diff --git a/.github/ISSUE_TEMPLATE/2-question.md b/.github/ISSUE_TEMPLATE/2-question.md index f2d0633def..80af4a5ea9 100644 --- a/.github/ISSUE_TEMPLATE/2-question.md +++ b/.github/ISSUE_TEMPLATE/2-question.md @@ -10,9 +10,9 @@ assignees: '' diff --git a/doc/guide/cli_quickstart.hpp b/doc/guide/cli_quickstart.hpp index 1518d4d9ac..41fc37ec0b 100644 --- a/doc/guide/cli_quickstart.hpp +++ b/doc/guide/cli_quickstart.hpp @@ -52,8 +52,8 @@ You can copy-paste this code directly into your shell to run it. @code{.sh} # Get the dataset and unpack it. -wget http://www.mlpack.org/datasets/covertype-small.data.csv.gz -wget http://www.mlpack.org/datasets/covertype-small.labels.csv.gz +wget https://www.mlpack.org/datasets/covertype-small.data.csv.gz +wget https://www.mlpack.org/datasets/covertype-small.labels.csv.gz gunzip covertype-small.data.csv.gz covertype-small.labels.csv.gz # Split the dataset; 70% into a training set and 30% into a test set. @@ -104,24 +104,13 @@ different mlpack learners, or to interface with other machine learning toolkits. @section cli_quickstart_whatelse What else does mlpack implement? The example above has only shown a little bit of the functionality of mlpack. -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. +Lots of other commands are available with different functionality. A full list +of commands and full documentation for each can be found on the following page: - - 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 + - CLI documentation - - Distance-based problems: mlpack_approx_kfn, mlpack_emst, mlpack_fastmks, mlpack_kfn, mlpack_knn, mlpack_krann, mlpack_lsh, mlpack_det, mlpack_range_search - - - Clustering: mlpack_kmeans, mlpack_mean_shift, mlpack_gmm_train, mlpack_gmm_generate, mlpack_gmm_probability, mlpack_dbscan - - - Transformations: mlpack_pca, mlpack_radical, mlpack_local_coordinate_coding, mlpack_sparse_coding, mlpack_nca, mlpack_kernel_pca - - - Regression: mlpack_linear_regression, mlpack_lars - - - 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 +For more information on what mlpack does, see https://www.mlpack.org/. Next, +let's go through another example for providing movie recommendations with mlpack. @section cli_quickstart_movierecs Using mlpack for movie recommendations @@ -134,8 +123,8 @@ train to give recommendations. You can copy-paste this code directly into the command line to run it. @code{.sh} -wget http://www.mlpack.org/datasets/ml-20m/ratings-only.csv.gz -wget http://www.mlpack.org/datasets/ml-20m/movies.csv.gz +wget https://www.mlpack.org/datasets/ml-20m/ratings-only.csv.gz +wget https://www.mlpack.org/datasets/ml-20m/movies.csv.gz gunzip ratings-only.csv.gz gunzip movies.csv.gz @@ -200,7 +189,7 @@ 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 @@ -208,13 +197,13 @@ 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 e9bc19db53..0b2d6793ae 100644 --- a/doc/guide/python_quickstart.hpp +++ b/doc/guide/python_quickstart.hpp @@ -31,7 +31,7 @@ build and install mlpack. You can copy-paste the commands into your shell. @code{.sh} sudo apt-get install libboost-all-dev g++ cmake libarmadillo-dev python-pip wget sudo pip install cython setuptools distutils numpy pandas -wget http://www.mlpack.org/files/mlpack-3.0.4.tar.gz +wget https://www.mlpack.org/files/mlpack-3.0.4.tar.gz tar -xvzpf mlpack-3.0.4.tar.gz mkdir -p mlpack-3.0.4/build/ && cd mlpack-3.0.4/build/ cmake ../ && make -j4 && sudo make install @@ -114,31 +114,20 @@ different mlpack learners, or to interface with other machine learning toolkits. @section python_quickstart_whatelse What else does mlpack implement? The example above has only shown a little bit of the functionality of mlpack. -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. +Lots of other commands are available with different functionality. A full list +of each of these commands and full documentation can be found on the following +page: - - 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() + - Python documentation - - Distance-based problems: approx_kfn(), emst(), fastmks(), kfn(), knn(), krann(), lsh(), det() - - - Clustering: kmeans(), mean_shift(), gmm_train(), gmm_generate(), gmm_probability() - - - Transformations: pca(), radical(), local_coordinate_coding(), sparse_coding(), nca(), kernel_pca() - - - Regression: linear_regression(), lars() - - - Preprocessing/other: preprocess_binarize(), preprocess_split(), preprocess_describe(), nmf() - -For more information on what mlpack does, see http://www.mlpack.org/about.html. +For more information on what mlpack does, see https://www.mlpack.org/about.html. Next, let's go through another example for providing movie recommendations with mlpack. @section python_quickstart_movierecs Using mlpack for movie recommendations In this example, we'll train a collaborative filtering model using mlpack's -cf() method. We'll train this on the MovieLens dataset from +cf() method. We'll train this on the MovieLens dataset from https://grouplens.org/datasets/movielens/, and then we'll use the model that we train to give recommendations. @@ -204,7 +193,7 @@ 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 @@ -212,13 +201,13 @@ 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 */ diff --git a/doc/tutorials/README.md b/doc/tutorials/README.md index 5bd6ea9aa8..fd60ffd11d 100644 --- a/doc/tutorials/README.md +++ b/doc/tutorials/README.md @@ -29,7 +29,6 @@ These tutorials introduce the various methods mlpack offers, aimed at users who * [Euclidean Minimum Spanning Trees tutorial (mlpack_emst)](https://www.mlpack.org/doc/mlpack-git/doxygen/emst_tutorial.html) * [Alternating Matrix Factorization Tutorial](https://www.mlpack.org/doc/mlpack-git/doxygen/amftutorial.html) * [Collaborative Filtering Tutorial](https://www.mlpack.org/doc/mlpack-git/doxygen/cftutorial.html) -* [Conventional Neural Evolution Tutorial](https://www.mlpack.org/doc/mlpack-git/doxygen/cnetutorial.html) ### Policy Class Documentation