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enable clang-format for gaussian_proces source files
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@agalasso
agalasso committed Dec 21, 2024
1 parent a8c54ba commit 4b57797
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7 changes: 6 additions & 1 deletion .github/workflows/clang-format-check.yml
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Expand Up @@ -4,10 +4,15 @@ jobs:
formatting-check:
name: Formatting Check
runs-on: ubuntu-latest
strategy:
matrix:
path:
- 'src'
- 'contributions/MPI_IS_gaussian_process'
steps:
- uses: actions/checkout@v4
- name: Run clang-format style check for C/C++ source files
uses: jidicula/[email protected]
with:
clang-format-version: '18'
check-path: 'src'
check-path: ${{ matrix.path }}
319 changes: 163 additions & 156 deletions contributions/MPI_IS_gaussian_process/src/covariance_functions.cpp
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Expand Up @@ -47,160 +47,167 @@
namespace covariance_functions
{

/* PeriodicSquareExponential */
PeriodicSquareExponential::PeriodicSquareExponential() :
hyperParameters(Eigen::VectorXd::Zero(4)), extraParameters(Eigen::VectorXd::Ones(1)*std::numeric_limits<double>::max()) { }

PeriodicSquareExponential::PeriodicSquareExponential(const Eigen::VectorXd& hyperParameters_) :
hyperParameters(hyperParameters_), extraParameters(Eigen::VectorXd::Ones(1)*std::numeric_limits<double>::max()) { }
/* PeriodicSquareExponential */
PeriodicSquareExponential::PeriodicSquareExponential()
: hyperParameters(Eigen::VectorXd::Zero(4)), extraParameters(Eigen::VectorXd::Ones(1) * std::numeric_limits<double>::max())
{
}

PeriodicSquareExponential::PeriodicSquareExponential(const Eigen::VectorXd& hyperParameters_)
: hyperParameters(hyperParameters_), extraParameters(Eigen::VectorXd::Ones(1) * std::numeric_limits<double>::max())
{
}

Eigen::MatrixXd PeriodicSquareExponential::evaluate(const Eigen::VectorXd& x, const Eigen::VectorXd& y)
{

double lsSE0 = exp(hyperParameters(0));
double svSE0 = exp(2 * hyperParameters(1));
double lsP = exp(hyperParameters(2));
double svP = exp(2 * hyperParameters(3));

double plP = exp(extraParameters(0));

// Work with arrays internally, convert to matrix for return value.
// This is because all the operations act elementwise, and Eigen::Arrays
// do so, too.

// Compute Distances
Eigen::ArrayXXd squareDistanceXY = math_tools::squareDistance(x.transpose(), y.transpose());

// fast version
return svSE0 * ((-0.5 / std::pow(lsSE0, 2)) * squareDistanceXY).exp() +
svP * (-2 * (((M_PI / plP) * squareDistanceXY.sqrt()).sin() / lsP).square()).exp();

/* // verbose version
// Square Exponential Kernel
Eigen::ArrayXXd K0 = squareDistanceXY / std::pow(lsSE0, 2);
K0 = svSE0 * (-0.5 * K0).exp();
// Periodic Kernel
Eigen::ArrayXXd K1 = (M_PI * squareDistanceXY.sqrt() / plP);
K1 = K1.sin() / lsP;
K1 = K1.square();
K1 = svP * (-2 * K1).exp();
// Combined Kernel
return K0 + K1;
*/
}

void PeriodicSquareExponential::setParameters(const Eigen::VectorXd& params)
{
this->hyperParameters = params;
}

void PeriodicSquareExponential::setExtraParameters(const Eigen::VectorXd& params)
{
this->extraParameters = params;
}

const Eigen::VectorXd& PeriodicSquareExponential::getParameters() const
{
return this->hyperParameters;
}

const Eigen::VectorXd& PeriodicSquareExponential::getExtraParameters() const
{
return this->extraParameters;
}

int PeriodicSquareExponential::getParameterCount() const
{
return 4;
}

int PeriodicSquareExponential::getExtraParameterCount() const
{
return 1;
}

/* PeriodicSquareExponential2 */
PeriodicSquareExponential2::PeriodicSquareExponential2()
: hyperParameters(Eigen::VectorXd::Zero(6)), extraParameters(Eigen::VectorXd::Ones(1) * std::numeric_limits<double>::max())
{
}

