gusucode.com > SOM算法,matlab程序,是经典的教科书的例子程序 > somtoolbox/som_demo3.m
%SOM_DEMO3 Self-organizing map visualization.
% 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 080200 070600
clf reset;
figure(gcf)
echo on
clc
% ==========================================================
% SOM_DEMO3 - VISUALIZATION
% ==========================================================
% som_show - Visualize map.
% som_grid - Visualization with free coordinates.
%
% som_show_add - Add markers on som_show visualization.
% som_show_clear - Remove markers from som_show visualization.
% som_recolorbar - Refresh and rescale colorbars in som_show
% visualization.
%
% som_cplane - Visualize component/color/U-matrix plane.
% som_pieplane - Visualize prototype vectors as pie charts.
% som_barplane - Visualize prototype vectors as bar charts.
% som_plotplane - Visualize prototype vectors as line graphs.
%
% pcaproj - Projection to principal component space.
% cca - Projection with Curvilinear Component Analysis.
% sammon - Projection with Sammon's mapping.
% som_umat - Calculate U-matrix.
% som_colorcode - Color coding for the map.
% som_normcolor - RGB values of indexed colors.
% som_hits - Hit histograms for the map.
% The basic functions for SOM visualization are SOM_SHOW and
% SOM_GRID. The SOM_SHOW has three auxiliary functions:
% SOM_SHOW_ADD, SOM_SHOW_CLEAR and SOM_RECOLORBAR which are used
% to add and remove markers and to control the colorbars.
% SOM_SHOW actually uses SOM_CPLANE to make the visualizations.
% Also SOM_{PIE,BAR,PLOT}PLANE can be used to visualize SOMs.
% The other functions listed above do not themselves visualize
% anything, but their results are used in the visualizations.
% There's an important limitation that visualization functions have:
% while the SOM Toolbox otherwise supports N-dimensional map grids,
% visualization only works for 1- and 2-dimensional map grids!!!
pause % Strike any key to create demo data and map...
clc
% DEMO DATA AND MAP
% =================
% The data set contructed for this demo consists of random vectors
% in three gaussian kernels the centers of which are at [0, 0, 0],
% [3 3 3] and [9 0 0]. The map is trained using default parameters.
D1 = randn(100,3);
D2 = randn(100,3) + 3;
D3 = randn(100,3); D3(:,1) = D3(:,1) + 9;
sD = som_data_struct([D1; D2; D3],'name','Demo3 data',...
'comp_names',{'X-coord','Y-coord','Z-coord'});
sM = som_make(sD);
% Since the data (and thus the prototypes of the map) are
% 3-dimensional, they can be directly plotted using PLOT3.
% Below, the data is plotted using red 'o's and the map
% prototype vectors with black '+'s.
plot3(sD.data(:,1),sD.data(:,2),sD.data(:,3),'ro',...
sM.codebook(:,1),sM.codebook(:,2),sM.codebook(:,3),'k+')
rotate3d on
% From the visualization it is pretty easy to see what the data is
% like, and how the prototypes have been positioned. One can see
% that there are three clusters, and that there are some prototype
% vectors between the clusters, although there is actually no
% data there. The map units corresponding to these prototypes
% are called 'dead' or 'interpolative' map units.
pause % Strike any key to continue...
clc
% VISUALIZATION OF MULTIDIMENSIONAL DATA
% ======================================
% Usually visualization of data sets is not this straightforward,
% since the dimensionality is much higher than three. In principle,
% one can embed additional information to the visualization by
% using properties other than position, for example color, size or
% shape.
