You need to install mlpack on your machine. We provide a script that install mlpack on the virtual machine that we provided. To install mlpack run the following commands in the terminal:

$> wget https://raw.githubusercontent.com/smover/mlpack_sample_project/master/install_mlpack.bash
$> bash install_mlpack.bash

The installation will take some time (the compilation of mlpack takes time). CMake shows you a percentage of completion --- it should not take more than ~30 minutes.

The script will ask you for the root password (e.g., to install the packages and libraries globally on your system).

We are compiling also the Python binding: you can use them to be fast in learning how the library works, but the final project should be in C++ (this also includes the learning part of the model).

The same script should work on your Ubuntu machine, but this is not guaranteed (different version, different dependencies). You can use the same script as a starting point to install mlpack for other Linux distributions.

This repository sets up a minimal project using CMake and including mlpack.

The project and the sample source code was extracted from https://github.com/mlpack/models

In a nutshell, CMake manages for you the process of building the project.

To build a CMake project you have to perform two steps:

1. Create and configure the build files (e.g., a Makefile) specific for your environment. The build file will take care of compiling and linking your code. When creating the build file CMake will perform tasks like determining your compiler, finding the libraries and headers your project depends on, ...

From the project main directory you can create the configuration files as follows:

$> mkdir build
$> cd build
$> cmake ../

Note that the directory build can be an arbitrary directory on your system and does not need to be nested in your project directory. The argument to the cmake command is the root directory of the project containing the configuration file (called CMakeLists.txt)

2. Compile your project

To compile your project just enter the build folder and type make.

$> cd build
$> make

Now the project is configured to create an executable called nnsample in the build directory. To run the sample code you also need the data to train and test the neural network.

$> cd build
$> wget https://github.com/mlpack/models/raw/master/Kaggle/kaggle_train_test_dataset.zip
$> unzip kaggle_train_test_dataset.zip
$> ./nnsample

You can copy this template and use it as starting point for your project.

You'll probably need to extend the configuration file CMakeLists.txt to organize your code or to include additinal libraries.

The CMake tutorial is a good place to start using CMake.