337 lines
12 KiB
C++
337 lines
12 KiB
C++
/***********************************************************
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* File generated by the HALCON-Compiler hcomp version 20.11
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* Usage: Interface to C++
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*
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* Software by: MVTec Software GmbH, www.mvtec.com
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***********************************************************/
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#ifndef HCPP_HCLASSMLP
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#define HCPP_HCLASSMLP
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namespace HalconCpp
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{
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// Represents an instance of a multilayer perceptron.
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class LIntExport HClassMlp : public HHandle
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{
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public:
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// Create an uninitialized instance
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HClassMlp():HHandle() {}
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// Copy constructor
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HClassMlp(const HClassMlp& source) : HHandle(source) {}
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// Copy constructor
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HClassMlp(const HHandle& handle);
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// Create HClassMlp from handle, taking ownership
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explicit HClassMlp(Hlong handle);
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bool operator==(const HHandle& obj) const
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{
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return HHandleBase::operator==(obj);
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}
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bool operator!=(const HHandle& obj) const
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{
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return HHandleBase::operator!=(obj);
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}
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protected:
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// Verify matching semantic type ('class_mlp')!
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virtual void AssertType(Hphandle handle) const;
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public:
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// Deep copy of all data represented by this object instance
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HClassMlp Clone() const;
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/*****************************************************************************
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* Operator-based class constructors
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*****************************************************************************/
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// read_class_mlp: Read a multilayer perceptron from a file.
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explicit HClassMlp(const HString& FileName);
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// read_class_mlp: Read a multilayer perceptron from a file.
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explicit HClassMlp(const char* FileName);
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#ifdef _WIN32
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// read_class_mlp: Read a multilayer perceptron from a file.
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explicit HClassMlp(const wchar_t* FileName);
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#endif
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// create_class_mlp: Create a multilayer perceptron for classification or regression.
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explicit HClassMlp(Hlong NumInput, Hlong NumHidden, Hlong NumOutput, const HString& OutputFunction, const HString& Preprocessing, Hlong NumComponents, Hlong RandSeed);
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// create_class_mlp: Create a multilayer perceptron for classification or regression.
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explicit HClassMlp(Hlong NumInput, Hlong NumHidden, Hlong NumOutput, const char* OutputFunction, const char* Preprocessing, Hlong NumComponents, Hlong RandSeed);
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#ifdef _WIN32
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// create_class_mlp: Create a multilayer perceptron for classification or regression.
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explicit HClassMlp(Hlong NumInput, Hlong NumHidden, Hlong NumOutput, const wchar_t* OutputFunction, const wchar_t* Preprocessing, Hlong NumComponents, Hlong RandSeed);
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#endif
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/***************************************************************************
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* Operators *
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***************************************************************************/
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// Classify an image with a multilayer perceptron.
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HRegion ClassifyImageClassMlp(const HImage& Image, double RejectionThreshold) const;
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// Add training samples from an image to the training data of a multilayer perceptron.
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void AddSamplesImageClassMlp(const HImage& Image, const HRegion& ClassRegions) const;
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// Get the training data of a multilayer perceptron (MLP).
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HClassTrainData GetClassTrainDataMlp() const;
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// Add training data to a multilayer perceptron (MLP).
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void AddClassTrainDataMlp(const HClassTrainData& ClassTrainDataHandle) const;
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// Selects an optimal combination of features to classify the provided data.
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HTuple SelectFeatureSetMlp(const HClassTrainData& ClassTrainDataHandle, const HString& SelectionMethod, const HTuple& GenParamName, const HTuple& GenParamValue, HTuple* Score);
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// Selects an optimal combination of features to classify the provided data.
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HTuple SelectFeatureSetMlp(const HClassTrainData& ClassTrainDataHandle, const HString& SelectionMethod, const HString& GenParamName, double GenParamValue, HTuple* Score);
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// Selects an optimal combination of features to classify the provided data.
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HTuple SelectFeatureSetMlp(const HClassTrainData& ClassTrainDataHandle, const char* SelectionMethod, const char* GenParamName, double GenParamValue, HTuple* Score);
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#ifdef _WIN32
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// Selects an optimal combination of features to classify the provided data.
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HTuple SelectFeatureSetMlp(const HClassTrainData& ClassTrainDataHandle, const wchar_t* SelectionMethod, const wchar_t* GenParamName, double GenParamValue, HTuple* Score);
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#endif
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// Create a look-up table using a multi-layer perceptron to classify byte images.
