def computeReferenceValues(self, uqSetting, n=2000):
# ----------------------------------------------------------
# dicretize the stochastic space with Monte Carlo
# ----------------------------------------------------------
if uqSetting.getSize() < n:
print("-" * 60)
print("Latin Hypercube sampling")
print("-" * 60)
mcSampler = MCSampler.withLatinHypercubeSampleGenerator(self.params, n - uqSetting.getSize())
while uqSetting.getSize() < n:
samples = mcSampler.nextSamples(n - uqSetting.getSize())
uqSetting.runSamples(samples)
uqSetting.writeToFile()
def f(y, t):
return [fi(y, t) for fi in self.f]
self.y0 = [1., .5, 0.]
self.n = ((self.tn - self.t0) / self.dt) + 1
self.t = np.linspace(self.t0, self.tn, self.n, endpoint=True)
# solve the ODEs
soln = odeint(f, self.y0, self.t)
self.y1 = soln[:, 0]
self.y2 = soln[:, 1]
self.y3 = soln[:, 2]
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()
def testSettings(self):
builder = ParameterBuilder()
dp = builder.defineDeterministicParameters()
up = builder.defineUncertainParameters()
# ============================================
# 1)
up.new().isCalled('v')\
.withDistribution(Uniform(0, 1))\
.withRosenblattTransformation()
# --------------------------------------------
# 2)
up.new().isCalled('density')\
.withDistribution(Uniform(-1, 1))\
.hasValue(0.0)
# --------------------------------------------
# 3)
up.new().isCalled('K')\
.withDistribution(TNormal(0, 1, -3, 2))\
.hasValue(-3)
# --------------------------------------------
# 4)
up.new().isCalled('theta')\
.withDistribution(TNormal(0, 1, -2, 2))\
.withLinearTransformation()
# --------------------------------------------
# 5)
up.new().isCalled('blub')\
.withUniformDistribution(-1, 1)
# --------------------------------------------
# 6)
dp.new().isCalled('radius').hasValue(2)
# ============================================
params = builder.andGetResult()
# test dimensions
assert params.getDim() == 6
assert params.getStochasticDim() == 3
assert len(params.activeParams()) == 3
assert params.getStochasticDim() == len(params.activeParams())
assert params.getDim() - len(params.uncertainParams()) == \
len(params.deterministicParams())
assert params.getStochasticDim() == len(params.getDistributions()) - 2
jsonStr = params.getJointTransformation().toJson()
jsonObject = json.loads(jsonStr)
trans = Transformation.fromJson(jsonObject)
# test transformations
ap = params.activeParams()
assert params.getStochasticDim() == len(ap)
sampler = MCSampler.withNaiveSampleGenerator(params)
for sample in sampler.nextSamples(100):
for x in sample.getActiveUnit():
assert 0 <= x <= 1
bounds = params.getBounds()
q = sample.getExpandedProbabilistic()
for xlim1, xlim2, x in np.vstack((bounds.T, q)).T:
assert xlim1 <= x <= xlim2
params.removeParam(0)
assert params.getStochasticDim() == len(ap) - 1
sampler = MCSampler.withNaiveSampleGenerator(params)
for sample in sampler.nextSamples(100):
for x in sample.getActiveUnit():
assert 0 <= x <= 1
bounds = params.getBounds()
q = sample.getExpandedProbabilistic()
for xlim1, xlim2, x in np.vstack((bounds.T, q)).T:
assert xlim1 <= x <= xlim2