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Data Structures | |
struct | LWPR_Workspace |
LWPR_Workspace is a structure that contains enough "working memory" for the LWPR computational routines. More... | |
struct | LWPR_ThreadData |
Data structure that is passed to each thread for updates or predictions. More... | |
Functions | |
void | lwpr_aux_dist_derivatives (int nIn, int nInS, double *dwdM, double *dJ2dM, double *ddwdMdM, double *ddJ2dMdM, double w, double dwdq, double ddwdqdq, const double *RF_D, const double *RF_M, const double *dx, int diag_only, double penalty, int meta) |
Computes the derivates of the activation w and a penalty term with respect to M, Cholesky factors of the distance metric. | |
double | lwpr_aux_update_distance_metric (LWPR_ReceptiveField *RF, double w, double dwdq, double ddwdqdq, double e_cv, double e, const double *xn, LWPR_Workspace *ws) |
Performs an update of a receptive field's distance metric. | |
double | lwpr_aux_update_means (LWPR_ReceptiveField *RF, const double *x, double y, double w, double *xmz) |
Performs an update of the receptive field's statistics (weighted mean input and output). | |
void | lwpr_aux_compute_projection_r (int nIn, int nInS, int nReg, double *s, double *xres, const double *x, const double *U, const double *P) |
Computes the PLS projections and its residuals given regression axes U, projection axes P, and an input vector x. | |
void | lwpr_aux_compute_projection (int nIn, int nInS, int nReg, double *s, const double *x, const double *U, const double *P, LWPR_Workspace *ws) |
Computes the PLS projections given regression axes U, projection axes P, and an input vector x. | |
void | lwpr_aux_compute_projection_d (int nIn, int nInS, int nReg, double *s, double *dsdx, const double *x, const double *U, const double *P, LWPR_Workspace *ws) |
Computes the PLS projections and their derivatives given regression axes U, projection axes P, and an input vector x. | |
void | lwpr_aux_update_regression (LWPR_ReceptiveField *RF, double *yp, double *e_cv, double *e, const double *x, double y, double w, LWPR_Workspace *ws) |
Performs an update on the regression parameters of one receptive field. | |
LWPR_ReceptiveField * | lwpr_aux_add_rf (LWPR_SubModel *sub, int nReg) |
Adds a new receptive field to the specified LWPR_SubModel. Regression parameters etc. are not initialised. | |
int | lwpr_aux_check_add_projection (LWPR_ReceptiveField *RF) |
Check if a receptive field needs another PLS regression axis, and modify the relevant variables. | |
int | lwpr_aux_init_rf (LWPR_ReceptiveField *RF, const LWPR_Model *model, const LWPR_ReceptiveField *RFT, const double *xc, double y) |
Allocates and initialises the variables of a receptive field. | |
int | lwpr_aux_update_one (LWPR_Model *model, int dim, const double *xn, double yn, double *y_pred, double *max_w) |
Update the receptive fields specific to one output dimension of the LWPR model. | |
void * | lwpr_aux_update_one_T (void *ptr) |
Thread function for updating a subset of receptive fields. | |
int | lwpr_aux_update_one_add_prune (LWPR_Model *model, LWPR_ThreadData *TD, int dim, const double *xn, double yn) |
Update the receptive fields specific to one output dimension of the LWPR model. | |
double | lwpr_aux_predict_one (const LWPR_Model *model, int dim, const double *xn, double cutoff, double *conf, double *max_w) |
Computes the prediction of an LWPR model for a specific output dimension. Can also return confidence bounds and the maximal activation of all receptive fields. | |
void * | lwpr_aux_predict_one_T (void *ptr) |
Thread function for predicting the output for one SubModel. | |
void * | lwpr_aux_predict_conf_one_T (void *ptr) |
Computes the prediction of an LWPR model for a specific output dimension, together with its confidence bounds. | |
double | lwpr_aux_predict_one_J (const LWPR_Model *model, int dim, const double *xn, double cutoff, double *dydx) |
Computes the prediction of an LWPR model for a specific output dimension, and also the gradient of that prediction with respect to the input vector. | |
double | lwpr_aux_predict_one_gH (const LWPR_Model *model, int dim, const double *xn, double cutoff, double *dydx, double *ddydxdx) |
Computes the prediction of an LWPR model for a specific output dimension, and also the gradient of that prediction with respect to the input vector. | |
void * | lwpr_aux_predict_one_J_T (void *ptr) |
Thread function for predicting output and gradient for one SubModel. | |
void * | lwpr_aux_predict_one_gH_T (void *ptr) |
Thread function for predicting output, gradient and Hessian for one SubModel. | |
void | lwpr_aux_update_model_stats (LWPR_Model *model, const double *x) |
Updates the global model statistics, i.e. the mean and variance of the input training data, and also the number of data points. |
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Adds a new receptive field to the specified LWPR_SubModel. Regression parameters etc. are not initialised.
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Check if a receptive field needs another PLS regression axis, and modify the relevant variables.
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Computes the PLS projections given regression axes U, projection axes P, and an input vector x.
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Computes the PLS projections and their derivatives given regression axes U, projection axes P, and an input vector x.
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Computes the PLS projections and its residuals given regression axes U, projection axes P, and an input vector x.
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Computes the derivates of the activation w and a penalty term with respect to M, Cholesky factors of the distance metric.
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Allocates and initialises the variables of a receptive field.
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Computes the prediction of an LWPR model for a specific output dimension, together with its confidence bounds.
On return, you may read the following fields:
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Computes the prediction of an LWPR model for a specific output dimension. Can also return confidence bounds and the maximal activation of all receptive fields.
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Computes the prediction of an LWPR model for a specific output dimension, and also the gradient of that prediction with respect to the input vector.
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Thread function for predicting output, gradient and Hessian for one SubModel.
On return, you may read the following fields:
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Computes the prediction of an LWPR model for a specific output dimension, and also the gradient of that prediction with respect to the input vector.
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Thread function for predicting output and gradient for one SubModel.
On return, you may read the following fields:
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Thread function for predicting the output for one SubModel.
On return, you may read the following fields:
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Performs an update of a receptive field's distance metric.
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Performs an update of the receptive field's statistics (weighted mean input and output).
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Updates the global model statistics, i.e. the mean and variance of the input training data, and also the number of data points.
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Update the receptive fields specific to one output dimension of the LWPR model.
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Update the receptive fields specific to one output dimension of the LWPR model.
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Thread function for updating a subset of receptive fields.
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Performs an update on the regression parameters of one receptive field.
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