Class cv::ml::TrainData#

Class encapsulating training data. View details

#include <opencv2/ml.hpp>

Collaboration diagram for cv::ml::TrainData:

Detailed Description#

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.

Constructor & Destructor Documentation#

~TrainData()#

cv::ml::TrainData::~TrainData()

Member Function Documentation#

create()#

static Ptr< TrainData > cv::ml::TrainData::create(
InputArray samples,
int layout,
InputArray responses,
InputArray varIdx = noArray(),
InputArray sampleIdx = noArray(),
InputArray sampleWeights = noArray(),
InputArray varType = noArray() )

Python:

cv.ml.TrainData.create(samples, layout, responses[, varIdx[, sampleIdx[, sampleWeights[, varType]]]]) -> retval

cv.ml.TrainData_create(samples, layout, responses[, varIdx[, sampleIdx[, sampleWeights[, varType]]]]) -> retval

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.

Here is the call graph for this function:

getSubMatrix()#

static Mat cv::ml::TrainData::getSubMatrix(
const Mat & matrix,
const Mat & idx,
int layout )

Python:

cv.ml.TrainData.getSubMatrix(matrix, idx, layout) -> retval

cv.ml.TrainData_getSubMatrix(matrix, idx, layout) -> retval

Extract from matrix rows/cols specified by passed indexes.

Parameters

matrix — input matrix (supported types: CV_32S, CV_32F, CV_64F)
idx — 1D index vector
layout — specifies to extract rows (cv::ml::ROW_SAMPLES) or to extract columns (cv::ml::COL_SAMPLES)

getSubVector()#

static Mat cv::ml::TrainData::getSubVector(
const Mat & vec,
const Mat & idx )

Python:

cv.ml.TrainData.getSubVector(vec, idx) -> retval

cv.ml.TrainData_getSubVector(vec, idx) -> retval

Extract from 1D vector elements specified by passed indexes.

Parameters

vec — input vector (supported types: CV_32S, CV_32F, CV_64F)
idx — 1D index vector

loadFromCSV()#

static Ptr< TrainData > cv::ml::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.

Note

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 starting 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 ord[n1-n2,n3,n4-n5,...]cat[n6,n7-n8,...]. That is, variables from n1 to n2 (inclusive range), n3, n4 to n5 … are considered ordered and n6, n7 to n8 … are considered as categorical. The range [n1..n2] + [n3] + [n4..n5] + ... + [n6] + [n7..n8] should cover all the variables. If varTypeSpec is not specified, then algorithm uses the following rules:
- all input variables are considered ordered by default. If some column contains has non- numerical values, e.g. ‘apple’, ‘pear’, ‘apple’, ‘apple’, ‘mango’, the corresponding variable is considered categorical.
- if there are several output variables, they are all considered as ordered. Error is reported when non-numerical values are used.
- if there is a single output variable, then if its values are non-numerical or are all integers, then it’s considered categorical. Otherwise, it’s considered ordered.
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.

missingValue()#

static float cv::ml::TrainData::missingValue()

getCatCount()#

int cv::ml::TrainData::getCatCount(int vi)

Python:

cv.ml.TrainData.getCatCount(vi) -> retval

getCatMap()#

Mat cv::ml::TrainData::getCatMap()

Python:

cv.ml.TrainData.getCatMap() -> retval

getCatOfs()#

Mat cv::ml::TrainData::getCatOfs()

Python:

cv.ml.TrainData.getCatOfs() -> retval

getClassLabels()#

Mat cv::ml::TrainData::getClassLabels()

Python:

cv.ml.TrainData.getClassLabels() -> retval

Returns the vector of class labels.

The function returns vector of unique labels occurred in the responses.

getDefaultSubstValues()#

Mat cv::ml::TrainData::getDefaultSubstValues()

Python:

cv.ml.TrainData.getDefaultSubstValues() -> retval

getLayout()#

int cv::ml::TrainData::getLayout()

Python:

cv.ml.TrainData.getLayout() -> retval

getMissing()#

Mat cv::ml::TrainData::getMissing()

Python:

cv.ml.TrainData.getMissing() -> retval

getNAllVars()#

int cv::ml::TrainData::getNAllVars()

Python:

cv.ml.TrainData.getNAllVars() -> retval

getNames()#

void cv::ml::TrainData::getNames(std::vector< String > & names)

Python:

cv.ml.TrainData.getNames(names)

Returns vector of symbolic names captured in loadFromCSV()

getNormCatResponses()#

Mat cv::ml::TrainData::getNormCatResponses()

Python:

cv.ml.TrainData.getNormCatResponses() -> retval

getNormCatValues()#

void cv::ml::TrainData::getNormCatValues(
int vi,
InputArray sidx,
int * values )

getNSamples()#

int cv::ml::TrainData::getNSamples()

