def testFixation(self):
intensity_of_selection=0.1
population_size =50
game_matrix = games.prisoners_dilemma_equal_gains()
fix = numerical.fixation_probability(mutant_index=0, resident_index=1, intensity_of_selection=intensity_of_selection, payoff_function=None, population_size=population_size, game_matrix=game_matrix,
number_of_strategies=None, mapping='EXP')
self.assertAlmostEqual(fix, 0.0006059821672805591)
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 testFixation(self):
intensity_of_selection = 0.1
population_size = 50
game_matrix = games.prisoners_dilemma_equal_gains()
fix = numerical.fixation_probability(
mutant_index=0,
resident_index=1,
intensity_of_selection=intensity_of_selection,
payoff_function=None,
population_size=population_size,
game_matrix=game_matrix,
number_of_strategies=None,
mapping='EXP')
self.assertAlmostEqual(fix, 0.0006059821672805591)
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)
