/// <summary>
/// SVMを最適なパラメータで学習する
/// </summary>
/// <param name="train_data"></param>
/// <param name="responses"></param>
/// <param name="var_idx"></param>
/// <param name="sample_idx"></param>
/// <param name="params"></param>
/// <param name="k_fold">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="C_grid"></param>
/// <param name="gamma_grid"></param>
/// <param name="p_grid"></param>
/// <param name="nu_grid"></param>
/// <param name="coef_grid"></param>
/// <param name="degree_grid"></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="params"></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>
/// <returns></returns>
#endif
public virtual bool TrainAuto(CvMat trainData, CvMat responses, CvMat varIdx, CvMat sampleIdx, CvSVMParams @params, int kFold,
CvParamGrid cGrid, CvParamGrid gammaGrid, CvParamGrid pGrid, CvParamGrid nuGrid, CvParamGrid coefGrid, CvParamGrid degreeGrid)
{
if (trainData == null)
throw new ArgumentNullException("trainData");
if (responses == null)
throw new ArgumentNullException("responses");
if(@params == null)
@params = new CvSVMParams();
IntPtr varIdxPtr = (varIdx == null) ? IntPtr.Zero : varIdx.CvPtr;
IntPtr sampleIdxPtr = (sampleIdx == null) ? IntPtr.Zero : sampleIdx.CvPtr;
return MLInvoke.CvSVM_train_auto(
ptr,
trainData.CvPtr,
responses.CvPtr,
varIdxPtr,
sampleIdxPtr,
@params.NativeStruct,
kFold,
cGrid,
gammaGrid,
pGrid,
nuGrid,
coefGrid,
degreeGrid
);
}
/// <summary>
/// SVM パラメータのためのグリッドを生成する
/// </summary>
/// <param name="param_id"></param>
/// <returns></returns>
#else
/// <summary>
/// Generates a grid for SVM parameters
/// </summary>
/// <param name="paramId"></param>
/// <returns></returns>
#endif
public static CvParamGrid GetDefaultGrid(SVMParamType paramId)
{
CvParamGrid grid = new CvParamGrid();
MLInvoke.CvSVM_get_default_grid(ref grid, (int)paramId);
return grid;
}
public static extern bool CvParamGrid_check(CvParamGrid grid);
public static extern bool CvSVM_train_auto(IntPtr model, IntPtr _train_data, IntPtr _responses, IntPtr _var_idx, IntPtr _sample_idx, WCvSVMParams _params,
int k_fold, CvParamGrid C_grid, CvParamGrid gamma_grid, CvParamGrid p_grid, CvParamGrid nu_grid, CvParamGrid coef_grid, CvParamGrid degree_grid);
public static extern void CvSVM_get_default_grid(ref CvParamGrid grid, int param_id);
