def main():
ga = GenAlg(chromoSize=len(solution),
dtype=chromo_type,
range=chromo_range,
fitnessFcn=calcFitness,
crossoverFcn=crossover2,
size=1024,
elitism=0.10,
crossover=0.60,
pureMutation=0.30,
minOrMax='max',
showBest=0)
ga.describe()
#
# if a data-file exists, we load it
if (os.path.isfile('ga_hello.dat')):
pop = IoOps.loadPopulation(ga, 'ga_hello.dat')
ga.loadPopulation(pop)
print('Read init data from file (' + str(len(pop) / ga.chromo_sz) +
' chromos)')
else:
# otherwise, init the gen-alg library from scratch
ga.initPopulation()
print('Created random init data')
# print( "init data best chromo:" )
# print( ga.population[range(len(solutionWord))], ga.fitnessVals[0] )
# print( ga.population[range(len(solutionWord),2*len(solutionWord))], ga.fitnessVals[1] )
#
# Run it !!
# : we'll just do 10 epochs of 10 steps each
for i in range(10):
ga.evolve(10)
# give some running feedback on our progress
print('iter ' + str(i) + ", best chromo:")
print(ga.population[range(len(solutionWord))], ga.fitnessVals[0])
#print( ga.population[range(len(solutionWord),2*len(solutionWord))], ga.fitnessVals[1] )
#
# all done ... output final results
print("\nfinal best chromos:")
print(ga.population[range(len(solutionWord))], ga.fitnessVals[0])
print(ga.population[range(len(solutionWord), 2 * len(solutionWord))],
ga.fitnessVals[1])
print('solution - maxchr=', len(letters))
print(solution[:])
#
# we'll always save the pickle-file, just delete it
# if you want to start over from scratch
IoOps.savePopulation(ga, 'ga_hello.dat')
print('Final data stored to file (rm ga_hello.dat to start fresh)')
ga.cudaAnalysis()
def main():
random.seed()
ga = GenAlg( size=popsize,
elitism = 0.03,
crossover = 0.47,
pureMutation = 0.50,
selectionFcn = GenAlgOps.simpleSelectionParentPct,
crossoverFcn = GenAlgOps.crossover22,
mutationFcn = GenAlgOps.mutateFew,
# for pure-mutation of all chromos .. no need to run tournament selection
# pureMutationSelectionFcn = lambda x: [0,0],
# pureMutationFcn = GenAlgOps.mutateAll,
chromoClass = MyChromo,
minOrMax = 'max',
showBest = 0,
# optional params ..
params = {
'mutateNum': 3, # for mutateFew .. make 2 mutations each time
'parentPct': 0.50 # for parent-pct .. only top 50% of chromos are eligible as parents
},
)
ga.describe()
#print( 'random state', random.getstate() )
#
# if a data-file exists, we load it
if( os.path.isfile('ga_mathsq.dat') ):
pop = IoOps.loadPopulation( ga, 'ga_mathsq.dat' )
ga.appendToPopulation( pop )
print( 'Read init data from file ('+str(len(pop))+' chromos)')
else:
# otherwise, init the gen-alg library from scratch
ga.initPopulation()
print( 'Created random init data' )
#
# Run it !!
# : we'll just do 10 epochs of 10 steps each
for i in range( outeriter ):
ga.evolve( inneriter )
sys.stdout.write( '.' )
sys.stdout.flush()
#
# all done ... output final results
print( "\nfinal best chromo: " + str(ga.population[0].fitness) )
ga.population[0].showGrid()
debuglevel = 100
ga.population[0].calcFitness()
#
# we'll always save the pickle-file, just delete it
# if you want to start over from scratch
IoOps.savePopulation( ga, 'ga_mathsq.dat' )
print('Final data stored to file (rm ga_mathsq.dat to start fresh)')
def main():
ga = GenAlg(
size=100,
elitism=0.10,
crossover=0.60,
pureMutation=0.30,
chromoClass=MyChromo,
#selectionFcn = GenAlgOps.tournamentSelection,
#crossoverFcn = GenAlgOps.crossover22,
#mutationFcn = GenAlgOps.mutateFew,
#pureMutationSelectionFcn = GenAlgOps.simpleSelection,
#pureMutationFcn = GenAlgOps.mutateFew,
#feasibleSolnFcn = GenAlgOps.disallowDupes,
minOrMax='max',
showBest=0,
)
ga.describe()
#
# if a data-file exists, we load it
if (False and os.path.isfile('ga_hello.dat')):
pop = IoOps.loadPopulation(ga, 'ga_hello.dat')
ga.appendToPopulation(pop)
print('Read init data from file (' + str(len(pop)) + ' chromos)')
else:
# otherwise, init the gen-alg library from scratch
ga.initPopulation()
print('Created random init data')
