def _getDistances(self, inputPattern, partitionId=None):
    """Return the distances from inputPattern to all stored patterns.

    :param inputPattern The pattern from which distances to all other patterns
        are returned

    :param partitionId If provided, ignore all training vectors with this
        partitionId.
    """
    if not self._finishedLearning:
      self.finishLearning()
      self._finishedLearning = True

    if self._vt is not None and len(self._vt) > 0:
      inputPattern = numpy.dot(self._vt, inputPattern - self._mean)

    sparseInput = self._sparsifyVector(inputPattern)

    # Compute distances
    dist = self._calcDistance(sparseInput)
    # Invalidate results where category is -1
    if self._specificIndexTraining:
      dist[numpy.array(self._categoryList) == -1] = numpy.inf

    # Ignore vectors with this partition id by setting their distances to inf
    if partitionId is not None:
      dist[self._partitionIdMap.get(partitionId, [])] = numpy.inf

    return dist
def _getDistances(self, inputPattern, partitionId=None):
    """Return the distances from inputPattern to all stored patterns.

    :param inputPattern The pattern from which distances to all other patterns
        are returned

    :param partitionId If provided, ignore all training vectors with this
        partitionId.
    """
    if not self._finishedLearning:
      self.finishLearning()
      self._finishedLearning = True

    if self._vt is not None and len(self._vt) > 0:
      inputPattern = numpy.dot(self._vt, inputPattern - self._mean)

    sparseInput = self._sparsifyVector(inputPattern)

    # Compute distances
    dist = self._calcDistance(sparseInput)
    # Invalidate results where category is -1
    if self._specificIndexTraining:
      dist[numpy.array(self._categoryList) == -1] = numpy.inf

    # Ignore vectors with this partition id by setting their distances to inf
    if partitionId is not None:
      dist[self._partitionIdMap.get(partitionId, [])] = numpy.inf

    return dist
def _getDistances ( self , inputPattern , partitionId = None ) : if not self . _finishedLearning : self . finishLearning ( ) self . _finishedLearning = True if self . _vt is not None and len ( self . _vt ) > 0 : inputPattern = numpy . dot ( self . _vt , inputPattern - self . _mean ) sparseInput = self . _sparsifyVector ( inputPattern ) # Compute distances dist = self . _calcDistance ( sparseInput ) # Invalidate results where category is -1 if self . _specificIndexTraining : dist [ numpy . array ( self . _categoryList ) == - 1 ] = numpy . inf # Ignore vectors with this partition id by setting their distances to inf if partitionId is not None : dist [ self . _partitionIdMap . get ( partitionId , [ ] ) ] = numpy . inf return dist
Return the distances from inputPattern to all stored patterns.

    :param inputPattern The pattern from which distances to all other patterns
        are returned

    :param partitionId If provided, ignore all training vectors with this
        partitionId.
Return the distances from inputPattern to all stored patterns .