Class for standardizing data (centering and scaling) More...

#include <Standardizer.h>

Public Member Functions
	Standardizer ()=default

void	set (const Real &n)
	Methods for setting mean and standard deviation directly Sets mean=0, std=1 for n variables.

void	set (const Real &mean, const Real &stdev)
	Sets mean and std for a single variable.

void	set (const Real &mean, const Real &stdev, const Real &n)
	Sets mean and std for a n variables variable.

void	set (const std::vector< Real > &mean, const std::vector< Real > &stdev)
	Sets mean and std directly using provided vectors.

const torch::Tensor &	getMean () const
	Get the mean vector.

const torch::Tensor &	getStdDev () const
	Get the standard deviation vector.

void	computeSet (const torch::Tensor &input)
	Methods for computing and setting mean and standard deviation.

void	dataStore (std::ostream &stream, void *context) const
	Helper for dataStore.

void	getStandardized (torch::Tensor &input) const
	Returns the standardized (centered and scaled) of the provided input.

void	getDestandardized (torch::Tensor &input) const
	De-standardizes (de-centered and de-scaled) the assumed standardized input.

void	getDescaled (torch::Tensor &input) const
	De-scales the assumed scaled input.

void	getScaled (torch::Tensor &input) const
	Scales the assumed de-scaled input.

Protected Attributes
torch::Tensor	_mean

torch::Tensor	_stdev

Detailed Description

Class for standardizing data (centering and scaling)

Definition at line 24 of file Standardizer.h.

Constructor & Destructor Documentation

◆ Standardizer()

StochasticTools::Standardizer::Standardizer ( )

default

Member Function Documentation

◆ computeSet()

void StochasticTools::Standardizer::computeSet ( const torch::Tensor & input )

Methods for computing and setting mean and standard deviation.

Definition at line 80 of file Standardizer.C.

{
  if (input.dim() != 2)
    mooseError("Standardizer input must be a rank-2 tensor.");
  // Compute mean and standard deviation
  _mean = torch::mean(input, 0, false);
  _stdev = torch::std(input, 0, 0, false);
}

Referenced by StochasticTools::GaussianProcess::standardizeData(), StochasticTools::GaussianProcess::standardizeParameters(), TEST(), TEST(), TEST(), TEST(), TEST(), and TEST().

◆ dataStore()

void StochasticTools::Standardizer::dataStore	(	std::ostream &	stream,
		void *	context
	)		const

Helper for dataStore.

Definition at line 119 of file Standardizer.C.

{
  const auto mean = LibtorchUtils::toCPUContiguous(_mean);
  const auto stdev = LibtorchUtils::toCPUContiguous(_stdev);
  auto mean_accessor = mean.accessor<Real, 1>();
  auto stdev_accessor = stdev.accessor<Real, 1>();
  unsigned int n = mean.size(0);
  ::dataStore(stream, n, context);
  for (unsigned int ii = 0; ii < n; ++ii)
    ::dataStore(stream, mean_accessor[ii], context);
  for (unsigned int ii = 0; ii < n; ++ii)
    ::dataStore(stream, stdev_accessor[ii], context);
}

Referenced by dataStore(), and dataStore().

◆ getDescaled()

void StochasticTools::Standardizer::getDescaled ( torch::Tensor & input ) const

De-scales the assumed scaled input.

Definition at line 104 of file Standardizer.C.

{
  checkInputCompatibility(input, _stdev);
  input.mul_(asFeatureVector(_stdev, input));
}

Referenced by GaussianProcessSurrogate::evaluate().

◆ getDestandardized()

void StochasticTools::Standardizer::getDestandardized ( torch::Tensor & input ) const

De-standardizes (de-centered and de-scaled) the assumed standardized input.

Definition at line 97 of file Standardizer.C.

{
  checkInputCompatibility(input, _mean);
  input.mul_(asFeatureVector(_stdev, input)).add_(asFeatureVector(_mean, input));
}

Referenced by GaussianProcessSurrogate::evaluate().

◆ getMean()

const torch::Tensor & StochasticTools::Standardizer::getMean ( ) const

inline

Get the mean vector.

Definition at line 40 of file Standardizer.h.

40{ return _mean; }

Referenced by LibtorchANNSurrogate::evaluate(), and TEST().

◆ getScaled()

void StochasticTools::Standardizer::getScaled ( torch::Tensor & input ) const

Scales the assumed de-scaled input.

Definition at line 111 of file Standardizer.C.

{
  checkInputCompatibility(input, _stdev);
  input.div_(asFeatureVector(_stdev, input));
}

◆ getStandardized()

void StochasticTools::Standardizer::getStandardized ( torch::Tensor & input ) const

Returns the standardized (centered and scaled) of the provided input.

Definition at line 90 of file Standardizer.C.

{
  checkInputCompatibility(input, _mean);
  input.sub_(asFeatureVector(_mean, input)).div_(asFeatureVector(_stdev, input));
}

Referenced by GaussianProcessSurrogate::evaluate(), StochasticTools::GaussianProcess::standardizeData(), and StochasticTools::GaussianProcess::standardizeParameters().

◆ getStdDev()

const torch::Tensor & StochasticTools::Standardizer::getStdDev ( ) const

inline

Get the standard deviation vector.

Definition at line 42 of file Standardizer.h.

42{ return _stdev; }

Referenced by LibtorchANNSurrogate::evaluate(), and TEST().

◆ set() [1/4]

void StochasticTools::Standardizer::set	(	const Real &	mean,
		const Real &	stdev
	)

Sets mean and std for a single variable.

Definition at line 56 of file Standardizer.C.

{
  _mean = torch::full({1}, mean, at::kDouble);
  _stdev = torch::full({1}, stdev, at::kDouble);
}

◆ set() [2/4]

void StochasticTools::Standardizer::set	(	const Real &	mean,
		const Real &	stdev,
		const Real &	n
	)

Sets mean and std for a n variables variable.

Definition at line 63 of file Standardizer.C.

{
  auto options = torch::TensorOptions().dtype(at::kDouble);
  _mean = torch::full({long(n)}, mean, options);
  _stdev = torch::full({long(n)}, stdev, options);
}

◆ set() [3/4]

void StochasticTools::Standardizer::set ( const Real & n )

Methods for setting mean and standard deviation directly Sets mean=0, std=1 for n variables.

Definition at line 49 of file Standardizer.C.

{
  _mean = torch::zeros({long(n)}, at::kDouble);
  _stdev = torch::ones({long(n)}, at::kDouble);
}

Referenced by dataLoad(), LibtorchANNTrainer::postTrain(), GaussianProcessTrainer::postTrain(), and ActiveLearningGaussianProcess::reTrain().

◆ set() [4/4]

void StochasticTools::Standardizer::set	(	const std::vector< Real > &	mean,
		const std::vector< Real > &	stdev
	)

Sets mean and std directly using provided vectors.

Definition at line 71 of file Standardizer.C.

{
  mooseAssert(mean.size() == stdev.size(),
              "Provided mean and standard deviation vectors are of differing size.");
  _mean = LibtorchUtils::vectorToTensorCopy(mean, {long(mean.size())});
  _stdev = LibtorchUtils::vectorToTensorCopy(stdev, {long(stdev.size())});
}

Member Data Documentation

◆ _mean

torch::Tensor StochasticTools::Standardizer::_mean

protected

Definition at line 63 of file Standardizer.h.

Referenced by computeSet(), dataStore(), getDestandardized(), getMean(), getStandardized(), set(), set(), set(), and set().

◆ _stdev

torch::Tensor StochasticTools::Standardizer::_stdev