## Copyright (C) 2022-2023 Andreas Bertsatos <abertsatos@biol.uoa.gr>
## Copyright (C) 2022 Andrew Penn <A.C.Penn@sussex.ac.uk>
##
## This file is part of the statistics package for GNU Octave.
##
## This program is free software; you can redistribute it and/or modify it under
## the terms of the GNU General Public License as published by the Free Software
## Foundation; either version 3 of the License, or (at your option) any later
## version.
##
## This program is distributed in the hope that it will be useful, but WITHOUT
## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
## details.
##
## You should have received a copy of the GNU General Public License along with
## this program; if not, see <http://www.gnu.org/licenses/>.

## -*- texinfo -*-
## @deftypefn  {statistics} {@var{paramhat} =} evfit (@var{x})
## @deftypefnx {statistics} {[@var{paramhat}, @var{paramci}] =} evfit (@var{x})
## @deftypefnx {statistics} {[@var{paramhat}, @var{paramci}] =} evfit (@var{x}, @var{alpha})
## @deftypefnx {statistics} {[@dots{}] =} evfit (@var{x}, @var{alpha}, @var{censor})
## @deftypefnx {statistics} {[@dots{}] =} evfit (@var{x}, @var{alpha}, @var{censor}, @var{freq})
## @deftypefnx {statistics} {[@dots{}] =} evfit (@var{x}, @var{alpha}, @var{censor}, @var{freq}, @var{options})
##
## Estimate parameters and confidence intervals for the extreme value
## distribution.
##
## @code{@var{paramhat} = evfit (@var{x})} returns the maximum likelihood
## estimates of the parameters of the extreme value distribution (also known as
## the Gumbel or the type I generalized extreme value distribution) given the
## data in @var{x}.  @qcode{@var{paramhat}(1)} is the location parameter,
## @var{mu}, and @qcode{@var{paramhat}(2)} is the scale parameter, @var{sigma}.
##
## @code{[@var{paramhat}, @var{paramci}] = evfit (@var{x})} returns the 95%
## confidence intervals for the parameter estimates.
##
## @code{[@dots{}] = evfit (@var{x}, @var{alpha})} also returns the
## @qcode{100 * (1 - @var{alpha})} percent confidence intervals for the
## parameter estimates.  By default, the optional argument @var{alpha} is
## 0.05 corresponding to 95% confidence intervals.  Pass in @qcode{[]} for
## @var{alpha} to use the default values.
##
## @code{[@dots{}] = evfit (@var{x}, @var{alpha}, @var{censor})} accepts a
## boolean vector, @var{censor}, of the same size as @var{x} with @qcode{1}s for
## observations that are right-censored and @qcode{0}s for observations that are
## observed exactly.  By default, or if left empty,
## @qcode{@var{censor} = zeros (size (@var{x}))}.
##
## @code{[@dots{}] = evfit (@var{x}, @var{alpha}, @var{censor}, @var{freq})}
## accepts a frequency vector, @var{freq}, of the same size as @var{x}.
## @var{freq} typically contains integer frequencies for the corresponding
## elements in @var{x}, but it can contain any non-integer non-negative values.
## By default, or if left empty, @qcode{@var{freq} = ones (size (@var{x}))}.
##
## @code{[@dots{}] = evfit (@dots{}, @var{options})} specifies control
## parameters for the iterative algorithm used to compute the maximum likelihood
## estimates.  @var{options} is a structure with the following field and its
## default value:
## @itemize
## @item @qcode{@var{options}.Display = "off"}
## @item @qcode{@var{options}.MaxFunEvals = 400}
## @item @qcode{@var{options}.MaxIter = 200}
## @item @qcode{@var{options}.TolX = 1e-6}
## @end itemize
##
## The Gumbel distribution is used to model the distribution of the maximum (or
## the minimum) of a number of samples of various distributions.  This version
## is suitable for modeling minima.  For modeling maxima, use the alternative
## Gumbel fitting function, @code{gumbelfit}.
##
## Further information about the Gumbel distribution can be found at
## @url{https://en.wikipedia.org/wiki/Gumbel_distribution}
##
## @seealso{evcdf, evinv, evpdf, evrnd, evlike, evstat, gumbelfit}
## @end deftypefn

function [paramhat, paramci] = evfit (x, alpha, censor, freq, options)

