Linear kernel function. More...
#include <FgLinearKernel.h>


Public Member Functions | |
| LinearKernel () | |
| Default constructor. More... | |
| LinearKernel (int numdims) | |
| Constructor. More... | |
| ~LinearKernel () | |
| Destructor. More... | |
| void | ComputeKernelMatrix (const af::array &inX1, const af::array &inX2, af::array &outMatrix) |
| Computes the kernel matrix of the kernel. More... | |
| void | ComputeDiagonal (const af::array &inX, af::array &outDiagonal) |
| Calculates only diagonal elements of K. More... | |
| void | LogLikGradientX (const af::array &inX, const af::array &indL_dK, af::array &outdL_dX) |
| Computes dL/dX for full fit GP. More... | |
| void | LogLikGradientX (const af::array &inXu, const af::array &indL_dKuu, const af::array &inX, const af::array &indL_dKuf, af::array &outdL_dXu, af::array &outdL_dX) |
| Computes dL / dX and dL / dXu for sparse approximation GP. More... | |
| virtual void | LogLikGradientX (const af::array &inX1, const af::array &inX2, const af::array &indL_dK, af::array &outdL_dX) override |
| Computes dL / dX for sparse approximation GP. More... | |
| void | LogLikGradientParam (const af::array &inX1, const af::array &inX2, const af::array &indL_dK, af::array &outdL_dParam) |
| Computes the gradient of the kernel parameters. More... | |
| virtual void | LogLikGradientParam (const af::array &inX1, const af::array &inX2, const af::array &indL_dK, af::array &outdL_dParam, const af::array *dlogZ_dv) override |
| Computes the gradient of LL w.r.t. the kernel parameters. More... | |
| void | GradX (const af::array &inX1, const af::array &inX2, int q, af::array &outdK_dX) |
| Computes dK/dX. More... | |
| void | DiagGradX (const af::array &inX, af::array &outDiagdK_dX) |
| Derivative of diagonal elemts of K w.r.t X. More... | |
| void | DiagGradParam (const af::array &inX, const af::array &inCovDiag, af::array &outDiagdK_dParam) |
| Derivative of diagonal elemts of K w.r.t kernel parameters. More... | |
| virtual void | InitParameters (Scalar inMedian) override |
Initializes the parameters based on the median of the distances of /f$\mathbf{X}/f$. More... | |
| virtual void | LogLikGradientCompundKfu (const af::array &indL_dKfu, const af::array &inX, const af::array &inXu, af::array *outdL_dParam, af::array *outdL_dXu, const af::array *dlogZ_dv=nullptr, af::array *outdL_dX=nullptr) override |
| virtual void | LogGradientCompoundKuu (const af::array &inXu, const af::array &inCovDiag, af::array *outdL_dParam, af::array *outdL_dXu) override |
| void | SetParameters (const af::array ¶m) |
| Sets the parameters. More... | |
| af::array | GetParameters () |
| Gets the parameters. More... | |
| virtual void | SetLogParameters (const af::array ¶m) override |
| Sets log parameters. More... | |
| virtual af::array | GetLogParameters () override |
| Gets log parameters. More... | |
| void | ComputePsiStatistics (const af::array &inXu, const af::array &inMu, const af::array &inS, af::array &outPsi0, af::array &outPsi1, af::array &outPsi2) override |
| PSI statistics. More... | |
| void | PsiDerivatives (const af::array &indL_dPsi0, const af::array &inPsi1, const af::array &indL_dPsi1, const af::array &inPsi2, const af::array &indL_dPsi2, const af::array &inXu, const af::array &inMu, const af::array &inS, af::array &outdL_dParam, af::array &outdL_dXu, af::array &outdL_dMu, af::array &outdL_dS, const af::array *dlogZ_dv=nullptr) override |
| Parameter and variable derivatives w.r.t. all Psi statistics. More... | |
Public Member Functions inherited from NeuralEngine::MachineLearning::IKernel< Scalar > | |
| IKernel (KernelType type, int numParameters) | |
| Constructor. More... | |
| virtual | ~IKernel () |
| Destructor. More... | |
| virtual int | GetNumParameter () |
| Gets number of parametes of the kernel. More... | |
| virtual KernelType | GetKernelType () |
| Gets kernel type. More... | |
| virtual void | ComputeKernelMatrix (const af::array &inX1, const af::array &inX2, af::array &outMatrix)=0 |
| Computes the kernel matrix of the kernel. More... | |
