Merge pull request #136 from joegomes/binarize

  def untransform(self, z):

    print("Cannot undo CoulombRandomizationTransformer.")

class CoulombBinarizationTransformer(Transformer):

class CoulombBinarizationTransformer(CoulombRandomizationTransformer):

  def __init__(self, transform_X=False, transform_y=False, dataset=None,

               theta=1):

                                                         transform_y=transform_y,

                                                         dataset=dataset)

    self.theta = theta

    self.max = 2 

    self.feature_max = np.zeros(dataset.get_data_shape()) 

  def set_max(self, df):

    for _, row in df.iterrows(): # Iterate over entire df by rows

      X = load_from_disk(row['X-transformed'])

      self.feature_max = np.maximum(self.feature_max,X.max(axis=0))

  def transform_row(self, i, df):

    """

    row = df.iloc[i]

    X_bin = []

    if i == 0: self.set_max(df)

    if self.transform_X:

      X = load_from_disk(row['X-transformed'])

      d = X[0].shape[0]

      for i in xrange(len(X)):

        for j in np.arange(0,self.max+self.theta,self.theta):

          if j == 0: Xi = np.tanh((X[i])/self.theta).reshape(d,1)

          else: Xi = np.vstack([Xi,np.tanh((X[i]-j)/self.theta).reshape(d,1)])

        X_bin.append(Xi)

      X_bin = np.array(np.squeeze(X_bin))

      for i in range(X.shape[1]):

        for k in np.arange(0,self.feature_max[i]+self.theta,self.theta):

          X_bin += [np.tanh((X[:,i]-k)/self.theta)]

      X_bin = np.array(X_bin).T

      save_to_disk(X_bin, row['X-transformed'])

    if self.transform_y: