content: #!/usr/bin/python
  # -*- coding: utf-8 -*-
  
  """
  =========================================================
  The Iris Dataset
  =========================================================
  This data sets consists of 3 different types of irises'
  (Setosa, Versicolour, and Virginica) petal and sepal
  length, stored in a 150x4 numpy.ndarray
  
  The rows being the samples and the columns being:
  Sepal Length, Sepal Width, Petal Length	and Petal Width.
  
  The below plot uses the first two features.
  See `here <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
  information on this dataset.
  """
  print(__doc__)
  
  
  # Code source: Gaël Varoquaux
  # Modified for documentation by Jaques Grobler
  # License: BSD 3 clause
  
  import matplotlib.pyplot as plt
  from mpl_toolkits.mplot3d import Axes3D
  from sklearn import datasets
  from sklearn.decomposition import PCA
  
  # import some data to play with
  iris = datasets.load_iris()
  X = iris.data[:, :2]  # we only take the first two features.
  Y = iris.target
  
  x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
  y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
  
  plt.figure(2, figsize=(8, 6))
  plt.clf()
  
  # Plot the training points
  plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
  plt.xlabel('Sepal length')
  plt.ylabel('Sepal width')
  
  plt.xlim(x_min, x_max)
  plt.ylim(y_min, y_max)
  plt.xticks(())
  plt.yticks(())
  
  # To getter a better understanding of interaction of the dimensions
  # plot the first three PCA dimensions
  fig = plt.figure(1, figsize=(8, 6))
  ax = Axes3D(fig, elev=-150, azim=110)
  X_reduced = PCA(n_components=3).fit_transform(iris.data)
  ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,
             cmap=plt.cm.Paired)
  ax.set_title("First three PCA directions")
  ax.set_xlabel("1st eigenvector")
  ax.w_xaxis.set_ticklabels([])
  ax.set_ylabel("2nd eigenvector")
  ax.w_yaxis.set_ticklabels([])
  ax.set_zlabel("3rd eigenvector")
  ax.w_zaxis.set_ticklabels([])
  
  plt.show()
  