ccl_learn_lambda.h File Reference

CCL header file for learning state dependent constraint with known jacobian. More...

#include <../include/ccl_math.h>
#include <../include/ccl_learn_alpha.h>
#include <gsl/gsl_linalg.h>
#include <gsl/gsl_blas.h>
#include <gsl/gsl_math.h>
#include <gsl/gsl_sf.h>
#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <time.h>

Go to the source code of this file.

Macros
#define	NUM_CONSTRAINT   2

Functions
void	Jacobian (const double *X, const int size, double *out)
	Computation routine for calculating jacobian for the robot inverse kinematic. More...
int	ccl_learn_lambda_model_alloc (LEARN_A_MODEL *model)
	Allocates the memory for the learn_lambda model. More...
int	ccl_learn_lambda_model_free (LEARN_A_MODEL *model)
	Free the memory for the learn_lambda model. More...
void	ccl_learn_lambda (const double *Un, const double *X, void(*J_func)(const double *, const int, double *), const int dim_b, const int dim_r, const int dim_n, const int dim_x, const int dim_u, LEARN_A_MODEL optimal)
	Main computation routine for learning state dependent constraint lambda. More...
void	predict_proj_lambda (double *x, LEARN_A_MODEL model, void(*J_func)(const double *, const int, double *), double *centres, double variance, double *Iu, double *A)
	Computation routine for prediction of the lambda matrix. More...
int	ccl_write_learn_lambda_model (char *filename, LEARN_A_MODEL *model)
	Write model parameters to .txt file. More...

Detailed Description

CCL header file for learning state dependent constraint with known jacobian.

Macro Definition Documentation

#define NUM_CONSTRAINT   2

NUM_CONSTRAINT is the default full rank of the task constraint

Function Documentation

void ccl_learn_lambda	(	const double *	Un,
		const double *	X,
		void(*)(const double *, const int, double *)	J_func,
		const int	dim_b,
		const int	dim_r,
		const int	dim_n,
		const int	dim_x,
		const int	dim_u,
		LEARN_A_MODEL	optimal
	)

Main computation routine for learning state dependent constraint lambda.

Parameters

[in]	Un	Observed actions, Must point to an array of dim_u * dim_n of doubles
[in]	X	Observed state variables, Must point to an array of dim_x * dim_n of doubles
[in]	J_func	Functor for calculating the jacobian matrix
[in]	dim_b	Number of basis functions
[in]	dim_r	Dimensionality of the task space
[in]	dim_n	NUmber of data points
[in]	dim_x	Dimensionality of the state variables
[in]	dim_u	Dimensionality of the action space
[out]		optimal model paramters

int ccl_learn_lambda_model_alloc

(

LEARN_A_MODEL *

model

)

Allocates the memory for the learn_lambda model.

Parameters

[in]

model

Must point to a valid LEARN_A_MODEL structure

Returns

• 1 in case of succes
• 0 in case of failure (e.g. memory could not be allocated)

int ccl_learn_lambda_model_free

(

LEARN_A_MODEL *

model

)

Free the memory for the learn_lambda model.

Parameters

[in]

model

Must point to a valid LEARN_A_MODEL structure

Returns

• 1 in case of succes
• 0 in case of failure (e.g. memory could not be freed)

int ccl_write_learn_lambda_model	(	char *	filename,
		LEARN_A_MODEL *	model
	)

Write model parameters to .txt file.

Parameters

[in]	filename	File name
[in]	model	Must be pointer to a valid LEARN_A_MODEL structure

void Jacobian	(	const double *	X,
		const int	size,
		double *	out
	)

Computation routine for calculating jacobian for the robot inverse kinematic.

Parameters

[in]	X	Observed state variables, Must point to an array of dim_x * 1 of doubles
[in]	size	Number of links (Todo)
[out]	out	Jacobian matrix, must point to an array of 2 * 3 doubles (3 links arm)

void predict_proj_lambda	(	double *	x,
		LEARN_A_MODEL	model,
		void(*)(const double *, const int, double *)	J_func,
		double *	centres,
		double	variance,
		double *	Iu,
		double *	A
	)

Computation routine for prediction of the lambda matrix.

Parameters

[in]	x	Input state variable, must point to an array of dim_x doubles
[in]	model	Must be pointer to a valid LEARN_A_MODEL structure
[in]	J_func	Functor for calculating the jacobian matrix
[in]	centres	Rbf centers, must point to an array of 1 * dim_b doubles.
[in]	variance	Variance of the rbf
[in]	Iu	Identity matrix, must point to an array of dim_u * dim_u doubles
[out]	A	Constraint lambda matrix, must point to an array of dim_k * dum_u doubles