import numpy as np
import utils
iters = 1 * 10 ** 5
rate = 10 ** -5
payoff_scale = 10 ** 0
display_game = True
use_LH = True
# str_row_init = np.array([0.1667, 0.5, 0.3333])
# str_col_init = np.array([0.3333, 0.5, 0.1667])
str_row_init = utils.randomize_mixed_strategy(3)
str_col_init = utils.randomize_mixed_strategy(3)
payoff_matrix_row = np.array([
[678, 110, -33],
[-673, 942, -995],
[-683, 725, -372]])
payoff_matrix_col = np.array([
[328.0, 976.0, 442.0],
[-19.0, 669.0, 701.0],
[587.0, -209.0, -55.0]])
def FPI(self):
s = self.init_strategy
for i in range(self.iters):
payoff = s.dot(self.vertex_payoff)
vertex_gain = np.where((self.vertex_payoff - payoff) > 0, self.vertex_payoff - payoff, 0)
# vertex_gain = np.apply_along_axis(lambda x: 10 ** 4 * x * x, 0, vertex_gain)
vertex_gain = np.apply_along_axis(lambda x: x, 0, vertex_gain)
s = utils.vector_update(s, vertex_gain, self.rate)
self.vertex_gain = vertex_gain
self.strategy = s
self.angle = utils.vector_angle(self.strategy, self.vertex_gain)
self.payoff = payoff
self.gamma = vertex_gain.dot(vertex_gain) / (1 / self.rate + vertex_gain.sum())
def stats(self):
print('strategy: %s\nvertex gain: %s\npayoff: %s vertex gain sum: %s angle: %s gamma: %s' % (
self.strategy.tolist(), self.vertex_gain.tolist(), self.payoff, self.vertex_gain.sum(), self.angle, self.gamma))
print('***')
if __name__ == "__main__":
size = 6
strategy = Strategy(
utils.randomize_mixed_strategy(size),
utils.randomize_payoff_vector(size) * 100,
200 * 10 ** 4,
10 ** -3)
strategy.FPI()
strategy.stats()
import numpy as np
import sys
sys.path.append('../')
import utils
iters = 10 * 10**4
rate = 10**-4
payoff_scale = 10**0
png_name = '60X40-disconv.png'
rows = 60
cols = 40
str_row_init = utils.randomize_mixed_strategy(rows)
str_col_init = utils.randomize_mixed_strategy(cols)
payoff_matrix_row, payoff_matrix_col = utils.randomize_payoff_matrix(
rows, cols)
[settings.payoff_matrix_row, settings.payoff_matrix_col])
if display_game:
fpi.print_game()
fpi.converge(rate, iters)
FPI_2P.display_eqpt(fpi.eqpt())
if use_LH:
fpi.lemke_howson()
if contraction_test:
pass
# ############################# contraction test #############################
if contraction_test:
import contraction
import utils
rows, cols = settings.payoff_matrix_row.shape
strategies_profile_1 = (utils.randomize_mixed_strategy(rows),
utils.randomize_mixed_strategy(cols))
strategies_profile_2 = (utils.randomize_mixed_strategy(rows),
utils.randomize_mixed_strategy(cols))
distance_function = contraction.distance_function_1
payoff_matrices = (settings.payoff_matrix_row, settings.payoff_matrix_col)
rate = settings.rate
iters = settings.iters
distance_l = []
for i in range(iters):
distance = distance_function(strategies_profile_1,
strategies_profile_2)
distance_l.append(np.log(distance))
strategies_profile_1 = contraction.FPI_transformation_once(
strategies_profile_1, payoff_matrices, rate)
import numpy as np
import utils
iters = 1 * 10**5
rate = 10**-5
payoff_scale = 10**0
display_game = True
use_LH = True
annotate_game_details = False
png_name = '3X3-1eq3sp-repellor'
payoff_matrix_row = np.array([[678, 110, -33], [-673, 942, -995],
[-683, 725, -372]])
payoff_matrix_col = np.array([[328.0, 976.0, 442.0], [-19.0, 669.0, 701.0],
[587.0, -209.0, -55.0]])
eqpts = [
(np.array([0.1555637623931138, 0.3850174710945712, 0.459418766512315]),
np.array([0.2116999757925876, 0.5821356134801096, 0.20616441072730282]))
]
devi = 0.00001
str_row_init_l = [
utils.pick_a_neighour(eqpts[0][0], devi=devi) for i in range(5)
] + [utils.randomize_mixed_strategy(3) for i in range(5)]
str_col_init_l = [
utils.pick_a_neighour(eqpts[0][1], devi=devi) for i in range(5)
] + [utils.randomize_mixed_strategy(3) for i in range(5)]
def init_mixed_strategies(self):
self.mixed_strategy = utils.randomize_mixed_strategy(
self.pure_strategies_num)
import numpy as np
import utils
iters = 10**5
rate = 10**-5
payoff_scale = 10**0
display_game = True
use_LH = True
annotate_game_details = False
png_name = '3X3-2eq2sp-attractor'
paths = 10
str_row_init_l = [np.array([0.2372, 0.4233, 0.3395]), np.array([0.1076, 0.4191, 0.4733])] + \
[utils.randomize_mixed_strategy(3) for i in range(paths - 2)]
str_col_init_l = [np.array([0.5481, 0.3365, 0.1154]), np.array([0.2, 0.4176, 0.3824])] + \
[utils.randomize_mixed_strategy(3) for i in range(paths - 2)]
payoff_matrix_row = np.array([[-231.0, -505.0, 525.0], [-552.0, 831.0, -928.0],
[-74.0, -96.0, -604.0]])
payoff_matrix_col = np.array([[175.0, -350.0, -770.0],
[-641.0, -222.0, -189.0], [302.0, 504.0, 767.0]])
eqpts = [(np.array([0.3297872340425532, 0.0, 0.6702127659574468]),
np.array([0.8779160186625193, 0.0, 0.12208398133748055])),
(np.array([0.0728476821192053, 0.9271523178807947, 0.0]),
np.array([0.0, 0.5209752599498029, 0.4790247400501973]))]
import numpy as np
import utils
iters = 1 * 10**5
rate = 10**-5
payoff_scale = 10**0
display_game = True
use_LH = True
str_row_init = utils.randomize_mixed_strategy(
3) # np.array([0.2811, 0.4424, 0.2765])
str_col_init = utils.randomize_mixed_strategy(
3) # np.array([0.3251, 0.3086, 0.3663])
payoff_matrix_row = np.array([[-503, -349, 177], [-868, 855, -199],
[660, 429, 206]])
payoff_matrix_col = np.array([[49.0, 780.0, -105.0], [789.0, -404.0, 226.0],
[387.0, 642.0, -756.0]])
import numpy as np import utils iters = 3 * 10**5 rate = 10**-5 payoff_scale = 10**0 display_game = False use_LH = False str_row_init = utils.randomize_mixed_strategy(1000) str_col_init = utils.randomize_mixed_strategy(800) payoff_matrix_row, payoff_matrix_col = utils.randomize_payoff_matrix(1000, 800)
import numpy as np
import sys
sys.path.append('../')
import utils
iters = 10 * 10**4
rate = 10**-4
payoff_scale = 10**0
# settings
rows = 120
cols = 100
str_row_init = utils.randomize_mixed_strategy(rows) * payoff_scale
str_col_init = utils.randomize_mixed_strategy(cols) * payoff_scale
payoff_matrix_row, payoff_matrix_col = utils.randomize_payoff_matrix(
rows, cols)
