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modified likelihood computing

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This commit is contained in:
Maria Dimashova
2012-04-10 10:52:11 +00:00
parent 74b38e978b
commit 3b02ee4b29
2 changed files with 23 additions and 28 deletions
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42
modules/ml/src/em.cpp
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@@ -44,7 +44,7 @@
namespace cv
{
const double minEigenValue = 1.e-5;
const double minEigenValue = DBL_MIN;
///////////////////////////////////////////////////////////////////////////////////////////////////////
@@ -121,7 +121,7 @@ bool EM::trainM(InputArray samples,
}
int EM::predict(InputArray _sample, OutputArray _probs, double* _logLikelihood) const
int EM::predict(InputArray _sample, OutputArray _probs, double* logLikelihood) const
{
Mat sample = _sample.getMat();
CV_Assert(isTrained());
@@ -135,16 +135,13 @@ int EM::predict(InputArray _sample, OutputArray _probs, double* _logLikelihood)
}
int label;
double logLikelihood = 0.;
Mat probs;
if( _probs.needed() )
{
_probs.create(1, nclusters, CV_64FC1);
probs = _probs.getMat();
}
computeProbabilities(sample, label, !probs.empty() ? &probs : 0, _logLikelihood ? &logLikelihood : 0);
if(_logLikelihood)
*_logLikelihood = logLikelihood;
computeProbabilities(sample, label, !probs.empty() ? &probs : 0, logLikelihood);
return label;
}
@@ -372,6 +369,7 @@ void EM::computeLogWeightDivDet()
CV_Assert(!covsEigenValues.empty());
Mat logWeights;
cv::max(weights, DBL_MIN, weights);
log(weights, logWeights);
logWeightDivDet.create(1, nclusters, CV_64FC1);
@@ -504,28 +502,24 @@ void EM::computeProbabilities(const Mat& sample, int& label, Mat* probs, double*
if(!probs && !logLikelihood)
return;
if(probs)
{
Mat expL_Lmax;
exp(L - L.at<double>(label), expL_Lmax);
double partSum = 0, // sum_j!=q (exp(L_ij - L_iq))
factor; // 1/(1 + partExpSum)
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
if(clusterIndex != label)
partSum += expL_Lmax.at<double>(clusterIndex);
factor = 1./(1 + partSum);
Mat buf, *sampleProbs = probs ? probs : &buf;
Mat expL_Lmax;
exp(L - L.at<double>(label), expL_Lmax);
double partSum = 0, // sum_j!=q (exp(L_ij - L_iq))
factor; // 1/(1 + partExpSum)
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
if(clusterIndex != label)
partSum += expL_Lmax.at<double>(clusterIndex);
factor = 1./(1 + partSum);
probs->create(1, nclusters, CV_64FC1);
expL_Lmax *= factor;
expL_Lmax.copyTo(*probs);
}
sampleProbs->create(1, nclusters, CV_64FC1);
expL_Lmax *= factor;
expL_Lmax.copyTo(*sampleProbs);
if(logLikelihood)
{
Mat expL;
exp(L, expL);
// note logLikelihood = log (sum_j exp(L_ij)) - 0.5 * dims * ln2Pi
*logLikelihood = std::log(sum(expL)[0]) - (double)(0.5 * dim * CV_LOG2PI);
double logWeightProbs = std::log(std::max(DBL_MIN, sum(*sampleProbs)[0]));
*logLikelihood = logWeightProbs;
}
}

9
modules/ml/test/test_emknearestkmeans.cpp
View File

@@ -83,7 +83,7 @@ void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat&
labels.create( data.rows, 1, labelType );
randn( data, Scalar::all(0.0), Scalar::all(1.0) );
randn( data, Scalar::all(-1.0), Scalar::all(1.0) );
vector<Mat> means(sizes.size());
for(int i = 0; i < _means.rows; i++)
means[i] = _means.row(i);
@@ -381,7 +381,7 @@ int CV_EMTest::runCase( int caseIndex, const EM_Params& params,
ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.006f )
else if( err > 0.008f )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on train data.\n", caseIndex, err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
@@ -401,7 +401,7 @@ int CV_EMTest::runCase( int caseIndex, const EM_Params& params,
ts->printf( cvtest::TS::LOG, "Case index %i : Bad output labels.\n", caseIndex );
code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if( err > 0.006f )
else if( err > 0.008f )
{
ts->printf( cvtest::TS::LOG, "Case index %i : Bad accuracy (%f) on test data.\n", caseIndex, err );
code = cvtest::TS::FAIL_BAD_ACCURACY;
@@ -505,7 +505,8 @@ protected:
virtual void run( int /*start_from*/ )
{
int code = cvtest::TS::OK;
cv::EM em(2);
const int nclusters = 2;
cv::EM em(nclusters);
Mat samples = Mat(3,1,CV_64FC1);
samples.at<double>(0,0) = 1;