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-/**
- * @page spec_training_file Training Input File Specification
- *
- * This page specifies the training file that can be parsed by
- * read_training_from_file(). Below is an example training file.
- *
- * @verbatim
- train: /path/to/training/dataset.txt
- test: /path/to/test/dataset.txt
- p: 1.0 1.5 2.0
- kappa: -0.9 0.0 1.0
- lambda: 64 16 4 1 0.25 0.0625 0.015625 0.00390625 0.0009765625 0.000244140625
- epsilon: 1e-6
- weight: 1 2
- folds: 10
- kernel: LINEAR
- gamma: 1e-3 1e-1 1e1 1e3
- coef: 1.0 2.0
- degree: 2.0 3.0
- @endverbatim
- *
- * Note that with a @c LINEAR kernel specification, the @c gamma, @c coef, and
- * @c degree parameters do not need to be specified. The above merely shows
- * all available parameters that can be specified in the grid search. Below 
- * each of the parameters are described in more detail. Arguments followed by
- * an asterisk are optional.
- *
- * @c train: @n
- * The location of the training dataset file. See @ref spec_data_file for the
- * specification of a dataset file.
- * 
- * @c test:* @n
- * The location of a test dataset file. See @ref spec_data_file for the
- * specification of a dataset file. This is optional, if specified the
- * train/test split will be used for training. 
- *
- * @c p: @n
- * The values of the @c p parameter of the algorithm to search over. The @c p
- * parameter is used in the @f$ \ell_p @f$ norm over the Huber weighted scalar
- * misclassification errors. Note: @f$ 1 \leq p \leq 2 @f$.
- *
- * @c kappa: @n
- * The values of the @c kappa parameter of the algorithm to search over. The
- * @c kappa parameter is used in the Huber hinge error over the scalar
- * misclassification errors. Note: @f$ \kappa > -1 @f$.
- *
- * @c lambda: @n
- * The values of the @c lambda parameter of the algorithm to search over. The
- * @c lambda parameter is used in the regularization term of the loss
- * function. Note: @f$ \lambda > 0 @f$.
- *
- * @c epsilon: @n
- * The values of the @c epsilon parameter of the algorithm to search over. The
- * @c epsilon parameter is used as the stopping parameter in the majorization
- * algorithm. Note that it often suffices to use only one epsilon value. Using
- * more than one value increases the size of the grid search considerably. 
- *
- * @c weight: @n
- * The weight specifications for the algorithm to use. Two weight
- * specifications are implemented: the unit weights (index = 1) and the group
- * size correction weights (index = 2). See also msvmmaj_initialize_weights().
- *
- * @c folds: @n
- * The number of cross validation folds to use. 
- *
- * @c kernel:* @n
- * Kernel to use in training. Only one kernel can be specified. See KernelType
- * for available kernel functions. Note: if multiple kernel types are
- * specified on this line, only the last value will be used (see the
- * implementation of parse_kernel_str() for details). If no kernel is
- * specified, the @c LINEAR kernel will be used.
- *
- * @c gamma:* @n
- * Gamma parameters for the @c RBF, @c POLY, and @c SIGMOID kernels. This
- * parameter is only optional if the @c LINEAR kernel is specified. See
- * msvmmaj_compute_rbf(), msvmmaj_compute_poly(), and
- * msvmmaj_compute_sigmoid() for kernel specifications.
- *
- * @c coef:* @n
- * Coefficients for the @c POLY and @c SIGMOID kernels. This parameter is only
- * optional if the @c LINEAR or @c RBF kernels are used. See
- * msvmmaj_compute_poly() and msvmmaj_compute_sigmoid() for kernel
- * specifications.
- *
- * @c degree:* @n
- * Degrees to search over in the grid search when the @c POLY kernel is
- * specified. With other kernel specifications this parameter is unnecessary.
- * See msvmmaj_compute_poly() for the polynomial kernel specification.
- *
- */
-
-
-/**
- * @page spec_data_file Data File Specification
- *
- * This page describes the input file format for a dataset. This specification
- * is used by msvmmaj_read_data() and msvmmaj_write_predictions(). The data
- * file specification is the same as that used in <a
- * href="http://www.loria.fr/~lauer/MSVMpack/MSVMpack.html">MSVMpack</a>
- * (verified in v. 1.3). 
- *
- * The file is expected to be as follows
- * @verbatim
-n
-m
-x_11 x_12 ... x_1m y_1
-x_21 x_22 ... x_2m y_2
-...
-x_n1 x_n2 ... x_nm y_n
-@endverbatim
- * 
- * Here, @c n denotes the number of instances and @c m denotes the number of
- * predictors. The class labels @c y_i are expected in the final column of
- * each line. 
- *
- * As an example, below the first 5 lines of the iris dataset are shown.
- *
- * @verbatim
-150
-4
-5.10000 3.50000 1.40000 0.20000 1.00000
-4.90000 3.00000 1.40000 0.20000 1.00000
-4.70000 3.20000 1.30000 0.20000 1.00000
-@endverbatim
- *
- */
-
-/**
- * @page spec_model_file Model File Specification
- *
- * This page describes the input file format for a MajModel. This
- * specification is used by msvmmaj_read_model() and msvmmaj_write_model().
- * The model file is designed to fully reproduce a MajModel. 
- *
- * The model output file follows the format
- * @verbatim
-Output file for MSVMMaj (version 0.1)
-Generated on: Tue Jan 14 12:00:00 2014 (UTC +01:00)
-
-Model:
-p = 2.00
-lambda = 0.001
-kappa = 1.0
-epsilon = 1e-06
-weight_idx = 1
-
-Data:
-filename = /path/to/data_file.txt
-n = 150
-m = 4
-K = 3
-
-Output:
--0.7693429935131153 -1.9335141926875414
-+0.3425555992439160 +1.0939198172438194
-+0.3100589593140404 +0.9872012663780092
-+0.1319873613546321 +0.1207806485439152
-+0.8052481376988456 +0.6507524553955120
-@endverbatim
- * 
- * The first two lines of the file mainly serve a logging purpose, and are
- * ignored when reading the model file. The model section fully describes the
- * model parameters. Next, the data section describes the data file that was
- * used in training and the size of the dataset. Finally, the output section
- * shows the augmented weight matrix MajModel::V, in row-major order.
- *
- * @todo
- * Write kernel specification to model file as well and adjust the format
- * above.
- */