Mercurial > hg > octave-lyh
view scripts/plot/hist.m @ 5443:ec8c33dcd1bf
[project @ 2005-09-08 01:40:57 by jwe]
author | jwe |
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date | Thu, 08 Sep 2005 01:40:58 +0000 |
parents | 4c8a2e4e0717 |
children | 3d9ddfdc6d3e |
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## Copyright (C) 1996, 1997 John W. Eaton ## ## 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 2, 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, write to the Free ## Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA ## 02110-1301, USA. ## -*- texinfo -*- ## @deftypefn {Function File} {} hist (@var{y}, @var{x}, @var{norm}) ## Produce histogram counts or plots. ## ## With one vector input argument, plot a histogram of the values with ## 10 bins. The range of the histogram bins is determined by the range ## of the data. ## ## Given a second scalar argument, use that as the number of bins. ## ## Given a second vector argument, use that as the centers of the bins, ## with the width of the bins determined from the adjacent values in ## the vector. ## ## If third argument is provided, the histogram is normalised such that ## the sum of the bars is equal to @var{norm}. ## ## Extreme values are lumped in the first and last bins. ## ## With two output arguments, produce the values @var{nn} and @var{xx} such ## that @code{bar (@var{xx}, @var{nn})} will plot the histogram. ## @end deftypefn ## ## @seealso{bar} ## Author: jwe function [nn, xx] = hist (y, x, norm) if (nargin < 1 || nargin > 3) usage ("[nn, xx] = hist (y, x, norm)"); endif arg_is_vector = isvector (y); if (rows (y) == 1) y = y(:); endif if (isreal (y)) max_val = max (y); min_val = min (y); else error ("hist: first argument must be a vector"); endif if (nargin == 1) n = 10; x = [0.5:n]'/n; x = x * (max_val - min_val) + ones(size(x)) * min_val; else ## nargin is either 2 or 3 if (isscalar (x)) n = x; if (n <= 0) error ("hist: number of bins must be positive"); endif x = [0.5:n]'/n; x = x * (max_val - min_val) + ones(size(x)) * min_val; elseif (isreal (x)) if (isvector (x)) x = x(:); endif tmp = sort (x); if (any (tmp != x)) warning ("hist: bin values not sorted on input"); x = tmp; endif else error ("hist: second argument must be a scalar or a vector"); endif endif cutoff = (x(1:end-1,:) + x(2:end,:)) / 2; n = rows (x); if (n < 30 && columns (x) == 1) ## The following algorithm works fastest for n less than about 30. chist = zeros (n+1, columns (y)); for i = 1:n-1 chist(i+1,:) = sum (y <= cutoff(i)); endfor chist(n+1,:) = rows (y); else ## The following algorithm works fastest for n greater than about 30. ## Put cutoff elements between boundaries, integrate over all ## elements, keep totals at boundaries. [s, idx] = sort ([y; cutoff]); len = rows (y); chist = cumsum (idx <= len); t1 = zeros (1, columns (y)); t2 = reshape (chist(idx > len), size (cutoff)); t3 = chist(end,:); chist = [t1; t2; t3]; endif freq = diff (chist); if (nargin == 3) ## Normalise the histogram. freq = freq / rows (y) * norm; endif if (nargout > 0) if (arg_is_vector) nn = freq'; xx = x'; else nn = freq; xx = x; endif else bar (x, freq); endif endfunction %!test %! [nn,xx]=hist([1:4],3); %! assert(xx, [1.5,2.5,3.5]); %! assert(nn, [2,1,1]); %!test %! [nn,xx]=hist([1:4]',3); %! assert(xx, [1.5,2.5,3.5]'); %! assert(nn, [2,1,1]'); %!test %! [nn,xx]=hist([[1:4]',[1:4]'],3); %! assert(xx, [[1.5,2.5,3.5]',[1.5,2.5,3.5]']); %! assert(nn, [[2,1,1]',[2,1,1]']); %!assert(hist(1,1),1); %!test %! for n = [10, 30, 100, 1000] %! assert( sum(hist([1:n], n)), n ); %! assert( sum(hist([1:n], [2:n-1])), n); %! assert( sum(hist([1:n], [1:n])), n ); %! assert( sum(hist([1:n], 29)), n); %! assert( sum(hist([1:n], 30)), n); %! endfor