def update(self,pop):
sz = len(pop)
if not sz:
raise GAError('srs_selector - the pop size is 0!')
f =pop.fitnesses()
f_max = max(f); f_min = min(f)
if not ( (f_max >= 0. and f_min >= 0.) or
(f_max <= 0. and f_min <= 0.)):
raise GAError('srs_selector requires all fitnesses values to be either strictly positive or strictly negative - min %f, max %f' %(f_min,f_max))
f_avg = sum(f,axis=0)/sz
if f_avg == 0.:
e = np.ones_like(f)
else:
if pop.min_or_max() == 'max':
e = f/f_avg
else:
e = (-f+f_max+f_min)/f_avg
self.expected_value = e
garauntee,chance = divmod(e,1.)
# garauntee = floor(e)
# chance = remainder(e,1)
choices = []
for i in xrange(sz):
choices = choices + [pop[i]] * int(garauntee[i])
#now deal with the remainder
dart_board = np.add.accumulate(chance / sum(chance,axis=0))
for i in range(len(choices),sz):
dart = prng.random()
idx = 0
while dart > dart_board[idx]:
idx = idx + 1
choices.append(pop[idx])
self.choices = choices
def mutate(self):
"""
Returns 1 if gene mutated, 0 otherwise.
Calls the **mutator.evaluate()** function to mutate the gene
mutation_rate of the time. Otherwise, it does nothing.
"""
#inlined 'flip_coin' for speed
if prng.random() < self.mutation_rate:
self._value = self.mutator.evaluate(self)
return 1
return 0
def select(self,pop,cnt = 1):
returns = []
for i in range(cnt):
dart = prng.random()
idx = 0
#binary search would be faster
while dart > self.dart_board[idx]:
idx = idx + 1
returns.append(self.pop[idx])
if cnt == 1:
return returns[0]
else:
return returns
def flip_coin(p):
""" Return True with probability p.
"""
return (prng.random() < p)
