def bm_height_optim(tree, traits, nrates=False):
if nrates != False:
start = [random.uniform(0.000001, 2.0)] * nrates
tree_utils.assign_node_nums(tree)
tree_utils.init_heights(tree, start)
tree_utils.assign_sigsq(tree, start)
nstart = [
i.height for i in tree.iternodes()
if i.istip == False and i.parent != None
]
start = start + nstart
opt = optimize.fmin_bfgs(calc_like_sigsq_nodes,
start,
args=(tree, traits, nrates),
full_output=True,
disp=True)
#bounds = [(0.0,100000.0)]*len(start)
#opt = optimize.fmin_l_bfgs_b(calc_like_sigsq_nodes,start,approx_grad = True,bounds =bounds,args=(tree,traits,nrates))
tree_utils.assign_node_heights(opt[0][1:], tree)
return [tree.get_newick_repr(True), opt]
elif nrates == False: ##estimate node heights with fixed rate
tree_utils.assign_node_nums(tree)
tree_utils.init_heights(tree)
sigsq_i = calc_bm_likelihood.sigsqML(tree)
tree_utils.assign_sigsq(tree, [sigsq_i])
start = [i.height for i in tree.iternodes()
if i.istip == False] # and i.parent!=None]
#print len(start)
opt = optimize.fmin_bfgs(calc_like_nodes,
start,
args=(tree, traits),
full_output=True,
disp=True)
return [tree.get_newick_repr(True), opt]
def bm_brlen_optim(tree, traits, rate=1):
tree_utils.assign_branch_nums(tree)
tree_utils.assign_sigsq(tree, [rate])
start = [i.length for i in tree.iternodes() if i != tree]
opt = optimize.fmin_bfgs(calc_like_brlens,
start,
args=(tree, traits),
full_output=False,
disp=True)
return [tree.get_newick_repr(True), opt]
def calc_like_sigsq_nodes(ht, tree, traits, nrates):
for i in ht:
if i < 0:
return LARGE
z = 0
while z < nrates:
if ht[z] > 500:
return LARGE
z += 1
if nrates == 1:
tree_utils.assign_sigsq(tree, ht[0])
elif nrates > 1:
tree_utils.assign_sigsq(tree, ht[0:nrates])
bad = assign_node_heights(ht[nrates:], tree)
if bad:
return LARGE
try:
val = -bm_like.bm_prune(tree, traits)
except:
return LARGE
#print ht[0]
#print (val,ht)
return val
def calc_like_strat_bm(p,
tree,
strat,
traits,
nrates=1): #p[0] should be lambda, p[1:nrates] = sig2
for i in p:
if i < 0:
return LARGE
hstart = nrates + 1
if nrates == 1:
tree_utils.assign_sigsq(tree, [p[1]])
elif nrates > 1:
tree_utils.assign_sigsq(tree, p[1:nrates])
bad = tree_utils.assign_node_heights(p[hstart:], tree)
if bad:
return LARGE
try:
sll = -stratoML.hr97_loglike(tree, p[0])
bmll = -calc_bm_likelihood.bm_prune(tree, traits)
ll = sll + bmll
except:
return LARGE
return ll