PeriodicSquareExponential2::PeriodicSquareExponential2(const Eigen::VectorXd& hyperParameters_)
: hyperParameters(hyperParameters_), extraParameters(Eigen::VectorXd::Ones(1) * std::numeric_limits<double>::max())
{
}

Eigen::MatrixXd PeriodicSquareExponential2::evaluate(const Eigen::VectorXd& x, const Eigen::VectorXd& y)
{

double lsSE0 = exp(hyperParameters(0));
double svSE0 = exp(2 * hyperParameters(1));
double lsP = exp(hyperParameters(2));
double svP = exp(2 * hyperParameters(3));
double lsSE1 = exp(hyperParameters(4));
double svSE1 = exp(2 * hyperParameters(5));

double plP = exp(extraParameters(0));

// Work with arrays internally, convert to matrix for return value.
// This is because all the operations act elementwise, and Eigen::Arrays
// do so, too.

// Compute Distances
Eigen::ArrayXXd squareDistanceXY = math_tools::squareDistance(x.transpose(), y.transpose());

// fast version
return svSE0 * ((-0.5 / std::pow(lsSE0, 2)) * squareDistanceXY).exp() +
svP * (-2 * (((M_PI / plP) * squareDistanceXY.sqrt()).sin() / lsP).square()).exp() +
svSE1 * ((-0.5 / std::pow(lsSE1, 2)) * squareDistanceXY).exp();

/* // verbose version
// Square Exponential Kernel
Eigen::ArrayXXd K0 = squareDistanceXY / pow(lsSE0, 2);
K0 = svSE0 * (-0.5 * K0).exp();
// Periodic Kernel
Eigen::ArrayXXd K1 = (M_PI * squareDistanceXY.sqrt() / plP);
K1 = K1.sin() / lsP;
K1 = K1.square();
K1 = svP * (-2 * K1).exp();
// Square Exponential Kernel
Eigen::ArrayXXd K2 = squareDistanceXY / pow(lsSE1, 2);
K2 = svSE1 * (-0.5 * K2).exp();
// Combined Kernel
return K0 + K1 + K2;
*/
}

void PeriodicSquareExponential2::setParameters(const Eigen::VectorXd& params)
{
this->hyperParameters = params;
}

void PeriodicSquareExponential2::setExtraParameters(const Eigen::VectorXd& params)
{
this->extraParameters = params;
}

const Eigen::VectorXd& PeriodicSquareExponential2::getParameters() const
{
return this->hyperParameters;
}

const Eigen::VectorXd& PeriodicSquareExponential2::getExtraParameters() const
{
return this->extraParameters;
}

int PeriodicSquareExponential2::getParameterCount() const
{
return 6;
}

int PeriodicSquareExponential2::getExtraParameterCount() const
{
return 1;
}

Eigen::MatrixXd PeriodicSquareExponential::evaluate(const Eigen::VectorXd& x, const Eigen::VectorXd& y)
{

double lsSE0 = exp(hyperParameters(0));
double svSE0 = exp(2 * hyperParameters(1));
double lsP = exp(hyperParameters(2));
double svP = exp(2 * hyperParameters(3));

double plP = exp(extraParameters(0));

// Work with arrays internally, convert to matrix for return value.
// This is because all the operations act elementwise, and Eigen::Arrays
// do so, too.

