def test_hardcoded_fixations(self):
game = games.two_times_two(a=3.0, b=0.5, c=0.5, d=2.0)
intensity_of_selection = 1.0
population_size = 5
# number_of_samples=100000
number_of_samples = 20000
index_of_the_incumbent = 1
index_of_the_mutant = 0
# numerical_value = numerical.fixation_probability_strategy_a(game, intensity_of_selection, population_size)
numerical_value = numerical.fixation_probability(
mutant_index=0,
resident_index=1,
intensity_of_selection=intensity_of_selection,
population_size=population_size,
game_matrix=game,
mapping='EXP')
print(numerical_value)
mp = MoranProcess(population_size,
intensity_of_selection,
game_matrix=game,
fitness_mapping='exp',
mutation_probability=0.1)
simulated_value = mp.simulate_fixation_probability(
index_of_the_incumbent=index_of_the_incumbent,
index_of_the_mutant=index_of_the_mutant,
number_of_samples=number_of_samples,
seed=None)
print(simulated_value)
self.assertAlmostEqual(numerical_value,
simulated_value,
places=2,
msg="",
delta=None)
def test_fixation_of_neutral_mutant(self):
number_of_strategies_value = 2
number_of_samples_=10000
for _ in xrange(0,5):
pop_size =np.random.randint(2,11)
mp = MoranProcess(population_size=pop_size,
intensity_of_selection=0.0, game_matrix=np.random.rand(number_of_strategies_value,number_of_strategies_value),
number_of_strategies=number_of_strategies_value, fitness_mapping='exp', mutation_probability=0.1)
fix = mp.simulate_fixation_probability(0, 1, number_of_samples=number_of_samples_, seed=None)
np.testing.assert_allclose(fix,1.0/pop_size, rtol=0.01, atol=0.01, err_msg="Paila", verbose=True)
def test_fixation_of_neutral_mutant(self):
number_of_strategies_value = 2
number_of_samples_ = 10000
for _ in range(0, 5):
pop_size = np.random.randint(2, 11)
mp = MoranProcess(population_size=pop_size,
intensity_of_selection=0.0,
game_matrix=np.random.rand(number_of_strategies_value, number_of_strategies_value),
number_of_strategies=number_of_strategies_value, fitness_mapping='exp',
mutation_probability=0.1)
fix = mp.simulate_fixation_probability(0, 1, number_of_samples=number_of_samples_, seed=None)
np.testing.assert_allclose(fix, 1.0 / pop_size, rtol=0.01, atol=0.01, err_msg="Paila", verbose=True)
def test_if_a_type_is_not_there_it_never_shows_up(self):
#np.random.seed(999)
for i in xrange(0,3):
pop = np.random.randint(1,10,5) #random population with 5 strategies
zero_element = np.random.randint(0,5)
pop[zero_element] = 0
pop_size = np.sum(pop)
mp = MoranProcess(population_size=pop_size,
intensity_of_selection=1.0, game_matrix=np.random.rand(5,5),
number_of_strategies=5, fitness_mapping='exp', mutation_probability=0.1)
for j in xrange(0,1000):
pop = mp.step(pop, mutation_step=False)[0]
self.assertEqual(pop[zero_element], 0, "Type " + str(zero_element) +" showed up in population "+ str(pop) +" at iteration "+ str(i)+" "+str(j))
def test_if_a_type_is_not_there_it_never_shows_up(self):
# np.random.seed(999)
for i in range(0, 3):
pop = np.random.randint(1, 10, 5) # random population with 5 strategies
zero_element = np.random.randint(0, 5)
pop[zero_element] = 0
pop_size = np.sum(pop)
mp = MoranProcess(population_size=pop_size,
intensity_of_selection=1.0, game_matrix=np.random.rand(5, 5),
number_of_strategies=5, fitness_mapping='exp', mutation_probability=0.1)
for j in range(0, 1000):
pop = mp.step(pop, mutation_step=False)[0]
self.assertEqual(pop[zero_element], 0, "Type " + str(zero_element) + " showed up in population " + str(
pop) + " at iteration " + str(i) + " " + str(j))
def test_hardcoded_fixations(self):
game= games.two_times_two(a=3.0, b=0.5, c=0.5, d=2.0)
intensity_of_selection=1.0
population_size=5
#number_of_samples=100000
number_of_samples=20000
index_of_the_incumbent=1
index_of_the_mutant=0
#numerical_value = numerical.fixation_probability_strategy_a(game, intensity_of_selection, population_size)
numerical_value = numerical.fixation_probability(mutant_index=0, resident_index=1, intensity_of_selection=intensity_of_selection, population_size=population_size, game_matrix=game, mapping='EXP')
print numerical_value
mp = MoranProcess(population_size, intensity_of_selection, game_matrix=game, fitness_mapping='exp', mutation_probability=0.1)
simulated_value = mp.simulate_fixation_probability(index_of_the_incumbent=index_of_the_incumbent, index_of_the_mutant=index_of_the_mutant, number_of_samples=number_of_samples, seed=None)
print simulated_value
self.assertAlmostEqual(numerical_value, simulated_value, places=2, msg="", delta=None)
