def selekcjaKolemRuletki(ustawienia, populacja=[]):
score = {}
lista_wynikow = []
Populacja_elity = []
najlepsi = []
if ustawienia.rodzaj_Optymalizacj == "Max":
# elita
for x in populacja:
lista_wynikow.append(x.wynik)
elita = heapq.nlargest(ustawienia.elita, lista_wynikow)
for i in elita:
x = populacja[elita.index(i)]
Populacja_elity.append(x)
populacja.remove(x)
lista_wynikow = []
# elita
for indiv in populacja:
score.update({indiv: indiv.wynik})
for x in range(0, populacja.__len__()):
najlepsi.append(
rouletteWheelSelect(getRouletteWheel(populacja, score)))
else:
if ustawienia.rodzaj_Optymalizacj == "Min":
# elita
for x in populacja:
lista_wynikow.append(x.wynik)
elita = heapq.nsmallest(ustawienia.elita, lista_wynikow)
for i in elita:
x = populacja[elita.index(i)]
Populacja_elity.append(x)
populacja.remove(x)
lista_wynikow = []
# elita
for indiv in populacja:
score.update({indiv: 1 / indiv.wynik})
for x in range(0, populacja.__len__()):
najlepsi.append(
rouletteWheelSelect(getRouletteWheel(populacja, score)))
return najlepsi, Populacja_elity
def runTSPGA(kwargs):
"""
pre:
isinstance(kwargs, dict)
'maxGens' in kwargs
kwargs['maxGens'] > 0
post[kwargs]:
__old__.kwargs == kwargs
__return__[0][1] >= kwargs['targetscore'] or __return__[1] == kwargs['maxGens']
isinstance(__return__[0][0], Individual)
"""
if 'sanity' not in kwargs:
raise TypeError("Expected argument 'sanity' not found")
arguments = kwargs['sanity']
if len(kwargs) < len(arguments):
raise TypeError("Missing Arguements: %s" %' '.join([a for a in arguments if a not in kwargs]))
# # # # # # PARAMETERS # # # # # #
testmode = kwargs['testmode']
maxGens = kwargs['maxGens']
targetscore = kwargs['targetscore']
genfunc = kwargs['genfunc']
genparams = kwargs['genparams']
scorefunc = kwargs['scorefunc']
scoreparams = kwargs['scoreparams']
selectfunc = kwargs['selectfunc']
selectparams = kwargs['selectparams']
numcross = kwargs['numcross']
crossfunc = kwargs['crossfunc']
crossprob = kwargs['crossprob']
crossparams = kwargs['crossparams']
mutfunc = kwargs['mutfunc']
mutprob = kwargs['mutprob']
mutparams = kwargs['mutparams']
SCORES = kwargs['SCORES']
visualize = kwargs['visualize']
getWheel = kwargs['getWheel']
if visualize:
makeScreenParams = kwargs['makeScreenParams']
drawParams = kwargs['drawParams']
font = kwargs['font']
fontParams = kwargs['fontParams']
labelParams = kwargs['labelParams']
# # # # # # /PARAMETERS # # # # # #
pop = genfunc(*genparams)
for p in pop:
if p not in SCORES:
SCORES[p] = scorefunc(p, *scoreparams)
best = max(SCORES, key=SCORES.__getitem__)
best = best, SCORES[best] # indiv, score
if visualize:
screen = vis.makeScreen(*makeScreenParams)
label = font.render("%d / %d" %(best[1], targetscore), *fontParams)
screen.blit(label, *labelParams)
pg.display.init()
vis.draw(best[0], screen, *drawParams)
g = 0
while g < maxGens:
if testmode:
assert g < maxGens
assert best[1] < targetscore
if getWheel:
wheel = selection.getRouletteWheel(pop, SCORES)
newpop = []
for _ in xrange(numcross):
if getWheel:
