Overhaul documentation homepage (#3836)
* Add pipeline to documentation homepage. * Fix for mobile devices. * Add little pipelines to go at the top of each page. * Overhaul index page. * Overhaul README to remove redundant material. * Add installation documentation. * Update pipelines. * Allow nesting of deeper details. * Add a pipeline to the top of the load/save page. * Add prerequisites link to main pipeline. * Add better but not finished sidebar. * Add a couple new documentation pages. * Fix URLs in svg. * Incremental checkin. * Fix Youtube URLs. * Incremental checkin. * Minor fixes. * Add first pass at evaluation/deployment pages. * Minor spacing and link fixes. * Flesh out a number of additional pages and write basic compilation documentation. * Fix some minor issues, and add Docker deployment page (not totally finished yet). * Add developer documentation landing page. * Hopefully getting close to the final set of changes here. * Remove this documentation for now. * Fix a few links, and the size of the sidebar. * Fix some additional links. * Fix a bunch more links. * Fix another link that now has a better place. * Refactor test-docs.sh to handle documentation that is a standalone program. * Fix file exclusions. * Fully qualify typename. * Update name of file. * Fix syntax error. * Remove files that are not meant to be compiled. * Also skip the quickstart. * Move quickstart entry to the top. * Remove gray coloring of binding documentation. * Update name of sidebar link. * Update to working link. * Fix Wikipedia anchor.
This commit is contained in:
Ryan Curtin
@@ -44,7 +44,7 @@ programs, Python bindings, Julia bindings, Go bindings and R bindings.
|
||||
- [mlpack homepage](https://www.mlpack.org/)
|
||||
- [mlpack documentation](https://www.mlpack.org/doc/index.html)
|
||||
- [Examples repository](https://github.com/mlpack/examples/)
|
||||
- [Tutorials](doc/tutorials/README.md)
|
||||
- [Tutorials](doc/user/tutorials.md)
|
||||
- [Development Site (Github)](https://github.com/mlpack/mlpack/)
|
||||
|
||||
[//]: # (numfocus-fiscal-sponsor-attribution)
|
||||
@@ -69,13 +69,9 @@ variety of other needs.
|
||||
|
||||
1. [Citation details](#1-citation-details)
|
||||
2. [Dependencies](#2-dependencies)
|
||||
3. [Installing and using mlpack in C++](#3-installing-and-using-mlpack-in-c)
|
||||
4. [Building mlpack bindings to other languages](#4-building-mlpack-bindings-to-other-languages)
|
||||
1. [Command-line programs](#4i-command-line-programs)
|
||||
2. [Python bindings](#4ii-python-bindings)
|
||||
3. [R bindings](#4iii-r-bindings)
|
||||
4. [Julia bindings](#4iv-julia-bindings)
|
||||
5. [Go bindings](#4v-go-bindings)
|
||||
3. [Installation](#3-installation)
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||||
4. [Usage from C++](#4-usage-from-c)
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||||
1. [Reducing compile time](#41-reducing-compile-time)
|
||||
5. [Building mlpack's test suite](#5-building-mlpacks-test-suite)
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||||
6. [Further resources](#6-further-resources)
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||||
|
||||
@@ -107,6 +103,7 @@ Citations are beneficial for the growth and improvement of mlpack.
|
||||
## 2. Dependencies
|
||||
|
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**mlpack** requires the following additional dependencies:
|
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|
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- C++17 compiler
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- [Armadillo](https://arma.sourceforge.net)  >= 10.8
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||||
- [ensmallen](https://ensmallen.org)  >= 2.10.0
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@@ -117,57 +114,12 @@ available.
|
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|
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If you are compiling Armadillo by hand, ensure that LAPACK and BLAS are enabled.
|
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|
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## 3. Installing and using mlpack in C++
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## 3. Installation
|
||||
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*See also the [C++ quickstart](doc/quickstart/cpp.md).*
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Detailed installation instructions can be found on the
|
||||
[Installing mlpack](doc/user/install.md) page.
|
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|
||||
Since mlpack is a header-only library, installing just the headers for use in a
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C++ application is trivial.
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|
||||
From the root of the sources, configure and install
|
||||
in the standard CMake way:
|
||||
|
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```sh
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mkdir build && cd build/
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cmake ..
