/* Function: esl_hxp_FitCompleteBinned()
*
* Purpose: Given a histogram <g> with binned observations, where each
* bin i holds some number of observed samples x with values from
* lower bound l to upper bound u (that is, $l < x \leq u$),
* and given a starting guess <h> for hyperexponential parameters;
*
* Find maximum likelihood parameters <h> by conjugate gradient
* descent, starting from the initial <h> and leaving the
* optimized solution in <h>.
*
* Returns: <eslOK> on success.
*
* Throws: <eslEMEM> on allocation error, and <h> is left in its
* initial state.
*/
int
esl_hxp_FitCompleteBinned(ESL_HISTOGRAM *g, ESL_HYPEREXP *h)
{
struct hyperexp_binned_data data;
int status;
double *p = NULL;
double *u = NULL;
double *wrk = NULL;
double fx;
int i;
double tol = 1e-6;
int np;
np = 0;
if (! h->fixmix) np = h->K-1; /* K-1 mix coefficients... */
for (i = 0; i < h->K; i++) /* ...and up to K lambdas free. */
if (! h->fixlambda[i]) np++;
ESL_ALLOC(p, sizeof(double) * np);
ESL_ALLOC(u, sizeof(double) * np);
ESL_ALLOC(wrk, sizeof(double) * np * 4);
/* Copy shared info into the "data" structure */
data.g = g;
data.h = h;
/* From h, create the parameter vector. */
hyperexp_pack_paramvector(p, np, h);
/* Define the step size vector u.
*/
for (i = 0; i < np; i++) u[i] = 1.0;
/* Feed it all to the mighty optimizer.
*/
status = esl_min_ConjugateGradientDescent(p, u, np,
&hyperexp_complete_binned_func,
&hyperexp_complete_binned_gradient,
(void *) (&data), tol, wrk, &fx);
if (status != eslOK) goto ERROR;
/* Convert the final parameter vector back to a hyperexponential
*/
hyperexp_unpack_paramvector(p, np, h);
free(p);
free(u);
free(wrk);
esl_hyperexp_SortComponents(h);
return eslOK;
ERROR:
if (p != NULL) free(p);
if (u != NULL) free(u);
if (wrk != NULL) free(wrk);
return status;
}
/* Function: esl_sxp_FitCompleteBinned()
*
* Purpose: Given a histogram <g> with binned observations, where each
* bin i holds some number of observed samples x with values from
* lower bound l to upper bound u (that is, $l < x \leq u$);
* find maximum likelihood parameters mu, lambda, tau by conjugate
* gradient descent optimization.
*/
int
esl_sxp_FitCompleteBinned(ESL_HISTOGRAM *g,
double *ret_mu, double *ret_lambda, double *ret_tau)
{
struct sxp_binned_data data;
double p[2], u[2], wrk[8];
double mu, tau, lambda;
double tol = 1e-6;
double fx;
int status;
double ai, mean;
int i;
/* Set the fixed mu.
* Make a good initial guess of lambda, based on exponential fit.
* Choose an arbitrary tau.
*/
if (g->is_tailfit) mu = g->phi; /* all x > mu in this case */
else if (g->is_rounded) mu = esl_histogram_Bin2LBound(g, g->imin);
else mu = g->xmin;
mean = 0.;
for (i = g->cmin; i <= g->imax; i++)
{
ai = esl_histogram_Bin2LBound(g, i);
ai += 0.5*g->w; /* midpoint in bin */
mean += (double)g->obs[i] * ai;
}
mean /= g->No;
lambda = 1 / (mean - mu);
tau = 0.9;
/* load data structure, param vector, and step vector */
data.g = g;
data.mu = mu;
p[0] = log(lambda);
p[1] = log(tau);
u[0] = 1.0;
u[1] = 1.0;
/* hand it off */
status = esl_min_ConjugateGradientDescent(p, u, 2,
&sxp_complete_binned_func,
NULL,
(void *) (&data), tol, wrk, &fx);
*ret_mu = mu;
*ret_lambda = exp(p[0]);
*ret_tau = exp(p[1]);
return status;
}
/* Function: esl_wei_FitComplete()
*
* Purpose: Given an array of <n> samples <x[0]..x[n-1>, fit
* them to a stretched exponential distribution starting
* at lower bound <mu> (all $x_i > \mu$), and
* return maximum likelihood parameters <ret_lambda>
* and <ret_tau>.
