src/gsl_helper.h File Reference

#include <stdio.h>
#include <stdlib.h>
#include <gsl/gsl_math.h>
#include <gsl/gsl_histogram.h>
#include <gsl/gsl_vector.h>
#include <gsl/gsl_matrix.h>
#include <assert.h>

Go to the source code of this file.

Functions
double	calc_vector_sum (const gsl_vector *v)
double	calc_vector_squaresum (const gsl_vector *v)
gsl_vector *	dup_vector (const gsl_vector *v)
gsl_vector *	calc_normalized (const gsl_vector *v)
int	calc_same (const gsl_vector *a, const gsl_vector *b)
void	max_vector (gsl_vector *a, const gsl_vector *b)
void	min_vector (gsl_vector *a, const gsl_vector *b)
void	sort (gsl_vector **vs, unsigned int nvectors, unsigned int vector_size)
double	min_column (const gsl_matrix *m, const unsigned int i)
double	min_row (const gsl_matrix *m, const unsigned int i)
double	max_column (const gsl_matrix *m, const unsigned int i)
double	max_row (const gsl_matrix *m, const unsigned int i)
double	calc_deviation (const gsl_matrix *x, const gsl_vector *y, const gsl_vector *k, const double d, const gsl_vector *weights)
gsl_vector *	linreg_n (const gsl_matrix *x, const gsl_vector *y, double *d, const gsl_vector *weights)

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Function Documentation

double calc_deviation	(	const gsl_matrix *	x,
		const gsl_vector *	y,
		const gsl_vector *	k,
		const double	d,
		const gsl_vector *	weights
	)

n-dimensional weighted square deviation

References assert.

Referenced by markov_chain_calibrate_multilinear_regression().

gsl_vector* calc_normalized

(

const gsl_vector *

v

)

normalizes the vector, i.e. the values are scaled so that the sum of all values is 1

The caller has to free the returned vector.

References calc_vector_sum(), dup_vector(), r, and require.

int calc_same	(	const gsl_vector *	a,
		const gsl_vector *	b
	)

Returns:

1 if vectors contain the same entries

References assert.

double calc_vector_squaresum

(

const gsl_vector *

v

)

sums the squared values

double calc_vector_sum

(

const gsl_vector *

v

)

sums the values

Referenced by calc_normalized().

gsl_vector* dup_vector

(

const gsl_vector *

v

)

returns a duplicate.

The caller has to free the returned vector.

References assert, r, and require.

Referenced by burn_in(), calc_beta_0(), calc_normalized(), calibrate_rest(), markov_chain_step(), restart_from_best(), update_min_max(), and write_calibration_summary().

gsl_vector* linreg_n	(	const gsl_matrix *	x,
		const gsl_vector *	y,
		double *	d,
		const gsl_vector *	weights
	)

n-dimensional weighted linear regression returns k and d.

References assert, IFDEBUG, p, xbar(), and xbar_j().

Referenced by markov_chain_calibrate_multilinear_regression().

double max_column	(	const gsl_matrix *	m,
		const unsigned int	i
	)

given the first index of the matrix, iterate through the second to find the largest entry

Referenced by markov_chain_calibrate_quadratic().

double max_row	(	const gsl_matrix *	m,
		const unsigned int	i
	)

given the second index of the matrix, iterate through the first to find the largest entry

void max_vector	(	gsl_vector *	a,
		const gsl_vector *	b
	)

a = max(a, b)

References assert.

Referenced by update_min_max().

double min_column	(	const gsl_matrix *	m,
		const unsigned int	i
	)

given the first index of the matrix, iterate through the second to find the smallest entry

Referenced by markov_chain_calibrate_quadratic().

double min_row	(	const gsl_matrix *	m,
		const unsigned int	i
	)

given the second index of the matrix, iterate through the first to find the smallest entry

void min_vector	(	gsl_vector *	a,
		const gsl_vector *	b
	)

a = min(a, b)

References assert.

Referenced by update_min_max().

void sort	(	gsl_vector **	vs,
		unsigned int	nvectors,
		unsigned int	vector_size
	)

sorts all vectors by the entries in the first vector.

selection sort

References IFDEBUG.