def run_jc_sample(layers, judge_types, auction_type, valuation_type):
interdep_net = indp.initialize_sample_network(layers=layers)
params = {
"NUM_ITERATIONS": 7,
"OUTPUT_DIR": '../results/jc_sample_12Node_results',
"V": len(layers),
"T": 1,
"L": layers,
"WINDOW_LENGTH": 1,
"ALGORITHM": "JC",
"N": interdep_net,
"MAGNITUDE": 0,
"SIM_NUMBER": 0,
"JUDGMENT_TYPE": judge_types,
"RES_ALLOC_TYPE": auction_type,
"VALUATION_TYPE": valuation_type
}
dindputils.run_judgment_call(params, save_jc_model=True, print_cmd=False)
for jt, rst, vt in itertools.product(judge_types, auction_type,
valuation_type):
print('\n\nPlot restoration plan by JC', jt, rst, vt)
if rst == 'UNIFORM':
indp.plot_indp_sample(params,
folderSuffix='_' + jt + '_' + rst,
suffix="real")
else:
indp.plot_indp_sample(params,
folderSuffix='_' + jt + '_AUCTION_' + rst +
'_' + vt,
suffix="real")
plt.show()
def run_indp_sample(layers):
interdep_net = indp.initialize_sample_network(layers=layers)
params = {
"NUM_ITERATIONS": 0,
"OUTPUT_DIR": '../results/indp_sample_12Node_results',
"V": {
'': len(layers)
},
"T": 1,
"L": layers,
"WINDOW_LENGTH": 1,
"ALGORITHM": "INDP",
"N": interdep_net,
"MAGNITUDE": 0,
"SIM_NUMBER": 0,
'DYNAMIC_PARAMS': None
}
indp.run_indp(params,
layers=layers,
T=params["T"],
suffix="",
save_model=True,
print_cmd_line=True)
print('\n\nPlot restoration plan by INDP')
indp.plot_indp_sample(params)
plt.show()
def run_game_sample(layers,
judge_types,
auction_type,
valuation_type,
game_type="NORMALGAME",
signals=None,
beliefs=None,
reduced_act=None):
interdep_net = indp.initialize_sample_network(layers=layers)
if game_type == "NORMALGAME":
out_dir = '../results/ng_sample_12Node_results'
elif game_type == "BAYESGAME":
out_dir = '../results/bg' + ''.join(signals.values()) + ''.join(beliefs.values()) + \
'_sample_12Node_results'
params = {
"NUM_ITERATIONS": 7,
"OUTPUT_DIR": out_dir,
"V": {
'': len(layers)
},
"T": 1,
"L": layers,
"WINDOW_LENGTH": 1,
"ALGORITHM": game_type,
'EQUIBALG': 'enumerate_pure',
"N": interdep_net,
"MAGNITUDE": 0,
"SIM_NUMBER": 0,
"JUDGMENT_TYPE": judge_types,
"RES_ALLOC_TYPE": auction_type,
"VALUATION_TYPE": valuation_type,
'DYNAMIC_PARAMS': None,
'PAYOFF_DIR': None,
"SIGNALS": signals,
"BELIEFS": beliefs,
'REDUCED_ACTIONS': reduced_act
}
gameutils.run_game(params,
save_results=True,
print_cmd=True,
save_model=True,
plot2D=True)
for jt, rst, vt in itertools.product(judge_types, auction_type,
valuation_type):
print('\n\nPlot restoration plan by Game', jt, rst, vt)
if rst == 'UNIFORM' or 'FIXED_LAYER':
indp.plot_indp_sample(params,
folder_suffix='_' + jt + '_' + rst,
suffix="")
else:
indp.plot_indp_sample(params,
folder_suffix='_' + jt + '_AUCTION_' + rst +
'_' + vt,
suffix="")
plt.show()
def run_tdindp_sample(layers):
interdep_net = indp.initialize_sample_network(layers=layers)
params = {
"OUTPUT_DIR": '../results/tdindp_sample_12Node_results',
"V": len(layers),
"T": 7,
"L": layers,
"ALGORITHM": "INDP",
"WINDOW_LENGTH": 3,
"N": interdep_net,
"MAGNITUDE": 0,
"SIM_NUMBER": 0
} #"WINDOW_LENGTH":6,
indp.run_indp(params,
layers=layers,
T=params["T"],
suffix="",
saveModel=True,
print_cmd_line=True)
print('\n\nPlot restoration plan by INDP')
indp.plot_indp_sample(params)
plt.show()