def get_weighted_values(self, weights):
if len(weights) == len(CisTrans.values_options):
self.values = [
CisTrans.values_options[find_one_in_list(sum(weights), weights)] for i in range(len(self.positions))
]
else:
self.values = [choice(CisTrans.values_options) for i in range(len(self.positions))]
def get_weighted_values(self, weights):
if len(weights) == len(CisTrans.values_options):
self.values = [
CisTrans.values_options[find_one_in_list(
sum(weights), weights)] for i in range(len(self.positions))
]
else:
self.values = [
choice(CisTrans.values_options)
for i in range(len(self.positions))
]
def get_weighted_values(self, weights):
"""Generate a random, but weighted value for each of the positions of
the Torsion object.
Args:
weights (list): weights for all allowed values options, if it is
not of the length of the values options, random values will be
generated
"""
if len(weights) == len(Torsion.values_options):
self.values = [
Torsion.values_options[find_one_in_list(sum(weights), weights)] for i in range(len(self.positions))
]
else:
self.values = [choice(Torsion.values_options) for i in range(len(self.positions))]
def mutation(list_for_mut, max_mutations, options, weights=None,
periodic=False):
"""Mutate a list of values.
Args(required):
list_for_mut (list): list of values to be mutated
max_mut: maximal number of changes to be made
options: list of options for new values
Args(optional):
weights (list): weights for options
periodic (bool)
Returns:
mutated list
"""
if not isinstance(max_mutations, int):
raise TypeError("The max. number of mutations needs to be an integer")
if max_mutations < 0:
raise ValueError("The max. number of mutations cannot be negative")
mut_numb = random.randint(1, min(max_mutations, len(list_for_mut)))
pos = random.sample(range(len(list_for_mut)), mut_numb)
for p in pos:
current_value = list_for_mut[p]
banned = find_closest(current_value, options, periodic)
cnt = 0
while cnt < 100:
if weights is None:
new_value = random.sample(options, 1)[0]
else:
new_value = options[find_one_in_list(sum(weights), weights)]
if len(banned) != len(options):
if new_value not in banned:
list_for_mut[p] = new_value
break
else:
cnt += 1
else:
# for very rare cases, i.e. if there are only two options
# and the value is the mean of them!
list_for_mut[p] = new_value
break
return list_for_mut
def get_weighted_values(self, weights):
"""Generate a random, but weighted value for each of the positions of
the Torsion object.
Args:
weights (list): weights for all allowed values options, if it is
not of the length of the values options, random values will be
generated
"""
if len(weights) == len(Torsion.values_options):
self.values = [
Torsion.values_options[find_one_in_list(sum(weights), weights)]
for i in range(len(self.positions))
]
else:
self.values = [
choice(Torsion.values_options)
for i in range(len(self.positions))
]
