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Add python and java support for ml module
This commit is contained in:
Maksim Shabunin
parent
d5b954c2d4
commit
5247eacc3c
@ -1,2 +1,2 @@
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set(the_description "Machine Learning")
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ocv_define_module(ml opencv_core)
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ocv_define_module(ml opencv_core WRAP java python)
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@ -104,7 +104,7 @@ enum SampleTypes
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It is used for optimizing statmodel accuracy by varying model parameters, the accuracy estimate
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being computed by cross-validation.
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*/
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class CV_EXPORTS_W_MAP ParamGrid
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class CV_EXPORTS ParamGrid
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{
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public:
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/** @brief Default constructor */
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@ -112,8 +112,8 @@ public:
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/** @brief Constructor with parameters */
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ParamGrid(double _minVal, double _maxVal, double _logStep);
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CV_PROP_RW double minVal; //!< Minimum value of the statmodel parameter. Default value is 0.
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CV_PROP_RW double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0.
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double minVal; //!< Minimum value of the statmodel parameter. Default value is 0.
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double maxVal; //!< Maximum value of the statmodel parameter. Default value is 0.
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/** @brief Logarithmic step for iterating the statmodel parameter.
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The grid determines the following iteration sequence of the statmodel parameter values:
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@ -122,7 +122,7 @@ public:
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\f[\texttt{minVal} * \texttt{logStep} ^n < \texttt{maxVal}\f]
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The grid is logarithmic, so logStep must always be greater then 1. Default value is 1.
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*/
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CV_PROP_RW double logStep;
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double logStep;
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};
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/** @brief Class encapsulating training data.
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@ -134,22 +134,22 @@ of this class into StatModel::train.
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@sa @ref ml_intro_data
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*/
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class CV_EXPORTS TrainData
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class CV_EXPORTS_W TrainData
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{
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public:
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static inline float missingValue() { return FLT_MAX; }
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virtual ~TrainData();
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virtual int getLayout() const = 0;
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virtual int getNTrainSamples() const = 0;
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virtual int getNTestSamples() const = 0;
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virtual int getNSamples() const = 0;
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virtual int getNVars() const = 0;
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virtual int getNAllVars() const = 0;
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CV_WRAP virtual int getLayout() const = 0;
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CV_WRAP virtual int getNTrainSamples() const = 0;
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CV_WRAP virtual int getNTestSamples() const = 0;
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CV_WRAP virtual int getNSamples() const = 0;
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CV_WRAP virtual int getNVars() const = 0;
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CV_WRAP virtual int getNAllVars() const = 0;
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virtual void getSample(InputArray varIdx, int sidx, float* buf) const = 0;
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virtual Mat getSamples() const = 0;
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virtual Mat getMissing() const = 0;
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CV_WRAP virtual void getSample(InputArray varIdx, int sidx, float* buf) const = 0;
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CV_WRAP virtual Mat getSamples() const = 0;
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CV_WRAP virtual Mat getMissing() const = 0;
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/** @brief Returns matrix of train samples
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@ -163,7 +163,7 @@ public:
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In current implementation the function tries to avoid physical data copying and returns the
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matrix stored inside TrainData (unless the transposition or compression is needed).
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*/
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virtual Mat getTrainSamples(int layout=ROW_SAMPLE,
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CV_WRAP virtual Mat getTrainSamples(int layout=ROW_SAMPLE,
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bool compressSamples=true,
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bool compressVars=true) const = 0;
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@ -172,7 +172,7 @@ public:
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The function returns ordered or the original categorical responses. Usually it's used in
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regression algorithms.
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*/
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virtual Mat getTrainResponses() const = 0;
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CV_WRAP virtual Mat getTrainResponses() const = 0;
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/** @brief Returns the vector of normalized categorical responses
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@ -180,38 +180,38 @@ public:
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classes>-1`. The actual label value can be retrieved then from the class label vector, see
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TrainData::getClassLabels.
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*/
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virtual Mat getTrainNormCatResponses() const = 0;
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virtual Mat getTestResponses() const = 0;
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virtual Mat getTestNormCatResponses() const = 0;
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virtual Mat getResponses() const = 0;
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virtual Mat getNormCatResponses() const = 0;
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virtual Mat getSampleWeights() const = 0;
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virtual Mat getTrainSampleWeights() const = 0;
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virtual Mat getTestSampleWeights() const = 0;
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virtual Mat getVarIdx() const = 0;
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virtual Mat getVarType() const = 0;
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virtual int getResponseType() const = 0;
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virtual Mat getTrainSampleIdx() const = 0;
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virtual Mat getTestSampleIdx() const = 0;
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virtual void getValues(int vi, InputArray sidx, float* values) const = 0;
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virtual void getNormCatValues(int vi, InputArray sidx, int* values) const = 0;
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virtual Mat getDefaultSubstValues() const = 0;
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CV_WRAP virtual Mat getTrainNormCatResponses() const = 0;
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CV_WRAP virtual Mat getTestResponses() const = 0;
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CV_WRAP virtual Mat getTestNormCatResponses() const = 0;
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CV_WRAP virtual Mat getResponses() const = 0;
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CV_WRAP virtual Mat getNormCatResponses() const = 0;
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CV_WRAP virtual Mat getSampleWeights() const = 0;
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CV_WRAP virtual Mat getTrainSampleWeights() const = 0;
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CV_WRAP virtual Mat getTestSampleWeights() const = 0;
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CV_WRAP virtual Mat getVarIdx() const = 0;
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CV_WRAP virtual Mat getVarType() const = 0;
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CV_WRAP virtual int getResponseType() const = 0;
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CV_WRAP virtual Mat getTrainSampleIdx() const = 0;
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CV_WRAP virtual Mat getTestSampleIdx() const = 0;
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CV_WRAP virtual void getValues(int vi, InputArray sidx, float* values) const = 0;
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CV_WRAP virtual void getNormCatValues(int vi, InputArray sidx, int* values) const = 0;
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CV_WRAP virtual Mat getDefaultSubstValues() const = 0;
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virtual int getCatCount(int vi) const = 0;
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CV_WRAP virtual int getCatCount(int vi) const = 0;
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/** @brief Returns the vector of class labels
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The function returns vector of unique labels occurred in the responses.
