Note
Click here to download the full example code
structural constantsΒΆ
Out:
/home/docs/checkouts/readthedocs.org/user_builds/glyph/envs/latest/lib/python3.6/site-packages/deap/tools/emo.py:138: ComplexWarning: Casting complex values to real discards the imaginary part
norm = nobj * float(crowd[-1][0][i] - crowd[0][0][i])
/home/docs/checkouts/readthedocs.org/user_builds/glyph/envs/latest/lib/python3.6/site-packages/deap/tools/emo.py:140: RuntimeWarning: invalid value encountered in double_scalars
distances[cur[1]] += (next[0][i] - prev[0][i]) / norm
-0.7884573603642701 + I*pi ((0.028662213987980155-3.127158238984308j), 2.0)
-0.7884573603642701 + I*pi ((0.028662213987980155-3.127158238984308j), 2.0)
-0.7884573603642701 + I*pi ((0.028662213987980155-3.127158238984308j), 2.0)
-0.7884573603642701 + I*pi ((0.028662213987980155-3.127158238984308j), 2.0)
-0.7884573603642701 + I*pi ((0.028662213987980155-3.127158238984308j), 2.0)
-0.7884573603642701 + I*pi ((0.028662213987980155-3.127158238984308j), 2.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
-0.5*log(2*x_0) - 0.5877866649021189 + I*pi ((0.005502350949910848-3.0735536742862175j), 9.0)
from functools import partial
import deap.gp
import deap.tools
import numpy as np
from glyph import gp
from glyph.utils import Memoize
from glyph.utils.numeric import nrmse, silent_numpy
pset = gp.numpy_primitive_set(arity=1, categories=["algebraic", "trigonometric", "exponential"])
pset = gp.individual.add_sc(pset, gp.individual.sc_mmqout)
Individual = gp.Individual(pset=pset)
@Memoize
@silent_numpy
def measure(ind):
g = lambda x: x ** 2 - 1.1
points = np.linspace(-1, 1, 100, endpoint=True)
y = g(points)
f = gp.individual.numpy_phenotype(ind)
try:
yhat = f(points)
except TypeError:
yhat = np.infty
if np.isscalar(yhat):
yhat = np.ones_like(y) * yhat
return nrmse(y, yhat), len(ind.resolve_sc())
def update_fitness(population, map=map):
invalid = [p for p in population if not p.fitness.valid]
fitnesses = map(measure, invalid)
for ind, fit in zip(invalid, fitnesses):
ind.fitness.values = fit
return population
def main():
pop_size = 400
mate = deap.gp.cxOnePoint
expr_mut = partial(deap.gp.genFull, min_=0, max_=2)
mutate = partial(deap.gp.mutUniform, expr=expr_mut, pset=Individual.pset)
algorithm = gp.algorithms.AgeFitness(mate, mutate, deap.tools.selNSGA2, Individual.create_population)
pop = update_fitness(Individual.create_population(pop_size))
for gen in range(50):
pop = algorithm.evolve(pop)
pop = update_fitness(pop)
best = deap.tools.selBest(pop, 1)[0]
print(gp.individual.simplify_this(best), best.fitness.values)
if best.fitness.values[0] <= 1e-3:
break
if __name__ == "__main__":
main()
Total running time of the script: ( 0 minutes 14.235 seconds)