% Here the data set and map prototypes are plotted again, but
% information of the cluster is shown using color: red for the
% first cluster, green for the second and blue for the last.
plot3(sD.data(1:100,1),sD.data(1:100,2),sD.data(1:100,3),'ro',...
sD.data(101:200,1),sD.data(101:200,2),sD.data(101:200,3),'go',...
sD.data(201:300,1),sD.data(201:300,2),sD.data(201:300,3),'bo',...
sM.codebook(:,1),sM.codebook(:,2),sM.codebook(:,3),'k+')
rotate3d on
% However, this works only for relatively small dimensionality, say
% less than 10. When the information is added this way, the
% visualization becomes harder and harder to understand. Also, not
% all properties are equal: the human visual system perceives
% colors differently from position, not to mention the complex
% rules governing perception of shape.
pause % Strike any key to learn about linking...
clc
% LINKING MULTIPLE VISUALIZATIONS
% ===============================
% The other option is to use *multiple visualizations*, so called
% small multiples, instead of only one. The problem is then how to
% link these visualizations together: one should be able to idetify
% the same object from the different visualizations.
% This could be done using, for example, color: each object has
% the same color in each visualization. Another option is to use
% similar position: each object has the same position in each
% small multiple.
% For example, here are four subplots, one for each component and
% one for cluster information, where color denotes the value and
% position is used for linking. The 2D-position is derived by
% projecting the data into the space spanned by its two greatest
% eigenvectors.
[Pd,V,me] = pcaproj(sD.data,2); % project the data
Pm = pcaproj(sM.codebook,V,me); % project the prototypes
colormap(hot); % colormap used for values
echo off
for c=1:3,
subplot(2,2,c), cla, hold on
som_grid('rect',[300 1],'coord',Pd,'Line','none',...
'MarkerColor',som_normcolor(sD.data(:,c)));
som_grid(sM,'Coord',Pm,'Line','none','marker','+');
hold off, title(sD.comp_names{c}), xlabel('PC 1'), ylabel('PC 2');
end
subplot(2,2,4), cla
plot(Pd(1:100,1),Pd(1:100,2),'ro',...
Pd(101:200,1),Pd(101:200,2),'go',...
Pd(201:300,1),Pd(201:300,2),'bo',...
Pm(:,1),Pm(:,2),'k+')
title('Cluster')
echo on
pause % Strike any key to use color for linking...
% Here is another example, where color is used for linking. On the
% top right triangle are the scatter plots of each variable without
% color coding, and on the bottom left triangle with the color
% coding. In the colored figures, each data sample can be
% identified by a unique color. Well, almost identified: there are
% quite a lot of samples with almost the same color. Color is not as
% precise linking method as position.
echo off
Col = som_normcolor([1:300]',jet(300));
k=1;
for i=1:3,
for j=1:3,
if i<j, i1=i; i2=j; else i1=j; i2=i; end
if i<j,
subplot(3,3,k); cla
plot(sD.data(:,i1),sD.data(:,i2),'ko')
xlabel(sD.comp_names{i1}), ylabel(sD.comp_names{i2})
elseif i>j,
subplot(3,3,k); cla
som_grid('rect',[300 1],'coord',sD.data(:,[i1 i2]),...
'Line','none','MarkerColor',Col);
xlabel(sD.comp_names{i1}), ylabel(sD.comp_names{i2})
end
k=k+1;
end
end
echo on
pause % Strike any key to learn about data visualization using SOM...
clc
% DATA VISUALIZATION USING SOM
% ============================
% The basic visualization functions and their usage have already
% been introduced in SOM_DEMO2. In this demo, a more structured
% presentation is given.
% Data visualization techniques using the SOM can be divided to
% three categories based on their goal:
% 1. visualization of clusters and shape of the data:
% projections, U-matrices and other distance matrices
%
% 2. visualization of components / variables:
% component planes, scatter plots
%
% 3. visualization of data projections:
% hit histograms, response surfaces
pause % Strike any key to visualize clusters with distance matrices...
clf
clc
% 1. VISUALIZATION OF CLUSTERS: DISTANCE MATRICES
% ===============================================
% Distance matrices are typically used to show the cluster
% structure of the SOM. They show distances between neighboring
% units, and are thus closely related to single linkage clustering
% techniques. The most widely used distance matrix technique is
% the U-matrix.
% Here, the U-matrix of the map is shown (using all three
% components in the distance calculation):
colormap(1-gray)
som_show(sM,'umat','all');
pause % Strike any key to see more examples of distance matrices...