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*/
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virtual Mat getClassLabels() const = 0;
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CV_WRAP virtual Mat getClassLabels() const = 0;
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virtual Mat getCatOfs() const = 0;
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virtual Mat getCatMap() const = 0;
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CV_WRAP virtual Mat getCatOfs() const = 0;
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CV_WRAP virtual Mat getCatMap() const = 0;
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/** @brief Splits the training data into the training and test parts
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@sa TrainData::setTrainTestSplitRatio
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*/
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virtual void setTrainTestSplit(int count, bool shuffle=true) = 0;
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CV_WRAP virtual void setTrainTestSplit(int count, bool shuffle=true) = 0;
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/** @brief Splits the training data into the training and test parts
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@ -221,10 +221,10 @@ public:
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subset can be retrieved and processed as well.
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@sa TrainData::setTrainTestSplit
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*/
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virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0;
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virtual void shuffleTrainTest() = 0;
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CV_WRAP virtual void setTrainTestSplitRatio(double ratio, bool shuffle=true) = 0;
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CV_WRAP virtual void shuffleTrainTest() = 0;
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static Mat getSubVector(const Mat& vec, const Mat& idx);
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CV_WRAP static Mat getSubVector(const Mat& vec, const Mat& idx);
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/** @brief Reads the dataset from a .csv file and returns the ready-to-use training data.
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@ -280,7 +280,7 @@ public:
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<number_of_variables_in_responses>`, containing types of each input and output variable. See
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ml::VariableTypes.
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*/
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static Ptr<TrainData> create(InputArray samples, int layout, InputArray responses,
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CV_WRAP static Ptr<TrainData> create(InputArray samples, int layout, InputArray responses,
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InputArray varIdx=noArray(), InputArray sampleIdx=noArray(),
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InputArray sampleWeights=noArray(), InputArray varType=noArray());
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};
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@ -297,15 +297,15 @@ public:
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COMPRESSED_INPUT=2,
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PREPROCESSED_INPUT=4
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};
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virtual void clear();
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CV_WRAP virtual void clear();
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/** @brief Returns the number of variables in training samples */
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virtual int getVarCount() const = 0;
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CV_WRAP virtual int getVarCount() const = 0;
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/** @brief Returns true if the model is trained */
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virtual bool isTrained() const = 0;
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CV_WRAP virtual bool isTrained() const = 0;
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/** @brief Returns true if the model is classifier */
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virtual bool isClassifier() const = 0;
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CV_WRAP virtual bool isClassifier() const = 0;
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/** @brief Trains the statistical model
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@ -314,7 +314,7 @@ public:
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@param flags optional flags, depending on the model. Some of the models can be updated with the
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new training samples, not completely overwritten (such as NormalBayesClassifier or ANN_MLP).
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*/
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virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
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CV_WRAP virtual bool train( const Ptr<TrainData>& trainData, int flags=0 );
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/** @brief Trains the statistical model
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@ -322,7 +322,7 @@ public:
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@param layout See ml::SampleTypes.
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@param responses vector of responses associated with the training samples.
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*/
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virtual bool train( InputArray samples, int layout, InputArray responses );
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CV_WRAP virtual bool train( InputArray samples, int layout, InputArray responses );
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/** @brief Computes error on the training or test dataset
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@ -337,7 +337,7 @@ public:
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The method uses StatModel::predict to compute the error. For regression models the error is
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computed as RMS, for classifiers - as a percent of missclassified samples (0%-100%).
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*/
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virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const;
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CV_WRAP virtual float calcError( const Ptr<TrainData>& data, bool test, OutputArray resp ) const;
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/** @brief Predicts response(s) for the provided sample(s)
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@ -345,7 +345,7 @@ public:
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@param results The optional output matrix of results.
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@param flags The optional flags, model-dependent. See cv::ml::StatModel::Flags.
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*/
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virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
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CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
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/** @brief Loads model from the file
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@ -393,11 +393,11 @@ public:
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/** Saves the model to a file.
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In order to make this method work, the derived class must implement Algorithm::write(FileStorage& fs). */
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virtual void save(const String& filename) const;
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CV_WRAP virtual void save(const String& filename) const;
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/** Returns model string identifier.
