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autodiff gaussian width parameter #4782

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autodiff gaussian width parameter #4782

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0d13a9e
autodiff gaussian width parameter
gf712 Oct 23, 2019
e7f2e62
include only the necessary header
gf712 Oct 23, 2019
0b0de2e
moved var instantiation outside the loop
gf712 Oct 24, 2019
e9bced7
autodiff gaussian width parameter
gf712 Oct 23, 2019
5815a3d
include only the necessary header
gf712 Oct 23, 2019
a35cf20
use eigen instead of stanmath
gf712 Nov 5, 2019
bdcebbf
generic kernel function for AD and kernel computation
gf712 Nov 6, 2019
bb85e1b
make expression template depend on class member
gf712 Nov 6, 2019
f94af02
fix race condition
gf712 Nov 6, 2019
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18 changes: 14 additions & 4 deletions src/shogun/kernel/GaussianKernel.cpp
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Expand Up @@ -6,12 +6,15 @@
* Tonmoy Saikia, Sergey Lisitsyn, Matt Aasted, Sanuj Sharma
*/

#include <Eigen/Core>
#include <unsupported/Eigen/AutoDiff>
#include <shogun/lib/common.h>
#include <shogun/kernel/GaussianKernel.h>
#include <shogun/features/DotFeatures.h>
#include <shogun/distance/EuclideanDistance.h>
#include <shogun/mathematics/Math.h>


using namespace shogun;

CGaussianKernel::CGaussianKernel() : CShiftInvariantKernel()
Expand Down Expand Up @@ -90,19 +93,26 @@ void CGaussianKernel::set_width(float64_t w)

SGMatrix<float64_t> CGaussianKernel::get_parameter_gradient(const TParameter* param, index_t index)
{
using std::exp;

require(lhs, "Left hand side features must be set!");
require(rhs, "Rightt hand side features must be set!");
require(rhs, "Right hand side features must be set!");

if (!strcmp(param->m_name, "log_width"))
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so I guess a next step could be to start thinking about getting rid of this explicit code, and rather automatically offer this derivative through registering something in the ctors ....

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I think for that we should maybe have a base class for classes that have parameters that we can take the derivative wrt. This class registers the gradient parameters in some vector and then we can get the index from there.
Basically when we do watch_param(...) this would add the variable in such a vector if it has the flag GRADIENT

{
SGMatrix<float64_t> derivative=SGMatrix<float64_t>(num_lhs, num_rhs);
using EigenScalar = Eigen::Matrix<float64_t, 1, 1>;
Eigen::AutoDiffScalar<EigenScalar> eigen_log_width = m_log_width;

for (int k=0; k<num_rhs; k++)
{
#pragma omp parallel for
for (int j=0; j<num_lhs; j++)
{
float64_t element=distance(j, k);
derivative(j, k) = std::exp(-element) * element * 2.0;
eigen_log_width.derivatives() = EigenScalar::Unit(1,0);
auto el = CShiftInvariantKernel::distance(j, k);
Eigen::AutoDiffScalar<EigenScalar> kernel = exp(-el / (exp(eigen_log_width * 2.0) * 2.0));
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It kinda sucks that the code for the kernel needs to be in here as well as in the kernel itself dont you think?

derivative(j, k) = kernel.derivatives()(0);
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this seems pretty automatable to me (at least say for scalar valued kernels with scalar parameters)

}
}
return derivative;
Expand All @@ -118,7 +128,7 @@ float64_t CGaussianKernel::compute(int32_t idx_a, int32_t idx_b)
{
float64_t result=distance(idx_a, idx_b);
return std::exp(-result);
}
}

void CGaussianKernel::load_serializable_post() noexcept(false)
{
Expand Down