old_contents: """
  ===================
  Latent Dynamics CRF
  ===================
  
  Solving a 2d grid problem by introducing latent variable interactions.
  The input data is the same as in plot_grid_crf, a cross pattern.
  But now, the center is not given an extra state. That makes the problem
  much harder to solve for a pairwise model.
  We can still solve it by introducing latent dynamics. In essence we allow
  an additional state with different interactions, that maps to the same
  state (the cross) in the ground truth.
  
  """
  import numpy as np
  import matplotlib.pyplot as plt
  
  from sklearn.cross_validation import train_test_split
  
  from pystruct.models import LatentGridCRF
  from pystruct.learners import LatentSSVM, OneSlackSSVM
  
  from pystruct.datasets import generate_crosses
  
  
  X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
  X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5)
  
  crf = LatentGridCRF(n_states_per_label=[1, 2])
  base_ssvm = OneSlackSSVM(model=crf, C=10., n_jobs=-1, inference_cache=20,
                           tol=.1)
  clf = LatentSSVM(base_ssvm=base_ssvm)
  clf.fit(X_train, Y_train)
  print("loss training set: %f" % clf.score(X_train, Y_train))
  print("loss test set: %f" % clf.score(X_test, Y_test))
  
  Y_pred = clf.predict(X_test)
  
  x, y, y_pred = X_test[1], Y_test[1], Y_pred[1]
  fig, ax = plt.subplots(3, 2)
  ax[0, 0].matshow(y, vmin=0, vmax=crf.n_labels - 1)
  ax[0, 0].set_title("ground truth")
  ax[0, 1].matshow(np.argmax(x, axis=-1),
                   vmin=0, vmax=crf.n_labels - 1)
  ax[0, 1].set_title("unaries only")
  ax[1, 0].set_visible(False)
  ax[1, 1].matshow(crf.latent(x, y, clf.w),
                   vmin=0, vmax=crf.n_states - 1)
  ax[1, 1].set_title("latent final")
  ax[2, 0].matshow(crf.inference(x, clf.w),
                   vmin=0, vmax=crf.n_states - 1)
  ax[2, 0].set_title("prediction latent")
  ax[2, 1].matshow(y_pred,
                   vmin=0, vmax=crf.n_labels - 1)
  ax[2, 1].set_title("prediction")
  for a in ax.ravel():
      a.set_xticks(())
      a.set_yticks(())
  
  plt.show()
  
new_contents: """
  ===================
  Latent Dynamics CRF
  ===================
  
  Solving a 2d grid problem by introducing latent variable interactions.
  The input data is the same as in plot_grid_crf, a cross pattern.
  But now, the center is not given an extra state. That makes the problem
  much harder to solve for a pairwise model.
  We can still solve it by introducing latent dynamics. In essence we allow
  an additional state with different interactions, that maps to the same
  state (the cross) in the ground truth.
  
  """
  import numpy as np
  import matplotlib.pyplot as plt
  
  from sklearn.cross_validation import train_test_split
  
  from pystruct.models import LatentGridCRF
  from pystruct.learners import LatentSSVM, OneSlackSSVM
  
  from pystruct.datasets import generate_crosses
  
  
  X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
  X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5,
                                                      allow_nd=True)
  
  crf = LatentGridCRF(n_states_per_label=[1, 2])
  base_ssvm = OneSlackSSVM(model=crf, C=10., n_jobs=-1, inference_cache=20,
                           tol=.1)
  clf = LatentSSVM(base_ssvm=base_ssvm)
  clf.fit(X_train, Y_train)
  print("loss training set: %f" % clf.score(X_train, Y_train))
  print("loss test set: %f" % clf.score(X_test, Y_test))
  
  Y_pred = clf.predict(X_test)
  
  x, y, y_pred = X_test[1], Y_test[1], Y_pred[1]
  fig, ax = plt.subplots(3, 2)
  ax[0, 0].matshow(y, vmin=0, vmax=crf.n_labels - 1)
  ax[0, 0].set_title("ground truth")
  ax[0, 1].matshow(np.argmax(x, axis=-1),
                   vmin=0, vmax=crf.n_labels - 1)
  ax[0, 1].set_title("unaries only")
  ax[1, 0].set_visible(False)
  ax[1, 1].matshow(crf.latent(x, y, clf.w),
                   vmin=0, vmax=crf.n_states - 1)
  ax[1, 1].set_title("latent final")
  ax[2, 0].matshow(crf.inference(x, clf.w),
                   vmin=0, vmax=crf.n_states - 1)
  ax[2, 0].set_title("prediction latent")
  ax[2, 1].matshow(y_pred,
                   vmin=0, vmax=crf.n_labels - 1)
  ax[2, 1].set_title("prediction")
  for a in ax.ravel():
      a.set_xticks(())
      a.set_yticks(())
  
  plt.show()
  
subject: Fix call to train_test_split in plot_latent_crf example. Scikit-learn's train_test_split changed its behaviour for nd samples in version 0.15.