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This string is used as top level xml/yml node tag when model is saved to a file or string. */
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virtual String getDefaultModelName() const = 0;
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CV_WRAP virtual String getDefaultModelName() const = 0;
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};
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/****************************************************************************************\
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@ -419,12 +419,12 @@ public:
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The vector outputProbs contains the output probabilities corresponding to each element of
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result.
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*/
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virtual float predictProb( InputArray inputs, OutputArray outputs,
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CV_WRAP virtual float predictProb( InputArray inputs, OutputArray outputs,
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OutputArray outputProbs, int flags=0 ) const = 0;
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/** Creates empty model
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Use StatModel::train to train the model after creation. */
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static Ptr<NormalBayesClassifier> create();
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CV_WRAP static Ptr<NormalBayesClassifier> create();
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};
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/****************************************************************************************\
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@ -441,27 +441,27 @@ public:
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/** Default number of neighbors to use in predict method. */
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/** @see setDefaultK */
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virtual int getDefaultK() const = 0;
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CV_WRAP virtual int getDefaultK() const = 0;
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/** @copybrief getDefaultK @see getDefaultK */
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virtual void setDefaultK(int val) = 0;
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CV_WRAP virtual void setDefaultK(int val) = 0;
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/** Whether classification or regression model should be trained. */
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/** @see setIsClassifier */
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virtual bool getIsClassifier() const = 0;
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CV_WRAP virtual bool getIsClassifier() const = 0;
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/** @copybrief getIsClassifier @see getIsClassifier */
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virtual void setIsClassifier(bool val) = 0;
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CV_WRAP virtual void setIsClassifier(bool val) = 0;
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/** Parameter for KDTree implementation. */
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/** @see setEmax */
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virtual int getEmax() const = 0;
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CV_WRAP virtual int getEmax() const = 0;
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/** @copybrief getEmax @see getEmax */
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virtual void setEmax(int val) = 0;
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CV_WRAP virtual void setEmax(int val) = 0;
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/** %Algorithm type, one of KNearest::Types. */
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/** @see setAlgorithmType */
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virtual int getAlgorithmType() const = 0;
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CV_WRAP virtual int getAlgorithmType() const = 0;
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/** @copybrief getAlgorithmType @see getAlgorithmType */
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virtual void setAlgorithmType(int val) = 0;
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CV_WRAP virtual void setAlgorithmType(int val) = 0;
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/** @brief Finds the neighbors and predicts responses for input vectors.
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@ -489,7 +489,7 @@ public:
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The function is parallelized with the TBB library.
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*/
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virtual float findNearest( InputArray samples, int k,
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CV_WRAP virtual float findNearest( InputArray samples, int k,
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OutputArray results,
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OutputArray neighborResponses=noArray(),
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OutputArray dist=noArray() ) const = 0;
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@ -506,7 +506,7 @@ public:
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The static method creates empty %KNearest classifier. It should be then trained using StatModel::train method.
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*/
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static Ptr<KNearest> create();
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CV_WRAP static Ptr<KNearest> create();
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};
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/****************************************************************************************\
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@ -531,51 +531,51 @@ public:
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/** Type of a %SVM formulation.
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See SVM::Types. Default value is SVM::C_SVC. */
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/** @see setType */
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virtual int getType() const = 0;
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CV_WRAP virtual int getType() const = 0;
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/** @copybrief getType @see getType */
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virtual void setType(int val) = 0;
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CV_WRAP virtual void setType(int val) = 0;
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/** Parameter \f$\gamma\f$ of a kernel function.
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For SVM::POLY, SVM::RBF, SVM::SIGMOID or SVM::CHI2. Default value is 1. */
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/** @see setGamma */
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virtual double getGamma() const = 0;
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CV_WRAP virtual double getGamma() const = 0;
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/** @copybrief getGamma @see getGamma */
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virtual void setGamma(double val) = 0;
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CV_WRAP virtual void setGamma(double val) = 0;
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/** Parameter _coef0_ of a kernel function.
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For SVM::POLY or SVM::SIGMOID. Default value is 0.*/
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/** @see setCoef0 */
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virtual double getCoef0() const = 0;
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CV_WRAP virtual double getCoef0() const = 0;
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/** @copybrief getCoef0 @see getCoef0 */
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virtual void setCoef0(double val) = 0;
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CV_WRAP virtual void setCoef0(double val) = 0;
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/** Parameter _degree_ of a kernel function.
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For SVM::POLY. Default value is 0. */
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/** @see setDegree */
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virtual double getDegree() const = 0;
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CV_WRAP virtual double getDegree() const = 0;
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/** @copybrief getDegree @see getDegree */
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virtual void setDegree(double val) = 0;
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CV_WRAP virtual void setDegree(double val) = 0;
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/** Parameter _C_ of a %SVM optimization problem.
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For SVM::C_SVC, SVM::EPS_SVR or SVM::NU_SVR. Default value is 0. */
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/** @see setC */
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virtual double getC() const = 0;
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CV_WRAP virtual double getC() const = 0;
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/** @copybrief getC @see getC */
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virtual void setC(double val) = 0;
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CV_WRAP virtual void setC(double val) = 0;
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/** Parameter \f$\nu\f$ of a %SVM optimization problem.
