def bestresponse_node(Game, dn, N, delta=1, tol=30, verbose=False):
"""Compute level-k best response at the DN given Game
:arg Game: the Network Form Game of interest
:type Game: SemiNFG or iterSemiNFG
:arg dn: the name of the decision node where MCEUs are estimated
:type dn: str
:arg N: the max number of iterations for the estimation
:type N: int
:arg tol: the minimum number of samples per parent value
:type tol: int
"""
G = copy.deepcopy(Game)
EUtable = mceu(G, dn, N, tol, delta, verbose)
G.node_dict[dn].CPT = convert_2_pureCPT(EUtable)
return G
def train_node(self, nodename, level, logit=False, setCPT=False, verbose=False):
"""Compute level-k best response at the DN given Game
:arg nodename: the name of the decision node where MCEUs are estimated
:type nodename: str
:arg level: The level at which to train that player
:type level: int
:arg setCPT: If the trained CPT should be set as the current CPT.
Otherwise, it can be accessed through node.LevelCPT. Default is
False
:type setCPT: bool
Notes
-----
If training a player at level k, the other players' CPT will be accessed
through self.Game.node_dict[other_player].LevelCPT[k-1]
"""
print 'Training ' + nodename + ' at level ' + str(level)
Game = copy.deepcopy(self.Game) # copy in order to maintain original CPT
ps = self.specs
for node in Game.node_dict.values(): # Game changes, self.Game doesn't
if type(node) is pynfg.DecisionNode:
try:
node.CPT = node.LevelCPT[level - 1]
except KeyError:
raise KeyError('Need to train other players at level %s'
% str(level-1))
EUtable = mceu(Game, nodename, Game.node_dict[nodename].N,
Game.node_dict[nodename].tol, Game.node_dict[nodename].delta,
verbose=verbose)
if not logit:
self.Game.node_dict[nodename].LevelCPT[level] = \
convert_2_pureCPT(EUtable)
if setCPT:
self.Game.node_dict[nodename].CPT = convert_2_pureCPT(EUtable)
else:
weight = np.exp(Game.node_dict[nodename].beta*EUtable)
norm = np.sum(weight, axis=-1)
self.Game.node_dict[nodename].LevelCPT[level] = \
weight/norm[..., np.newaxis]
if setCPT:
self.Game.node_dict[nodename].CPT = weight/norm[..., np.newaxis]
def logitresponse_node(Game, dn, N, delta=1, beta=1, tol=30, verbose=False):
"""Compute level-k logit response at the DN given Game
:arg Game: the Network Form Game of interest
:type Game: SemiNFG or iterSemiNFG
:arg dn: the name of the decision node where MCEUs are estimated
:type dn: str
:arg N: the max number of iterations for the estimation
:type N: int
:arg tol: the minimum number of samples per parent value
:type tol: int
"""
G = copy.deepcopy(Game)
EUtable = mceu(G, dn, N, tol, delta, verbose)
weight = np.exp(beta*EUtable)
norm = np.sum(weight, axis=-1)
G.node_dict[dn].CPT = weight/norm[...,np.newaxis]
return G