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sudo make install
|
||||
```
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||||
|
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If the `cmake ..` command fails due to unavailable dependencies, consider either using the
|
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`-DDOWNLOAD_DEPENDENCIES=ON` option as detailed in [the following
|
||||
subsection](#31-additional-build-options), or ensure that mlpack's dependencies
|
||||
are installed, e.g. using the system package manager. For example, on Debian
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and Ubuntu, all relevant dependencies can be installed with `sudo apt-get
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install libarmadillo-dev libensmallen-dev libcereal-dev libstb-dev g++ cmake`.
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|
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Alternatively, since CMake v3.14.0 the `cmake` command can create the build
|
||||
folder itself, and so the above commands can be rewritten as follows:
|
||||
|
||||
```sh
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cmake -S . -B build
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sudo cmake --build build --target install
|
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```
|
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|
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During configuration, CMake adjusts the file `mlpack/config.hpp` using the
|
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details of the local system. This file can be modified by hand as necessary
|
||||
before or after installation.
|
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|
||||
### 3.1. Additional build options
|
||||
|
||||
You can add a few arguments to the `cmake` command to control the behavior of
|
||||
the configuration and build process. Simply add these to the `cmake` command.
|
||||
Some options are given below:
|
||||
|
||||
- `-DDOWNLOAD_DEPENDENCIES=ON` will automatically download mlpack's
|
||||
dependencies (ensmallen, Armadillo, and cereal). Installing Armadillo this
|
||||
way is not recommended and it is better to use your system package manager
|
||||
when possible (see [below](#31a-linking-with-autodownloaded-armadillo)).
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||||
- `-DCMAKE_INSTALL_PREFIX=/install/root/` will set the root of the install
|
||||
directory to `/install/root` when `make install` is run.
|
||||
- `-DDEBUG=ON` will enable debugging symbols in any compiled bindings or tests.
|
||||
|
||||
There are also options to enable building bindings to each language that mlpack
|
||||
supports; those are detailed in the following sections.
|
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## 4. Usage from C++
|
||||
|
||||
Once headers are installed with `make install`, using mlpack in an application
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||||
consists only of including it. So, your program should include mlpack:
|
||||
@@ -189,44 +141,19 @@ add `#define MLPACK_ENABLE_ANN_SERIALIZATION` before including `<mlpack.hpp>`.
|
||||
If you don't define `MLPACK_ENABLE_ANN_SERIALIZATION` and your code serializes a
|
||||
neural network, a compilation error will occur.
|
||||
|
||||
See the [C++ quickstart](doc/quickstart/cpp.md) and the
|
||||
[examples](https://github.com/mlpack/examples) repository for some examples
|
||||
of mlpack applications in C++, with corresponding `Makefile`s.
|
||||
See also:
|
||||
|
||||
#### 3.1.a. Linking with autodownloaded Armadillo
|
||||
* the [test program compilation section](doc/user/install.md#compiling-a-test-program)
|
||||
of the installation documentation,
|
||||
* the [C++ quickstart](doc/quickstart/cpp.md), and
|
||||
* the [examples repository](https://github.com/mlpack/examples) repository for
|
||||
some examples of mlpack applications in C++, with corresponding `Makefile`s.
|
||||
|
||||
When the autodownloader is used to download Armadillo
|
||||
(`-DDOWNLOAD_DEPENDENCIES=ON`), the Armadillo runtime library is not built and
|
||||
Armadillo must be used in header-only mode. The autodownloader also does not
|
||||
download dependencies of Armadillo such as OpenBLAS. For this reason, it is
|
||||
recommended to instead install Armadillo using your system package manager,
|
||||
which will also install the dependencies of Armadillo. For example, on Ubuntu
|
||||
and Debian systems, Armadillo can be installed with
|
||||
|
||||
```sh
|
||||
sudo apt-get install libarmadillo-dev
|
||||
```
|
||||
|
||||
and other package managers such as `dnf` and `brew` and `pacman` also have
|
||||
Armadillo packages available.
|
||||
|
||||
If the autodownloader is used to provide Armadillo, mlpack programs cannot be
|
||||
linked with `-larmadillo`. Instead, you must link directly with the
|
||||
dependencies of Armadillo. For example, on a system that has OpenBLAS
|
||||
available, compilation can be done like this:
|
||||
|
||||
```sh
|
||||
g++ -O3 -std=c++17 -o my_program my_program.cpp -lopenblas -fopenmp
|
||||
```
|
||||
|
||||
See [the Armadillo documentation](https://arma.sourceforge.net/faq.html#linking)
|
||||
for more information on linking Armadillo programs.