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HClassLUT CreateClassLutMlp(const HTuple& GenParamName, const HTuple& GenParamValue) const;
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// Clear a multilayer perceptron.
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static void ClearClassMlp(const HClassMlpArray& MLPHandle);
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// Clear a multilayer perceptron.
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void ClearClassMlp() const;
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// Clear the training data of a multilayer perceptron.
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static void ClearSamplesClassMlp(const HClassMlpArray& MLPHandle);
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// Clear the training data of a multilayer perceptron.
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void ClearSamplesClassMlp() const;
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// Deserialize a serialized multilayer perceptron.
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void DeserializeClassMlp(const HSerializedItem& SerializedItemHandle);
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// Serialize a multilayer perceptron (MLP).
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HSerializedItem SerializeClassMlp() const;
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// Read a multilayer perceptron from a file.
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void ReadClassMlp(const HString& FileName);
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// Read a multilayer perceptron from a file.
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void ReadClassMlp(const char* FileName);
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#ifdef _WIN32
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// Read a multilayer perceptron from a file.
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void ReadClassMlp(const wchar_t* FileName);
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#endif
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// Write a multilayer perceptron to a file.
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void WriteClassMlp(const HString& FileName) const;
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// Write a multilayer perceptron to a file.
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void WriteClassMlp(const char* FileName) const;
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#ifdef _WIN32
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// Write a multilayer perceptron to a file.
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void WriteClassMlp(const wchar_t* FileName) const;
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#endif
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// Read the training data of a multilayer perceptron from a file.
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void ReadSamplesClassMlp(const HString& FileName) const;
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// Read the training data of a multilayer perceptron from a file.
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void ReadSamplesClassMlp(const char* FileName) const;
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#ifdef _WIN32
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// Read the training data of a multilayer perceptron from a file.
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void ReadSamplesClassMlp(const wchar_t* FileName) const;
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#endif
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// Write the training data of a multilayer perceptron to a file.
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void WriteSamplesClassMlp(const HString& FileName) const;
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// Write the training data of a multilayer perceptron to a file.
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void WriteSamplesClassMlp(const char* FileName) const;
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#ifdef _WIN32
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// Write the training data of a multilayer perceptron to a file.
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void WriteSamplesClassMlp(const wchar_t* FileName) const;
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#endif
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// Calculate the class of a feature vector by a multilayer perceptron.
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HTuple ClassifyClassMlp(const HTuple& Features, const HTuple& Num, HTuple* Confidence) const;
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// Calculate the class of a feature vector by a multilayer perceptron.
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Hlong ClassifyClassMlp(const HTuple& Features, const HTuple& Num, double* Confidence) const;
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// Calculate the evaluation of a feature vector by a multilayer perceptron.
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HTuple EvaluateClassMlp(const HTuple& Features) const;
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// Train a multilayer perceptron.
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double TrainClassMlp(Hlong MaxIterations, double WeightTolerance, double ErrorTolerance, HTuple* ErrorLog) const;
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// Compute the information content of the preprocessed feature vectors of a multilayer perceptron.
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HTuple GetPrepInfoClassMlp(const HString& Preprocessing, HTuple* CumInformationCont) const;
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// Compute the information content of the preprocessed feature vectors of a multilayer perceptron.
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HTuple GetPrepInfoClassMlp(const char* Preprocessing, HTuple* CumInformationCont) const;
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#ifdef _WIN32
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// Compute the information content of the preprocessed feature vectors of a multilayer perceptron.
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HTuple GetPrepInfoClassMlp(const wchar_t* Preprocessing, HTuple* CumInformationCont) const;
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#endif
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// Return the number of training samples stored in the training data of a multilayer perceptron.
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Hlong GetSampleNumClassMlp() const;
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// Return a training sample from the training data of a multilayer perceptron.
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HTuple GetSampleClassMlp(Hlong IndexSample, HTuple* Target) const;
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// Get the parameters of a rejection class.
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HTuple GetRejectionParamsClassMlp(const HTuple& GenParamName) const;
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// Get the parameters of a rejection class.
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HTuple GetRejectionParamsClassMlp(const HString& GenParamName) const;
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// Get the parameters of a rejection class.
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HTuple GetRejectionParamsClassMlp(const char* GenParamName) const;
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#ifdef _WIN32
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// Get the parameters of a rejection class.
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HTuple GetRejectionParamsClassMlp(const wchar_t* GenParamName) const;
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#endif
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// Set the parameters of a rejection class.
