Mercurial > octave-nkf
view scripts/statistics/distributions/empirical_cdf.m @ 20642:9d2023d1a63c
binoinv.m: Implement binary search algorithm for 28X performance increase (bug #34363).
* binoinv.m: Call new functions scalar_binoinv or vector_binoinv to calculate
binoinv. If there are still uncalculated values then call bin_search_binoinv
to perform binary search for remaining values. Add more BIST tests.
* binoinv.m (scalar_binoinv): New subfunction to calculate binoinv for scalar x.
Stops when x > 1000.
* binoinv.m (vector_binoinv): New subfunction to calculate binoinv for scalar x.
Stops when x > 1000.
author | Lachlan Andrew <lachlanbis@gmail.com> |
---|---|
date | Sun, 11 Oct 2015 19:49:40 -0700 |
parents | d9341b422488 |
children |
line wrap: on
line source
## Copyright (C) 2012 Rik Wehbring ## Copyright (C) 1996-2015 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} {} empirical_cdf (@var{x}, @var{data}) ## For each element of @var{x}, compute the cumulative distribution function ## (CDF) at @var{x} of the empirical distribution obtained from ## the univariate sample @var{data}. ## @end deftypefn ## Author: KH <Kurt.Hornik@wu-wien.ac.at> ## Description: CDF of the empirical distribution function cdf = empirical_cdf (x, data) if (nargin != 2) print_usage (); endif if (! isvector (data)) error ("empirical_cdf: DATA must be a vector"); endif cdf = discrete_cdf (x, data, ones (size (data))); endfunction %!shared x,v,y %! x = [-1 0.1 1.1 1.9 3]; %! v = 0.1:0.2:1.9; %! y = [0 0.1 0.6 1 1]; %!assert (empirical_cdf (x, v), y, eps) %!assert (empirical_cdf ([x(1) NaN x(3:5)], v), [0 NaN 0.6 1 1], eps) ## Test class of input preserved %!assert (empirical_cdf ([x, NaN], v), [y, NaN], eps) %!assert (empirical_cdf (single ([x, NaN]), v), single ([y, NaN]), eps) %!assert (empirical_cdf ([x, NaN], single (v)), single ([y, NaN]), eps) ## Test input validation %!error empirical_cdf () %!error empirical_cdf (1) %!error empirical_cdf (1,2,3) %!error empirical_cdf (1, ones (2))