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)
Exemple #10
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 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)
Exemple #11
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# 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")