def test_extrema_moran_5(lim=1e-16):
"""
Test for extrema of the stationary distribution.
"""
n = 3
N = 60
mu = (3. / 2) * 1. / N
m = [[0, 1, 1], [1, 0, 1], [1, 1, 0]]
maxes = set([(20, 20, 20), (0, 0, 60), (0, 60, 0), (60, 0, 0), (30, 0, 30),
(0, 30, 30), (30, 30, 0)])
fitness_landscape = linear_fitness_landscape(m)
incentive = fermi(fitness_landscape, beta=0.1)
edges = incentive_process.multivariate_transitions(N,
incentive,
num_types=n,
mu=mu)
s = stationary_distribution(edges, lim=lim)
s2 = expected_divergence(edges, q_d=0)
flow = inflow_outflow(edges)
# These sets should all correspond
assert_equal(find_local_maxima(s), set(maxes))
assert_equal(find_local_minima(s2), set(maxes))
assert_equal(find_local_minima(flow), set(maxes))
# The minima are pathological
assert_equal(find_local_minima(s), set([(3, 3, 54), (3, 54, 3),
(54, 3, 3)]))
assert_equal(find_local_maxima(s2),
set([(4, 52, 4), (4, 4, 52), (52, 4, 4)]))
assert_equal(find_local_maxima(flow),
set([(1, 58, 1), (1, 1, 58), (58, 1, 1)]))
def test_extrema_moran_5(lim=1e-16):
"""
Test for extrema of the stationary distribution.
"""
n = 3
N = 60
mu = (3./2) * 1./N
m = [[0, 1, 1], [1, 0, 1], [1, 1, 0]]
maxes = set([(20, 20, 20), (0, 0, 60), (0, 60, 0), (60, 0, 0),
(30, 0, 30), (0, 30, 30), (30, 30, 0)])
fitness_landscape = linear_fitness_landscape(m)
incentive = fermi(fitness_landscape, beta=0.1)
edges = incentive_process.multivariate_transitions(
N, incentive, num_types=n, mu=mu)
s = stationary_distribution(edges, lim=lim)
s2 = expected_divergence(edges, q_d=0)
flow = inflow_outflow(edges)
# These sets should all correspond
assert_equal(find_local_maxima(s), set(maxes))
assert_equal(find_local_minima(s2), set(maxes))
assert_equal(find_local_minima(flow), set(maxes))
# The minima are pathological
assert_equal(find_local_minima(s),
set([(3, 3, 54), (3, 54, 3), (54, 3, 3)]))
assert_equal(find_local_maxima(s2),
set([(4, 52, 4), (4, 4, 52), (52, 4, 4)]))
assert_equal(find_local_maxima(flow),
set([(1, 58, 1), (1, 1, 58), (58, 1, 1)]))
def test_extrema_moran_3(lim=1e-12):
"""
Test for extrema of the stationary distribution.
"""
n = 2
N = 100
mu = 6. / 25
m = [[1, 0], [0, 1]]
maxes = set([(38, 62), (62, 38)])
mins = set([(50, 50), (100, 0), (0, 100)])
fitness_landscape = linear_fitness_landscape(m)
incentive = replicator(fitness_landscape)
edges = incentive_process.multivariate_transitions(N,
incentive,
num_types=n,
mu=mu)
s = stationary_distribution(edges, lim=lim)
flow = inflow_outflow(edges)
for q_d in [0, 1]:
s2 = incentive_process.kl(edges, q_d=1)
assert_equal(find_local_maxima(s), set(maxes))
assert_equal(find_local_minima(s), set(mins))
assert_equal(find_local_minima(s2), set([(50, 50), (40, 60),
(60, 40)]))
assert_equal(find_local_maxima(flow), set(mins))
def test_extrema_moran_3(lim=1e-12):
"""
Test for extrema of the stationary distribution.
"""
n = 2
N = 100
mu = 6./ 25
m = [[1, 0], [0, 1]]
maxes = set([(38, 62), (62, 38)])
mins = set([(50, 50), (100, 0), (0, 100)])
fitness_landscape = linear_fitness_landscape(m)
incentive = replicator(fitness_landscape)
edges = incentive_process.multivariate_transitions(N, incentive, num_types=n, mu=mu)
s = stationary_distribution(edges, lim=lim)
flow = inflow_outflow(edges)
for q_d in [0, 1]:
s2 = expected_divergence(edges, q_d=1)
assert_equal(find_local_maxima(s), set(maxes))
assert_equal(find_local_minima(s), set(mins))
assert_equal(find_local_minima(s2), set([(50,50), (40, 60), (60, 40)]))
assert_equal(find_local_maxima(flow), set(mins))