p1 = selectfunc(wheel, *selectparams)
p2 = selectfunc(wheel, *selectparams)
else:
p1, p2 = selectfunc(pop, *selectparams)
if rand() <= crossprob:
c1 = crossfunc(p1, p2, *crossparams)
c2 = crossfunc(p2, p1, *crossparams)
newpop.extend([c1,c2])
for i,p in enumerate(newpop):
if rand() <= mutprob:
newpop[i] = mutfunc(p, *mutparams)
p = newpop[i]
SCORES[p] = scorefunc(p, *scoreparams)
pop = sorted(pop+newpop, key=SCORES.__getitem__, reverse=True)[:len(pop)]
fittest = max(pop, key=SCORES.__getitem__)
fittest = fittest, SCORES[fittest]
log.info("Generation %03d | highest fitness: %s | fittest indiv: %r" %(g, fittest[1], fittest[0].chromosomes[0]) )
if fittest[1] > best[1]:
best = fittest
if visualize:
screen = vis.makeScreen(*makeScreenParams)
label = font.render("%d / %d" %(best[1], targetscore), *fontParams)
screen.blit(label, *labelParams)
pg.display.init()
vis.draw(fittest[0], screen, *drawParams)
if best[1] >= targetscore:
if visualize:
raw_input("Hit <ENTER> to kill visualization: ")
vis.killscreen()
return best[0], g
g += 1
if visualize:
raw_input("Hit <ENTER> to kill visualization: ")
vis.killscreen()
if testmode:
assert (g == maxGens) or best[1] >= targetscore
return best, g
def runGA(kwargs, testmode=False):
"""
pre:
isinstance(kwargs, dict)
'maxGens' in kwargs
kwargs['maxGens'] > 0
post[kwargs]:
__old__.kwargs == kwargs
__return__[0][1] >= kwargs['targetscore'] or __return__[1] == kwargs['maxGens']
isinstance(__return__[0][0], Individual)
"""
if 'sanity' not in kwargs:
raise TypeError("Expected argument 'sanity' not found")
arguments = kwargs['sanity']
if len(kwargs) < len(arguments):
raise TypeError("Missing Arguements: %s" %' '.join([a for a in arguments if a not in kwargs]))
# # # # # # PARAMETERS # # # # # #
maxGens = kwargs['maxGens']
targetscore = kwargs['targetscore']
genfunc = kwargs['genfunc']
genparams = kwargs['genparams']
scorefunc = kwargs['scorefunc']
scoreparams = kwargs['scoreparams']
selectfunc = kwargs['selectfunc']
selectparams = kwargs['selectparams']
numcross = kwargs['numcross']
crossfunc = kwargs['crossfunc']
crossprob = kwargs['crossprob']
crossparams = kwargs['crossparams']
mutfunc = kwargs['mutfunc']
mutprob = kwargs['mutprob']
mutparams = kwargs['mutparams']
SCORES = kwargs['SCORES']
diff = kwargs['diff']
getWheel = kwargs['getWheel']
# # # # # # /PARAMETERS # # # # # #
df = pd.read_csv('Genetic/Data/simSEAData.csv',header=False)
dfY = df.iloc[:,3]
dfX = df.iloc[:,0:3]
genparams = (genparams[0],genparams[1],[(8,2)])
crossover.X = dfX.iloc[0:100].values
crossover.Y = dfY.iloc[0:100].values
population.X = dfX.iloc[0:100].values
population.Y = dfY.iloc[0:100].values
pop = genfunc(*genparams)
N = len(pop)
start = 100
end = start + diff
fitness.currX = dfX.iloc[start:min(end,len(dfX))].values
fitness.currY = dfY.iloc[start:min(end,len(dfX))].values
for p in pop:
if p not in SCORES:
SCORES[p] = scorefunc(p, *scoreparams)
best = max(SCORES, key=SCORES.__getitem__)
best = best, SCORES[best] # indiv, score
g = 1
acc = []
while g < maxGens and end < len(dfX):
