def learning(self):
for agent in self.Pop: agent.assessment() # storing current scores (with possible cross-benefits)
# some agents learn
Learners = sample(self.Pop, ET.chances(self.Param('LearningProbability')/100.0, len(self.Pop)))
for agent in self.Pop:
agent.wins(agent.Points) # Stores points for learning
if agent in Learners:
if not agent.Learns(self.neighbours(agent), hot=self.Obs.hot_phase()):
# agent.update() # this is a newborn - update position for display
pass
agent.update() # update position for display
def learning(self):
for agent in self.Pop: agent.assessment() # storing current scores (with possible cross-benefits)
# some agents learn
Learners = sample(self.Pop, ET.chances(self.Param('LearningProbability')/100.0, len(self.Pop)))
for agent in self.Pop:
agent.wins(agent.Points) # Stores points for learning
if agent in Learners:
if not agent.Learns(self.neighbours(agent), hot=self.Obs.hot_phase()):
# agent.update() # this is a newborn - update position for display
pass
agent.update() # update position for display
def mutate(self, mutation_rate = -1):
" computing the expected number of mutations "
if mutation_rate < 0: mutation_rate = self.Scenario.Parameter('MutationRate')
mutation_number = Tools.chances(mutation_rate/1000.0, self.nb_nucleotides)
## mutation_number = (mutation_rate * self.nb_nucleotides) / 1000
## if randint(1,1000) < 1 + ((mutation_rate * self.nb_nucleotides) % 1000) :
## mutation_number += 1
# performing mutations
for mutation in range(mutation_number):
pos = random.randint(0, self.nb_nucleotides - 1)
self.__dna[pos] = 1 - self.__dna[pos]
return mutation_number