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For SVM::NU_SVC, SVM::ONE_CLASS or SVM::NU_SVR. Default value is 0. */
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/** @see setNu */
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virtual double getNu() const = 0;
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CV_WRAP virtual double getNu() const = 0;
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/** @copybrief getNu @see getNu */
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virtual void setNu(double val) = 0;
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CV_WRAP virtual void setNu(double val) = 0;
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/** Parameter \f$\epsilon\f$ of a %SVM optimization problem.
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For SVM::EPS_SVR. Default value is 0. */
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/** @see setP */
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virtual double getP() const = 0;
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CV_WRAP virtual double getP() const = 0;
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/** @copybrief getP @see getP */
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virtual void setP(double val) = 0;
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CV_WRAP virtual void setP(double val) = 0;
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/** Optional weights in the SVM::C_SVC problem, assigned to particular classes.
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They are multiplied by _C_ so the parameter _C_ of class _i_ becomes `classWeights(i) * C`. Thus
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@ -583,26 +583,26 @@ public:
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the larger penalty on misclassification of data from the corresponding class. Default value is
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empty Mat. */
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/** @see setClassWeights */
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virtual cv::Mat getClassWeights() const = 0;
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CV_WRAP virtual cv::Mat getClassWeights() const = 0;
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/** @copybrief getClassWeights @see getClassWeights */
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virtual void setClassWeights(const cv::Mat &val) = 0;
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CV_WRAP virtual void setClassWeights(const cv::Mat &val) = 0;
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/** Termination criteria of the iterative %SVM training procedure which solves a partial
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case of constrained quadratic optimization problem.
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You can specify tolerance and/or the maximum number of iterations. Default value is
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`TermCriteria( TermCriteria::MAX_ITER + TermCriteria::EPS, 1000, FLT_EPSILON )`; */
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/** @see setTermCriteria */
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virtual cv::TermCriteria getTermCriteria() const = 0;
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CV_WRAP virtual cv::TermCriteria getTermCriteria() const = 0;
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/** @copybrief getTermCriteria @see getTermCriteria */
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virtual void setTermCriteria(const cv::TermCriteria &val) = 0;
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CV_WRAP virtual void setTermCriteria(const cv::TermCriteria &val) = 0;
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/** Type of a %SVM kernel.
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See SVM::KernelTypes. Default value is SVM::RBF. */
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virtual int getKernelType() const = 0;
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CV_WRAP virtual int getKernelType() const = 0;
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/** Initialize with one of predefined kernels.
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See SVM::KernelTypes. */
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virtual void setKernel(int kernelType) = 0;
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CV_WRAP virtual void setKernel(int kernelType) = 0;
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/** Initialize with custom kernel.
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See SVM::Kernel class for implementation details */
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@ -734,7 +734,7 @@ public:
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The method returns rho parameter of the decision function, a scalar subtracted from the weighted
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sum of kernel responses.
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*/
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virtual double getDecisionFunction(int i, OutputArray alpha, OutputArray svidx) const = 0;
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CV_WRAP virtual double getDecisionFunction(int i, OutputArray alpha, OutputArray svidx) const = 0;
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/** @brief Generates a grid for %SVM parameters.
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@ -749,7 +749,7 @@ public:
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/** Creates empty model.
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Use StatModel::train to train the model. Since %SVM has several parameters, you may want to
|
||||
find the best parameters for your problem, it can be done with SVM::trainAuto. */
|
||||
static Ptr<SVM> create();
|
||||
CV_WRAP static Ptr<SVM> create();
|
||||
};
|
||||
|
||||
/****************************************************************************************\
|
||||
@ -795,37 +795,37 @@ public:
|
||||
determine the optimal number of mixtures within a specified value range, but that is not the
|
||||
case in ML yet. */
|
||||
/** @see setClustersNumber */
|
||||
virtual int getClustersNumber() const = 0;
|
||||
CV_WRAP virtual int getClustersNumber() const = 0;
|
||||
/** @copybrief getClustersNumber @see getClustersNumber */
|
||||
virtual void setClustersNumber(int val) = 0;
|
||||
CV_WRAP virtual void setClustersNumber(int val) = 0;
|
||||
|
||||
/** Constraint on covariance matrices which defines type of matrices.
|
||||
See EM::Types. */
|
||||
/** @see setCovarianceMatrixType */
|
||||
virtual int getCovarianceMatrixType() const = 0;
|
||||
CV_WRAP virtual int getCovarianceMatrixType() const = 0;
|
||||
/** @copybrief getCovarianceMatrixType @see getCovarianceMatrixType */
|
||||
virtual void setCovarianceMatrixType(int val) = 0;
|
||||
CV_WRAP virtual void setCovarianceMatrixType(int val) = 0;
|
||||
|
||||
/** The termination criteria of the %EM algorithm.
|
||||
The %EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of
|
||||
M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default
|
||||
maximum number of iterations is EM::DEFAULT_MAX_ITERS=100. */
|
||||
/** @see setTermCriteria */
|
||||
virtual TermCriteria getTermCriteria() const = 0;
|
||||
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
|
||||
/** @copybrief getTermCriteria @see getTermCriteria */
|
||||
virtual void setTermCriteria(const TermCriteria &val) = 0;
|
||||
CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0;
|
||||
|
||||
/** @brief Returns weights of the mixtures
|
||||
|
||||
Returns vector with the number of elements equal to the number of mixtures.
