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Calcerror uses now weighted samples (#10346)

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* Calcerror uses now sample weights

* catree comment in #10319
This commit is contained in:
LaurentBerger 2017-12-20 15:24:46 +01:00 committed by Vadim Pisarevsky
parent b8a24b36ce
commit e43997dbb5
2 changed files with 14 additions and 21 deletions
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17
modules/ml/src/ann_mlp.cpp
View File

@ -155,7 +155,7 @@ int SimulatedAnnealingSolver::run()
if (newEnergy < previousEnergy) if (newEnergy < previousEnergy)
{ {
previousEnergy = newEnergy; previousEnergy = newEnergy;
//??? exchange++; exchange++;
} }
else else
{ {
@ -405,21 +405,6 @@ public:
param2 = 0.1; param2 = 0.1;
params.bpMomentScale = std::min(param2, 1.); params.bpMomentScale = std::min(param2, 1.);
} }
/* else if (method == ANN_MLP::ANNEAL)
{
if (param1 <= 0)
param1 = 10;
if (param2 <= 0 || param2>param1)
param2 = 0.1;
if (param3 <= 0 || param3 >=1)
param3 = 0.95;
if (param4 <= 0)
param4 = 10;
params.initialT = param1;
params.finalT = param2;
params.coolingRatio = param3;
params.itePerStep = param4;
}*/
} }
int getTrainMethod() const int getTrainMethod() const

18
modules/ml/src/inner_functions.cpp
View File

@ -94,26 +94,29 @@ public:
int idxErr = range.start; int idxErr = range.start;
CV_TRACE_FUNCTION_SKIP_NESTED(); CV_TRACE_FUNCTION_SKIP_NESTED();
Mat samples = data->getSamples(); Mat samples = data->getSamples();
Mat weights=testerr? data->getTestSampleWeights() : data->getTrainSampleWeights();
int layout = data->getLayout(); int layout = data->getLayout();
Mat sidx = testerr ? data->getTestSampleIdx() : data->getTrainSampleIdx(); Mat sidx = testerr ? data->getTestSampleIdx() : data->getTrainSampleIdx();
const int* sidx_ptr = sidx.ptr<int>(); const int* sidx_ptr = sidx.ptr<int>();
bool isclassifier = s.isClassifier(); bool isclassifier = s.isClassifier();
Mat responses = data->getResponses(); Mat responses = data->getResponses();
int responses_type = responses.type(); int responses_type = responses.type();
double err = 0; double err = 0;
const float* sw = weights.empty() ? 0 : weights.ptr<float>();
for (int i = range.start; i < range.end; i++) for (int i = range.start; i < range.end; i++)
{ {
int si = sidx_ptr ? sidx_ptr[i] : i; int si = sidx_ptr ? sidx_ptr[i] : i;
double sweight = sw ? static_cast<double>(sw[i]) : 1.;
Mat sample = layout == ROW_SAMPLE ? samples.row(si) : samples.col(si); Mat sample = layout == ROW_SAMPLE ? samples.row(si) : samples.col(si);
float val = s.predict(sample); float val = s.predict(sample);
float val0 = (responses_type == CV_32S) ? (float)responses.at<int>(si) : responses.at<float>(si); float val0 = (responses_type == CV_32S) ? (float)responses.at<int>(si) : responses.at<float>(si);
if (isclassifier) if (isclassifier)
err += fabs(val - val0) > FLT_EPSILON; err += sweight * fabs(val - val0) > FLT_EPSILON;
else else
err += (val - val0)*(val - val0); err += sweight * (val - val0)*(val - val0);
if (!resp.empty()) if (!resp.empty())
resp.at<float>(i) = val; resp.at<float>(i) = val;
} }
@ -133,12 +136,17 @@ float StatModel::calcError(const Ptr<TrainData>& data, bool testerr, OutputArray
CV_TRACE_FUNCTION_SKIP_NESTED(); CV_TRACE_FUNCTION_SKIP_NESTED();
Mat samples = data->getSamples(); Mat samples = data->getSamples();
Mat sidx = testerr ? data->getTestSampleIdx() : data->getTrainSampleIdx(); Mat sidx = testerr ? data->getTestSampleIdx() : data->getTrainSampleIdx();
Mat weights = testerr ? data->getTestSampleWeights() : data->getTrainSampleWeights();
int n = (int)sidx.total(); int n = (int)sidx.total();
bool isclassifier = isClassifier(); bool isclassifier = isClassifier();
Mat responses = data->getResponses(); Mat responses = data->getResponses();
if (n == 0) if (n == 0)
{
n = data->getNSamples(); n = data->getNSamples();
weights = data->getTrainSampleWeights();
testerr =false;
}
if (n == 0) if (n == 0)
return -FLT_MAX; return -FLT_MAX;
@ -155,11 +163,11 @@ float StatModel::calcError(const Ptr<TrainData>& data, bool testerr, OutputArray
for (size_t i = 0; i < errStrip.size(); i++) for (size_t i = 0; i < errStrip.size(); i++)
err += errStrip[i]; err += errStrip[i];
float weightSum= weights.empty() ? n: static_cast<float>(sum(weights)(0));
if (_resp.needed()) if (_resp.needed())
resp.copyTo(_resp); resp.copyTo(_resp);
return (float)(err / n * (isclassifier ? 100 : 1)); return (float)(err/ weightSum * (isclassifier ? 100 : 1));
} }
/* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */ /* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */