
        while True:
            if ite % update_interval == 0:
                print('\n')
                for view in range(len(x)):
                    avg_loss[view] = Loss[view] / update_interval
                    kl_loss[view] = kl_loss[view] / update_interval

                Q_and_X = self.model.predict(input_dic)
                # Coo
                for view in range(len(x)):
                    # print(Q_and_X[view * 2][0])
                    y_pred_sp[view] = Q_and_X[view * 2].argmax(1)

                features = self.encoder.predict(input_dic)
                mu = []
                for view in range(len(x)):
                    muu = self.model.get_layer(name='clustering' + str(view + 1)).get_weights()
                    # print(muu)
                    mu.append(muu)

                # np.save(save_dir + '/Features/' + str(ite) + '.npy', features)
                # np.save(save_dir + '/Mu/' + str(ite) + '.npy', mu)
                # print(features[0][0])
                # print(features[1][0])

                if len(x) >= 6:
                    scaler = 1
                else:
                    scaler = 0
                # print("scaler ? :"+str(scaler))
                # if views' number is too many (eg, >= 6), we can scale the features to [0,1] to build global features
                if scaler == 1:
                    n_features = []
                    for view in range(len(x)):
                        n_features.append(min_max_scaler.fit_transform(features[view]))
                    z = hstack(n_features)
                else:
                    z = hstack(features)

                kmean = KMeans(n_clusters=self.n_clusters, n_init=100)
                y_pred = kmean.fit_predict(z)     # k-means on global features
                # print(kmeans.cluster_centers_.shape)
                # print(y_pred[0:9])

                acc = np.round(Nmetrics.acc(y, y_pred), 5)
                nmi = np.round(Nmetrics.nmi(y, y_pred), 5)
                vmea = np.round(Nmetrics.vmeasure(y, y_pred), 5)
                ari = np.round(Nmetrics.ari(y, y_pred), 5)
                print('ACC=%.5f, NMI=%.5f, ARI=%.5f' % (acc, nmi, ari))
                ACC.append(acc)
                NMI.append(nmi)
                ARI.append(ari)
                the = np.sum(kl_loss) / len(x)
                # print(kl_loss)
                # print(Loss)
                # print(np.sum(kl_loss), np.sum(Loss))

                if y is not None:

                    scale = len(y)
                    for i in range(len(y)):
                        predict = y_pred_sp[0][i]
                        for view in range(len(x) - 1):
                            if predict == y_pred_sp[view + 1][i]:
                                continue
                            else:
                                scale -= 1
                                break

                    # alignment_before = alignment
                    alignment = (scale / len(y))
                    print('Aligned Ratio: %.2f%%. %d' % (alignment * 100, len(y)))
                    Rate.append(alignment)
                    tmpACC = []
                    tmpNMI = []
                    tmpARI = []
                    for view in range(len(x)):
                        acc = np.round(Nmetrics.acc(y, y_pred_sp[view]), 5)
                        nmi = np.round(Nmetrics.nmi(y, y_pred_sp[view]), 5)
                        vme = np.round(Nmetrics.vmeasure(y, y_pred_sp[view]), 5)
                        ari = np.round(Nmetrics.ari(y, y_pred_sp[view]), 5)
                        logdict = dict(iter=ite, nmi=nmi, vmea=vme, ari=ari, loss=avg_loss[view])
                        logwriter.writerow(logdict)
                        logfile.flush()
                        print('V' + str(
                            view + 1) + '-Iter %d: ACC=%.5f, NMI=%.5f, ARI=%.5f; Loss=%.5f' % (
                                  ite, acc, nmi, ari, avg_loss[view]))
                        tmpACC.append(acc)
                        tmpNMI.append(nmi)
                        tmpARI.append(ari)
                    vACC.append(tmpACC)
                    vNMI.append(tmpNMI)
                    vARI.append(tmpARI)
                    ting = time() - t1

                if alignment > arg.AR:
                    alignment_large += 1
                else:
                    alignment_large = 0
                # print("Over AR times:" + str(alignment_large))
                if alignment_large < arg.ARtime:
                    Center_init = kmean.cluster_centers_    # k-means on global features
                    new_P = self.new_P(z, Center_init)      # similarity measure
                    print("Self-Supervised Multi-View Discriminative Feature Learning")
                    p = self.target_distribution(new_P)     # enhance discrimination
                    center_init += 1
                else:
                    break
                P = []
                if arg.Coo == 1:
                    print("Unified Target Distribution for Multiple KL Losses")
                    for view in range(len(x)):
                        P.append(p)
                else:
                    print("self clustering")
                    for view in range(len(x)):
                        P.append(self.target_distribution(Q_and_X[view * 2]))

