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[Octave-cvsupdate] SF.net SVN: octave:[11097] trunk/octave-forge/main/fuzzy-logic-toolkit/inst
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From: <lm...@us...> - 2012-10-03 00:38:17
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Revision: 11097
http://octave.svn.sourceforge.net/octave/?rev=11097&view=rev
Author: lmarkov
Date: 2012-10-03 00:38:09 +0000 (Wed, 03 Oct 2012)
Log Message:
-----------
M inst/hamacher_product.m
M inst/partition_coeff.m
M inst/heart_disease_risk.fis
M inst/einstein_product.m
M inst/mamdani_tip_calculator.fis
M inst/sugeno_tip_demo.m
M inst/cubic_approx_demo.m
M inst/drastic_product.m
M inst/linear_tip_demo.m
M inst/hamacher_sum.m
M inst/rmmf.m
M inst/einstein_sum.m
M inst/fcm.m
M inst/xie_beni_index.m
M inst/sugeno_tip_calculator.fis
M inst/drastic_sum.m
M inst/investment_portfolio.fis
M inst/gustafson_kessel.m
M inst/cubic_approximator.fis
M inst/linear_tip_calculator.fis
M inst/heart_disease_demo_1.m
M inst/heart_disease_demo_2.m
M inst/partition_entropy.m
Modified Paths:
--------------
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/cubic_approx_demo.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/cubic_approximator.fis
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/drastic_product.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/drastic_sum.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/einstein_product.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/einstein_sum.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/fcm.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/gustafson_kessel.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/hamacher_product.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/hamacher_sum.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/heart_disease_demo_1.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/heart_disease_demo_2.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/heart_disease_risk.fis
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/investment_portfolio.fis
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/linear_tip_calculator.fis
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/linear_tip_demo.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/mamdani_tip_calculator.fis
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/partition_coeff.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/partition_entropy.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/rmmf.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/sugeno_tip_calculator.fis
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/sugeno_tip_demo.m
trunk/octave-forge/main/fuzzy-logic-toolkit/inst/xie_beni_index.m
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/cubic_approx_demo.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/cubic_approx_demo.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/cubic_approx_demo.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -41,7 +41,7 @@
## Keywords: fuzzy-logic-toolkit fuzzy tests demos
## Directory: fuzzy-logic-toolkit/inst
## Filename: cubic_approx_demo.m
-## Last-Modified: 12 Nov 2011
+## Last-Modified: 20 Aug 2012
## Read the FIS structure from a file.
fis = readfis ('cubic_approximator.fis');
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/cubic_approximator.fis
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/cubic_approximator.fis 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/cubic_approximator.fis 2012-10-03 00:38:09 UTC (rev 11097)
@@ -1,3 +1,27 @@
+## Copyright (C) 2011-2012 L. Markowsky <lm...@us...>
+##
+## 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/>.
+
+## Author: L. Markowsky
+## Keywords: fuzzy-logic-toolkit fis
+## Directory: fuzzy-logic-toolkit/inst/
+## Filename: cubic_approximator.fis
+## Last-Modified: 28 Aug 2012
+
[System]
Name='Cubic-Approximator'
Type='sugeno'
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/drastic_product.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/drastic_product.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/drastic_product.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -44,7 +44,7 @@
## Keywords: fuzzy-logic-toolkit fuzzy drastic_product
## Directory: fuzzy-logic-toolkit/inst/
## Filename: drastic_product.m
-## Last-Modified: 12 Nov 2011
+## Last-Modified: 20 Aug 2012
function retval = drastic_product (x, y = 0)
if (nargin == 0 || nargin > 2 ||
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/drastic_sum.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/drastic_sum.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/drastic_sum.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -44,7 +44,7 @@
## Keywords: fuzzy-logic-toolkit fuzzy drastic_sum
## Directory: fuzzy-logic-toolkit/inst/
## Filename: drastic_sum.m
-## Last-Modified: 12 Nov 2011
+## Last-Modified: 20 Aug 2012
function retval = drastic_sum (x, y = 0)
if (nargin == 0 || nargin > 2 ||
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/einstein_product.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/einstein_product.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/einstein_product.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -39,7 +39,7 @@
## Keywords: fuzzy-logic-toolkit fuzzy einstein_product
## Directory: fuzzy-logic-toolkit/inst/
## Filename: einstein_product.m
-## Last-Modified: 12 Nov 2011
+## Last-Modified: 20 Aug 2012
function retval = einstein_product (x, y = 0)
if (nargin == 0 || nargin > 2 ||
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/einstein_sum.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/einstein_sum.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/einstein_sum.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -38,7 +38,7 @@
## Keywords: fuzzy-logic-toolkit fuzzy einstein_sum
## Directory: fuzzy-logic-toolkit/inst/
## Filename: einstein_sum.m
-## Last-Modified: 12 Nov 2011
+## Last-Modified: 20 Aug 2012
function retval = einstein_sum (x, y = 0)
if (nargin == 0 || nargin > 2 ||
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/fcm.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/fcm.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/fcm.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -25,10 +25,12 @@
## @deftypefnx {Function File} {[@var{cluster_centers}, @var{soft_partition}, @var{obj_fcn_history}] =} fcm (@var{input_data}, @var{num_clusters}, [@var{m}, @var{max_iterations}, @var{epsilon}, @var{display_intermediate_results}])
##
## Using the Fuzzy C-Means algorithm, calculate and return the soft partition
-## of a set of unlabeled data points.
+## of a set of unlabeled data points.
##
## Also, if @var{display_intermediate_results} is true, display intermediate
-## results after each iteration.
+## results after each iteration. Note that because the initial cluster
+## prototypes are randomly selected locations in the ranges determined by the
+## input data, the results of this function are nondeterministic.
