content: #!/usr/bin/env python
  """
  =================================
  Path with L1- Logistic Regression
  =================================
  
  Computes path on IRIS dataset.
  
  """
  print(__doc__)
  
  # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
  # License: BSD 3 clause
  
  from datetime import datetime
  import numpy as np
  import matplotlib.pyplot as plt
  
  from sklearn import linear_model
  from sklearn import datasets
  from sklearn.svm import l1_min_c
  
  iris = datasets.load_iris()
  X = iris.data
  y = iris.target
  
  X = X[y != 2]
  y = y[y != 2]
  
  X -= np.mean(X, 0)
  
  ###############################################################################
  # Demo path functions
  
  cs = l1_min_c(X, y, loss='log') * np.logspace(0, 3)
  
  
  print("Computing regularization path ...")
  start = datetime.now()
  clf = linear_model.LogisticRegression(C=1.0, penalty='l1', tol=1e-6)
  coefs_ = []
  for c in cs:
      clf.set_params(C=c)
      clf.fit(X, y)
      coefs_.append(clf.coef_.ravel().copy())
  print("This took ", datetime.now() - start)
  
  coefs_ = np.array(coefs_)
  plt.plot(np.log10(cs), coefs_)
  ymin, ymax = plt.ylim()
  plt.xlabel('log(C)')
  plt.ylabel('Coefficients')
  plt.title('Logistic Regression Path')
  plt.axis('tight')
  plt.show()
  