                # ge = np.random.randint(0, x[0].shape[0], 1, dtype=int)
                # ge = int(ge)
                # print('Number of sample:' + str(ge))
                # for view in range(len(x)):
                #     for i in Q_and_X[view * 2][ge]:
                #         print("%.3f  " % i, end="")
                #     print("\n")

                # evaluate the clustering performance
                for view in range(len(x)):
                    Loss[view] = 0.
                    kl_loss[view] = 0.

            # train on batch
            idx = index_array[index * batch_size: min((index + 1) * batch_size, x[0].shape[0])]
            x_batch = []
            y_batch = []
            for view in range(len(x)):
                x_batch.append(x[view][idx])
                y_batch.append(P[view][idx])
                y_batch.append(x[view][idx])

            tmp = self.train_on_batch(xin=x_batch, yout=y_batch)  # [sum, q, xn, q, x]
            # print(tmp)
            KLLoss = []
            for view in range(len(x)):
                Loss[view] += tmp[2 * view + 2]       # lr
                kl_loss[view] += tmp[2 * view + 1]    # lc
                # KLLoss += tmp[2 * view + 1]
                KLLoss.append(tmp[2 * view + 1])
            MVKLLoss.append(KLLoss)
            index = index + 1 if (index + 1) * batch_size <= x[0].shape[0] else 0
            # print(ite)
            ite += 1
            if ite >= int(maxiter):
                ite = 0
                # # Train from scratch
                # print("Pretrain self.autoencoder")
                # optimizer = Adam(lr=0.001)
                # self.pretrain(x, y, optimizer=optimizer, epochs=500, batch_size=batch_size,
                #               save_dir=save_dir, verbose=1)
                print("self.autoencoder.load_weights(args.pretrain_dir)")
                self.autoencoder.load_weights(arg.pretrain_dir)
                features = self.encoder.predict(input_dic)
                for view in range(len(x)):
                    kmeans.fit_predict(features[view])
                    self.model.get_layer(name='clustering' + str(view + 1)).set_weights([kmeans.cluster_centers_])

        # save the trained model
        logfile.close()
        # print('saving model to:', save_dir + '/model_final.h5')
        # self.model.save_weights(save_dir + '/model_final.h5')
        # # self.autoencoder.save_weights(save_dir + '/pre_model.h5')
        # np.save(save_dir + '/AccNmiAriRate/ACC.npy', ACC)
        # np.save(save_dir + '/AccNmiAriRate/NMI.npy', NMI)
        # np.save(save_dir + '/AccNmiAriRate/ARI.npy', ARI)
        # np.save(save_dir + '/AccNmiAriRate/vACC.npy', vACC)
        # np.save(save_dir + '/AccNmiAriRate/vNMI.npy', vNMI)
        # np.save(save_dir + '/AccNmiAriRate/vARI.npy', vARI)
        # np.save(save_dir + '/AccNmiAriRate/Rate.npy', Rate)
        # np.save(save_dir + '/AccNmiAriRate/MVKLLoss.npy', MVKLLoss)
        print('Clustering time: %ds' % (time() - t1))
        print('End clustering:', '-' * 60)

        Q_and_X = self.model.predict(input_dic)
        y_pred = []
        for view in range(len(x)):
            y_pred.append(Q_and_X[view * 2].argmax(1))

        y_q = Q_and_X[(len(x) - 1) * 2]
        for view in range(len(x) - 1):
            y_q += Q_and_X[view * 2]
        # y_q = y_q/len(x)
        y_mean_pred = y_q.argmax(1)
        return y_pred, y_mean_pred

    def new_P(self, inputs, centers):
        alpha = 1
        q = 1.0 / (1.0 + (np.sum(np.square(np.expand_dims(inputs, axis=1) - centers), axis=2) / alpha))
        q **= (alpha + 1.0) / 2.0
        q = np.transpose(np.transpose(q) / np.sum(q, axis=1))
        return q