void generateData(int length, double a, double b, double std){
genericFunctions gen;
gen.initialiseRand();
//generate data for the linear model calibration
observations = new double[length];
predictions = new double[length];
double *residuals; residuals = new double[length];
for (int i = 0; i < length; i++){
linearModel(a, b,i+1,observations[i]);
gen.sampleNormDist(std,residuals[i]);
observations[i] += residuals[i];
}
double actualStd = 0;
gen.calcStd(residuals,length,actualStd);
delete [] residuals;
}
int _tmain(int argc, _TCHAR* argv[])
{
tObs = 1000;
parameters = 3;
retainedParameterSets = 15000;
generateData(tObs, 0.6, 0.5, 2);
//set prior parameter distributions for the model.
double * minpar; minpar = new double[parameters];
double *maxpar; maxpar = new double[parameters];
minpar[0] = 0.5; maxpar[0] = 0.7;
minpar[1] = 0.35; maxpar[1] = 0.65;
minpar[2] = 1; maxpar[2] = 3;
//initialise the calibration class.
modelCalibration cal;
cal.initialise(parameters, tObs, retainedParameterSets);
//initialise the formal likelihood class.
informalLikelihoods like;
like.initialiseNSE(observations,tObs,2);
//initialise the sampling class.
mcSampling samp;
samp.initialise(parameters, minpar, maxpar);
informalBayesAnalysis iba;
//loop through making model runs.
for (int i = 0; i < 200000; i++){
std::cout<<i<<std::endl;
//sample from the specified prior distributions.
samp.sample();
//run the model.
for (int i = 0; i < tObs; i++){
linearModel(samp.par[0], samp.par[1], i+1, predictions[i]);
}
//calculate the result of the predictions.
double result;
like.runNSE(predictions,result);
//if sufficiently good, store in calibration master storage.
cal.addSample(result, samp.par, predictions);
}
//intitialise the formal bayesian analysis class.
iba.initialise(cal.par, cal.like, cal.tSamples, cal.tPar, samp.parMin, samp.parMax, cal.pred, cal.tObs,observations,0.95);
//define the thresholds for running the informal bayesian analysis.
int tThresh = 5;
double *thresholds; thresholds = new double[tThresh];
thresholds[0] = 0.1;
thresholds[1] = 0.2;
thresholds[2] = 0.4;
thresholds[3] = 0.7;
thresholds[4] = 1;
//run model performance evaluation.
iba.runAnalysis(tThresh,thresholds);
//output summary tables of model parameter sensitivity to file.
iba.outputTables("C:/Users/desktop/toolbox_demo/output tables.txt");
//output parameter probability (and cumulative) distribution functions to file.
iba.outputPDFCDF("C:/Users/desktop/toolbox_demo/output pdfcdf.txt");
//output prediction intervals and confidence intervals to file.
iba.outputPredInt("C:/Users/desktop/toolbox_demo/output confidence intervals.txt");
return 0;
}
#include "catch.hpp"
#include <iterator>
#include "dataset.h"
#include "libnested.h"
#include "models.h"
#include "params.h"
#include "csv.h"
TEST_CASE( "Linear model correct", "[Linear model]") {
vector<double> params {0.5, 0.5};
vector<double> data {0, 1, 2, 3, 4, 5};
vector<double> results = linearModel(data, params);
vector<double> expected_results {0.5, 1, 1.5, 2, 2.5, 3};
REQUIRE( results == expected_results );
}
TEST_CASE( "Uniform prior correct", "[Uniform prior]") {
double r = uniformPrior(1.5, 0, 2);
REQUIRE( r == 3 );
}
TEST_CASE ( "DataSet load correct", "[DataSet]") {
DataSet ftdata("data/ftdata.csv");
REQUIRE ( ftdata.t[0] == 0.923884 );