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-rw-r--r--gi/pyp-topics/src/slice-sampler.h192
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diff --git a/gi/pyp-topics/src/slice-sampler.h b/gi/pyp-topics/src/slice-sampler.h
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-//! slice-sampler.h is an MCMC slice sampler
-//!
-//! Mark Johnson, 1st August 2008
-
-#ifndef SLICE_SAMPLER_H
-#define SLICE_SAMPLER_H
-
-#include <algorithm>
-#include <cassert>
-#include <cmath>
-#include <iostream>
-#include <limits>
-
-//! slice_sampler_rfc_type{} returns the value of a user-specified
-//! function if the argument is within range, or - infinity otherwise
-//
-template <typename F, typename Fn, typename U>
-struct slice_sampler_rfc_type {
- F min_x, max_x;
- const Fn& f;
- U max_nfeval, nfeval;
- slice_sampler_rfc_type(F min_x, F max_x, const Fn& f, U max_nfeval)
- : min_x(min_x), max_x(max_x), f(f), max_nfeval(max_nfeval), nfeval(0) { }
-
- F operator() (F x) {
- if (min_x < x && x < max_x) {
- assert(++nfeval <= max_nfeval);
- F fx = f(x);
- assert(std::isfinite(fx));
- return fx;
- }
- else
- return -std::numeric_limits<F>::infinity();
- }
-}; // slice_sampler_rfc_type{}
-
-//! slice_sampler1d() implements the univariate "range doubling" slice sampler
-//! described in Neal (2003) "Slice Sampling", The Annals of Statistics 31(3), 705-767.
-//
-template <typename F, typename LogF, typename Uniform01>
-F slice_sampler1d(const LogF& logF0, //!< log of function to sample
- F x, //!< starting point
- Uniform01& u01, //!< uniform [0,1) random number generator
- F min_x = -std::numeric_limits<F>::infinity(), //!< minimum value of support
- F max_x = std::numeric_limits<F>::infinity(), //!< maximum value of support
- F w = 0.0, //!< guess at initial width
- unsigned nsamples=1, //!< number of samples to draw
- unsigned max_nfeval=200) //!< max number of function evaluations
-{
- typedef unsigned U;
- slice_sampler_rfc_type<F,LogF,U> logF(min_x, max_x, logF0, max_nfeval);
-
- assert(std::isfinite(x));
-
- if (w <= 0.0) { // set w to a default width
- if (min_x > -std::numeric_limits<F>::infinity() && max_x < std::numeric_limits<F>::infinity())
- w = (max_x - min_x)/4;
- else
- w = std::max(((x < 0.0) ? -x : x)/4, (F) 0.1);
- }
- assert(std::isfinite(w));
-
- F logFx = logF(x);
- for (U sample = 0; sample < nsamples; ++sample) {
- F logY = logFx + log(u01()+1e-100); //! slice logFx at this value
- assert(std::isfinite(logY));
-
- F xl = x - w*u01(); //! lower bound on slice interval
- F logFxl = logF(xl);
- F xr = xl + w; //! upper bound on slice interval
- F logFxr = logF(xr);
-
- while (logY < logFxl || logY < logFxr) // doubling procedure
- if (u01() < 0.5)
- logFxl = logF(xl -= xr - xl);
- else
- logFxr = logF(xr += xr - xl);
-
- F xl1 = xl;
- F xr1 = xr;
- while (true) { // shrinking procedure
- F x1 = xl1 + u01()*(xr1 - xl1);
- if (logY < logF(x1)) {
- F xl2 = xl; // acceptance procedure
- F xr2 = xr;
- bool d = false;
- while (xr2 - xl2 > 1.1*w) {
- F xm = (xl2 + xr2)/2;
- if ((x < xm && x1 >= xm) || (x >= xm && x1 < xm))
- d = true;
- if (x1 < xm)
- xr2 = xm;
- else
- xl2 = xm;
- if (d && logY >= logF(xl2) && logY >= logF(xr2))
- goto unacceptable;
- }
- x = x1;
- goto acceptable;
- }
- goto acceptable;
- unacceptable:
- if (x1 < x) // rest of shrinking procedure
- xl1 = x1;
- else
- xr1 = x1;
- }
- acceptable:
- w = (4*w + (xr1 - xl1))/5; // update width estimate
- }
- return x;
-}
-
-/*
-//! slice_sampler1d() implements a 1-d MCMC slice sampler.
