For the machine learning algorithms, the data set is often stored in a file of the ``.csv``-like format. The file contains a table of predictor and response values where each row of the table corresponds to a sample. Missing values are supported. The UC Irvine Machine Learning Repository (http://archive.ics.uci.edu/ml/) provides many data sets stored in such a format to the machine learning community. The class ``MLData`` is implemented to easily load the data for training one of the OpenCV machine learning algorithms. For float values, only the ``'.'`` separator is supported.
While reading the data, the method tries to define the type of variables (predictors and responses): ordered or categorical. If a value of the variable is not numerical (except for the label for a missing value), the type of the variable is set to ``CV_VAR_CATEGORICAL``. If all existing values of the variable are numerical, the type of the variable is set to ``CV_VAR_ORDERED``. So, the default definition of variables types works correctly for all cases except the case of a categorical variable with numerical class labeles. In this case, the type ``CV_VAR_ORDERED`` is set. You should change the type to ``CV_VAR_CATEGORICAL`` using the method :ocv:func:`CvMLData::change_var_type`. For categorical variables, a common map is built to convert a string class label to the numerical class label. Use :ocv:func:`CvMLData::get_class_labels_map` to obtain this map.
Also, when reading the data, the method constructs the mask of missing values. For example, values are egual to `'?'`.
The method returns a pointer to the matrix of predictor and response ``values`` or ``0`` if the data has not been loaded from the file yet.
The row count of this matrix equals the sample count. The column count equals predictors ``+ 1`` for the response (if exists) count. This means that each row of the matrix contains values of one sample predictor and response. The matrix type is ``CV_32FC1``.
The method sets the index of a response column in the ``values`` matrix (see :ocv:func:`CvMLData::get_values`) or throws an exception if the data has not been loaded from the file yet.
The old response columns become predictors. If ``idx < 0``, there is no response.
The method returns the index of a response column in the ``values`` matrix (see :ocv:func:`CvMLData::get_values`) or throws an exception if the data has not been loaded from the file yet.
This method sets parameters for such a split using ``spl`` (see :ocv:class:`CvTrainTestSplit`) or throws an exception if the data has not been loaded from the file yet.
The method returns the matrix of sample indices for a training subset. This is a single-row matrix of the type ``CV_32SC1``. If data split is not set, the method returns ``0``. If the data has not been loaded from the file yet, an exception is thrown.
The method shuffles the indices of training and test samples preserving sizes of training and test subsets if the data split is set by :ocv:func:`CvMLData::get_values`. If the data has not been loaded from the file yet, an exception is thrown.
The method returns the indices of variables (columns) used in the ``values`` matrix (see :ocv:func:`CvMLData::get_values`).
It returns ``0`` if the used subset is not set. It throws an exception if the data has not been loaded from the file yet. Returned matrix is a single-row matrix of the type ``CV_32SC1``. Its column count is equal to the size of the used variable subset.
By default, after reading the data set all variables in the ``values`` matrix (see :ocv:func:`CvMLData::get_values`) are used. But you may want to use only a subset of variables and include/exclude (depending on ``state`` value) a variable with the ``vi`` index from the used subset. If the data has not been loaded from the file yet, an exception is thrown.
The function returns a single-row matrix of the type ``CV_8UC1``, where each element is set to either ``CV_VAR_ORDERED`` or ``CV_VAR_CATEGORICAL``. The number of columns is equal to the number of variables. If data has not been loaded from file yet an exception is thrown.
In the string, a variable type is followed by a list of variables indices. For example: ``"ord[0-17],cat[18]"``, ``"ord[0,2,4,10-12], cat[1,3,5-9,13,14]"``, ``"cat"`` (all variables are categorical), ``"ord"`` (all variables are ordered).
The method sets the delimiter for variables in a file. For example: ``','`` (default), ``';'``, ``' '`` (space), or other characters. The floating-point separator ``'.'`` is not allowed.
The method sets the character used to specify missing values. For example: ``'?'`` (default), ``'-'``. The floating-point separator ``'.'`` is not allowed.
* Set the training sample count (subset size) ``train_sample_count``. Other existing samples are located in a test subset.
* Set a training sample portion in ``[0,..1]``. The flag ``mix`` is used to mix training and test samples indices when the split is set. Otherwise, the data set is split in the storing order: the first part of samples of a given size is a training subset, the second part is a test subset.
