new_contents: import mdp
  import numpy as np
  import scipy
  import inaugural as data
  
  occurrences, row_labels, col_labels = data.get_adjacency_matrix(sorted(data.DEFAULT_FILE_LIST), entropy_threshold=0.1, normalized=False, verbosity=1)
  def reduce(occurrences=occurrences, dim=2):
      pca = mdp.nodes.PCANode(output_dim=dim, dtype='float64')
  A = scipy.sparse.csr_matrix(A)
  A.size
  len(A.A)
  len(A.A[0])
  A.dtype
  A = scipy.sparse.csr_matrix(A, dtype='float64')
  A.A.nbytes
  2500 * 56 * 8
  pcanode80d = mdp.nodes.PCANode(output_dim=.8, dtype='float64')
  pcanode80d.train(A)
  pcanode80d.train(A.A)
  pcanode80d.stop_training()
  pcanode80d.avg
  pca = pcanode80d
  del(pcanode80d)
  pca.d
  pca.explained_variance
  pca.get_current_train_phase
  pca.get_current_train_phase()
  pca.output_dim
  pca.input_dim
  pca.is_invertible
  pca.is_invertible()
  pca.has_multiple_training_phases
  pca.has_multiple_training_phases()
  pca.save
  pca.save('pca.pickled')
  pca.svd
  pca.v
  pca.var_abs
  pca.var_part
  pca.var_rel
  pca.tlen
  pca.svg??
  pca.svg
  pca.svd
  pca.svd??
  len(pca.v)
  len(words_
  )
  len(words
  )
  words[pca.v.index(max(pca.v))]
  max(pca.v)
  words[np.where(pca.v==max(pca.v))]
  words[np.where(pca.v==max(pca.v))[0]]
  words
  for var in pca.v:
      if var[0] > .8:
          print var[0], words[i]
  for i, var in enumerate(pca.v):
      if var[0] > .8:
          print var[0], words[i]
  for i, var in enumerate(pca.v):
      if var[0] > .7:
          print var[0], words[i]
  for i, var in enumerate(pca.v):
      if var[0] > .5:
          print var[0], words[i]
  for i, var in enumerate(pca.v):
      if var[0] > .3:
          print var[0], words[i]
  for i, var in enumerate(pca.v):
      if var[0] > .2:
          print var[0], words[i]
  for i, var in enumerate(pca.v):
      if var[0] > .1:
          print var[0], words[i]
  for i, var in enumerate(pca.v):
      if var[0] > .05:
          print var[0], words[i]
  word_var = []
  for i, var in enumerate(pca.v):
      word_var += [(var[0], words[i])]
  sorted(word_var)[:50]
  sorted(word_var, reverse=True)[:50]
  history
  
subject: rename the only file that uses mdp