Mercurial > forge
changeset 10729:e890606bd234 octave-forge
M inst/fcm.m
M inst/xie_beni_index.m
M inst/gustafson_kessel.m
D inst/fcm_demo_1.m
M inst/partition_entropy.m
D inst/fcm_demo_2.m
D inst/gustafson_kessel_demo_1.m
D inst/gustafson_kessel_demo_2.m
M inst/partition_coeff.m
| author | lmarkov |
|---|---|
| date | Sun, 26 Aug 2012 14:54:12 +0000 |
| parents | e08c9a29b4e1 |
| children | a2187ff706f6 |
| files | main/fuzzy-logic-toolkit/inst/fcm.m main/fuzzy-logic-toolkit/inst/fcm_demo_1.m main/fuzzy-logic-toolkit/inst/fcm_demo_2.m main/fuzzy-logic-toolkit/inst/gustafson_kessel.m main/fuzzy-logic-toolkit/inst/gustafson_kessel_demo_1.m main/fuzzy-logic-toolkit/inst/gustafson_kessel_demo_2.m main/fuzzy-logic-toolkit/inst/partition_coeff.m main/fuzzy-logic-toolkit/inst/partition_entropy.m main/fuzzy-logic-toolkit/inst/xie_beni_index.m |
| diffstat | 9 files changed, 477 insertions(+), 448 deletions(-) [+] |
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--- a/main/fuzzy-logic-toolkit/inst/fcm.m Sat Aug 25 08:27:39 2012 +0000 +++ b/main/fuzzy-logic-toolkit/inst/fcm.m Sun Aug 26 14:54:12 2012 +0000 @@ -106,7 +106,7 @@ ## @end group ## @end example ## -## @seealso{fcm_demo_1, fcm_demo_2, gustafson_kessel, gustafson_kessel_demo_1, gustafson_kessel_demo_2, partition_coeff, partition_entropy, xie_beni_index} +## @seealso{gustafson_kessel, partition_coeff, partition_entropy, xie_beni_index} ## ## @end deftypefn @@ -114,7 +114,7 @@ ## Keywords: fuzzy-logic-toolkit fuzzy partition clustering fcm ## Directory: fuzzy-logic-toolkit/inst/ ## Filename: fcm.m -## Last-Modified: 19 Aug 2012 +## Last-Modified: 26 Aug 2012 function [cluster_centers, soft_partition, obj_fcn_history] = ... fcm (input_data, num_clusters, options = [2.0, 100, 1e-5, 1]) @@ -212,3 +212,126 @@ endif endfunction + +##---------------------------------------------------------------------- +## FCM Demo #1 +##---------------------------------------------------------------------- + +%!demo +%! ## This demo: +%! ## - 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 +%! ## +%! ## 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; +%! [cluster_centers, soft_partition, obj_fcn_history] = ... +%! fcm (input_data, number_of_clusters) +%! +%! ## Plot the data points as small blue x's. +%! figure ('NumberTitle', 'off', 'Name', 'FCM Demo 1'); +%! for i = 1 : rows (input_data) +%! plot (input_data(i, 1), input_data(i, 2), 'LineWidth', 2, ... +%! '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 +%! xlim ([0 15]); +%! ylim ([0 15]); +%! xlabel ('Feature 1'); +%! ylabel ('Feature 2'); +%! grid +%! hold + +##---------------------------------------------------------------------- +## FCM Demo #2 +##---------------------------------------------------------------------- + +%!demo +%! ## This demo: +%! ## - 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 +%! ## +%! ## 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]; +%! number_of_clusters = 3; +%! [cluster_centers, soft_partition, obj_fcn_history] = ... +%! fcm (input_data, number_of_clusters, [NaN NaN NaN 0]) +%! +%! ## Plot the data points in two dimensions (using features 1 & 2) +%! ## 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, 2), 'LineWidth', 2, ... +%! 'marker', 'x', 'color', 'b'); +%! hold on; +%! endfor +%! +%! ## 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'); +%! hold on; +%! endfor +%! +%! ## Make the figure look a little better: +%! ## - scale and label the axes +%! ## - show gridlines +%! xlim ([0 15]); +%! ylim ([0 15]); +%! xlabel ('Feature 1'); +%! ylabel ('Feature 2'); +%! grid +%! hold +%! +%! ## 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, ... +%! 'marker', 'x', 'color', 'b'); +%! hold on; +%! endfor +%! +%! ## 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'); +%! hold on; +%! endfor +%! +%! ## Make the figure look a little better: +%! ## - scale and label the axes +%! ## - show gridlines +%! xlim ([0 15]); +%! ylim ([0 15]); +%! xlabel ('Feature 1'); +%! ylabel ('Feature 3'); +%! grid +%! hold
