text: from sklearn import datasets as d
  import numpy as np
  from sklearn import preprocessing
  
  boston = d.load_boston()
  print boston.DESCR
  
  complex_reg_data = d.make_regression(1000, 10, 5, 2, 1.0)
  print complex_reg_data[0].shape
  
  classification_set = d.make_classification(weights=[0.1])
  print np.bincount(classification_set[1])
  
  n_samples=1000
  n_features = 10
  n_target=5
  n_informative=2
  bias=np.random.rand(n_samples, n_target)
  X = np.random.randn(n_samples, n_features)
  ground_truth = np.zeros((n_features, n_target))
  ground_truth[:n_informative, :] = 100*np.random.rand(n_informative,n_target)
  
  print '#######################'
  print X.shape
  print X
  print '#######################'
  print ground_truth.shape
  print ground_truth
  print '#######################'
  
  y = np.dot(X, ground_truth) + bias
  print '#######################'
  print X
  print '#######################'
  print y
  print '#######################'
  
  print X[:, :3].mean(axis=0) #mean of the first 3 features
  print '#######################'
  print X[:, :3].std(axis=0)
  print '#######################'
  
  
  
  
  