| virtual void | ComputeDiagonal (const af::array &inX, af::array &outDiagonal)=0 |
| Calculates only diagonal elements of K. More... | |
| virtual void | LogLikGradientX (const af::array &inX, const af::array &indL_dK, af::array &outdL_dX)=0 |
| Computes dL/dX for full fit GP. More... | |
| virtual void | LogLikGradientX (const af::array &inXu, const af::array &indL_dKuu, const af::array &inX, const af::array &indL_dKuf, af::array &outdL_dXu, af::array &outdL_dX)=0 |
| Computes dL / dX and dL / dXu for sparse approximation GP. More... | |
| virtual void | LogLikGradientX (const af::array &inX1, const af::array &inX2, const af::array &indL_dK, af::array &outdL_dX) |
| Computes dL / dX for sparse approximation GP. More... | |
| virtual void | LogLikGradientParam (const af::array &inX1, const af::array &inX2, const af::array &indL_dK, af::array &outdL_dParam)=0 |
| Computes the gradient of LL w.r.t. the kernel parameters. More... | |
| virtual void | LogLikGradientParam (const af::array &inX1, const af::array &inX2, const af::array &indL_dK, af::array &outdL_dParam, const af::array *dlogZ_dv) |
| Computes the gradient of LL w.r.t. the kernel parameters. More... | |
| virtual void | LogLikGradientCompundKfu (const af::array &indL_dKfu, const af::array &inX, const af::array &inXu, af::array *outdL_dParam, af::array *outdL_dXu, const af::array *dlogZ_dv=nullptr, af::array *outdL_dX=nullptr) |
| virtual void | LogGradientCompoundKuu (const af::array &inXu, const af::array &inCovDiag, af::array *outdL_dParam, af::array *outdL_dXu) |
| virtual void | GradX (const af::array &inX1, const af::array &inX2, int q, af::array &outdK_dX)=0 |
| Computes dK/dX. More... | |
| virtual void | DiagGradX (const af::array &inX, af::array &outDiagdK_dX)=0 |
| Derivative of diagonal elemts of K w.r.t X. More... | |
| virtual void | DiagGradParam (const af::array &inX, const af::array &inCovDiag, af::array &outDiagdK_dParam)=0 |
| Derivative of diagonal elemts of K w.r.t kernel parameters. More... | |
| virtual void | SetParameters (const af::array ¶m)=0 |
| Sets the parameters. More... | |
| virtual void | SetLogParameters (const af::array ¶m) |
| Sets log parameters. More... | |
| virtual af::array | GetParameters ()=0 |
| Gets the parameters. More... | |
| virtual af::array | GetLogParameters () |
| Gets log parameters. More... | |
| virtual void | InitParameters (Scalar inMedian) |
Initializes the parameters based on the median of the distances of /f$\mathbf{X}/f$. More... | |
| virtual void | ComputePsiStatistics (const af::array &inXu, const af::array &inMu, const af::array &inS, af::array &outPsi0, af::array &outPsi1, af::array &outPsi2) |
| PSI statistics. More... | |
| virtual void | PsiDerivatives (const af::array &indL_dPsi0, const af::array &inPsi1, const af::array &indL_dPsi1, const af::array &inPsi2, const af::array &indL_dPsi2, const af::array &inXu, const af::array &inMu, const af::array &inS, af::array &outdL_dParam, af::array &outdL_dXu, af::array &outdL_dMu, af::array &outdL_dS, const af::array *dlogZ_dv=nullptr) |
| Psi derivatives. More... | |
Protected Member Functions | |
| void | Psi2Derivative (const af::array &indL_dPsi2, const af::array &inXu, const af::array &inMu, const af::array &inS, af::array &outdL_dParam, af::array &outdL_dXu, af::array &outdL_dMu, af::array &outdL_dS) |
| Parameter and variable derivatives w.r.t. Psi2. More... | |
Protected Member Functions inherited from NeuralEngine::MachineLearning::IKernel< Scalar > | |
| IKernel () | |
| Default constructor. More... | |
Private Member Functions | |
| template<class Archive > | |
| void | serialize (Archive &ar, unsigned int version) |
Private Attributes | |
| af::array | dVariance |
Friends | |
| class | boost::serialization::access |
Additional Inherited Members | |
Protected Attributes inherited from NeuralEngine::MachineLearning::IKernel< Scalar > | |
| KernelType | eType |
| int | iNumParam |
| af::dtype | m_dType |
| Scalar | sDegree |
| af::array | afGHx |
| af::array | afGHw |
| af::array | afXs |
| bool | bCacheK |
Linear kernel function.
