Use Imitation Dynamics

One of the algorithms implemented in Nashpy is called imitation_dynamics(), this is implemented as a method on the Game class:

>>> import nashpy as nash
>>> import numpy as np
>>> A = np.array([[3, -1], [-1, 3]])
>>> B = np.array([[-3, 1], [1, -3]])
>>> rps = nash.Game(A,B)

This imitation_dynamics method returns a generator of the outcomes of the imitation dynamics algorithm:

>>> ne_imitation_dynamics = rps.imitation_dynamics()
>>> print(list(ne_imitation_dynamics))
[(array([0., 1.]), array([1., 0.]))]

imitation_dynamics takes the following parameters iterations, population_size, random_seed and threshold within the function imitation_dynamics.

>>> import nashpy as nash
>>> import numpy as np
>>> A = np.array([[3, -1,3], [-1, 3,6], [-1, 1,2]])
>>> B = np.array([[-3, 1,4], [1, -3,3], [-1, 3,4]])
>>> rps = nash.Game(A,B)
>>> population_size=200
>>> iterations=100
>>> random_seed=30
>>> threshold=0.3
>>> ne_imitation_dynamics = rps.imitation_dynamics(population_size=population_size,iterations=iterations,random_seed=random_seed,threshold=threshold)
>>> list(ne_imitation_dynamics)
[(array([0., 1., 0.]), array([1., 0., 1.]))]