|
||||
|
||||
### 3.2. Reducing compile time
|
||||
### 4.1. Reducing compile time
|
||||
|
||||
mlpack is a template-heavy library, and if care is not used, compilation time of
|
||||
a project can be increased greatly. Fortunately, there are a number of ways to
|
||||
reduce compilation time:
|
||||
a project can be very high. Fortunately, there are a number of ways to reduce
|
||||
compilation time:
|
||||
|
||||
* Include individual headers, like `<mlpack/methods/decision_tree.hpp>`, if you
|
||||
are only using one component, instead of `<mlpack.hpp>`. This reduces the
|
||||
@@ -248,241 +175,15 @@ reduce compilation time:
|
||||
Other strategies exist too, such as precompiled headers, compiler options,
|
||||
[`ccache`](https://ccache.dev), and others.
|
||||
|
||||
## 4. Building mlpack bindings to other languages
|
||||
|
||||
mlpack is not just a header-only library: it also comes with bindings to a
|
||||
number of other languages, this allows flexible use of mlpack's efficient
|
||||
implementations from languages that aren't C++.
|
||||
|
||||
In general, you should *not* need to build these by hand---they should be
|
||||
provided by either your system package manager or your language's package
|
||||
manager.
|
||||
|
||||
Building the bindings for a particular language is done by calling `cmake` with
|
||||
different options; each example below shows how to configure an individual set
|
||||
of bindings, but it is of course possible to combine the options and build
|
||||
bindings for many languages at once.
|
||||
|
||||
### 4.i. Command-line programs
|
||||
|
||||
*See also the [command-line quickstart](doc/quickstart/cli.md).*
|
||||
|
||||
The command-line programs have no extra dependencies. The set of programs that
|
||||
will be compiled is detailed and documented on the [command-line program
|
||||
documentation page](doc/user/bindings/cli.md).
|
||||
|
||||
From the root of the mlpack sources, run the following commands to build and
|
||||
install the command-line bindings:
|
||||
|
||||
```sh
|
||||
mkdir build && cd build/
|
||||
cmake -DBUILD_CLI_PROGRAMS=ON ../
|
||||
make
|
||||
sudo make install
|
||||
```
|
||||
|
||||
You can use `make -j<N>`, where `N` is the number of cores on your machine, to
|
||||
build in parallel; e.g., `make -j4` will use 4 cores to build.
|
||||
|
||||
### 4.ii. Python bindings
|
||||
|
||||
*See also the [Python quickstart](doc/quickstart/python.md).*
|
||||
|
||||
mlpack's Python bindings are available on
|
||||
[PyPI](https://pypi.org/project/mlpack/) and
|
||||
[conda-forge](https://anaconda.org/conda-forge/mlpack), and can be installed
|
||||
with either `pip install mlpack` or `conda install -c conda-forge mlpack`.
|
||||
These sources are recommended, as building the Python bindings by hand can be
|
||||
complex.
|
||||
|
||||
With that in mind, if you would still like to manually build the mlpack Python
|
||||
bindings, first make sure that the following Python packages are installed:
|
||||
|
||||
- setuptools
|
||||
- wheel
|
||||
- cython >= 0.24
|
||||
- numpy
|
||||
- pandas >= 0.15.0
|
||||
|
||||
Now, from the root of the mlpack sources, run the following commands to build
|
||||
and install the Python bindings:
|
||||
|
||||
```sh
|
||||
mkdir build && cd build/
|
||||
cmake -DBUILD_PYTHON_BINDINGS=ON ../
|
||||
make
|
||||
sudo make install
|
||||
```
|
||||
|
||||
You can use `make -j<N>`, where `N` is the number of cores on your machine, to
|
||||
build in parallel; e.g., `make -j4` will use 4 cores to build. You can also
|
||||
specify a custom Python interpreter with the CMake option
|
||||
`-DPYTHON_EXECUTABLE=/path/to/python`.
|
||||
|
||||
### 4.iii. R bindings
|
||||
|
||||
*See also the [R quickstart](doc/quickstart/r.md).*
|
||||
|
||||
mlpack's R bindings are available as the R package
|
||||
[mlpack](https://cran.r-project.org/web/packages/mlpack/index.html) on CRAN.
|
||||
You can install the package by running `install.packages('mlpack')`, and this is
|
||||
the recommended way of getting mlpack in R.
