def main(args):
print "[Solve ID by local search]"
N, M, K = int(args[1]), int(args[2]), int(args[3])
from Factor import Factor
# sample network
ChanceVars, DecVars, CPT, Strategy, Utility = sampleChainID(N, M)
#print "loading model from file...",
#stime = time.clock()
#Variables, Factors = read_network_from_file(args[1], useLog=logScale)
#etime = time.clock() - stime
#print "done: %d variables, %d factors. \033[91m[%gs]\033[0m" % (len(Variables), len(Factors), etime)
run(ChanceVars, DecVars, CPT, Strategy, Utility, K, False, 100, True)
sol = len(DecVars) * [None]
for n in range(len(DecVars)):
sol[n] = Factor([DecVars[n]], defaultValue=1.0 / M)
kPUvalue, kPUit, kPUtime, KPUsize = run(ChanceVars, DecVars, CPT, sol,
Utility, K, False, 100, True)
sol2 = len(DecVars) * [None]
for n in range(len(DecVars)):
sol2[n] = Factor([DecVars[n]], defaultValue=1.0 / M)
kPUvalue2, kPUit2, kPUtime2, KPUsize2 = run(ChanceVars, DecVars, CPT, sol2,
Utility, K, False, 100, False)
print
print "METHOD \tTIME(s) \tMAX SIZE \tVALUE"
print "Pruned \t%10s\t%10s\t%g" % (str(kPUtime), str(KPUsize), kPUvalue)
print "Exhaustive\t%10s\t%10s\t%g" % (str(kPUtime2), str(KPUsize2),
kPUvalue2)
def main(args):
print "[Solve ID by local search]"
N,M,K,T=int(args[1]),int(args[2]),int(args[3]),int(args[4])
from Factor import Factor
# sample network
ChanceVars, DecVars, CPT, Strategy, Utility = sampleChainID(N,M)
print "SPU"
print
sys.stdout.flush()
SPUvalue, SPUit, SPUtime, SPUsize = run(ChanceVars, DecVars, CPT, Strategy, Utility, 1, False, 4*N, False)
print "Exact"
print
sys.stdout.flush()
sol = len(DecVars)*[ None ]
for n in range(len(DecVars)):
sol[n] = Factor([DecVars[n]], defaultValue=1.0/M)
kPUvalue, kPUit, kPUtime, kPUsize = run(ChanceVars, DecVars, CPT, sol, Utility, K, True, 2*N, False)
print "Approximate"
print
sys.stdout.flush()
sol2 = len(DecVars)*[ None ]
for n in range(len(DecVars)):
sol2[n] = Factor([DecVars[n]], defaultValue=1.0/M)
akPUvalue, akPUit, akPUtime, akPUsize = run(ChanceVars, DecVars, CPT, sol2, Utility, K, True, 2*N, True, T)
print
print "METHOD \tTIME(s) \tMAX SIZE \tVALUE"
print "SPU \t%10s\t%10s\t%g" % (str(SPUtime), str(SPUsize), SPUvalue)
print "Exact \t%10s\t%10s\t%g" % (str(kPUtime), str(kPUsize), kPUvalue)
print "Approximate\t%10s\t%10s\t%g" % (str(akPUtime), str(akPUsize), akPUvalue)
def main(args):
print "[Solve ID by local search]"
N,M,K=int(args[1]),int(args[2]),int(args[3])
from Factor import Factor
# sample network
ChanceVars, DecVars, CPT, Strategy, Utility = sampleChainID(N,M)
#print "loading model from file...",
#stime = time.clock()
#Variables, Factors = read_network_from_file(args[1], useLog=logScale)
#etime = time.clock() - stime
#print "done: %d variables, %d factors. \033[91m[%gs]\033[0m" % (len(Variables), len(Factors), etime)
run(ChanceVars, DecVars, CPT, Strategy, Utility, K, False, 100, True)
sol = len(DecVars)*[ None ]
for n in range(len(DecVars)):
sol[n] = Factor([DecVars[n]], defaultValue=1.0/M)
kPUvalue, kPUit, kPUtime, KPUsize = run(ChanceVars, DecVars, CPT, sol, Utility, K, False, 100, True)
sol2 = len(DecVars)*[ None ]
for n in range(len(DecVars)):
sol2[n] = Factor([DecVars[n]], defaultValue=1.0/M)
kPUvalue2, kPUit2, kPUtime2, KPUsize2 = run(ChanceVars, DecVars, CPT, sol2, Utility, K, False, 100, False)
print
print "METHOD \tTIME(s) \tMAX SIZE \tVALUE"
print "Pruned \t%10s\t%10s\t%g" % (str(kPUtime), str(KPUsize), kPUvalue)
print "Exhaustive\t%10s\t%10s\t%g" % (str(kPUtime2), str(KPUsize2), kPUvalue2)
def main(args):
print "[multiple policy updating]"
N,M=int(args[1]),int(args[2])
ChanceVars, DecVars, CPT, Strategy, Utility = sampleChainID(N,M)
mpu,t1,numtables = run(ChanceVars, DecVars, CPT, Strategy, Utility, verbose=True, ComputeHull=True)
print "Total elapsed time: \033[91m%gs\033[0m" % t1
mpu,t1,numtables = run(ChanceVars, DecVars, CPT, Strategy, Utility, verbose=True, ComputeHull=False)
print "Total elapsed time: \033[91m%gs\033[0m" % t1
def main(args):
print "[Solve ID by local search]"
N,M=int(args[1]),int(args[2])
