          var_c = np.sum(np.multiply(test_face, test_face))
          return 1 - (var_a / (np.sqrt(var_b) * np.sqrt(var_c)))
  
      def sorted_similarity(self, predictions, method="ward"):
          """ 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)
          """
          logger.info("Sorting face distances. Depending on your dataset this may take some time...")
          num_predictions, dims = predictions.shape
  
          clustering_method = self._get_clustering_method(num_predictions, dims)
 
</s> remove     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
 </s> add     def _seriation(self, tree, points, current_index):
        """ Seriation method for sorted similarity. </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         clustering_method = self._get_clustering_method(num_predictions, dims)

 </s> add  </s> remove         """ Find the cosine similarity between a source face and a test face """
 </s> add         """ Find the cosine similarity between two faces.

        Parameters
        ----------
        source_face: numpy.ndarray
            The first face to test against :attr:`test_face`
        test_face: numpy.ndarray
            The second face to test against :attr:`source_face`

        Returns
        -------
        float:
            The cosine similarity between the two faces
        """ </s> remove             str: 'linkage' or 'vector'
 </s> add             bool:
                ``True`` if vector_linkage should be used. ``False`` if linkage should be used
        """ 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
          var_c = np.sum(np.multiply(test_face, test_face))
          return 1 - (var_a / (np.sqrt(var_b) * np.sqrt(var_c)))
  
      def sorted_similarity(self, predictions, method="ward"):
        """ 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
        """ 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
        """ 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
        """ 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
        """ 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
        """ 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
        """ 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
        """ 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
        """ 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
        """ 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
          """
          logger.info("Sorting face distances. Depending on your dataset this may take some time...")
          num_predictions, dims = predictions.shape
  
          clustering_method = self._get_clustering_method(num_predictions, dims)
keep keep keep keep replace replace 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