#
# Run it !!
# : we'll just do 10 epochs of 10 steps each
for i in range(10):
ga.evolve(10)
# give some running feedback on our progress
print('iter ' + str(i) + ", best chromo:")
for i in range(10):
print(ga.population[i])
#
# all done ... output final results
print("\nfinal best chromos:")
for i in range(10):
print(ga.population[i])
#
# we'll always save the pickle-file, just delete it
# if you want to start over from scratch
IoOps.savePopulation(ga, 'ga_hello.dat')
print('Final data stored to file (rm ga_hello.dat to start fresh)')
def main():
global debuglevel
ga = PsoAlg(size=popsize,
omega=0.75,
phi_p=1.50,
phi_g=1.00,
learning_rate=1.0,
chromoClass=MyChromo,
minOrMax='max',
showBest=0)
#
# if a data-file exists, we load it
if (os.path.isfile('ga_maze2_pso.dat')):
pop = IoOps.loadPopulation(ga, 'ga_maze2_pso.dat')
ga.appendToPopulation(pop)
print('Read init data from file (' + str(len(pop)) + ' chromos)')
else:
# otherwise, init the gen-alg library from scratch
ga.initPopulation()
print('Created random init data')
#
# Run it !!
# : we'll just do 10 epochs of 10 steps each
for i in range(outeriter):
ga.evolve(inneriter)
sys.stdout.write('.')
sys.stdout.flush()
#
# all done ... output final results
print("\nfinal best chromo: " + str(ga.population[0].fitness))
ga.population[0].chromo.showGrid()
debuglevel = 100
ga.population[0].calcFitness()
#
# we'll always save the pickle-file, just delete it
# if you want to start over from scratch
IoOps.savePopulation(ga, 'ga_maze2_pso.dat')
print('Final data stored to file (rm ga_maze2_pso.dat to start fresh)')
def run(ctx, save_out, load_in):
ctxobj = ctx.obj
verbose = ctxobj.get('verbose')
epoch_it = ctxobj.get('epoch_it')
inner_it = ctxobj.get('inner_it')
ga = GenAlg(
chromoSize=3 * num_sprayers,
dtype=np.float32,
range=dataRanges,
fitnessFcn=calcFitness,
crossoverFcn=crossover,
userDataI=cutout_region, # user-provided cut-out region
userDataF=scoring, # user-provided scoring system
size=ctxobj.get('popsize'),
elitism=ctxobj.get('elitism'),
crossover=ctxobj.get('crossover'),
pureMutation=ctxobj.get('puremutation'),
parentPct=ctxobj.get('parent_pct'),
minOrMax='max',
showBest=0)
#
# if a pickle-file exists, we load it
if (load_in):
pop = IoOps.loadPopulation(ga, 'ga_sprinkler.dat')
ga.loadPopulation(pop)
print('Read init data from file (' + str(len(pop)) + ' chromos)')
else:
# otherwise, init the gen-alg library from scratch
ga.initPopulation()
print('Created random init data')
if (verbose > 0):
ga.describe()
print('Epoch/Inner iters:', epoch_it, inner_it)
maxspray = 0.0
for i in range(num_sprayers):
maxspray = maxspray + 3.1415 * dataRanges[
3 * i + 2][1] * dataRanges[3 * i + 2][1]
maxfield = 100 * 100 - cutout_region[0] * cutout_region[1]
mx = max(scoring)
print('Max spray: %.2f ( %d )' % (mx * maxspray, mx * maxfield))
