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## 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} {} plotmf (@var{fis}, @var{in_or_out}, @var{var_index})
## @deftypefnx {Function File} {} plotmf (@var{fis}, @var{in_or_out}, @var{var_index}, @var{y_lower_limit})
## @deftypefnx {Function File} {} plotmf (@var{fis}, @var{in_or_out}, @var{var_index}, @var{y_lower_limit}, @var{y_upper_limit})
##
## Plot the membership functions defined for the specified FIS input or output
## variable on a single set of axes. Fuzzy output membership functions are
## represented by the [0, 1]-valued fuzzy functions, and constant output
## membership functions are represented by unit-valued singleton spikes.
## Linear output membership functions, however, are represented by
## two-dimensional lines y = ax + c, regardless of how many dimensions the
## linear function is defined to have. In effect, all of the other dimensions
## of the linear function are set to 0.
##
## If both constant and linear membership functions are used for a single FIS
## output, then two sets of axes are used: one for the constant membership
## functions, and another for the linear membership functions. To plot both
## constant and linear membership functions together, or to plot constant
## membership functions as horizontal lines instead of unit-valued spikes,
## represent the constant membership functions using 'linear' functions, with
## 0 for all except the last parameter, and with the desired constant value as
## the last parameter.
##
## The types/values of the arguments are expected to be:
##
## @multitable @columnfractions .30 .65
## @headitem Argument @tab Expected Type or Value
## @item @var{fis}
## @tab an FIS structure
## @item @var{in_or_out}
## @tab either 'input' or 'output' (case-insensitive)
## @item @var{var_index}
## @tab an FIS input or output variable index
## @item @var{y_lower_limit}
## @tab a real scalar (default value = -0.1)
## @item @var{y_upper_limit}
## @tab a real scalar (default value = 1.1)
## @end multitable
## @sp 1
## Six examples that use plotmf are:
## @itemize @bullet
## @item
## cubic_approx_demo.m
## @item
## heart_disease_demo_1.m
## @item
## heart_disease_demo_2.m
## @item
## investment_portfolio_demo.m
## @item
## linear_tip_demo.m
## @item
## mamdani_tip_demo.m
## @item
## sugeno_tip_demo.m
## @end itemize
##
## @seealso{gensurf}
## @end deftypefn
## Author: L. Markowsky
## Keywords: fuzzy-logic-toolkit fuzzy membership plot
## Directory: fuzzy-logic-toolkit/inst/
## Filename: plotmf.m
## Last-Modified: 12 Jun 2024
function plotmf (fis, in_or_out, var_index, ...
y_lower_limit = -0.1, y_upper_limit = 1.1)
## If the caller did not supply 3 argument values with the correct
## types, print an error message and halt.
if ((nargin < 3) || (nargin > 5))
error ("plotmf requires 3 - 5 arguments\n");
elseif (!is_fis (fis))
error ("plotmf's first argument must be an FIS structure\n");
elseif (!(is_string (in_or_out) && ...
ismember (tolower (in_or_out), {'input', 'output'})))
error ("plotmf's second argument must be 'input' or 'output'\n");
elseif (!is_var_index (fis, in_or_out, var_index))
error ("plotmf's third argument must be a variable index\n");
elseif (!(is_real (y_lower_limit) && is_real (y_upper_limit)))
error ("plotmf's 4th and 5th arguments must be real scalars\n");
endif
## Select specified variable and construct the window title.
if (strcmpi (in_or_out, 'input'))
var = fis.input(var_index);
window_title = [' Input ' num2str(var_index) ' Term Set'];
else
var = fis.output(var_index);
window_title = [' Output ' num2str(var_index) ' Term Set'];
endif
## Plot the membership functions for the specified variable.
## Cycle through the five colors: red, blue, green, magenta, cyan.
## Display the membership function names in a legend.
colors = ["r" "b" "g" "m" "c"];
x = linspace (var.range(1), var.range(2), 1001);
num_mfs = columns (var.mf);
## Define vectors to keep track of linear and non-linear mfs.
linear_mfs = zeros (1, num_mfs);
for i = 1 : num_mfs
if (strcmp ('linear', var.mf(i).type))
linear_mfs(i) = 1;
endif
endfor
fuzzy_and_constant_mfs = 1 - linear_mfs;
## Plot the fuzzy or constant membership functions together on a set
## of axes.
if (sum (fuzzy_and_constant_mfs))
figure ('NumberTitle', 'off', 'Name', window_title);
## Plot the mfs.
for i = 1 : num_mfs
if (fuzzy_and_constant_mfs(i))
y = evalmf_private (x, var.mf(i).params, var.mf(i).type);
y_label = [colors(mod(i-1,5)+1) ";" var.mf(i).name ";"];
plot (x, y, y_label, 'LineWidth', 2);
hold on;
endif
endfor
## Adjust the y-axis, label both axes, and display a dotted grid.
ylim ([y_lower_limit y_upper_limit]);
xlabel (var.name, 'FontWeight', 'bold');
ylabel ('Degree of Membership', 'FontWeight', 'bold');
grid;
hold;
endif
## Plot the linear membership functions together on a separate set
## of axes.
if (sum (linear_mfs))
figure ('NumberTitle', 'off', 'Name', window_title);
## Plot the mfs.
for i = 1 : num_mfs
if (linear_mfs(i))
y = evalmf_private (x, var.mf(i).params, var.mf(i).type);
y_label = [colors(mod(i-1,5)+1) ";" var.mf(i).name ";"];
plot (x, y, y_label, 'LineWidth', 2);
hold on;
endif
endfor
## Adjust the y-axis, label both axes, and display a dotted grid.
ylim ([y_lower_limit y_upper_limit]);
xlabel ('X', 'FontWeight', 'bold');
ylabel (var.name, 'FontWeight', 'bold');
grid;
hold;
endif
endfunction
%!shared fis
%! fis = readfis ('cubic_approximator.fis');
## Test input validation
%!error <plotmf requires 3 - 5 arguments>
%! plotmf()
%!error <plotmf requires 3 - 5 arguments>
%! plotmf(1)
%!error <plotmf requires 3 - 5 arguments>
%! plotmf(1, 2)
%!error <plotmf: function called with too many inputs>
%! plotmf(1, 2, 3, 4, 5, 6)
%!error <plotmf's first argument must be an FIS structure>
%! plotmf(1, 2, 3)
%!error <plotmf's second argument must be 'input' or 'output'>
%! plotmf(fis, 2, 3)
%!error <plotmf's third argument must be a variable index>
%! plotmf(fis, 'input', 3)
%!error <plotmf's 4th and 5th arguments must be real scalars>
%! plotmf(fis, 'input', 1, 2j)
%!error <plotmf's 4th and 5th arguments must be real scalars>
%! plotmf(fis, 'input', 1, 0, 2j)
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