def do_search(self, nseconds, sampling_function):
"""
@param nseconds: allowed search time or None
@param sampling_function: a function that samples a branch length
@return: True if a tree was found that met the criteria
"""
if not self.is_initialized():
raise RuntimeError("the search was not sufficiently initialized")
true_splits = self.tree.get_nontrivial_splits()
start_time = time.time()
while True:
elapsed_time = time.time() - start_time
if nseconds and elapsed_time > nseconds:
return False
# assign new sampled branch lengths
for branch in self.tree.get_branches():
branch.length = sampling_function()
# get the distance matrix so we can use a library function to get the split
D = np.array(self.tree.get_distance_matrix())
ntips = len(D)
# get the Laplacian matrix of the full tree and the corresponding Fiedler split of the leaves
if self.force_difference or self.informative_full_split:
A_aug = np.array(self.tree.get_weighted_adjacency_matrix(self.id_to_index))
L_aug = Euclid.adjacency_to_laplacian(A_aug)
v_aug = BuildTreeTopology.laplacian_to_fiedler(L_aug)
left_aug, right_aug = BuildTreeTopology.eigenvector_to_split(v_aug)
left = [x for x in left_aug if x in range(ntips)]
right = [x for x in right_aug if x in range(ntips)]
leaf_eigensplit_aug = BuildTreeTopology.make_split(left, right)
if self.force_difference:
if leaf_eigensplit_aug == self.desired_primary_split:
self.aug_split_collision_count += 1
continue
if self.informative_full_split:
if min(len(s) for s in leaf_eigensplit_aug) < 2:
self.aug_split_degenerate_count += 1
continue
# get the eigensplit
try:
eigensplit = BuildTreeTopology.split_using_eigenvector(D)
except BuildTreeTopology.DegenerateSplitException, e:
self.degenerate_primary_split_count += 1
continue
except BuildTreeTopology.InvalidSpectralSplitException, e:
self.error_primary_split_count += 1
continue
def do_search(self, nseconds, sampling_function):
"""
@param nseconds: allowed search time or None
@param sampling_function: a function that samples a branch length
@return: True if a tree was found that met the criteria
"""
if not self.is_initialized():
raise RuntimeError('the search was not sufficiently initialized')
true_splits = self.tree.get_nontrivial_splits()
start_time = time.time()
while True:
elapsed_time = time.time() - start_time
if nseconds and elapsed_time > nseconds:
return False
# assign new sampled branch lengths
for branch in self.tree.get_branches():
branch.length = sampling_function()
# get the distance matrix so we can use a library function to get the split
D = np.array(self.tree.get_distance_matrix())
ntips = len(D)
# get the Laplacian matrix of the full tree and the corresponding Fiedler split of the leaves
if self.force_difference or self.informative_full_split:
A_aug = np.array(self.tree.get_weighted_adjacency_matrix(self.id_to_index))
L_aug = Euclid.adjacency_to_laplacian(A_aug)
v_aug = BuildTreeTopology.laplacian_to_fiedler(L_aug)
left_aug, right_aug = BuildTreeTopology.eigenvector_to_split(v_aug)
left = [x for x in left_aug if x in range(ntips)]
right = [x for x in right_aug if x in range(ntips)]
leaf_eigensplit_aug = BuildTreeTopology.make_split(left, right)
if self.force_difference:
if leaf_eigensplit_aug == self.desired_primary_split:
self.aug_split_collision_count += 1
continue
if self.informative_full_split:
if min(len(s) for s in leaf_eigensplit_aug) < 2:
self.aug_split_degenerate_count += 1
continue
# get the eigensplit
try:
eigensplit = BuildTreeTopology.split_using_eigenvector(D)
except BuildTreeTopology.DegenerateSplitException, e:
self.degenerate_primary_split_count += 1
continue
except BuildTreeTopology.InvalidSpectralSplitException, e:
self.error_primary_split_count += 1
continue