|
||||
*/
|
||||
virtual Mat getWeights() const = 0;
|
||||
CV_WRAP virtual Mat getWeights() const = 0;
|
||||
/** @brief Returns the cluster centers (means of the Gaussian mixture)
|
||||
|
||||
Returns matrix with the number of rows equal to the number of mixtures and number of columns
|
||||
equal to the space dimensionality.
|
||||
*/
|
||||
virtual Mat getMeans() const = 0;
|
||||
CV_WRAP virtual Mat getMeans() const = 0;
|
||||
/** @brief Returns covariation matrices
|
||||
|
||||
Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures,
|
||||
@ -845,7 +845,7 @@ public:
|
||||
the sample. First element is an index of the most probable mixture component for the given
|
||||
sample.
|
||||
*/
|
||||
CV_WRAP virtual Vec2d predict2(InputArray sample, OutputArray probs) const = 0;
|
||||
CV_WRAP CV_WRAP virtual Vec2d predict2(InputArray sample, OutputArray probs) const = 0;
|
||||
|
||||
/** @brief Estimate the Gaussian mixture parameters from a samples set.
|
||||
|
||||
@ -875,7 +875,7 @@ public:
|
||||
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
|
||||
CV_64FC1 type.
|
||||
*/
|
||||
virtual bool trainEM(InputArray samples,
|
||||
CV_WRAP virtual bool trainEM(InputArray samples,
|
||||
OutputArray logLikelihoods=noArray(),
|
||||
OutputArray labels=noArray(),
|
||||
OutputArray probs=noArray()) = 0;
|
||||
@ -907,7 +907,7 @@ public:
|
||||
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
|
||||
CV_64FC1 type.
|
||||
*/
|
||||
virtual bool trainE(InputArray samples, InputArray means0,
|
||||
CV_WRAP virtual bool trainE(InputArray samples, InputArray means0,
|
||||
InputArray covs0=noArray(),
|
||||
InputArray weights0=noArray(),
|
||||
OutputArray logLikelihoods=noArray(),
|
||||
@ -932,7 +932,7 @@ public:
|
||||
mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
|
||||
CV_64FC1 type.
|
||||
*/
|
||||
virtual bool trainM(InputArray samples, InputArray probs0,
|
||||
CV_WRAP virtual bool trainM(InputArray samples, InputArray probs0,
|
||||
OutputArray logLikelihoods=noArray(),
|
||||
OutputArray labels=noArray(),
|
||||
OutputArray probs=noArray()) = 0;
|
||||
@ -941,7 +941,7 @@ public:
|
||||
The model should be trained then using StatModel::train(traindata, flags) method. Alternatively, you
|
||||
can use one of the EM::train\* methods or load it from file using StatModel::load\<EM\>(filename).
|
||||
*/
|
||||
static Ptr<EM> create();
|
||||
CV_WRAP static Ptr<EM> create();
|
||||
};
|
||||
|
||||
/****************************************************************************************\
|
||||
@ -975,9 +975,9 @@ public:
|
||||
efficiently without employing clustering, thus the parameter is not used in these cases.
|
||||
Default value is 10.*/
|
||||
/** @see setMaxCategories */
|
||||
virtual int getMaxCategories() const = 0;
|
||||
CV_WRAP virtual int getMaxCategories() const = 0;
|
||||
/** @copybrief getMaxCategories @see getMaxCategories */
|
||||
virtual void setMaxCategories(int val) = 0;
|
||||
CV_WRAP virtual void setMaxCategories(int val) = 0;
|
||||
|
||||
/** The maximum possible depth of the tree.
|
||||
That is the training algorithms attempts to split a node while its depth is less than maxDepth.
|
||||
@ -985,59 +985,59 @@ public:
|
||||
are met (see the outline of the training procedure @ref ml_intro_trees "here"), and/or if the
|
||||
tree is pruned. Default value is INT_MAX.*/
|
||||
/** @see setMaxDepth */
|
||||
virtual int getMaxDepth() const = 0;
|
||||
CV_WRAP virtual int getMaxDepth() const = 0;
|
||||
/** @copybrief getMaxDepth @see getMaxDepth */
|
||||
virtual void setMaxDepth(int val) = 0;
|
||||
CV_WRAP virtual void setMaxDepth(int val) = 0;
|
||||
|
||||
/** If the number of samples in a node is less than this parameter then the node will not be split.
|
||||
|
||||
Default value is 10.*/
|
||||
/** @see setMinSampleCount */
|
||||
virtual int getMinSampleCount() const = 0;
|
||||
CV_WRAP virtual int getMinSampleCount() const = 0;
|
||||
/** @copybrief getMinSampleCount @see getMinSampleCount */
|
||||
virtual void setMinSampleCount(int val) = 0;
|
||||
CV_WRAP virtual void setMinSampleCount(int val) = 0;
|
||||
|
||||
/** If CVFolds \> 1 then algorithms prunes the built decision tree using K-fold
|
||||
cross-validation procedure where K is equal to CVFolds.
