def optimize_branch_func(self, parent, child, t_a): """ <t_a> is the new branch length between <parent> --- <child> (t_a is dressed in an array...should always be just [t_a]) We can cheat & speed up likelihood calculation by re-using likelihods of subtrees below child and T' where T' is the subtree under root that does not contain parent/child Returns: POSITIVE sum of log likelihood (so we try to minimize it) currently wrapped up by ::optimize_branch:: NOTE: it is ::optimize_branch::'s responsibility to update self.order and self.like when all is done!!! p.s. potentialy speed-up by simultaneously optimizing two child branches of the same parent?? """ #assert len(t_a) == 1 g = MyMat.calc_likelihood child.edge_length = t_a[0] # TODO: make this more efficient! cheat_order = Tree.postorder_cheat_traverse(parent) L_single = g(self.single_model.gtr.R, self.S, self.log_freq_single, \ cheat_order, range(self.ncol), self.nnode, self.ncol, self.nbase) L_paired = g(self.paired_model.gtr.R, self.P, self.log_freq_paired, \ cheat_order, range(self.ncol_p), self.nnode_p, self.ncol_p, self.nbase_p) return -(L_single.sum() + L_paired.sum())
def reversible_subtree_func(copy_S, copy_P, parent, child, t_a): #assert len(t_a) == 1 child.edge_length = t_a[0] order = Tree.postorder_cheat_traverse(parent) L_single = g(tlobj.single_model.gtr.R, copy_S, tlobj.log_freq_single, \ order, range(tlobj.ncol), tlobj.nnode, tlobj.ncol, tlobj.nbase) L_paired = g(tlobj.paired_model.gtr.R, copy_P, tlobj.log_freq_paired, \ order, range(tlobj.ncol_p), tlobj.nnode_p, tlobj.ncol_p, tlobj.nbase_p) ans = -(L_single.sum() + L_paired.sum()) return ans