Python:

cv.ml.TrainData.getNSamples() -> retval

getNTestSamples()#

int cv::ml::TrainData::getNTestSamples()

Python:

cv.ml.TrainData.getNTestSamples() -> retval

getNTrainSamples()#

int cv::ml::TrainData::getNTrainSamples()

Python:

cv.ml.TrainData.getNTrainSamples() -> retval

getNVars()#

int cv::ml::TrainData::getNVars()

Python:

cv.ml.TrainData.getNVars() -> retval

getResponses()#

Mat cv::ml::TrainData::getResponses()

Python:

cv.ml.TrainData.getResponses() -> retval

getResponseType()#

int cv::ml::TrainData::getResponseType()

Python:

cv.ml.TrainData.getResponseType() -> retval

getSample()#

void cv::ml::TrainData::getSample(
InputArray varIdx,
int sidx,
float * buf )

Python:

cv.ml.TrainData.getSample(varIdx, sidx, buf)

getSamples()#

Mat cv::ml::TrainData::getSamples()

Python:

cv.ml.TrainData.getSamples() -> retval

getSampleWeights()#

Mat cv::ml::TrainData::getSampleWeights()

Python:

cv.ml.TrainData.getSampleWeights() -> retval

getTestNormCatResponses()#

Mat cv::ml::TrainData::getTestNormCatResponses()

Python:

cv.ml.TrainData.getTestNormCatResponses() -> retval

getTestResponses()#

Mat cv::ml::TrainData::getTestResponses()

Python:

cv.ml.TrainData.getTestResponses() -> retval

getTestSampleIdx()#

Mat cv::ml::TrainData::getTestSampleIdx()

Python:

cv.ml.TrainData.getTestSampleIdx() -> retval

getTestSamples()#

Mat cv::ml::TrainData::getTestSamples()

Python:

cv.ml.TrainData.getTestSamples() -> retval

Returns matrix of test samples.

getTestSampleWeights()#

Mat cv::ml::TrainData::getTestSampleWeights()

Python:

cv.ml.TrainData.getTestSampleWeights() -> retval

getTrainNormCatResponses()#

Mat cv::ml::TrainData::getTrainNormCatResponses()

Python:

cv.ml.TrainData.getTrainNormCatResponses() -> retval

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.

getTrainResponses()#

Mat cv::ml::TrainData::getTrainResponses()

Python:

cv.ml.TrainData.getTrainResponses() -> retval

Returns the vector of responses.

The function returns ordered or the original categorical responses. Usually it’s used in regression algorithms.

getTrainSampleIdx()#

Mat cv::ml::TrainData::getTrainSampleIdx()

Python:

cv.ml.TrainData.getTrainSampleIdx() -> retval

getTrainSamples()#

Mat cv::ml::TrainData::getTrainSamples(
int layout = ROW_SAMPLE,
bool compressSamples = true,
bool compressVars = true )

Python:

cv.ml.TrainData.getTrainSamples([, layout[, compressSamples[, compressVars]]]) -> retval

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.

getTrainSampleWeights()#

Mat cv::ml::TrainData::getTrainSampleWeights()

Python:

cv.ml.TrainData.getTrainSampleWeights() -> retval

getValues()#

void cv::ml::TrainData::getValues(
int vi,
InputArray sidx,
float * values )

Python:

cv.ml.TrainData.getValues(vi, sidx, values)

getVarIdx()#

Mat cv::ml::TrainData::getVarIdx()

Python:

cv.ml.TrainData.getVarIdx() -> retval

getVarSymbolFlags()#

Mat cv::ml::TrainData::getVarSymbolFlags()

Python:

cv.ml.TrainData.getVarSymbolFlags() -> retval

getVarType()#

Mat cv::ml::TrainData::getVarType()

Python:

cv.ml.TrainData.getVarType() -> retval

setTrainTestSplit()#

void cv::ml::TrainData::setTrainTestSplit(
int count,
bool shuffle = true )

Python:

cv.ml.TrainData.setTrainTestSplit(count[, shuffle])

Splits the training data into the training and test parts.

setTrainTestSplitRatio()#

void cv::ml::TrainData::setTrainTestSplitRatio(
double ratio,
bool shuffle = true )

Python:

cv.ml.TrainData.setTrainTestSplitRatio(ratio[, shuffle])

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.

shuffleTrainTest()#

void cv::ml::TrainData::shuffleTrainTest()

Python:

cv.ml.TrainData.shuffleTrainTest()

Source file#

The documentation for this class was generated from the following file:

opencv2/ml.hpp