Kernel function constructed as follows:
\[k(\mathbf{x}, \mathbf{x}') = \gamma \mathbf{x}^T \mathbf{x}'.\]
Note: GP's constructed with a linear kernel are equivalent with the dual version of probabilistic PCA.
Admin, 5/24/2017.
Definition at line 34 of file FgLinearKernel.h.
| NeuralEngine::MachineLearning::LinearKernel< Scalar >::LinearKernel | ( | ) |
Default constructor.
Admin, 5/24/2017.
| NeuralEngine::MachineLearning::LinearKernel< Scalar >::LinearKernel | ( | int | numdims | ) |
Constructor.
Hmetal T, 16/10/2020.
| numdims | (Optional) The numdims. |
| NeuralEngine::MachineLearning::LinearKernel< Scalar >::~LinearKernel | ( | ) |
Destructor.
Admin, 5/24/2017.
|
virtual |
Computes the kernel matrix of the kernel.
Admin, 5/23/2017.
| inX1 | [in,out] First n times q matrix of latent points. |
| inX2 | [in,out] Second m times q matrix of latent points (X'). |
| outMatrix | [in,out] Resulting kernel matrix.< / param> |
Implements NeuralEngine::MachineLearning::IKernel< Scalar >.
|
virtual |
Calculates only diagonal elements of K.
Admin, 5/24/2017.
| inX | [in,out] Nxq matrix X. |
| outDiagonal | [in,out] The out diagonal. |
Implements NeuralEngine::MachineLearning::IKernel< Scalar >.
|
virtual |
Computes dL/dX for full fit GP.
Admin, 5/23/2017.
| inX | [in,out] Nxq Matrix of latent points. |
| indL_dK | [in,out] Derivative of the loglikelihood w.r.t kernel matrix K. |
| outdL_dX | [in,out] Derivative of the loglikelihood w.r.t latent points. |
Implements NeuralEngine::MachineLearning::IKernel< Scalar >.
|
virtual |
Computes dL / dX and dL / dXu for sparse approximation GP.
Admin, 5/23/2017.
| inXu | [in,out] kxq Matrix of latent subset points. |
| indL_dKuu | [in,out] Derivative of the loglikelihood w.r.t subset kernel matrix Kuu. |
| inX | [in,out] Nxq Matrix of latent points. |
| indL_dKuf | [in,out] Derivative of the loglikelihood w.r.t the kernel matrix K. |
| outdL_dXu | [in,out] Derivative of the loglikelihood w.r.t latent subset points Xu. |
| outdL_dX | [in,out] Derivative of the loglikelihood w.r.t latent points X. |
Implements NeuralEngine::MachineLearning::IKernel< Scalar >.
|
overridevirtual |
Computes dL / dX for sparse approximation GP.
Hmetal T, 25/11/2020.
| inX1 | The first latent points. |
| inX2 | The second latent points. |
| indL_dK | The derivative dL / dK. |
| outdL_dX | [in,out] The derivative dL / dX. |
Reimplemented from NeuralEngine::MachineLearning::IKernel< Scalar >.
|
virtual |
Computes the gradient of the kernel parameters.
Admin, 5/23/2017.
| indL_dK | [in,out] Derivative of the loglikelihood w.r.t the kernel matrix K. |
| outdL_dParam | [in,out] Gradient of kernel parameters. |
Implements NeuralEngine::MachineLearning::IKernel< Scalar >.
|
overridevirtual |
Computes the gradient of LL w.r.t. the kernel parameters.
Admin, 5/23/2017.
| indL_dK | [in,out] Derivative of the loglikelihood w.r.t the kernel matrix K. |
| outdL_dParam | [in,out] Gradient of kernel parameters. |
Reimplemented from NeuralEngine::MachineLearning::IKernel< Scalar >.
|
virtual |
Computes dK/dX.
Admin, 5/23/2017.
| inX1 | [in,out] First n times q matrix of latent points. |
| inX2 | [in,out] Second n times q matrix of latent points (X'). |
| q | The latent dimension to process. |
| outdK_dX | [in,out] dK/dX. |
Implements NeuralEngine::MachineLearning::IKernel< Scalar >.
|
virtual |
Derivative of diagonal elemts of K w.r.t X.
