AdaptiveMonteCarloUtils Namespace Reference

Functions
Real	computeSTD (const std::vector< Real > &data, const unsigned int &start_index)
	compute the standard deviation of a data vector by only considering values from a specific index.
Real	computeMean (const std::vector< Real > &data, const unsigned int &start_index)
	compute the mean of a data vector by only considering values from a specific index.
std::vector< std::vector< Real > >	sortInput (const std::vector< std::vector< Real > > &inputs, const std::vector< Real > &outputs, const unsigned int samplessub, const Real subset_prob)
	return input values corresponding to the largest po percentile output values.
std::vector< Real >	sortOutput (const std::vector< Real > &outputs, const unsigned int samplessub, const Real subset_prob)
	return the largest po percentile output values.
Real	computeMin (const std::vector< Real > &data)
	return the minimum value in a vector.
std::vector< Real >	computeVectorABS (const std::vector< Real > &data)
	return the absolute values in a vector.
unsigned int	weightedResample (const std::vector< Real > &weights, Real rnd)
	return a resampled vector from a population given a weight vector.

Function Documentation

computeMean()

Real AdaptiveMonteCarloUtils::computeMean	(	const std::vector< Real > &	data,
		const unsigned int &	start_index
	)

compute the mean of a data vector by only considering values from a specific index.

Parameters

the	data vector
the	starting index

Definition at line 38 of file AdaptiveMonteCarloUtils.C.

{
  if (data.size() < start_index)
    return 0.0;
  else
    return std::accumulate(data.begin() + start_index, data.end(), 0.0) /
           (data.size() - start_index);
}

Referenced by computeSTD(), and AdaptiveImportanceSampler::executeSetUp().

computeMin()

Real AdaptiveMonteCarloUtils::computeMin

(

const std::vector< Real > &

data

)

return the minimum value in a vector.

Parameters

the	data vector

Definition at line 80 of file AdaptiveMonteCarloUtils.C.

{
  return *std::min_element(data.begin(), data.end());
}

Referenced by AdaptiveMonteCarloDecision::execute().

computeSTD()

Real AdaptiveMonteCarloUtils::computeSTD	(	const std::vector< Real > &	data,
		const unsigned int &	start_index
	)

compute the standard deviation of a data vector by only considering values from a specific index.

Parameters

the	data vector
the	starting index

Definition at line 21 of file AdaptiveMonteCarloUtils.C.

{
  if (data.size() < start_index)
    return 0.0;
  else
  {
    const Real mean = computeMean(data, start_index);
    const Real sq_diff =
        std::accumulate(data.begin() + start_index,
                        data.end(),
                        0.0,
                        [&mean](Real x, Real y) { return x + (y - mean) * (y - mean); });
    return std::sqrt(sq_diff / (data.size() - start_index));
  }
}

Referenced by AdaptiveImportanceSampler::executeSetUp().

computeVectorABS()

std::vector< Real > AdaptiveMonteCarloUtils::computeVectorABS

(

const std::vector< Real > &

data

)

return the absolute values in a vector.

Parameters

the	data vector

Definition at line 86 of file AdaptiveMonteCarloUtils.C.

{
  std::vector<Real> data_abs(data.size());
  for (unsigned int i = 0; i < data.size(); ++i)
    data_abs[i] = std::abs(data[i]);
  return data_abs;
}

Referenced by AdaptiveMonteCarloDecision::execute(), and ParallelSubsetSimulation::executeSetUp().

sortInput()

std::vector< std::vector< Real > > AdaptiveMonteCarloUtils::sortInput	(	const std::vector< std::vector< Real > > &	inputs,
		const std::vector< Real > &	outputs,
		const unsigned int	samplessub,
		const Real	subset_prob
	)

return input values corresponding to the largest po percentile output values.

 FOR PARALLEL SUBSET SIMULATION SAMPLER ********

Parameters

the	input vector
the	output vector
the	number of samples per subset
the	subset index
the	subset intermediate failure probability

Definition at line 48 of file AdaptiveMonteCarloUtils.C.

{
  std::vector<size_t> ind;
  Moose::indirectSort(outputs.begin(), outputs.end(), ind);
 
  std::vector<std::vector<Real>> out(inputs.size(), std::vector<Real>(samplessub * subset_prob));
  const size_t offset = std::ceil(samplessub * (1 - subset_prob));
  for (const auto & j : index_range(out))
    for (const auto & i : index_range(out[j]))
      out[j][i] = inputs[j][ind[i + offset]];
 
  return out;
}

Referenced by AdaptiveMonteCarloDecision::execute(), and ParallelSubsetSimulation::executeSetUp().

sortOutput()

std::vector< Real > AdaptiveMonteCarloUtils::sortOutput	(	const std::vector< Real > &	outputs,
		const unsigned int	samplessub,
		const Real	subset_prob
	)

return the largest po percentile output values.

 FOR PARALLEL SUBSET SIMULATION SAMPLER ********

Parameters

the	output vector
the	number of samples per subset
the	subset index
the	subset intermediate failure probability

Definition at line 66 of file AdaptiveMonteCarloUtils.C.

{
  std::vector<size_t> ind;
  Moose::indirectSort(outputs.begin(), outputs.end(), ind);
 
  std::vector<Real> out(samplessub * subset_prob);
  const size_t offset = std::round(samplessub * (1 - subset_prob));
  for (const auto & i : index_range(out))
    out[i] = outputs[ind[i + offset]];
 
  return out;
}

Referenced by AdaptiveMonteCarloDecision::execute().

weightedResample()

unsigned int AdaptiveMonteCarloUtils::weightedResample	(	const std::vector< Real > &	weights,
		Real	rnd
	)

return a resampled vector from a population given a weight vector.

Parameters

the	given inputs (population)
the	weight vector
the	number of dimensions
a	random number between 0 and 1

Definition at line 95 of file AdaptiveMonteCarloUtils.C.

{
  unsigned int req_index = 0;
  for (unsigned int i = 0; i < weights.size(); ++i)
  {
    if (rnd < weights[i])
    {
      req_index = i;
      break;
    }
    rnd -= weights[i];
  }
  return req_index;
}

Referenced by IndependentMHDecision::nextSamples().