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void SetRejectionParamsClassMlp(const HTuple& GenParamName, const HTuple& GenParamValue) const;
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// Set the parameters of a rejection class.
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void SetRejectionParamsClassMlp(const HString& GenParamName, const HString& GenParamValue) const;
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// Set the parameters of a rejection class.
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void SetRejectionParamsClassMlp(const char* GenParamName, const char* GenParamValue) const;
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#ifdef _WIN32
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// Set the parameters of a rejection class.
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void SetRejectionParamsClassMlp(const wchar_t* GenParamName, const wchar_t* GenParamValue) const;
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#endif
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// Add a training sample to the training data of a multilayer perceptron.
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void AddSampleClassMlp(const HTuple& Features, const HTuple& Target) const;
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// Add a training sample to the training data of a multilayer perceptron.
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void AddSampleClassMlp(const HTuple& Features, Hlong Target) const;
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// Return the regularization parameters of a multilayer perceptron.
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HTuple GetRegularizationParamsClassMlp(const HString& GenParamName) const;
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// Return the regularization parameters of a multilayer perceptron.
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HTuple GetRegularizationParamsClassMlp(const char* GenParamName) const;
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#ifdef _WIN32
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// Return the regularization parameters of a multilayer perceptron.
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HTuple GetRegularizationParamsClassMlp(const wchar_t* GenParamName) const;
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#endif
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// Set the regularization parameters of a multilayer perceptron.
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void SetRegularizationParamsClassMlp(const HString& GenParamName, const HTuple& GenParamValue) const;
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// Set the regularization parameters of a multilayer perceptron.
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void SetRegularizationParamsClassMlp(const HString& GenParamName, double GenParamValue) const;
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// Set the regularization parameters of a multilayer perceptron.
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void SetRegularizationParamsClassMlp(const char* GenParamName, double GenParamValue) const;
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#ifdef _WIN32
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// Set the regularization parameters of a multilayer perceptron.
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void SetRegularizationParamsClassMlp(const wchar_t* GenParamName, double GenParamValue) const;
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#endif
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// Return the parameters of a multilayer perceptron.
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Hlong GetParamsClassMlp(Hlong* NumHidden, Hlong* NumOutput, HString* OutputFunction, HString* Preprocessing, Hlong* NumComponents) const;
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// Create a multilayer perceptron for classification or regression.
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void CreateClassMlp(Hlong NumInput, Hlong NumHidden, Hlong NumOutput, const HString& OutputFunction, const HString& Preprocessing, Hlong NumComponents, Hlong RandSeed);
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// Create a multilayer perceptron for classification or regression.
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void CreateClassMlp(Hlong NumInput, Hlong NumHidden, Hlong NumOutput, const char* OutputFunction, const char* Preprocessing, Hlong NumComponents, Hlong RandSeed);
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#ifdef _WIN32
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// Create a multilayer perceptron for classification or regression.
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void CreateClassMlp(Hlong NumInput, Hlong NumHidden, Hlong NumOutput, const wchar_t* OutputFunction, const wchar_t* Preprocessing, Hlong NumComponents, Hlong RandSeed);
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#endif
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};
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// forward declarations and types for internal array implementation
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template<class T> class HSmartPtr;
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template<class T> class HHandleBaseArrayRef;
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typedef HHandleBaseArrayRef<HClassMlp> HClassMlpArrayRef;
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typedef HSmartPtr< HClassMlpArrayRef > HClassMlpArrayPtr;
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// Represents multiple tool instances
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class LIntExport HClassMlpArray : public HHandleBaseArray
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{
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public:
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// Create empty array
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HClassMlpArray();
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// Create array from native array of tool instances
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HClassMlpArray(HClassMlp* classes, Hlong length);
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// Copy constructor
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HClassMlpArray(const HClassMlpArray &tool_array);
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// Destructor
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virtual ~HClassMlpArray();
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// Assignment operator
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HClassMlpArray &operator=(const HClassMlpArray &tool_array);
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// Clears array and all tool instances
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virtual void Clear();
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// Get array of native tool instances
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const HClassMlp* Tools() const;
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// Get number of tools
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virtual Hlong Length() const;
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// Create tool array from tuple of handles
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virtual void SetFromTuple(const HTuple& handles);
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// Get tuple of handles for tool array
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virtual HTuple ConvertToTuple() const;
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protected:
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// Smart pointer to internal data container
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HClassMlpArrayPtr *mArrayPtr;
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};
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}
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#endif
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