def test_generate_XOR_with_addition(self):
minNodes = 5 # and( 1, +(a, b))
geneset = {
'A': Operation(lambda a, b: a, False, False),
'B': Operation(lambda a, b: b, False, False),
'AND': Operation(lambda a, b: a & b, True, True),
'NOT': Operation(lambda a, b: a == 0, True, False),
'+': Operation(lambda a, b: a + b, True, True),
'1': Operation(lambda a, b: 1, False, False)
}
rules = [[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0]]
maxNodes = 50
optimalValue = 1000 - minNodes
startTime = datetime.datetime.now()
fnDisplay = lambda candidate: displayDot(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate, geneset, rules)
fnCreateGene = lambda index, length: createGene(index, length, geneset)
fnMutate = lambda child: mutate(child, fnCreateGene)
best = genetic.getBest(fnGetFitness,
fnDisplay,
minNodes,
optimalValue,
createGene=fnCreateGene,
maxLen=maxNodes,
customMutate=fnMutate,
customCrossover=crossover)
self.assertTrue(best.Fitness >= optimalValue)
def test(self):
geneset = [0, 1, 2, 3, 4, 5, 6, 7]
optimalValue = 8 + 8 + 8 + 8
startTime = datetime.datetime.now()
fnDisplay = lambda candidate: display(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate)
best = genetic.getBest(fnGetFitness, fnDisplay, 16, optimalValue, geneset)
self.assertEqual(best.Fitness, optimalValue)
def test(self):
geneset = ' abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!.'
startTime = datetime.datetime.now()
target = "Not all those who wander are lost."
fnDisplay = lambda candidate: display(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate, target)
best = genetic.getBest(fnGetFitness, fnDisplay, len(target), len(target), geneset)
self.assertEqual((''.join(best.Genes)), target)
def test(self):
geneset = [0, 1, 2, 3, 4, 5, 6, 7]
optimalValue = 8 + 8 + 8 + 8
startTime = datetime.datetime.now()
fnDisplay = lambda candidate: display(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate)
best = genetic.getBest(fnGetFitness, fnDisplay, 16, optimalValue,
geneset)
self.assertEqual(best.Fitness, optimalValue)
def test(self):
geneset = ' abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!.'
startTime = datetime.datetime.now()
target = "Not all those who wander are lost."
fnDisplay = lambda candidate: display(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate, target)
best = genetic.getBest(fnGetFitness, fnDisplay, len(target),
len(target), geneset)
self.assertEqual((''.join(best.Genes)), target)
def test(self):
geneset = [1, 2, 3, 4, 5, 6, 7, '+', '-']
minNodes = 5 # (+ (+ ->[a] [a](+ 7 [b]5)) [b])
expectedTotal = 29
maxNodes = 30
optimalValue = 1000 - minNodes
startTime = datetime.datetime.now()
fnDisplay = lambda candidate: displayRaw(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate, expectedTotal)
fnCreateGene = lambda index, length: createGene(index, length, geneset)
fnCrossover = lambda child, parent: crossover(child, parent)
fnMutate = lambda child: mutate(child, fnCreateGene)
best = genetic.getBest(fnGetFitness, fnDisplay, minNodes, optimalValue,
createGene=fnCreateGene, maxLen=maxNodes,
customMutate=fnMutate, customCrossover=fnCrossover)
self.assertTrue(best.Fitness >= optimalValue)
def test(self):
states = loadData("adjacent_states.csv")
rules = buildRules(states)
colors = ["Orange", "Yellow", "Green", "Blue"]
colorLookup = {}
for color in colors:
colorLookup[color[0]] = color
geneset = list(colorLookup.keys())
optimalValue = len(rules)
startTime = datetime.datetime.now()
fnDisplay = lambda candidate: display(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate, rules)
best = genetic.getBest(fnGetFitness, fnDisplay, len(states), optimalValue, geneset)
self.assertEqual(best.Fitness, optimalValue)
keys = sorted(states.keys())
for index in range(len(states)):
print(keys[index] + " is " + colorLookup[best.Genes[index]])
def test(self):
states = loadData("adjacent_states.csv")
rules = buildRules(states)
colors = ["Orange", "Yellow", "Green", "Blue"]
colorLookup = {}
for color in colors:
colorLookup[color[0]] = color
geneset = list(colorLookup.keys())
optimalValue = len(rules)
startTime = datetime.datetime.now()
fnDisplay = lambda candidate: display(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate, rules)
best = genetic.getBest(fnGetFitness, fnDisplay, len(states),
optimalValue, geneset)
self.assertEqual(best.Fitness, optimalValue)
keys = sorted(states.keys())
for index in range(len(states)):
print(keys[index] + " is " + colorLookup[best.Genes[index]])
def test_generate_XOR(self):
minNodes = 9 # and( not( and(a, b)), not( and( not(a), not(b))))
rules = [[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 0]]
maxNodes = 50
optimalValue = 1000 - minNodes
startTime = datetime.datetime.now()
fnDisplay = lambda candidate: displayDot(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate, self.geneset,
rules)
fnCreateGene = lambda index, length: createGene(
index, length, self.geneset)
fnMutate = lambda child: mutate(child, fnCreateGene)
best = genetic.getBest(fnGetFitness,
fnDisplay,
minNodes,
optimalValue,
createGene=fnCreateGene,
maxLen=maxNodes,
customMutate=fnMutate,
customCrossover=crossover)
self.assertTrue(best.Fitness >= optimalValue)
def test(self):
geneset = [1, 2, 3, 4, 5, 6, 7, '+', '-']
minNodes = 5 # (+ (+ ->[a] [a](+ 7 [b]5)) [b])
expectedTotal = 29
maxNodes = 30
optimalValue = 1000 - minNodes
startTime = datetime.datetime.now()
fnDisplay = lambda candidate: displayRaw(candidate, startTime)
fnGetFitness = lambda candidate: getFitness(candidate, expectedTotal)
fnCreateGene = lambda index, length: createGene(index, length, geneset)
fnCrossover = lambda child, parent: crossover(child, parent)
fnMutate = lambda child: mutate(child, fnCreateGene)
best = genetic.getBest(fnGetFitness,
fnDisplay,
minNodes,
optimalValue,
createGene=fnCreateGene,
maxLen=maxNodes,
customMutate=fnMutate,
customCrossover=fnCrossover)
self.assertTrue(best.Fitness >= optimalValue)
# popcpy = g.mutate(pop)
# print(popcpy)
# popcpy = g.mutate(pop)
# print(popcpy)
# popcpy = g.mutate(pop)
# print(popcpy)
# print(type(popcpy))
generations = 0
# print(pop[1])
while generations <= 100:
generations += 1
pop = g.mutate(pop)
newpop = [0 for _ in range(100)]
newpop[0] = pop[g.getBest(pop, fn.rast, -5.12, 5.12, 17)]
for i in range(1, len(newpop)):
newpop[i] = g.select(pop, fn.rast, -5.12, 5.12, 17)
pop = newpop
for i in range(100):
print(fn.rast(fn.decode(pop[i], -5.12, 5.12, 17)), end=" ")
print("\n")
