def test_single_simulation_moran(self):
s = GameDynamicsWrapper(CWOLOnlyL,
Moran,
dynamics_kwargs=dict(pop_size=3000),
game_kwargs=dict(a=0.2,
equilibrium_tolerance=.5))
s.simulate(num_gens=30000)
def test_many_simulation(self):
s = GameDynamicsWrapper(CWOL,
WrightFisher,
dynamics_kwargs=dict(),
game_kwargs=dict(a=.2,
equilibrium_tolerance=.2))
print s.simulate_many(num_iterations=10, num_gens=150)
def test_single_simulation(self):
s = GameDynamicsWrapper(CtsDisc,
WrightFisher,
dynamics_kwargs=dict(selection_strength=.3))
s.simulate(num_gens=190,
graph=dict(shading='redblue',
options=[
'area', 'meanStratLine', 'payoffLine',
'largeFont', 'noLegend'
]),
start_state=state)
def test_many_simulation(self): # Determines which equilibria result based upon several simulations, text output
s = GameDynamicsWrapper(CtsDisc, WrightFisher, dynamics_kwargs=dict(selection_strength=0.3))
def test_single_simulation(self):
s = GameDynamicsWrapper(HawkDoveBourgeois, WrightFisher)
s.simulate(num_gens=1000)#, graph=dict(options=['area', 'largeFont']))
def calculate_stationary(self):
s = GameDynamicsWrapper(Coordination, WrightFisher)
s.stationaryDistribution()
def test_unclassified_d(self):
s = GameDynamicsWrapper(CWOLOnlyL,
WrightFisher,
game_kwargs=dict(d=-1.0))
s.simulate(num_gens=500)
def test_single_simulation(self):
s = GameDynamicsWrapper(CWOL, WrightFisher, game_kwargs=dict())
s.simulate(num_gens=300)
def test_unclassified_d(self):
s = GameDynamicsWrapper(CWOLOnlyL, WrightFisher, game_kwargs=dict(d=-1.0))
s.simulate(num_gens=500)
def test_many_simulation(self):
s = GameDynamicsWrapper(CWOL, WrightFisher, dynamics_kwargs=dict(), game_kwargs=dict(a=.2, equilibrium_tolerance=.2))
print s.simulate_many(num_iterations=10, num_gens=150)
def test_single_simulation(self):
s = GameDynamicsWrapper(CWOL, WrightFisher, game_kwargs=dict())
s.simulate(num_gens=300)
def test_single_simulation_moran(self):
s = GameDynamicsWrapper(CWOLOnlyL, Moran, dynamics_kwargs=dict(pop_size=3000), game_kwargs=dict(a=0.2, equilibrium_tolerance=.5))
s.simulate(num_gens=30000)
def test_many_simulation(self): # Determines which equilibria result based upon several simulations, text output
s = GameDynamicsWrapper(HawkDove, WrightFisher)
print(s.simulate_many(num_iterations=100, num_gens=100, graph=dict(options=['area', 'smallFont'])))
def test_single_simulation(self):
s = GameDynamicsWrapper(HawkDove, WrightFisher)
s.simulate(num_gens=100, graph=dict(options=['area', 'smallFont']))