##
## The required arguments to fcm are:
## @itemize @w
@@ -111,10 +113,10 @@
## @end deftypefn
## Author: L. Markowsky
-## Keywords: fuzzy-logic-toolkit fuzzy partition clustering fcm
+## Keywords: fuzzy-logic-toolkit fuzzy partition clustering
## Directory: fuzzy-logic-toolkit/inst/
## Filename: fcm.m
-## Last-Modified: 26 Aug 2012
+## Last-Modified: 5 Sep 2012
function [cluster_centers, soft_partition, obj_fcn_history] = ...
fcm (input_data, num_clusters, options = [2.0, 100, 1e-5, 1])
@@ -175,8 +177,8 @@
fcm_private (X, k, m, max_iterations, epsilon, ...
display_intermediate_results)
- ## Initialize the prototype and the calculation.
- V = fcm_init_prototype (X, k);
+ ## Initialize the prototypes and the calculation.
+ V = init_cluster_prototypes (X, k);
obj_fcn_history = zeros (max_iterations);
convergence_criterion = epsilon + 1;
iteration = 0;
@@ -191,18 +193,18 @@
while (convergence_criterion > epsilon && ...
++iteration <= max_iterations)
V_previous = V;
- Mu = fcm_update_membership_fcn (V, X, m, k, n, sqr_dist);
+ Mu = update_cluster_membership (V, X, m, k, n, sqr_dist);
Mu_m = Mu .^ m;
- V = fcm_update_cluster_centers (Mu_m, X, k);
+ V = update_cluster_prototypes (Mu_m, X, k);
sqr_dist = square_distance_matrix (X, V);
obj_fcn_history(iteration) = ...
- fcm_compute_objective_fcn (Mu_m, sqr_dist);
+ compute_cluster_obj_fcn (Mu_m, sqr_dist);
if (display_intermediate_results)
printf ("Iteration count = %d, Objective fcn = %8.6f\n", ...
iteration, obj_fcn_history(iteration));
endif
convergence_criterion = ...
- fcm_compute_convergence_criterion (V, V_previous);
+ compute_cluster_convergence (V, V_previous);
endwhile
## Remove extraneous entries from the tail of the objective
@@ -219,15 +221,18 @@
%!demo
%! ## This demo:
-%! ## - classifies a small set of unlabeled data points using the
-%! ## Fuzzy C-Means algorithm into two fuzzy clusters
+%! ## - classifies a small set of unlabeled data points using
+%! ## the Fuzzy C-Means algorithm into two fuzzy clusters
%! ## - plots the input points together with the cluster centers
+%! ## - evaluates the quality of the resulting clusters using
+%! ## three validity measures: the partition coefficient, the
+%! ## partition entropy, and the Xie-Beni validity index
%! ##
%! ## Note: The input_data is taken from Chapter 13, Example 17 in
%! ## Fuzzy Logic: Intelligence, Control and Information, by
%! ## J. Yen and R. Langari, Prentice Hall, 1999, page 381
%! ## (International Edition).
-%!
+%!
%! ## Use fcm to classify the input_data.
%! input_data = [2 12; 4 9; 7 13; 11 5; 12 7; 14 4];
%! number_of_clusters = 2;
@@ -241,14 +246,14 @@
%! 'marker', 'x', 'color', 'b');
%! hold on;
%! endfor
-%!
+%!
%! ## Plot the cluster centers as larger red *'s.
%! for i = 1 : number_of_clusters
%! plot (cluster_centers(i, 1), cluster_centers(i, 2), ...
%! 'LineWidth', 4, 'marker', '*', 'color', 'r');
%! hold on;
%! endfor
-%!
+%!
%! ## Make the figure look a little better:
%! ## - scale and label the axes
%! ## - show gridlines
@@ -258,6 +263,15 @@
%! ylabel ('Feature 2');
%! grid
%! hold
+%!
+%! ## Calculate and print the three validity measures.
+%! printf ("Partition Coefficient: %f\n", ...
+%! partition_coeff (soft_partition));
+%! printf ("Partition Entropy (with a = 2): %f\n", ...
+%! partition_entropy (soft_partition, 2));
+%! printf ("Xie-Beni Index: %f\n\n", ...
+%! xie_beni_index (input_data, cluster_centers, ...
+%! soft_partition));
##----------------------------------------------------------------------
## FCM Demo #2
@@ -265,19 +279,23 @@
%!demo
%! ## This demo:
-%! ## - classifies three-dimensional unlabeled data points using the
-%! ## Fuzzy C-Means algorithm into three fuzzy clusters
+%! ## - classifies three-dimensional unlabeled data points using
+%! ## the Fuzzy C-Means algorithm into three fuzzy clusters
%! ## - plots the input points together with the cluster centers
+%! ## - evaluates the quality of the resulting clusters using
+%! ## three validity measures: the partition coefficient, the
+%! ## partition entropy, and the Xie-Beni validity index
%! ##
%! ## Note: The input_data was selected to form three areas of
%! ## different shapes.
%!
%! ## Use fcm to classify the input_data.
