mean = np.average(vals)
stdv = np.std(vals)
checkAnswer('mean of 10x100 batch samples for local engine provided',mean,0.5,tol=1e-3)
checkAnswer('stdv of 10x100 batch samples for local engine provided',stdv,np.sqrt(1./12.),tol=1e-3)
### randomNormal
## first test box muller
mean,stdev = randomUtils.BoxMullerGenerator().testSampling(1e5,engine=None)
checkAnswer('Box Muller generator mean for engine not provided',mean,0.0,tol=5e-3)
checkAnswer('Box Muller generator stdv for engine not provided',stdev,1.0,tol=1e-3)
mean,stdev = randomUtils.BoxMullerGenerator().testSampling(1e5,engine=eng)
checkAnswer('Box Muller generator mean for local engine provided',mean,0.0,tol=5e-3)
checkAnswer('Box Muller generator stdv for local engine provided',stdev,1.0,tol=1e-3)
## test single value
vals = randomUtils.randomNormal(engine=None)
checkAnswer('random normal single value for engine not provided',vals,1.90167449657)
vals = randomUtils.randomNormal(engine=eng)
checkAnswer('random normal single value for local engine provided',vals,1.90167449657)
## test single point
right = [1.11130480322, 0.698326166056, 2.82788725018]
vals = randomUtils.randomNormal(3,engine=None)
checkArray('random normal single point for engine not provided',vals,right)
vals = randomUtils.randomNormal(3,engine=eng)
checkArray('random normal single point for local engine provided',vals,right)
## test many points
vals = randomUtils.randomNormal(3,5,engine=None)
checkAnswer('randomNormal number of samples for engine not provided',len(vals),5)
checkAnswer('randomNormal size of sample for engine not provided',len(vals[0]),3)
def _nextNeighbour(self, rlz, fraction=1):
"""
Perturbs the state to find the next random neighbour based on the cooling schedule
@ In, rlz, dict, current realization
@ In, fraction, float, optional, the current iteration divided by the iteration limit i.e., $\frac{iter}{Limit}$
@ Out, nextNeighbour, dict, the next random state
for exponential cooling:
.. math::
fraction = \\frac{iter}{Limit}
amp = 1-fraction
delta = \\frac{-amp}{2} + amp * r
where :math: `r \sim \mathcal{U}(0,1)`
for boltzmann cooling:
.. math::
amp = min(\\sqrt(T), \\frac{1}{3*alpha}
delta = r * alpha * amp
where :math: `r \\sim \\mathcal{N}(0,1)`
for cauchy cooling:
.. math::
amp = r
delta = alpha * T * tan(amp)
where :math: `r \\sim \\mathcal{U}(-\\pi,\\pi)`
for veryfast cooling:
.. math::
amp = r
delta = \\sign(amp-0.5)*T*((1.0+\\frac{1.0}{T})^{\\abs{2*amp-1}-1.0}
where :math: `r \\sim \\mathcal{U}(0,1)`
"""
nextNeighbour = {}
D = len(self.toBeSampled.keys())
alpha = 0.94
if self._coolingMethod in ['exponential', 'geometric']:
amp = ((fraction)**-1) / 20
r = randomUtils.random(dim=D, samples=1)
delta = (-amp / 2.) + amp * r
elif self._coolingMethod == 'boltzmann':
amp = min(np.sqrt(self.T), 1 / 3.0 / alpha)
delta = randomUtils.randomNormal(dim=D, size=1) * alpha * amp
elif self._coolingMethod == 'veryfast':
amp = randomUtils.random(dim=D, samples=1)
delta = np.sign(amp - 0.5) * self.T * (
(1 + 1.0 / self.T)**abs(2 * amp - 1) - 1.0)
elif self._coolingMethod == 'cauchy':
amp = (np.pi -
(-np.pi)) * randomUtils.random(dim=D, samples=1) - np.pi
delta = alpha * self.T * np.tan(amp)
for i, var in enumerate(self.toBeSampled.keys()):
nextNeighbour[var] = rlz[var] + delta[i]
self.info['amp_' + var] = amp
self.info['delta_' + var] = delta[i]
self.info['fraction'] = fraction
return nextNeighbour
## 2d batch sampling
randomUtils.randomSeed(42)
vals = randomUtils.random(10, 1000)
# check statistics
mean = np.average(vals)
stdv = np.std(vals)
checkAnswer('mean of 10x100 batch samples', mean, 0.5, tol=1e-3)
checkAnswer('stdv of 10x100 batch samples', stdv, np.sqrt(1. / 12.), tol=1e-3)
### randomNormal
## first test box muller
mean, stdev = randomUtils.BoxMullerGenerator().testSampling(1e5)
checkAnswer('Box Muller generator mean', mean, 0.0, tol=5e-3)
checkAnswer('Box Muller generator stdv', stdev, 1.0, tol=1e-3)
## test single value
vals = randomUtils.randomNormal()
checkAnswer('random normal single value', vals, 1.90167449657)
## test single point
right = [1.11130480322, 0.698326166056, 2.82788725018]
vals = randomUtils.randomNormal(3)
checkArray('random normal single point', vals, right)
## test many points
right = [0, 0, 0]
vals = randomUtils.randomNormal(3, 5)
checkAnswer('randomNormal number of samples', len(vals), 5)
checkAnswer('randomNormal size of sample', len(vals[0]), 3)
### randomIntegers(), sampling integers in a range
randomUtils.randomSeed(42)
right = [14, 18, 20, 12, 17]
for i in range(5):