--- a/main/fuzzy-logic-toolkit/inst/fcm_demo_1.m Sat Aug 25 08:27:39 2012 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,94 +0,0 @@ -## Copyright (C) 2011-2012 L. Markowsky <lmarkov@users.sourceforge.net> -## -## 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 {Script File} {} fcm_demo_1 -## Use the Fuzzy C-Means algorithm to classify a small set of unlabeled -## data points and evaluate the quality of the resulting clusters. -## -## This demo: -## @itemize @minus -## @item -## classifies a small set of unlabeled data points using the Fuzzy C-Means -## algorithm into two fuzzy clusters -## @item -## calculates and prints (on standard output) three validity measures: -## the partition coefficient, the partition entropy, and the Xie-Beni -## validity index -## @item -## plots the input points together with the cluster centers -## @end itemize -## -## For a description of the data structures used in the script, see the -## documentation for fcm. -## -## @seealso{fcm, fcm_demo_2, partition_coeff, partition_entropy, xie_beni_index, gustafson_kessel, gustafson_kessel_demo_1, gustafson_kessel_demo_2} -## -## @end deftypefn - -## Author: L. Markowsky -## Keywords: fuzzy-logic-toolkit fuzzy partition clustering fcm -## Directory: fuzzy-logic-toolkit/inst/ -## Filename: fcm_demo_1.m -## Last-Modified: 19 Aug 2012 - -##---------------------------------------------------------------------- -## 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 data in matrix x. -input_data = [2 12; 4 9; 7 13; 11 5; 12 7; 14 4] -number_of_clusters = 2 -[cluster_centers, soft_partition, obj_fcn_history] = ... - fcm (input_data, number_of_clusters) - -## 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)); - -## Plot the data points as small blue x's. -figure ('NumberTitle', 'off', 'Name', 'FCM Demo 1'); -for i = 1 : rows (input_data) - plot (input_data(i, 1), input_data(i, 2), 'LineWidth', 2, ... - '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 -xlim ([0 15]); -ylim ([0 15]); -xlabel ('Feature 1'); -ylabel ('Feature 2'); -grid -hold
--- a/main/fuzzy-logic-toolkit/inst/fcm_demo_2.m Sat Aug 25 08:27:39 2012 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,125 +0,0 @@ -## Copyright (C) 2011-2012 L. Markowsky <lmarkov@users.sourceforge.net> -## -## 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 {Script File} {} fcm_demo_2 -## Use the Fuzzy C-Means algorithm to classify three-dimensional unlabeled -## data points and evaluate the quality of the resulting clusters. -## -## The demo: -## @itemize @minus -## @item -## classifies three-dimensional unlabeled data points using the Fuzzy -## C-Means algorithm into three fuzzy clusters -## @item -## calculates and prints (on standard output) three validity measures: -## the partition coefficient, the partition entropy, and the Xie-Beni -## validity index -## @item -## plots the input points together with the cluster centers -## @end itemize -## -## For a description of the data structures used in the script, see the -## documentation for fcm. -## -## @seealso{fcm, fcm_demo_1, partition_coeff, partition_entropy, xie_beni_index, gustafson_kessel, gustafson_kessel_demo_1, gustafson_kessel_demo_2} -## -## @end deftypefn - -## Author: L. Markowsky -## Keywords: fuzzy-logic-toolkit fuzzy partition clustering fcm -## Directory: fuzzy-logic-toolkit/inst/ -## Filename: fcm_demo_2.m -## Last-Modified: 19 Aug 2012 - -##---------------------------------------------------------------------- -## Note: The input_data was selected to form three areas of different -## shapes. -##---------------------------------------------------------------------- - -## Use fcm to classify the data in matrix x. -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]) - -## 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)); - -## Plot the data points in two dimensions (using features 1 and 2) -## 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, 2), 'LineWidth', 2, ... - 'marker', 'x', 'color', 'b'); - hold on; -endfor - -## Plot the cluster centers in two dimensions (using features 1 and 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'); - hold on; -endfor - -## Make the figure look a little better: -## -- scale and label the axes -## -- show gridlines -xlim ([0 15]); -ylim ([0 15]); -xlabel ('Feature 1'); -ylabel ('Feature 2'); -grid -hold - -## Plot the data points in two dimensions (using features 1 and 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, ... - 'marker', 'x', 'color', 'b'); - hold on; -endfor - -## Plot the cluster centers in two dimensions (using features 1 and 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'); - hold on; -endfor - -## Make the figure look a little better: -## -- scale and label the axes -## -- show gridlines -xlim ([0 15]); -ylim ([0 15]); -xlabel ('Feature 1'); -ylabel ('Feature 3'); -grid -hold
--- a/main/fuzzy-logic-toolkit/inst/gustafson_kessel.m Sat Aug 25 08:27:39 2012 +0000 +++ b/main/fuzzy-logic-toolkit/inst/gustafson_kessel.m Sun Aug 26 14:54:12 2012 +0000 @@ -111,7 +111,7 @@ ## @end group ## @end example ## -## @seealso{fcm, fcm_demo_1, fcm_demo_2, gustafson_kessel_demo_1, gustafson_kessel_demo_2, partition_coeff, partition_entropy, xie_beni_index} +## @seealso{fcm, partition_coeff, partition_entropy, xie_beni_index} ## ## @end deftypefn @@ -119,7 +119,7 @@ ## Keywords: fuzzy-logic-toolkit fuzzy partition clustering ## Directory: fuzzy-logic-toolkit/inst/ ## Filename: gustafson_kessel.m -## Last-Modified: 20 Aug 2012 +## Last-Modified: 26 Aug 2012 function [cluster_centers, soft_partition, obj_fcn_history] = ... gustafson_kessel (input_data, num_clusters, ... @@ -283,3 +283,125 @@ endfunction +##---------------------------------------------------------------------- +## Gustafson-Kessel Demo #1 +##---------------------------------------------------------------------- + +%!demo +%! ## This demo: +%! ## - 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 +%! ## +%! ## 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 gustafson_kessel to classify the input_data. +%! input_data = [2 12; 4 9; 7 13; 11 5; 12 7; 14 4]; +%! number_of_clusters = 2; +%! [cluster_centers, soft_partition, obj_fcn_history] = ... +%! gustafson_kessel (input_data, number_of_clusters) +%! +%! ## Plot the data points as small blue x's. +%! figure ('NumberTitle', 'off', 'Name', 'Gustafson-Kessel Demo 1'); +%! for i = 1 : rows (input_data) +%! plot (input_data(i, 1), input_data(i, 2), 'LineWidth', 2, ... +%! '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 +%! xlim ([0 15]); +%! ylim ([0 15]); +%! xlabel ('Feature 1'); +%! ylabel ('Feature 2'); +%! grid +%! hold + +##---------------------------------------------------------------------- +## Gustafson-Kessel Demo #2 +##---------------------------------------------------------------------- + +%!demo +%! ## This demo: +%! ## - classifies three-dimensional unlabeled data points using the +%! ## Gustafson-Kessel algorithm into three fuzzy clusters +%! ## - plots the input points together with the cluster centers +%! ## +%! ## 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]; +%! number_of_clusters = 3; +%! [cluster_centers, soft_partition, obj_fcn_history] = ... +%! gustafson_kessel (input_data, number_of_clusters, [1 1 1], ... +%! [NaN NaN NaN 0]) +%! +%! ## Plot the data points in two dimensions (using features 1 & 2) +%! ## 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, 2), 'LineWidth', 2, ... +%! 'marker', 'x', 'color', 'b'); +%! hold on; +%! endfor +%! +%! ## 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'); +%! hold on; +%! endfor +%! +%! ## Make the figure look a little better: +%! ## - scale and label the axes +%! ## - show gridlines +%! xlim ([0 15]); +%! ylim ([0 15]); +%! xlabel ('Feature 1'); +%! ylabel ('Feature 2'); +%! grid +%! +%! ## 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, ... +%! 'marker', 'x', 'color', 'b'); +%! hold on; +%! endfor +%! +%! ## 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'); +%! hold on; +%! endfor +%! +%! ## Make the figure look a little better: +%! ## - scale and label the axes +%! ## - show gridlines +%! xlim ([0 15]); +%! ylim ([0 15]); +%! xlabel ('Feature 1'); +%! ylabel ('Feature 3'); +%! grid +%! hold
