def __init__(self, gcode, config, **kwargs):
self.code = gcode
self.simtime = config.getint('default', 'simtime')
prob = kwargs['problem']
promfun = bindparams(config, buildpromlist)
productsfun = bindparams(config, buildproducts)
#TODO: add nout to the parameters file or bring the problem instance here?
self.promlist,self.effectorproms = \
promfun(gcode,
productprom='11111111',
nout = prob.nout)
self.proteins = productsfun(gcode, self.promlist)
self.effectors = []
if self.effectorproms:
self.effectors = productsfun(gcode, self.effectorproms)
self.receptors = build_customproducts()
self.receptorproms = [r[0] for r in self.receptors]
pbindfun = bindparams(config, getbindings)
weightsfun = bindparams(config, _getweights)
self.numtf = len(self.proteins)
if prob.nout == 1:
self.numeff = len(self.effectors)
else:
self.numeff = min(len(self.effectors), prob.nout)
self.effectorproms = self.effectorproms[:self.numeff]
self.effectors = self.effectors[:self.numeff]
self.numrec = min(len(self.receptorproms), prob.ninp)
self.receptorproms = self.receptorproms[:self.numrec]
self.receptors = self.receptors[:self.numrec]
self.ccs = []
if self.promlist or self.effectorproms:
self.ccs = nparray(
[1.0 / (self.numtf + self.numeff + self.numrec)] *
(self.numtf + self.numeff + self.numrec))
self._initializehistory()
self._initializebindings(pbindfun)
self._initializeweights(weightsfun)
for i in range(len(self.proteins)):
self.proteins[i].append(self.ccs[i])
self.simfun = bindparams(config, iterate)
self.delta = config.getfloat('default', 'delta')
log.debug(self.snapshot())
self.output_idx = 0
def __init__(self, config, gcode = None, parentfit = 1e4):
Agent.__init__(self, parentfit)
generator = arn.bindparams(config, arn.generatechromo)
if gcode == None:
gcode = generator()
self.genotype = arn.ARNetwork(gcode,config)
self.phenotype = []
self.fitness = 1e4
def __init__(self, gcode, config, **kwargs):
self.code = gcode
self.simtime = config.getint('default', 'simtime')
promfun = bindparams(config, buildpromlist)
productsfun = bindparams(config, buildproducts)
self.promlist = promfun(gcode)
self.proteins = productsfun(gcode, self.promlist)
self.effectors = []
self.effectorproms = promfun(gcode, promoter='00000000')
if self.effectorproms:
#print 'EFFECTORS:', self.effectorproms
self.effectors = productsfun(gcode, self.effectorproms)
self.receptors = []
self.receptorproms = promfun(gcode, promoter='11111111')
if self.receptorproms:
#print 'RECEPTORS:', self.receptorproms
self.receptors = productsfun(gcode, self.receptorproms)
pbindfun = bindparams(config, getbindings)
weightsfun = bindparams(config, _getweights)
prob = kwargs['problem']
self.numtf = len(self.proteins)
self.numeff = min(len(self.effectors), prob.nout)
self.effectors = self.effectors[:self.numeff]
self.numrec = min(len(self.receptors), prob.ninp)
self.receptors = self.receptors[:self.numrec]
self.ccs = []
if self.promlist:
self.ccs = nparray(
[1.0 / (self.numtf + self.numeff + self.numrec)] *
(self.numtf + self.numeff + self.numrec))
self._initializehistory()
self._initializebindings(pbindfun)
self._initializeweights(weightsfun)
for i in range(len(self.proteins)):
self.proteins[i].append(self.ccs[i])
self.simfun = bindparams(config, iterate)
self.delta = config.getfloat('default', 'delta')
def __init__(self, gcode, config, **kwargs):
self.code = gcode
self.simtime = config.getint('default','simtime')
promfun = bindparams(config, buildpromlist)
productsfun = bindparams(config, buildproducts)
self.promlist = promfun(gcode)
self.proteins = productsfun( gcode, self.promlist)
self.effectors=[]
self.effectorproms = promfun(gcode, promoter='00000000')
if self.effectorproms:
#print 'EFFECTORS:', self.effectorproms
self.effectors = productsfun(gcode,self.effectorproms)
self.receptors=[]
self.receptorproms = promfun(gcode, promoter='11111111')
if self.receptorproms:
#print 'RECEPTORS:', self.receptorproms
self.receptors = productsfun(gcode,self.receptorproms)
pbindfun = bindparams(config, getbindings)
weightsfun = bindparams(config, _getweights)
prob = kwargs['problem']
self.numtf = len(self.proteins)
self.numeff = min(len(self.effectors),prob.nout)
self.effectors = self.effectors[:self.numeff]
self.numrec = min(len(self.receptors),prob.ninp)
self.receptors = self.receptors[:self.numrec]
self.ccs = []
if self.promlist:
self.ccs = nparray([1.0/(self.numtf+self.numeff+self.numrec)]*
(self.numtf+self.numeff+self.numrec))
self._initializehistory()
self._initializebindings(pbindfun)
self._initializeweights(weightsfun)
for i in range(len(self.proteins)):
self.proteins[i].append(self.ccs[i])
self.simfun = bindparams(config,iterate)
self.delta = config.getfloat('default','delta')
def __init__(self, config, problem, gcode = None, parent = None):
Agent.__init__(self, parent)
generator = arn.bindparams(config, self.generate)
if gcode == None:
gcode = generator()
arnet = arn.ARNetwork(gcode,config)
self.genotype = arnet
self.phenotype = P(arnet,buildcircuit(self,problem), problem)
self.problem = problem
self.fitness = 1e6
def __init__(self, config, problem, gcode=None, parent=None):
Agent.__init__(self, parent)
generator = arn.bindparams(config, self.generate)
if gcode == None:
gcode = generator()
arnet = arn.ARNetwork(gcode, config)
self.genotype = arnet
self.phenotype = P(arnet, buildcircuit(self, problem), problem)
self.problem = problem
self.fitness = 1e6
def getmapping(arncfg, fback = False): """ Returns the mapping (from genome and expression to circuit) """ return functools.partial( bindparams(arncfg,_buildcircuitfunctions), feedback = fback)