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TestFunctions.m
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%Copyright (C) 2016 Piotr Beben
%This program 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.
%This program 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 this program. If not, see http://www.gnu.org/licenses/.
classdef TestFunctions
properties
end
methods (Static)
%---------------------------------------------------------------------------------------------
function test_2D(cloud, complex, complexPts, sensitivity, closed, ...
iterations, stepOver, delay, cloudColor, printProgress, varargin)
if isempty(varargin)
figureType = 'default';
else
figureType = varargin{1};
end;
fh = figure(1);
set(fh,'Units','Normalized','Position',[0.2, 0.1, 0.6, 0.75])
hold off
PointCloud.plot2D(cloud,cloudColor);
hold on
grid on
[p0,colors] = complex.plot2D(complexPts,[]);
set(p0,'facealpha',0.35);
switch figureType
case 'figure_fit_data'
m1 = min(cloud(1,:));
M1 = max(cloud(1,:));
m2 = min(cloud(2,:));
M2 = max(cloud(2,:));
setAxis = true;
case 'figure_fit_complex'
m1 = min(complexPts(1,:));
M1 = max(complexPts(1,:));
m2 = min(complexPts(2,:));
M2 = max(complexPts(2,:));
setAxis = true;
case 'figure_fit_all'
m1 = min( min(complexPts(1,:)), min(cloud(1,:)) );
M1 = max( max(complexPts(1,:)), max(cloud(1,:)) );
m2 = min( min(complexPts(2,:)), min(cloud(2,:)) );
M2 = max( max(complexPts(2,:)), max(cloud(2,:)) );
setAxis = true;
otherwise
setAxis = false;
end
if setAxis
P1 = 0.1*(M1-m1);
P2 = 0.1*(M2-m2);
if P1==0; P1 = 0.01; end;
if P2==0; P2 = 0.01; end;
vaxis = [m1-P1, M1+P1, m2-P2, M2+P2];
axis(vaxis)
end
set(gca,'FontSize',10);
pause(1+delay);
if closed
vertexStars = complex.get_vertex_stars(1:size(complexPts,2));
else
vertexStars = [];
end
fittedPts = complexPts;
for i = 1:iterations
for j=1:stepOver
[fittedPts,stats] = SMeans.next_fitting(cloud,complex,fittedPts,sensitivity,closed,printProgress,vertexStars);
end
%%{
delete(p0)
[p0,~] = complex.plot2D(fittedPts,colors);
set(p0,'facealpha',0.35);
%}
if setAxis; axis(vaxis); end
title(sprintf('iter #%d, avg. change %0.4f, max. change %0.4f, sum sq. dist. %0.4f',i*stepOver,stats(1),stats(2),stats(3)),'FontSize',14)
pause(delay);
end
end
%---------------------------------------------------------------------------------------------
function test_3D(cloud, complex, complexPts, sensitivity, closed, ...
iterations, stepOver, delay, cloudColor, alpha, printProgress, varargin)
if complex.get_dimension() < 3
bdComplex = complex;
else
bdComplex = complex.get_boundary();
end
if isempty(varargin)
figureType = 'default';
else
figureType = varargin{1};
end;
fh = figure(1);
set(fh,'Units','Normalized','Position',[0.2, 0.1, 0.6, 0.75])
hold off
PointCloud.plot3D(cloud,cloudColor);
hold on
grid on
[p0,colors] = bdComplex.plot3D(complexPts,[]);
set(p0,'facealpha',alpha);
switch figureType
case 'figure_fit_data'
m1 = min(cloud(1,:));
M1 = max(cloud(1,:));
m2 = min(cloud(2,:));
M2 = max(cloud(2,:));
m3 = min(cloud(3,:));
M3 = max(cloud(3,:));
setAxis = true;
case 'figure_fit_complex'
m1 = min(complexPts(1,:));
M1 = max(complexPts(1,:));
m2 = min(complexPts(2,:));
M2 = max(complexPts(2,:));
m3 = min(complexPts(3,:));
M3 = max(complexPts(3,:));
setAxis = true;
case 'figure_fit_all'
m1 = min( min(complexPts(1,:)), min(cloud(1,:)) );
M1 = max( max(complexPts(1,:)), max(cloud(1,:)) );
m2 = min( min(complexPts(2,:)), min(cloud(2,:)) );
M2 = max( max(complexPts(2,:)), max(cloud(2,:)) );
m3 = min( min(complexPts(3,:)), min(cloud(3,:)) );
M3 = max( max(complexPts(3,:)), max(cloud(3,:)) );
setAxis = true;
otherwise
setAxis = false;
end
if setAxis
P1 = 0.1*(M1-m1);
P2 = 0.1*(M2-m2);
P3 = 0.1*(M3-m3);
if P1==0; P1 = 0.01; end;
if P2==0; P2 = 0.01; end;
if P3==0; P3 = 0.01; end;
vaxis = [m1-P1, M1+P1, m2-P2, M2+P2, m3-P3, M3+P3];
axis(vaxis)
end
set(gca,'FontSize',10);
pause(1+delay);
if closed
vertexStars = complex.get_vertex_stars(1:size(complexPts,2));
else
vertexStars = [];
end
fittedPts = complexPts;
for i = 1:iterations
for j=1:stepOver
[fittedPts,stats] = ...
SMeans.next_fitting(cloud,complex,fittedPts,sensitivity,closed,printProgress,vertexStars);
end
%%{
delete(p0)
[p0,~] = bdComplex.plot3D(fittedPts,colors);
set(p0,'facealpha',alpha);
%}
if setAxis; axis(vaxis); end
title(sprintf('iter #%d, avg. change %0.4f, max. change %0.4f, sum sq. dist. %0.4f',i*stepOver,stats(1),stats(2),stats(3)),'FontSize',14)
pause(delay);
end
end
%---------------------------------------------------------------------------------------------
function test_high_dimension(cloud, complex, complexPts, sensitivity, closed, iterations, printProgress)
if closed
vertexStars = complex.get_vertex_stars(1:size(complexPts,2));
else
vertexStars = [];
end
fittedPts = complexPts;
distSq = zeros(1,iterations);
for i = 1:iterations
[fittedPts,stats] = SMeans.next_fitting(cloud,complex,fittedPts,sensitivity,closed,printProgress,vertexStars);
distSq(i) = stats(3);
sprintf('iter #%d, avg. change %0.4f, max. change %0.4f, sum sq. dist. %0.4f',i,stats(1),stats(2),stats(3))
end
plot(1:iterations,distSq./size(cloud,2),'-o');
xlabel('iterations')
ylabel('mean sum squared distances')
set(gca,'xtick',0:iterations)
hold on
end
%---------------------------------------------------------------------------------------------
end
end