/* * ttestwcpp * * Compare a WinMine-generated Bayesian network to an NBE model by  * computing the log likelihoods of the observations in a user-specified * test set.  Performs a paired t-test for significance testing. * * Author: Daniel Lowd <lowd@cs.washington.edu> */#include "BayesNet.h"#include "AbsVarAbstractionSet.h"#include <math.h>#include <fstream>using namespace std;double getLogLikelihood(const BayesNet* model, const VarSet& t){    double ll = 0.0;    int numvars = t.getNumVars();    double* allvars = new double[numvars];    for (int i = 0; i < numvars; i++) {        allvars[i] = t[i];    }    for (int i = 0; i < numvars; i++) {        ll += model->getLogProb(i, allvars);    }    delete[] allvars;    return ll;}int main(int argc, char* argv[]){    // Check arguments    if (argc < 4) {        cerr << "Usage: ./ttest <nbe model> <winmine model> <testdata>\n";        return 1;    }    int nextArg = 1;    bool reportfull = false;    if (!strcmp(argv[nextArg], "-v")) {        reportfull = true;        nextArg++;    }    // Load NBE model    ifstream modelin(argv[nextArg]);    if (!modelin) {        cerr << "Error: could not open NBE model file \""             << argv[nextArg] << "\".\n";        return 1;    }    nextArg++;    AbsVarAbstractionSet nbe;    modelin >> nbe;    // Load WinMine model    FILE* inFile = fopen(argv[nextArg], "r");    if (!inFile) {        cerr << "ERROR: could not open WinMine model file \""             << argv[nextArg] << "\"\n";        exit(-1);    }    nextArg++;    BayesNet* model = new BayesNet();    loadModel(inFile, *model);    // DEBUG    //cout << model;    // Open test file    ifstream testin(argv[nextArg]);    if (!testin) {        cerr << "Error: could not open test file \"" << argv[nextArg] << "\".\n";        return 1;    }    nextArg++;    // Read in each observation and compute its log likelihood    double loglikelihood = 0.0;    double nbe_loglikelihood = 0.0;    double wm_loglikelihood = 0.0;    double sq_loglikelihood = 0.0;    int observations = 0;    VarSet t;    while (testin >> t) {        double l_nbe = nbe.getLogLikelihood(t);        double l_wm = getLogLikelihood(model, t);        nbe_loglikelihood += l_nbe;        wm_loglikelihood  += l_wm;        loglikelihood += (l_nbe - l_wm);        sq_loglikelihood += (l_nbe - l_wm) * (l_nbe - l_wm);        observations++;        // DEBUG        //cout << loglikelihood/observations << endl;    }    cout << "NBE:     " << nbe_loglikelihood/observations << endl;    cout << "WinMine: " << wm_loglikelihood/observations << endl;    // Output results (both log likelihood and stdev)    cout << "log(likelihood) = " << loglikelihood/observations << endl;    double sigma_sq = (sq_loglikelihood - (loglikelihood*loglikelihood)/observations)          /(observations*observations);    cout << "sigma = " << sqrt(sigma_sq) << endl;    if (reportfull) {        cout << "sum = " << loglikelihood << endl;        cout << "sumsq = " << sq_loglikelihood << endl;        cout << "n = " << observations << endl;    }    return 0;}