if testmode:
assert g < maxGens
assert best[1] < targetscore
if getWheel:
wheel = selection.getRouletteWheel(pop, SCORES)
newpop = []
for _ in xrange(numcross):
if getWheel:
p1 = selectfunc(wheel, *selectparams)
p2 = selectfunc(wheel, *selectparams)
else:
p1, p2 = selectfunc(pop, *selectparams)
if rand() <= crossprob:
l = crossfunc(p1,p2,g,SCORES[p1],SCORES[p2])
for a in l:
SCORES[a] = scorefunc(a, *scoreparams)
newpop.extend(l)
else:
newpop.extend([p1,p2])
for i,p in enumerate(newpop):
if rand() <= mutprob:
newpop[i] = mutfunc(p,SCORES[p],crossover.Y)
p = newpop[i]
SCORES[p] = scorefunc(p, *scoreparams)
pop = newpop
sorted(pop,key = fitness.scoreAccuracy)
pop = pop[0:N]
#print(len(pop))
crossover.X = fitness.currX
crossover.Y = fitness.currY
fitness.currX = dfX.iloc[start:min(end,len(dfX))].values
fitness.currY = dfY.iloc[start:min(end,len(dfX))].values
fittest = max(pop, key=SCORES.__getitem__)
fittest = fittest, SCORES[fittest]
#log.info("Generation %03d | highest fitness: %s | fittest indiv: %r" %(g, fittest[1], ''.join(fittest[0].chromosomes[0])) )
a = fittest[0].chromosomes[0].score(fitness.currX,fitness.currY)
#print("Generation %03d | highest fitness: %s | Born: %d | prediction: %s" %(g, fittest[1],fittest[0].chromosomes[1],a))
acc.append(a)
if fittest[1] > best[1]:
best = fittest
if best[1] >= targetscore:
return best, g
g += 1
fitness.evolution(pop)
start += diff
end += diff
if testmode:
assert (g == maxGens) or best[1] >= targetscore
return acc
def runGA(kwargs, testmode=False):
"""
pre:
isinstance(kwargs, dict)
'maxGens' in kwargs
kwargs['maxGens'] > 0
post[kwargs]:
__old__.kwargs == kwargs
__return__[0][1] >= kwargs['targetscore'] or __return__[1] == kwargs['maxGens']
isinstance(__return__[0][0], Individual)
"""
# # # # # # PARAMETERS # # # # # #
maxGens = kwargs['maxGens']
targetscore = kwargs['targetscore']
genfunc = kwargs['genfunc']
genparams = kwargs['genparams']
scorefunc = kwargs['scorefunc']
scoreparams = kwargs['scoreparams']
selectfunc = kwargs['selectfunc']
selectparams = kwargs['selectparams']
numcross = kwargs['numcross']
crossfunc = kwargs['crossfunc']
crossfuncs = kwargs['crossfuncs']
crossprob = kwargs['crossprob']
crossparams = kwargs['crossparams']
mutfunc = kwargs['mutfunc']
mutprob = kwargs['mutprob']
mutparams = kwargs['mutparams']
SCORES = kwargs['SCORES']
getWheel = kwargs['getWheel']
# # # # # # /PARAMETERS # # # # # #
pop = genfunc(*genparams)
for p in pop:
if p not in SCORES:
SCORES[p] = scorefunc(p, *scoreparams)
best = max(SCORES, key=SCORES.__getitem__)
best = best, SCORES[best] # indiv, score
g = 0
while g < maxGens:
if testmode:
assert g < maxGens
assert best[1] < targetscore
if getWheel:
wheel = selection.getRouletteWheel(pop, SCORES)
newpop = []
for _ in xrange(numcross):
if getWheel:
p1 = selectfunc(wheel, *selectparams)
p2 = selectfunc(wheel, *selectparams)
else:
p1, p2 = selectfunc(pop, *selectparams)
if rand() <= crossprob:
p1, p2 = crossfunc(p1, p2, crossfuncs, crossparams)
newpop.extend([p1,p2])