% The function SOM_UMAT can be used to calculate U-matrix. The
% resulting matrix holds distances between neighboring map units,
% as well as the median distance from each map unit to its
% neighbors. These median distances corresponding to each map unit
% can be easily extracted. The result is a distance matrix using
% median distance.
U = som_umat(sM);
Um = U(1:2:size(U,1),1:2:size(U,2));
% A related technique is to assign colors to the map units such
% that similar map units get similar colors.
% Here, four clustering figures are shown:
% - U-matrix
% - median distance matrix (with grayscale)
% - median distance matrix (with map unit size)
% - similarity coloring, made by spreading a colormap
% on top of the principal component projection of the
% prototype vectors
subplot(2,2,1)
h=som_cplane([sM.topol.lattice,'U'],sM.topol.msize, U(:));
set(h,'Edgecolor','none'); title('U-matrix')
subplot(2,2,2)
h=som_cplane(sM, Um(:));
set(h,'Edgecolor','none'); title('D-matrix (grayscale)')
subplot(2,2,3)
som_cplane(sM,'none',1-Um(:)/max(Um(:)))
title('D-matrix (marker size)')
subplot(2,2,4)
C = som_colorcode(Pm); % Pm is the PC-projection calculated earlier
som_cplane(sM,C)
title('Similarity coloring')
pause % Strike any key to visualize shape and clusters with projections...
clf
clc
% 1. VISUALIZATION OF CLUSTERS AND SHAPE: PROJECTIONS
% ===================================================
% In vector projection, a set of high-dimensional data samples is
% projected to a lower dimensional such that the distances between
% data sample pairs are preserved as well as possible. Depending
% on the technique, the projection may be either linear or
% non-linear, and it may place special emphasis on preserving
% local distances.
% For example SOM is a projection technique, since the prototypes
% have well-defined positions on the 2-dimensional map grid. SOM as
% a projection is however a very crude one. Other projection
% techniques include the principal component projection used
% earlier, Sammon's mapping and Curvilinear Component Analysis
% (to name a few). These have been implemented in functions
% PCAPROJ, SAMMON and CCA.
% Projecting the map prototype vectors and joining neighboring map
% units with lines gives the SOM its characteristic net-like look.
% The projection figures can be linked to the map planes using
% color coding.
% Here is the distance matrix, color coding, a projection without
% coloring and a projection with one. In the last projection,
% the size of interpolating map units has been set to zero.
subplot(2,2,1)
som_cplane(sM,Um(:));
title('Distance matrix')
subplot(2,2,2)
C = som_colorcode(sM,'rgb4');
som_cplane(sM,C);
title('Color code')
subplot(2,2,3)
som_grid(sM,'Coord',Pm,'Linecolor','k');
title('PC-projection')
subplot(2,2,4)
h = som_hits(sM,sD); s=6*(h>0);
som_grid(sM,'Coord',Pm,'MarkerColor',C,'Linecolor','k','MarkerSize',s);
title('Colored PC-projection')
pause % Strike any key to visualize component planes...
clf
clc
% 2. VISUALIZATION OF COMPONENTS: COMPONENT PLANES
% ================================================
% The component planes visualizations shows what kind of values the
% prototype vectors of the map units have for different vector
% components.
% Here is the U-matrix and the three component planes of the map.
som_show(sM)
pause % Strike any key to continue...
% Besides SOM_SHOW and SOM_CPLANE, there are three other
% functions specifically designed for showing the values of the
% component planes: SOM_PIEPLANE, SOM_BARPLANE, SOM_PLOTPLANE.
% SOM_PIEPLANE shows a single pie chart for each map unit. Each
% pie shows the relative proportion of each component of the sum of
% all components in that map unit. The component values must be
% positive.
% SOM_BARPLANE shows a barchart in each map unit. The scaling of
% bars can be made unit-wise or variable-wise. By default it is
% determined variable-wise.
% SOM_PLOTPLANE shows a linegraph in each map unit.