|
||||
|
||||
If you still wish to build the R bindings by hand, first make sure the following
|
||||
dependencies are installed:
|
||||
|
||||
- R >= 4.0
|
||||
- Rcpp >= 0.12.12
|
||||
- RcppArmadillo >= 0.10.8.0
|
||||
- RcppEnsmallen >= 0.2.10.0
|
||||
- roxygen2
|
||||
- testthat
|
||||
- pkgbuild
|
||||
|
||||
These can be installed with `install.packages()` inside of your R environment.
|
||||
Once the dependencies are available, you can configure mlpack and build the R
|
||||
bindings by running the following commands from the root of the mlpack sources:
|
||||
|
||||
```sh
|
||||
mkdir build && cd build/
|
||||
cmake -DBUILD_R_BINDINGS=ON ../
|
||||
make
|
||||
sudo make install
|
||||
```
|
||||
|
||||
You may need to specify the location of the R program in the `cmake` command
|
||||
with the option `-DR_EXECUTABLE=/path/to/R`.
|
||||
|
||||
Once the build is complete, a tarball can be found under the build directory in
|
||||
`src/mlpack/bindings/R/`, and then that can be installed into your R environment
|
||||
with a command like `install.packages(mlpack_3.4.3.tar.gz, repos=NULL,
|
||||
type='source')`.
|
||||
|
||||
### 4.iv. Julia bindings
|
||||
|
||||
*See also the [Julia quickstart](doc/quickstart/julia.md).*
|
||||
|
||||
mlpack's Julia bindings are available by installing the
|
||||
[mlpack.jl](https://github.com/mlpack/mlpack.jl) package using
|
||||
`Pkg.add("mlpack.jl")`. The process of building, packaging, and distributing
|
||||
mlpack's Julia bindings is very nontrivial, so it is recommended to simply use
|
||||
the version available in `Pkg`, but if you want to build the bindings by hand
|
||||
anyway, you can configure and build them by running the following commands from
|
||||
the root of the mlpack sources:
|
||||
|
||||
```sh
|
||||
mkdir build && cd build/
|
||||
cmake -DBUILD_JULIA_BINDINGS=ON ../
|
||||
make
|
||||
```
|
||||
|
||||
If CMake cannot find your Julia installation, you can add
|
||||
`-DJULIA_EXECUTABLE=/path/to/julia` to the CMake configuration step.
|
||||
|
||||
Note that the `make install` step is not done above, since the Julia binding
|
||||
build system was not meant to be installed directly. Instead, to use handbuilt
|
||||
bindings (for instance, to test them), one option is to start Julia with
|
||||
`JULIA_PROJECT` set as an environment variable:
|
||||
|
||||
```sh
|
||||
cd build/src/mlpack/bindings/julia/mlpack/
|
||||
JULIA_PROJECT=$PWD julia
|
||||
```
|
||||
|
||||
and then `using mlpack` should work.
|
||||
|
||||
### 4.v. Go bindings
|
||||
|
||||
*See also the [Go quickstart](doc/quickstart/go.md).*
|
||||
|
||||
To build mlpack's Go bindings, ensure that Go >= 1.11.0 is installed, and that
|
||||
the Gonum package is available. You can use `go get` to install mlpack as a
|
||||
module in a Go project:
|
||||
|
||||
```sh
|
||||
go get -u mlpack.org/v1/mlpack
|
||||
```
|
||||
|
||||
The Go bindings themselves will then need to be compiled. Find the mlpack
|
||||
directory under `$GOMODCACHE/mlpack.org/v1/mlpack` and run these commands:
|
||||
|
||||
```sh
|
||||
make
|
||||
sudo make install
|
||||
```
|
||||
|
||||
Then, `go run my_code.go` will be able to correctly link against mlpack's Go
|
||||
bindings and run.
|
||||
|
||||
The process of building the Go bindings by hand is a little tedious, so
|
||||
following the steps above is recommended. However, if you wish to build the Go
|
||||
bindings by hand anyway, you can do this by running the following commands from
|
||||
the root of the mlpack sources:
|
||||
|
||||
```sh
|
||||
mkdir build && cd build/
|
||||
cmake -DBUILD_GO_BINDINGS=ON ../
|
||||
make
|
||||
sudo make install
|
||||
```
|
||||
|
||||
## 5. Building mlpack's test suite
|
||||
|
||||
mlpack contains an extensive test suite that exercises every part of the
|
||||
codebase. It is easy to build and run the tests with CMake and CTest, as below:
|
||||
|
||||
```sh
|
||||
mkdir build && cd build/
|
||||
cmake -DBUILD_TESTS=ON ../
|
||||
make
|
||||
ctest .
