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📄 discrete_inv.m

📁 similer program for matlab
💻 M
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## Copyright (C) 1996, 1997, 1998, 2000, 2002, 2004, 2005, 2006, 2007##               Kurt Hornik#### This file is part of Octave.#### Octave 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.#### Octave 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 Octave; see the file COPYING.  If not, see## <http://www.gnu.org/licenses/>.## -*- texinfo -*-## @deftypefn {Function File} {} discrete_inv (@var{x}, @var{v}, @var{p})## For each component of @var{x}, compute the quantile (the inverse of## the CDF) at @var{x} of the univariate distribution which assumes the## values in @var{v} with probabilities @var{p}.## @end deftypefn## Author: KH <Kurt.Hornik@wu-wien.ac.at>## Description: Quantile function of a discrete distributionfunction inv = discrete_inv (x, v, p)  if (nargin != 3)    print_usage ();  endif  sz = size (x);  if (! isvector (v))    error ("discrete_inv: v must be a vector");  elseif (! isvector (p) || (length (p) != length (v)))    error ("discrete_inv: p must be a vector with length (v) elements");  elseif (! (all (p >= 0) && any (p)))    error ("discrete_inv: p must be a nonzero, nonnegative vector");  endif  n = numel (x);  x = reshape (x, 1, n);  m = length (v);  v = sort (v);  s = reshape (cumsum (p / sum (p)), m, 1);  ## Allow storage allocated for P to be reclaimed.  p = [];  inv = NaN * ones (sz);  if (any (k = find (x == 0)))    inv(k) = -Inf;  endif  if (any (k = find (x == 1)))    inv(k) = v(m) * ones (size (k));  endif  if (any (k = find ((x > 0) & (x < 1))))    n = length (k);    ## The following loop is a space/time tradeoff in favor of space,    ## since the dataset may be large.    ##    ## Vectorized code is:    ##    ##     inv(k) = v(sum ((ones (m, 1) * x(k)) > (s * ones (1, n))) + 1);    for q = 1:n      inv(k(q)) = v(sum (x(k(q)) > s) + 1);    endfor  endifendfunction

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