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genUniformPoints.m
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genUniformPoints.m
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%{
* Copyright (C) 2020-2030, The Regents of The University of Michigan.
* All rights reserved.
* This software was developed in the Biped Lab (https://www.biped.solutions/)
* under the direction of Jessy Grizzle, [email protected]. This software may
* be available under alternative licensing terms; contact the address above.
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
* The views and conclusions contained in the software and documentation are those
* of the authors and should not be interpreted as representing official policies,
* either expressed or implied, of the Regents of The University of Michigan.
*
* AUTHOR: Bruce JK Huang (bjhuang[at]umich.edu)
* WEBSITE: https://www.brucerobot.com/
%}
function [x, y, z] = genUniformPoints(app)
% Gaussian noise
% rand_z = getNoise(app, app.noise_mu_(3), app.noise_sigma_(3), app.num_points_);
% rand_y = getNoise(app, app.noise_mu_(2), app.noise_sigma_(2), app.num_points_);
% rand_x = getNoise(app, app.noise_mu_(1), app.noise_sigma_(1), app.num_points_);
% uniform noise
rand_x = app.tag_size_ * app.noise_sigma_(1) + ...
(-app.tag_size_ * app.noise_sigma_(1) - app.tag_size_ * app.noise_sigma_(1) ) * ...
rand(1, app.num_points_);
rand_y = app.tag_size_ * app.noise_sigma_(2) + ...
(-app.tag_size_ * app.noise_sigma_(2) - app.tag_size_ * app.noise_sigma_(2) ) * ...
rand(1, app.num_points_);
rand_z = app.tag_size_ * app.noise_sigma_(3) + ...
(-app.tag_size_ * app.noise_sigma_(3) - app.tag_size_ * app.noise_sigma_(3) ) * ...
rand(1, app.num_points_);
% generate two sets of points seperately
% noise free points
% noise_free_z = rand(1, app.num_points_target) * app.tag_size_ - app.tag_size_/2;
% noise_free_y = rand(1, app.num_points_target) * app.tag_size_ - app.tag_size_/2;
% noise_free_x = rand(1, app.num_points_target) * 0.0001;
% app.noise_free_points_ = [noise_free_x;
% noise_free_y;
% noise_free_z;
% ones(1, app.num_points_target)];
%
% % noisy data
% z = rand(1, app.num_points_) * app.tag_size_ - app.tag_size_/2 + rand_z;
% y = rand(1, app.num_points_) * app.tag_size_ - app.tag_size_/2 + rand_y;
% x = rand(1, app.num_points_) * 0.0001 + rand_x;
noise_free_z = rand(1, app.num_points_) * app.tag_size_ - app.tag_size_/2;
noise_free_y = rand(1, app.num_points_) * app.tag_size_ - app.tag_size_/2;
noise_free_x = rand(1, app.num_points_) * 0.0001;
app.noise_free_points_ = [noise_free_x;
noise_free_y;
noise_free_z;
ones(1, app.num_points_)];
% noisy data
z = noise_free_z + rand_z;
y = noise_free_y + rand_y;
x = noise_free_x + rand_x;
end