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refactored likelihood computing

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This commit is contained in:
Maria Dimashova 2012-04-11 15:28:50 +00:00
parent 51385ac73a
commit 04d24a8824
1 changed files with 10 additions and 12 deletions
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22
modules/ml/src/em.cpp
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@ -58,7 +58,7 @@ EM::EM(int _nclusters, int _covMatType, const TermCriteria& _criteria)
EM::~EM() EM::~EM()
{ {
clear(); //clear();
} }
void EM::clear() void EM::clear()
@ -322,6 +322,8 @@ void EM::clusterTrainSamples()
int nsamples = trainSamples.rows; int nsamples = trainSamples.rows;
// Cluster samples, compute/update means // Cluster samples, compute/update means
// Convert samples and means to 32F, because kmeans requires this type.
Mat trainSamplesFlt, meansFlt; Mat trainSamplesFlt, meansFlt;
if(trainSamples.type() != CV_32FC1) if(trainSamples.type() != CV_32FC1)
trainSamples.convertTo(trainSamplesFlt, CV_32FC1); trainSamples.convertTo(trainSamplesFlt, CV_32FC1);
@ -338,6 +340,7 @@ void EM::clusterTrainSamples()
Mat labels; Mat labels;
kmeans(trainSamplesFlt, nclusters, labels, TermCriteria(TermCriteria::COUNT, means.empty() ? 10 : 1, 0.5), 10, KMEANS_PP_CENTERS, meansFlt); kmeans(trainSamplesFlt, nclusters, labels, TermCriteria(TermCriteria::COUNT, means.empty() ? 10 : 1, 0.5), 10, KMEANS_PP_CENTERS, meansFlt);
// Convert samples and means back to 64F.
CV_Assert(meansFlt.type() == CV_32FC1); CV_Assert(meansFlt.type() == CV_32FC1);
if(trainSamples.type() != CV_64FC1) if(trainSamples.type() != CV_64FC1)
{ {
@ -476,6 +479,8 @@ void EM::computeProbabilities(const Mat& sample, int& label, Mat* probs, double*
// L_ik = log(weight_k) - 0.5 * log(|det(cov_k)|) - 0.5 *(x_i - mean_k)' cov_k^(-1) (x_i - mean_k)] // L_ik = log(weight_k) - 0.5 * log(|det(cov_k)|) - 0.5 *(x_i - mean_k)' cov_k^(-1) (x_i - mean_k)]
// q = arg(max_k(L_ik)) // q = arg(max_k(L_ik))
// probs_ik = exp(L_ik - L_iq) / (1 + sum_j!=q (exp(L_ij - L_iq)) // probs_ik = exp(L_ik - L_iq) / (1 + sum_j!=q (exp(L_ij - L_iq))
// see Alex Smola's blog http://blog.smola.org/page/2 for
// details on the log-sum-exp trick
CV_Assert(!means.empty()); CV_Assert(!means.empty());
CV_Assert(sample.type() == CV_64FC1); CV_Assert(sample.type() == CV_64FC1);
@ -511,29 +516,22 @@ void EM::computeProbabilities(const Mat& sample, int& label, Mat* probs, double*
if(!probs && !logLikelihood) if(!probs && !logLikelihood)
return; return;
Mat expL_Lmax(L.size(), CV_64FC1);
double maxLVal = L.at<double>(label); double maxLVal = L.at<double>(label);
Mat expL_Lmax = L; // exp(L_ij - L_iq)
for(int i = 0; i < L.cols; i++) for(int i = 0; i < L.cols; i++)
expL_Lmax.at<double>(i) = std::exp(L.at<double>(i) - maxLVal); expL_Lmax.at<double>(i) = std::exp(L.at<double>(i) - maxLVal);
double expDiffSum = sum(expL_Lmax)[0]; // sum_j(exp(L_ij - L_iq))
double partSum = 0; // sum_j!=q (exp(L_ij - L_iq))
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
if(clusterIndex != label)
partSum += expL_Lmax.at<double>(clusterIndex);
if(probs) if(probs)
{ {
probs->create(1, nclusters, CV_64FC1); probs->create(1, nclusters, CV_64FC1);
double factor = 1./(1 + partSum); double factor = 1./expDiffSum;
expL_Lmax *= factor; expL_Lmax *= factor;
expL_Lmax.copyTo(*probs); expL_Lmax.copyTo(*probs);
} }
if(logLikelihood) if(logLikelihood)
{ *logLikelihood = std::log(expDiffSum) + maxLVal - 0.5 * dim * CV_LOG2PI;
double logWeightProbs = std::log((1 + partSum) * std::exp(maxLVal)) - 0.5 * dim * CV_LOG2PI;
*logLikelihood = logWeightProbs;
}
} }
void EM::eStep() void EM::eStep()