Builds the tree finding an augmenting path. Alternates along
        matched and unmatched edges between X and Y. The paths are
        stored in _pred (new predecessor of nodes in Y), and
        self._x and self._y

def _build_tree(self):
        """
        Builds the tree finding an augmenting path. Alternates along
        matched and unmatched edges between X and Y. The paths are
        stored in _pred (new predecessor of nodes in Y), and
        self._x and self._y
        """
        #find unassigned i*
        istar = np.argmin(self._x)

        #compute distances
        self._d = self.c[istar] - self._v
        _pred = np.zeros(self.n, dtype=np.int) + istar

        #initialize sets
        #READY: set of nodes visited and in the path (whose price gets
        #updated in augment)
        #SCAN: set of nodes at the bottom of the tree, which we need to
        #look at
        #T0DO: unvisited nodes
        _ready = np.zeros(self.n, dtype=np.bool)
        _scan = np.zeros(self.n, dtype=np.bool)
        _todo = np.zeros(self.n, dtype=np.bool) + True

        while True:
            #populate scan with minimum reduced distances
            if True not in _scan:
                mu = np.min(self._d[_todo])
                _scan[self._d == mu] = True
                _todo[_scan] = False
                j = np.argmin(self._y * _scan)
                if self._y[j] == -1 and _scan[j]:
                    return _pred, _ready, istar, j, mu

            #pick jstar from scan (scan always has at least 1)
            _jstar = np.argmax(_scan)

            #pick i associated with jstar
            i = self._y[_jstar]

            _scan[_jstar] = False
            _ready[_jstar] = True

            #find shorter distances
            newdists = mu + self.cred[i, :]
            shorter = np.logical_and(newdists < self._d, _todo)

            #update distances
            self._d[shorter] = newdists[shorter]

            #update predecessors
            _pred[shorter] = i

            for j in np.nonzero(np.logical_and(self._d == mu, _todo))[0]:
                if self._y[j] == -1:
                    return _pred, _ready, istar, j, mu
                _scan[j] = True
                _todo[j] = False