for i,p in enumerate(newpop):
if rand() <= mutprob:
newpop[i] = mutfunc(p, *mutparams)
p = newpop[i]
SCORES[p] = scorefunc(p, *scoreparams)
pop = newpop
fittest = max(pop, key=SCORES.__getitem__)
fittest = fittest, SCORES[fittest]
log.info("Generation %03d | highest fitness: %s | fittest indiv: %r" %(g, fittest[1], ''.join(fittest[0].chromosomes[0])) )
if fittest[1] > best[1]:
best = fittest
if best[1] >= targetscore:
return best, g
g += 1
if testmode:
assert (g == maxGens) or best[1] >= targetscore
return best[0], g
def runTSPGA(kwargs):
"""
pre:
isinstance(kwargs, dict)
'maxGens' in kwargs
kwargs['maxGens'] > 0
post[kwargs]:
__old__.kwargs == kwargs
__return__[0][1] >= kwargs['targetscore'] or __return__[1] == kwargs['maxGens']
isinstance(__return__[0][0], Individual)
"""
if 'sanity' not in kwargs:
raise TypeError("Expected argument 'sanity' not found")
arguments = kwargs['sanity']
if len(kwargs) < len(arguments):
raise TypeError("Missing Arguements: %s" %
' '.join([a for a in arguments if a not in kwargs]))
# # # # # # PARAMETERS # # # # # #
testmode = kwargs['testmode']
maxGens = kwargs['maxGens']
targetscore = kwargs['targetscore']
genfunc = kwargs['genfunc']
genparams = kwargs['genparams']
scorefunc = kwargs['scorefunc']
scoreparams = kwargs['scoreparams']
selectfunc = kwargs['selectfunc']
selectparams = kwargs['selectparams']
numcross = kwargs['numcross']
crossfunc = kwargs['crossfunc']
crossprob = kwargs['crossprob']
crossparams = kwargs['crossparams']
mutfunc = kwargs['mutfunc']
mutprob = kwargs['mutprob']
mutparams = kwargs['mutparams']
SCORES = kwargs['SCORES']
visualize = kwargs['visualize']
getWheel = kwargs['getWheel']
if visualize:
makeScreenParams = kwargs['makeScreenParams']
drawParams = kwargs['drawParams']
font = kwargs['font']
fontParams = kwargs['fontParams']
labelParams = kwargs['labelParams']
# # # # # # /PARAMETERS # # # # # #
pop = genfunc(*genparams)
for p in pop:
if p not in SCORES:
SCORES[p] = scorefunc(p, *scoreparams)
best = max(SCORES, key=SCORES.__getitem__)
best = best, SCORES[best] # indiv, score
if visualize:
screen = vis.makeScreen(*makeScreenParams)
label = font.render("%d / %d" % (best[1], targetscore), *fontParams)
screen.blit(label, *labelParams)
pg.display.init()
vis.draw(best[0], screen, *drawParams)
g = 0
while g < maxGens:
if testmode:
assert g < maxGens
assert best[1] < targetscore
if getWheel:
wheel = selection.getRouletteWheel(pop, SCORES)
newpop = []
for _ in xrange(numcross):
if getWheel:
p1 = selectfunc(wheel, *selectparams)
p2 = selectfunc(wheel, *selectparams)
else:
p1, p2 = selectfunc(pop, *selectparams)
if rand() <= crossprob:
c1 = crossfunc(p1, p2, *crossparams)
c2 = crossfunc(p2, p1, *crossparams)
newpop.extend([c1, c2])
for i, p in enumerate(newpop):
if rand() <= mutprob:
newpop[i] = mutfunc(p, *mutparams)
p = newpop[i]
SCORES[p] = scorefunc(p, *scoreparams)
pop = sorted(pop + newpop, key=SCORES.__getitem__,
reverse=True)[:len(pop)]
fittest = max(pop, key=SCORES.__getitem__)
fittest = fittest, SCORES[fittest]