# sample network
ChanceVars, DecVars, CPT, Strategy, Utility = sampleChainID(N,M)
#print "loading model from file...",
#stime = time.clock()
#Variables, Factors = read_network_from_file(args[1], useLog=logScale)
#etime = time.clock() - stime
#print "done: %d variables, %d factors. \033[91m[%gs]\033[0m" % (len(Variables), len(Factors), etime)
MEU, iteration, time = run(ChanceVars, DecVars, CPT, Strategy, Utility)
def main(args):
print "[Solve ID by local search]"
N, M = int(args[1]), int(args[2])
# sample network
ChanceVars, DecVars, CPT, Strategy, Utility = sampleChainID(N, M)
#print "loading model from file...",
#stime = time.clock()
#Variables, Factors = read_network_from_file(args[1], useLog=logScale)
#etime = time.clock() - stime
#print "done: %d variables, %d factors. \033[91m[%gs]\033[0m" % (len(Variables), len(Factors), etime)
MEU, iteration, time = run(ChanceVars, DecVars, CPT, Strategy, Utility)
def main(args):
import pickle
import time
N,M,L = int(args[1]), int(args[2]), int(args[3]) # num vars, var cardinality, no. of limids
print "generating %d chain-like limids with %d variables taking on %d values..." % (L,N,M),
sys.stdout.flush()
start = time.clock()
for t in range(L):
#ChanceVars, DecVars, CPT, Strategy, Utility = sampleChainID(N,M)
exp = sampleChainID(N,M)
pickle.dump( exp, open( "experiments/exp-%d-%d-%d.p" % (N,M,t), "wb" ) )
end = time.clock()
print "done [%gs]" % (end-start)
print "saved in experiments/exp-%d-%d-L" % (N,M)
def main(args):
import pickle
import time
N, M, L = int(args[1]), int(args[2]), int(
args[3]) # num vars, var cardinality, no. of limids
print "generating %d chain-like limids with %d variables taking on %d values..." % (
L, N, M),
sys.stdout.flush()
start = time.clock()
for t in range(L):
#ChanceVars, DecVars, CPT, Strategy, Utility = sampleChainID(N,M)
exp = sampleChainID(N, M)
pickle.dump(exp, open("experiments/exp-%d-%d-%d.p" % (N, M, t), "wb"))
end = time.clock()
print "done [%gs]" % (end - start)
print "saved in experiments/exp-%d-%d-L" % (N, M)
def main(args):
print "[multiple policy updating]"
N, M = int(args[1]), int(args[2])
ChanceVars, DecVars, CPT, Strategy, Utility = sampleChainID(N, M)
mpu, t1, numtables = run(ChanceVars,
DecVars,
CPT,
Strategy,
Utility,
verbose=True,
ComputeHull=True)
print "Total elapsed time: \033[91m%gs\033[0m" % t1
mpu, t1, numtables = run(ChanceVars,
DecVars,
CPT,
Strategy,
Utility,
verbose=True,
ComputeHull=False)
print "Total elapsed time: \033[91m%gs\033[0m" % t1
def main(args):
print "[Solve ID by local search]"
N, M, K, T = int(args[1]), int(args[2]), int(args[3]), int(args[4])
from Factor import Factor
# sample network
ChanceVars, DecVars, CPT, Strategy, Utility = sampleChainID(N, M)
print "SPU"
print
sys.stdout.flush()
SPUvalue, SPUit, SPUtime, SPUsize = run(ChanceVars, DecVars, CPT, Strategy,
Utility, 1, False, 4 * N, False)
print "Exact"
print
sys.stdout.flush()
sol = len(DecVars) * [None]
for n in range(len(DecVars)):
sol[n] = Factor([DecVars[n]], defaultValue=1.0 / M)
kPUvalue, kPUit, kPUtime, kPUsize = run(ChanceVars, DecVars, CPT, sol,
Utility, K, True, 2 * N, False)
print "Approximate"
print
sys.stdout.flush()
sol2 = len(DecVars) * [None]
for n in range(len(DecVars)):
sol2[n] = Factor([DecVars[n]], defaultValue=1.0 / M)
akPUvalue, akPUit, akPUtime, akPUsize = run(ChanceVars, DecVars, CPT, sol2,
Utility, K, True, 2 * N, True,
T)
print
print "METHOD \tTIME(s) \tMAX SIZE \tVALUE"
print "SPU \t%10s\t%10s\t%g" % (str(SPUtime), str(SPUsize),
SPUvalue)
print "Exact \t%10s\t%10s\t%g" % (str(kPUtime), str(kPUsize),
kPUvalue)
print "Approximate\t%10s\t%10s\t%g" % (str(akPUtime), str(akPUsize),
akPUvalue)
def main(args):
print "[Solve ID by exhaustive search]"
N,M=int(args[1]),int(args[2])
# sample network
ChanceVars, DecVars, CPT, Strategy, Utility = sampleChainID(N,M)
run(ChanceVars, DecVars, CPT, Strategy, Utility)
def main(args):
print "[Solve ID by exhaustive search]"
N, M = int(args[1]), int(args[2])
# sample network
ChanceVars, DecVars, CPT, Strategy, Utility = sampleChainID(N, M)
run(ChanceVars, DecVars, CPT, Strategy, Utility)