*
* Args: x - complete GEV-distributed data [0..n-1]
* n - number of samples in <x>
* ret_mu - RETURN: lower bound of the distribution (all x_i >= mu)
* ret_lambda - RETURN: maximum likelihood estimate of lambda
* ret_tau - RETURN: maximum likelihood estimate of tau
*
* Returns: <eslOK> on success.
*
* Throws: <eslENOHALT> if the fit doesn't converge.
*
* Xref: STL9/136-137
*/
int
esl_wei_FitComplete(double *x, int n, double *ret_mu,
double *ret_lambda, double *ret_tau)
{
struct wei_data data;
double p[2]; /* parameter vector */
double u[2]; /* max initial step size vector */
double wrk[8]; /* 4 tmp vectors of length 2 */
double mean;
double mu, lambda, tau; /* initial param guesses */
double tol = 1e-6; /* convergence criterion for CG */
double fx; /* f(x) at minimum; currently unused */
int status;
/* Make a good initial guess, based on exponential fit;
* set an arbitrary tau.
*/
mu = esl_vec_DMin(x, n);
esl_stats_DMean(x, n, &mean, NULL);
lambda = 1 / (mean - mu);
tau = 0.9;
/* Load the data structure
*/
data.x = x;
data.n = n;
data.mu = mu;
/* Change of variables;
* lambda > 0, so c.o.v. lambda = exp^w, w = log(lambda);
* tau > 0, same c.o.v.
*/
p[0] = log(lambda);
p[1] = log(tau);
u[0] = 1.0;
u[1] = 1.0;
/* pass problem to the optimizer
*/
status = esl_min_ConjugateGradientDescent(p, u, 2,
&wei_func, NULL,
(void *)(&data),
tol, wrk, &fx);
*ret_mu = mu;
*ret_lambda = exp(p[0]);
*ret_tau = exp(p[1]);
return status;
}
/* Function: esl_gumbel_FitTruncated()
* Synopsis: Estimates $\mu$, $\lambda$ from truncated data.
* Incept: SRE, Wed Jun 29 14:14:17 2005 [St. Louis]
*
* Purpose: Given a left-truncated array of Gumbel-distributed
* samples <x[0]..x[n-1]> and the truncation threshold
* <phi> (such that all <x[i]> $\geq$ <phi>).
* Find maximum likelihood parameters <mu> and <lambda>.
*
* <phi> should not be much greater than <mu>, the
* mode of the Gumbel, or the fit will become unstable
* or may even fail to converge. The problem is
* that for <phi> $>$ <mu>, the tail of the Gumbel
* becomes a scale-free exponential, and <mu> becomes
* undetermined.
*
* Algorithm: Uses conjugate gradient descent to optimize the
* log likelihood of the data. Follows a general
* approach to fitting missing data problems outlined
* in [Gelman95].
*
* Args: x - observed data samples [0..n-1]
* n - number of samples
* phi - truncation threshold; all x[i] >= phi
* ret_mu - RETURN: ML estimate of mu
* ret_lambda - RETURN: ML estimate of lambda
*
* Returns: <eslOK> on success.
*
* Throws: <eslENOHALT> if the fit doesn't converge.
*/
int
esl_gumbel_FitTruncated(double *x, int n, double phi,
double *ret_mu, double *ret_lambda)
{
struct tevd_data data;
double wrk[8]; /* workspace for CG: 4 tmp vectors of size 2 */
double p[2]; /* mu, w; lambda = e^w */
double u[2]; /* max initial step size for mu, lambda */
int status;
double mean, variance;
double mu, lambda;
double fx;
data.x = x;
data.n = n;
data.phi = phi;
/* The source of the following magic is Evans/Hastings/Peacock,
* Statistical Distributions, 3rd edition (2000), p.86, which gives
* eq's for the mean and variance of a Gumbel in terms of mu and lambda;
* we turn them around to get mu and lambda in terms of the mean and variance.
* These would be reasonable estimators if we had a full set of Gumbel
* distributed variates. They'll be off for a truncated sample, but
* close enough to be a useful starting point.
*/
esl_stats_DMean(x, n, &mean, &variance);
lambda = eslCONST_PI / sqrt(6.*variance);
mu = mean - 0.57722/lambda;
p[0] = mu;
p[1] = log(lambda); /* c.o.v. because lambda is constrained to >0 */
u[0] = 2.0;
u[1] = 0.1;
/* Pass the problem to the optimizer. The work is done by the
* equations in tevd_func() and tevd_grad().