  ## Check X for being a double precision vector
  if (! isvector (x) || ! isa (x, 'double'))
    error ("evfit: X must be a double-precision vector.");
  endif

  ## Check that X does not contain missing values (NaNs)
  if (any (isnan (x)))
    error ("evfit: X must NOT contain missing values (NaNs).");
  endif

  ## Check alpha
  if (nargin < 2 || isempty (alpha))
    alpha = 0.05;
  else
    if (! isscalar (alpha) || ! isreal (alpha) || alpha <= 0 || alpha >= 1)
      error ("evfit: wrong value for ALPHA.");
    endif
  endif

  ## Check censor vector
  if (nargin < 3 || isempty (censor))
    censor = zeros (size (x));
  elseif (! isequal (size (x), size (censor)))
    error ("evfit: X and CENSOR vectors mismatch.");
  endif

  ## Parse FREQ argument or add default
  if (nargin < 4 || isempty (freq))
    freq = ones (size (x));
  elseif (! isequal (size (x), size (freq)))
    error ("evfit: X and FREQ vectors mismatch.");
  elseif (any (freq < 0))
    error ("evfit: FREQ must not contain negative values.");
  endif

  ## Get options structure or add defaults
  if (nargin < 5)
    options.Display = 'off';
    options.MaxFunEvals = 400;
    options.MaxIter = 200;
    options.TolX = 1e-6;
  else
    if (! isstruct (options) || ! isfield (options, 'Display') ||
        ! isfield (options, 'MaxFunEvals') || ! isfield (options, 'MaxIter')
                                           || ! isfield (options, 'TolX'))
      error (strcat ("evfit: 'options' 5th argument must be a", ...
                     " structure with 'Display', 'MaxFunEvals',", ...
                     " 'MaxIter', and 'TolX' fields present."));
    endif
  endif

  ## Remove zeros and NaNs from frequency vector (if necessary)
  if (! all (freq == 1))
    remove = freq == 0 | isnan (freq);
    x(remove) = [];
    censor(remove) = [];
    freq(remove) = [];
  endif

  ## If X is a column vector, make X, CENSOR, and FREQ row vectors
  if (size (x, 1) > 1)
    x = x(:)';
    censor = censor(:)';
    freq = freq(:)';
  endif

  ## Censor x and get number of samples
  sample_size = sum (freq);
  censored_sample_size = sum (freq .* censor);
  uncensored_sample_size = sample_size - censored_sample_size;
  x_range = range (x);
  x_max = max (x);

  ## Check cases that cannot make a fit.
  ## 1. All observations are censored
  if (sample_size == 0 || uncensored_sample_size == 0 || ! isfinite (x_range))
    paramhat = NaN (1, 2);
    paramci = NaN (2, 2);
    return
  endif

  ## 2. Constant x in X
  if (censored_sample_size == 0 && x_range == 0)
    paramhat = [x(1), 0];
    if (sample_size == 1)
      paramci = [-Inf, 0; Inf, Inf];
    else
      paramci = [paramhat, paramhat];
    endif
    return
  elseif (censored_sample_size == 0 && x_range != 0)
    ## Data can fit, so preprocess them to make likelihood eqn more stable.
    ## Shift x to max(x) == 0, min(x) = -1.
    x_0 = (x - x_max) ./ x_range;
    ## Get a rough initial estimate for scale parameter
    initial_sigma_parm = (sqrt (6) * std (x_0)) / pi;
    uncensored_weights = sum (freq .* x_0) ./ sample_size;
  endif