log.info("Generation %03d | highest fitness: %s | fittest indiv: %r" %
(g, fittest[1], fittest[0].chromosomes[0]))
if fittest[1] > best[1]:
best = fittest
if visualize:
screen = vis.makeScreen(*makeScreenParams)
label = font.render("%d / %d" % (best[1], targetscore),
*fontParams)
screen.blit(label, *labelParams)
pg.display.init()
vis.draw(fittest[0], screen, *drawParams)
if best[1] >= targetscore:
if visualize:
raw_input("Hit <ENTER> to kill visualization: ")
vis.killscreen()
return best[0], g
g += 1
if visualize:
raw_input("Hit <ENTER> to kill visualization: ")
vis.killscreen()
if testmode:
assert (g == maxGens) or best[1] >= targetscore
return best, g
def runGA(kwargs, testmode=False):
"""
pre:
isinstance(kwargs, dict)
'maxGens' in kwargs
kwargs['maxGens'] > 0
post[kwargs]:
__old__.kwargs == kwargs
__return__[0][1] >= kwargs['targetscore'] or __return__[1] == kwargs['maxGens']
isinstance(__return__[0][0], Individual)
"""
if 'sanity' not in kwargs:
raise TypeError("Expected argument 'sanity' not found")
arguments = kwargs['sanity']
if len(kwargs) < len(arguments):
raise TypeError("Missing Arguements: %s" %
' '.join([a for a in arguments if a not in kwargs]))
# # # # # # PARAMETERS # # # # # #
maxGens = kwargs['maxGens']
targetscore = kwargs['targetscore']
genfunc = kwargs['genfunc']
genparams = kwargs['genparams']
scorefunc = kwargs['scorefunc']
scoreparams = kwargs['scoreparams']
selectfunc = kwargs['selectfunc']
selectparams = kwargs['selectparams']
numcross = kwargs['numcross']
crossfunc = kwargs['crossfunc']
crossprob = kwargs['crossprob']
crossparams = kwargs['crossparams']
mutfunc = kwargs['mutfunc']
mutprob = kwargs['mutprob']
mutparams = kwargs['mutparams']
SCORES = kwargs['SCORES']
diff = kwargs['diff']
getWheel = kwargs['getWheel']
# # # # # # /PARAMETERS # # # # # #
df = pd.read_csv('Genetic/Data/simSEAData.csv', header=False)
dfY = df.iloc[:, 3]
dfX = df.iloc[:, 0:3]
genparams = (genparams[0], genparams[1], [(8, 2)])
crossover.X = dfX.iloc[0:100].values
crossover.Y = dfY.iloc[0:100].values
population.X = dfX.iloc[0:100].values
population.Y = dfY.iloc[0:100].values
pop = genfunc(*genparams)
N = len(pop)
start = 100
end = start + diff
fitness.currX = dfX.iloc[start:min(end, len(dfX))].values
fitness.currY = dfY.iloc[start:min(end, len(dfX))].values
for p in pop:
if p not in SCORES:
SCORES[p] = scorefunc(p, *scoreparams)
best = max(SCORES, key=SCORES.__getitem__)
best = best, SCORES[best] # indiv, score
g = 1
acc = []
while g < maxGens and end < len(dfX):
if testmode:
assert g < maxGens
assert best[1] < targetscore
if getWheel:
wheel = selection.getRouletteWheel(pop, SCORES)
newpop = []
for _ in xrange(numcross):
if getWheel:
p1 = selectfunc(wheel, *selectparams)
p2 = selectfunc(wheel, *selectparams)
else:
p1, p2 = selectfunc(pop, *selectparams)
if rand() <= crossprob:
l = crossfunc(p1, p2, g, SCORES[p1], SCORES[p2])
for a in l:
SCORES[a] = scorefunc(a, *scoreparams)
newpop.extend(l)
else:
newpop.extend([p1, p2])
for i, p in enumerate(newpop):
if rand() <= mutprob:
newpop[i] = mutfunc(p, SCORES[p], crossover.Y)
p = newpop[i]
SCORES[p] = scorefunc(p, *scoreparams)
pop = newpop
sorted(pop, key=fitness.scoreAccuracy)
pop = pop[0:N]
#print(len(pop))
crossover.X = fitness.currX
crossover.Y = fitness.currY
fitness.currX = dfX.iloc[start:min(end, len(dfX))].values
fitness.currY = dfY.iloc[start:min(end, len(dfX))].values
fittest = max(pop, key=SCORES.__getitem__)
fittest = fittest, SCORES[fittest]
#log.info("Generation %03d | highest fitness: %s | fittest indiv: %r" %(g, fittest[1], ''.join(fittest[0].chromosomes[0])) )
a = fittest[0].chromosomes[0].score(fitness.currX, fitness.currY)
#print("Generation %03d | highest fitness: %s | Born: %d | prediction: %s" %(g, fittest[1],fittest[0].chromosomes[1],a))
acc.append(a)
if fittest[1] > best[1]:
best = fittest
if best[1] >= targetscore:
return best, g
g += 1
fitness.evolution(pop)
start += diff
end += diff
if testmode:
assert (g == maxGens) or best[1] >= targetscore
return acc
def runGA(kwargs, testmode=False):
"""
pre:
isinstance(kwargs, dict)
'maxGens' in kwargs
kwargs['maxGens'] > 0
post[kwargs]:
__old__.kwargs == kwargs
__return__[0][1] >= kwargs['targetscore'] or __return__[1] == kwargs['maxGens']
isinstance(__return__[0][0], Individual)
"""
# # # # # # PARAMETERS # # # # # #
maxGens = kwargs['maxGens']
targetscore = kwargs['targetscore']
genfunc = kwargs['genfunc']
genparams = kwargs['genparams']
scorefunc = kwargs['scorefunc']
scoreparams = kwargs['scoreparams']
selectfunc = kwargs['selectfunc']
selectparams = kwargs['selectparams']
numcross = kwargs['numcross']
crossfunc = kwargs['crossfunc']
crossfuncs = kwargs['crossfuncs']
crossprob = kwargs['crossprob']
crossparams = kwargs['crossparams']
mutfunc = kwargs['mutfunc']
mutprob = kwargs['mutprob']
mutparams = kwargs['mutparams']
SCORES = kwargs['SCORES']
getWheel = kwargs['getWheel']
# # # # # # /PARAMETERS # # # # # #
pop = genfunc(*genparams)
for p in pop:
if p not in SCORES:
SCORES[p] = scorefunc(p, *scoreparams)
best = max(SCORES, key=SCORES.__getitem__)
best = best, SCORES[best] # indiv, score
g = 0
while g < maxGens:
if testmode:
assert g < maxGens
assert best[1] < targetscore
if getWheel:
wheel = selection.getRouletteWheel(pop, SCORES)
newpop = []
for _ in range(numcross):
if getWheel:
p1 = selectfunc(wheel, *selectparams)
p2 = selectfunc(wheel, *selectparams)
else:
p1, p2 = selectfunc(pop, *selectparams)
if rand() <= crossprob:
p1, p2 = crossfunc(p1, p2, crossfuncs, crossparams)
newpop.extend([p1, p2])
for i, p in enumerate(newpop):
if rand() <= mutprob:
newpop[i] = mutfunc(p, *mutparams)
p = newpop[i]
SCORES[p] = scorefunc(p, *scoreparams)
pop = newpop
fittest = max(pop, key=SCORES.__getitem__)
fittest = fittest, SCORES[fittest]
log.info("Generation %03d | highest fitness: %s | fittest indiv: %r" %
(g, fittest[1], ''.join(fittest[0].chromosomes[0])))
if fittest[1] > best[1]:
best = fittest
if best[1] >= targetscore:
return best, g
g += 1
if testmode:
assert (g == maxGens) or best[1] >= targetscore
return best[0], g
