def mutant_hero(population, crossover_fnc, cmp_fnc):
hero = population[0]
hero_o = hero['org'][0]
hero_a = hero['amplitude']
child_o = [ele for ele in hero_o]
child_o = [child_o, [0,0,0]]
child = {'org' : child_o, 'amplitude' : hero_a}
child = mut.mutation(child, True)
population.append(child)
population.sort(lambda x,y : mtr.comparator(x,y,cmp_fnc))
population = population[:-1]
return population
def greedy_selection(population, crossover_fnc, cmp_fnc):
indices = four_indices(len(population))
parents = [population[indices[i]] for i in range(4)]
parents.sort(lambda x,y : mtr.comparator(x,y,cmp_fnc))
winner1, winner2 = parents[0], parents[1]
# Fitness is measured before crossover is complete and automatically
# cached in a tuple. child1 and child2 should be tuples now.
child1, child2 = cvr.crossover(winner1, winner2, crossover_fnc)
# Mutate (if the check passes)
child1 = mut.mutation(child1)
child2 = mut.mutation(child2)
# Add the children, resort, remove the worst 2
population.append(child1)
population.append(child2)
population.sort(lambda x,y : mtr.comparator(x,y,cmp_fnc))
population = population[:-2]
return population
def do_experiment(cmp_fnc, c_over_op, select_op, trial, force=False):
ID = str(cmp_fnc)+"."+str(c_over_op)+"."+str(select_op)+"."+str(trial)
cmp_fnc = mtr.fn_list[cmp_fnc]
c_over_op = cvr.fn_list[c_over_op]
select_op = sel.fn_list[select_op]
print "ID:", ID,
print str(cmp_fnc)[10:-15],str(c_over_op)[10:-15],str(select_op)[10:-15]
pop = None
generation = 0
while ( generation < max_gens ):
# First check to see if this experiment is already done...
d = shelve.open("dat/"+ID+"." + str(generation) + ".pop")
if 'pop' in d:
prog = 100.0 * float(generation)/(max_gens-1)
print "\rAlready computed. Skipping ahead. %",prog," f:",first,
sys.stdout.flush() # Update our percentage ticker.
generation = generation + 1 # Advance the generation counter.
pop = d['pop'] # Load that population into memory.
d.close()
continue
d.close()
# Initialize our population if we haven't already
if not pop:
pop = initialize_pop()
# Otherwise we need to compute!
pop.sort(lambda x,y : mtr.comparator(x,y, cmp_fnc) ) # Eval and sort
IO_update(ID, generation, pop, max_gens) # Spit out status
pop = select_op(pop, c_over_op, cmp_fnc) # Breed
generation = generation + 1 # Tick
# Forcibly revaluate the fitness of the hero.
try:
del pop[0]['fitness']
except KeyError:
pass
print " Done."
def non_greedy_selection(population, crossover_fnc, cmp_fnc):
indices = four_indices(len(population))
parents = [population[indices[i]] for i in range(4)]
parents.sort(lambda x,y : mtr.comparator(x,y,cmp_fnc))
winner1, winner2 = parents[0], parents[1]
loser1, loser2 = parents[2], parents[3]
# Fitness is measured before crossover is complete and automatically
# cached in a tuple. child1 and child2 should be tuples now.
child1, child2 = cvr.crossover(winner1, winner2, crossover_fnc)
# Mutate (if the check passes)
child1 = mut.mutation(child1)
child2 = mut.mutation(child2)
# Replace the losers from selection
population[population.index(loser1)] = child1
population[population.index(loser2)] = child2
return population
def do_experiment(cmp_fnc, c_over_op, select_op, trial, force=False):
ID = combo_to_ID(cmp_fnc, c_over_op, select_op, trial)
cmp_fnc = mtr.fn_list[cmp_fnc]
c_over_op = cvr.fn_list[c_over_op]
select_op = sel.fn_list[select_op]
print "ID:", ID,
print str(cmp_fnc)[10:-15],str(c_over_op)[10:-15],str(select_op)[10:-15]
pop = already_computed(ID, max_gens-1)
if pop:
return
pop = None
generation = 0
while ( generation < max_gens ):
tmp = already_computed(ID, generation)
if tmp:
# Iteratively load saved generations up to where we stopped before
pop, generation = tmp, generation + 1
continue
# Initialize our population if we haven't already
if not pop:
pop = initialize_pop()
# Otherwise we need to compute!
pop.sort(lambda x,y : mtr.comparator(x,y, cmp_fnc) ) # Eval and sort
IO_update(ID, generation, pop, max_gens) # Spit out status
pop = select_op(pop, c_over_op, cmp_fnc) # Breed
generation = generation + 1 # Tick
# Forcibly revaluate the fitness of the hero.
try:
del pop[0]['fitness']
except KeyError:
pass
