Package | Description |
---|---|
org.bytedeco.opencv.opencv_ml |
Modifier and Type | Method and Description |
---|---|
static TrainData |
TrainData.create(GpuMat samples,
int layout,
GpuMat responses) |
static TrainData |
TrainData.create(GpuMat samples,
int layout,
GpuMat responses,
GpuMat varIdx,
GpuMat sampleIdx,
GpuMat sampleWeights,
GpuMat varType) |
static TrainData |
TrainData.create(Mat samples,
int layout,
Mat responses) |
static TrainData |
TrainData.create(Mat samples,
int layout,
Mat responses,
Mat varIdx,
Mat sampleIdx,
Mat sampleWeights,
Mat varType)
\brief Creates training data from in-memory arrays.
|
static TrainData |
TrainData.create(UMat samples,
int layout,
UMat responses) |
static TrainData |
TrainData.create(UMat samples,
int layout,
UMat responses,
UMat varIdx,
UMat sampleIdx,
UMat sampleWeights,
UMat varType) |
static TrainData |
TrainData.loadFromCSV(BytePointer filename,
int headerLineCount) |
static TrainData |
TrainData.loadFromCSV(BytePointer filename,
int headerLineCount,
int responseStartIdx,
int responseEndIdx,
BytePointer varTypeSpec,
byte delimiter,
byte missch)
\brief Reads the dataset from a .csv file and returns the ready-to-use training data.
|
static TrainData |
TrainData.loadFromCSV(String filename,
int headerLineCount) |
static TrainData |
TrainData.loadFromCSV(String filename,
int headerLineCount,
int responseStartIdx,
int responseEndIdx,
String varTypeSpec,
byte delimiter,
byte missch) |
Modifier and Type | Method and Description |
---|---|
float |
StatModel.calcError(TrainData data,
boolean test,
GpuMat resp) |
float |
StatModel.calcError(TrainData data,
boolean test,
Mat resp)
\brief Computes error on the training or test dataset
|
float |
StatModel.calcError(TrainData data,
boolean test,
UMat resp) |
boolean |
StatModel.train(TrainData trainData) |
boolean |
StatModel.train(TrainData trainData,
int flags)
\brief Trains the statistical model
|
boolean |
SVM.trainAuto(TrainData data) |
boolean |
SVM.trainAuto(TrainData data,
int kFold,
ParamGrid Cgrid,
ParamGrid gammaGrid,
ParamGrid pGrid,
ParamGrid nuGrid,
ParamGrid coeffGrid,
ParamGrid degreeGrid,
boolean balanced)
\brief Trains an %SVM with optimal parameters.
|
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