M = som_normalize(sM.codebook,'range');
subplot(1,3,1)
som_pieplane(sM, M);
title('som\_pieplane')
subplot(1,3,2)
som_barplane(sM, M, '', 'unitwise');
title('som\_barplane')
subplot(1,3,3)
som_plotplane(sM, M, 'b');
title('som\_plotplane')
pause % Strike any key to visualize cluster properties...
clf
clc
% 2. VISUALIZATION OF COMPONENTS: CLUSTERS
% ========================================
% An interesting question is of course how do the values of the
% variables relate to the clusters: what are the values of the
% components in the clusters, and which components are the ones
% which *make* the clusters.
som_show(sM)
% From the U-matrix and component planes, one can easily see
% what the typical values are in each cluster.
pause % Strike any key to continue...
% The significance of the components with respect to the clustering
% is harder to visualize. One indication of importance is that on
% the borders of the clusters, values of important variables change
% very rabidly.
% Here, the distance matrix is calculated with respect to each
% variable.
u1 = som_umat(sM,'mask',[1 0 0]'); u1=u1(1:2:size(u1,1),1:2:size(u1,2));
u2 = som_umat(sM,'mask',[0 1 0]'); u2=u2(1:2:size(u2,1),1:2:size(u2,2));
u3 = som_umat(sM,'mask',[0 0 1]'); u3=u3(1:2:size(u3,1),1:2:size(u3,2));
% Here, the distance matrices are shown, as well as a piechart
% indicating the relative importance of each variable in each
% map unit. The size of piecharts has been scaled by the
% distance matrix calculated from all components.
subplot(2,2,1)
som_cplane(sM,u1(:));
title(sM.comp_names{1})
subplot(2,2,2)
som_cplane(sM,u2(:));
title(sM.comp_names{2})
subplot(2,2,3)
som_cplane(sM,u3(:));
title(sM.comp_names{3})
subplot(2,2,4)
som_pieplane(sM, [u1(:), u2(:), u3(:)], hsv(3), Um(:)/max(Um(:)));
title('Relative importance')
% From the last subplot, one can see that in the area where the
% bigger cluster border is, the 'X-coord' component (red color)
% has biggest effect, and thus is the main factor in separating
% that cluster from the rest.
pause % Strike any key to learn about correlation hunting...
clf
clc
% 2. VISUALIZATION OF COMPONENTS: CORRELATION HUNTING
% ===================================================
% Finally, the component planes are often used for correlation
% hunting. When the number of variables is high, the component
% plane visualization offers a convenient way to visualize all
% components at once and hunt for correlations (as opposed to
% N*(N-1)/2 scatterplots).
% Hunting correlations this way is not very accurate. However, it
% is easy to select interesting combinations for further
% investigation.
% Here, the first and third components are shown with scatter
% plot. As with projections, a color coding is used to link the
% visualization to the map plane. In the color coding, size shows
% the distance matrix information.
C = som_colorcode(sM);
subplot(1,2,1)
som_cplane(sM,C,1-Um(:)/max(Um(:)));
title('Color coding + distance matrix')
subplot(1,2,2)
som_grid(sM,'Coord',sM.codebook(:,[1 3]),'MarkerColor',C);
title('Scatter plot'); xlabel(sM.comp_names{1}); ylabel(sM.comp_names{3})
axis equal
pause % Strike any key to visualize data responses...
clf
clc
% 3. DATA ON MAP
% ==============
% The SOM is a map of the data manifold. An interesting question
% then is where on the map a specific data sample is located, and
% how accurate is that localization? One is interested in the
% response of the map to the data sample.
% The simplest answer is to find the BMU of the data sample.
% However, this gives no indication of the accuracy of the
% match. Is the data sample close to the BMU, or is it actually
% equally close to the neighboring map units (or even approximately
% as close to all map units)? Sometimes accuracy doesn't really
% matter, but if it does, it should be visualized somehow.
% Here are different kinds of response visualizations for two
% vectors: [0 0 0] and [99 99 99].
