/// <summary>
/// 決定木を学習する
/// </summary>
/// <param name="trainData"></param>
/// <param name="param"></param>
/// <returns></returns>
#else
/// <summary>
/// Trains decision tree
/// </summary>
/// <param name="trainData"></param>
/// <param name="param"></param>
/// <returns></returns>
#endif
public virtual bool Train(CvMLData trainData, CvDTreeParams param)
{
if (trainData == null)
{
throw new ArgumentNullException(nameof(trainData));
}
if (param == null)
{
param = new CvDTreeParams();
}
return(NativeMethods.ml_CvDTree_train3(
ptr, trainData.CvPtr, param.CvPtr) != 0);
}
/// <summary>
///
/// </summary>
/// <param name="trainData"></param>
/// <param name="tflag"></param>
/// <param name="responses"></param>
/// <param name="varIdx"></param>
/// <param name="sampleIdx"></param>
/// <param name="varType"></param>
/// <param name="missingMask"></param>
/// <param name="param"></param>
/// <param name="shared"></param>
/// <param name="addLabels"></param>
/// <param name="updateData"></param>
/// <returns></returns>
#else
/// <summary>
///
/// </summary>
/// <param name="trainData"></param>
/// <param name="tflag"></param>
/// <param name="responses"></param>
/// <param name="varIdx"></param>
/// <param name="sampleIdx"></param>
/// <param name="varType"></param>
/// <param name="missingMask"></param>
/// <param name="param"></param>
/// <param name="shared"></param>
/// <param name="addLabels"></param>
/// <param name="updateData"></param>
/// <returns></returns>
#endif
public void SetData(
CvMat trainData,
DTreeDataLayout tflag,
CvMat responses,
CvMat varIdx = null,
CvMat sampleIdx = null,
CvMat varType = null,
CvMat missingMask = null,
CvDTreeParams param = null,
bool shared = false,
bool addLabels = false,
bool updateData = false)
{
if (trainData == null)
{
throw new ArgumentNullException(nameof(trainData));
}
if (responses == null)
{
throw new ArgumentNullException(nameof(responses));
}
if (param == null)
{
param = new CvDTreeParams();
}
NativeMethods.ml_CvDTreeTrainData_set_data(
ptr,
trainData.CvPtr,
(int)tflag,
responses.CvPtr,
Cv2.ToPtr(varIdx),
Cv2.ToPtr(sampleIdx),
Cv2.ToPtr(varType),
Cv2.ToPtr(missingMask),
param.CvPtr,
shared ? 1 : 0,
addLabels ? 1 : 0,
updateData ? 1 : 0
);
}
/// <summary>
/// 学習データを与えて初期化
/// </summary>
/// <param name="trainData"></param>
/// <param name="tflag"></param>
/// <param name="responses"></param>
/// <param name="varIdx"></param>
/// <param name="sampleIdx"></param>
/// <param name="varType"></param>
/// <param name="missingMask"></param>
/// <param name="param"></param>
/// <param name="shared"></param>
/// <param name="addLabels"></param>
/// <returns></returns>
#else
/// <summary>
/// Training constructor
/// </summary>
/// <param name="trainData"></param>
/// <param name="tflag"></param>
/// <param name="responses"></param>
/// <param name="varIdx"></param>
/// <param name="sampleIdx"></param>
/// <param name="varType"></param>
/// <param name="missingMask"></param>
/// <param name="param"></param>
/// <param name="shared"></param>
/// <param name="addLabels"></param>
/// <returns></returns>
#endif
public CvDTreeTrainData(
CvMat trainData,
DTreeDataLayout tflag,
CvMat responses,
CvMat varIdx = null,
CvMat sampleIdx = null,
CvMat varType = null,
CvMat missingMask = null,
CvDTreeParams param = null,
bool shared = false,
bool addLabels = false)
{
if (trainData == null)
{
throw new ArgumentNullException("trainData");
}
if (responses == null)
{
throw new ArgumentNullException("responses");
}
trainData.ThrowIfDisposed();
responses.ThrowIfDisposed();
if (param == null)
{
param = new CvDTreeParams();
}
ptr = NativeMethods.ml_CvDTreeTrainData_new2(
trainData.CvPtr,
(int)tflag,
responses.CvPtr,
Cv2.ToPtr(varIdx),
Cv2.ToPtr(sampleIdx),
Cv2.ToPtr(varType),
Cv2.ToPtr(missingMask),
param.CvPtr,
shared ? 1 : 0,
addLabels ? 1 : 0
);
}
/// <summary>
/// 決定木を学習する
/// </summary>
/// <param name="trainData"></param>
/// <param name="tflag"></param>
/// <param name="responses"></param>
/// <param name="varIdx"></param>
