#"EqualSay_G1_Default",
#"Random_Dictator",
#"Player1_Dictator",
#"EqualSay_G2_Default",
#"EqualSay_G1_Default",
#"Player1_Dictator",
#"Random_Dictator",
]
#transition = transitions[0]
transition = "EqualSay_G1_Default"
# 1.0 - 10**(-2) = 0.99 -> exponent = num decimal places
delta = 1.0 - 10**(-11)
S8_game = S_8_Game(c=c, b1=1.8, b2=b2, game_transition_dynamics=transition)
S12_game = S_12_Game(c=c, b1=1.8, b2=b2, game_transition_dynamics=transition)
S16_game = S_16_Game(c=c, b1=1.8, b2=b2, game_transition_dynamics=transition)
games = [S8_game, S12_game, S16_game]
folder_timestamp = time.strftime("date_%Y_%m_%d_%H_%M_%S")
params_str = "c_{:.2f}_b2_{:.2f}_delta_{:.10f}".format(c, b2, delta)
b1_list = np.arange(1.0, 3.2, 0.14)
def run_dict(b1_list):
d = {}
parent_folder = "data/full_coop_spe/dict/{:s}/"\
.format(params_str)
transitions = [
#"EqualSay_G1_Default",
#"Random_Dictator",
#"Player1_Dictator",
"EqualSay_G2_Default",
#"EqualSay_G1_Default",
#"Player1_Dictator",
#"Random",
]
transition = transitions[0]
# 1.0 - 10**(-2) = 0.99 -> exponent = num decimal places
delta = 1.0 - 10**(-5)
S08_game = S_8_Game(c=c, b1=1.8, b2=b2, game_transition_dynamics=transition)
S12_game = S_12_Game(c=c, b1=1.8, b2=b2, game_transition_dynamics=transition)
S16_game = S_16_Game(c=c, b1=1.8, b2=b2, game_transition_dynamics=transition)
games = [S08_game, S12_game, S16_game]
s_08_strat = (1, 0, 0, 1, 1, 0, 0, 0)
s_12_strat = (1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0)
v_s08 = S08_game.get_stationary_dist(s_08_strat, s_08_strat, S08_game.f)
v_s12 = S12_game.get_stationary_dist(s_12_strat, s_12_strat, S12_game.f)
print("v_s08: ", v_s08[0])
print("v_s12: ", v_s12[0])
s_08_strat_allc2 = (1, 1, 1, 1, 0, 0, 0, 0)
s_08_strat_allc1 = (1, 1, 1, 1, 1, 1, 1, 1)
lst1 = [10**-4, 10**-3, 10**-2, 10**-1, 10**0, 10**1, 10**2]
lst2 = [2, 4, 8, 16, 32, 64, 128]
lst3 = [10**-1, 10**0, 10**1]
lst4 = [10**-4, 10**-3, 10**-2, 10**-2, 10**-1]
lst5 = [10**-4]
param_to_change, param_list = ("N", lst2)
fixed_b1 = 1.8
hacky_spe_b1 = 1.84
transition = "EqualSay_G2_Default"
game = S_12_Game(c=c, b1=fixed_b1, b2=b2, game_transition_dynamics=transition)
# define folder name
folder_timestamp = time.strftime("date_%Y_%m_%d_%H_%M_%S")
subfolder = "param_effect_fixed/{:s}/{:s}/".format(param_to_change,
folder_timestamp)
data_folder = "data/{:s}".format(subfolder)
img_folder = data_folder
def do_runs(param_value, params_dict, spe_d):
data_filename = "{:s}{:s}_{:2f}.csv".format(
data_folder, param_to_change, param_value) #get_filename(param_value)
data_folder = get_folder(folder_timestamp, directory="data/game_comp/")
img_folder = get_folder(folder_timestamp, directory="imgs/game_comp/")
print("\nData Folder: {:s}\n".format(data_folder))
#b1_list = np.arange(1.0, 3.2, 0.56)
#b1_list = [1.8]
#b1_list = np.arange(1.0, 3.2, 0.14)
transition = transitions[0]
games = [
#S_2_Game(c=1.0, b1=1.2),
#S_4_Game(c=1.0, b1=b2),
#S_4_Game(c=1.0, b1=b1),
#S_8_Game(c=1.0, b1=b1, b2=b2, game_transition_dynamics=transitions[0]),
S_12_Game(c=1.0, b1=b1, b2=b2, game_transition_dynamics=transitions[0]),
#S_16_Game(c=1.0, b1=b1, b2=b2, game_transition_dynamics=transitions[0]),
]
def do_run(run_id, data_folder, game_str, num_timesteps, params_dict, game_b1):
data_filename = data_folder + game_str + "_game_b1_{:.2f}".format(game_b1) \
