dynamics.c File Reference

Collection of functions for stochastic dynamics on a function to generate random samples. More...

#include <jwsc/config.h>
#include <stdlib.h>
#include <stdio.h>
#include <inttypes.h>
#include <math.h>
#include <assert.h>
#include "jwsc/base/error.h"
#include "jwsc/base/limits.h"
#include "jwsc/vector/vector.h"
#include "jwsc/vector/vector_math.h"
#include "jwsc/vector/vector_io.h"
#include "jwsc/matrix/matrix.h"
#include "jwsc/prob/pdf.h"
#include "jwsc/prob/mv_pdf.h"
#include "jwsc/stat/dynamics.h"

Include dependency graph for dynamics.c:

Go to the source code of this file.

Functions
stochastic_dynamics
Leapfrog discretization of stochastic dynamics to sample from an energy function.
Error *	stochastic_dynamics_1_d (uint32_t iterations, double delta_t, double alpha, uint32_t kick, void *params, void(*get_pt_from_params)(Vector_d **p_out, const void *params), Error *(*grad_energy_func)(Vector_d **grad_out, void *params), Error *(*accept_sample)(const Vector_d *sample, const Vector_d *momenta, void *params))
	Double precision leapfrog stochastic dynamics.
Error *	stochastic_dynamics_2_d (uint32_t iterations, const Vector_d *delta_t, double alpha, uint32_t kick, void *params, void(*get_pt_from_params)(Vector_d **p_out, const void *params), Error *(*grad_energy_func)(Vector_d **grad_out, void *params), Error *(*accept_sample)(const Vector_d *sample, const Vector_d *momenta, void *params))
	Double precision leapfrog stochastic dynamics.
hybrid_monte_carlo
Stochastic dynamics combined with Metropolis-Hastings for quickly converging and unbiased sampling.
Error *	hybrid_monte_carlo_1_d (uint32_t iterations, uint32_t leap_iter, double delta_t, double alpha, uint32_t kick, void *params, void(*get_pt_from_params)(Vector_d **p_out, const void *params), Error *(*set_params_from_pt)(const Vector_d *p, void *params), Error *(*energy_func)(double *energy_out, void *params), Error *(*grad_energy_func)(Vector_d **grad_out, void *params), Error *(*accept_sample)(const Vector_d *sample, const Vector_d *momenta, void *params), Error *(*reject_sample)(const Vector_d *sample, const Vector_d *momenta, void *params))
	Double precision hybrid monte carlo.
Error *	hybrid_monte_carlo_2_d (uint32_t iterations, uint32_t leap_iter, const Vector_d *delta_t, double alpha, uint32_t kick, void *params, void(*get_pt_from_params)(Vector_d **p_out, const void *params), Error *(*set_params_from_pt)(const Vector_d *p, void *params), Error *(*energy_func)(double *energy_out, void *params), Error *(*grad_energy_func)(Vector_d **grad_out, void *params), Error *(*accept_sample)(const Vector_d *sample, const Vector_d *momenta, void *params), Error *(*reject_sample)(const Vector_d *sample, const Vector_d *momenta, void *params))
	Double precision hybrid monte carlo.
langevin
Langevin dynamics for generating samples from an energy function.
Error *	langevin_1_f (uint32_t iterations, float delta_t, void *params, void(*get_pt_from_params)(Vector_f **p_out, const void *params), Error *(*grad_energy_func)(Vector_f **grad_out, void *params), Error *(*accept_sample)(const Vector_f *sample, void *params))
	Single precision Langevin dynamics.
Error *	langevin_2_f (uint32_t iterations, const Vector_f *delta_t, void *params, void(*get_pt_from_params)(Vector_f **p_out, const void *params), Error *(*grad_energy_func)(Vector_f **grad_out, void *params), Error *(*accept_sample)(const Vector_f *sample, void *params))
	Single precision Langevin dynamics.
Error *	langevin_1_d (uint32_t iterations, double delta_t, void *params, void(*get_pt_from_params)(Vector_d **p_out, const void *params), Error *(*grad_energy_func)(Vector_d **grad_out, void *params), Error *(*accept_sample)(const Vector_d *sample, void *params))
	Double precision Langevin dynamics.
