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# This file is part of EAP.
#
# EAP is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# EAP is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with EAP. If not, see <http://www.gnu.org/licenses/>.
import operator
import math
import random
import numpy
from deap import algorithms
from deap import base
from deap import creator
from deap import tools
from deap import gp
# Define new functions
def protectedDiv(left, right):
with numpy.errstate(divide='ignore',invalid='ignore'):
x = numpy.divide(left, right)
if isinstance(x, numpy.ndarray):
x[numpy.isinf(x)] = 1
x[numpy.isnan(x)] = 1
elif numpy.isinf(x) or numpy.isnan(x):
x = 1
return x
pset = gp.PrimitiveSet("MAIN", 1)
pset.addPrimitive(numpy.add, 2, name="vadd")
pset.addPrimitive(numpy.subtract, 2, name="vsub")
pset.addPrimitive(numpy.multiply, 2, name="vmul")
pset.addPrimitive(protectedDiv, 2)
pset.addPrimitive(numpy.negative, 1, name="vneg")
pset.addPrimitive(numpy.cos, 1, name="vcos")
pset.addPrimitive(numpy.sin, 1, name="vsin")
pset.addEphemeralConstant("rand101", lambda: random.randint(-1,1))
pset.renameArguments(ARG0='x')
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMin)
toolbox = base.Toolbox()
toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=2)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("compile", gp.compile, pset=pset)
samples = numpy.linspace(-1, 1, 10000)
values = samples**4 + samples**3 + samples**2 + samples
def evalSymbReg(individual):
# Transform the tree expression in a callable function
func = toolbox.compile(expr=individual)
# Evaluate the sum of squared difference between the expression
# and the real function values : x**4 + x**3 + x**2 + x
diff = numpy.sum((func(samples) - values)**2)
return diff,
toolbox.register("evaluate", evalSymbReg)
toolbox.register("select", tools.selTournament, tournsize=3)
toolbox.register("mate", gp.cxOnePoint)
toolbox.register("expr_mut", gp.genFull, min_=0, max_=2)
toolbox.register('mutate', gp.mutUniform, expr=toolbox.expr_mut, pset=pset)
def main():
random.seed(318)
pop = toolbox.population(n=300)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
algorithms.eaSimple(pop, toolbox, 0.5, 0.1, 40, stats, halloffame=hof)
return pop, stats, hof
if __name__ == "__main__":
main()
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