                                  "the size of  your dataset. Free RAM: {}MB. "
                                  "Required RAM: {}MB".format(int(free_ram), int(vector_required)))
          return retval
  
      def seriation(self, tree, points, current_index):
          """ Seriation method for sorted similarity
              input:
                  - tree is a hierarchical tree (dendrogram)
                  - points is the number of points given to the clustering process
                  - current_index is the position in the tree for the recursive traversal
              output:
                  - order implied by the hierarchical tree
  
              seriation computes the order implied by a hierarchical tree (dendrogram)
          """
          if current_index < points:
              return [current_index]
 
</s> remove             seriation computes the order implied by a hierarchical tree (dendrogram)
 </s> add         Seriation computes the order implied by a hierarchical tree (dendrogram).

        Parameters
        ----------
        tree: numpy.ndarray
           A hierarchical tree (dendrogram)
        points: int
            The number of points given to the clustering process
        current_index: int
            The position in the tree for the recursive traversal

        Returns
        -------
        list:
            The indices in the order implied by the hierarchical tree </s> remove         """ Sort a matrix of predictions by similarity Adapted from:
            https://gmarti.gitlab.io/ml/2017/09/07/how-to-sort-distance-matrix.html
        input:
            - predictions is a stacked matrix of vgg_face predictions shape: (x, 4096)
            - method = ["ward","single","average","complete"]
        output:
            - result_order is a list of indices with the order implied by the hierarhical tree

        sorted_similarity transforms a distance matrix into a sorted distance matrix according to
        the order implied by the hierarchical tree (dendrogram)
 </s> add         """ Sort a matrix of predictions by similarity.

        Transforms a distance matrix into a sorted distance matrix according to the order implied
        by the hierarchical tree (dendrogram).

        Parameters
        ----------
        predictions: numpy.ndarray
            A stacked matrix of vgg_face2 predictions of the shape (`N`, `D`) where `N` is the
            number of observations and `D` are the number of dimensions.  NB: The given
            :attr:`predictions` will be overwritten to save memory. If you still require the 
            original values you should take a copy prior to running this method
        method: ['single','centroid','median','ward']
            The clustering method to use.

        Returns
        -------
        list:
            List of indices with the order implied by the hierarchical tree </s> remove             retval = "vector"
 </s> add             retval = True </s> remove         return self.seriation(tree, points, left) + self.seriation(tree, points, right)
 </s> add         return self._seriation(tree, points, left) + self._seriation(tree, points, right)
    def _seriation(self, tree, points, current_index):
        """ Seriation method for sorted similarity.
                                  "the size of  your dataset. Free RAM: {}MB. "
                                  "Required RAM: {}MB".format(int(free_ram), int(vector_required)))
          return retval
  
    def _seriation(self, tree, points, current_index):
        """ Seriation method for sorted similarity.
    def _seriation(self, tree, points, current_index):
        """ Seriation method for sorted similarity.
    def _seriation(self, tree, points, current_index):
        """ Seriation method for sorted similarity.
    def _seriation(self, tree, points, current_index):
        """ Seriation method for sorted similarity.
    def _seriation(self, tree, points, current_index):
        """ Seriation method for sorted similarity.
    def _seriation(self, tree, points, current_index):
        """ Seriation method for sorted similarity.
    def _seriation(self, tree, points, current_index):
        """ Seriation method for sorted similarity.
    def _seriation(self, tree, points, current_index):
        """ Seriation method for sorted similarity.
  
              seriation computes the order implied by a hierarchical tree (dendrogram)
          """
          if current_index < points:
              return [current_index]
keep keep keep keep replace replace replace replace replace replace replace replace keep keep keep keep keep
 bugfix: tools.preview - Fix for new icons
bugfix: lib.vgg_face2_keras - Remove debug code
documentation: lib.vgg_face2_keras 
lib/vgg_face2_keras.py