def test1(ctx):
ctxobj = ctx.obj
verbose = ctxobj.get('verbose')
ga = GenAlg(
size=4,
elitism=ctxobj.get('elitism'),
crossover=ctxobj.get('crossover'),
pureMutation=ctxobj.get('puremutation'),
chromoClass=MyChromo,
#selectionFcn = GenAlgOps.tournamentSelection,
crossoverFcn=MyCrossover212,
mutationFcn=GenAlgOps.mutateFew,
# for pure-mutation of all chromos .. no need to run tournament selection
#pureMutationSelectionFcn = lambda x: [0,0],
#pureMutationFcn = GenAlgOps.mutateAll,
pureMutationSelectionFcn=GenAlgOps.simpleSelection2,
pureMutationFcn=MyMutate,
#feasibleSolnFcn = GenAlgOps.disallowDupes,
minOrMax='min',
showBest=0,
# optional params ..
params={
'mutateNum': ctxobj.get('mutatenum'),
'parentPct': 0.50,
})
# otherwise, init the gen-alg library from scratch
ga.initPopulation()
print('Created random init data')
# simulate a cross-over
mother = ga.population[0]
print('mother', str(mother))
father = ga.population[1]
print('father', str(father))
children = ga.crossoverFcn(mother, father, ga.params)
for child in children:
print('child', str(child))
asm_prog = [
'ROM0', 'XYZ0', 'MPY', 'ROM1', 'XYZ1', 'MPY', 'ADD', 'ROM2', 'ADD'
]
prog = ga.population[2].cpu.compile(asm_prog)
print('prog', prog)
prog.extend([1, 1, 1]) # add the 3 rom/coeffs
# but make these the last N steps of prog, so the output _is_ the prog output
ga.population[2].data[-len(prog):] = prog
fit = ga.population[2].calcFitness()
print('code', ga.population[2].cpu.show_prog(show_pc=False, nl='/'))
print('code', ga.population[2].cpu.show_prog_as_func())
print('fitness', fit)
def test1(ctx):
ctxobj = ctx.obj
verbose = ctxobj.get('verbose')
ga = GenAlg(
size=4,
elitism=ctxobj.get('elitism'),
crossover=ctxobj.get('crossover'),
pureMutation=ctxobj.get('puremutation'),
chromoClass=MyChromo,
#selectionFcn = GenAlgOps.tournamentSelection,
crossoverFcn=GenAlgOps.crossover12,
mutationFcn=GenAlgOps.mutateNone,
# for pure-mutation of all chromos .. no need to run tournament selection
#pureMutationSelectionFcn = lambda x: [0,0],
#pureMutationFcn = GenAlgOps.mutateAll,
pureMutationSelectionFcn=GenAlgOps.simpleSelectionParentPct,
pureMutationFcn=GenAlgOps.mutateAll,
#feasibleSolnFcn = GenAlgOps.disallowDupes,
minOrMax='min',
showBest=0,
# optional params ..
params={
'mutateNum': ctxobj.get('mutatenum'),
'parentPct': 0.50,
})
# otherwise, init the gen-alg library from scratch
ga.initPopulation()
print('Created random init data')
mother = ga.population[0]
mother.fitness = mother.calcFitness()
print('mother', str(mother))
#print( ' ', mother.dataRange )
print(' ', mother.cpu.show_prog_as_func())
father = ga.population[1]
father.fitness = father.calcFitness()
print('father', str(father))
#print( ' ', father.dataRange )
print(' ', father.cpu.show_prog_as_func())
# simulate a cross-over
children = ga.crossoverFcn(mother, father, ga.params)
for child in children:
child.fitness = child.calcFitness()
print('child', str(child))