#
# Run it !!
# : we'll just do 10 epochs of 10 steps each
with click.progressbar(range(epoch_it)) as bar:
for i in bar:
ga.evolve(inner_it)
#
# all done ... output final results
print("\nfinal best chromos:")
print(ga.population[range(3 * num_sprayers)], ga.fitnessVals[0])
drawImage(ga.population[range(3 * num_sprayers)])
#
# we'll always save the pickle-file, just delete it
# if you want to start over from scratch
if (save_out):
IoOps.savePopulation(ga, 'ga_sprinkler.dat')
print('Final data stored to file (rm ga_sprinkler.dat to start fresh)')
def run(ctx):
ctxobj = ctx.obj
verbose = ctxobj.get('verbose')
epoch_it = ctxobj.get('epoch_it')
inner_it = ctxobj.get('inner_it')
ga = GenAlg(
size=ctxobj.get('popsize'),
elitism=ctxobj.get('elitism'),
crossover=ctxobj.get('crossover'),
pureMutation=ctxobj.get('puremutation'),
chromoClass=MyChromo,
#selectionFcn = GenAlgOps.tournamentSelection,
crossoverFcn=MyCrossover212,
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=MyMutate,
feasibleSolnFcn=GenAlgOps.disallowDupes,
minOrMax='min',
showBest=0,
# optional params ..
params={
'mutateNum': ctxobj.get('mutatenum'),
'parentPct': 0.50,
})
#
# if a data-file exists, we load it
if (ctxobj.get('load_in')):
pop = IoOps.loadPopulation(ga, 'ga_cpu.dat')
ga.appendToPopulation(pop)
print('Read init data from file (' + str(len(pop)) + ' chromos)')
else:
# otherwise, init the gen-alg library from scratch
ga.initPopulation()
print('Created random init data')
# add some 'good' candidate solns ... ROM0*XYZ0, ROM1*XYZ1, etc.
cpu = ga.population[0].cpu
prog = cpu.compile(['ROM0', 'XYZ0', 'MPY', 'ROM1', 'XYZ1', 'MPY'])
for i in range(0, prog_size - 6):
ga.population[i].data[-len(prog):] = prog
if (verbose > 0):
ga.describe()
print('Chromo size: %d :: %d %d' %
(len(ga.population[0].data), prog_size, rom_size))
print('Epoch/Inner iters:', epoch_it, inner_it)
print('Instruction set:',
' '.join(ga.population[0].cpu.PARSEops.keys()))
#
# Run it !!
# : we'll just do 10 epochs of 10 steps each
for i in range(epoch_it):
print('it=', i, time.time())
ga.evolve(inner_it)
# give some running feedback on our progress
txt = ''
for j in range(10):
txt = txt + ' %d' % (ga.population[j].fitness)
print('iter ' + str(i) + ", best fitnesses:" + txt)
print(' ' + ga.population[0].cpu.show_prog(show_pc=False, nl='/') +
' :: ' + ga.population[0].cpu.dump_rommemory())
print(' ' + ga.population[0].cpu.show_prog_as_func())
#
# all done ... output final results
print("\nfinal best chromos:")
for i in range(5):
#print( ga.population[i] )
print(' fit=%d' % (ga.population[i].fitness))
print(' ' + ga.population[i].cpu.show_prog(show_pc=False, nl='/') +
' :: ' + ga.population[i].cpu.dump_rommemory())
print(' ' + ga.population[i].cpu.show_prog_as_func())
#
# we'll always save the pickle-file, just delete it
# if you want to start over from scratch
if (ctxobj.get('save_out')):
IoOps.savePopulation(ga, 'ga_cpu.dat')
print('Final data stored to file (rm ga_cpu.dat to start fresh)')
def run(ctx):
ctxobj = ctx.obj
verbose = ctxobj.get('verbose')
epoch_it = ctxobj.get('epoch_it')
inner_it = ctxobj.get('inner_it')
