TrainData Class Reference
[Machine Learning]

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.

Returns the vector of class labels.

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

Returns vector of symbolic names captured in loadFromCSV()

Returns matrix of test samples.

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.

Returns the vector of responses.

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

Returns matrix of train samples.

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.

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).

Reads the dataset from a .csv file and returns the ready-to-use training data.

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 `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.

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.

Splits the training data into the training and test parts.

See also:

TrainData::setTrainTestSplitRatio

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:

TrainData::setTrainTestSplit