max package for machine learning using jitter matrices
The package is most reliant on the mlpack and armadillo. Copyright info for both are below.
Many of the description of classes and their use comes from the main source code or the many wonderful examples included with mlpack.
This package is using the min-devkit however there are a couple fixes in the local version related to issue #189 #168 #184 #183
Armadillo matrices are organized in memory using column major order. When used with mlpack classes each column in the matrix is a data point or feature vector and each row is elements of vector.
mlpack really uses two data types for matrices - double or long. Objects are currently setup to convert to one of these types depending on what is needed.
To deal with converting matrices to/from armadillo, objects have 3 possible modes that are set using the @mode attribute.
This is most interoperable with jitter matrix. This mode expects a 1 or 2d jitter matrix where each cell is a data point and each plane are the values for that data. This will obviously not work for feature vectors with more than 32 elements but it is very efficient otherwise. In this mode objects may output 1d, 2d or 3d, matrices depending on the function of the object.
In this mode jitter matrices are going to be 1 or 2d with a single plane. Each row is a data point and each column in the row is an element of that point.
In this mode jitter matrices are going to be 1 or 2d with a single plane. Each column is a data point and each row in the column is an element of that point. This corresponds to the typical layout used by mlpack for armadillo matrices and would make it easier to replicate examples using mlpack.
Over view 0. Clone repo
- Build armadillo static lib
- Install prereqs for buildng mlpack
- Build mlpack static lib
- Generate projects and build objects
remember this to get all min-api stuff :
git submodule initgit submodule update --init --recursive
cd source/min-api
git apply ../../better_docs.patch
cd source/armadillo to go to armadillo directory
run mkdir build to create build directory
cd build
This is the cmake I use. I do not install superlu, ARPACK, OpenBLAS, or hdf5 at the moment.
cmake -DBUILD_SHARED_LIBS=OFF -DALLOW_BLAS_LAPACK_MACOS=ON -DALLOW_OPENBLAS_MACOS=OFF -DCMAKE_OSX_ARCHITECTURES="x86_64;arm64" -DCMAKE_OSX_DEPLOYMENT_TARGET=10.15 -G Xcode ..
You can now use
cmake --build . --config Release to build release version of library
if wanting to debug also do
cmake --build . --config Debug
You should now have static library at build/Release/libarmadillo.a
in source directory:
cd source/ensmallen
mkdir build
cmake -DARMADILLO_INCLUDE_DIR="../../armadillo/include" ..
this just copies headers into ./include so nothing more to do here.
only headers being used so no cmake needed
From the source/mlpack directory run
mkdir build
cd build
These are the cmake options I use. mlpack has a number of potential bindings but these are not of use for this project
cmake -DBUILD_PYTHON_BINDINGS=OFF -DBUILD_JULIA_BINDINGS=OFF -DBUILD_GO_BINDINGS=OFF -DBUILD_R_BINDINGS=OFF -DCMAKE_OSX_ARCHITECTURES="x86_64;arm64" -DCMAKE_OSX_DEPLOYMENT_TARGET=10.15 -DARMADILLO_LIBRARY="../armadillo/build/Release/libarmadillo.a" -DENSMALLEN_INCLUDE_DIR="../../ensmallen/include" -DARMADILLO_INCLUDE_DIR="../../armadillo-coed/include" -DCEREAL_INCLUDE_DIR="../../cereal/include" -DCMAKE_INSTALL_PREFIX=./ -G Xcode ..
You can now use
cmake --build . --config Release to build release version of library
if wanting to debug also do
cmake --build . --config Debug
cd into main mlmat directory
run
mkdir build
cd build
cmake -GXcode ..
You should now be able to do the following
cmake --build . --config Release to build release version of library
if wanting to debug also do
cmake --build . --config Debug
Armadillo C++ Linear Algebra Library Copyright 2008-2022 Conrad Sanderson (http://conradsanderson.id.au) Copyright 2008-2016 National ICT Australia (NICTA) Copyright 2017-2022 Data61 / CSIRO
This product includes software developed by Conrad Sanderson (http://conradsanderson.id.au) This product includes software developed at National ICT Australia (NICTA) This product includes software developed at Data61 / CSIRO
Attribution Notice.
mlpack is provided without any warranty of fitness for any purpose. You can redistribute the library and/or modify it under the terms of the 3-clause BSD license. The text of the 3-clause BSD license is contained below.
mlpack contains some reproductions of the source code of Armadillo, which is licensed under the Mozilla Public License v2.0 (MPL2). This code is found in src/mlpack/core/arma_extend/ and more details on the licensing are available there.
mlpack also contains some reproductions of the source code of Boost, which is licensed under the Boost Software License, version 1.0. This code is found in src/mlpack/core/boost_backport/ and more details on the licensing are available there.
mlpack contain some usage of the source code of MNMLSTC Core library, which is a backport of C++17 features to C++11. MNMLSTC is licensed under the Apache 2.0 License. This code can be found in src/mlpack/core/std_backport/ and more details about licensing can be found there.
mlpack may contain some usage of the source code of stb, which is licensed under the MIT License and the Public Domain (www.unlicense.org). This code is used in src/mlpack/core/data/load_image.hpp.
Copyright (c) 2007-2020, mlpack contributors (see COPYRIGHT.txt) All rights reserved.
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Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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