comparison toolboxes/MIRtoolbox1.3.2/somtoolbox/som_demo2.m @ 0:e9a9cd732c1e tip

first hg version after svn

+
+%SOM_DEMO2 Basic usage of the SOM Toolbox.
+
+% Contributed to SOM Toolbox 2.0, February 11th, 2000 by Juha Vesanto
+% http://www.cis.hut.fi/projects/somtoolbox/
+
+% Version 1.0beta juuso 071197 
+% Version 2.0beta juuso 070200
+
+clf reset;
+figure(gcf)
+echo on
+
+
+
+clc
+%    ==========================================================
+%    SOM_DEMO2 - BASIC USAGE OF SOM TOOLBOX
+%    ==========================================================
+
+%    som_data_struct    - Create a data struct.
+%    som_read_data      - Read data from file.
+%
+%    som_normalize      - Normalize data.
+%    som_denormalize    - Denormalize data.
+%
+%    som_make           - Initialize and train the map. 
+%
+%    som_show           - Visualize map.
+%    som_show_add       - Add markers on som_show visualization.
+%    som_grid           - Visualization with free coordinates.
+%
+%    som_autolabel      - Give labels to map.
+%    som_hits           - Calculate hit histogram for the map.
+
+%    BASIC USAGE OF THE SOM TOOLBOX
+
+%    The basic usage of the SOM Toolbox proceeds like this: 
+%      1. construct data set
+%      2. normalize it
+%      3. train the map
+%      4. visualize map
+%      5. analyse results
+
+%    The four first items are - if default options are used - very
+%    simple operations, each executable with a single command.  For
+%    the last, several different kinds of functions are provided in
+%    the Toolbox, but as the needs of analysis vary, a general default
+%    function or procedure does not exist. 
+
+pause % Strike any key to construct data...
+
+
+
+clc
+%    STEP 1: CONSTRUCT DATA
+%    ======================
+
+%    The SOM Toolbox has a special struct, called data struct, which
+%    is used to group information regarding the data set in one
+%    place.
+
+%    Here, a data struct is created using function SOM_DATA_STRUCT.
+%    First argument is the data matrix itself, then is the name 
+%    given to the data set, and the names of the components
+%    (variables) in the data matrix.
+
+D = rand(1000,3); % 1000 samples from unit cube
+sData = som_data_struct(D,'name','unit cube','comp_names',{'x','y','z'});
+			
+%    Another option is to read the data directly from an ASCII file.
+%    Here, the IRIS data set is loaded from a file (please make sure
+%    the file can be found from the current path):
+
+try, 
+  sDiris = som_read_data('iris.data');
+catch
+  echo off
+
+  warning('File ''iris.data'' not found. Using simulated data instead.')
+  
+  D = randn(50,4); 
+  D(:,1) = D(:,1)+5;     D(:,2) = D(:,2)+3.5; 
+  D(:,3) = D(:,3)/2+1.5; D(:,4) = D(:,4)/2+0.3;
+  D(find(D(:)<=0)) = 0.01; 
+  
+  D2 = randn(100,4); D2(:,2) = sort(D2(:,2));
+  D2(:,1) = D2(:,1)+6.5; D2(:,2) = D2(:,2)+2.8; 
+  D2(:,3) = D2(:,3)+5;   D2(:,4) = D2(:,4)/2+1.5;
+  D2(find(D2(:)<=0)) = 0.01; 
+  
+  sDiris = som_data_struct([D; D2],'name','iris (simulated)',...
+			  'comp_names',{'SepalL','SepalW','PetalL','PetalW'});
+  sDiris = som_label(sDiris,'add',[1:50]','Setosa');
+  sDiris = som_label(sDiris,'add',[51:100]','Versicolor');
+  sDiris = som_label(sDiris,'add',[101:150]','Virginica');
+  
+  echo on
+end
+
+%     Here are the histograms and scatter plots of the four variables.
+
+echo off 
+k=1;
+for i=1:4, 
+  for j=1:4, 
+    if i==j, 
+      subplot(4,4,k); 
+      hist(sDiris.data(:,i)); title(sDiris.comp_names{i})
+    elseif i<j, 
+      subplot(4,4,k); 
+      plot(sDiris.data(:,i),sDiris.data(:,j),'k.')
+      xlabel(sDiris.comp_names{i})
+      ylabel(sDiris.comp_names{j})
+    end
+    k=k+1;
+  end
+end
+echo on
+
+%     Actually, as you saw in SOM_DEMO1, most SOM Toolbox functions
+%     can also handle plain data matrices, but then one is without the
+%     convenience offered by component names, labels and
+%     denormalization operations.
+
+
+pause % Strike any key to normalize the data...
+
+
+
+
+
+clc
+%    STEP 2: DATA NORMALIZATION
+%    ==========================
+
+%    Since SOM algorithm is based on Euclidian distances, the scale of
+%    the variables is very important in determining what the map will
+%    be like. If the range of values of some variable is much bigger
+%    than of the other variables, that variable will probably dominate
+%    the map organization completely. 