-%! input_data = [1 11 5; 1 12 6; 1 13 5; 2 11 7; 2 12 6; 2 13 7; 3 11 6;
-%! 3 12 5; 3 13 7; 1 1 10; 1 3 9; 2 2 11; 3 1 9; 3 3 10;
-%! 3 5 11; 4 4 9; 4 6 8; 5 5 8; 5 7 9; 6 6 10; 9 10 12;
-%! 9 12 13; 9 13 14; 10 9 13; 10 13 12; 11 10 14;
-%! 11 12 13; 12 6 12; 12 7 15; 12 9 15; 14 6 14; 14 8 13];
+%! input_data = [1 11 5; 1 12 6; 1 13 5; 2 11 7; 2 12 6; 2 13 7;
+%! 3 11 6; 3 12 5; 3 13 7; 1 1 10; 1 3 9; 2 2 11;
+%! 3 1 9; 3 3 10; 3 5 11; 4 4 9; 4 6 8; 5 5 8; 5 7 9;
+%! 6 6 10; 9 10 12; 9 12 13; 9 13 14; 10 9 13; 10 13 12;
+%! 11 10 14; 11 12 13; 12 6 12; 12 7 15; 12 9 15;
+%! 14 6 14; 14 8 13];
%! number_of_clusters = 3;
%! [cluster_centers, soft_partition, obj_fcn_history] = ...
%! fcm (input_data, number_of_clusters, [NaN NaN NaN 0])
@@ -291,8 +309,8 @@
%! hold on;
%! endfor
%!
-%! ## Plot the cluster centers in two dimensions (using features 1 & 2)
-%! ## as larger red *'s.
+%! ## Plot the cluster centers in two dimensions
+%! ## (using features 1 & 2) as larger red *'s.
%! for i = 1 : number_of_clusters
%! plot (cluster_centers(i, 1), cluster_centers(i, 2), ...
%! 'LineWidth', 4, 'marker', '*', 'color', 'r');
@@ -309,8 +327,8 @@
%! grid
%! hold
%!
-%! ## Plot the data points in two dimensions (using features 1 & 3)
-%! ## as small blue x's.
+%! ## Plot the data points in two dimensions
+%! ## (using features 1 & 3) as small blue x's.
%! figure ('NumberTitle', 'off', 'Name', 'FCM Demo 2');
%! for i = 1 : rows (input_data)
%! plot (input_data(i, 1), input_data(i, 3), 'LineWidth', 2, ...
@@ -318,8 +336,8 @@
%! hold on;
%! endfor
%!
-%! ## Plot the cluster centers in two dimensions (using features 1 & 3)
-%! ## as larger red *'s.
+%! ## Plot the cluster centers in two dimensions
+%! ## (using features 1 & 3) as larger red *'s.
%! for i = 1 : number_of_clusters
%! plot (cluster_centers(i, 1), cluster_centers(i, 3), ...
%! 'LineWidth', 4, 'marker', '*', 'color', 'r');
@@ -335,3 +353,12 @@
%! ylabel ('Feature 3');
%! grid
%! hold
+%!
+%! ## Calculate and print the three validity measures.
+%! printf ("Partition Coefficient: %f\n", ...
+%! partition_coeff (soft_partition));
+%! printf ("Partition Entropy (with a = 2): %f\n", ...
+%! partition_entropy (soft_partition, 2));
+%! printf ("Xie-Beni Index: %f\n\n", ...
+%! xie_beni_index (input_data, cluster_centers, ...
+%! soft_partition));
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/gustafson_kessel.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/gustafson_kessel.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/gustafson_kessel.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -27,10 +27,12 @@
## @deftypefnx {Function File} {[@var{cluster_centers}, @var{soft_partition}, @var{obj_fcn_history}] =} gustafson_kessel (@var{input_data}, @var{num_clusters}, @var{cluster_volume}, [@var{m}, @var{max_iterations}, @var{epsilon}, @var{display_intermediate_results}])
##
## Using the Gustafson-Kessel algorithm, calculate and return the soft partition
-## of a set of unlabeled data points.
+## of a set of unlabeled data points.
##
## Also, if @var{display_intermediate_results} is true, display intermediate
-## results after each iteration.
+## results after each iteration. Note that because the initial cluster
+## prototypes are randomly selected locations in the ranges determined by the
+## input data, the results of this function are nondeterministic.
##
## The required arguments to gustafson_kessel are:
## @itemize @w
@@ -119,7 +121,7 @@
## Keywords: fuzzy-logic-toolkit fuzzy partition clustering
## Directory: fuzzy-logic-toolkit/inst/
## Filename: gustafson_kessel.m
-## Last-Modified: 26 Aug 2012
+## Last-Modified: 5 Sep 2012
function [cluster_centers, soft_partition, obj_fcn_history] = ...
gustafson_kessel (input_data, num_clusters, ...
@@ -194,8 +196,8 @@
gustafson_kessel_private (X, k, cluster_volume, m, max_iterations, ...
epsilon, display_intermediate_results)
- ## Initialize the prototype and the calculation.
- V = fcm_init_prototype (X, k);
+ ## Initialize the prototypes and the calculation.
+ V = init_cluster_prototypes (X, k);
obj_fcn_history = zeros (max_iterations);
convergence_criterion = epsilon + 1;
iteration = 0;
@@ -210,18 +212,18 @@
while (convergence_criterion > epsilon && ...
++iteration <= max_iterations)
V_previous = V;
- Mu = fcm_update_membership_fcn (V, X, m, k, n, sqr_dist);
+ Mu = update_cluster_membership (V, X, m, k, n, sqr_dist);
Mu_m = Mu .^ m;
- V = fcm_update_cluster_centers (Mu_m, X, k);
+ V = update_cluster_prototypes (Mu_m, X, k);
sqr_dist = gk_square_distance_matrix (X, V, Mu_m, cluster_volume);
obj_fcn_history(iteration) = ...
- fcm_compute_objective_fcn (Mu_m, sqr_dist);
+ compute_cluster_obj_fcn (Mu_m, sqr_dist);
if (display_intermediate_results)
printf ("Iteration count = %d, Objective fcn = %8.6f\n", ...
iteration, obj_fcn_history(iteration));
endif
convergence_criterion = ...
