wip
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9f02e5d123
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1 changed files with 204 additions and 20 deletions
224
tim.py
224
tim.py
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@ -3,6 +3,8 @@ import functools
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import itertools
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from collections import defaultdict as dd
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import pprint
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import numpy
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from numpy.random import random
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def ResistanceGame(n_players):
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@ -12,11 +14,109 @@ def ResistanceGame(n_players):
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return full_set[:n_players]
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def BuildNbyNBeliefMatrix(id, n_players):
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all_vars = []
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matrix = []
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for player_id in range(n_players):
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vec = mc.Uniform("%d_trust_%d" % (id, player_id),
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0.0, 1.0,
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trace=False,
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size=n_players)
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matrix.append(vec)
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all_vars.append(vec)
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# Enforce that there's only one role for each player.
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for i, player_vec in enumerate(matrix):
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det_var = sum(vec)
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all_vars.append(det_var)
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obs = mc.Bernoulli(
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"player_%d_one_role_constraint_%d" % (id, i),
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det_var,
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value=1.0,
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trace=False,
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observed=True)
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all_vars.append(obs)
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# Enforce that there's only one player per role.
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for i in range(n_players):
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det_var = sum([vec[i] for vec in matrix])
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all_vars.append(det_var)
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obs = mc.Bernoulli(
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"player_%d_one_player_constraint_%d" % (id, i),
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det_var,
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value=1.0,
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trace=False,
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observed=True)
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all_vars.append(obs)
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return matrix, all_vars
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class Player(object):
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def __init__(self, id, card, game, side):
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self.id_num = id
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self.role_str, _ = card
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''' Representing all that the player is and knows '''
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def __init__(self, id, game, side_var, role_var):
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self.id = id
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self.game = game
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self.side_var = side_var
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self.role_var = role_var
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self.all_vars = []
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self.build_belief_matrix()
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def side(self):
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return self.side_var
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def role(self):
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return self.role_var
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def build_belief_matrix(self):
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self.matrix = []
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self.matrix, self.all_vars = \
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BuildNbyNBeliefMatrix(self.id, self.game.n_players)
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# Enforce that the player completely knows her own role.
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def player_knows_self(self_role=self.role_var):
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return self.matrix[self.id][self_role]
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knows = mc.Deterministic(eval=player_knows_self,
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name="player_knows_self_%d" % self.id,
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parents={"self_role": self.role_var},
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doc="Player knows self %d" % self.id,
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dtype=float,
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trace=True,
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plot=False)
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self.all_vars.append(knows)
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obs = mc.Bernoulli(
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"player_knows_self_constraint_%d" % self.id,
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knows,
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value=1.0,
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observed=True)
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self.all_vars.append(obs)
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# Convenience vars for trust
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self.side_belief = []
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def side_belief(player_id, role_vec):
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out = 0.0
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for i, role in enumerate(role_vec):
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if self.game.role_id_is_good(i):
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out += role
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out = out / len(role_vec)
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return out
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for i in range(self.game.n_players):
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is_good = mc.Deterministic(eval=functools.partial(side_belief, i),
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name="player%d_trust_%d" % (self.id, i),
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parents={"role_vec": self.matrix[i]},
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doc="Does player trust %d?" % i,
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dtype=float,
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trace=True,
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plot=False)
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self.side_belief.append(is_good)
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self.all_vars.append(is_good)
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def get_good_belief_for(self, player_id):
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return self.side_belief[player_id]
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def get_role_belief_for(self, player_id, role_id):
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return self.matrix[player_id][role_id]
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class DeceptionGame(object):
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@ -32,6 +132,7 @@ class DeceptionGame(object):
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self.player_role_vars = []
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self.role_ids = {}
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self.role_side = {}
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i = 0
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for card in player_array:
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@ -41,18 +142,22 @@ class DeceptionGame(object):
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i += 1
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def player_side(x, role):
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return self.role_side[role]
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if self.role_side[role]:
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return True
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return False
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def player_role(x, deck_var=self.deck_var):
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role_str = self.all_permutations[deck_var][x][0]
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return self.role_ids[role_str]
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return int(self.role_ids[role_str])
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self.players = []
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for x in range(self.n_players):
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role = mc.Deterministic(eval=functools.partial(player_role, x),
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name="player_role_%d" % x,
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parents={"deck_var": self.deck_var},
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doc="Who is player %d?" % x,
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dtype=str,
