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Updated ml module interfaces and documentation

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This commit is contained in:
Maksim Shabunin
2015-02-11 13:24:14 +03:00
parent da383e65e2
commit 79e8f0680c
32 changed files with 1403 additions and 1528 deletions
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148
modules/ml/src/em.cpp
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@@ -48,37 +48,49 @@ namespace ml
const double minEigenValue = DBL_EPSILON;
EM::Params::Params(int _nclusters, int _covMatType, const TermCriteria& _termCrit)
{
nclusters = _nclusters;
covMatType = _covMatType;
termCrit = _termCrit;
}
class CV_EXPORTS EMImpl : public EM
{
public:
EMImpl(const Params& _params)
int nclusters;
int covMatType;
TermCriteria termCrit;
CV_IMPL_PROPERTY_S(TermCriteria, TermCriteria, termCrit)
void setClustersNumber(int val)
{
setParams(_params);
nclusters = val;
CV_Assert(nclusters > 1);
}
int getClustersNumber() const
{
return nclusters;
}
void setCovarianceMatrixType(int val)
{
covMatType = val;
CV_Assert(covMatType == COV_MAT_SPHERICAL ||
covMatType == COV_MAT_DIAGONAL ||
covMatType == COV_MAT_GENERIC);
}
int getCovarianceMatrixType() const
{
return covMatType;
}
EMImpl()
{
nclusters = DEFAULT_NCLUSTERS;
covMatType=EM::COV_MAT_DIAGONAL;
termCrit = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, EM::DEFAULT_MAX_ITERS, 1e-6);
}
virtual ~EMImpl() {}
void setParams(const Params& _params)
{
params = _params;
CV_Assert(params.nclusters > 1);
CV_Assert(params.covMatType == COV_MAT_SPHERICAL ||
params.covMatType == COV_MAT_DIAGONAL ||
params.covMatType == COV_MAT_GENERIC);
}
Params getParams() const
{
return params;
}
void clear()
{
trainSamples.release();
@@ -100,10 +112,10 @@ public:
bool train(const Ptr<TrainData>& data, int)
{
Mat samples = data->getTrainSamples(), labels;
return train_(samples, labels, noArray(), noArray());
return trainEM(samples, labels, noArray(), noArray());
}
bool train_(InputArray samples,
bool trainEM(InputArray samples,
OutputArray logLikelihoods,
OutputArray labels,
OutputArray probs)
@@ -157,7 +169,7 @@ public:
{
if( _outputs.fixedType() )
ptype = _outputs.type();
_outputs.create(samples.rows, params.nclusters, ptype);
_outputs.create(samples.rows, nclusters, ptype);
}
else
nsamples = std::min(nsamples, 1);
@@ -193,7 +205,7 @@ public:
{
if( _probs.fixedType() )
ptype = _probs.type();
_probs.create(1, params.nclusters, ptype);
_probs.create(1, nclusters, ptype);
probs = _probs.getMat();
}
@@ -311,7 +323,6 @@ public:
const std::vector<Mat>* covs0,
const Mat* weights0)
{
int nclusters = params.nclusters, covMatType = params.covMatType;
clear();
checkTrainData(startStep, samples, nclusters, covMatType, probs0, means0, covs0, weights0);
@@ -350,7 +361,6 @@ public:
void decomposeCovs()
{
int nclusters = params.nclusters, covMatType = params.covMatType;
CV_Assert(!covs.empty());
covsEigenValues.resize(nclusters);
if(covMatType == COV_MAT_GENERIC)
@@ -383,7 +393,6 @@ public:
void clusterTrainSamples()
{
int nclusters = params.nclusters;
int nsamples = trainSamples.rows;
// Cluster samples, compute/update means
@@ -443,7 +452,6 @@ public:
void computeLogWeightDivDet()
{
int nclusters = params.nclusters;
CV_Assert(!covsEigenValues.empty());
Mat logWeights;
@@ -458,7 +466,7 @@ public:
double logDetCov = 0.;
const int evalCount = static_cast<int>(covsEigenValues[clusterIndex].total());
for(int di = 0; di < evalCount; di++)
logDetCov += std::log(covsEigenValues[clusterIndex].at<double>(params.covMatType != COV_MAT_SPHERICAL ? di : 0));
logDetCov += std::log(covsEigenValues[clusterIndex].at<double>(covMatType != COV_MAT_SPHERICAL ? di : 0));
logWeightDivDet.at<double>(clusterIndex) = logWeights.at<double>(clusterIndex) - 0.5 * logDetCov;
}
@@ -466,7 +474,6 @@ public:
bool doTrain(int startStep, OutputArray logLikelihoods, OutputArray labels, OutputArray probs)
{
int nclusters = params.nclusters;
int dim = trainSamples.cols;
// Precompute the empty initial train data in the cases of START_E_STEP and START_AUTO_STEP
if(startStep != START_M_STEP)
@@ -488,9 +495,9 @@ public:
mStep();
double trainLogLikelihood, prevTrainLogLikelihood = 0.;
int maxIters = (params.termCrit.type & TermCriteria::MAX_ITER) ?
params.termCrit.maxCount : DEFAULT_MAX_ITERS;
double epsilon = (params.termCrit.type & TermCriteria::EPS) ? params.termCrit.epsilon : 0.;
int maxIters = (termCrit.type & TermCriteria::MAX_ITER) ?