# ga = GenAlg( size= ctxobj.get('popsize'),
# 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.simpleSelection,
# pureMutationFcn = GenAlgOps.mutateAll,
# #feasibleSolnFcn = GenAlgOps.disallowDupes,
# minOrMax = 'min',
# showBest = 0,
# # optional params ..
# params = {
# 'mutateNum': ctxobj.get('mutatenum'),
# 'parentPct': 0.50,
# }
# )
ga = PsoAlg(
size=ctxobj.get('popsize'),
omega=0.75,
phi_p=1.50,
phi_g=1.00,
learning_rate=1.0,
chromoClass=MyChromo,
minOrMax='min',
showBest=0,
# optional params ..
params={
'mutateNum': ctxobj.get('mutatenum'),
'parentPct': 0.50,
})
#
# if a data-file exists, we load it
if (ctxobj.get('load_in')):
pop = IoOps.loadPopulation(ga, 'ga_cpuDAG.dat')
ga.appendToPopulation(pop)
print('Read init data from file (' + str(len(pop)) + ' chromos)')
else:
# otherwise, init the gen-alg library from scratch
ga.initPopulation()
print('Created random init data')
if (verbose > 0):
ga.describe()
print('Chromo size: %d :: %d %d' %
(len(ga.population[0].chromo.data), num_levels, nodes_per_level))
print('Epoch/Inner iters:', epoch_it, inner_it)
print('Instruction set:',
' '.join(ga.population[0].chromo.cpu.PARSEops.keys()))
#
# Run it !!
# : we'll just do 10 epochs of 10 steps each
for i in range(epoch_it):
print('it=', i, time.time())
ga.evolve(inner_it)
# give some running feedback on our progress
txt = ''
for j in range(10):
txt = txt + ' %d' % (ga.population[j].fitness)
print('iter ' + str(i) + ", best fitnesses:" + txt)
print(' ' +
ga.population[0].chromo.cpu.show_prog(show_pc=False, nl='/'))
print(' ' + ga.population[0].chromo.cpu.show_prog_as_func())
#
# all done ... output final results
print("\nfinal best chromos:")
for i in range(5):
#print( ga.population[i] )
print(' fit=%d' % (ga.population[i].fitness))
print(' ' +
ga.population[i].chromo.cpu.show_prog(show_pc=False, nl='/'))
print(' ' + ga.population[i].chromo.cpu.show_prog_as_func())
#
# we'll always save the pickle-file, just delete it
# if you want to start over from scratch
if (ctxobj.get('save_out')):
IoOps.savePopulation(ga, 'ga_cpuDAG.dat')
print('Final data stored to file (rm ga_cpuDAG.dat to start fresh)')
def main():
ga = GenAlg(
size=100,
elitism=0.10,
crossover=0.60,
pureMutation=0.30,
chromoClass=MyChromo,
#selectionFcn = GenAlgOps.tournamentSelection,
#crossoverFcn = GenAlgOps.crossover22,
#mutationFcn = GenAlgOps.mutateFew,
#pureMutationSelectionFcn = GenAlgOps.simpleSelection,
#pureMutationFcn = GenAlgOps.mutateFew,
#feasibleSolnFcn = GenAlgOps.disallowDupes,
minOrMax='min',
showBest=0,
)
#
# if a pickle-file exists, we load it
if (os.path.isfile('ga_voting2.dat')):
pop = IoOps.loadPopulation(ga, 'ga_voting2.dat')
ga.appendToPopulation(pop)
print('Read init data from file (' + str(len(pop)) + ' chromos)')
if (len(pop) < ga.population_sz):
# we need to fill this out with random data
pop = IoOps.randomPopulation(ga, ga.population_sz - len(pop))
ga.appendToPopulation(pop)
print(' appended ' + str(len(pop)) + ' random chromos')
else:
# otherwise, init the gen-alg library from scratch
ga.initPopulation()
print('Created random init data')
#
# Run it !!
# : we'll just do 10 epochs of 10 steps each
for i in range(10):
ga.evolve(10)
# give some running feedback on our progress
print(str(i) + " best chromo:")
for i in range(1):
print(ga.population[i])
#
# all done ... output final results
print("\nfinal best chromos:")
for i in range(1):
pop = ga.population[i]
# print( ga.population[i] )
pop_ct = pop.population_per_district()
for j in range(len(pop_ct)):
ct = pop_ct[j]
x = pop.data[2 * j]
y = pop.data[2 * j + 1]
print(' %10.4f %10.4f : %10d' % (x, y, ct))
ga.population[0].drawImage()
ga.population[0].drawImage(showCurrent=True)
#
# we'll always save the pickle-file, just delete it
# if you want to start over from scratch
IoOps.savePopulation(ga, 'ga_voting2.dat')
print('Final data stored to file (rm ga_voting2.dat to start fresh)')