Error *	langevin_2_d (uint32_t iterations, const Vector_d *delta_t, void *params, void(*get_pt_from_params)(Vector_d **p_out, const void *params), Error *(*grad_energy_func)(Vector_d **grad_out, void *params), Error *(*accept_sample)(const Vector_d *sample, void *params))
	Double precision Langevin dynamics.
hyperdynamics_hessian_eigenvalue
Estimates the lowest eigenvalue for the energy function Hessian matrix.
static Error *	hyperdynamics_hessian_eigenvalue_f (float *e_out, const Vector_f *v, float eta, void *params, void(*get_pt_from_params)(Vector_f **, const void *), Error *(*set_params_from_pt)(const Vector_f *, void *), Error *(*energy_func)(float *, void *))
static Error *	hyperdynamics_hessian_eigenvalue_d (double *e_out, const Vector_d *v, double eta, void *params, void(*get_pt_from_params)(Vector_d **, const void *), Error *(*set_params_from_pt)(const Vector_d *, void *), Error *(*energy_func)(double *, void *))
hyperdynamics_hessian_eigenvector
Estimates the lowest eigenvector for the energy function Hessian matrix.
static Error *	hyperdynamics_hessian_eigenvector_f (Vector_f **v_out, float eta, float gamma, void *params, void(*get_pt_from_params)(Vector_f **, const void *), Error *(*set_params_from_pt)(const Vector_f *, void *), Error *(*energy_func)(float *, void *), Error *(*grad_energy_func)(Vector_f **, void *))
static Error *	hyperdynamics_hessian_eigenvector_d (Vector_d **v_out, double eta, double gamma, void *params, void(*get_pt_from_params)(Vector_d **, const void *), Error *(*set_params_from_pt)(const Vector_d *, void *), Error *(*energy_func)(double *, void *), Error *(*grad_energy_func)(Vector_d **, void *))
hyperdynamics_dx_hessian_eigenvalue
Estimates the gradient of the lowest eigenvalue for the energy function Hessian matrix w.r.t. the parameters.
static Error *	hyperdynamics_dx_hessian_eigenvalue_f (Vector_f **e_dx_out, const Vector_f *v, float eta, void *params, void(*get_pt_from_params)(Vector_f **, const void *), Error *(*set_params_from_pt)(const Vector_f *, void *), Error *(*grad_energy_func)(Vector_f **, void *))
static Error *	hyperdynamics_dx_hessian_eigenvalue_d (Vector_d **e_dx_out, const Vector_d *v, double eta, void *params, void(*get_pt_from_params)(Vector_d **, const void *), Error *(*set_params_from_pt)(const Vector_d *, void *), Error *(*grad_energy_func)(Vector_d **, void *))
hyperdynamics_hessgrad_eigenvalue
Estimates the lowest eigenvalue for the energy function Hessian matrix plus the gradient (squared) matrix w.r.t. the parameters.
static Error *	hyperdynamics_hessgrad_eigenvalue_f (float *e_out, float lambda, const Vector_f *v, float eta, void *params, void(*get_pt_from_params)(Vector_f **, const void *), Error *(*set_params_from_pt)(const Vector_f *, void *), Error *(*energy_func)(float *, void *), uint8_t positive)
static Error *	hyperdynamics_hessgrad_eigenvalue_d (double *e_out, double lambda, const Vector_d *v, double eta, void *params, void(*get_pt_from_params)(Vector_d **, const void *), Error *(*set_params_from_pt)(const Vector_d *, void *), Error *(*energy_func)(double *, void *), uint8_t positive)
static Error *	hyperdynamics_dx_hessgrad_eigenvalue_f (Vector_f **e_lambda_dx_out, float lambda, const Vector_f *v, float eta, void *params, void(*get_pt_from_params)(Vector_f **, const void *), Error *(*set_params_from_pt)(const Vector_f *, void *), Error *(*energy_func)(float *, void *), Error *(*grad_energy_func)(Vector_f **, void *), uint8_t positive)
static Error *	hyperdynamics_dx_hessgrad_eigenvalue_d (Vector_d **e_lambda_dx_out, double lambda, const Vector_d *v, double eta, void *params, void(*get_pt_from_params)(Vector_d **, const void *), Error *(*set_params_from_pt)(const Vector_d *, void *), Error *(*energy_func)(double *, void *), Error *(*grad_energy_func)(Vector_d **, void *), uint8_t positive)
hyperdynamics_proj_hessian_eigenvector