/// <param name="sampleIdx"></param>
/// <param name="varType"></param>
/// <param name="missingMask"></param>
/// <param name="param"></param>
/// <returns></returns>
#else
/// <summary>
/// Trains decision tree
/// </summary>
/// <param name="trainData"></param>
/// <param name="tflag"></param>
/// <param name="responses"></param>
/// <param name="varIdx"></param>
/// <param name="sampleIdx"></param>
/// <param name="varType"></param>
/// <param name="missingMask"></param>
/// <param name="param"></param>
/// <returns></returns>
#endif
public virtual bool Train(
Mat trainData,
DTreeDataLayout tflag,
Mat responses,
Mat varIdx,
Mat sampleIdx,
Mat varType,
Mat missingMask,
CvDTreeParams param)
{
if (trainData == null)
{
throw new ArgumentNullException(nameof(trainData));
}
if (responses == null)
{
throw new ArgumentNullException(nameof(responses));
}
trainData.ThrowIfDisposed();
responses.ThrowIfDisposed();
if (param == null)
{
param = new CvDTreeParams();
}
return(NativeMethods.ml_CvDTree_train_Mat(
ptr,
trainData.CvPtr,
(int)tflag,
responses.CvPtr,
Cv2.ToPtr(varIdx),
Cv2.ToPtr(sampleIdx),
Cv2.ToPtr(varType),
Cv2.ToPtr(missingMask),
param.CvPtr) != 0);
}
/// <summary>
/// 学習データを与えて初期化
/// </summary>
/// <param name="trainData"></param>
/// <param name="tflag"></param>
/// <param name="responses"></param>
/// <param name="varIdx"></param>
/// <param name="sampleIdx"></param>
/// <param name="varType"></param>
/// <param name="missingMask"></param>
/// <param name="param"></param>
/// <param name="shared"></param>
/// <param name="addLabels"></param>
/// <returns></returns>
#else
/// <summary>
/// Training constructor
/// </summary>
/// <param name="trainData"></param>
/// <param name="tflag"></param>
/// <param name="responses"></param>
/// <param name="varIdx"></param>
/// <param name="sampleIdx"></param>
/// <param name="varType"></param>
/// <param name="missingMask"></param>
/// <param name="param"></param>
/// <param name="shared"></param>
/// <param name="addLabels"></param>
/// <returns></returns>
#endif
public CvDTreeTrainData(
CvMat trainData,
DTreeDataLayout tflag,
CvMat responses,
CvMat varIdx = null,
CvMat sampleIdx = null,
CvMat varType = null,
CvMat missingMask = null,
CvDTreeParams param = null,
bool shared = false,
bool addLabels = false)
{
if (trainData == null)
throw new ArgumentNullException("trainData");
if (responses == null)
throw new ArgumentNullException("responses");
trainData.ThrowIfDisposed();
responses.ThrowIfDisposed();
if(param == null)
param = new CvDTreeParams();
ptr = NativeMethods.ml_CvDTreeTrainData_new2(
trainData.CvPtr,
(int)tflag,
responses.CvPtr,
Cv2.ToPtr(varIdx),
Cv2.ToPtr(sampleIdx),
Cv2.ToPtr(varType),
Cv2.ToPtr(missingMask),
param.CvPtr,
shared ? 1 : 0,
addLabels ? 1 : 0
);
}
/// <summary>
///
/// </summary>
/// <param name="data"></param>
/// <param name="missing"></param>
/// <param name="responses"></param>
/// <param name="pWeight"></param>
/// <returns></returns>
private CvDTree MushroomCreateDTree(CvMat data, CvMat missing, CvMat responses, float pWeight)
{
float[] priors = { 1, pWeight };
CvMat varType = new CvMat(data.Cols + 1, 1, MatrixType.U8C1);
Cv.Set(varType, CvScalar.ScalarAll(CvStatModel.CV_VAR_CATEGORICAL)); // all the variables are categorical
CvDTree dtree = new CvDTree();
CvDTreeParams p = new CvDTreeParams(8, // max depth
10, // min sample count
0, // regression accuracy: N/A here
true, // compute surrogate split, as we have missing data
15, // max number of categories (use sub-optimal algorithm for larger numbers)
10, // the number of cross-validation folds
true, // use 1SE rule => smaller tree
true, // throw away the pruned tree branches
priors // the array of priors, the bigger p_weight, the more attention
// to the poisonous mushrooms
// (a mushroom will be judjed to be poisonous with bigger chance)
);
dtree.Train(data, DTreeDataLayout.RowSample, responses, null, null, varType, missing, p);
// compute hit-rate on the training database, demonstrates predict usage.
