public static HClassTrainData Deserialize(Stream stream)
{
HClassTrainData hclassTrainData = new HClassTrainData();
hclassTrainData.DeserializeClassTrainData(HSerializedItem.Deserialize(stream));
return(hclassTrainData);
}
/// <summary>
/// Selects an optimal subset from a set of features to solve a certain classification problem.
/// Modified instance represents: A trained k-NN classifier using only the selected features.
/// </summary>
/// <param name="classTrainDataHandle">Handle of the training data.</param>
/// <param name="selectionMethod">Method to perform the selection. Default: "greedy"</param>
/// <param name="genParamName">Names of generic parameters to configure the selection process and the classifier. Default: []</param>
/// <param name="genParamValue">Values of generic parameters to configure the selection process and the classifier. Default: []</param>
/// <param name="score">The achieved score using two-fold cross-validation.</param>
/// <returns>The selected feature set, contains indices or names.</returns>
public HTuple SelectFeatureSetKnn(
HClassTrainData classTrainDataHandle,
string selectionMethod,
string genParamName,
double genParamValue,
out HTuple score)
{
this.Dispose();
IntPtr proc = HalconAPI.PreCall(1802);
HalconAPI.Store(proc, 0, (HTool)classTrainDataHandle);
HalconAPI.StoreS(proc, 1, selectionMethod);
HalconAPI.StoreS(proc, 2, genParamName);
HalconAPI.StoreD(proc, 3, genParamValue);
HalconAPI.InitOCT(proc, 0);
HalconAPI.InitOCT(proc, 1);
HalconAPI.InitOCT(proc, 2);
int err1 = HalconAPI.CallProcedure(proc);
int err2 = this.Load(proc, 0, err1);
HTuple tuple;
int err3 = HTuple.LoadNew(proc, 1, err2, out tuple);
int procResult = HTuple.LoadNew(proc, 2, HTupleType.DOUBLE, err3, out score);
HalconAPI.PostCall(proc, procResult);
GC.KeepAlive((object)this);
GC.KeepAlive((object)classTrainDataHandle);
return(tuple);
}
public HClassTrainData Clone()
{
HSerializedItem serializedItemHandle = this.SerializeClassTrainData();
HClassTrainData hclassTrainData = new HClassTrainData();
hclassTrainData.DeserializeClassTrainData(serializedItemHandle);
serializedItemHandle.Dispose();
return(hclassTrainData);
}
internal static int LoadNew(IntPtr proc, int parIndex, int err, out HClassTrainData[] obj)
{
HTuple tuple;
err = HTuple.LoadNew(proc, parIndex, err, out tuple);
obj = new HClassTrainData[tuple.Length];
for (int index = 0; index < tuple.Length; ++index)
{
obj[index] = new HClassTrainData(tuple[index].IP);
}
return(err);
}
/// <summary>
/// Add training data to a k-nearest neighbors (k-NN) classifier.
/// Instance represents: Handle of a k-NN which receives the training data.
/// </summary>
/// <param name="classTrainDataHandle">Training data for a classifier.</param>
public void AddClassTrainDataKnn(HClassTrainData classTrainDataHandle)
{
IntPtr proc = HalconAPI.PreCall(1790);
this.Store(proc, 0);
HalconAPI.Store(proc, 1, (HTool)classTrainDataHandle);
int procResult = HalconAPI.CallProcedure(proc);
HalconAPI.PostCall(proc, procResult);
GC.KeepAlive((object)this);
GC.KeepAlive((object)classTrainDataHandle);
}
/// <summary>
/// Get the training data of a k-nearest neighbors (k-NN) classifier.
/// Instance represents: Handle of the k-NN classifier that contains training data.
/// </summary>
/// <returns>Handle of the training data of the classifier.</returns>
public HClassTrainData GetClassTrainDataKnn()
{
IntPtr proc = HalconAPI.PreCall(1789);
this.Store(proc, 0);
HalconAPI.InitOCT(proc, 0);
int err = HalconAPI.CallProcedure(proc);
HClassTrainData hclassTrainData;
int procResult = HClassTrainData.LoadNew(proc, 0, err, out hclassTrainData);
HalconAPI.PostCall(proc, procResult);
GC.KeepAlive((object)this);
return(hclassTrainData);
}
/// <summary>
/// Select certain features from training data to create training data containing less features.
/// Instance represents: Handle of the training data.
/// </summary>
/// <param name="subFeatureIndices">Indices or names to select the subfeatures or columns.</param>
/// <returns>Handle of the reduced training data.</returns>
public HClassTrainData SelectSubFeatureClassTrainData(HTuple subFeatureIndices)
{
IntPtr proc = HalconAPI.PreCall(1783);
this.Store(proc, 0);
HalconAPI.Store(proc, 1, subFeatureIndices);
HalconAPI.InitOCT(proc, 0);
int err = HalconAPI.CallProcedure(proc);
HalconAPI.UnpinTuple(subFeatureIndices);
HClassTrainData hclassTrainData;
int procResult = HClassTrainData.LoadNew(proc, 0, err, out hclassTrainData);
HalconAPI.PostCall(proc, procResult);
GC.KeepAlive((object)this);
return(hclassTrainData);
}
internal static int LoadNew(IntPtr proc, int parIndex, int err, out HClassTrainData obj)
{
obj = new HClassTrainData(HTool.UNDEF);
return(obj.Load(proc, parIndex, err));
}