// Compute Distances
Eigen::ArrayXXd squareDistanceXY = math_tools::squareDistance( x.transpose(), y.transpose());

// fast version
return svSE0 * ((-0.5 / std::pow(lsSE0, 2)) * squareDistanceXY).exp()
+ svP * (-2 * (((M_PI / plP) * squareDistanceXY.sqrt()).sin() / lsP).square()).exp();

/* // verbose version
// Square Exponential Kernel
Eigen::ArrayXXd K0 = squareDistanceXY / std::pow(lsSE0, 2);
K0 = svSE0 * (-0.5 * K0).exp();
// Periodic Kernel
Eigen::ArrayXXd K1 = (M_PI * squareDistanceXY.sqrt() / plP);
K1 = K1.sin() / lsP;
K1 = K1.square();
K1 = svP * (-2 * K1).exp();
// Combined Kernel
return K0 + K1;
*/
}

void PeriodicSquareExponential::setParameters(const Eigen::VectorXd& params)
{
this->hyperParameters = params;
}

void PeriodicSquareExponential::setExtraParameters(const Eigen::VectorXd& params)
{
this->extraParameters = params;
}

const Eigen::VectorXd& PeriodicSquareExponential::getParameters() const
{
return this->hyperParameters;
}

const Eigen::VectorXd& PeriodicSquareExponential::getExtraParameters() const
{
return this->extraParameters;
}

int PeriodicSquareExponential::getParameterCount() const
{
return 4;
}

int PeriodicSquareExponential::getExtraParameterCount() const
{
return 1;
}


/* PeriodicSquareExponential2 */
PeriodicSquareExponential2::PeriodicSquareExponential2() :
hyperParameters(Eigen::VectorXd::Zero(6)), extraParameters(Eigen::VectorXd::Ones(1)*std::numeric_limits<double>::max()) { }

PeriodicSquareExponential2::PeriodicSquareExponential2(const Eigen::VectorXd& hyperParameters_) :
hyperParameters(hyperParameters_), extraParameters(Eigen::VectorXd::Ones(1)*std::numeric_limits<double>::max()) { }

Eigen::MatrixXd PeriodicSquareExponential2::evaluate(const Eigen::VectorXd& x, const Eigen::VectorXd& y)
{

double lsSE0 = exp(hyperParameters(0));
double svSE0 = exp(2 * hyperParameters(1));
double lsP = exp(hyperParameters(2));
double svP = exp(2 * hyperParameters(3));
double lsSE1 = exp(hyperParameters(4));
double svSE1 = exp(2 * hyperParameters(5));

double plP = exp(extraParameters(0));

// Work with arrays internally, convert to matrix for return value.
// This is because all the operations act elementwise, and Eigen::Arrays
// do so, too.

// Compute Distances
Eigen::ArrayXXd squareDistanceXY = math_tools::squareDistance( x.transpose(), y.transpose());

// fast version
return svSE0 * ((-0.5 / std::pow(lsSE0, 2)) * squareDistanceXY).exp()
+ svP * (-2 * (((M_PI / plP) * squareDistanceXY.sqrt()).sin() / lsP).square()).exp()
+ svSE1 * ((-0.5 / std::pow(lsSE1, 2)) * squareDistanceXY).exp();

/* // verbose version
// Square Exponential Kernel
Eigen::ArrayXXd K0 = squareDistanceXY / pow(lsSE0, 2);
K0 = svSE0 * (-0.5 * K0).exp();
// Periodic Kernel
Eigen::ArrayXXd K1 = (M_PI * squareDistanceXY.sqrt() / plP);
K1 = K1.sin() / lsP;
K1 = K1.square();
K1 = svP * (-2 * K1).exp();
// Square Exponential Kernel
Eigen::ArrayXXd K2 = squareDistanceXY / pow(lsSE1, 2);
K2 = svSE1 * (-0.5 * K2).exp();
// Combined Kernel
return K0 + K1 + K2;
*/
}

void PeriodicSquareExponential2::setParameters(const Eigen::VectorXd& params)
{
this->hyperParameters = params;
}

void PeriodicSquareExponential2::setExtraParameters(const Eigen::VectorXd& params)
{
this->extraParameters = params;
}

const Eigen::VectorXd& PeriodicSquareExponential2::getParameters() const
{
return this->hyperParameters;
}

const Eigen::VectorXd& PeriodicSquareExponential2::getExtraParameters() const
{
return this->extraParameters;
}

int PeriodicSquareExponential2::getParameterCount() const
{
return 6;
}

int PeriodicSquareExponential2::getExtraParameterCount() const
{
return 1;
}

} // namespace covariance_functions
} // namespace covariance_functions
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