Estimates the projected gradient vector on the lowest eigenvector of the Hessian matrix.
static Error *	hyperdynamics_proj_hessian_eigenvector_f (float *g1p_out, float e_pos, float e_neg, float lambda)
static Error *	hyperdynamics_proj_hessian_eigenvector_d (double *g1p_out, double e_pos, double e_neg, double lambda)
hyperdynamics_hessgrad_eigenvector
Estimates the lowest eigenvector for the energy function Hessian matrix plus the gradient (squared) matrix w.r.t. the parameters.
static Error *	hyperdynamics_hessgrad_eigenvector_f (float *lambda_out, Vector_f **v_pos_out, Vector_f **v_neg_out, float eta, float gamma, void *params, void(*get_pt_from_params)(Vector_f **, const void *), Error *(*set_params_from_pt)(const Vector_f *, void *), Error *(*energy_func)(float *, void *), Error *(*grad_energy_func)(Vector_f **, void *))
static Error *	hyperdynamics_hessgrad_eigenvector_d (double *lambda_out, Vector_d **v_pos_out, Vector_d **v_neg_out, double eta, double gamma, void *params, void(*get_pt_from_params)(Vector_d **, const void *), Error *(*set_params_from_pt)(const Vector_d *, void *), Error *(*energy_func)(double *, void *), Error *(*grad_energy_func)(Vector_d **, void *))
hyperdynamics_dx_proj_hessian_eigenvector
Estimates the derivative of the projected gradient vector on the lowest eigenvector of the Hessian matrix.
static Error *	hyperdynamics_dx_proj_hessian_eigenvector_f (Vector_f **g1p_dx_out, const Vector_f *e_pos_dx, const Vector_f *e_neg_dx, float g1p, float lambda)
static Error *	hyperdynamics_dx_proj_hessian_eigenvector_d (Vector_d **g1p_dx_out, const Vector_d *e_pos_dx, const Vector_d *e_neg_dx, double g1p, double lambda)
langevin_hyperdynamics
Voter's hyperdynamics biasing for generating samples from an energy function using Langevin dynamics.
Error *	langevin_hyperdynamics_1_f (uint32_t iterations, float delta_t, float gamma, float eta, float h, float d, void *params, void(*get_pt_from_params)(Vector_f **p_out, const void *params), Error *(*set_params_from_pt)(const Vector_f *p, void *params), Error *(*energy_func)(float *energy_out, void *params), Error *(*grad_energy_func)(Vector_f **grad_out, void *params), Error *(*accept_sample)(const Vector_f *sample, void *params, float energy_bias))
	Single precision Voter hyperdynamics.
Error *	langevin_hyperdynamics_2_f (uint32_t iterations, const Vector_f *delta_t, float gamma, float eta, float h, float d, void *params, void(*get_pt_from_params)(Vector_f **p_out, const void *params), Error *(*set_params_from_pt)(const Vector_f *p, void *params), Error *(*energy_func)(float *energy_out, void *params), Error *(*grad_energy_func)(Vector_f **grad_out, void *params), Error *(*accept_sample)(const Vector_f *sample, void *params, float energy_bias))
	Single precision Voter hyperdynamics.
Error *	langevin_hyperdynamics_1_d (uint32_t iterations, double delta_t, double gamma, double eta, double h, double d, void *params, void(*get_pt_from_params)(Vector_d **p_out, const void *params), Error *(*set_params_from_pt)(const Vector_d *p, void *params), Error *(*energy_func)(double *energy_out, void *params), Error *(*grad_energy_func)(Vector_d **grad_out, void *params), Error *(*accept_sample)(const Vector_d *sample, void *params, double energy_bias))
	Double precision Voter hyperdynamics.
Error *	langevin_hyperdynamics_2_d (uint32_t iterations, const Vector_d *delta_t, double gamma, double eta, double h, double d, void *params, void(*get_pt_from_params)(Vector_d **p_out, const void *params), Error *(*set_params_from_pt)(const Vector_d *p, void *params), Error *(*energy_func)(double *energy_out, void *params), Error *(*grad_energy_func)(Vector_d **grad_out, void *params), Error *(*accept_sample)(const Vector_d *sample, void *params, double energy_bias))
	Double precision Voter hyperdynamics.