Admin, 5/23/2017.
| inX | [in,out] The in x coordinate. |
| outDiagdK_dX | [in,out] Derivative of diagonal elemts of K w.r.t X. |
Implements NeuralEngine::MachineLearning::IKernel< Scalar >.
|
virtual |
Derivative of diagonal elemts of K w.r.t kernel parameters.
Admin, 5/23/2017.
| inX | [in,out] Nxq matrix of Latent points X. |
| inCovDiag | [in,out] Diagonal of kernel matrix K. |
| outDiagdK_dParam | [in,out] Derivative of diagonal elemts of K w.r.t kernel parameters. |
Implements NeuralEngine::MachineLearning::IKernel< Scalar >.
|
overridevirtual |
Initializes the parameters based on the median of the distances of /f$\mathbf{X}/f$.
Hmetal T, 06/11/2020.
| inMedian | The in median. |
Reimplemented from NeuralEngine::MachineLearning::IKernel< Scalar >.
|
overridevirtual |
Reimplemented from NeuralEngine::MachineLearning::IKernel< Scalar >.
|
overridevirtual |
Reimplemented from NeuralEngine::MachineLearning::IKernel< Scalar >.
|
virtual |
Sets the parameters.
, 26.06.2018.
| param | The parameter. |
Implements NeuralEngine::MachineLearning::IKernel< Scalar >.
|
virtual |
Gets the parameters.
, 26.06.2018.
Implements NeuralEngine::MachineLearning::IKernel< Scalar >.
|
overridevirtual |
Sets log parameters.
Hmetal T, 15/07/2019.
| param | The parameter. |
Reimplemented from NeuralEngine::MachineLearning::IKernel< Scalar >.
|
overridevirtual |
Gets log parameters.
Hmetal T, 15/07/2019.
Reimplemented from NeuralEngine::MachineLearning::IKernel< Scalar >.
|
overridevirtual |
PSI statistics.
PSI statistics computation.
Hmetal T, 15/07/2019.
| inXu | Inducing points. |
| inMu | Posterior mean. |
| inS | Posterior covariance. |
| outPsi0 | [in,out] Psi0. |
| outPsi1 | [in,out] Psi1. |
| outPsi2 | [in,out] Psi2. |
Reimplemented from NeuralEngine::MachineLearning::IKernel< Scalar >.
|
overridevirtual |
Parameter and variable derivatives w.r.t. all Psi statistics.
Hmetal T, 15/07/2019.
| inPsi1 | Psi1. |
| indL_dPsi1 | Psi1 derivative w.r.t. objective. |
| inPsi1 | Psi2. |
| indL_dPsi1 | Psi2 derivative w.r.t. objective. |
| inXu | Inducing points. |
| inMu | Posterior mean. |
| inS | Posterior covariance. |
| outdL_dParam | [in,out] Kernel parameter derivatives w.r.t. Psi statistics. |
| outdL_dXu | [in,out] Inducing inputs derivatives w.r.t. Psi statistics. |
| outdL_dMu | [in,out] Posterior mean derivatives w.r.t. Psi statistics. |
| outdL_dS | [in,out] Posterior covariance derivatives w.r.t. Psi statistics. |
Reimplemented from NeuralEngine::MachineLearning::IKernel< Scalar >.
|
protected |
Parameter and variable derivatives w.r.t. Psi2.
Hmetal T, 15/07/2019.
| inPsi1 | Psi2. |
| indL_dPsi1 | Psi2 derivative w.r.t. objective. |
| inXu | Inducing points. |
| inMu | Posterior mean. |
| inS | Posterior covariance. |
| outdL_dParam | [in,out] Kernel parameter derivatives w.r.t. Psi2. |
| outdL_dXu | [in,out] Inducing inputs derivatives w.r.t. Psi2. |
| outdL_dMu | [in,out] Posterior mean derivatives w.r.t. Psi2. |
| outdL_dS | [in,out] Posterior covariance derivatives w.r.t. Psi2. |
|
inlineprivate |
Definition at line 300 of file FgLinearKernel.h.
|
friend |
Definition at line 297 of file FgLinearKernel.h.
|
private |
Definition at line 308 of file FgLinearKernel.h.