-//! It should be correct for unimodal distributions, but
-//! not for multimodal ones.
-//
-template <typename F, typename LogP, typename Uniform01>
-F slice_sampler1d(const LogP& logP, //!< log of distribution to sample
- F x, //!< initial sample
- Uniform01& u01, //!< uniform random number generator
- F min_x = -std::numeric_limits<F>::infinity(), //!< minimum value of support
- F max_x = std::numeric_limits<F>::infinity(), //!< maximum value of support
- F w = 0.0, //!< guess at initial width
- unsigned nsamples=1, //!< number of samples to draw
- unsigned max_nfeval=200) //!< max number of function evaluations
-{
- typedef unsigned U;
- assert(std::isfinite(x));
- if (w <= 0.0) {
- if (min_x > -std::numeric_limits<F>::infinity() && max_x < std::numeric_limits<F>::infinity())
- w = (max_x - min_x)/4;
- else
- w = std::max(((x < 0.0) ? -x : x)/4, 0.1);
- }
- // TRACE4(x, min_x, max_x, w);
- F logPx = logP(x);
- assert(std::isfinite(logPx));
- U nfeval = 1;
- for (U sample = 0; sample < nsamples; ++sample) {
- F x0 = x;
- F logU = logPx + log(u01()+1e-100);
- assert(std::isfinite(logU));
- F r = u01();
- F xl = std::max(min_x, x - r*w);
- F xr = std::min(max_x, x + (1-r)*w);
- // TRACE3(x, logPx, logU);
- while (xl > min_x && logP(xl) > logU) {
- xl -= w;
- w *= 2;
- ++nfeval;
- if (nfeval >= max_nfeval)
- std::cerr << "## Error: nfeval = " << nfeval << ", max_nfeval = " << max_nfeval << ", sample = " << sample << ", nsamples = " << nsamples << ", r = " << r << ", w = " << w << ", xl = " << xl << std::endl;
- assert(nfeval < max_nfeval);
- }
- xl = std::max(xl, min_x);
- while (xr < max_x && logP(xr) > logU) {
- xr += w;
- w *= 2;
- ++nfeval;
- if (nfeval >= max_nfeval)
- std::cerr << "## Error: nfeval = " << nfeval << ", max_nfeval = " << max_nfeval << ", sample = " << sample << ", nsamples = " << nsamples << ", r = " << r << ", w = " << w << ", xr = " << xr << std::endl;
- assert(nfeval < max_nfeval);
- }
- xr = std::min(xr, max_x);
- while (true) {
- r = u01();
- x = r*xl + (1-r)*xr;
- assert(std::isfinite(x));
- logPx = logP(x);
- // TRACE4(logPx, x, xl, xr);
- assert(std::isfinite(logPx));
- ++nfeval;
- if (nfeval >= max_nfeval)
- std::cerr << "## Error: nfeval = " << nfeval << ", max_nfeval = " << max_nfeval << ", sample = " << sample << ", nsamples = " << nsamples << ", r = " << r << ", w = " << w << ", xl = " << xl << ", xr = " << xr << ", x = " << x << std::endl;
- assert(nfeval < max_nfeval);
- if (logPx > logU)
- break;
- else if (x > x0)
- xr = x;
- else
- xl = x;
- }
- // w = (4*w + (xr-xl))/5; // gradually adjust w
- }
- // TRACE2(logPx, x);
- return x;
-} // slice_sampler1d()
-*/
-
-#endif // SLICE_SAMPLER_H
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