*/
status = esl_min_ConjugateGradientDescent(p, u, 2,
&tevd_func, &tevd_grad,(void *)(&data),
1e-4, wrk, &fx);
*ret_mu = p[0];
*ret_lambda = exp(p[1]); /* reverse the c.o.v. */
return status;
}
/* fitting_engine()
* Fitting code shared by the FitComplete() and FitCensored() API.
*
* The fitting_engine(), in turn, is just an adaptor wrapped around
* the conjugate gradient descent minimizer.
*/
static int
fitting_engine(struct gev_data *data,
double *ret_mu, double *ret_lambda, double *ret_alpha)
{
double p[3]; /* parameter vector */
double u[3]; /* max initial step size vector */
double wrk[12]; /* 4 tmp vectors of length 3 */
double mean, variance;
double mu, lambda, alpha; /* initial param guesses */
double tol = 1e-6; /* convergence criterion for CG */
double fx; /* f(x) at minimum; currently unused */
int status;
/* Make an initial guess.
* (very good guess for complete data; merely sufficient for censored)
*/
esl_stats_DMean(data->x, data->n, &mean, &variance);
lambda = eslCONST_PI / sqrt(6.*variance);
mu = mean - 0.57722/lambda;
alpha = 0.0001;
p[0] = mu;
p[1] = log(lambda); /* c.o.v. from lambda to w */
p[2] = alpha;
/* max initial step sizes: keeps bracketing from exploding */
u[0] = 1.0;
u[1] = fabs(log(0.02));
u[2] = 0.02;
/* pass problem to the optimizer
*/
status = esl_min_ConjugateGradientDescent(p, u, 3,
&gev_func,
&gev_gradient,
(void *)data,
tol, wrk, &fx);
*ret_mu = p[0];
*ret_lambda = exp(p[1]);
*ret_alpha = p[2];
return status;
}
/* Function: esl_sxp_FitComplete()
*
* Purpose: Given a vector of <n> observed data samples <x[]>,
* find maximum likelihood parameters by conjugate gradient
* descent optimization.
*/
int
esl_sxp_FitComplete(double *x, int n,
double *ret_mu, double *ret_lambda, double *ret_tau)
{
struct sxp_data data;
double p[2], u[2], wrk[8];
double mu, tau, lambda;
double mean;
double tol = 1e-6;
double fx;
int status;
/* initial guesses; mu is definitely = minimum x,
* and just use arbitrary #'s to init lambda, tau
*/
mu = esl_vec_DMin(x, n);
esl_stats_DMean(x, n, &mean, NULL);
lambda = 1 / (mean - mu);
tau = 0.9;
/* load data structure, param vector, and step vector */
data.x = x;
data.n = n;
data.mu = mu;
p[0] = log(lambda);
p[1] = log(tau);
u[0] = 1.0;
u[1] = 1.0;
/* hand it off */
status = esl_min_ConjugateGradientDescent(p, u, 2,
&sxp_complete_func,
NULL,
(void *) (&data), tol, wrk, &fx);
*ret_mu = mu;
*ret_lambda = exp(p[0]);
*ret_tau = exp(p[1]);
return status;
}
int
main(int argc, char **argv)
{
int n = 6;
double a[6] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
double x[6] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
double u[6] = { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
double wrk[24];
double fx;
int i;
esl_min_ConjugateGradientDescent(x, u, n,
&example_func, &example_dfunc, (void *) a,
0.0001, wrk, &fx);
printf("At minimum: f(x) = %g\n", fx);
printf("vector x = ");
for (i = 0; i < 6; i++) printf("%g ", x[i]);
printf("\n");
return 0;
}
/* Function: esl_mixgev_FitComplete()
*
* Purpose: Given <n> observed data values <x[0..n-1]>, and
* an initial guess at a mixture GEV fit to those data
* <mg>, use conjugate gradient descent to perform
* a locally optimal maximum likelihood mixture
* GEV parameter fit to the data.
*
* To obtain a reasonable initial guess for <mg>,
* see <esl_mixgev_FitGuess()>.
*
* Args: x - observed data, <x[0..n-1]>.
* n - number of samples in <x>
* mg - mixture GEV to estimate, w/ params set to
* an initial guess.
*
* Returns: <eslOK> on success, and <mg> contains local
* ML estimate for mixture GEV parameters.
*
* Throws: <eslEMEM> on allocation error, and <mg> is unchanged
* from its initial state.