|
||||
Default value is 10.*/
|
||||
/** @see setCVFolds */
|
||||
virtual int getCVFolds() const = 0;
|
||||
CV_WRAP virtual int getCVFolds() const = 0;
|
||||
/** @copybrief getCVFolds @see getCVFolds */
|
||||
virtual void setCVFolds(int val) = 0;
|
||||
CV_WRAP virtual void setCVFolds(int val) = 0;
|
||||
|
||||
/** If true then surrogate splits will be built.
|
||||
These splits allow to work with missing data and compute variable importance correctly.
|
||||
Default value is false.
|
||||
@note currently it's not implemented.*/
|
||||
/** @see setUseSurrogates */
|
||||
virtual bool getUseSurrogates() const = 0;
|
||||
CV_WRAP virtual bool getUseSurrogates() const = 0;
|
||||
/** @copybrief getUseSurrogates @see getUseSurrogates */
|
||||
virtual void setUseSurrogates(bool val) = 0;
|
||||
CV_WRAP virtual void setUseSurrogates(bool val) = 0;
|
||||
|
||||
/** If true then a pruning will be harsher.
|
||||
This will make a tree more compact and more resistant to the training data noise but a bit less
|
||||
accurate. Default value is true.*/
|
||||
/** @see setUse1SERule */
|
||||
virtual bool getUse1SERule() const = 0;
|
||||
CV_WRAP virtual bool getUse1SERule() const = 0;
|
||||
/** @copybrief getUse1SERule @see getUse1SERule */
|
||||
virtual void setUse1SERule(bool val) = 0;
|
||||
CV_WRAP virtual void setUse1SERule(bool val) = 0;
|
||||
|
||||
/** If true then pruned branches are physically removed from the tree.
|
||||
Otherwise they are retained and it is possible to get results from the original unpruned (or
|
||||
pruned less aggressively) tree. Default value is true.*/
|
||||
/** @see setTruncatePrunedTree */
|
||||
virtual bool getTruncatePrunedTree() const = 0;
|
||||
CV_WRAP virtual bool getTruncatePrunedTree() const = 0;
|
||||
/** @copybrief getTruncatePrunedTree @see getTruncatePrunedTree */
|
||||
virtual void setTruncatePrunedTree(bool val) = 0;
|
||||
CV_WRAP virtual void setTruncatePrunedTree(bool val) = 0;
|
||||
|
||||
/** Termination criteria for regression trees.
|
||||
If all absolute differences between an estimated value in a node and values of train samples
|
||||
in this node are less than this parameter then the node will not be split further. Default
|
||||
value is 0.01f*/
|
||||
/** @see setRegressionAccuracy */
|
||||
virtual float getRegressionAccuracy() const = 0;
|
||||
CV_WRAP virtual float getRegressionAccuracy() const = 0;
|
||||
/** @copybrief getRegressionAccuracy @see getRegressionAccuracy */
|
||||
virtual void setRegressionAccuracy(float val) = 0;
|
||||
CV_WRAP virtual void setRegressionAccuracy(float val) = 0;
|
||||
|
||||
/** @brief The array of a priori class probabilities, sorted by the class label value.
|
||||
|
||||
@ -1055,9 +1055,9 @@ public:
|
||||
the second category is equivalent to making 10 mistakes in predicting the first category.
|
||||
Default value is empty Mat.*/
|
||||
/** @see setPriors */
|
||||
virtual cv::Mat getPriors() const = 0;
|
||||
CV_WRAP virtual cv::Mat getPriors() const = 0;
|
||||
/** @copybrief getPriors @see getPriors */
|
||||
virtual void setPriors(const cv::Mat &val) = 0;
|
||||
CV_WRAP virtual void setPriors(const cv::Mat &val) = 0;
|
||||
|
||||
/** @brief The class represents a decision tree node.
|
||||
*/
|
||||
@ -1129,7 +1129,7 @@ public:
|
||||
trained using train method (see StatModel::train). Alternatively, you can load the model from
|
||||
file using StatModel::load\<DTrees\>(filename).
|
||||
*/
|
||||
static Ptr<DTrees> create();
|
||||
CV_WRAP static Ptr<DTrees> create();
|
||||
};
|
||||
|
||||
/****************************************************************************************\
|
||||
@ -1147,18 +1147,18 @@ public:
|
||||
/** If true then variable importance will be calculated and then it can be retrieved by RTrees::getVarImportance.
|
||||
Default value is false.*/
|
||||
/** @see setCalculateVarImportance */
|
||||
virtual bool getCalculateVarImportance() const = 0;
|
||||
CV_WRAP virtual bool getCalculateVarImportance() const = 0;
|
||||
/** @copybrief getCalculateVarImportance @see getCalculateVarImportance */
|
||||
virtual void setCalculateVarImportance(bool val) = 0;
|
||||
CV_WRAP virtual void setCalculateVarImportance(bool val) = 0;
|
||||
|
||||
/** The size of the randomly selected subset of features at each tree node and that are used
|
||||
to find the best split(s).
