int pkmGaussianMixtureModel::writeToFile(ofstream &fileStream, bool writeClusterNums, bool writeWeights, bool writeMeans, bool writeCovs, bool verbose)
{
if(!fileStream.is_open())
return -1;
// use the best-model
CvEM myModel = emModel[bestModel];
const CvMat **modelCovs = myModel.get_covs();
const CvMat *modelMus = myModel.get_means();
const CvMat *modelWeights = myModel.get_weights();
int numClusters = myModel.get_nclusters();
// output the total number of clusters
if(writeClusterNums)
{
if(verbose)
fileStream << "Number of Clusters: " << numClusters << "\n";
else
fileStream << numClusters << "\n";
}
if(writeWeights)
{
// output the weights of each cluster
if(verbose)
fileStream << "Weight of Clusters\n";
for (int k = 0; k < numClusters; k++)
{
const double *weightPtr = (const double *)(modelWeights->data.ptr + k*modelWeights->step);
double weight = weightPtr[0];
if(verbose)
fileStream << k << ": " << weight << "\n";
else
fileStream << weight << " ";
}
fileStream << "\n";
}
if(writeMeans)
{
// output the means of each cluster
if(verbose)
fileStream << "Means of Clusters\n";
for (int k = 0; k < numClusters; k++)
{
if(verbose)
fileStream << "Cluster " << k << ":\n";
for (int i = 0; i < m_nVariables; i++)
{
if(verbose)
fileStream << i << ": " << cvmGet(modelMus, k, i) << " ";
else
fileStream << cvmGet(modelMus, k, i) << " ";
}
fileStream << "\n";
}
}
if(writeCovs)
{
// output the covariances of each cluster
if(verbose)
fileStream << "Covariances of Clusters\n";
for (int k = 0; k < numClusters; k++)
{
const CvMat * covar = modelCovs[k];
if(verbose)
fileStream << "Cluster " << k << ":\n";
for (int i = 0; i < m_nVariables; i++)
{
for (int j = 0; j < m_nVariables; j++)
{
if(verbose)
fileStream << i << "," << j << ": " << cvmGet(covar, i, j) << " ";
else
fileStream << cvmGet(covar, i, j) << " ";
}
}
fileStream << "\n";
}
}
return 0;
}
/*void pkmGaussianMixtureModel::getLikelihood(int x, int y)
{
}
*/
void pkmGaussianMixtureModel::getLikelihoodMap(int rows, int cols, unsigned char *map, ofstream &filePtr, int widthstep)
{
if(widthstep == 0)
widthstep = cols;
if(!bModeled)
return;
CvEM myModel = emModel[bestModel];
const CvMat **modelCovs = myModel.get_covs();
const CvMat *modelMus = myModel.get_means();
const CvMat *modelWeights = myModel.get_weights();
int numClusters = myModel.get_nclusters();
CvMat *pts = cvCreateMat(m_nVariables, 1, CV_64FC1);
CvMat *mean = cvCreateMat(m_nVariables, 1, CV_64FC1);
double p = 0;
double weight;
double max = 0;
double prob;
filePtr << "clusters: " << numClusters << "\n";
filePtr << "likelihood: " << m_Likelihood << "\n";
filePtr << "BIC: " << m_BIC << "\n";
//printf("clusters: %d\n", numClusters);
float best_weight = 0;
bestCluster = 0;
for (int k = 0; k < numClusters; k++)
{
const CvMat * covar = modelCovs[k];
//printf("covar: (%f, %f, %f, %f)\n", covar->data.ptr[0], \
covar->data.ptr[1], \
covar->data.ptr[2], \
covar->data.ptr[3]);
//const double *meanVals = (const double *)(modelMus->data.ptr + k*modelWeights->step);
cvmSet(mean, 0, 0, cvmGet(modelMus, k, 0));
cvmSet(mean, 1, 0, cvmGet(modelMus, k, 1));
//const double *weightPtr = (const double *)(modelWeights->data.ptr + k*modelWeights->step);
//weight = weightPtr[k];
weight = cvmGet(modelWeights, 0, k);
if (best_weight < weight) {
best_weight = weight;
bestCluster = k;
}
filePtr << "mean: " << cvmGet(modelMus, k, 0)*(double)m_nScale << " " << cvmGet(modelMus, k, 1)*(double)m_nScale << "\n";
filePtr << "covar: " << cvmGet(covar, 0, 0) << "\n";
filePtr << "weight: " << weight << "\n";
for (int i = 0; i < rows; i++)
{
for (int j = 0; j < cols; j++)
{
cvmSet(pts, 0, 0, (double)j);
cvmSet(pts, 1, 0, (double)i);
prob = multinormalDistribution(pts, mean, covar);
map[j+i*widthstep] += (int)((weight * prob)*(double)(rows*cols));
}
}
}
cvReleaseMat(&mean);
cvReleaseMat(&pts);
}