  ## 3. All uncensored observations are equal and greater than all censored ones
  uncensored_x_range = range (x(censor == 0));
  uncensored_x = x(censor == 0);
  if (censored_sample_size > 0 && uncensored_x_range == 0 ...
                               && uncensored_x(1) >= x_max)
    paramhat = [uncensored_x(1), 0];
    if uncensored_sample_size == 1
      paramci = [-Inf, 0; Inf, Inf];
    else
      paramci = [paramhat; paramhat];
    endif
    return
  else
    ## Data can fit, so preprocess them to make likelihood eqn more stable.
    ## Shift x to max(x) == 0, min(x) = -1.
    x_0 = (x - x_max) ./ x_range;
    ## Get a rough initial estimate for scale parameter
    if (uncensored_x_range > 0)
      [F_y, y] = ecdf (x_0, 'censoring', censor', 'frequency', freq');
      pmid = (F_y(1:(end-1)) + F_y(2:end)) / 2;
      linefit = polyfit (log (- log (1 - pmid)), y(2:end), 1);
      initial_sigma_parm = linefit(1);
    else
      initial_sigma_parm = 1;
    endif
    uncensored_weights = sum (freq .* x_0 .* (1 - censor)) ./ ...
                              uncensored_sample_size;
  endif

  ## Find lower and upper boundaries for bracketing the likelihood equation for
  ## the extreme value scale parameter
  if (evscale_lkeq (initial_sigma_parm, x_0, freq, uncensored_weights) > 0)
    upper = initial_sigma_parm;
    lower = 0.5 * upper;
    while (evscale_lkeq (lower, x_0, freq, uncensored_weights) > 0)
      upper = lower;
      lower = 0.5 * upper;
      if (lower <= realmin ('double'))
        error ("evfit: no solution for maximum likelihood estimates.");
      endif
    endwhile
    boundaries = [lower, upper];
  else
    lower = initial_sigma_parm;
    upper = 2 * lower;
    while (evscale_lkeq (upper, x_0, freq, uncensored_weights) < 0)
      lower = upper;
      upper = 2 * lower;
      if (upper > realmax ('double'))
        error ("evfit: no solution for maximum likelihood estimates.");
      endif
    endwhile
    boundaries = [lower, upper];
  endif

  ## Compute maximum likelihood for scale parameter as the root of the equation
  ## Custom code for finding the value within the boundaries [lower, upper] that
  ## evscale_lkeq function returns zero
  ## First check that there is a root within the boundaries
  new_lo = boundaries(1);
  new_up = boundaries(2);
  v_lower = evscale_lkeq (new_lo, x_0, freq, uncensored_weights);
  v_upper = evscale_lkeq (new_up, x_0, freq, uncensored_weights);
  if (! (sign (v_lower) * sign (v_upper) <= 0))
    error ("evfit: no solution for maximum likelihood estimates.");
  endif
  ## Get a value at mid boundary range
  old_sigma = new_lo;
  new_sigma = (new_lo + new_up) / 2;
  new_fzero = evscale_lkeq (new_sigma, x_0, freq, uncensored_weights);
  ## Start searching
  cur_iter = 0;
  max_iter = 1e+3;
  while (cur_iter < max_iter && abs (old_sigma - new_sigma) > options.TolX)
    cur_iter++;
    if (new_fzero < 0)
      old_sigma = new_sigma;
      new_lo = new_sigma;
      new_sigma = (new_lo + new_up) / 2;
      new_fzero = evscale_lkeq (new_sigma, x_0, freq, uncensored_weights);
    else
      old_sigma = new_sigma;
      new_up = new_sigma;
      new_sigma = (new_lo + new_up) / 2;
      new_fzero = evscale_lkeq (new_sigma, x_0, freq, uncensored_weights);
    endif
  endwhile

  ## Check for maximum number of iterations
  if (cur_iter == max_iter)
    warning (strcat ("evfit: maximum number of function ", ...
                     " evaluations (1e+4) has been reached."));
  endif

  ## Compute MU
  muhat = new_sigma .* log (sum (freq .* exp (x_0 ./ new_sigma)) ./ ...
                                             uncensored_sample_size);

  ## Transform MU and SIGMA back to original location and scale
  paramhat = [(x_range*muhat)+x_max, x_range*new_sigma];

  ## Compute the CI for MU and SIGMA
  if (nargout == 2)
    probs = [alpha/2; 1-alpha/2];
    [~, acov] = evlike (paramhat, x, censor, freq);
    transfhat = [paramhat(1), log(paramhat(2))];
    se = sqrt (diag (acov))';
    se(2) = se(2) ./ paramhat(2);
    paramci = norminv ([probs, probs], [transfhat; transfhat], [se; se]);
    paramci(:,2) = exp (paramci(:,2));
  endif
endfunction