% - BMUs indicated with labels
% - BMUs indicated with markers, relative quantization errors
% (in this case, proportion between distances to BMU and
% Worst-MU) with vertical lines
% - quantization error between the samples and all map units
% - fuzzy response (a non-linear function of quantization
% error) of all map units
echo off
[bm,qe] = som_bmus(sM,[0 0 0; 99 99 99],'all'); % distance to all map units
[dummy,ind] = sort(bm(1,:)); d0 = qe(1,ind)';
[dummy,ind] = sort(bm(2,:)); d9 = qe(2,ind)';
bmu0 = bm(1,1); bmu9 = bm(2,1); % bmus
h0 = zeros(prod(sM.topol.msize),1); h0(bmu0) = 1; % crisp hits
h9 = zeros(prod(sM.topol.msize),1); h9(bmu9) = 1;
lab = cell(prod(sM.topol.msize),1);
lab{bmu0} = '[0,0,0]'; lab{bmu9} = '[99,99,99]';
hf0 = som_hits(sM,[0 0 0],'fuzzy'); % fuzzy response
hf9 = som_hits(sM,[99 99 99],'fuzzy');
som_show(sM,'umat',{'all','BMU'},...
'color',{d0,'Qerror 0'},'color',{hf0,'Fuzzy response 0'},...
'empty','BMU+qerror',...
'color',{d9,'Qerror 99'},'color',{hf9,'Fuzzy response 99'});
som_show_add('label',lab,'Subplot',1,'Textcolor','r');
som_show_add('hit',[h0, h9],'Subplot',4,'MarkerColor','r');
hold on
Co = som_vis_coords(sM.topol.lattice,sM.topol.msize);
plot3(Co(bmu0,[1 1]),Co(bmu0,[2 2]),[0 10*qe(1,1)/qe(1,end)],'r-')
plot3(Co(bmu9,[1 1]),Co(bmu9,[2 2]),[0 10*qe(2,1)/qe(2,end)],'r-')
view(3), axis equal
echo on
% Here are the distances to BMU, 2-BMU and WMU:
qe(1,[1,2,end]) % [0 0 0]
qe(2,[1,2,end]) % [99 99 99]
% One can see that for [0 0 0] the accuracy is pretty good as the
% quantization error of the BMU is much lower than that of the
% WMU. On the other hand [99 99 99] is very far from the map:
% distance to BMU is almost equal to distance to WMU.
pause % Strike any key to visualize responses of multiple samples...
clc
clf
% 3. DATA ON MAP: HIT HISTOGRAMS
% ==============================
% One can also investigate whole data sets using the map. When the
% BMUs of multiple data samples are aggregated, a hit histogram
% results. Instead of BMUs, one can also aggregate for example
% fuzzy responses.
% The hit histograms (or aggregated responses) can then be compared
% with each other.
% Here are hit histograms of three data sets: one with 50 first
% vectors of the data set, one with 150 samples from the data
% set, and one with 50 randomly selected samples. In the last
% subplot, the fuzzy response of the first data set.
dlen = size(sD.data,1);
Dsample1 = sD.data(1:50,:); h1 = som_hits(sM,Dsample1);
Dsample2 = sD.data(1:150,:); h2 = som_hits(sM,Dsample2);
Dsample3 = sD.data(ceil(rand(50,1)*dlen),:); h3 = som_hits(sM,Dsample3);
hf = som_hits(sM,Dsample1,'fuzzy');
som_show(sM,'umat','all','umat','all','umat','all','color',{hf,'Fuzzy'})
som_show_add('hit',h1,'Subplot',1,'Markercolor','r')
som_show_add('hit',h2,'Subplot',2,'Markercolor','r')
som_show_add('hit',h3,'Subplot',3,'Markercolor','r')
pause % Strike any key to visualize trajectories...
clc
clf
% 3. DATA ON MAP: TRAJECTORIES
% ============================
% A special data mapping technique is trajectory. If the samples
% are ordered, forming a time-series for example, their response on
% the map can be tracked. The function SOM_SHOW_ADD can be used to
% show the trajectories in two different modes: 'traj' and 'comet'.
