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import logging

from functools import partial

from bitstring import BitStream

import math

import arn

from evodevo import Problem, Agent

from utils import *

from utils.bitstrutils import *

from utils.mathlogic import *

log = logging.getLogger(__name__)

def printdotcircuit(circuit, labels=None):

circuit = circuit.circuit

s = 'digraph best {\nordering = out;\n'

for c in circuit:

s += '%i [label="%s"];\n' % (c[0], c[1])# if not labels

#else labels[c[1]])

for inp in c[2]:

aux = "dir=back"

if inp < 0:

aux += ",style=dotted"

s += '%i -> %i [%s];\n' % (c[0],abs(inp),aux)

s += '}'

return s

#TODO: remove! behavior is identical to nnlikefun

def regressionfun(mapped, node_inputs, inputs ):

if not node_inputs:

return eval(mapped)

mainmod = __import__('__main__')

if mapped in ['if_']:

return getattr(mainmod, mapped)(*node_inputs)

#print r

# return r

#else:

if len(node_inputs) == 1:

return getattr(mainmod, mapped)(node_inputs[0])

return reduce(lambda m,n: getattr(mainmod, mapped)(m,n),

node_inputs)

def nnlikefun(mapped, node_inputs, inputs):

mainmod = __import__('__main__')

if not node_inputs:

try:

return eval(mapped)

except NameError:

return getattr(mainmod, mapped)(inputs)

if len(node_inputs) == 1:

return getattr(mainmod, mapped)(node_inputs[0])

if mapped not in ReNCoDeProb.funs:

return getattr(mainmod, mapped)(*node_inputs)

#print mapped, node_inputs

return reduce(lambda m,n: getattr(mainmod, mapped)(m,n),

node_inputs)

def mergefun(mapped, node_inputs, inputs):

if not node_inputs:

return mapped

else:

result = mapped + '('

result += reduce(lambda m, n: m + ',' + n, node_inputs)

result += ')'

return result

def defaultnodemap(signature, mappingset):

if len(mappingset) < 2:

return mappingset[0]

index = BitArray(bin=applymajority(

signature,

int(math.ceil(math.log(len(mappingset),2)))))

intindex = index.uint

if intindex >= len(mappingset):

intindex -= len(mappingset)

return mappingset[intindex]

def evaluatecircuit(circuit, circuitmap, resultdict, *inputs,**kwargs):

try:

nout = kwargs['nout']

except KeyError:

nout = 1

if resultdict == None:

resultdict = dict()

for i in range(len(circuit)):

inputvalues = [resultdict[abs(v)] for v in circuit[-1-i][2]]

result = circuitmap(circuit[-1-i][1],

inputvalues,

inputs)

resultdict[circuit[-1-i][0]] = result

if nout == 1:

return result

else:

results = list()

for i in range(nout):

if len(circuit) > i:

results.append(resultdict[circuit[i][0]])

else:

results.append(0)

return results

def buildcircuit(agent, problem, **kwargs):

"""Returns the circuit to be fed into the evaluation function"""

arn = agent.genotype

if not arn.promlist:

return []

#arn.simulate()

#orderedps = sorted(arn.proteins, key = lambda x: x[-1], reverse=True)

graph = arn.ebindings - arn.ibindings

cleanpairs(graph)

promlist = [(p[0],

_getinputlist(

arn.promlist,

graph[:,arn.promlist.index(p[0])].tolist()))

for p in arn.proteins]

promlist.sort(key=lambda x: len(x[1]),reverse=True)

circuit = []

pdict = dict(zip(arn.promlist,arn.proteins))

recursivebuild(circuit, problem, pdict, dict(promlist),

graph, [promlist[0][0]], [], [])

return circuit

def recursivebuild(circuit, problem, proteindict, inputdict, graph,

pqueue, secondqueue, blacklist):

if not pqueue:

if not secondqueue: return circuit

else:

secondqueue.sort(key=lambda x: len(inputdict[x]),

reverse=True)

pqueue.append(secondqueue.pop(0))

next = pqueue.pop(0)

pnext = proteindict[next]

blacklist.append(next)

inputs = []

for i in inputdict[next]:

if i in blacklist:

if problem.feedback:

inputs.append(-i)

else:

inputs.append(i)

if inputs:

fun = problem.nodemap_(pnext[4],problem.funs)

ar = problem.arity[fun]

if ar > 0 and len(inputs) > ar:

del inputs[ar:]

else:

fun = problem.nodemap_(pnext[4],problem.terms)

circuit.append((next, fun , inputs))

