Source code for bob.bio.base.extractor.Linearize

#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# @author: Manuel Guenther <Manuel.Guenther@idiap.ch>
# @date: Fri Oct 26 17:05:40 CEST 2012
#
# Copyright (C) 2011-2012 Idiap Research Institute, Martigny, Switzerland
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, version 3 of the License.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.

from .Extractor import Extractor
import numpy

class Linearize (Extractor):
  """Extracts features by simply concatenating all elements of the data into one long vector.

  If a ``dtype`` is specified in the contructor, it is assured that the resulting
  """

  def __init__(self, dtype=None):
    """If the ``dtype`` parameter is given, it specifies the data type that is enforced for the features."""
    Extractor.__init__(self, dtype = dtype)
    self.dtype = dtype

  def __call__(self, data):
    """__call__(data) -> data

    Takes data of arbitrary dimensions and linearizes it into a 1D vector; enforcing the data type, if desired.

    **Parameters:**

    data : :py:class:`numpy.ndarray`
      The preprocessed data to be transformed into one vector.

    **Returns:**

    data : 1D :py:class:`numpy.ndarray`
      The extracted feature vector, of the desired ``dtype`` (if specified).
    """
    assert isinstance(data, numpy.ndarray)

    linear = numpy.reshape(data, data.size)
    if self.dtype is not None:
      linear = linear.astype(self.dtype)
    return linear


  # re-define unused functions, just so that they do not get documented
  def train(*args,**kwargs): raise NotImplementedError()
  def load(*args,**kwargs): pass