toc = time.time() dt1 = toc - tic print("time=%f" % dt1) print("dimension=%d, size=%d,sa=%s" % (dimension, size, sa)) print( str(result.getOptimalValue()) + " c2=" + str(result.getC2()) + " phiP=" + str(result.getPhiP()) + " minDist=" + str(result.getMinDist())) crit = result.drawHistoryCriterion() proba = result.drawHistoryProbability() temp = result.drawHistoryTemperature() pp = PdfPages('small_OTLHS.pdf') # Criterion fig = View(crit, plot_kwargs={'color': 'blue'}).getFigure() fig.savefig("crit_sa_geom.png") pp.savefig(fig) plt.close(fig) # Proba fig = View(proba, plot_kwargs={ 'marker': 'o', 'ms': 0.6 }, axes_kwargs={ 'ylim': [-0.05, 1.05] }).getFigure() fig.savefig("lhs_c2_proba.png") pp.savefig(fig) plt.close(fig) # Temperature
design = sa.generate() result = sa.getResult() toc = time.time() dt1 = toc-tic print("time=%f"%dt1) print("dimension=%d, size=%d,sa=%s"%(dimension, size, sa)) print(str(result.getOptimalValue())+" c2="+str(result.getC2())+" phiP="+str(result.getPhiP())+" minDist="+str(result.getMinDist())) crit = result.drawHistoryCriterion() proba = result.drawHistoryProbability() temp = result.drawHistoryTemperature() pp = PdfPages('large_OTLHS.pdf') # Criterion fig = View(crit, plot_kwargs={'color':'blue'}).getFigure() fig.savefig("otlhs_c2_crit_big.png") pp.savefig(fig) plt.close(fig) # Proba fig = View(proba, plot_kwargs={'marker': 'o', 'ms': 0.6}, axes_kwargs={'ylim': [-0.05, 1.05]}).getFigure() fig.savefig("lhs_c2_proba_big.png") pp.savefig(fig) plt.close(fig) # Temperature fig = View(temp).getFigure() pp.savefig(fig) plt.close(fig) minDist = ot.SpaceFillingMinDist() sa = ot.SimulatedAnnealingLHS(lhsDesign, geomProfile, minDist) tic = time.time()
toc = time.time() dt1 = toc - tic print("time=%f" % dt1) print("dimension=%d, size=%d,sa=%s" % (dimension, size, sa)) print( str(result.getOptimalValue()) + " c2=" + str(result.getC2()) + " phiP=" + str(result.getPhiP()) + " minDist=" + str(result.getMinDist())) crit = result.drawHistoryCriterion() proba = result.drawHistoryProbability() temp = result.drawHistoryTemperature() pp = PdfPages('large_OTLHS.pdf') # Criterion fig = View(crit, plot_kwargs={'color': 'blue'}).getFigure() fig.savefig("otlhs_c2_crit_big.png") pp.savefig(fig) plt.close(fig) # Proba fig = View(proba, plot_kwargs={ 'marker': 'o', 'ms': 0.6 }, axes_kwargs={ 'ylim': [-0.05, 1.05] }).getFigure() fig.savefig("lhs_c2_proba_big.png") pp.savefig(fig) plt.close(fig) # Temperature
mc = otlhs.MonteCarloLHS(lhsDesign, nSimu, c2) tic = time.time() result = mc.generate() toc = time.time() print("%d %f %f" % (nSimu, result.getOptimalValue(), toc - tic)) pp = PdfPages("small_mc_OTLHS.pdf") # plot criterion & save it crit = result.drawHistoryCriterion() fig = View(crit, plot_kwargs={"color": "blue"}).getFigure() pp.savefig(fig) plt.close(fig) # plot design fig = PyPlotDesign(result.getOptimalDesign(), bounds, size, size, plot_kwargs={"color": "blue", "marker": "o", "ms": 6}) plt.suptitle("LHS design of size=%d - Optimization of %s criterion using %d MC sample" % (size, c2.getName(), nSimu)) fig.savefig("lhs_mc_c2_%d.png" % size) plt.close(fig) minDist = otlhs.SpaceFillingMinDist() # Factory: lhs generates lhsDesign = otlhs.LHSDesign(bounds, size) mc = otlhs.MonteCarloLHS(lhsDesign, nSimu, minDist) tic = time.time() result = mc.generate() toc = time.time() print("cpu time=%f" % (toc - tic)) print("dimension=%d, size=%d,mc=%s" % (dimension, size, mc)) print( "optimal value=" + str(result.getOptimalValue())
tic = time.time() result = sa.generate() toc = time.time() dt1 = toc-tic print("time=%f"%dt1) print("dimension=%d, size=%d,sa=%s"%(dimension, size, sa)) print(str(result.getOptimalValue())+" c2="+str(result.getC2())+" phiP="+str(result.getPhiP())+" minDist="+str(result.getMinDist())) crit = result.drawHistoryCriterion() proba = result.drawHistoryProbability() temp = result.drawHistoryTemperature() pp = PdfPages('small_OTLHS.pdf') # Criterion fig = View(crit, plot_kwargs={'color':'blue'}).getFigure() fig.savefig("crit_sa_geom.png") pp.savefig(fig) plt.close(fig) # Proba fig = View(proba, plot_kwargs={'marker': 'o', 'ms': 0.6}, axes_kwargs={'ylim': [-0.05, 1.05]}).getFigure() fig.savefig("lhs_c2_proba.png") pp.savefig(fig) plt.close(fig) # Temperature fig = View(temp).getFigure() pp.savefig(fig) plt.close(fig) linearProfile = ot.LinearProfile(10.0, 50000) minDist = ot.SpaceFillingMinDist() sa = ot.SimulatedAnnealingLHS(lhsDesign, linearProfile, minDist)