1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138
|
## Copyright (C) 2011-2025 L. Markowsky <lmarkowsky@gmail.com>
##
## This file is part of the fuzzy-logic-toolkit.
##
## The fuzzy-logic-toolkit 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.
##
## The fuzzy-logic-toolkit 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 the fuzzy-logic-toolkit; see the file COPYING. If not,
## see <http://www.gnu.org/licenses/>.
## -*- texinfo -*-
## @deftypefn {Function File} {@var{y} =} gaussmf (@var{x}, @var{params})
## @deftypefnx {Function File} {@var{y} =} gaussmf (@var{[x1 x2 ... xn]}, @var{[sig c]})
##
## For a given domain @var{x} and parameters @var{params} (or @var{[sig c]}),
## return the corresponding @var{y} values for the Gaussian membership
## function. This membership function is shaped like the Gaussian (normal)
## distribution, but scaled to have a maximum value of 1. By contrast, the
## area under the Gaussian distribution curve is 1.
##
## The argument @var{x} must be a real number or a non-empty vector of strictly
## increasing real numbers, and @var{sig} and @var{c} must be real numbers.
## This membership function satisfies the equation:
##
## @verbatim
## f(x) = exp((-(x - c)^2)/(2 * sig^2))
## @end verbatim
##
## which always returns values in the range [0, 1].
##
## Just as for the Gaussian (normal) distribution, the parameters @var{sig} and
## @var{c} represent:
##
## @verbatim
## sig^2 == the variance (a measure of the width of the curve)
## c == the center (the mean; the x value of the peak)
## @end verbatim
##
## For larger values of @var{sig}, the curve is flatter, and for smaller values
## of sig, the curve is narrower. The @var{y} value at the center is always 1:
##
## @verbatim
## f(c) == 1
## @end verbatim
##
## To run the demonstration code, type "@t{demo gaussmf}" (without the quotation
## marks) at the Octave prompt.
##
## @seealso{dsigmf, gauss2mf, gbellmf, pimf, psigmf, sigmf, smf, trapmf, trimf, zmf}
## @end deftypefn
## Author: L. Markowsky
## Keywords: fuzzy-logic-toolkit fuzzy membership gaussian
## Directory: fuzzy-logic-toolkit/inst/
## Filename: gaussmf.m
## Last-Modified: 13 Jun 2024
function y = gaussmf (x, params)
## If the caller did not supply 2 argument values with the correct
## types, print an error message and halt.
if (nargin != 2)
error ("gaussmf requires 2 arguments\n");
elseif (!is_domain (x))
error ("gaussmf's first argument must be a valid domain\n");
elseif (!are_mf_params ('gaussmf', params))
error ("gaussmf's second argument must be a parameter vector\n");
endif
## Calculate and return the y values of the membership function on the
## domain x.
sig = params(1);
c = params(2);
y_val = @(x_val) exp ((-(x_val - c)^2)/(2 * sig^2));
y = arrayfun (y_val, x);
endfunction
%!demo
%! x = -5:0.1:5;
%! params = [0.5 0];
%! y1 = gaussmf(x, params);
%! params = [1 0];
%! y2 = gaussmf(x, params);
%! params = [2 0];
%! y3 = gaussmf(x, params);
%! figure('NumberTitle', 'off', 'Name', 'gaussmf demo');
%! plot(x, y1, 'r;params = [0.5 0];', 'LineWidth', 2);
%! hold on ;
%! plot(x, y2, 'b;params = [1 0];', 'LineWidth', 2);
%! hold on ;
%! plot(x, y3, 'g;params = [2 0];', 'LineWidth', 2);
%! ylim([-0.1 1.1]);
%! xlabel('Crisp Input Value');
%! ylabel('Degree of Membership');
%! grid;
%! hold;
%!test
%! x = -5:5;
%! params = [2 0];
%! y = [0.043937 0.1353 0.3247 0.6065 0.8825 1 ...
%! 0.8825 0.6065 0.3247 0.1353 0.043937];
%! z = gaussmf(x, params);
%! assert(z, y, 1e-4);
## Test input validation
%!error <gaussmf requires 2 arguments>
%! gaussmf()
%!error <gaussmf requires 2 arguments>
%! gaussmf(1)
%!error <gaussmf: function called with too many inputs>
%! gaussmf(1, 2, 3)
%!error <gaussmf's first argument must be a valid domain>
%! gaussmf([1 0], 2)
%!error <gaussmf's second argument must be a parameter vector>
%! gaussmf(1, 2)
%!error <gaussmf's second argument must be a parameter vector>
%! gaussmf(0:100, [])
%!error <gaussmf's second argument must be a parameter vector>
%! gaussmf(0:100, [30])
%!error <gaussmf's second argument must be a parameter vector>
%! gaussmf(0:100, [2 3 4 5])
%!error <gaussmf's second argument must be a parameter vector>
%! gaussmf(0:100, [90 80 30])
%!error <gaussmf's second argument must be a parameter vector>
%! gaussmf(0:100, 'abc')
|