def minMinVal(b, pmin, P): """ MAX MAX MIN probability scheduler: b: belief state pmin: minimum probability for every state P: pomdp """ ret = 0 inv_obs = func.inverseObservation(b.M.O) for st,pr in b.d.iteritems(): ctrl = b.M.L.names[st[0]] # controller state env = b.M.M.names[st[1]] # environment state inner = 0 for obs in P.O: obspr = P.Z[st][obs] if obs in P.Z[st] else 0 if obspr == 0: continue env = tuple(item for t in env for item in t) key = tuple(item for t in (ctrl,env) for item in t) key = key + (inv_obs[obs],) if key in pmin: inner += obspr * pmin[key] if pr * inner < ret: ret = pr * inner return ret
def avgMinVal(b, pmin, P): """ MAX AVG MIN probability value: b: belief state pmin: minimum probability for every state P: pomdp """ ret = 0 inv_obs = func.inverseObservation(b.M.O) for st,pr in b.d.iteritems(): ctrl = st[0] # controller state env = st[1] # environment state inner = 0 for obs in P.O: obspr = P.Z[st][obs] if obs in P.Z[st] else 0 if obspr == 0: continue key = (ctrl,env,inv_obs[obs]) if key in pmin: inner += obspr * pmin[key] ret += pr * inner return ret
def export2sta(P,L,M,O,head,filename):
start = time.time()
f = open(filename,'w')
# first line
#head = "("
#for i in range(N):
# head += "x"+str(i)+",y"+str(i)+","
#head+="obs)\n"
f.write(head+'\n')
inv_obs = func.inverseObservation(O)
for i,((c,e),o) in enumerate(P.S):
#print 'i',i,'c',L.names[c],'e',M.names[e],'o',o
line = str(i)+':('+ \
str(c)+','+ \
str(e) + ',' + \
str(inv_obs[o]) + ')\n'
f.write(line)
end = time.time()
print "-> EXPORT TO STATES (time: "+str(end - start)+")"
