old_contents: #!/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 Style.
  
  from datetime import datetime
  import numpy as np
  import pylab as pl
  
  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_ = [clf.fit(X, y, C=c).coef_.ravel().copy() for c in cs]
  print "This took ", datetime.now() - start
  
  coefs_ = np.array(coefs_)
  pl.plot(np.log10(cs), coefs_)
  ymin, ymax = pl.ylim()
  pl.xlabel('log(C)')
  pl.ylabel('Coefficients')
  pl.title('Logistic Regression Path')
  pl.axis('tight')
  pl.show()
  
  
new_contents: #!/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 Style.
  
  from datetime import datetime
  import numpy as np
  import pylab as pl
  
  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_)
  pl.plot(np.log10(cs), coefs_)
  ymin, ymax = pl.ylim()
  pl.xlabel('log(C)')
  pl.ylabel('Coefficients')
  pl.title('Logistic Regression Path')
  pl.axis('tight')
  pl.show()
  
  
subject: FIX example logisitic regression.