[Insight-users] ExpectationMaximizationMixtureModelEstimator error

[smrolfe at u.washington.edu]

Hello,

I am using the ExpectationMaximizationMixtureModelEstimator filter to estimate a mixture model from a set of observations.  I am closely following the example
ExpectationMaximizationMixtureModelEstimator_2D.cxx

When I run the program, I am getting the error:

Description: itk::ERROR: WeightedMeanSampleFilter(00000000038A66C0): Total weight was too close to zero. Value = 0

It looks like this filter computes the mean of a set of weighted measurement vectors, but I'm not clear on what might cause the total weight to be zero.  I'd appreciate any thoughts.
An excerpt of my code is below.

Thanks!
Sara


typedef itk::Array< double > ParametersType;
typedef itk::Statistics::GaussianMixtureModelComponent< SampleType > ComponentType;
typedef itk::Statistics::ExpectationMaximizationMixtureModelEstimator< SampleType > EstimatorType;

SampleType::Pointer sample = SampleType::New();

for (int pointIter = 0; pointIter < pointArray[i].size(); pointIter++){ //put all points in current histogram bin in the sample vector
              sample->PushBack( pointArray[i][pointIter] ); //pointArray[i] is a vector of measurement vectors
}
int distributionNumber = initialParameters.meanVectors[i].size();
int paramNumber = (MeasurementVectorLength)+(MeasurementVectorLength*MeasurementVectorLength);
ParametersType params( paramNumber );
std::vector< ParametersType > inputParameters( distributionNumber );
itk::Array< double > initialProportions(distributionNumber);

for(int j = 0; j < distributionNumber; j++){         // for each distribution in initial estimate
initialProportions[j] = initialParameters.subHistogram[i][j];   // set initial proportions to the sub-bin size
for(int k = 0; k < MeasurementVectorLength; k++){ // assign values in distribution parameter matrix
params[k] = initialParameters.meanVectors[i][j][k];
}
int paramCount = MeasurementVectorLength;
for(int l=0; l < MeasurementVectorLength; l++){
for(int m=0; m < MeasurementVectorLength; m++){
params[paramCount] = initialParameters.covarianceMats[i][j][l][m];
paramCount++;
}
}

inputParameters[j] = params; // push parameters for current distribution into parameter vector
}


std::vector< ComponentType::Pointer > components;                       // Create the Gausian model components
for ( unsigned int k = 0 ; k < distributionNumber ; k++ ){
components.push_back( ComponentType::New() );
(components[k])->SetSample( sample );
(components[k])->SetParameters( inputParameters[k] );
}

EstimatorType::Pointer estimator = EstimatorType::New();         // set up estimator for bin i
estimator->SetSample( sample );
estimator->SetMaximumIteration( 200 );
estimator->SetInitialProportions( initialProportions );

for ( unsigned int k = 0 ; k < distributionNumber ; k++){
estimator->AddComponent( (ComponentType::Superclass*)(components[k]).GetPointer() );
}

try{
estimator->Update();
}catch( itk::ExceptionObject & err ){
std::cerr << "ExceptionObject caught !" << std::endl;
std::cerr << err << std::endl;
}