def test_halt_expected(self):
"""
Ensure the function returns true if we're in a halting state.
"""
g1 = Genome([1, 2, 3])
g1.fitness = MAX_REWARD
g2 = Genome([1, 2, 3])
g2.fitness = 3
g3 = Genome([1, 2, 3])
g3.fitness = 2
# Any fittest solution with fitness >= MAX_REWARD means call a halt.
population = [g1, g2, g3]
result = halt(population, 1)
self.assertTrue(result)
def test_halt_expected(self):
"""
Ensure the function returns true if we're in a halting state.
"""
g1 = Genome([1, 2, 3])
g1.fitness = MAX_REWARD
g2 = Genome([1, 2, 3])
g2.fitness = 3
g3 = Genome([1, 2, 3])
g3.fitness = 2
# Any fittest solution with fitness >= MAX_REWARD means call a halt.
population = [g1, g2, g3]
result = halt(population, 1)
self.assertTrue(result)
def test_halt_not(self):
"""
Ensures if the fittest genome has fitness < 4 then halt doesn't
succeed.
"""
g1 = Genome([1, 2, 3])
g1.fitness = MAX_REWARD - 0.1
g2 = Genome([1, 2, 3])
g2.fitness = 3
g3 = Genome([1, 2, 3])
g3.fitness = 2
# Any fittest solution with fitness >= 4 means call a halt.
population = [g1, g2, g3]
result = halt(population, 1)
self.assertFalse(result)
def test_halt_not(self):
"""
Ensures if the fittest genome has fitness < 4 then halt doesn't
succeed.
"""
g1 = Genome([1, 2, 3])
g1.fitness = MAX_REWARD - 0.1
g2 = Genome([1, 2, 3])
g2.fitness = 3
g3 = Genome([1, 2, 3])
g3.fitness = 2
# Any fittest solution with fitness >= 4 means call a halt.
population = [g1, g2, g3]
result = halt(population, 1)
self.assertFalse(result)
