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import unittest
import random
import numpy as np
from genetic import individuals, recombination, selection, populations
#TODO test recombination and selection
class TestSingleChromosomeIndividual(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.engine = lambda x: random.randint(0, 50)
self.mutrate = 0.1
def test_mutrate(self):
with self.assertRaises(ValueError):
individuals.SingleChromosomeIndividual(self.engine,
1.2, l=10)
with self.assertRaises(ValueError):
individuals.SingleChromosomeIndividual(self.engine,
-0.1, l=10)
with self.assertRaises(ValueError):
individuals.SingleChromosomeIndividual(self.engine,
1, l=10)
individuals.SingleChromosomeIndividual(self.engine, self.mutrate, l=10)
def test_engine_and_length(self):
with self.assertRaises(ValueError):
individuals.SingleChromosomeIndividual([self.engine],
self.mutrate, l=10)
with self.assertRaises(ValueError):
individuals.SingleChromosomeIndividual(self.engine, self.mutrate)
with self.assertRaises(ValueError):
individuals.SingleChromosomeIndividual([self.engine, 1],
self.mutrate)
individuals.SingleChromosomeIndividual(self.engine, self.mutrate, l=10)
individuals.SingleChromosomeIndividual([self.engine], self.mutrate, l=1)
class TestPanmicticPopulation(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.engine = lambda x: random.randint(0, 50)
self.fitness = lambda x: - abs(200 - sum(x.genome))
self.size = 100
self.indiv = individuals.SingleChromosomeIndividual(self.engine, 0.1, 50)
self.select = selection.bimodal(0.2, 0.05)
self.nlegends = 10
def test_single_thread(self):
ancestors = [self.indiv] * 2
pop = populations.PanmicticPopulation(ancestors, self.size,
self.fitness, self.select,
self.nlegends)
errors1 = list(map(abs, pop.evolve(5, jobs=1)))
errors2 = list(map(abs, pop.evolve(100, jobs=1)))
self.assertTrue(np.mean(errors2) < np.mean(errors1))
def test_two_threads(self):
ancestors = [self.indiv] * 2
pop = populations.PanmicticPopulation(ancestors, self.size,
self.fitness, self.select,
self.nlegends)
errors1 = list(map(abs, pop.evolve(5, jobs=2)))
errors2 = list(map(abs, pop.evolve(100, jobs=2)))
self.assertTrue(np.mean(errors2) < np.mean(errors1))
def test_legends(self):
ancestors = [self.indiv] * 2
with self.assertRaises(ValueError):
populations.PanmicticPopulation(ancestors, self.size,
self.fitness, self.select,
-10)
with self.assertRaises(ValueError):
populations.PanmicticPopulation(ancestors, self.size,
self.fitness, self.select,
0.1)
pop = populations.PanmicticPopulation(ancestors, self.size,
self.fitness, self.select,
self.nlegends)
errors1 = list(map(abs, pop.evolve(1, jobs=1)))
legends1 = pop.legends
errors2 = list(map(abs, pop.evolve(49, jobs=1)))
legends2 = pop.legends
legendary_scores1 = [abs(legend[0]) for legend in legends1]
legendary_scores2 = [abs(legend[0]) for legend in legends2]
self.assertTrue(np.mean(legendary_scores2) < np.mean(legendary_scores1))
if __name__ == "__main__":
unittest.main()
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