#update input dict if not using feedback

if not problem.feedback:

for k,v in inputdict.items():

inputdict[k] = filter(

lambda i: i not in blacklist, v)

secondqueue.extend([inp for inp in inputs

if inp not in (pqueue+secondqueue+blacklist)

and (inp >= 0)])

pqueue, secondqueue = _mergequeues(pqueue, secondqueue, inputdict)

pqueue.sort(key=lambda x: len(inputdict[x]), reverse = True)

return recursivebuild(circuit, problem, proteindict, inputdict,

graph, pqueue,secondqueue, blacklist)

def cleanpairs(matrix):

s = len(matrix)

for i in range(s):

for j in range(i):

if matrix[i][j] >= matrix[j][i]:

matrix[j][i] = 0

else:

matrix[i][j] = 0

def _mergequeues(q1, q2,inputdict):

disjunction = q2[:]

demoted = []

for e in q1:

dependent = False

if e in [p

for pq_el in q1

for p in inputdict[pq_el]

if pq_el != e]:

dependent = True

if e in [p

for pq_el in q2

for p in inputdict[pq_el]]:

dependent = True

if dependent:

demoted.append(e)

for e in q2:

dependent = False

if e in [p

for pq_el in q1

for p in inputdict[pq_el]]:

dependent = True

if e in [p

for pq_el in q2

for p in inputdict[pq_el]

if pq_el != e]:

dependent = True

if dependent:

disjunction.remove(e)

for e in demoted: q1.remove(e)

q2.extend(demoted)

for e in disjunction: q2.remove(e)

q1.extend(disjunction)

return q1,q2

def _getinputlist(promlist, weights):

"""Returns the relevant inputs given the weights."""

inputs=[]

pmap = zip(promlist,weights)

for p,w in pmap:

if w > 0:

inputs.append(p)

return inputs

def printcircuit(circuit):

s = ''

for c in circuit:

s += "%i [%s]: %s\n" % (c[0], c[1],

reduce(lambda m,n: "%s %s " % (m,n),c[2],

""))

return s[:-2]

### Problem base to use with ReNCoDe

class ReNCoDeProb(Problem):

#TODO: read fun set from config file

funs = [ 'add_', 'sub_', 'mul_', 'div_']

terms = [ 'inputs[0]' ]

labels = {'add_':'+', 'sub_':'-', 'mul_':'*', 'div_':'/',

'inputs[0]':'x', 'inputs[1]':'1.0'}

arity = {}#{'add_':0, 'sub_':0, 'mul_':0, 'div_':0}

feedback = False

nout=1

def __init__(self, evaluate, nodemap = defaultnodemap):

Problem.__init__(self, evaluate)

self.nodemap_ = nodemap

#callable phenotype

class P:

def __init__(self, arnet, circuit, problem, skeleton = regressionfun):

self.geno = arnet

self.circuit = circuit

self.problem = problem

#memory (disabled by default)

self.memory = None

self.funskel = skeleton

if not problem.feedback:

self._str = compile(evaluatecircuit(self.circuit, mergefun, self.memory, *problem.terms),

'<string>',

'eval')

def getcircuit(self, *args):

return self.circuit

def __call__(self, *inputs):

return evaluatecircuit(self.circuit,self.funskel , self.memory,

*inputs, nout = self.problem.nout)

def __len__(self):

return len(self.circuit)

def __eq__(self,other):

return self.circuit == other.circuit

def printgraph(self):

return "GRAPH NOT AVAILABLE"

### Agent model to use with this CoDe module

class ReNCoDeAgent(Agent):

genotype = None

phenotype = None

fitness = None

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 __str__(self):

return "### Agent ###\n%s\n%s: %f" % (self.arn,self.phenotype,

self.fitness)

def pickled(self):

return self.print_()

def print_(self):

return printdotcircuit(self.phenotype)

class DMAgent(ReNCoDeAgent):

def __init__(self, config, problem, gcode = None, parent = None):

self.generate = arn.generatechromo

ReNCoDeAgent.__init__(self, config, problem, gcode, parent)

class RndAgent(ReNCoDeAgent):

def __init__(self, config, problem, gcode = None, parent = None):

self.generate = partial(arn.generatechromo_rnd,

genomesize = 32 * pow(2,config.getint('default','initdm')))

ReNCoDeAgent.__init__(self, config, problem, gcode, parent)

###########################################################################

### Test ###

###########################################################################

if __name__ == '__main__':

import ConfigParser

import random

from bitstring import BitStream

from bitstringutils import dm_event

from arn import ARNetwork

from evodevo import Problem

log.setLevel(logging.DEBUG)

cfg = ConfigParser.ConfigParser()

cfg.readfp(open('test.cfg'))

arncfg = ConfigParser.ConfigParser()

arncfg.readfp(open('arnsim.cfg'))

proteins=[]

nump = 0

while nump < 3:

genome = BitStream(float=random.random(), length=32)

for i in range(5):

genome = dm_event(genome,

.02)

arnet = ARNetwork(genome, arncfg)

nump = len(arnet.promlist)

for p in arnet.proteins: print p

prob = Problem(defaultnodemap,regressionfun, None)

circuit = buildcircuit(arnet, prob,False)

_printcircuit(circuit)

print printdotcircuit(circuit, prob.labels)