|
||||
```
|
||||
|
||||
If you want to test the bindings, too, you will have to adapt the CMake
|
||||
configuration command to turn on the language bindings that you want to
|
||||
test---see the previous sections for details.
|
||||
See the [installation instruction section](doc/user/install.md#build-tests).
|
||||
|
||||
## 6. Further Resources
|
||||
|
||||
More documentation is available for both users and developers.
|
||||
|
||||
***User documentation***:
|
||||
|
||||
- [Matrices in mlpack](doc/user/matrices.md)
|
||||
- [Loading and saving mlpack objects](doc/user/load_save.md)
|
||||
- [Cross-Validation](doc/user/cv.md)
|
||||
- [Hyper-parameter Tuning](doc/user/hpt.md)
|
||||
- [Building mlpack from source on Windows](doc/user/build_windows.md)
|
||||
- [Sample C++ ML App for Windows](doc/user/sample_ml_app.md)
|
||||
- [mlpack core library documentation](doc/user/core.md)
|
||||
- [Examples repository](https://github.com/mlpack/examples/)
|
||||
|
||||
***Tutorials:***
|
||||
|
||||
- [Alternating Matrix Factorization (AMF)](doc/tutorials/amf.md)
|
||||
- [Artificial Neural Networks (ANN)](doc/tutorials/ann.md)
|
||||
- [Approximate k-Furthest Neighbor Search (`approx_kfn`)](doc/tutorials/approx_kfn.md)
|
||||
- [Collaborative Filtering (CF)](doc/tutorials/cf.md)
|
||||
- [DatasetMapper](doc/tutorials/datasetmapper.md)
|
||||
- [Density Estimation Trees (DET)](doc/tutorials/det.md)
|
||||
- [Euclidean Minimum Spanning Trees (EMST)](doc/tutorials/emst.md)
|
||||
- [Fast Max-Kernel Search (FastMKS)](doc/tutorials/fastmks.md)
|
||||
- [Image Utilities](doc/tutorials/image.md)
|
||||
- [k-Means Clustering](doc/tutorials/kmeans.md)
|
||||
- [Linear Regression](doc/tutorials/linear_regression.md)
|
||||
- [Neighbor Search (k-Nearest-Neighbors)](doc/tutorials/neighbor_search.md)
|
||||
- [Range Search](doc/tutorials/range_search.md)
|
||||
- [Reinforcement Learning](doc/tutorials/reinforcement_learning.md)
|
||||
|
||||
***Developer documentation***:
|
||||
|
||||
- [Writing an mlpack binding](doc/developer/iodoc.md)
|
||||
- [mlpack Timers](doc/developer/timer.md)
|
||||
- [mlpack automatic bindings to other languages](doc/developer/bindings.md)
|
||||
- [The ElemType policy in mlpack](doc/developer/elemtype.md)
|
||||
- [The KernelType policy in mlpack](doc/developer/kernels.md)
|
||||
- [The DistanceType policy in mlpack](doc/developer/distances.md)
|
||||
- [The TreeType policy in mlpack](doc/developer/trees.md)
|
||||
* [Documentation homepage](https://www.mlpack.org/doc/index.html)
|
||||
|
||||
To learn about the development goals of mlpack in the short- and medium-term
|
||||
future, see the [vision document](https://www.mlpack.org/papers/vision.pdf).
|
||||
|
||||
+1
-1
@@ -43,7 +43,7 @@ internal design.