- fcm_compute_convergence_criterion (V, V_previous);
+ compute_cluster_convergence (V, V_previous);
endwhile
## Remove extraneous entries from the tail of the objective ...
@@ -289,9 +291,12 @@
%!demo
%! ## This demo:
-%! ## - classifies a small set of unlabeled data points using the
-%! ## Gustafson-Kessel algorithm into two fuzzy clusters
+%! ## - classifies a small set of unlabeled data points using
+%! ## the Gustafson-Kessel algorithm into two fuzzy clusters
%! ## - plots the input points together with the cluster centers
+%! ## - evaluates the quality of the resulting clusters using
+%! ## three validity measures: the partition coefficient, the
+%! ## partition entropy, and the Xie-Beni validity index
%! ##
%! ## Note: The input_data is taken from Chapter 13, Example 17 in
%! ## Fuzzy Logic: Intelligence, Control and Information, by
@@ -314,8 +319,8 @@
%!
%! ## Plot the cluster centers as larger red *'s.
%! for i = 1 : number_of_clusters
-%! plot (cluster_centers(i, 1), cluster_centers(i, 2), 'LineWidth', ...
-%! 4, 'marker', '*', 'color', 'r');
+%! plot (cluster_centers(i, 1), cluster_centers(i, 2), ...
+%! 'LineWidth', 4, 'marker', '*', 'color', 'r');
%! hold on;
%! endfor
%!
@@ -328,26 +333,39 @@
%! ylabel ('Feature 2');
%! grid
%! hold
-
+%!
+%! ## Calculate and print the three validity measures.
+%! printf ("Partition Coefficient: %f\n", ...
+%! partition_coeff (soft_partition));
+%! printf ("Partition Entropy (with a = 2): %f\n", ...
+%! partition_entropy (soft_partition, 2));
+%! printf ("Xie-Beni Index: %f\n\n", ...
+%! xie_beni_index (input_data, cluster_centers, ...
+%! soft_partition));
+
##----------------------------------------------------------------------
## Gustafson-Kessel Demo #2
##----------------------------------------------------------------------
%!demo
%! ## This demo:
-%! ## - classifies three-dimensional unlabeled data points using the
-%! ## Gustafson-Kessel algorithm into three fuzzy clusters
+%! ## - classifies three-dimensional unlabeled data points using
+%! ## the Gustafson-Kessel algorithm into three fuzzy clusters
%! ## - plots the input points together with the cluster centers
+%! ## - evaluates the quality of the resulting clusters using
+%! ## three validity measures: the partition coefficient, the
+%! ## partition entropy, and the Xie-Beni validity index
%! ##
%! ## Note: The input_data was selected to form three areas of
%! ## different shapes.
%!
%! ## Use gustafson_kessel to classify the input_data.
-%! input_data = [1 11 5; 1 12 6; 1 13 5; 2 11 7; 2 12 6; 2 13 7; 3 11 6;
-%! 3 12 5; 3 13 7; 1 1 10; 1 3 9; 2 2 11; 3 1 9; 3 3 10;
-%! 3 5 11; 4 4 9; 4 6 8; 5 5 8; 5 7 9; 6 6 10; 9 10 12;
-%! 9 12 13; 9 13 14; 10 9 13; 10 13 12; 11 10 14;
-%! 11 12 13; 12 6 12; 12 7 15; 12 9 15; 14 6 14; 14 8 13];
+%! input_data = [1 11 5; 1 12 6; 1 13 5; 2 11 7; 2 12 6; 2 13 7;
+%! 3 11 6; 3 12 5; 3 13 7; 1 1 10; 1 3 9; 2 2 11;
+%! 3 1 9; 3 3 10; 3 5 11; 4 4 9; 4 6 8; 5 5 8; 5 7 9;
+%! 6 6 10; 9 10 12; 9 12 13; 9 13 14; 10 9 13; 10 13 12;
+%! 11 10 14; 11 12 13; 12 6 12; 12 7 15; 12 9 15;
+%! 14 6 14; 14 8 13];
%! number_of_clusters = 3;
%! [cluster_centers, soft_partition, obj_fcn_history] = ...
%! gustafson_kessel (input_data, number_of_clusters, [1 1 1], ...
@@ -362,11 +380,11 @@
%! hold on;
%! endfor
%!
-%! ## Plot the cluster centers in two dimensions (using features 1 & 2)
-%! ## as larger red *'s.
+%! ## Plot the cluster centers in two dimensions
+%! ## (using features 1 & 2) as larger red *'s.
%! for i = 1 : number_of_clusters
-%! plot (cluster_centers(i, 1), cluster_centers(i, 2), 'LineWidth', ...
-%! 4, 'marker', '*', 'color', 'r');
+%! plot (cluster_centers(i, 1), cluster_centers(i, 2), ...
+%! 'LineWidth', 4, 'marker', '*', 'color', 'r');
%! hold on;
%! endfor
%!
@@ -379,8 +397,8 @@
%! ylabel ('Feature 2');
%! grid
%!
-%! ## Plot the data points in two dimensions (using features 1 & 3)
-%! ## as small blue x's.
+%! ## Plot the data points in two dimensions
+%! ## (using features 1 & 3) as small blue x's.
%! figure ('NumberTitle', 'off', 'Name', 'Gustafson-Kessel Demo 2');
%! for i = 1 : rows (input_data)
%! plot (input_data(i, 1), input_data(i, 3), 'LineWidth', 2, ...
@@ -388,11 +406,11 @@
%! hold on;
%! endfor
%!
-%! ## Plot the cluster centers in two dimensions (using features 1 & 3)
-%! ## as larger red *'s.
+%! ## Plot the cluster centers in two dimensions
+%! ## (using features 1 & 3) as larger red *'s.