--- a/main/fuzzy-logic-toolkit/inst/gustafson_kessel_demo_1.m Sat Aug 25 08:27:39 2012 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,94 +0,0 @@ -## Copyright (C) 2011-2012 L. Markowsky <lmarkov@users.sourceforge.net> -## -## 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 {Script File} {} gustafson_kessel_demo_1 -## Use the Gustafson-Kessel algorithm to classify a small set of unlabeled -## data points and evaluate the quality of the resulting clusters. -## -## The demo: -## @itemize @minus -## @item -## classifies a small set of unlabeled data points using the Gustafson-Kessel -## algorithm into two fuzzy clusters -## @item -## calculates and prints (on standard output) three validity measures: -## the partition coefficient, the partition entropy, and the Xie-Beni -## validity index -## @item -## plots the input points together with the cluster centers -## @end itemize -## -## For a description of the data structures used in the script, see the -## documentation for gustafson_kessel. -## -## @seealso{gustafson_kessel, gustafson_kessel_demo_2, partition_coeff, partition_entropy, xie_beni_index, fcm, fcm_demo_1, fcm_demo_2} -## -## @end deftypefn - -## Author: L. Markowsky -## Keywords: fuzzy-logic-toolkit fuzzy partition clustering -## Directory: fuzzy-logic-toolkit/inst/ -## Filename: gustafson_kessel_demo_1.m -## Last-Modified: 20 Aug 2012 - -##---------------------------------------------------------------------- -## 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 gustafson_kessel to classify the data in matrix x. -input_data = [2 12; 4 9; 7 13; 11 5; 12 7; 14 4] -number_of_clusters = 2 -[cluster_centers, soft_partition, obj_fcn_history] = ... - gustafson_kessel (input_data, number_of_clusters) - -## 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)); - -## Plot the data points as small blue x's. -figure ('NumberTitle', 'off', 'Name', 'Gustafson-Kessel Demo 1'); -for i = 1 : rows (input_data) - plot (input_data(i, 1), input_data(i, 2), 'LineWidth', 2, ... - '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 -xlim ([0 15]); -ylim ([0 15]); -xlabel ('Feature 1'); -ylabel ('Feature 2'); -grid -hold
--- a/main/fuzzy-logic-toolkit/inst/gustafson_kessel_demo_2.m Sat Aug 25 08:27:39 2012 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,125 +0,0 @@ -## Copyright (C) 2011-2012 L. Markowsky <lmarkov@users.sourceforge.net> -## -## 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 {Script File} {} gustafson_kessel_demo_2 -## Use the Gustafson-Kessel algorithm to classify three-dimensional unlabeled -## data points and evaluate the quality of the resulting clusters. -## -## The demo: -## @itemize @minus -## @item -## classifies three-dimensional unlabeled data points using the -## Gustafson-Kessel algorithm into three fuzzy clusters -## @item -## calculates and prints (on standard output) three validity measures: -## the partition coefficient, the partition entropy, and the Xie-Beni -## validity index -## @item -## plots the input points together with the cluster centers -## @end itemize -## -## For a description of the data structures used in the script, see the -## documentation for gustafson_kessel. -## -## @seealso{gustafson_kessel, gustafson_kessel_demo_1, partition_coeff, partition_entropy, xie_beni_index, fcm, fcm_demo_1, fcm_demo_2} -## -## @end deftypefn - -## Author: L. Markowsky -## Keywords: fuzzy-logic-toolkit fuzzy partition clustering -## Directory: fuzzy-logic-toolkit/inst/ -## Filename: gustafson_kessel_demo_2.m -## Last-Modified: 20 Aug 2012 - -##---------------------------------------------------------------------- -## Note: The input_data was selected to form three areas of -## different shapes. -##---------------------------------------------------------------------- - -## Use gustafson_kessel to classify the data