if "calibrate" in cmd:
pyapemost.calibrate()
# run APEMoST
if "run" in cmd:
w = pyapemost.watch.ProgressWatcher()
w.start()
pyapemost.run(max_iterations = 10000, append = False)
w.stop()
# lets analyse the results
if "analyse" in cmd:
plotter = pyapemost.analyse.VisitedAllPlotter()
plotter.plot()
show("chain0.pdf")
histograms = pyapemost.create_histograms()
i = 1
plt.clf()
plt.figure(figsize=(7, 4 * len(histograms)))
for k,(v,stats) in histograms.iteritems():
plt.subplot(len(histograms), 1, i)
plt.plot(v[:,0], v[:,2], ls='steps--', label=k)
plt.legend()
print k, stats
i = i + 1
plt.savefig("marginals.pdf")
show("marginals.pdf")
print pyapemost.model_probability()
cmd = sys.argv[1:]
if "calibrate" in cmd:
pyapemost.calibrate()
# run APEMoST
if "run" in cmd:
w = pyapemost.watch.ProgressWatcher()
w.start()
pyapemost.run(max_iterations=10000, append=False)
w.stop()
# lets analyse the results
if "analyse" in cmd:
plotter = pyapemost.analyse.VisitedAllPlotter()
plotter.plot()
show("chain0.pdf")
histograms = pyapemost.create_histograms()
i = 1
plt.clf()
plt.figure(figsize=(7, 4 * len(histograms)))
for k, (v, stats) in histograms.items():
plt.subplot(len(histograms), 1, i)
plt.plot(v[:, 0], v[:, 2], ls='steps--', label=k)
plt.legend()
print(k, stats)
i = i + 1
plt.savefig("marginals.pdf")
show("marginals.pdf")
print(pyapemost.model_probability())
if "calibrate" in cmd:
pyapemost.calibrate()
# run APEMoST
if "run" in cmd:
w = pyapemost.watch.ProgressWatcher()
w.start()
pyapemost.run(max_iterations = 10000, append = False)
w.stop()
# lets analyse the results
if "analyse" in cmd:
plotter = pyapemost.analyse.VisitedAllPlotter()
plotter.plot()
show("chain0.pdf")
histograms = pyapemost.create_histograms()
i = 1
plt.clf()
plt.figure(figsize=(7, 4 * len(histograms)))
for k,(v,stats) in histograms.items():
plt.subplot(len(histograms), 1, i)
plt.plot(v[:,0], v[:,2], ls='steps--', label=k)
plt.legend()
print(k, stats)
i = i + 1
plt.savefig("marginals.pdf")
show("marginals.pdf")
print(pyapemost.model_probability())