def update_stats(self):
"""Gather statistics from the various populations to the mga's population.
"""
sum_fields = [
'selections', 'crossovers', 'mutations', 'replacements',
'ind_evals'
]
s = []
for ga in self.GAs:
for field in sum_fields:
self.pop.stats[field] = self.pop.stats[field] + ga.stats[field]
s = s + ga.pop.scores().tolist()
self.pop.stats['current']['max'] = self.pop.best().score()
self.pop.stats['current']['avg'] = my_mean(s)
self.pop.stats['current']['min'] = min(s)
if len(s) > 1: self.pop.stats['current']['dev'] = my_std(s)
else: self.pop.stats['current']['dev'] = 0
try:
self.pop.stats['overall']['max'] = max(
self.pop.stats['overall']['max'],
self.pop.stats['current']['max'])
except KeyError:
self.pop.stats['overall']['max'] = self.pop.stats['current']['max']
try:
self.pop.stats['overall']['min'] = min(
self.pop.stats['overall']['min'],
self.pop.stats['current']['min'])
except KeyError:
self.pop.stats['overall']['min'] = self.pop.stats['current']['min']
self.pop.stats # XXX: Is this a no-op?
self.pop.stats['pop_evals'] = self.GAs[0].stats['pop_evals']
self.stats.update(self.pop.stats)
def pop_deviation(self):
"""calculate the std deviation across all populations"""
all_scores = []
for ga in self.GAs:
all_scores = all_scores + ga.pop.scores().tolist()
if len(all_scores) > 1:
denom = my_mean(all_scores)
if denom == 0.: denom = .0001 # what should I do here?
return abs(my_std(all_scores)/denom)
return 0
def pop_deviation(self):
"""calculate the std deviation across all populations"""
all_scores = []
for ga in self.GAs:
all_scores = all_scores + ga.pop.scores().tolist()
if len(all_scores) > 1:
denom = my_mean(all_scores)
if denom == 0.: denom = .0001 # what should I do here?
return abs(my_std(all_scores) / denom)
return 0
def scale(self, pop):
sc = pop.scores()
avg = my_mean(sc)
if len(sc) > 1:
dev = my_std(sc)
else:
dev = 0
f = clip(sc - avg + self.scaling * dev, 0, inf)
for i in range(len(pop)):
pop[i].fitness(f[i])
return pop
def update_stats(self):
"""Update the statistics for the population."""
s = self.scores()
self.stats['current']['max'] = max(s)
self.stats['current']['avg'] = my_mean(s)
self.stats['current']['min'] = min(s)
if len(s) > 1:
self.stats['current']['dev'] = my_std(s)
else:
self.stats['current']['dev'] = 0
try:
self.stats['overall']['max'] = max(self.stats['overall']['max'],
self.stats['current']['max'])
except KeyError:
self.stats['overall']['max'] = self.stats['current']['max']
try:
self.stats['overall']['min'] = min(self.stats['overall']['min'],
self.stats['current']['min'])
except KeyError:
self.stats['overall']['min'] = self.stats['current']['min']
def scale(self, pop):
sc = pop.scores()
pmin = min(sc)
if pmin < 0:
raise GAError("linear scaling does not work with objective scores < 0")
pmax = max(sc)
pavg = my_mean(sc)
if pavg == pmax:
a = 1.0
b = 0.0
elif pmin > (self.mult * pavg - pmax) / (self.mult - 1.0):
delta = pmax - pavg
a = (self.mult - 1.0) * pavg / delta
b = pavg * (pmax - self.mult * pavg) / delta
else:
delta = pavg - pmin
a = pavg / delta
b = -pmin * pavg / delta
f = clip(sc * a + b, 0, inf)
for i in range(len(pop)):
pop[i].fitness(f[i])
def update_stats(self):
"""Gather statistics from the various populations to the mga's population.
"""
sum_fields = ['selections','crossovers','mutations','replacements','ind_evals']
s = []
for ga in self.GAs:
for field in sum_fields:
self.pop.stats[field] = self.pop.stats[field] + ga.stats[field]
s = s + ga.pop.scores().tolist()
self.pop.stats['current']['max'] = self.pop.best().score()
self.pop.stats['current']['avg'] = my_mean(s)
self.pop.stats['current']['min'] = min(s)
if len(s) > 1: self.pop.stats['current']['dev'] = my_std(s)
else: self.pop.stats['current']['dev'] = 0
try: self.pop.stats['overall']['max'] = max(self.pop.stats['overall']['max'],
self.pop.stats['current']['max'])
except KeyError: self.pop.stats['overall']['max'] = self.pop.stats['current']['max']
try: self.pop.stats['overall']['min'] = min(self.pop.stats['overall']['min'],
self.pop.stats['current']['min'])
except KeyError: self.pop.stats['overall']['min'] = self.pop.stats['current']['min']
self.pop.stats # XXX: Is this a no-op?
self.pop.stats['pop_evals'] = self.GAs[0].stats['pop_evals']
self.stats.update(self.pop.stats)
def pop_deviation(self):
#calculate the std deviation across all populations as a percent of mean
scores = self.pop.scores()
denom = my_mean(scores)
if denom == 0.: denom = .0001 # what should I do here?
return abs(my_std(scores)/denom)
def pop_deviation(self):
#calculate the std deviation across all populations as a percent of mean
scores = self.pop.scores()
denom = my_mean(scores)
if denom == 0.: denom = .0001 # what should I do here?
return abs(my_std(scores) / denom)
