text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  import sys
  
  import input_data
  import tsgl
  import numpy as np
  import time
  
  FLAGS = None
  
  
  def train(group):
    # Import data
    mnist = input_data.read_data_sets(FLAGS.data_dir,
                                      one_hot=True,
                                      fake_data=FLAGS.fake_data)
    tr_data, tr_label = mnist.train.next_batch(mnist.train._num_examples);
    # Dictionary Initialization
    M=len(tr_data[0]);
    D=tsgl.dict_initializer(M,FLAGS.P);
    # Learning
    lr=FLAGS.learning_rate; pre_mse=10;
    for i in range(1,FLAGS.max_steps+1):
      # Data Shuffle
      idx=range(len(tr_data));np.random.shuffle(idx);
      batch=tr_data[idx[:FLAGS.batch_num]].transpose();
      # Learning Rate Decay
      if(i%FLAGS.decay_num==0):
        lr=lr/float(FLAGS.decay_rate);
      # Sparse Coding
      A=tsgl.sparse_coding(D,batch,FLAGS,group);
      print(A[:,0]);print(A[:,1]);
      # Dictionary Learning
      D=tsgl.dictionary_learning(D,batch,A,lr,FLAGS);
      loss=np.linalg.norm(np.matmul(D,A)-batch,axis=0);mse=np.mean(loss);
      print(str(i)+"th MSE: "+str(mse));
      mse_diff=abs(mse-pre_mse);
      if(mse_diff<FLAGS.mse_diff_threshold):
        print("Learning Done");
        exit(1);
      pre_mse=mse;
    print("Max Iterations Done");
  
  if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--fake_data', nargs='?', const=True, type=bool,
                        default=False,
                        help='If true, uses fake data for unit testing.')
    parser.add_argument('--learning_rate', type=float, default=0.1,
                        help='Initial learning rate')
    parser.add_argument('--decay_num', type=int, default=10,
                        help='Every decay_num, learning rate is decayed')
    parser.add_argument('--decay_rate', type=int, default=2,
                        help='Decay Late')
    parser.add_argument('--max_steps', type=float, default=100,
                        help='Max Iterations')
    parser.add_argument('--mse_diff_threshold', type=int, default=0.001,
                        help='The threshold of Loss Diff.')
    parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',
                        help='Directory for storing input data')
    parser.add_argument('--P', type=int, default=30,
                        help='The Number of dictionary atoms')
    parser.add_argument('--batch_num', type=int, default=20,
                        help='The Number of data per each batch')
    parser.add_argument('--sc_max_steps', type=int, default=100,
                        help='Sparse Coding Max Iterations')
    parser.add_argument('--sc_mse_diff_threshold', type=int, default=0.001,
                        help='Sparse Coding threshold of Loss Diff');
    parser.add_argument('--sc_lambda', type=int, default=0.8,
                        help='The ratio between loss and regularization terms');
    parser.add_argument('--sc_Wg', type=int, default=1,
                        help='The Weight for Group');
    FLAGS, unparsed = parser.parse_known_args()
    group=np.zeros(3,dtype=object);
    group[0]=range(30);group[1]=range(10,20);group[2]=range(20,30);
    train(group);
  