---

Detailed Description

Collection of functions for stochastic dynamics on a function to generate random samples.

Author:

Joseph Schlecht

License:

Creative Commons BY-NC-SA 3.0

Definition in file dynamics.c.

---

Function Documentation

Error* stochastic_dynamics_1_d	(	uint32_t	iterations,
		double	delta_t,
		double	alpha,
		uint32_t	kick,
		void *	params,
		void(*)(Vector_d **p_out, const void *params)	get_pt_from_params,
		Error *(*)(Vector_d **grad_out, void *params)	grad_energy_func,
		Error *(*)(const Vector_d *sample, const Vector_d *momenta, void *params)	accept_sample
	)

Double precision leapfrog stochastic dynamics.

See Neal '93 for details.

Preserves phase space volume.

Parameters:

iterations	Number of leapfrog steps to take; number of samples to generate.
delta_t	Time discretization step size.
alpha	Parameter for stochastic dynamics. Should be in in the interval [0,1].
kick	Frequency (in iterations) to kick the particle and reset the momenta. Set to zero if no kicking is needed.
params	Parameter(s) for the callback functions.
get_pt_from_params	Gets the parameters as a point from params.
grad_energy_func	Computes the gradient of the energy function at a point specified in params
accept_sample	Accepts a generated sample by. Updates the params with the sample.

Returns:

On success, NULL is returned. On error, an Error is returned.

Definition at line 97 of file dynamics.c.

Error* stochastic_dynamics_2_d	(	uint32_t	iterations,
		const Vector_d *	delta_t,
		double	alpha,
		uint32_t	kick,
		void *	params,
		void(*)(Vector_d **p_out, const void *params)	get_pt_from_params,
		Error *(*)(Vector_d **grad_out, void *params)	grad_energy_func,
		Error *(*)(const Vector_d *sample, const Vector_d *momenta, void *params)	accept_sample
	)

Double precision leapfrog stochastic dynamics.

See Neal '93 for details.

Preserves phase space volume.

Parameters:

iterations	Number of leapfrog steps to take; number of samples to generate.
delta_t	Time discretization step size.
alpha	Parameter for stochastic dynamics. Should be in in the interval [0,1].
kick	Frequency (in iterations) to kick the particle and reset the momenta. Set to zero if no kicking is needed.
params	Parameter(s) for the callback functions.
get_pt_from_params	Gets the parameters as a point from params.
grad_energy_func	Computes the gradient of the energy function at a point specified in params
accept_sample	Accepts a generated sample by. Updates the params with the sample.

Returns:

On success, NULL is returned. On error, an Error is returned.

Definition at line 152 of file dynamics.c.

Error* hybrid_monte_carlo_1_d	(	uint32_t	iterations,
		uint32_t	leap_iter,
		double	delta_t,
		double	alpha,
		uint32_t	kick,
		void *	params,
		void(*)(Vector_d **p_out, const void *params)	get_pt_from_params,
		Error *(*)(const Vector_d *p, void *params)	set_params_from_pt,
		Error *(*)(double *energy_out, void *params)	energy_func,
		Error *(*)(Vector_d **grad_out, void *params)	grad_energy_func,
		Error *(*)(const Vector_d *sample, const Vector_d *momenta, void *params)	accept_sample,
		Error *(*)(const Vector_d *sample, const Vector_d *momenta, void *params)	reject_sample
	)

Double precision hybrid monte carlo.

See Neal '93 for details.

Preserves phase space volume and generates unbiased samples.

Parameters:

iterations	Number of Metropolis-Hastings iterations. Proportional to the number of samples generated, depending on the rejection rate.
leap_iter	Number of leapfrog iterations between each Metropolis-Hastings iterations.
delta_t	Time discretization step size.
alpha	Parameter for stochastic dynamics. Should be in in the interval [0,1].
kick	Frequency (in iterations) to kick the particle and reset the momenta. Set to zero if no kicking is needed.
params	Parameter(s) for the callback functions.
get_pt_from_params	Gets the parameters as a point from params.
set_params_from_pt	Sets the parameters in params from a point.
energy_func	Computes the energy function at a point specified in params
grad_energy_func	Computes the gradient of the energy function at a point specified in params
accept_sample	Accepts a generated sample by. Updates the params with the sample.
reject_sample	Rejects a generated sample. This is optional and can be set to NULL.

