public static extern bool CvRTreesTrain(
IntPtr model,
IntPtr trainData,
MlEnum.DataLayoutType tFlag,
IntPtr responses,
IntPtr varIdx,
IntPtr sampleIdx,
IntPtr varType,
IntPtr missingMask,
ref MCvRTParams param);
public static extern bool CvGBTreesTrain(
IntPtr model,
IntPtr trainData,
MlEnum.DataLayoutType tFlag,
IntPtr responses,
IntPtr varIdx,
IntPtr sampleIdx,
IntPtr varType,
IntPtr missingMask,
ref MCvGBTreesParams param,
[MarshalAs(CvInvoke.BoolMarshalType)]
bool update);
/// <summary>
/// Train the decision tree using the specific traning data
/// </summary>
/// <param name="trainData">The training data. A 32-bit floating-point, single-channel matrix, one vector per row</param>
/// <param name="tflag">data layout type</param>
/// <param name="responses">A floating-point matrix of the corresponding output vectors, one vector per row. </param>
/// <param name="varType">The types of input variables</param>
/// <param name="missingMask">Can be null if not needed. When specified, it is an 8-bit matrix of the same size as <paramref name="trainData"/>, is used to mark the missed values (non-zero elements of the mask)</param>
/// <param name="param">The parameters for training the decision tree</param>
/// <returns></returns>
public bool Train(
Matrix <float> trainData,
MlEnum.DataLayoutType tflag,
Matrix <float> responses,
Matrix <Byte> varType,
Matrix <Byte> missingMask,
MCvDTreeParams param)
{
return(MlInvoke.CvDTreeTrain(
_ptr,
trainData.Ptr,
tflag,
responses.Ptr,
IntPtr.Zero,
IntPtr.Zero,
varType == null ? IntPtr.Zero : varType.Ptr,
missingMask == null ? IntPtr.Zero : missingMask.Ptr,
ref param));
}
/// <summary>
/// Train the boost tree using the specific traning data
/// </summary>
/// <param name="trainData">The training data. A 32-bit floating-point, single-channel matrix, one vector per row</param>
/// <param name="tflag">data layout type</param>
/// <param name="responses">A floating-point matrix of the corresponding output vectors, one vector per row. </param>
/// <param name="varMask">Can be null if not needed. When specified, it is a mask that identifies variables (features) of interest. It must be a Matrix>Byte< of n x 1 where n is the number of rows in <paramref name="trainData"/></param>
/// <param name="sampleMask">Can be null if not needed. When specified, it is a mask identifies samples of interest. It must be a Matrix>Byte< of nx1, where n is the number of rows in <paramref name="trainData"/></param>
/// <param name="varType">The types of input variables</param>
/// <param name="missingMask">Can be null if not needed. When specified, it is an 8-bit matrix of the same size as <paramref name="trainData"/>, is used to mark the missed values (non-zero elements of the mask)</param>
/// <param name="param">The parameters for training the boost tree</param>
/// <param name="update">specifies whether the classifier needs to be updated (i.e. the new weak tree classifiers added to the existing ensemble), or the classifier needs to be rebuilt from scratch</param>
/// <returns></returns>
public bool Train(
Matrix <float> trainData,
MlEnum.DataLayoutType tflag,
Matrix <float> responses,
Matrix <Byte> varMask,
Matrix <Byte> sampleMask,
Matrix <Byte> varType,
Matrix <Byte> missingMask,
MCvBoostParams param,
bool update)
{
return(MlInvoke.CvBoostTrain(
_ptr,
trainData.Ptr,
tflag,
responses.Ptr,
varMask == null ? IntPtr.Zero : varMask.Ptr,
sampleMask == null ? IntPtr.Zero : sampleMask.Ptr,
varType == null ? IntPtr.Zero : varType.Ptr,
missingMask == null ? IntPtr.Zero : missingMask.Ptr,
ref param,
update));
}
