old_contents: """
  Kernel Density Estimation
  =========================
  
  This example shows how kernel density estimation (KDE), a powerful
  non-parametric density estimation technique, can be used to learn
  a generative model for a dataset.  With this generative model in place,
  new samples can be drawn.  These new samples reflect the underlying model
  of the data.
  """
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  from sklearn.datasets import load_digits
  from sklearn.neighbors import KernelDensity
  from sklearn.decomposition import PCA
  from sklearn.grid_search import GridSearchCV
  
  # load the data
  digits = load_digits()
  data = digits.data
  
  # project the 64-dimensional data to a lower dimension
  pca = PCA(n_components=15, whiten=False)
  data = pca.fit_transform(digits.data)
  
  # use grid search cross-validation to optimize the bandwidth
  params = {'bandwidth':np.logspace(-1, 1, 20)}
  grid = GridSearchCV(KernelDensity(), params)
  grid.fit(data)
  
  print "best bandwidth: {0}".format(grid.best_estimator_.bandwidth)
  
  # use the best estimator to compute the kernel density estimate
  kde = grid.best_estimator_
  
  # sample 44 new points from the data
  new_data = kde.sample(44)
  new_data = pca.inverse_transform(new_data)
  
  # turn data into a 4x11 grid
  new_data = new_data.reshape((4, 11, -1))
  real_data = digits.data[:44].reshape((4, 11, -1))
  
  # plot real digits and resampled digits
  fig, ax = plt.subplots(9, 11, subplot_kw=dict(xticks=[], yticks=[]))
  for j in range(11):
      ax[4, j].set_visible(False)
      for i in range(4):
          im = ax[i, j].imshow(real_data[i, j].reshape((8, 8)),
                               cmap=plt.cm.binary, interpolation='nearest')
          im.set_clim(0, 16)
          im = ax[i + 5, j].imshow(new_data[i, j].reshape((8, 8)),
                                   cmap=plt.cm.binary, interpolation='nearest')
          im.set_clim(0, 16)
  
  ax[0, 5].set_title('Selection from the input data')
  ax[5, 5].set_title('"New" digits drawn from the kernel density model')
  
  plt.show()
  
new_contents: """
  Kernel Density Estimation
  =========================
  
  This example shows how kernel density estimation (KDE), a powerful
  non-parametric density estimation technique, can be used to learn
  a generative model for a dataset.  With this generative model in place,
  new samples can be drawn.  These new samples reflect the underlying model
  of the data.
  """
  
  import numpy as np
  import matplotlib.pyplot as plt
  
  from sklearn.datasets import load_digits
  from sklearn.neighbors import KernelDensity
  from sklearn.decomposition import PCA
  from sklearn.grid_search import GridSearchCV
  
  # load the data
  digits = load_digits()
  data = digits.data
  
  # project the 64-dimensional data to a lower dimension
  pca = PCA(n_components=15, whiten=False)
  data = pca.fit_transform(digits.data)
  
  # use grid search cross-validation to optimize the bandwidth
  params = {'bandwidth':np.logspace(-1, 1, 20)}
  grid = GridSearchCV(KernelDensity(), params)
  grid.fit(data)
  
  print "best bandwidth: {0}".format(grid.best_estimator_.bandwidth)
  
  # use the best estimator to compute the kernel density estimate
  kde = grid.best_estimator_
  
  # sample 44 new points from the data
  new_data = kde.sample(44, random_state=0)
  new_data = pca.inverse_transform(new_data)
  
  # turn data into a 4x11 grid
  new_data = new_data.reshape((4, 11, -1))
  real_data = digits.data[:44].reshape((4, 11, -1))
  
  # plot real digits and resampled digits
  fig, ax = plt.subplots(9, 11, subplot_kw=dict(xticks=[], yticks=[]))
  for j in range(11):
      ax[4, j].set_visible(False)
      for i in range(4):
          im = ax[i, j].imshow(real_data[i, j].reshape((8, 8)),
                               cmap=plt.cm.binary, interpolation='nearest')
          im.set_clim(0, 16)
          im = ax[i + 5, j].imshow(new_data[i, j].reshape((8, 8)),
                                   cmap=plt.cm.binary, interpolation='nearest')
          im.set_clim(0, 16)
  
  ax[0, 5].set_title('Selection from the input data')
  ax[5, 5].set_title('"New" digits drawn from the kernel density model')
  
  plt.show()
  
subject: set random seed in KDE example