diff --git a/doc/guide/cli_quickstart.hpp b/doc/guide/cli_quickstart.hpp index 3ec4d40b7c..6392f2546e 100644 --- a/doc/guide/cli_quickstart.hpp +++ b/doc/guide/cli_quickstart.hpp @@ -108,28 +108,17 @@ Lots of other commands are available with different functionality. Below is a list of all the mlpack functionality offered through the command-line, split into some categories. - - Classification techniques: @c mlpack_adaboost, @c mlpack_decision_stump, @c - mlpack_decision_tree, @c mlpack_hmm_train, @c mlpack_hmm_generate, @c -mlpack_hmm_loglik, @c mlpack_hmm_viterbi, @c mlpack_hoeffding_tree, @c -mlpack_logistic_regression, @c mlpack_nbc, @c mlpack_perceptron, @c -mlpack_random_forest, @c mlpack_softmax_regression, @c mlpack_cf + - Classification techniques: mlpack_adaboost, mlpack_decision_stump, mlpack_decision_tree, mlpack_hmm_train, mlpack_hmm_generate, mlpack_hmm_loglik, mlpack_hmm_viterbi, mlpack_hoeffding_tree, mlpack_logistic_regression, mlpack_nbc, mlpack_perceptron, mlpack_random_forest, mlpack_softmax_regression, mlpack_cf - - Distance-based problems: @c mlpack_approx_kfn, @c mlpack_emst, @c - mlpack_fastmks, @c mlpack_kfn, @c mlpack_knn, @c mlpack_krann, @c mlpack_lsh, -@c mlpack_det, @c mlpack_range_search + - Distance-based problems: mlpack_approx_kfn, mlpack_emst, mlpack_fastmks, mlpack_kfn, mlpack_knn, mlpack_krann, mlpack_lsh, mlpack_det, mlpack_range_search - - Clustering: @c mlpack_kmeans, @c mlpack_mean_shift, @c mlpack_gmm_train, @c - mlpack_gmm_generate, @c mlpack_gmm_probability, @c mlpack_dbscan + - Clustering: mlpack_kmeans, mlpack_mean_shift, mlpack_gmm_train, mlpack_gmm_generate, mlpack_gmm_probability, mlpack_dbscan - - Transformations: @c mlpack_pca, @c mlpack_radical, @c - mlpack_local_coordinate_coding, @c mlpack_sparse_coding, @c mlpack_nca, @c -mlpack_kernel_pca + - Transformations: mlpack_pca, mlpack_radical, mlpack_local_coordinate_coding, mlpack_sparse_coding, mlpack_nca, mlpack_kernel_pca - - Regression: @c mlpack_linear_regression, @c mlpack_lars + - Regression: mlpack_linear_regression, mlpack_lars - - Preprocessing/other: @c mlpack_preprocess_binarize, @c - mlpack_preprocess_split, @c mlpack_preprocess_describe, @c - mlpack_preprocess_imputer, @c mlpack_nmf + - Preprocessing/other: mlpack_preprocess_binarize, mlpack_preprocess_split, mlpack_preprocess_describe, mlpack_preprocess_imputer, mlpack_nmf For more information on what mlpack does, see http://www.mlpack.org/about.html. Next, let's go through another example for providing movie recommendations with @@ -211,7 +200,7 @@ easily plug into a data science production workflow for the command line. A great thing to do next would be to look at more documentation for the mlpack command-line programs: - - mlpack + - mlpack command-line program documentation Also, mlpack is much more flexible from C++ and allows much greater @@ -219,13 +208,13 @@ functionality. So, more complicated tasks are possible if you are willing to write C++. To get started learning about mlpack in C++, the following resources might be helpful: - - mlpack + - mlpack C++ tutorials - - mlpack + - mlpack build and installation guide - - Simple + - Simple sample C++ mlpack programs - - mlpack + - mlpack Doxygen documentation homepage */ diff --git a/doc/guide/python_quickstart.hpp b/doc/guide/python_quickstart.hpp index 46e21b8f72..fc04ecc139 100644 --- a/doc/guide/python_quickstart.hpp +++ b/doc/guide/python_quickstart.hpp @@ -94,17 +94,18 @@ Lots of other commands are available with different functionality. Below is a list of all the mlpack functionality offered through Python, split into some categories. - - Classification techniques: @c adaboost(), @c decision_stump(), @c decision_tree(), @c hmm_train(), @c hmm_generate(), @c hmm_loglik(), @c hmm_viterbi(), @c hoeffding_tree(), @c logistic_regression(), @c nbc(), @c perceptron(), @c random_forest(), @c softmax_regression(), @c cf() + - Classification techniques: adaboost(), decision_stump(), decision_tree(), hmm_train(), hmm_generate(), hmm_loglik(), hmm_viterbi(), hoeffding_tree(), logistic_regression(), nbc(), perceptron(), random_forest(), softmax_regression(), cf() - - Distance-based problems: @c approx_kfn(), @c emst(), @c fastmks(), @c kfn(), @c knn(), @c krann(), @c lsh(), @c det() + - Distance-based problems: approx_kfn(), emst(), fastmks(), kfn(), knn(), krann(), lsh(), det() - - Clustering: @c kmeans(), @c mean_shift(), @c gmm_train(), @c gmm_generate(), @c gmm_probability() + - Clustering: kmeans(), mean_shift(), gmm_train(), gmm_generate(), gmm_probability() - - Transformations: @c pca(), @c radical(), @c local_coordinate_coding(), @c sparse_coding(), @c nca(), @c kernel_pca() + - Transformations: pca(), radical(), local_coordinate_coding(), sparse_coding(), nca(), kernel_pca() - - Regression: @c linear_regression(), @c lars() + - Regression: linear_regression(), lars() - - Preprocessing/other: @c preprocess_binarize(), @c preprocess_split(), @c preprocess_describe(), @c nmf() + - Preprocessing/other: preprocess_binarize(), preprocess_split(), preprocess_describe(), nmf() For more information on what mlpack does, see http://www.mlpack.org/about.html. Next, let's go through another example for providing movie recommendations with @@ -179,7 +180,7 @@ Now that you have done some simple work with mlpack, you have seen how it can easily plug into a data science workflow in Python. A great thing to do next would be to look at more documentation for the Python mlpack bindings: - - Python mlpack + - Python mlpack binding documentation Also, mlpack is much more flexible from C++ and allows much greater @@ -187,13 +188,13 @@ functionality. So, more complicated tasks are possible if you are willing to write C++ (or perhaps Cython). To get started learning about mlpack in C++, the following resources might be helpful: - - mlpack + - mlpack C++ tutorials - - mlpack + - mlpack build and installation guide - - Simple + - Simple sample C++ mlpack programs - - mlpack + - mlpack Doxygen documentation homepage */