Returns:

On success, NULL is returned. On error, an Error is returned.

Definition at line 301 of file dynamics.c.

Error* hybrid_monte_carlo_2_d	(	uint32_t	iterations,
		uint32_t	leap_iter,
		const Vector_d *	delta_t,
		double	alpha,
		uint32_t	kick,
		void *	params,
		void(*)(Vector_d **p_out, const void *params)	get_pt_from_params,
		Error *(*)(const Vector_d *p, void *params)	set_params_from_pt,
		Error *(*)(double *energy_out, void *params)	energy_func,
		Error *(*)(Vector_d **grad_out, void *params)	grad_energy_func,
		Error *(*)(const Vector_d *sample, const Vector_d *momenta, void *params)	accept_sample,
		Error *(*)(const Vector_d *sample, const Vector_d *momenta, void *params)	reject_sample
	)

Double precision hybrid monte carlo.

See Neal '93 for details.

Preserves phase space volume and generates unbiased samples.

Parameters:

iterations	Number of Metropolis-Hastings iterations. Proportional to the number of samples generated, depending on the rejection rate.
leap_iter	Number of leapfrog iterations between each Metropolis-Hastings iterations.
delta_t	Time discretization step size.
alpha	Parameter for stochastic dynamics. Should be in in the interval [0,1].
kick	Frequency (in iterations) to kick the particle and reset the momenta. Set to zero if no kicking is needed.
params	Parameter(s) for the callback functions.
get_pt_from_params	Gets the parameters as a point from params.
set_params_from_pt	Sets the parameters in params from a point.
energy_func	Computes the energy function at a point specified in params
grad_energy_func	Computes the gradient of the energy function at a point specified in params
accept_sample	Accepts a generated sample. Updates the params with the sample.
reject_sample	Rejects a generated sample. This is optional and can be set to NULL.

Returns:

On success, NULL is returned. On error, an Error is returned.

Definition at line 368 of file dynamics.c.

Error* langevin_1_f	(	uint32_t	iterations,
		float	delta_t,
		void *	params,
		void(*)(Vector_f **p_out, const void *params)	get_pt_from_params,
		Error *(*)(Vector_f **grad_out, void *params)	grad_energy_func,
		Error *(*)(const Vector_f *sample, void *params)	accept_sample
	)

Single precision Langevin dynamics.

Follows the negative gradient of an energy function. Note that this will focus samples in areas of low energy. If you are interested in maxima, have your grad_energy_func negate things.

Parameters:

iterations	Number of Langevin dynamcs steps to take; number of samples to generate.
delta_t	Stochastics dynamics integration step size.
params	Parameter(s) for the callback functions.
get_pt_from_params	Gets the parameters as a point from params.
grad_energy_func	Computes the gradient of the energy function at a point specified in params
accept_sample	Accepts a generated sample by. Updates the params with the sample.

Returns:

On success, NULL is returned. On error, an Error is returned.

Definition at line 598 of file dynamics.c.

Error* langevin_2_f	(	uint32_t	iterations,
		const Vector_f *	delta_t,
		void *	params,
		void(*)(Vector_f **p_out, const void *params)	get_pt_from_params,
		Error *(*)(Vector_f **grad_out, void *params)	grad_energy_func,
		Error *(*)(const Vector_f *sample, void *params)	accept_sample
	)

Single precision Langevin dynamics.

Follows the negative gradient of an energy function. Note that this will focus samples in areas of low energy. If you are interested in maxima, have your grad_energy_func negate things.

Parameters:

iterations	Number of Langevin dynamcs steps to take; number of samples to generate.
delta_t	Stochastics dynamics integration step size.
params	Parameter(s) for the callback functions.
get_pt_from_params	Gets the parameters as a point from params.
grad_energy_func	Computes the gradient of the energy function at a point specified in params
accept_sample	Accepts a generated sample by. Updates the params with the sample.

Returns:

On success, NULL is returned. On error, an Error is returned.

Definition at line 645 of file dynamics.c.

Error* langevin_1_d	(	uint32_t	iterations,
		double	delta_t,
		void *	params,
		void(*)(Vector_d **p_out, const void *params)	get_pt_from_params,
		Error *(*)(Vector_d **grad_out, void *params)	grad_energy_func,
		Error *(*)(const Vector_d *sample, void *params)	accept_sample
	)

Double precision Langevin dynamics.

