text: from sklearn.utils import shuffle
  from matplotlib import pyplot as plt 
  import tensorflow as tf
  import numpy as np
  
  
  def generate(sample_size, mean, cov, diff, regression):
  	num_classes = 2
  	samples_per_class = int(sample_size/2)
  
  	X0 = np.random.multivariate_normal(mean, cov, samples_per_class)  #samples_per_class=500
  	#print X0
  	
  	Y0 = np.zeros(samples_per_class)
  	#print Y0
  
  	for ci, d in enumerate(diff):
  		X1 = np.random.multivariate_normal(mean+d, cov, samples_per_class)
  		Y1 = (ci+1)*np.ones(samples_per_class)
  
  		X0 = np.concatenate((X0, X1))
  		Y0 = np.concatenate((Y0, Y1))
  
  	print X0
  	print Y0
  	
  	if regression==False:
  		class_ind = [Y==class_number for class_number in range(num_classes)]
  		Y = np.asarray(np.hstack(class_ind), dtype=np.float32)
  	X, Y = shuffle(X0, Y0)
  	print X
  	print Y
  	return X,Y
  
  
  np.random.seed(10)
  mean = np.random.randn(2)
  cov = np.eye(2)
  
  X, Y = generate(10, mean, cov, [3.0], True)
  
  
  
  
  