def testSettings(self):
builder = ParameterBuilder()
dp = builder.defineDeterministicParameters()
up = builder.defineUncertainParameters()
up.new().isCalled('v').withUniformDistribution(
0, 10).withRosenblattTransformation()
dp.new().isCalled('density').hasValue(.3)
up.new().isCalled('theta').withTNormalDistribution(
1, 1, -2, 2).withLinearTransformation()
dp.new().isCalled('radius').hasValue(10)
up.new().isCalled('height').withBetaDistribution(
3, 3, 0, 2).withRosenblattTransformation()
params = builder.andGetResult()
ap = params.activeParams()
dist = ap.getIndependentJointDistribution()
trans = ap.getJointTransformation()
for _ in range(1000):
prob1 = dist.rvs()[0]
unit = trans.probabilisticToUnit(prob1)
prob2 = trans.unitToProbabilistic(unit)
assert all(["%g" % x == "%g" % y for x, y in zip(prob1, prob2)])
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