Follows the negative gradient of an energy function. Note that this will focus samples in areas of low energy. If you are interested in maxima, have your grad_energy_func negate things.

Parameters:

iterations	Number of Langevin dynamcs steps to take; number of samples to generate.
delta_t	Stochastics dynamics integration step size.
params	Parameter(s) for the callback functions.
get_pt_from_params	Gets the parameters as a point from params.
grad_energy_func	Computes the gradient of the energy function at a point specified in params
accept_sample	Accepts a generated sample by. Updates the params with the sample.

Returns:

On success, NULL is returned. On error, an Error is returned.

Definition at line 718 of file dynamics.c.

Error* langevin_2_d	(	uint32_t	iterations,
		const Vector_d *	delta_t,
		void *	params,
		void(*)(Vector_d **p_out, const void *params)	get_pt_from_params,
		Error *(*)(Vector_d **grad_out, void *params)	grad_energy_func,
		Error *(*)(const Vector_d *sample, void *params)	accept_sample
	)

Double precision Langevin dynamics.

Follows the negative gradient of an energy function. Note that this will focus samples in areas of low energy. If you are interested in maxima, have your grad_energy_func negate things.

Parameters:

iterations	Number of Langevin dynamcs steps to take; number of samples to generate.
delta_t	Stochastics dynamics integration step size.
params	Parameter(s) for the callback functions.
get_pt_from_params	Gets the parameters as a point from params.
grad_energy_func	Computes the gradient of the energy function at a point specified in params
accept_sample	Accepts a generated sample by. Updates the params with the sample.

Returns:

On success, NULL is returned. On error, an Error is returned.

Definition at line 765 of file dynamics.c.

Error* langevin_hyperdynamics_1_f	(	uint32_t	iterations,
		float	delta_t,
		float	gamma,
		float	eta,
		float	h,
		float	d,
		void *	params,
		void(*)(Vector_f **p_out, const void *params)	get_pt_from_params,
		Error *(*)(const Vector_f *p, void *params)	set_params_from_pt,
		Error *(*)(float *energy_out, void *params)	energy_func,
		Error *(*)(Vector_f **grad_out, void *params)	grad_energy_func,
		Error *(*)(const Vector_f *sample, void *params, float energy_bias)	accept_sample
	)

Single precision Voter hyperdynamics.

Follows the negative gradient of a (biased) energy function. Note that this will focus samples in areas of low energy. If you are interested in maxima, have your energy_func and grad_energy_func negate things.

Parameters:

iterations	Number of Langevin dynamcs steps to take; number of samples to generate.
delta_t	Stochastics dynamics integration step size.
gamma	Step size for gradient descent for each parameter. Very small positive value.
eta	Difference parameter for derivative numerical approximations. Small positive value.
h	Bias scaling of the Voter's function (h). Height of the smallest transition area is good starting value.
d	Distance scale from one minima to another in terms of 2*pi*d.
params	Parameter(s) for the callback functions.
get_pt_from_params	Gets the parameters as a point from params.
set_params_from_pt	Sets the params from a point.
energy_func	Computes the energy function at a point specified in params.
grad_energy_func	Computes the gradient of the energy function at a point specified in params
accept_sample	Accepts a generated sample by. Updates the params with the sample.

Returns:

On success, NULL is returned. On error, an Error is returned.

Definition at line 2711 of file dynamics.c.

Error* langevin_hyperdynamics_2_f	(	uint32_t	iterations,
		const Vector_f *	delta_t,
		float	gamma,
		float	eta,
		float	h,
		float	d,
		void *	params,
		void(*)(Vector_f **p_out, const void *params)	get_pt_from_params,
		Error *(*)(const Vector_f *p, void *params)	set_params_from_pt,
		Error *(*)(float *energy_out, void *params)	energy_func,
		Error *(*)(Vector_f **grad_out, void *params)	grad_energy_func,
		Error *(*)(const Vector_f *sample, void *params, float energy_bias)	accept_sample
	)

Single precision Voter hyperdynamics.

Follows the negative gradient of a (biased) energy function. Note that this will focus samples in areas of low energy. If you are interested in maxima, have your energy_func and grad_energy_func negate things.

