/**
* Trains the statistical model
*
* param trainData training data that can be loaded from file using TrainData::loadFromCSV or
* created with TrainData::create.
* new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
* return automatically generated
*/
public bool train(TrainData trainData)
{
ThrowIfDisposed();
if (trainData != null)
{
trainData.ThrowIfDisposed();
}
return(ml_StatModel_train_12(nativeObj, trainData.getNativeObjAddr()));
}
//
// C++: float cv::ml::StatModel::calcError(Ptr_TrainData data, bool test, Mat& resp)
//
/**
* Computes error on the training or test dataset
*
* param data the training data
* param test if true, the error is computed over the test subset of the data, otherwise it's
* computed over the training subset of the data. Please note that if you loaded a completely
* different dataset to evaluate already trained classifier, you will probably want not to set
* the test subset at all with TrainData::setTrainTestSplitRatio and specify test=false, so
* that the error is computed for the whole new set. Yes, this sounds a bit confusing.
* param resp the optional output responses.
*
* The method uses StatModel::predict to compute the error. For regression models the error is
* computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
* return automatically generated
*/
public float calcError(TrainData data, bool test, Mat resp)
{
ThrowIfDisposed();
if (data != null)
{
data.ThrowIfDisposed();
}
if (resp != null)
{
resp.ThrowIfDisposed();
}
return(ml_StatModel_calcError_10(nativeObj, data.getNativeObjAddr(), test, resp.nativeObj));
}
//javadoc: StatModel::train(trainData)
public bool train(TrainData trainData)
{
ThrowIfDisposed();
if (trainData != null)
{
trainData.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
bool retVal = ml_StatModel_train_12(nativeObj, trainData.getNativeObjAddr());
return(retVal);
#else
return(false);
#endif
}
//
// C++: float cv::ml::StatModel::calcError(Ptr_TrainData data, bool test, Mat& resp)
//
//javadoc: StatModel::calcError(data, test, resp)
public float calcError(TrainData data, bool test, Mat resp)
{
ThrowIfDisposed();
if (data != null)
{
data.ThrowIfDisposed();
}
if (resp != null)
{
resp.ThrowIfDisposed();
}
#if ((UNITY_ANDROID || UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR) || UNITY_5 || UNITY_5_3_OR_NEWER
float retVal = ml_StatModel_calcError_10(nativeObj, data.getNativeObjAddr(), test, resp.nativeObj);
return(retVal);
#else
return(-1);
#endif
}