88 lines
3.6 KiB
C++
88 lines
3.6 KiB
C++
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/****************************************************************************
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*
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* Copyright (C) 2015 PX4 Development Team. All rights reserved.
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* Author: Eddy Scott <scott.edward@aurora.aero>
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in
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* the documentation and/or other materials provided with the
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* distribution.
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* 3. Neither the name PX4 nor the names of its contributors may be
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* used to endorse or promote products derived from this software
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* without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
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* OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
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* AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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****************************************************************************/
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#include <cstdlib>
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#include <cmath>
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#include <ctime>
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#include <limits>
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#include <iostream> // not needed
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#include <fstream> // not needed
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using namespace std;
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float generateGaussianNoise(float mu, float variance)
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{
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/* Calculate normally distributed variable noise with mean = mu and variance = variance. Calculated according to
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Box-Muller transform */
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static const float epsilon = std::numeric_limits<float>::min(); //used to ensure non-zero uniform numbers
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static const float two_pi = 2.0*3.14159265358979323846; // 2*pi
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static float z0; //calculated normal distribution random variables with mu = 0, var = 1;
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float u1, u2; //random variables generated from c++ rand();
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/*Generate random variables in range (0 1] */
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do
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{
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u1 = rand() * (1.0 / RAND_MAX);
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u2 = rand() * (1.0 / RAND_MAX);
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}
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while ( u1 <= epsilon ); //Have a catch to ensure non-zero for log()
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z0 = sqrt(-2.0 * log(u1)) * cos(two_pi * u2); //calculate normally distributed variable with mu = 0, var = 1
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float noise = z0 * sqrt(variance) + mu; //calculate normally distributed variable with mu = mu, std = var^2
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return noise;
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}
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int main(int argc, char *argv[])
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{
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ofstream fid;
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fid.open ("generated_noise.csv");
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float mu = atof(argv[1]); // Define the mean of the noise, for gaussian = 0
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float variance = atof(argv[2]); //Define the variance of the noise
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int num_runs = atoi(argv[3]); //Define number of runs
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int num_samples = atoi(argv[4]);
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srand(time(NULL)); //Seed rand() function so same random variables are not calculated
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cout << "Desired Mean: " << mu << "\n";
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cout << "Desired Variance: " << variance << "\n";
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cout << "Desired number of runs: " << num_runs << "\n";
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cout << "Desired number of samples per run: " << num_samples << "\n";
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for(int j=0;j<num_runs;j++){
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if(j!=0){
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fid <<"\n";
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}
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for(int i=0;i<num_samples;i++){
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fid << generateGaussianNoise(mu, variance) << ",";
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}
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}
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fid.close();
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return 0;
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}
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