termCrit.maxCount : DEFAULT_MAX_ITERS;
double epsilon = (termCrit.type & TermCriteria::EPS) ? termCrit.epsilon : 0.;
for(int iter = 0; ; iter++)
{
@@ -521,12 +528,12 @@ public:
covs.resize(nclusters);
for(int clusterIndex = 0; clusterIndex < nclusters; clusterIndex++)
{
if(params.covMatType == COV_MAT_SPHERICAL)
if(covMatType == COV_MAT_SPHERICAL)
{
covs[clusterIndex].create(dim, dim, CV_64FC1);
setIdentity(covs[clusterIndex], Scalar(covsEigenValues[clusterIndex].at<double>(0)));
}
else if(params.covMatType == COV_MAT_DIAGONAL)
else if(covMatType == COV_MAT_DIAGONAL)
{
covs[clusterIndex] = Mat::diag(covsEigenValues[clusterIndex]);
}
@@ -555,7 +562,6 @@ public:
// see Alex Smola's blog http://blog.smola.org/page/2 for
// details on the log-sum-exp trick
int nclusters = params.nclusters, covMatType = params.covMatType;
int stype = sample.type();
CV_Assert(!means.empty());
CV_Assert((stype == CV_32F || stype == CV_64F) && (ptype == CV_32F || ptype == CV_64F));
@@ -621,7 +627,7 @@ public:
void eStep()
{
// Compute probs_ik from means_k, covs_k and weights_k.
trainProbs.create(trainSamples.rows, params.nclusters, CV_64FC1);
trainProbs.create(trainSamples.rows, nclusters, CV_64FC1);
trainLabels.create(trainSamples.rows, 1, CV_32SC1);
trainLogLikelihoods.create(trainSamples.rows, 1, CV_64FC1);
@@ -642,8 +648,6 @@ public:
void mStep()
{
// Update means_k, covs_k and weights_k from probs_ik
int nclusters = params.nclusters;
int covMatType = params.covMatType;
int dim = trainSamples.cols;
// Update weights
@@ -755,12 +759,12 @@ public:
void write_params(FileStorage& fs) const
{
fs << "nclusters" << params.nclusters;
fs << "cov_mat_type" << (params.covMatType == COV_MAT_SPHERICAL ? String("spherical") :
params.covMatType == COV_MAT_DIAGONAL ? String("diagonal") :
params.covMatType == COV_MAT_GENERIC ? String("generic") :
format("unknown_%d", params.covMatType));
writeTermCrit(fs, params.termCrit);
fs << "nclusters" << nclusters;
fs << "cov_mat_type" << (covMatType == COV_MAT_SPHERICAL ? String("spherical") :
covMatType == COV_MAT_DIAGONAL ? String("diagonal") :
covMatType == COV_MAT_GENERIC ? String("generic") :
format("unknown_%d", covMatType));
writeTermCrit(fs, termCrit);
}
void write(FileStorage& fs) const
@@ -781,15 +785,13 @@ public:
void read_params(const FileNode& fn)
{
Params _params;
_params.nclusters = (int)fn["nclusters"];
nclusters = (int)fn["nclusters"];
String s = (String)fn["cov_mat_type"];
_params.covMatType = s == "spherical" ? COV_MAT_SPHERICAL :
covMatType = s == "spherical" ? COV_MAT_SPHERICAL :
s == "diagonal" ? COV_MAT_DIAGONAL :
s == "generic" ? COV_MAT_GENERIC : -1;
CV_Assert(_params.covMatType >= 0);
_params.termCrit = readTermCrit(fn);
setParams(_params);
CV_Assert(covMatType >= 0);
termCrit = readTermCrit(fn);
}
void read(const FileNode& fn)
@@ -820,8 +822,6 @@ public:
std::copy(covs.begin(), covs.end(), _covs.begin());
}
Params params;
// all inner matrices have type CV_64FC1
Mat trainSamples;
Mat trainProbs;
@@ -838,41 +838,9 @@ public:
Mat logWeightDivDet;
};
Ptr<EM> EM::train(InputArray samples, OutputArray logLikelihoods,
OutputArray labels, OutputArray probs,
const EM::Params& params)
Ptr<EM> EM::create()
{
Ptr<EMImpl> em = makePtr<EMImpl>(params);
if(!em->train_(samples, logLikelihoods, labels, probs))
em.release();
return em;
}
Ptr<EM> EM::train_startWithE(InputArray samples, InputArray means0,
InputArray covs0, InputArray weights0,
OutputArray logLikelihoods, OutputArray labels,
OutputArray probs, const EM::Params& params)
{
Ptr<EMImpl> em = makePtr<EMImpl>(params);
if(!em->trainE(samples, means0, covs0, weights0, logLikelihoods, labels, probs))
em.release();
return em;
}
Ptr<EM> EM::train_startWithM(InputArray samples, InputArray probs0,
OutputArray logLikelihoods, OutputArray labels,
OutputArray probs, const EM::Params& params)
{
Ptr<EMImpl> em = makePtr<EMImpl>(params);
if(!em->trainM(samples, probs0, logLikelihoods, labels, probs))
em.release();
return em;
}
Ptr<EM> EM::create(const Params& params)
{
return makePtr<EMImpl>(params);
return makePtr<EMImpl>();
}
}