/// <summary>
/// SVMを最適なパラメータで学習する
/// </summary>
/// <param name="trainData"></param>
/// <param name="responses"></param>
/// <param name="varIdx"></param>
/// <param name="sampleIdx"></param>
/// <param name="param"></param>
/// <param name="kFold">交差検定（Cross-validation）パラメータ．学習集合は，k_foldの部分集合に分割され，一つの部分集合がモデルの学習に用いられ，その他の部分集合はテスト集合となる．つまり，SVM アルゴリズムは，k_fold回実行される.</param>
/// <param name="cGrid"></param>
/// <param name="gammaGrid"></param>
/// <param name="pGrid"></param>
/// <param name="nuGrid"></param>
/// <param name="coefGrid"></param>
/// <param name="degreeGrid"></param>
/// <param name="balanced"></param>
/// <returns></returns>
#else
/// <summary>
/// Trains SVM with optimal parameters
/// </summary>
/// <param name="trainData"></param>
/// <param name="responses"></param>
/// <param name="varIdx"></param>
/// <param name="sampleIdx"></param>
/// <param name="param"></param>
/// <param name="kFold">Cross-validation parameter. The training set is divided into k_fold subsets, one subset being used to train the model, the others forming the test set. So, the SVM algorithm is executed k_fold times. </param>
/// <param name="cGrid"></param>
/// <param name="gammaGrid"></param>
/// <param name="pGrid"></param>
/// <param name="nuGrid"></param>
/// <param name="coefGrid"></param>
/// <param name="degreeGrid"></param>
/// <param name="balanced"></param>
/// <returns></returns>
#endif
public virtual bool TrainAuto(
Mat trainData,
Mat responses,
Mat varIdx,
Mat sampleIdx,
CvSVMParams param,
int kFold = 10,
CvParamGrid? cGrid = null,
CvParamGrid? gammaGrid = null,
CvParamGrid? pGrid = null,
CvParamGrid? nuGrid = null,
CvParamGrid? coefGrid = null,
CvParamGrid? degreeGrid = null,
bool balanced = false)
{
if (trainData == null)
throw new ArgumentNullException("trainData");
if (responses == null)
throw new ArgumentNullException("responses");
if (varIdx == null)
throw new ArgumentNullException("varIdx");
if (sampleIdx == null)
throw new ArgumentNullException("sampleIdx");
trainData.ThrowIfDisposed();
responses.ThrowIfDisposed();
varIdx.ThrowIfDisposed();
sampleIdx.ThrowIfDisposed();
if (param == null)
param = new CvSVMParams();
var defaultGrid = GetDefaultGrid(SVMParamType.C);
var cGrid0 = cGrid.GetValueOrDefault(defaultGrid);
var gammaGrid0 = gammaGrid.GetValueOrDefault(defaultGrid);
var pGrid0 = pGrid.GetValueOrDefault(defaultGrid);
var nuGrid0 = nuGrid.GetValueOrDefault(defaultGrid);
var coefGrid0 = coefGrid.GetValueOrDefault(defaultGrid);
var degreeGrid0 = degreeGrid.GetValueOrDefault(defaultGrid);
return NativeMethods.ml_CvSVM_train_auto_CvMat(
ptr,
trainData.CvPtr,
responses.CvPtr,
varIdx.CvPtr,
sampleIdx.CvPtr,
param.NativeStruct,
kFold,
cGrid0,
gammaGrid0,
pGrid0,
nuGrid0,
coefGrid0,
degreeGrid0,
balanced ? 1 : 0) != 0;
}
