Source code for pynfg.pgtsolutions.intelligence.uncoordinated

# -*- coding: utf-8 -*-
"""
Implements PGT intelligence for iterSemiNFG objects

Part of: PyNFG - a Python package for modeling and solving Network Form Games

Created on Wed Jan  2 16:33:36 2013

Copyright (C) 2013 James Bono (jwbono@gmail.com)

GNU Affero General Public License

"""
from __future__ import division
import copy
import numpy as np
from pynfg import DecisionNode
from pynfg import iterSemiNFG
import scipy.stats.distributions as randvars
from pynfg.utilities.utilities import mh_decision

[docs]def uncoordinated_MC(G, S, noise, X, M, innoise, delta=1, integrand=None, \ mix=False, satisfice=None): """Run MC outer loop on random policy sequences for iterSemiNFG IQ calcs :arg G: the game to be evaluated :type G: iterSemiNFG or SemiNFG :arg S: number of policy sequences to sample :type S: int :arg X: number of samples of each policy profile :type X: int :arg M: number of random alt policies to compare :type M: int :arg delta: the discount factor :type delta: float :arg integrand: a user-supplied function of G that is evaluated for each s in S :type integrand: func .. note:: This is the uncoordinated approach because intelligence is assigned to a DN instead of being assigned to a player. """ dnlist = [d.name for d in G.nodes if isinstance(d, DecisionNode)] intel = {} #keys are MC iterations s, values are iq dicts iq = dict(zip(dnlist, np.zeros(len(dnlist)))) #keys are node names, vals are iqs funcout = {} #keys are s in S, vals are eval of integrand of G(s) w = {} weight = {} for s in xrange(1, S+1): #sampling S sequences of policy profiles print s GG = copy.deepcopy(G) for dn in dnlist: #drawing current policy w[dn] = GG.node_dict[dn].perturbCPT(noise, mixed=mix, \ returnweight=True) for dn in dnlist: #find the iq of each player's policy in turn iq[dn] = uncoordinated_calciq(dn, GG, X, M, mix, delta, innoise, \ satisfice) if integrand is not None: funcout[s] = integrand(GG) #eval integrand GG(s), assign to funcout intel[s] = copy.deepcopy(iq) weight[s] = copy.deepcopy(w) return intel, funcout, weight
[docs]def uncoordinated_MH(G, S, density, noise, X, M, innoise=1, delta=1, \ integrand=None, mix=False, satisfice=None): """Run MH for iterSemiNFG IQ calcs :arg G: the iterated semiNFG to be evaluated :type G: iterSemiNFG :arg S: number of MH iterations :type S: int :arg noise: the degree of independence of the proposal distribution on the current value. :type noise: float :arg density: the function that assigns weights to iq :type density: func :arg X: number of samples of each policy profile :type X: int :arg M: number of random alt policies to compare :type M: int :arg delta: the discount factor :type delta: float :arg integrand: a user-supplied function of G that is evaluated for each s in S :type integrand: func .. warning:: This will throw an error if there is a decision node in G.starttime that is not repeated throughout the net. .. note:: This is the agent-approach because intelligence is assigned to a DN instead of being assigned to a player. """ dnlist = [d.name for d in G.nodes if isinstance(d, DecisionNode)] intel = {} #keys are s in S, vals are iq dict (dict of dicts) iq = {} #keys are base names, iq timestep series funcout = {} #keys are s in S, vals are eval of integrand of G(s) dens = np.zeros(S+1) #storing densities for return for s in xrange(1, S+1): #sampling S sequences of policy profiles print s GG = copy.deepcopy(G) for dn in dnlist: GG.node_dict[dn].perturbCPT(noise, mixed=mix, setCPT=False) for dn in dnlist:#getting iq iq[dn] = uncoordinated_calciq(dn, GG, X, M, mix, delta, innoise, \ satisfice) # The MH decision current_dens = density(iq) verdict = mh_decision(current_dens, dens[s-1]) if verdict: #accepting new CPT intel[s] = copy.deepcopy(iq) G = copy.deepcopy(GG) dens[s] = current_dens else: intel[s] = intel[s-1] dens[s] = dens[s-1] if integrand is not None: funcout[s] = integrand(GG) #eval integrand G(s), assign to funcout return intel, funcout, dens[1::]
[docs]def uncoordinated_calciq(dn, G, X, M, mix, delta, innoise, satisfice=None): """Calc IQ of CPT at dn, holding all other dns constant :arg dn: the name of the decision node where intelligence is being evaluated. :type dn: str :arg G: the semiNFG to be evaluated :type G: SemiNFG or iterSemiNFG :arg X: number of samples of each policy profile :type X: int :arg M: number of random alt policies with which to compare :type M: int :arg mix: if true, proposal distribution is over mixed CPTs. Default is False. :type mix: bool :arg delta: the discount factor (ignored if SemiNFG) :type delta: float :arg innoise: the perturbation noise for the inner loop to draw alt CPTs :type innoise: float :returns: the fraction of alternative CPTs that have a lower utility than the current CPT. """ util = 0 altutil = [0]*M weight = np.ones(M) tick = 0 p = G.node_dict[dn].player oldCPT = copy.copy(G.node_dict[dn].CPT) GG = copy.deepcopy(G) if isinstance(G, iterSemiNFG): ufoo = G.npv_reward uargs = [p, G.starttime, delta] else: ufoo = G.utility uargs = [p] for x in xrange(1,X+1): G.sample() util = (ufoo(*uargs)+(x-1)*util)/x if satisfice: #using the satisficing distribution for drawing alternatives G = satisfice for m in range(M): #Sample M alt CPTs for the player at the DN if innoise == 1 or satisfice: GG.node_dict[dn].perturbCPT(innoise, mixed=mix) denw=1 else: denw = GG.node_dict[dn].perturbCPT(innoise, mixed=mix, \ returnweight=True) if not tick: numw = denw #scaling constant num to ~ magnitude of den weight[m] *= (numw/denw) tick += 1 GG.sample() #sample altpolicy prof. to end of net if isinstance(GG, iterSemiNFG): altutil[m] = GG.npv_reward(p, GG.starttime, delta) else: altutil[m] = GG.utility(p) GG.node_dict[dn].CPT = oldCPT #resetting the CPT for the next draw #weight of alts worse than G worse = [weight[m] for m in range(M) if altutil[m]<util] return np.sum(worse)/np.sum(weight) #fraction of alts worse than G is IQ