|
||||
If you set it to 0 then the size will be set to the square root of the total number of
|
||||
features. Default value is 0.*/
|
||||
/** @see setActiveVarCount */
|
||||
virtual int getActiveVarCount() const = 0;
|
||||
CV_WRAP virtual int getActiveVarCount() const = 0;
|
||||
/** @copybrief getActiveVarCount @see getActiveVarCount */
|
||||
virtual void setActiveVarCount(int val) = 0;
|
||||
CV_WRAP virtual void setActiveVarCount(int val) = 0;
|
||||
|
||||
/** The termination criteria that specifies when the training algorithm stops.
|
||||
Either when the specified number of trees is trained and added to the ensemble or when
|
||||
@ -1168,22 +1168,22 @@ public:
|
||||
prediction time linearly. Default value is TermCriteria(TermCriteria::MAX_ITERS +
|
||||
TermCriteria::EPS, 50, 0.1)*/
|
||||
/** @see setTermCriteria */
|
||||
virtual TermCriteria getTermCriteria() const = 0;
|
||||
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
|
||||
/** @copybrief getTermCriteria @see getTermCriteria */
|
||||
virtual void setTermCriteria(const TermCriteria &val) = 0;
|
||||
CV_WRAP virtual void setTermCriteria(const TermCriteria &val) = 0;
|
||||
|
||||
/** Returns the variable importance array.
|
||||
The method returns the variable importance vector, computed at the training stage when
|
||||
CalculateVarImportance is set to true. If this flag was set to false, the empty matrix is
|
||||
returned.
|
||||
*/
|
||||
virtual Mat getVarImportance() const = 0;
|
||||
CV_WRAP virtual Mat getVarImportance() const = 0;
|
||||
|
||||
/** Creates the empty model.
|
||||
Use StatModel::train to train the model, StatModel::train to create and train the model,
|
||||
StatModel::load to load the pre-trained model.
|
||||
*/
|
||||
static Ptr<RTrees> create();
|
||||
CV_WRAP static Ptr<RTrees> create();
|
||||
};
|
||||
|
||||
/****************************************************************************************\
|
||||
@ -1200,24 +1200,24 @@ public:
|
||||
/** Type of the boosting algorithm.
|
||||
See Boost::Types. Default value is Boost::REAL. */
|
||||
/** @see setBoostType */
|
||||
virtual int getBoostType() const = 0;
|
||||
CV_WRAP virtual int getBoostType() const = 0;
|
||||
/** @copybrief getBoostType @see getBoostType */
|
||||
virtual void setBoostType(int val) = 0;
|
||||
CV_WRAP virtual void setBoostType(int val) = 0;
|
||||
|
||||
/** The number of weak classifiers.
|
||||
Default value is 100. */
|
||||
/** @see setWeakCount */
|
||||
virtual int getWeakCount() const = 0;
|
||||
CV_WRAP virtual int getWeakCount() const = 0;
|
||||
/** @copybrief getWeakCount @see getWeakCount */
|
||||
virtual void setWeakCount(int val) = 0;
|
||||
CV_WRAP virtual void setWeakCount(int val) = 0;
|
||||
|
||||
/** A threshold between 0 and 1 used to save computational time.
|
||||
Samples with summary weight \f$\leq 1 - weight_trim_rate\f$ do not participate in the *next*
|
||||
iteration of training. Set this parameter to 0 to turn off this functionality. Default value is 0.95.*/
|
||||
/** @see setWeightTrimRate */
|
||||
virtual double getWeightTrimRate() const = 0;
|
||||
CV_WRAP virtual double getWeightTrimRate() const = 0;
|
||||
/** @copybrief getWeightTrimRate @see getWeightTrimRate */
|
||||
virtual void setWeightTrimRate(double val) = 0;
|
||||
CV_WRAP virtual void setWeightTrimRate(double val) = 0;
|
||||
|
||||
/** Boosting type.
|
||||
Gentle AdaBoost and Real AdaBoost are often the preferable choices. */
|
||||
@ -1232,7 +1232,7 @@ public:
|
||||
|
||||
/** Creates the empty model.
|
||||
Use StatModel::train to train the model, StatModel::load\<Boost\>(filename) to load the pre-trained model. */
|
||||
static Ptr<Boost> create();
|
||||
CV_WRAP static Ptr<Boost> create();
|
||||
};
|
||||
|
||||
/****************************************************************************************\
|
||||
@ -1282,7 +1282,7 @@ Additional flags for StatModel::train are available: ANN_MLP::TrainFlags.
