text: print(__doc__)
  
  # Code source: Gaël Varoquaux
  # Modified for documentation by Jaques Grobler
  # License: BSD 3 clause
  #https://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html
  #https://towardsdatascience.com/logistic-regression-using-python-sklearn-numpy-mnist-handwriting-recognition-matplotlib-a6b31e2b166a
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.linear_model import LogisticRegression
  from sklearn import datasets
  import pandas as pd
  
  # import some data to play with
  iris = datasets.load_iris()
  X = iris.data[:, :2]  # we only take the first two features.
  Y = iris.target
  
  logreg = LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial')
  
  # Create an instance of Logistic Regression Classifier and fit the data.
  logreg.fit(X, Y)
  
  # Plot the decision boundary. For that, we will assign a color to each
  # point in the mesh [x_min, x_max]x[y_min, y_max].
  x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
  y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
  h = .02  # step size in the mesh
  xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
  Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])
  
  # Put the result into a color plot
  Z = Z.reshape(xx.shape)
  plt.figure(1, figsize=(4, 3))
  plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
  
  # Plot also the training points
  plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
  plt.xlabel('Sepal length')
  plt.ylabel('Sepal width')
  
  plt.xlim(xx.min(), xx.max())
  plt.ylim(yy.min(), yy.max())
  plt.xticks(())
  plt.yticks(())
  
  plt.show()