*/
int
esl_mixgev_FitComplete(double *x, int n, ESL_MIXGEV *mg)
{
struct mixgev_data data;
int status;
double *p = NULL;
double *u = NULL;
double *wrk = NULL;
double tol;
int np;
double fx;
int k;
int i;
tol = 1e-6;
/* Determine number of free parameters and allocate
*/
np = mg->K-1; /* K-1 mix coefficients free */
for (k = 0; k < mg->K; k++)
np += (mg->isgumbel[k])? 2 : 3;
ESL_ALLOC(p, sizeof(double) * np);
ESL_ALLOC(u, sizeof(double) * np);
ESL_ALLOC(wrk, sizeof(double) * np * 4);
/* Copy shared info into the "data" structure
*/
data.x = x;
data.n = n;
data.wrk = wrk;
data.mg = mg;
/* From mg, create the parameter vector.
*/
mixgev_pack_paramvector(p, np, mg);
/* Define the step size vector u.
*/
i = 0;
for (k = 1; k < mg->K; k++) u[i++] = 1.0;
for (k = 0; k < mg->K; k++)
{
u[i++] = 1.0;
u[i++] = 1.0;
if (! mg->isgumbel[k]) u[i++] = 0.02;
}
ESL_DASSERT1( (np == i) );
/* Feed it all to the mighty optimizer.
*/
status = esl_min_ConjugateGradientDescent(p, u, np, &mixgev_complete_func, NULL,
(void *) (&data), tol, wrk, &fx);
if (status != eslOK) goto ERROR;
/* Convert the final parameter vector back to a mixture GEV
*/
mixgev_unpack_paramvector(p, np, mg);
free(p);
free(u);
free(wrk);
return eslOK;
ERROR:
if (p != NULL) free(p);
if (u != NULL) free(u);
if (wrk != NULL) free(wrk);
return status;
}
/* Function: esl_wei_FitCompleteBinned()
*
* Purpose: Given a histogram <g> with binned observations, where each
* bin i holds some number of observed samples x with values from
* lower bound l to upper bound u (that is, $l < x \leq u$), and
* <mu>, the known offset (minimum value) of the distribution;
* return maximum likelihood parameters <ret_lambda>
* and <ret_tau>.
*
* Args: x - complete GEV-distributed data [0..n-1]
* n - number of samples in <x>
* ret_mu - lower bound of the distribution (all x_i > mu)
* ret_lambda - RETURN: maximum likelihood estimate of lambda
* ret_tau - RETURN: maximum likelihood estimate of tau
*
* Returns: <eslOK> on success.
*
* Throws: <eslENOHALT> if the fit doesn't converge.
*
* Xref: STL9/136-137
*/
int
esl_wei_FitCompleteBinned(ESL_HISTOGRAM *h, double *ret_mu,
double *ret_lambda, double *ret_tau)
{
struct wei_binned_data data;
double p[2]; /* parameter vector */
double u[2]; /* max initial step size vector */
double wrk[8]; /* 4 tmp vectors of length 2 */
double mean;
double mu, lambda, tau; /* initial param guesses */
double tol = 1e-6; /* convergence criterion for CG */
double fx; /* f(x) at minimum; currently unused */
int status;
int i;
double ai;
/* Set the fixed mu.
* Make a good initial guess of lambda, based on exponential fit.
* Choose an arbitrary tau.
*/
if (h->is_tailfit) mu = h->phi; /* all x > mu in this case */
else if (h->is_rounded) mu = esl_histogram_Bin2LBound(h, h->imin);
else mu = h->xmin;
mean = 0.;
for (i = h->cmin; i <= h->imax; i++)
{
ai = esl_histogram_Bin2LBound(h, i);
ai += 0.5*h->w; /* midpoint in bin */
mean += (double)h->obs[i] * ai;
}
mean /= h->No;
lambda = 1 / (mean - mu);
tau = 0.9;
/* load the data structure */
data.h = h;
data.mu = mu;
/* Change of variables;
* lambda > 0, so c.o.v. lambda = exp^w, w = log(lambda);
* tau > 0, same c.o.v.
*/
p[0] = log(lambda);
p[1] = log(tau);
u[0] = 1.0;
u[1] = 1.0;
/* pass problem to the optimizer
*/
status = esl_min_ConjugateGradientDescent(p, u, 2,
&wei_binned_func, NULL,
(void *)(&data),
tol, wrk, &fx);
*ret_mu = mu;
*ret_lambda = exp(p[0]);
*ret_tau = exp(p[1]);
return status;
}