%! for i = 1 : number_of_clusters
-%! plot (cluster_centers(i, 1), cluster_centers(i, 3), 'LineWidth', ...
-%! 4, 'marker', '*', 'color', 'r');
+%! plot (cluster_centers(i, 1), cluster_centers(i, 3), ...
+%! 'LineWidth', 4, 'marker', '*', 'color', 'r');
%! hold on;
%! endfor
%!
@@ -405,3 +423,12 @@
%! ylabel ('Feature 3');
%! grid
%! hold
+%!
+%! ## Calculate and print the three validity measures.
+%! printf ("Partition Coefficient: %f\n", ...
+%! partition_coeff (soft_partition));
+%! printf ("Partition Entropy (with a = 2): %f\n", ...
+%! partition_entropy (soft_partition, 2));
+%! printf ("Xie-Beni Index: %f\n\n", ...
+%! xie_beni_index (input_data, cluster_centers, ...
+%! soft_partition));
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/hamacher_product.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/hamacher_product.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/hamacher_product.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -39,7 +39,7 @@
## Keywords: fuzzy-logic-toolkit fuzzy hamacher_product
## Directory: fuzzy-logic-toolkit/inst/
## Filename: hamacher_product.m
-## Last-Modified: 12 Nov 2011
+## Last-Modified: 20 Aug 2012
function retval = hamacher_product (x, y = 0)
if (nargin == 0 || nargin > 2 ||
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/hamacher_sum.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/hamacher_sum.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/hamacher_sum.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -39,7 +39,7 @@
## Keywords: fuzzy-logic-toolkit fuzzy hamacher_sum
## Directory: fuzzy-logic-toolkit/inst/
## Filename: hamacher_sum.m
-## Last-Modified: 12 Nov 2011
+## Last-Modified: 20 Aug 2012
function retval = hamacher_sum (x, y = 0)
if (nargin == 0 || nargin > 2 ||
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/heart_disease_demo_1.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/heart_disease_demo_1.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/heart_disease_demo_1.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -47,7 +47,7 @@
## Keywords: fuzzy-logic-toolkit fuzzy tests demos
## Directory: fuzzy-logic-toolkit/inst
## Filename: heart_disease_demo_1.m
-## Last-Modified: 12 Nov 2011
+## Last-Modified: 20 Aug 2012
## Create new FIS.
a = newfis ('Heart-Disease-Risk', 'sugeno', ...
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/heart_disease_demo_2.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/heart_disease_demo_2.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/heart_disease_demo_2.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -45,7 +45,7 @@
## Dr. Bruce Segee (University of Maine Dept. of ECE).
## Directory: fuzzy-logic-toolkit/inst
## Filename: heart_disease_demo_2.m
-## Last-Modified: 12 Nov 2011
+## Last-Modified: 20 Aug 2012
## Read the FIS structure from a file.
## (Alternatively, to select heart_disease_risk.fis using the dialog,
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/heart_disease_risk.fis
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/heart_disease_risk.fis 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/heart_disease_risk.fis 2012-10-03 00:38:09 UTC (rev 11097)
@@ -1,5 +1,29 @@
-# Heart Disease Risk FIS
+## Copyright (C) 2011-2012 L. Markowsky <lm...@us...>
+##
+## 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/>.
+## Author: L. Markowsky
+## Keywords: fuzzy-logic-toolkit fis
+## Directory: fuzzy-logic-toolkit/inst/
+## Filename: heart_disease_risk.fis
+## Last-Modified: 28 Aug 2012
+
+## Heart Disease Risk FIS
+
[System]
Name = 'Heart-Disease-Risk'
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/investment_portfolio.fis
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/investment_portfolio.fis 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/investment_portfolio.fis 2012-10-03 00:38:09 UTC (rev 11097)
@@ -1,3 +1,27 @@
+## Copyright (C) 2011-2012 L. Markowsky <lm...@us...>
+##
+## 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/>.
+
+## Author: L. Markowsky
+## Keywords: fuzzy-logic-toolkit fis
+## Directory: fuzzy-logic-toolkit/inst/
+## Filename: investment_portfolio.fis
+## Last-Modified: 28 Aug 2012
+
[System]
Name='Investment-Portfolio'
Type='mamdani'
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/linear_tip_calculator.fis
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/linear_tip_calculator.fis 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/linear_tip_calculator.fis 2012-10-03 00:38:09 UTC (rev 11097)
@@ -1,3 +1,27 @@
+## Copyright (C) 2011-2012 L. Markowsky <lm...@us...>
+##
+## 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/>.
+
+## Author: L. Markowsky
+## Keywords: fuzzy-logic-toolkit fis
+## Directory: fuzzy-logic-toolkit/inst/
+## Filename: linear_tip_calculator.fis
+## Last-Modified: 28 Aug 2012
+
[System]
Name='Linear-Tip-Calculator'
Type='sugeno'
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/linear_tip_demo.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/linear_tip_demo.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/linear_tip_demo.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -43,7 +43,7 @@
## Dr. Bruce Segee (University of Maine Dept. of ECE).
## Directory: fuzzy-logic-toolkit/inst
## Filename: linear_tip_demo.m
-## Last-Modified: 12 Nov 2011
+## Last-Modified: 20 Aug 2012
## Read the FIS structure from a file.
fis = readfis ('linear_tip_calculator.fis');
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/mamdani_tip_calculator.fis
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/mamdani_tip_calculator.fis 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/mamdani_tip_calculator.fis 2012-10-03 00:38:09 UTC (rev 11097)
@@ -1,3 +1,27 @@
+## Copyright (C) 2011-2012 L. Markowsky <lm...@us...>
+##
+## 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/>.