## Likelihood equation for the extreme value scale parameter.
function v = evscale_lkeq (sigma, x, freq, x_weighted_uncensored)
  freq = freq .* exp (x ./ sigma);
  v = sigma + x_weighted_uncensored - sum (x .* freq) / sum (freq);
endfunction

%!demo
%! ## Sample 3 populations from different extreme value distributions
%! rand ('seed', 1);    # for reproducibility
%! r1 = evrnd (2, 5, 400, 1);
%! rand ('seed', 12);    # for reproducibility
%! r2 = evrnd (-5, 3, 400, 1);
%! rand ('seed', 13);    # for reproducibility
%! r3 = evrnd (14, 8, 400, 1);
%! r = [r1, r2, r3];
%!
%! ## Plot them normalized and fix their colors
%! hist (r, 25, 0.4);
%! h = findobj (gca, 'Type', 'patch');
%! set (h(1), 'facecolor', 'c');
%! set (h(2), 'facecolor', 'g');
%! set (h(3), 'facecolor', 'r');
%! ylim ([0, 0.28])
%! xlim ([-30, 30]);
%! hold on
%!
%! ## Estimate their MU and SIGMA parameters
%! mu_sigmaA = evfit (r(:,1));
%! mu_sigmaB = evfit (r(:,2));
%! mu_sigmaC = evfit (r(:,3));
%!
%! ## Plot their estimated PDFs
%! x = [min(r(:)):max(r(:))];
%! y = evpdf (x, mu_sigmaA(1), mu_sigmaA(2));
%! plot (x, y, '-pr');
%! y = evpdf (x, mu_sigmaB(1), mu_sigmaB(2));
%! plot (x, y, '-sg');
%! y = evpdf (x, mu_sigmaC(1), mu_sigmaC(2));
%! plot (x, y, '-^c');
%! legend ({'Normalized HIST of sample 1 with μ=2 and σ=5', ...
%!          'Normalized HIST of sample 2 with μ=-5 and σ=3', ...
%!          'Normalized HIST of sample 3 with μ=14 and σ=8', ...
%!          sprintf("PDF for sample 1 with estimated μ=%0.2f and σ=%0.2f", ...
%!                  mu_sigmaA(1), mu_sigmaA(2)), ...
%!          sprintf("PDF for sample 2 with estimated μ=%0.2f and σ=%0.2f", ...
%!                  mu_sigmaB(1), mu_sigmaB(2)), ...
%!          sprintf("PDF for sample 3 with estimated μ=%0.2f and σ=%0.2f", ...
%!                  mu_sigmaC(1), mu_sigmaC(2))})
%! title ('Three population samples from different extreme value distributions')
%! hold off

## Test output
%!test
%! x = 1:50;
%! [paramhat, paramci] = evfit (x);
%! paramhat_out = [32.6811, 13.0509];
%! paramci_out = [28.8504, 10.5294; 36.5118, 16.1763];
%! assert_equal (paramhat, paramhat_out, 1e-4);
%! assert_equal (paramci, paramci_out, 1e-4);
%!test
%! x = 1:50;
%! [paramhat, paramci] = evfit (x, 0.01);
%! paramci_out = [27.6468, 9.8426; 37.7155, 17.3051];
%! assert_equal (paramci, paramci_out, 1e-4);

## Test input validation
%!error<evfit: X must be a double-precision vector.> evfit (ones (2,5));
%!error<evfit: X must be a double-precision vector.> evfit (single (ones (1,5)));
%!error<evfit: X must NOT contain missing values> evfit ([1, 2, 3, 4, NaN]);
%!error<evfit: wrong value for ALPHA.> evfit ([1, 2, 3, 4, 5], 1.2);
%!error<evfit: X and FREQ vectors mismatch.>
%! evfit ([1 2 3], 0.05, [], [1 5])
%!error<evfit: FREQ must not contain negative values.>
%! evfit ([1 2 3], 0.05, [], [1 5 -1])
%!error<evfit: 'options' 5th argument must be a structure> ...
%! evfit ([1:10], 0.05, [], [], 5)
