text: # -*- coding:utf-8 -*-
  
  """Logistic regression intuition and conditional probabilities"""
  
  import matplotlib.pyplot as plt
  import numpy as np
  
  
  def sigmoid(z):
      return 1.0 / (1.0 + np.exp(-z))
  
  
  z = np.arange(-7, 7, 0.1)
  phi_z = sigmoid(z)
  
  plt.plot(z, phi_z)
  plt.axvline(0.0, color='k')
  plt.ylim(-0.1, 1.1)
  plt.xlabel('z')
  plt.ylabel('$\phi (z)$')
  
  # y axis ticks and gridline
  plt.yticks([0.0, 0.5, 1.0])
  ax = plt.gca()
  ax.yaxis.grid(True)
  
  plt.tight_layout()
  plt.show()
  