Log likelihood output of EM in openCV - c++

Question

I am using the EM model in openCV and have run in to difficulty with the log likelihood value output from EM::train. I am training the EM models on separate (but correlated) data and the log likelihood values are linked to the absolute values of the data, i.e. when the training values are greater than 100, EM::train returns strongly positive log likelihood values, when the training values are less than one large negative values are returned, my understanding is this doesn't make any sense given the way EM works.
The section of code that trains the models is below, in case I am making any silly mistakes (I am quite new to opencv) std::cout confirms that the values are larger than one when the training values are larger than 100 and smaller than -1 when the training values are less than 1.
My problem is similar to this post:
OpenCV: Output of the predict function of Expectation Maximization
except there is no way around using the log-likelihoods without implementing something like a a DPGMM.
Many Thanks,
Sam
{
double logs =0;
cv::EM model (g,1,parameters);
model.train(sample_input.col(i),log_likelihoods);
for(int j = 0; j < number_selected; j++ )
{
logs += log_likelihoods.at<double>(j);
std::cout << log_likelihoods.at<double>(j) <<' ';
}
mean_of_logs[i] += 0.1*logs;
reduced_models.push_back(model);
}