+
+%    For this reason, the components of the data set are usually
+%    normalized, for example so that each component has unit
+%    variance. This can be done with function SOM_NORMALIZE:
+
+sDiris = som_normalize(sDiris,'var');
+
+%    The function has also other normalization methods.
+
+%    However, interpreting the values may be harder when they have
+%    been normalized. Therefore, the normalization operations can be
+%    reversed with function SOM_DENORMALIZE:
+
+x = sDiris.data(1,:)
+
+orig_x = som_denormalize(x,sDiris)
+
+pause % Strike any key to to train the map...
+
+
+
+
+
+clc
+%    STEP 3: MAP TRAINING
+%    ====================
+
+%    The function SOM_MAKE is used to train the SOM. By default, it
+%    first determines the map size, then initializes the map using
+%    linear initialization, and finally uses batch algorithm to train
+%    the map.  Function SOM_DEMO1 has a more detailed description of
+%    the training process.
+
+sMap = som_make(sDiris);
+
+
+pause % Strike any key to continues...
+
+%    The IRIS data set also has labels associated with the data
+%    samples. Actually, the data set consists of 50 samples of three
+%    species of Iris-flowers (a total of 150 samples) such that the
+%    measurements are width and height of sepal and petal leaves. The
+%    label associated with each sample is the species information:
+%    'Setosa', 'Versicolor' or 'Virginica'.
+
+%    Now, the map can be labelled with these labels. The best
+%    matching unit of each sample is found from the map, and the
+%    species label is given to the map unit. Function SOM_AUTOLABEL 
+%    can be used to do this: 
+
+sMap = som_autolabel(sMap,sDiris,'vote');
+
+pause % Strike any key to visualize the map...
+
+
+
+
+
+clc
+%    STEP 4: VISUALIZING THE SELF-ORGANIZING MAP: SOM_SHOW
+%    =====================================================
+
+%    The basic visualization of the SOM is done with function SOM_SHOW.
+
+colormap(1-gray)
+som_show(sMap,'norm','d')
+
+%    Notice that the names of the components are included as the
+%    titles of the subplots. Notice also that the variable values
+%    have been denormalized to the original range and scale.
+
+%    The component planes ('PetalL', 'PetalW', 'SepalL' and 'SepalW')
+%    show what kind of values the prototype vectors of the map units
+%    have. The value is indicated with color, and the colorbar on the
+%    right shows what the colors mean.
+
+%    The 'U-matrix' shows distances between neighboring units and thus
+%    visualizes the cluster structure of the map. Note that the
+%    U-matrix visualization has much more hexagons that the
+%    component planes. This is because distances *between* map units
+%    are shown, and not only the distance values *at* the map units. 
+
+%    High values on the U-matrix mean large distance between
+%    neighboring map units, and thus indicate cluster
+%    borders. Clusters are typically uniform areas of low
+%    values. Refer to colorbar to see which colors mean high
+%    values. In the IRIS map, there appear to be two clusters.
+
+pause % Strike any key to continue...
+
+%    The subplots are linked together through similar position. In
+%    each axis, a particular map unit is always in the same place. For
+%    example:
+
+h=zeros(sMap.topol.msize); h(1,2) = 1;
+som_show_add('hit',h(:),'markercolor','r','markersize',0.5,'subplot','all')
+
+%    the red marker is on top of the same unit on each axis. 
+
+pause % Strike any key to continue...
+
+
+
+clf
+
+clc
+
+%    STEP 4: VISUALIZING THE SELF-ORGANIZING MAP: SOM_SHOW_ADD
+%    =========================================================
+
+%    The SOM_SHOW_ADD function can be used to add markers, labels and
+%    trajectories on top of SOM_SHOW created figures. The function
+%    SOM_SHOW_CLEAR can be used to clear them away.
+
+%    Here, the U-matrix is shown on the left, and an empty grid
+%    named 'Labels' is shown on the right.
+
+som_show(sMap,'umat','all','empty','Labels')
+
+pause % Strike any key to add labels...
+
+%    Here, the labels added to the map with SOM_AUTOLABEL function
+%    are shown on the empty grid.
+
+som_show_add('label',sMap,'Textsize',8,'TextColor','r','Subplot',2)
+
+pause % Strike any key to add hits...
+
+%    An important tool in data analysis using SOM are so called hit
+%    histograms. They are formed by taking a data set, finding the BMU
+%    of each data sample from the map, and increasing a counter in a
+%    map unit each time it is the BMU. The hit histogram shows the
+%    distribution of the data set on the map.
+
+%    Here, the hit histogram for the whole data set is calculated
+%    and visualized on the U-matrix.
+
+h = som_hits(sMap,sDiris);
+som_show_add('hit',h,'MarkerColor','w','Subplot',1)
+
+pause % Strike any key to continue...
