old_contents: #!/usr/bin/python
  # -*- coding: utf-8 -*-
  
  """
  =========================================================
  PCA example with Iris Data-set
  =========================================================
  
  Principal Component Analysis applied to the Iris dataset.
  
  See `here <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
  information on this dataset.
  
  """
  print(__doc__)
  
  
  # Code source: Gaël Varoquaux
  # License: BSD 3 clause
  
  import numpy as np
  import matplotlib.pyplot as plt
  from mpl_toolkits.mplot3d import Axes3D
  
  
  from sklearn import decomposition
  from sklearn import datasets
  
  np.random.seed(5)
  
  centers = [[1, 1], [-1, -1], [1, -1]]
  iris = datasets.load_iris()
  X = iris.data
  y = iris.target
  
  fig = plt.figure(1, figsize=(4, 3))
  plt.clf()
  ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)
  
  plt.cla()
  pca = decomposition.PCA(n_components=3)
  pca.fit(X)
  X = pca.transform(X)
  
  for name, label in [('Setosa', 0), ('Versicolour', 1), ('Virginica', 2)]:
      ax.text3D(X[y == label, 0].mean(),
                X[y == label, 1].mean() + 1.5,
                X[y == label, 2].mean(), name,
                horizontalalignment='center',
                bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))
  # Reorder the labels to have colors matching the cluster results
  y = np.choose(y, [1, 2, 0]).astype(np.float)
  ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.spectral)
  
  ax.w_xaxis.set_ticklabels([])
  ax.w_yaxis.set_ticklabels([])
  ax.w_zaxis.set_ticklabels([])
  
  plt.show()
  
new_contents: #!/usr/bin/python
  # -*- coding: utf-8 -*-
  
  """
  =========================================================
  PCA example with Iris Data-set
  =========================================================
  
  Principal Component Analysis applied to the Iris dataset.
  
  See `here <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
  information on this dataset.
  
  """
  print(__doc__)
  
  
  # Code source: Gaël Varoquaux
  # License: BSD 3 clause
  
  import numpy as np
  import matplotlib.pyplot as plt
  from mpl_toolkits.mplot3d import Axes3D
  
  
  from sklearn import decomposition
  from sklearn import datasets
  
  np.random.seed(5)
  
  centers = [[1, 1], [-1, -1], [1, -1]]
  iris = datasets.load_iris()
  X = iris.data
  y = iris.target
  
  fig = plt.figure(1, figsize=(4, 3))
  plt.clf()
  ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)
  
  plt.cla()
  pca = decomposition.PCA(n_components=3)
  pca.fit(X)
  X = pca.transform(X)
  
  for name, label in [('Setosa', 0), ('Versicolour', 1), ('Virginica', 2)]:
      ax.text3D(X[y == label, 0].mean(),
                X[y == label, 1].mean() + 1.5,
                X[y == label, 2].mean(), name,
                horizontalalignment='center',
                bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))
  # Reorder the labels to have colors matching the cluster results
  y = np.choose(y, [1, 2, 0]).astype(np.float)
  ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.spectral,
             edgecolor='k')
  
  ax.w_xaxis.set_ticklabels([])
  ax.w_yaxis.set_ticklabels([])
  ax.w_zaxis.set_ticklabels([])
  
  plt.show()
  
subject: add edgecolor to plot_pca_iris.py (#8514)