def run_mc(self, N, minExp=4, maxExp=12, out=False, plot=False):
# ----------------------------------------------------------
# dicretize the stochastic space with Monte Carlo
# ----------------------------------------------------------
np.random.seed(1234567)
print("-" * 60)
print("Latin Hypercube Sampling")
print("-" * 60)
mcSampler, mcUQSetting = TestEnvironmentMC().buildSetting(
self.params, self.simulation, 2**maxExp)
# ----------------------------------------------------------
# Monte Carlo Estimator
# ----------------------------------------------------------
samples = mcSampler.nextSamples(N)
mcUQSetting.runSamples(samples)
samples = mcUQSetting.getResults()[0]
stats = {}
for iN in np.logspace(minExp, maxExp, maxExp - minExp + 1, base=2):
# split the results into chunk of Ni samples
num_samples = int(iN)
isamples = {0: dict(list(samples.items())[:num_samples])}
analysis = MCAnalysis(self.params, isamples)
analysis.setVerbose(False)
stats[iN] = {
"num_model_evaluations": num_samples,
"mean_estimated": analysis.mean(),
"var_estimated": analysis.var()
}
print("-" * 60)
print("#samples = %i" % (num_samples, ))
print("E[x] = %g ~ %g (err=%g)" %
(self.E_ana[0], analysis.mean()[0],
np.abs(self.E_ana[0] - analysis.mean()[0])))
print("V[x] = %g ~ %g (err=%g)" %
(self.V_ana[0], analysis.var()[0],
np.abs(self.V_ana[0] - analysis.var()[0])))
if out:
# store results
filename = os.path.join(
self.pathResults, "%s_mc_d%i_%s_N%i.pkl" %
(self.radix, self.numDims, "latinHypercube", N))
fd = open(filename, "w")
pkl.dump(
{
'surrogate': 'mc',
'num_dims': self.numDims,
'sampling_strategy': "latin_hypercube",
'num_model_evaluations': N,
'mean_analytic': self.E_ana[0],
'var_analytic': self.V_ana[0],
'results': stats
}, fd)
fd.close()
def run_mc(self, N=10, out=False, plot=False):
# ----------------------------------------------------------
# dicretize the stochastic space with Monte Carlo
# ----------------------------------------------------------
np.random.seed(1234567)
print("-" * 60)
print("Latin Hypercube Sampling")
print("-" * 60)
mcSampler = MCSampler.withLatinHypercubeSampleGenerator(self.params, N)
mcUQSettingBuilder = UQBuilder()
self.defineUQSetting(mcUQSettingBuilder)
mcUQSetting = mcUQSettingBuilder.andGetResult()
# ----------------------------------------------------------
# Monte Carlo Estimator
# ----------------------------------------------------------
samples = mcSampler.nextSamples(N)
mcUQSetting.runSamples(samples)
samples = mcUQSetting.getTimeDependentResults(self.toi, qoi=self.qoi)
# split the results into chunk of Ni samples
num_samples = len(next(iter(samples.values())))
analysis = MCAnalysis(self.params, samples)
analysis.setVerbose(False)
stats = {"num_model_evaluations": num_samples,
"mean_estimated": analysis.mean(),
"var_estimated": analysis.var()}
if out:
# store results
filename = os.path.join(self.pathResults,
"%s-qoi%s_%s.pkl" % (self.radix,
self.qoi,
"mc"))
fd = open(filename, "w")
pkl.dump({'surrogate': 'mc',
'num_dims': self.numDims,
'sampling_strategy': "latin_hypercube",
'num_model_evaluations': num_samples,
'time_steps': self.toi,
'setting': self.setting,
'qoi': self.qoi,
'results': stats},
fd)
fd.close()