+
+## Author: L. Markowsky
+## Keywords: fuzzy-logic-toolkit fis
+## Directory: fuzzy-logic-toolkit/inst/
+## Filename: mamdani_tip_calculator.fis
+## Last-Modified: 28 Aug 2012
+
[System]
Name='Mamdani-Tip-Calculator'
Type='mamdani'
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/partition_coeff.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/partition_coeff.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/partition_coeff.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -33,6 +33,12 @@
## @var{vpc} - the partition coefficient for the given soft partition
## @end itemize
##
+## For demos of this function, please type:
+## @example
+## demo 'fcm'
+## demo 'gustafson_kessel'
+## @end example
+##
## For more information about the @var{soft_partition} matrix, please see the
## documentation for function fcm.
##
@@ -44,7 +50,7 @@
## Keywords: fuzzy-logic-toolkit partition coefficient cluster
## Directory: fuzzy-logic-toolkit/inst/
## Filename: partition_coeff.m
-## Last-Modified: 26 Aug 2012
+## Last-Modified: 4 Sep 2012
##----------------------------------------------------------------------
## Note: This function is an implementation of Equation 13.9 (corrected
@@ -78,77 +84,3 @@
vpc = (sum (sum (soft_part_sqr))) / columns (soft_partition);
endfunction
-
-##----------------------------------------------------------------------
-## Partition Coefficient Demo #1
-##----------------------------------------------------------------------
-
-%!demo
-%! ## Use the Fuzzy C-Means and Gustafson-Kessel algorithms to classify
-%! ## a small set of unlabeled data points and evaluate the quality
-%! ## of the resulting clusters.
-%!
-%! ## Note: The input_data is taken from Chapter 13, Example 17 in
-%! ## Fuzzy Logic: Intelligence, Control and Information, by
-%! ## J. Yen and R. Langari, Prentice Hall, 1999, page 381
-%! ## (International Edition).
-%!
-%! input_data = [2 12; 4 9; 7 13; 11 5; 12 7; 14 4]
-%! number_of_clusters = 2
-%!
-%! ## Using fcm, classify the input data, print the cluster centers,
-%! ## and calculate and print the partition coefficient.
-%! [cluster_centers, soft_partition, obj_fcn_history] = ...
-%! fcm (input_data, number_of_clusters, [NaN NaN NaN 0]);
-%! puts ("\nResults using the Fuzzy C-Means algorithm:\n\n");
-%! cluster_centers
-%! printf ("partition coefficient: %f\n", ...
-%! partition_coeff (soft_partition));
-%!
-%! ## Using gustafson_kessel, classify the input data, print the cluster
-%! ## centers, and calculate and print the partition coefficient.
-%! [cluster_centers, soft_partition, obj_fcn_history] = ...
-%! gustafson_kessel (input_data, number_of_clusters, [1 1 1], ...
-%! [NaN NaN NaN 0]);
-%! puts ("\nResults using the Gustafson-Kessel algorithm:\n\n");
-%! cluster_centers
-%! printf ("partition coefficient: %f\n\n", ...
-%! partition_coeff (soft_partition));
-
-##----------------------------------------------------------------------
-## Partition Coefficient Demo #2
-##----------------------------------------------------------------------
-
-%!demo
-%! ## Use the Fuzzy C-Means and Gustafson-Kessel algorithms to classify
-%! ## three-dimensional unlabeled data points and evaluate the quality
-%! ## of the resulting clusters.
-%!
-%! ## Note: The input_data was selected to form three areas of
-%! ## different shapes.
-%!
-%! input_data = [1 11 5; 1 12 6; 1 13 5; 2 11 7; 2 12 6; 2 13 7; 3 11 6;
-%! 3 12 5; 3 13 7; 1 1 10; 1 3 9; 2 2 11; 3 1 9; 3 3 10;
-%! 3 5 11; 4 4 9; 4 6 8; 5 5 8; 5 7 9; 6 6 10; 9 10 12;
-%! 9 12 13; 9 13 14; 10 9 13; 10 13 12; 11 10 14;
-%! 11 12 13; 12 6 12; 12 7 15; 12 9 15; 14 6 14; 14 8 13]
-%! number_of_clusters = 3
-%!
-%! ## Using fcm, classify the input data, print the cluster centers,
-%! ## and calculate and print the partition coefficient.
-%! [cluster_centers, soft_partition, obj_fcn_history] = ...
-%! fcm (input_data, number_of_clusters, [NaN NaN NaN 0]);
-%! puts ("\nResults using the Fuzzy C-Means algorithm:\n\n");
-%! cluster_centers
-%! printf ("partition coefficient: %f\n", ...
-%! partition_coeff (soft_partition));
-%!
-%! ## Using gustafson_kessel, classify the input data, print the cluster
-%! ## centers, and calculate and print the partition coefficient.
-%! [cluster_centers, soft_partition, obj_fcn_history] = ...
-%! gustafson_kessel (input_data, number_of_clusters, [1 1 1], ...
-%! [NaN NaN NaN 0]);
-%! puts ("\nResults using the Gustafson-Kessel algorithm:\n\n");
-%! cluster_centers
-%! printf ("partition coefficient: %f\n\n", ...