in matrix x. -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], ... - [NaN NaN NaN 0]) - -## 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)); - -## Plot the data points in two dimensions (using features 1 and 2) -## 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, 2), 'LineWidth', 2, ... - 'marker', 'x', 'color', 'b'); - hold on; -endfor - -## Plot the cluster centers in two dimensions (using features 1 and 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'); - hold on; -endfor - -## Make the figure look a little better: -## -- scale and label the axes -## -- show gridlines -xlim ([0 15]); -ylim ([0 15]); -xlabel ('Feature 1'); -ylabel ('Feature 2'); -grid - -## Plot the data points in two dimensions (using features 1 and 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, ... - 'marker', 'x', 'color', 'b'); - hold on; -endfor - -## Plot the cluster centers in two dimensions (using features 1 and 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'); - hold on; -endfor - -## Make the figure look a little better: -## -- scale and label the axes -## -- show gridlines -xlim ([0 15]); -ylim ([0 15]); -xlabel ('Feature 1'); -ylabel ('Feature 3'); -grid -hold
--- a/main/fuzzy-logic-toolkit/inst/partition_coeff.m Sat Aug 25 08:27:39 2012 +0000 +++ b/main/fuzzy-logic-toolkit/inst/partition_coeff.m Sun Aug 26 14:54:12 2012 +0000 @@ -36,7 +36,7 @@ ## For more information about the @var{soft_partition} matrix, please see the ## documentation for function fcm. ## -## @seealso{fcm, fcm_demo_1, fcm_demo_2, gustafson_kessel, gustafson_kessel_demo_1, gustafson_kessel_demo_2, partition_entropy, xie_beni_index} +## @seealso{fcm, gustafson_kessel, partition_entropy, xie_beni_index} ## ## @end deftypefn @@ -44,7 +44,7 @@ ## Keywords: fuzzy-logic-toolkit partition coefficient cluster ## Directory: fuzzy-logic-toolkit/inst/ ## Filename: partition_coeff.m -## Last-Modified: 19 Aug 2012 +## Last-Modified: 26 Aug 2012 ##---------------------------------------------------------------------- ## Note: This function is an implementation of Equation 13.9 (corrected @@ -78,3 +78,77 @@ 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));
--- a/main/fuzzy-logic-toolkit/inst/partition_entropy.m Sat Aug 25 08:27:39 2012 +0000 +++ b/main/fuzzy-logic-toolkit/inst/partition_entropy.m Sun Aug 26 14:54:12 2012 +0000 @@ -38,7 +38,7 @@ ## For more information about the @var{soft_partition} matrix, please see the ## documentation for function fcm. ## -## @seealso{fcm, fcm_demo_1, fcm_demo_2, gustafson_kessel, gustafson_kessel_demo_1, gustafson_kessel_demo_2, partition_coeff, xie_beni_index} +## @seealso{fcm, gustafson_kessel, partition_coeff, xie_beni_index} ## ## @end deftypefn @@ -46,7 +46,7 @@ ## Keywords: fuzzy-logic-toolkit partition entropy cluster ## Directory: fuzzy-logic-toolkit/inst/ ## Filename: partition_entropy.m -## Last-Modified: 19 Aug 2012 +## Last-Modified: 26 Aug 2012 ##---------------------------------------------------------------------- ## Note: This function is an implementation of Equation 13.10 in @@ -86,3 +86,77 @@ 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));
--- a/main/fuzzy-logic-toolkit/inst/xie_beni_index.m Sat Aug 25 08:27:39 2012 +0000 +++ b/main/fuzzy-logic-toolkit/inst/xie_beni_index.m Sun Aug 26 14:54:12 2012 +0000 @@ -41,7 +41,7 @@ ## and @var{soft_partition} matrices, please see the documentation for function ## fcm. ## -## @seealso{fcm, fcm_demo_1, fcm_demo_2, gustafson_kessel, gustafson_kessel_demo_1, gustafson_kessel_demo_2, partition_coeff, partition_entropy} +## @seealso{fcm, gustafson_kessel, partition_coeff, partition_entropy} ## ## @end deftypefn @@ -49,7 +49,7 @@ ## Keywords: fuzzy-logic-toolkit fuzzy xie beni cluster validity ## Directory: fuzzy-logic-toolkit/inst/ ## Filename: xie_beni_index.m -## Last-Modified: 19 Aug 2012 +## Last-Modified: 26 Aug 2012 function vxb = xie_beni_index (input_data, cluster_centers, ... soft_partition) @@ -126,3 +126,77 @@ 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));