% Here, a series of data points is formed which go from [8,0,0]
% to [2,2,2]. The trajectory is plotted using the two modes.
Dtraj = [linspace(9,2,20); linspace(0,2,20); linspace(0,2,20)]';
T = som_bmus(sM,Dtraj);
som_show(sM,'comp',[1 1]);
som_show_add('traj',T,'Markercolor','r','TrajColor','r','subplot',1);
som_show_add('comet',T,'MarkerColor','r','subplot',2);
% There's also a function SOM_TRAJECTORY which lauches a GUI
% specifically designed for displaying trajectories (in 'comet'
% mode).
pause % Strike any key to learn about color handling...
clc
clf
% COLOR HANDLING
% ==============
% Matlab offers flexibility in the colormaps. Using the COLORMAP
% function, the colormap may be changed. There are several useful
% colormaps readily available, for example 'hot' and 'jet'. The
% default number of colors in the colormaps is 64. However, it is
% often advantageous to use less colors in the colormap. This way
% the components planes visualization become easier to interpret.
% Here the three component planes are visualized using the 'hot'
% colormap and only three colors.
som_show(sM,'comp',[1 2 3])
colormap(hot(3));
som_recolorbar
pause % Press any key to change the colorbar labels...
% The function SOM_RECOLORBAR can be used to reconfigure
% the labels beside the colorbar.
% Here the colorbar of the first subplot is labeled using labels
% 'small', 'medium' and 'big' at values 0, 1 and 2. For the
% colorbar of the second subplot, values are calculated for the
% borders between colors.
som_recolorbar(1,{[0 4 9]},'',{{'small','medium','big'}});
som_recolorbar(2,'border','');
pause % Press any key to learn about SOM_NORMCOLOR...
% Some SOM Toolbox functions do not use indexed colors if the
% underlying Matlab function (e.g. PLOT) do not use indexed
% colors. SOM_NORMCOLOR is a convenient function to simulate
% indexed colors: it calculates fixed RGB colors that
% are similar to indexed colors with the specified colormap.
% Here, two SOM_GRID visualizations are created. One uses the
% 'surf' mode to show the component colors in indexed color
% mode, and the other uses SOM_NORMALIZE to do the same.
clf
colormap(jet(64))
subplot(1,2,1)
som_grid(sM,'Surf',sM.codebook(:,3));
title('Surf mode')
subplot(1,2,2)
som_grid(sM,'Markercolor',som_normcolor(sM.codebook(:,3)));
title('som\_normcolor')
pause % Press any key to visualize different map shapes...
clc
clf
% DIFFERENT MAP SHAPES
% ====================
% There's no direct way to visualize cylinder or toroid maps. When
% visualized, they are treated exactly as if they were sheet
% shaped. However, if function SOM_UNIT_COORDS is used to provide
% unit coordinates, then SOM_GRID can be used to visualize these
% alternative map shapes.
% Here the grids of the three possible map shapes (sheet, cylinder
% and toroid) are visualized. The last subplot shows a component
% plane visualization of the toroid map.
Cor = som_unit_coords(sM.topol.msize,'hexa','sheet');
Coc = som_unit_coords(sM.topol.msize,'hexa','cyl');
Cot = som_unit_coords(sM.topol.msize,'hexa','toroid');
subplot(2,2,1)
som_grid(sM,'Coord',Cor,'Markersize',3,'Linecolor','k');
title('sheet'), view(0,-90), axis tight, axis equal
subplot(2,2,2)
som_grid(sM,'Coord',Coc,'Markersize',3,'Linecolor','k');
title('cylinder'), view(5,1), axis tight, axis equal
subplot(2,2,3)
som_grid(sM,'Coord',Cot,'Markersize',3,'Linecolor','k');
title('toroid'), view(-100,0), axis tight, axis equal
subplot(2,2,4)
som_grid(sM,'Coord',Cot,'Surf',sM.codebook(:,3));
colormap(jet), colorbar
title('toroid'), view(-100,0), axis tight, axis equal
echo off