-%! partition_coeff (soft_partition));
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/partition_entropy.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/partition_entropy.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/partition_entropy.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -35,6 +35,12 @@
## @var{vpe} - the partition entropy for the given soft partition
## @end itemize
##
+## For demos of this function, please type:
+## @example
+## demo 'fcm'
+## demo 'gustafson_kessel'
+## @end example
+##
## For more information about the @var{soft_partition} matrix, please see the
## documentation for function fcm.
##
@@ -46,7 +52,7 @@
## Keywords: fuzzy-logic-toolkit partition entropy cluster
## Directory: fuzzy-logic-toolkit/inst/
## Filename: partition_entropy.m
-## Last-Modified: 26 Aug 2012
+## Last-Modified: 4 Sep 2012
##----------------------------------------------------------------------
## Note: This function is an implementation of Equation 13.10 in
@@ -86,77 +92,3 @@
vpe = -(sum (sum (Mu .* log_a_Mu))) / n;
endfunction
-
-##----------------------------------------------------------------------
-## Partition Entropy Demo #1
-##----------------------------------------------------------------------
-
-%!demo
-%! ## Use the Fuzzy C-Means and Gustafson-Kessel algorithms to classify
-%! ## a small set of unlabeled data points and evaluate the quality
-%! ## of the resulting clusters.
-%!
-%! ## Note: The input_data is taken from Chapter 13, Example 17 in
-%! ## Fuzzy Logic: Intelligence, Control and Information, by
-%! ## J. Yen and R. Langari, Prentice Hall, 1999, page 381
-%! ## (International Edition).
-%!
-%! input_data = [2 12; 4 9; 7 13; 11 5; 12 7; 14 4]
-%! number_of_clusters = 2
-%!
-%! ## Using fcm, classify the input data, print the cluster centers,
-%! ## and calculate and print the partition coefficient.
-%! [cluster_centers, soft_partition, obj_fcn_history] = ...
-%! fcm (input_data, number_of_clusters, [NaN NaN NaN 0]);
-%! puts ("\nResults using the Fuzzy C-Means algorithm:\n\n");
-%! cluster_centers
-%! printf ("partition entropy (with a = 2): %f\n", ...
-%! partition_entropy (soft_partition, 2));
-%!
-%! ## Using gustafson_kessel, classify the input data, print the cluster
-%! ## centers, and calculate and print the partition coefficient.
-%! [cluster_centers, soft_partition, obj_fcn_history] = ...
-%! gustafson_kessel (input_data, number_of_clusters, [1 1 1], ...
-%! [NaN NaN NaN 0]);
-%! puts ("\nResults using the Gustafson-Kessel algorithm:\n\n");
-%! cluster_centers
-%! printf ("partition entropy (with a = 2): %f\n\n", ...
-%! partition_entropy (soft_partition, 2));
-
-##----------------------------------------------------------------------
-## Partition Entropy Demo #2
-##----------------------------------------------------------------------
-
-%!demo
-%! ## Use the Fuzzy C-Means and Gustafson-Kessel algorithms to classify
-%! ## three-dimensional unlabeled data points and evaluate the quality
-%! ## of the resulting clusters.
-%!
-%! ## Note: The input_data was selected to form three areas of
-%! ## different shapes.
-%!
-%! input_data = [1 11 5; 1 12 6; 1 13 5; 2 11 7; 2 12 6; 2 13 7; 3 11 6;
-%! 3 12 5; 3 13 7; 1 1 10; 1 3 9; 2 2 11; 3 1 9; 3 3 10;
-%! 3 5 11; 4 4 9; 4 6 8; 5 5 8; 5 7 9; 6 6 10; 9 10 12;
-%! 9 12 13; 9 13 14; 10 9 13; 10 13 12; 11 10 14;
-%! 11 12 13; 12 6 12; 12 7 15; 12 9 15; 14 6 14; 14 8 13]
-%! number_of_clusters = 3
-%!
-%! ## Using fcm, classify the input data, print the cluster centers,
-%! ## and calculate and print the partition coefficient.
-%! [cluster_centers, soft_partition, obj_fcn_history] = ...
-%! fcm (input_data, number_of_clusters, [NaN NaN NaN 0]);
-%! puts ("\nResults using the Fuzzy C-Means algorithm:\n\n");
-%! cluster_centers
-%! printf ("partition entropy (with a = 2): %f\n", ...
-%! partition_entropy (soft_partition, 2));
-%!
-%! ## Using gustafson_kessel, classify the input data, print the cluster
-%! ## centers, and calculate and print the partition coefficient.
-%! [cluster_centers, soft_partition, obj_fcn_history] = ...
-%! gustafson_kessel (input_data, number_of_clusters, [1 1 1], ...
-%! [NaN NaN NaN 0]);
-%! puts ("\nResults using the Gustafson-Kessel algorithm:\n\n");
-%! cluster_centers
-%! printf ("partition entropy (with a = 2): %f\n\n", ...
-%! partition_entropy (soft_partition, 2));
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/rmmf.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/rmmf.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/rmmf.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -46,7 +46,7 @@
## Keywords: fuzzy-logic-toolkit fuzzy membership
## Directory: fuzzy-logic-toolkit/inst/
## Filename: rmmf.m
-## Last-Modified: 16 Jul 2011
+## Last-Modified: 20 Aug 2012
function fis = rmmf (fis, in_or_out, var_index, mf, mf_index)
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/sugeno_tip_calculator.fis
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/sugeno_tip_calculator.fis 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/sugeno_tip_calculator.fis 2012-10-03 00:38:09 UTC (rev 11097)
@@ -1,3 +1,27 @@
+## Copyright (C) 2011-2012 L. Markowsky <lm...@us...>
+##
+## 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/>.
