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diff --git a/doc/specifications.c b/doc/specifications.c new file mode 100644 index 0000000..5b5a8ae --- /dev/null +++ b/doc/specifications.c @@ -0,0 +1,170 @@ +/** + * @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. + */ |