+ "_run_{:d}".format(run_id) + ".csv"
# announce what we are calculating
print("Game: {:s}, Run: {:d}".format(game_str, run_id))
# get data
start_time = time.time()
player_c_list = get_evolution_all(num_timesteps, params_dict, game_b1)
elapsed_time = time.time() - start_time
transitions = [
#"NA",
"EqualSay_G2_Default",
#"EqualSay_G1_Default",
#"Unilateral_Dictator",
#"Random_Dictator",
#"Random",
]
# b1_list = np.arange(1.0, 3.2, 0.14)
games = [
#S_2_Game(c=1.0, b1=1.2),
#S_4_Game(c=1.0, b1=2.0),
#S_8_Game(c=1.0, b1=2.0, b2=1.2, game_transition_dynamics=transitions[0]),
S_12_Game(c=1.0, b1=2.0, b2=1.2, game_transition_dynamics=transitions[0]),
S_16_Game(c=1.0, b1=2.0, b2=1.2, game_transition_dynamics=transitions[0]),
]
save_params_dict = {
# test params
"num_runs": num_runs,
"num_timesteps": num_timesteps,
# evolutionary params
"N": 100,
"eps": 0.001,
"beta": 2.0,
"strategy_type": "pure", # or "stochastic"
print("\nFolder: {:s}\n".format(folder))
b1_list = [1.8]
b1 = 1.8
c = 1.0
b2 = 1.2
#np.arange(1.0, 3.2, 0.14)
# games = [
# S_4_Game(c=1.0, b1=2.0),
# S_12_Game(c=1.0, b1=2.0, b2=1.2), S_16_Game(c=1.0, b1=2.0, b2=1.2)
# ]
games = [S_8_Game(c, b1, b2), S_12_Game(c, b1, b2), S_16_Game(c, b1, b2)]
def get_filepath(game_str, eps, beta, num_timesteps):
params_str = "{:s}_T_{:.2e}".format(game_str, num_timesteps)
filepath = folder + params_str + ".csv"
return filepath
def write_b1_effect_data():
eps, beta = get_params(["eps", "beta"], params_dict)
for num_timesteps in num_timesteps_list:
for game in games:
from class_one_games import S_2_Game, S_4_Game
from class_two_games import S_8_Game, S_12_Game, S_16_Game
from simulation_evolution_avgs import *
import pandas as pd
import pathlib # to create directory if needed
from parameters import *
from helper_functions import save_dict, read_dict
param_to_change, param_list = ("N", [128])
b1_list = np.arange(1.0, 3.2, 0.14)
game = S_12_Game(c=c,
b1=1.8,
b2=b2,
game_transition_dynamics="EqualSay_G2_Default")
# define folder name
folder_timestamp = time.strftime("date_%Y_%m_%d_%H_%M_%S")
subfolder = "param_effect/{:s}/{:s}/".format(param_to_change, folder_timestamp)
data_folder = "data/{:s}".format(subfolder)
img_folder = data_folder
def save_params(folder, params_filename="params"):
# create directory
pathlib.Path(folder).mkdir(parents=True, exist_ok=True)
dict_to_save = {key: val for key, val in save_params_dict.items()}
import numpy as np
import matplotlib.pyplot as plt
import time
from class_one_games import S_2_Game, S_4_Game
from class_two_games import S_12_Game, S_16_Game
from helper_functions import *
from simulation_evolution_avgs import *
# Parameters
num_timesteps = 10**4
game = S_12_Game(c=1.0, b1=2.0, b2=1.2, \
game_transition_dynamics = "EqualSay_G2_Default", num_timesteps= num_timesteps)
params_dict = {
"game": game,
"N": 100,
"eps": 0.001,
"beta": 2.0,
"host": tuple(0.005 for _ in range(game.strat_len)),
"strategy_type": "pure", # or "stochastic"
}
b1_list = np.arange(1.0, 3.2, 0.42)
start_time = time.time()
#g1_cc_avg, g2_cc_avg, g1_rate = get_evolution_avgs(num_timesteps, params_dict)
get_b1_evolution_data(num_timesteps, b1_list, params_dict)
elapsed_time = time.time() - start_time
print("Elapsed Time: {:.2f} min".format(elapsed_time / 60.0))