+
+## Author: L. Markowsky
+## Keywords: fuzzy-logic-toolkit fis
+## Directory: fuzzy-logic-toolkit/inst/
+## Filename: sugeno_tip_calculator.fis
+## Last-Modified: 28 Aug 2012
+
% Sugeno Tip Calculator
% Computes cheap, average, and generous tips
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/sugeno_tip_demo.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/sugeno_tip_demo.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/sugeno_tip_demo.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -49,7 +49,7 @@
## Dr. Bruce Segee (University of Maine Dept. of ECE).
## Directory: fuzzy-logic-toolkit/inst
## Filename: sugeno_tip_demo.m
-## Last-Modified: 10 Nov 2011
+## Last-Modified: 20 Aug 2012
## Read the FIS structure from a file.
fis = readfis ('sugeno_tip_calculator.fis');
Modified: trunk/octave-forge/main/fuzzy-logic-toolkit/inst/xie_beni_index.m
===================================================================
--- trunk/octave-forge/main/fuzzy-logic-toolkit/inst/xie_beni_index.m 2012-10-03 00:37:18 UTC (rev 11096)
+++ trunk/octave-forge/main/fuzzy-logic-toolkit/inst/xie_beni_index.m 2012-10-03 00:38:09 UTC (rev 11097)
@@ -37,6 +37,12 @@
## @var{vxb} - the Xie-Beni validity index for the given partition
## @end itemize
##
+## For demos of this function, please type:
+## @example
+## demo 'fcm'
+## demo 'gustafson_kessel'
+## @end example
+##
## For more information about the @var{input_data}, @var{cluster_centers},
## and @var{soft_partition} matrices, please see the documentation for function
## fcm.
@@ -49,7 +55,7 @@
## Keywords: fuzzy-logic-toolkit fuzzy xie beni cluster validity
## Directory: fuzzy-logic-toolkit/inst/
## Filename: xie_beni_index.m
-## Last-Modified: 26 Aug 2012
+## Last-Modified: 4 Sep 2012
function vxb = xie_beni_index (input_data, cluster_centers, ...
soft_partition)
@@ -126,77 +132,3 @@
d_sqr_min = min (min (d_sqr_matrix));
endfunction
-
-##----------------------------------------------------------------------
-## Xie-Beni Index Demo #1
-##----------------------------------------------------------------------
-
-%!demo
-%! ## Use the Fuzzy C-Means and Gustafson-Kessel algorithms to classify
-%! ## a small set of unlabeled data points and evaluate the quality
-%! ## of the resulting clusters.
-%!
-%! ## Note: The input_data is taken from Chapter 13, Example 17 in
-%! ## Fuzzy Logic: Intelligence, Control and Information, by
-%! ## J. Yen and R. Langari, Prentice Hall, 1999, page 381
-%! ## (International Edition).
-%!
-%! input_data = [2 12; 4 9; 7 13; 11 5; 12 7; 14 4]
-%! number_of_clusters = 2
-%!
-%! ## Using fcm, classify the input data, print the cluster centers,
-%! ## and calculate and print the partition coefficient.
-%! [cluster_centers, soft_partition, obj_fcn_history] = ...
-%! fcm (input_data, number_of_clusters, [NaN NaN NaN 0]);
-%! puts ("\nResults using the Fuzzy C-Means algorithm:\n\n");
-%! cluster_centers
-%! printf ("Xie-Beni index: %f\n", ...
-%! xie_beni_index (input_data, cluster_centers, soft_partition));
-%!
-%! ## Using gustafson_kessel, classify the input data, print the cluster
-%! ## centers, and calculate and print the partition coefficient.
-%! [cluster_centers, soft_partition, obj_fcn_history] = ...
-%! gustafson_kessel (input_data, number_of_clusters, [1 1 1], ...
-%! [NaN NaN NaN 0]);
-%! puts ("\nResults using the Gustafson-Kessel algorithm:\n\n");
-%! cluster_centers
-%! printf ("Xie-Beni index: %f\n\n", ...
-%! xie_beni_index (input_data, cluster_centers, soft_partition));
-
-##----------------------------------------------------------------------
-## Xie-Beni Index Demo #2
-##----------------------------------------------------------------------
-
-%!demo
-%! ## Use the Fuzzy C-Means and Gustafson-Kessel algorithms to classify
-%! ## three-dimensional unlabeled data points and evaluate the quality
-%! ## of the resulting clusters.
-%!
-%! ## Note: The input_data was selected to form three areas of
-%! ## different shapes.
-%!
-%! input_data = [1 11 5; 1 12 6; 1 13 5; 2 11 7; 2 12 6; 2 13 7; 3 11 6;
-%! 3 12 5; 3 13 7; 1 1 10; 1 3 9; 2 2 11; 3 1 9; 3 3 10;
-%! 3 5 11; 4 4 9; 4 6 8; 5 5 8; 5 7 9; 6 6 10; 9 10 12;
-%! 9 12 13; 9 13 14; 10 9 13; 10 13 12; 11 10 14;
-%! 11 12 13; 12 6 12; 12 7 15; 12 9 15; 14 6 14; 14 8 13]
-%! number_of_clusters = 3
-%!
-%! ## Using fcm, classify the input data, print the cluster centers,
-%! ## and calculate and print the partition coefficient.
-%! [cluster_centers, soft_partition, obj_fcn_history] = ...
-%! fcm (input_data, number_of_clusters, [NaN NaN NaN 0]);
-%! puts ("\nResults using the Fuzzy C-Means algorithm:\n\n");
-%! cluster_centers
-%! printf ("Xie-Beni index: %f\n", ...
-%! xie_beni_index (input_data, cluster_centers, soft_partition));
-%!
-%! ## Using gustafson_kessel, classify the input data, print the cluster
-%! ## centers, and calculate and print the partition coefficient.
-%! [cluster_centers, soft_partition, obj_fcn_history] = ...
-%! gustafson_kessel (input_data, number_of_clusters, [1 1 1], ...
-%! [NaN NaN NaN 0]);
-%! puts ("\nResults using the Gustafson-Kessel algorithm:\n\n");
-%! cluster_centers
-%! printf ("Xie-Beni index: %f\n\n", ...
-%! xie_beni_index (input_data, cluster_centers, soft_partition));
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