class cv::ml::TrainData
Overview
Class encapsulating training data. More…
#include <ml.hpp> class TrainData { public: // methods static Ptr<TrainData> create( InputArray samples, int layout, InputArray responses, InputArray varIdx = noArray(), InputArray sampleIdx = noArray(), InputArray sampleWeights = noArray(), InputArray varType = noArray() ); static Mat getSubVector( const Mat& vec, const Mat& idx ); static Ptr<TrainData> loadFromCSV( const String& filename, int headerLineCount, int responseStartIdx = -1, int responseEndIdx = -1, const String& varTypeSpec = String(), char delimiter = ',', char missch = '?' ); static float missingValue(); virtual int getCatCount(int vi) const = 0; virtual Mat getCatMap() const = 0; virtual Mat getCatOfs() const = 0; virtual Mat getClassLabels() const = 0; virtual Mat getDefaultSubstValues() const = 0; virtual int getLayout() const = 0; virtual Mat getMissing() const = 0; virtual int getNAllVars() const = 0; void getNames(std::vector<String>& names) const; virtual Mat getNormCatResponses() const = 0; virtual void getNormCatValues( int vi, InputArray sidx, int* values ) const = 0; virtual int getNSamples() const = 0; virtual int getNTestSamples() const = 0; virtual int getNTrainSamples() const = 0; virtual int getNVars() const = 0; virtual Mat getResponses() const = 0; virtual int getResponseType() const = 0; virtual void getSample( InputArray varIdx, int sidx, float* buf ) const = 0; virtual Mat getSamples() const = 0; virtual Mat getSampleWeights() const = 0; virtual Mat getTestNormCatResponses() const = 0; virtual Mat getTestResponses() const = 0; virtual Mat getTestSampleIdx() const = 0; Mat getTestSamples() const; virtual Mat getTestSampleWeights() const = 0; virtual Mat getTrainNormCatResponses() const = 0; virtual Mat getTrainResponses() const = 0; virtual Mat getTrainSampleIdx() const = 0; virtual Mat getTrainSamples( int layout = ROW_SAMPLE, bool compressSamples = true, bool compressVars = true ) const = 0; virtual Mat getTrainSampleWeights() const = 0; virtual void getValues( int vi, InputArray sidx, float* values ) const = 0; virtual Mat getVarIdx() const = 0; Mat getVarSymbolFlags() const; virtual Mat getVarType() const = 0; virtual void setTrainTestSplit( int count, bool shuffle = true ) = 0; virtual void setTrainTestSplitRatio( double ratio, bool shuffle = true ) = 0; virtual void shuffleTrainTest() = 0; };
Detailed Documentation
Class encapsulating training data.
Please note that the class only specifies the interface of training data, but not implementation. All the statistical model classes in ml module accepts Ptr <TrainData> as parameter. In other words, you can create your own class derived from TrainData and pass smart pointer to the instance of this class into StatModel::train.
See also:
Methods
static Ptr<TrainData> create( InputArray samples, int layout, InputArray responses, InputArray varIdx = noArray(), InputArray sampleIdx = noArray(), InputArray sampleWeights = noArray(), InputArray varType = noArray() )
Creates training data from in-memory arrays.
Parameters:
| samples | matrix of samples. It should have CV_32F type. |
| layout | see ml::SampleTypes. |
| responses | matrix of responses. If the responses are scalar, they should be stored as a single row or as a single column. The matrix should have type CV_32F or CV_32S (in the former case the responses are considered as ordered by default; in the latter case - as categorical) |
| varIdx | vector specifying which variables to use for training. It can be an integer vector (CV_32S) containing 0-based variable indices or byte vector (CV_8U) containing a mask of active variables. |
| sampleIdx | vector specifying which samples to use for training. It can be an integer vector (CV_32S) containing 0-based sample indices or byte vector (CV_8U) containing a mask of training samples. |
| sampleWeights | optional vector with weights for each sample. It should have CV_32F type. |
| varType | optional vector of type CV_8U and size <number_of_variables_in_samples> + <number_of_variables_in_responses>, containing types of each input and output variable. See ml::VariableTypes. |
static Ptr<TrainData> loadFromCSV( const String& filename, int headerLineCount, int responseStartIdx = -1, int responseEndIdx = -1, const String& varTypeSpec = String(), char delimiter = ',', char missch = '?' )
Reads the dataset from a .csv file and returns the ready-to-use training data.
If the dataset only contains input variables and no responses, use responseStartIdx = -2 and responseEndIdx = 0. The output variables vector will just contain zeros.
Parameters:
| filename | The input file name |
| headerLineCount | The number of lines in the beginning to skip; besides the header, the function also skips empty lines and lines staring with # |
| responseStartIdx | Index of the first output variable. If -1, the function considers the last variable as the response |
| responseEndIdx | Index of the last output variable + 1. If -1, then there is single response variable at responseStartIdx. |
| varTypeSpec | The optional text string that specifies the variables’ types. It has the format
|
| delimiter | The character used to separate values in each line. |
| missch | The character used to specify missing measurements. It should not be a digit. Although it’s a non-numerical value, it surely does not affect the decision of whether the variable ordered or categorical. |
virtual Mat getClassLabels() const = 0
Returns the vector of class labels.
The function returns vector of unique labels occurred in the responses.
void getNames(std::vector<String>& names) const
Returns vector of symbolic names captured in loadFromCSV()
Mat getTestSamples() const
Returns matrix of test samples.
virtual Mat getTrainNormCatResponses() const = 0
Returns the vector of normalized categorical responses.
The function returns vector of responses. Each response is integer from 0 to <number of classes>-1. The actual label value can be retrieved then from the class label vector, see TrainData::getClassLabels.
virtual Mat getTrainResponses() const = 0
Returns the vector of responses.
The function returns ordered or the original categorical responses. Usually it’s used in regression algorithms.
virtual Mat getTrainSamples( int layout = ROW_SAMPLE, bool compressSamples = true, bool compressVars = true ) const = 0
Returns matrix of train samples.
In current implementation the function tries to avoid physical data copying and returns the matrix stored inside TrainData (unless the transposition or compression is needed).
Parameters:
| layout | The requested layout. If it’s different from the initial one, the matrix is transposed. See ml::SampleTypes. |
| compressSamples | if true, the function returns only the training samples (specified by sampleIdx) |
| compressVars | if true, the function returns the shorter training samples, containing only the active variables. |
virtual void setTrainTestSplit( int count, bool shuffle = true ) = 0
Splits the training data into the training and test parts.
See also:
TrainData::setTrainTestSplitRatio
virtual void setTrainTestSplitRatio( double ratio, bool shuffle = true ) = 0
Splits the training data into the training and test parts.
The function selects a subset of specified relative size and then returns it as the training set. If the function is not called, all the data is used for training. Please, note that for each of TrainData::getTrain* there is corresponding TrainData::getTest*, so that the test subset can be retrieved and processed as well.
See also: