def __init__(self, evaluate, numinputs):
self.nout = 5
self.ninp = numinputs
self.labels = None
ReNCoDeProb.__init__(self,evaluate,printf=printmultiplecircuit)
self.terms.extend(["inputs[%i]"%i
for i in range(1,numinputs)])
self.funs.extend(self.extrafuns)
self.arity.update(zip(self.extrafuns,[0]*len(self.extrafuns)))
def __init__(self, evaluate, **kwargs):
self.labels = None
ReNCoDeProb.__init__(self,evaluate,**kwargs)
self.terms.extend(["inputs[%i]"%i
for i in range(1,8)])
#self.funs.extend(self.extrafuns)
self.arity.update(zip(self.funs,[0]*len(self.funs)))
#print self.arity
self.nout = 2
self.ninp = 8
def __init__(self, nbits, evalf, **kwargs):
self.ninp = nbits * 2
self.nout = nbits * 2
self.terms = ['inputs[%i]' % (idx, ) for idx in range(nbits * 2)]
ReNCoDeProb.__init__(self, evalf, **kwargs)
def __init__(self, nbits, evalf, **kwargs):
self.ninp = nbits*2
self.nout = nbits*2
self.terms = ['inputs[%i]'%(idx,) for idx in range(nbits*2)]
ReNCoDeProb.__init__(self,evalf, **kwargs)
circuit.output_idx = outidx
else:
print 'output index is ', circuit.output_idx
bestfit = evaluatekeijzer(circuit, target, inputs, test)
return bestfit
if __name__ == '__main__':
#log.setLevel(logging.INFO)
#random.seed(1234*int(os.getenv('SGE_TASK_ID')))
#random.seed(1234)
evalfun = partial(evaluate,
target=kozapolynomial,
inputs=list(drange(-1, 1.1, .1)))
##fromlist is needed for classes. Functions may be called directly
#mod = __import__(sys.argv[3], fromlist=[sys.argv[2]])
#agentclass = getattr(mod, sys.argv[2])
p = ReNCoDeProb(evalfun)
cfg = loadconfig(parsecmd())
edw = EvoDevoWorkbench(cfg, p)
edw.run(terminate=(lambda x, y: x <= 1e-3 or y <= 0))
#print wrapevaluate(edw.best.phenotype,
# target=kozapolynomial,
# inputs=list(drange(-1,1.1,.1)),
# device=edw.device,
# test = True)
def __init__(self, evalf):
self.ninp = 4
self.nout = 1
ReNCoDeProb.__init__(self, evalf, printf=printrencode2)