+
+%    Multiple hit histograms can be shown simultaniously. Here, three
+%    hit histograms corresponding to the three species of Iris
+%    flowers is calculated and shown. 
+
+%    First, the old hit histogram is removed.
+
+som_show_clear('hit',1)
+
+%    Then, the histograms are calculated. The first 50 samples in
+%    the data set are of the 'Setosa' species, the next 50 samples
+%    of the 'Versicolor' species and the last 50 samples of the
+%    'Virginica' species. 
+
+h1 = som_hits(sMap,sDiris.data(1:50,:));
+h2 = som_hits(sMap,sDiris.data(51:100,:));
+h3 = som_hits(sMap,sDiris.data(101:150,:));
+
+som_show_add('hit',[h1, h2, h3],'MarkerColor',[1 0 0; 0 1 0; 0 0 1],'Subplot',1)
+
+%    Red color is for 'Setosa', green for 'Versicolor' and blue for
+%    'Virginica'. One can see that the three species are pretty well
+%    separated, although 'Versicolor' and 'Virginica' are slightly
+%    mixed up.
+
+pause % Strike any key to continue...
+
+
+
+clf
+clc
+
+%    STEP 4: VISUALIZING THE SELF-ORGANIZING MAP: SOM_GRID
+%    =====================================================
+
+%    There's also another visualization function: SOM_GRID.  This
+%    allows visualization of the SOM in freely specified coordinates,
+%    for example the input space (of course, only upto 3D space). This
+%    function has quite a lot of options, and is pretty flexible.
+
+%    Basically, the SOM_GRID visualizes the SOM network: each unit is
+%    shown with a marker and connected to its neighbors with lines.
+%    The user has control over: 
+%     - the coordinate of each unit (2D or 3D)
+%     - the marker type, color and size of each unit
+%     - the linetype, color and width of the connecting lines
+%    There are also some other options.
+
+pause % Strike any key to see some visualizations...
+
+%    Here are four visualizations made with SOM_GRID: 
+%     - The map grid in the output space.
+
+subplot(2,2,1)
+som_grid(sMap,'Linecolor','k')
+view(0,-90), title('Map grid')
+
+%     - A surface plot of distance matrix: both color and 
+%       z-coordinate indicate average distance to neighboring 
+%       map units. This is closely related to the U-matrix.
+
+subplot(2,2,2)
+Co=som_unit_coords(sMap); U=som_umat(sMap); U=U(1:2:size(U,1),1:2:size(U,2));
+som_grid(sMap,'Coord',[Co, U(:)],'Surf',U(:),'Marker','none');
+view(-80,45), axis tight, title('Distance matrix')
+
+%     - The map grid in the output space. Three first components
+%       determine the 3D-coordinates of the map unit, and the size
+%       of the marker is determined by the fourth component.
+%       Note that the values have been denormalized.
+
+subplot(2,2,3)
+M = som_denormalize(sMap.codebook,sMap);
+som_grid(sMap,'Coord',M(:,1:3),'MarkerSize',M(:,4)*2)
+view(-80,45), axis tight, title('Prototypes')
+
+%     - Map grid as above, but the original data has been plotted
+%       also: coordinates show the values of three first components
+%       and color indicates the species of each sample.  Fourth
+%       component is not shown.
+
+subplot(2,2,4)
+som_grid(sMap,'Coord',M(:,1:3),'MarkerSize',M(:,4)*2)
+hold on
+D = som_denormalize(sDiris.data,sDiris); 
+plot3(D(1:50,1),D(1:50,2),D(1:50,3),'r.',...
+      D(51:100,1),D(51:100,2),D(51:100,3),'g.',...
+      D(101:150,1),D(101:150,2),D(101:150,3),'b.')
+view(-72,64), axis tight, title('Prototypes and data')
+
+pause % Strike any key to continue...
+
+%    STEP 5: ANALYSIS OF RESULTS
+%    ===========================
+
+%    The purpose of this step highly depends on the purpose of the
+%    whole data analysis: is it segmentation, modeling, novelty
+%    detection, classification, or something else? For this reason, 
+%    there is not a single general-purpose analysis function, but 
+%    a number of individual functions which may, or may not, prove 
+%    useful in any specific case.
+
+%    Visualization is of course part of the analysis of
+%    results. Examination of labels and hit histograms is another
+%    part. Yet another is validation of the quality of the SOM (see
+%    the use of SOM_QUALITY in SOM_DEMO1).
+
+[qe,te] = som_quality(sMap,sDiris)
+
+%    People have contributed a number of functions to the Toolbox
+%    which can be used for the analysis. These include functions for 
+%    vector projection, clustering, pdf-estimation, modeling,
+%    classification, etc. However, ultimately the use of these
+%    tools is up to you.
+
+%    More about visualization is presented in SOM_DEMO3.
+%    More about data analysis is presented in SOM_DEMO4.
+
+echo off
+warning on
+
+
+
+