def build(self, X, Y, w=None, edges=None):
        """ Assigns data to this object and builds the Merge Tree
            @ In, X, an m-by-n array of values specifying m
            n-dimensional samples
            @ In, Y, a m vector of values specifying the output
            responses corresponding to the m samples specified by X
            @ In, w, an optional m vector of values specifying the
            weights associated to each of the m samples used. Default of
            None means all points will be equally weighted
            @ In, edges, an optional list of custom edges to use as a
            starting point for pruning, or in place of a computed graph.
        """
        super(MergeTree, self).build(X, Y, w, edges)

        if self.debug:
            sys.stdout.write("Merge Tree Computation: ")
            start = time.clock()

        self.__tree = MergeTreeFloat(
            vectorFloat(self.Xnorm.flatten()),
            vectorFloat(self.Y),
            str(self.gradient),
            self.graph_rep.full_graph(),
            self.debug,
        )

        self._internal_build()

        if self.debug:
            end = time.clock()
            sys.stdout.write("%f s\n" % (end - start))
Assigns data to this object and builds the Merge Tree
            @ In, X, an m-by-n array of values specifying m
            n-dimensional samples
            @ In, Y, a m vector of values specifying the output
            responses corresponding to the m samples specified by X
            @ In, w, an optional m vector of values specifying the
            weights associated to each of the m samples used. Default of
            None means all points will be equally weighted
            @ In, edges, an optional list of custom edges to use as a
            starting point for pruning, or in place of a computed graph.