def runTest(self):
from dcgpy import symbolic_regression, generate_koza_quintic, kernel_set_double
import pygmo as pg
X, Y = generate_koza_quintic()
udp = symbolic_regression(points=X,
labels=Y,
rows=1,
cols=20,
levels_back=21,
arity=2,
kernels=kernel_set_double(["sum", "diff"])(),
n_eph=2,
multi_objective=False,
parallel_batches=0)
prob = pg.problem(udp)
pop = pg.population(prob, 10)
udp.pretty(pop.champion_x)
udp.prettier(pop.champion_x)
# Unconstrained
self.assertEqual(prob.get_nc(), 0)
self.assertEqual(prob.get_nic(), 0)
# Single objective
self.assertEqual(prob.get_nobj(), 1)
# Dimensions
self.assertEqual(prob.get_nix(), 20 * (2 + 1) + 1)
self.assertEqual(prob.get_nx(), 2 + prob.get_nix())
# Has gradient and hessians
self.assertEqual(prob.has_gradient(), True)
self.assertEqual(prob.has_hessians(), True)
def runTest(self):
from dcgpy import symbolic_regression, generate_koza_quintic, kernel_set_double, gd4cgp
import pygmo as pg
import pickle
X, Y = generate_koza_quintic()
# Interface for the UDPs
udp = symbolic_regression(points=X,
labels=Y,
rows=1,
cols=20,
levels_back=21,
arity=2,
kernels=kernel_set_double(
["sum", "diff", "mul", "pdiv"])(),
n_eph=2,
multi_objective=False,
parallel_batches=0)
prob = pg.problem(udp)
pop = pg.population(prob, 10)
# Interface for the UDAs
uda = gd4cgp(max_iter=10, lr=0.1, lr_min=1e-6)
algo = pg.algorithm(uda)
algo.set_verbosity(0)
# Testing some evolutions
pop = algo.evolve(pop)
# In parallel
archi = pg.archipelago(prob=prob, algo=algo, n=16, pop_size=4)
archi.evolve()
archi.wait_check()
# Pickling.
self.assertTrue(repr(algo) == repr(pickle.loads(pickle.dumps(algo))))