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iCAPs_v2/packages/gspbox/sgwt_require/create_synthetic_dataset.m
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create_synthetic_dataset.m
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function data = create_synthetic_dataset(data)
% create_synthetic_dataset creates test data for running nldr algorithms.
%
% inputs:
% data a struct describing the test data
% .dataset the number of the example, see code for more infos
% .n the number of data points (default=400)
% .state the initial state for the random numbers (default=0)
% .noise the variance of Gaussian noise to add (default=0)
% other options for some of the data sets (see code)
% alternatively, data = 1 chooses the dataset directly,
% the number of points defaults to 1000
%
% outputs:
% data a struct containing .x the generated data, each column is
% a data point, and other stuff:
% .z the "correct" embedding
% .e some random noise of same dimensionality
% .x_noisefree the noisefree version of .x, i.e.
% .x = .xnoise_free + sqrt(.noise) .e
%
% Adapted from create.m, originally written by
% (c) Stefan Harmeling, 2006
% using the examples of the original LLE and ISOMAP code.
%
% Url: http://lts2research.epfl.ch/gsp/doc/sgwt_require/create_synthetic_dataset.php
% Copyright (C) 2013-2016 Nathanael Perraudin, Johan Paratte, David I Shuman.
% This file is part of GSPbox version 0.7.0
%
% 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/>.
% If you use this toolbox please kindly cite
% N. Perraudin, J. Paratte, D. Shuman, V. Kalofolias, P. Vandergheynst,
% and D. K. Hammond. GSPBOX: A toolbox for signal processing on graphs.
% ArXiv e-prints, Aug. 2014.
% http://arxiv.org/abs/1408.5781
if ~isfield(data, 'dataset'),
number = data;
clear data
data.dataset = number;
end
if ~isfield(data, 'n'), data.n = 400; end
if ~isfield(data, 'noise'), data.noise = 0.0; end
if ~isfield(data, 'state'), data.state = 0; end
% set the randomness
rand('state', data.state);
randn('state', data.state);
data.typ = 'data';
switch data.dataset
case 0 % "swiss roll with hole"
data.name = 'swiss roll with hole';
n = data.n;
a = 1; % swiss roll goes from a*pi to b*pi
b = 4;
y = rand(2,n);
% punch a rectangular hole at the center
l1 = 0.05; l2 = 0.15;
y = y - 0.5;
ok = find((abs(y(1,:))>l1) | (abs(y(2,:))>l2));
i = length(ok);
y(:, 1:i) = y(:, ok);
while (i<n)
p = rand(2,1) - 0.5;
if (abs(p(1))>l1) || (abs(p(2))>l2)
i = i + 1;
y(:,i) = p;
end
end
y = y + 0.5;
tt = (b-a)*y(1,:) + a;
tt = pi*tt;
height = 21*y(2,:);
data.col = tt;
data.x = [tt.*cos(tt); height; tt.*sin(tt)];
data.z = [tt; height]; % the ground truth
data.az = -4;
data.el = 13;
case -1 % "swiss roll" dataset extracted from LLE's swissroll.m
data.name = 'uniform swiss roll';
n = data.n;
a = 1; % swiss roll goes from a*pi to b*pi
b = 4;
y = rand(2,n);
data.z = y; % the ground truth
switch 1
case 1
% uniform distribution along the manifold (in data space)
tt = sqrt((b*b-a*a)*y(1,:)+a*a);
case 2
% error('do not use this case')
% nonuniform distribution along the manifold (in data space)
tt = (b-a)*y(1,:) + a;
end
tt = pi*tt;
% now tt should go from a*pi to b*pi
height = 21*y(2,:);
data.col = tt;
data.x = [tt.*cos(tt); height; tt.*sin(tt)];
data.az = -4;
data.el = 13;
case 1 % "swiss roll (uniform in embedding space)"
% dataset extracted from LLE's swissroll.m
data.name = 'classic swiss roll';
n = data.n;
a = 1; % swiss roll goes from a*pi to b*pi
b = 4;
y = rand(2,n);
tt = (b-a)*y(1,:) + a;
tt = pi*tt;
height = 21*y(2,:);
data.col = tt;
data.x = [tt.*cos(tt); height; tt.*sin(tt)];
data.z = [tt; height]; % the ground truth
data.az = -4;
data.el = 13;
case 11 % "undersampled swiss roll"
% dataset extracted from LLE's swissroll.m
data.name = 'undersampled swiss roll';
data.n = 100;
n = data.n;
a = 1; % swiss roll goes from a*pi to b*pi
b = 4;
y = rand(2,n);
tt = (b-a)*y(1,:) + a;
tt = pi*tt;
height = 21*y(2,:);
data.col = tt;
data.x = [tt.*cos(tt); height; tt.*sin(tt)];
data.z = [tt; height]; % the ground truth
data.az = -4;
data.el = 13;
case 12 % "swiss roll"
% dataset extracted from LLE's swissroll.m
data.name = 'classic swiss roll';
data.n = 400;
n = data.n;
a = 1; % swiss roll goes from a*pi to b*pi
b = 4;
y = rand(2,n);
tt = (b-a)*y(1,:) + a;
tt = pi*tt;
height = 21*y(2,:);
data.col = tt;
data.x = [tt.*cos(tt); height; tt.*sin(tt)];
data.z = [tt; height]; % the ground truth
data.az = -4;
data.el = 13;
case 2 % "scurve" dataset extracted from LLE's scurve.m
data.name = 'scurve';
n = data.n;
% I added 'ceil' and 'floor' to account for the case that n is odd
angle = pi*(1.5*rand(1,ceil(n/2))-1); height = 5*rand(1,n);
data.x = [[cos(angle), -cos(angle(1:floor(n/2)))]; height;[ sin(angle), 2-sin(angle)]];
data.col = [angle, 1.5*pi + angle];
data.z = [angle, 1.5*pi+angle; height]; % the ground truth
case 3 % "square" dataset, a uniformly sampled 2D square randomly
% rotated into higher dimensions
data.name = 'square';
n = data.n;
d = 2; % intrinsic dimension
% optional parameter for dataset==3
% data.D dimension of the data
if ~isfield(data, 'D'), data.D = 3; end
% generate random rotation matrix
D = data.D;
A = randn(D, D);
options.disp = 0;
[R, dummy] = eigs(A*A', d, 'LM', options);
tt = rand(d, n);
data.col = tt(1,:);
data.x = R*tt;
data.z = tt; % the ground truth
data.az = 7;
data.el = 40;
case 4 % spiral: two dimensional "swiss roll"
data.name = 'spiral';
n = data.n;
tt = (3*pi/2)*(1+2*rand(1, n));
data.col = tt;
data.x = [tt.*cos(tt); tt.*sin(tt)];
data.z = tt; % the ground truth
case -4 % spiral: two dimensional "swiss roll"
data.name = 'noisy spiral';
n = data.n;
tt = (3*pi/2)*(1+2*rand(1, n));
data.col = tt;
data.x = [tt.*cos(tt); tt.*sin(tt)];
data.x = data.x + randn(size(data.x));
data.z = tt; % the ground truth
case 5 % hole: a dataset with a hole
data.name = 'hole';
n = data.n;
data.x = rand(2,n) - 0.5;
% punch a rectangular hole at the center
l1 = 0.2; l2 = 0.2;
ok = find((abs(data.x(1,:))>l1) | (abs(data.x(2,:))>l2));
i = length(ok);
data.x(:, 1:i) = data.x(:, ok);
while (i<n)
p = rand(2,1) - 0.5;
if (abs(p(1))>l1) || (abs(p(2))>l2)
i = i + 1;
data.x(:,i) = p;
end
end
data.col = data.x(2,:);
data.z = data.x;
case 6 % P : taken from Saul's slides
% note that for k=20, isomap and lle work fine which is very different
% from the plots that Saul showed in his slides.
data.name = 'P';
load x
x(2,:) = 500-x(2,:);
data.x = x;
data.z = x;
data.col = data.z(2,:);
data.n = size(x, 2);
case 7 % fishbowl: uniform in data space
gamma = 0.8;
data.name = 'fishbowl (uniform in data space)';
n = data.n;
data.x = rand(3,n)-0.5;
%project all data onto the surface of the unit sphere
data.x = data.x ./ repmat(sqrt(sum(data.x.*data.x, 1)), [3 1]);
ok = find(data.x(3,:) < gamma);
i = length(ok);
data.x(:, 1:i) = data.x(:, ok);
while (i < n)
p = rand(3,1)-0.5;
p = p / sqrt(p'*p);
if (p(3) < gamma)
i = i+1;
data.x(:, i) = p;
end
end
% the projection on the plane works as follows:
% start a beam from (0,0,1) through each surface point on the sphere
% and look where it hits the xy plane.
data.z = data.x(1:2,:) ./ repmat(1-data.x(3,:), [2 1]);
data.col = data.x(3,:);
data.az = -18;
data.el = 16;
case 8 % fishbowl: uniform in embedding space
data.name = 'fishbowl (uniform in embedding space)';
n = data.n;
data.z = rand(2, n) - 0.5;
% keep the disc
ok = find(sum(data.z .* data.z) <= 0.25);
i = length(ok);
data.z(:, 1:i) = data.z(:, ok);
while (i < n)
p = rand(2,1) - 0.5;
if (p'*p <= 0.25)
i = i + 1;
data.z(:, i) = p;
end
end
gamma = 0.8; % same role/parameter as in case 7
data.z = 2*sqrt((1+gamma)/(1-gamma))*data.z;
% project the disc onto the sphere
alpha = 2 ./ (1 + sum(data.z .* data.z, 1));
data.x = [repmat(alpha, [2 1]).*data.z; zeros(1, n)];
data.x(3,:) = 1-alpha;
data.col = data.x(3,:);
data.az = -18;
data.el = 16;
case 9 % a gaussian blob
data.name = 'gaussian blob';
n = data.n;
data.x = randn(3,n);
data.z = data.x(2:3,:);
data.col = data.x(3,:);
end
data.D = size(data.x, 1); % dimensionality of the data
% finally generate noise
data.e = randn(size(data.x));
data.x_noisefree = data.x; % the noise free data
data.x = data.x_noisefree + sqrt(data.noise)*data.e;
% precalculate the distanzmatrix
data.distances = gsp_distanz(data.x);
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