int hr1 = 0, hr2 = 0, pTotal = 0;
for (int i = 0; i < data.Rows; i++)
{
CvMat sample, mask;
Cv.GetRow(data, out sample, i);
Cv.GetRow(missing, out mask, i);
double r = dtree.Predict(sample, mask).Value;
bool d = Math.Abs(r - responses.DataArraySingle[i]) >= float.Epsilon;
if (d)
{
if (r != 'p')
hr1++;
else
hr2++;
}
//Console.WriteLine(responses.DataArraySingle[i]);
pTotal += (responses.DataArraySingle[i] == (float)'p') ? 1 : 0;
}
Console.WriteLine("Results on the training database");
Console.WriteLine("\tPoisonous mushrooms mis-predicted: {0} ({1}%)", hr1, (double)hr1 * 100 / pTotal);
Console.WriteLine("\tFalse-alarms: {0} ({1}%)", hr2, (double)hr2 * 100 / (data.Rows - pTotal));
varType.Dispose();
return dtree;
}
/// <summary>
/// 決定木を学習する
/// </summary>
/// <param name="trainData"></param>
/// <param name="param"></param>
/// <returns></returns>
#else
/// <summary>
/// Trains decision tree
/// </summary>
/// <param name="trainData"></param>
/// <param name="param"></param>
/// <returns></returns>
#endif
public virtual bool Train(CvMLData trainData, CvDTreeParams param)
{
if (trainData == null)
throw new ArgumentNullException("trainData");
if (param == null)
param = new CvDTreeParams();
return NativeMethods.ml_CvDTree_train3(
ptr, trainData.CvPtr, param.CvPtr) != 0;
}
/// <summary>
/// 決定木を学習する
/// </summary>
/// <param name="trainData"></param>
/// <param name="tflag"></param>
/// <param name="responses"></param>
/// <param name="varIdx"></param>
/// <param name="sampleIdx"></param>
/// <param name="varType"></param>
/// <param name="missingMask"></param>
/// <param name="param"></param>
/// <returns></returns>
#else
/// <summary>
/// Trains decision tree
/// </summary>
/// <param name="trainData"></param>
/// <param name="tflag"></param>
/// <param name="responses"></param>
/// <param name="varIdx"></param>
/// <param name="sampleIdx"></param>
/// <param name="varType"></param>
/// <param name="missingMask"></param>
/// <param name="param"></param>
/// <returns></returns>
#endif
public virtual bool Train(
Mat trainData,
DTreeDataLayout tflag,
Mat responses,
Mat varIdx,
Mat sampleIdx,
Mat varType,
Mat missingMask,
CvDTreeParams param)
{
if (trainData == null)
throw new ArgumentNullException("trainData");
if (responses == null)
throw new ArgumentNullException("responses");
trainData.ThrowIfDisposed();
responses.ThrowIfDisposed();
if (param == null)
param = new CvDTreeParams();
return NativeMethods.ml_CvDTree_train_Mat(
ptr,
trainData.CvPtr,
(int)tflag,
responses.CvPtr,
Cv2.ToPtr(varIdx),
Cv2.ToPtr(sampleIdx),
Cv2.ToPtr(varType),
Cv2.ToPtr(missingMask),
param.CvPtr) != 0;
}