Parameters:

iterations	Number of Langevin dynamcs steps to take; number of samples to generate.
delta_t	Stochastics dynamics integration step size.
gamma	Step size for gradient descent for each parameter. Very small positive value.
eta	Difference parameter for derivative numerical approximations. Small positive value.
h	Bias scaling of the Voter's function (h). Height of the smallest transition area is good starting value.
d	Distance scale from one minima to another in terms of 2*pi*d.
params	Parameter(s) for the callback functions.
get_pt_from_params	Gets the parameters as a point from params.
set_params_from_pt	Sets the params from a point.
energy_func	Computes the energy function at a point specified in params.
grad_energy_func	Computes the gradient of the energy function at a point specified in params
accept_sample	Accepts a generated sample by. Updates the params with the sample.

Returns:

On success, NULL is returned. On error, an Error is returned.

Definition at line 2777 of file dynamics.c.

Error* langevin_hyperdynamics_1_d	(	uint32_t	iterations,
		double	delta_t,
		double	gamma,
		double	eta,
		double	h,
		double	d,
		void *	params,
		void(*)(Vector_d **p_out, const void *params)	get_pt_from_params,
		Error *(*)(const Vector_d *p, void *params)	set_params_from_pt,
		Error *(*)(double *energy_out, void *params)	energy_func,
		Error *(*)(Vector_d **grad_out, void *params)	grad_energy_func,
		Error *(*)(const Vector_d *sample, void *params, double energy_bias)	accept_sample
	)

Double precision Voter hyperdynamics.

Follows the negative gradient of a (biased) energy function. Note that this will focus samples in areas of low energy. If you are interested in maxima, have your energy_func and grad_energy_func negate things.

Parameters:

iterations	Number of Langevin dynamcs steps to take; number of samples to generate.
delta_t	Stochastics dynamics integration step size.
gamma	Step size for gradient descent for each parameter. Very small positive value.
eta	Difference parameter for derivative numerical approximations. Small positive value.
h	Bias scaling of the Voter's function (h). Height of the smallest transition area is good starting value.
d	Distance scale from one minima to another in terms of 2*pi*d.
params	Parameter(s) for the callback functions.
get_pt_from_params	Gets the parameters as a point from params.
set_params_from_pt	Sets the params from a point.
energy_func	Computes the energy function at a point specified in params.
grad_energy_func	Computes the gradient of the energy function at a point specified in params
accept_sample	Accepts a generated sample.

Returns:

On success, NULL is returned. On error, an Error is returned.

Definition at line 2970 of file dynamics.c.

Error* langevin_hyperdynamics_2_d	(	uint32_t	iterations,
		const Vector_d *	delta_t,
		double	gamma,
		double	eta,
		double	h,
		double	d,
		void *	params,
		void(*)(Vector_d **p_out, const void *params)	get_pt_from_params,
		Error *(*)(const Vector_d *p, void *params)	set_params_from_pt,
		Error *(*)(double *energy_out, void *params)	energy_func,
		Error *(*)(Vector_d **grad_out, void *params)	grad_energy_func,
		Error *(*)(const Vector_d *sample, void *params, double energy_bias)	accept_sample
	)

Double precision Voter hyperdynamics.

Follows the negative gradient of a (biased) energy function. Note that this will focus samples in areas of low energy. If you are interested in maxima, have your energy_func and grad_energy_func negate things.

Parameters:

iterations	Number of Langevin dynamcs steps to take; number of samples to generate.
delta_t	Stochastics dynamics integration step size.
gamma	Step size for gradient descent for each parameter. Very small positive value.
eta	Difference parameter for derivative numerical approximations. Small positive value.
h	Bias scaling of the Voter's function (h). Height of the smallest transition area is good starting value.
d	Distance scale from one minima to another in terms of 2*pi*d.
params	Parameter(s) for the callback functions.
get_pt_from_params	Gets the parameters as a point from params.
set_params_from_pt	Sets the params from a point.
energy_func	Computes the energy function at a point specified in params.
grad_energy_func	Computes the gradient of the energy function at a point specified in params
accept_sample	Accepts a generated sample.

Returns:

On success, NULL is returned. On error, an Error is returned.

Definition at line 3035 of file dynamics.c.