def get_mc_trees_ben_slow(N):
"""
This is written much later in a way that I hope is clearer and slower.
"""
if N <= 3:
raise ValueError('not enough states')
all_splits = list(splitalg.get_informative_partial_splits(N))
mc_trees = []
for nsplits in range(N - 2):
for splits in itertools.combinations(all_splits, nsplits):
pass
def get_mc_trees_ben_slow(N):
"""
This is written much later in a way that I hope is clearer and slower.
"""
if N <= 3:
raise ValueError('not enough states')
all_splits = list(splitalg.get_informative_partial_splits(N))
mc_trees = []
for nsplits in range(N-2):
for splits in itertools.combinations(all_splits, nsplits):
pass
def get_mc_trees_ben(N):
"""
Use Ben's nine rules to generate the set of mc trees with a given number of leaves.
"""
assert N > 3
nsplits = N - 3
available_splits = list(splitalg.get_informative_partial_splits(N))
mc_trees = []
initial_split_set = set()
initial_offset = 0
get_mc_trees_ben_helper(initial_split_set, initial_offset, N, available_splits, mc_trees)
return mc_trees
def get_mc_trees_ben(N):
"""
Use Ben's nine rules to generate the set of mc trees with a given number of leaves.
"""
assert N > 3
nsplits = N - 3
available_splits = list(splitalg.get_informative_partial_splits(N))
mc_trees = []
initial_split_set = set()
initial_offset = 0
get_mc_trees_ben_helper(initial_split_set, initial_offset, N,
available_splits, mc_trees)
return mc_trees
def __init__(self, N):
self.N = N
self.all_quartets = list(splitalg.get_quartets(N))
self.all_splits = list(splitalg.get_informative_partial_splits(N))
def __init__(self, N):
self.N = N
self.all_quartets = list(splitalg.get_quartets(N))
self.all_splits = list(splitalg.get_informative_partial_splits(N))