|
||||
|
||||
@sa @ref ml_intro_ann
|
||||
*/
|
||||
class CV_EXPORTS_W ANN_MLP : public StatModel
|
||||
class CV_EXPORTS ANN_MLP : public StatModel
|
||||
{
|
||||
public:
|
||||
/** Available training methods */
|
||||
@ -1430,47 +1430,47 @@ public:
|
||||
|
||||
@sa @ref ml_intro_lr
|
||||
*/
|
||||
class CV_EXPORTS LogisticRegression : public StatModel
|
||||
class CV_EXPORTS_W LogisticRegression : public StatModel
|
||||
{
|
||||
public:
|
||||
|
||||
/** Learning rate. */
|
||||
/** @see setLearningRate */
|
||||
virtual double getLearningRate() const = 0;
|
||||
CV_WRAP virtual double getLearningRate() const = 0;
|
||||
/** @copybrief getLearningRate @see getLearningRate */
|
||||
virtual void setLearningRate(double val) = 0;
|
||||
CV_WRAP virtual void setLearningRate(double val) = 0;
|
||||
|
||||
/** Number of iterations. */
|
||||
/** @see setIterations */
|
||||
virtual int getIterations() const = 0;
|
||||
CV_WRAP virtual int getIterations() const = 0;
|
||||
/** @copybrief getIterations @see getIterations */
|
||||
virtual void setIterations(int val) = 0;
|
||||
CV_WRAP virtual void setIterations(int val) = 0;
|
||||
|
||||
/** Kind of regularization to be applied. See LogisticRegression::RegKinds. */
|
||||
/** @see setRegularization */
|
||||
virtual int getRegularization() const = 0;
|
||||
CV_WRAP virtual int getRegularization() const = 0;
|
||||
/** @copybrief getRegularization @see getRegularization */
|
||||
virtual void setRegularization(int val) = 0;
|
||||
CV_WRAP virtual void setRegularization(int val) = 0;
|
||||
|
||||
/** Kind of training method used. See LogisticRegression::Methods. */
|
||||
/** @see setTrainMethod */
|
||||
virtual int getTrainMethod() const = 0;
|
||||
CV_WRAP virtual int getTrainMethod() const = 0;
|
||||
/** @copybrief getTrainMethod @see getTrainMethod */
|
||||
virtual void setTrainMethod(int val) = 0;
|
||||
CV_WRAP virtual void setTrainMethod(int val) = 0;
|
||||
|
||||
/** Specifies the number of training samples taken in each step of Mini-Batch Gradient
|
||||
Descent. Will only be used if using LogisticRegression::MINI_BATCH training algorithm. It
|
||||
has to take values less than the total number of training samples. */
|
||||
/** @see setMiniBatchSize */
|
||||
virtual int getMiniBatchSize() const = 0;
|
||||
CV_WRAP virtual int getMiniBatchSize() const = 0;
|
||||
/** @copybrief getMiniBatchSize @see getMiniBatchSize */
|
||||
virtual void setMiniBatchSize(int val) = 0;
|
||||
CV_WRAP virtual void setMiniBatchSize(int val) = 0;
|
||||
|
||||
/** Termination criteria of the algorithm. */
|
||||
/** @see setTermCriteria */
|
||||
virtual TermCriteria getTermCriteria() const = 0;
|
||||
CV_WRAP virtual TermCriteria getTermCriteria() const = 0;
|
||||
/** @copybrief getTermCriteria @see getTermCriteria */
|
||||
virtual void setTermCriteria(TermCriteria val) = 0;
|
||||
CV_WRAP virtual void setTermCriteria(TermCriteria val) = 0;
|
||||
|
||||
//! Regularization kinds
|
||||
enum RegKinds {
|
||||
@ -1492,20 +1492,20 @@ public:
|
||||
@param results Predicted labels as a column matrix of type CV_32S.
|
||||
@param flags Not used.
|
||||
*/
|
||||
virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
|
||||
CV_WRAP virtual float predict( InputArray samples, OutputArray results=noArray(), int flags=0 ) const = 0;
|
||||
|
||||
/** @brief This function returns the trained paramters arranged across rows.
|
||||
|
||||
For a two class classifcation problem, it returns a row matrix. It returns learnt paramters of
|
||||
the Logistic Regression as a matrix of type CV_32F.
|
||||
*/
|
||||
virtual Mat get_learnt_thetas() const = 0;
|
||||
CV_WRAP virtual Mat get_learnt_thetas() const = 0;
|
||||
|
||||
/** @brief Creates empty model.
|
||||
|
||||
Creates Logistic Regression model with parameters given.
|
||||
*/
|
||||
static Ptr<LogisticRegression> create();
|
||||
CV_WRAP static Ptr<LogisticRegression> create();
|
||||
};
|
||||
|
||||
/****************************************************************************************\
|
||||
@ -1519,14 +1519,14 @@ public:
|
||||
@param nsamples returned samples count
|
||||
@param samples returned samples array
|
||||
*/
|
||||
CV_EXPORTS void randMVNormal( InputArray mean, InputArray cov, int nsamples, OutputArray samples);
|
||||
CV_EXPORTS_W void randMVNormal( InputArray mean, InputArray cov, int nsamples, OutputArray samples);
|
||||
|
||||
/** @brief Generates sample from gaussian mixture distribution */
|
||||
CV_EXPORTS void randGaussMixture( InputArray means, InputArray covs, InputArray weights,
|
||||
CV_EXPORTS_W void randGaussMixture( InputArray means, InputArray covs, InputArray weights,
|
||||
int nsamples, OutputArray samples, OutputArray sampClasses );
|
||||
|
||||
/** @brief Creates test set */
|
||||
CV_EXPORTS void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses,
|
||||
CV_EXPORTS_W void createConcentricSpheresTestSet( int nsamples, int nfeatures, int nclasses,
|
||||
OutputArray samples, OutputArray responses);
|
||||
|
||||
//! @} ml
|
||||
|
||||
Loading…
Reference in New Issue
Block a user