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dtype=int,
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trace=True,
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plot=False)
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self.player_role_vars.append(role)
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@ -67,37 +172,107 @@ class DeceptionGame(object):
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plot=False)
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self.player_side_vars.append(side)
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self.players.append(Player(x,
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player_array[x]))
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self.players.append(Player(x, self, side, role))
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for x in range(50000):
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self.deck_var.random()
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self.observations = []
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self.tid = 0
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def get_role_id_for(self, name):
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return self.role_ids[name]
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def role_id_is_good(self, role_id):
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return self.role_side[role_id]
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def add_known_side(self, player_id, is_good):
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transaction = []
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obs = mc.Bernoulli("player_seen_tid%d" % self.tid,
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self.player_side_vars[player_id],
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value=is_good,
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observed=True)
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def player_is_good(side_var):
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if side_var == is_good:
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return 1.0
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return 0.0
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det = mc.Deterministic(
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eval=player_is_good,
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name="player_seen_det_tid%d" % self.tid,
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parents={"side_var": self.player_side_vars[player_id]},
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doc="Det TID%d" % self.tid,
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dtype=float,
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trace=False,
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plot=False)
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transaction.append(det)
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obs = mc.Degenerate("player_seen_tid%d" % self.tid,
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det,
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value=1.0,
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observed=True)
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transaction.append(obs)
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self.observations.append(transaction)
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self.tid += 1
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def add_known_role(self, player_id, role_str):
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transaction = []
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obs = mc.Bernoulli("player_seen_role_tid%d" % self.tid,
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self.player_role_vars[player_id],
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value=self.role_ids[role_str],
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observed=True)
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known_role_id = self.get_role_id_for(role_str)
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def bool_stoch_logp(value, in_val):
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if value == in_val:
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return -numpy.log(1)
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else:
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return -numpy.inf
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def bool_stoch_rand(in_val):
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return bool(numpy.round(random()))
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def is_known_role(role_id):
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if role_id == known_role_id:
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return True
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return False
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det = mc.Deterministic(
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eval=is_known_role,
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name="player_seen_role_det_tid%d" % self.tid,
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parents={"role_id": self.player_role_vars[player_id]},
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doc="Det TID%d" % self.tid,
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dtype=bool,
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trace=True,
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plot=False)
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transaction.append(det)
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obs = mc.Stochastic(
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logp=bool_stoch_logp,
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doc="Boolean stochastic observation",
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name="player_seen_role_tid%d" % self.tid,
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parents={"in_val": det},
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random=bool_stoch_rand,
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trace=True,
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value=True,
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dtype=bool,
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observed=True,
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cache_depth=2,
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plot=False,
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verbose=0)
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#obs = mc.Uniform("player_seen_role_tid%d" % self.tid,
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#0, det,
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#value=1,
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#observed=True)
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transaction.append(obs)
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self.observations.append(transaction)
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self.tid += 1
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def eval(self, length=40000):
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def eval(self, length=40000, burn=10000):
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mcmc = mc.MCMC(self._build_model_list())
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mcmc.sample(length, 2000)
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mcmc.sample(length, burn)
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self.model = mcmc
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def eval_model_sans_players(self, length=40000, burn=500):
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mcmc = mc.MCMC(self._build_restricted_model_list())
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mcmc.sample(length, burn)
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def report(self):
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if self.model is None:
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self.eval()
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@ -129,19 +304,28 @@ class DeceptionGame(object):
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pp = pprint.PrettyPrinter(indent=4)
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pp.pprint(self.report())
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def _build_restricted_model_list(self):
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out = []
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out.append(self.deck_var)
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out.extend(self.player_side_vars[:])
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out.extend(self.player_role_vars[:])
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return out
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def _build_model_list(self):
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out = []
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out.append(self.deck_var)
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out.extend(self.player_side_vars[:])
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out.extend(self.player_role_vars[:])
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for player in self.players:
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out.extend(player.all_vars[:])
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flattened = [item for transaction in self.observations
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for item in transaction]
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out.extend(flattened[:])
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return list(set(out))
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base_game = DeceptionGame(ResistanceGame(5))
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base_game.eval(20000)
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base_game = DeceptionGame(ResistanceGame(10))
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base_game.eval_model_sans_players()
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base_game.add_known_role(0, "G1")
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base_game.add_known_side(1, False)
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base_game.eval()
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