|
||||
library](https://joss.theoj.org/papers/10.21105/joss.00726) (2018)
|
||||
|
||||
* [mlpack open-source machine learning library and
|
||||
community](http://kurg.org/pub/pdf/2018mlossmlpack.pdf) (2018)
|
||||
community](https://openreview.net/pdf?id=rJxx0Y6NhX) (2018)
|
||||
|
||||
* [Designing and building the mlpack open-source machine learning
|
||||
library](https://arxiv.org/abs/1708.05279) (2017)
|
||||
|
||||
+19
-7
@@ -21,8 +21,8 @@ body {
|
||||
div#content {
|
||||
padding-top: 10px;
|
||||
padding-bottom: 10px;
|
||||
padding: 30px;
|
||||
max-width: 920px;
|
||||
padding: 10px;
|
||||
max-width: 880px;
|
||||
margin: auto;
|
||||
}
|
||||
|
||||
@@ -1060,9 +1060,9 @@ div#sidebar {
|
||||
float: left;
|
||||
position: fixed;
|
||||
top: 55px;
|
||||
min-width: calc(50% - 460px);
|
||||
min-width: calc(50% - 450px);
|
||||
font-size: 90%;
|
||||
max-width: calc(50% - 460px);
|
||||
max-width: calc(50% - 450px);
|
||||
overflow-y: scroll;
|
||||
bottom: 0;
|
||||
}
|
||||
@@ -1071,7 +1071,7 @@ div#sidebar ul {
|
||||
border-top: 2px solid #ccc;
|
||||
padding: 0.5em;
|
||||
list-style-type: none;
|
||||
padding-left: 0.5em;
|
||||
padding-right: 0em;
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
@@ -1120,6 +1120,17 @@ div#sidebar details[open] details[open] summary::after {
|
||||
content: " [-]";
|
||||
}
|
||||
|
||||
div#sidebar details[open] details[open] details summary::after {
|
||||
content: " [+]";
|
||||
}
|
||||
|
||||
div#sidebar details[open] details[open] details[open] summary::after {
|
||||
content: " [-]";
|
||||
}
|
||||
|
||||
object#pipeline-wide { display: block; }
|
||||
object#pipeline-narrow { display: none; }
|
||||
|
||||
@media screen and (max-width: 1140px) {
|
||||
div#sidebar {
|
||||
display: none;
|
||||
@@ -1150,6 +1161,7 @@ a.textlink {
|
||||
color: #333;
|
||||
}
|
||||
|
||||
ul#binding_sidebar {
|
||||
background: #eee;
|
||||
@media screen and (max-width: 980px) {
|
||||
object#pipeline-wide { display: none; }
|
||||
object#pipeline-narrow { display: block; width: 100%; max-width: 400px; margin: auto; }
|
||||
}
|
||||
|
||||
@@ -0,0 +1,156 @@
|
||||
# mlpack continuous integration (CI) systems
|
||||
|
||||
Every pull request submitted to mlpack goes through a number of automated checks
|
||||
to make sure that all unit tests pass, all code matches the desired style guide,
|
||||
documentation does not contain any broken links, and so on and so forth.
|
||||
|
||||
In general, all CI checks need to pass for PRs to be merged, but like any
|
||||
complex project, there are occasionally spurious failures or other unrelated
|
||||
problems.
|
||||
|
||||
* [Basic compilation and test jobs](#basic-compilation-and-test-jobs)
|
||||
* [R build](#r-build)
|
||||
* [Documentation build and test](#documentation-build-and-test)
|
||||
* [Style checks](#style-checks)
|
||||
* [Cross-compilation checks](#cross-compilation-checks)
|
||||
* [Static code analysis checks](#static-code-analysis-checks)
|
||||
|
||||
Also you can see the [list of CI infrastructure](#list-of-ci-infrastructure).
|
||||
|
||||
## Basic compilation and test jobs
|
||||
|
||||
Basic compilation and testing is done on Azure Pipelines.
|
||||
We use Azure Pipelines primarily because of the large number of resources that
|
||||
an mlpack build takes; our own [internal resources](#list-of-ci-infrastructure)
|
||||
are thus preserved for more specific usage.
|
||||
|
||||
Link: [***mlpack on Azure Pipelines***](https://dev.azure.com/mlpack/mlpack/_build/)
|
||||
|
||||
* Builds and tests mlpack for Linux, OS X, and Windows.
|
||||
|
||||
* Also builds and tests bindings on Linux and OS X.
|
||||
|
||||
* Configurations for these jobs can be found in the mlpack repository under the
|
||||
`.ci/` directory.
|
||||
|
||||
* *These jobs are most of what's shown in the jobs in a PR.*
|
||||
|
||||
***If your build is failing on Azure Pipelines:***
|
||||
|
||||
* Take a look at the build log to identify the issue.
|
||||
|
||||
* If the failure is during `mlpack_test`, look through the test output to find
|
||||
where the actual failed test is.
|
||||
- If the failed test is related to your code, you probably have a bug to fix.
|
||||
:)
|
||||
- If the failed test does not seem related at all, it could be a spurious
|
||||
error in another test.
|
||||
- You can run the test locally with `bin/mlpack_test NameOfTest`.
|
||||
- If the test seems like a random failure, try different random seeds:
|
||||
`bin/mlpack_test --rng-seed=X NameOfTest`.
|
||||
|
||||
## R build
|
||||
|
||||
The R build uses Github Actions (not for any particular reason).
|
||||
|
||||
Link: [***mlpack R build actions***](https://github.com/mlpack/mlpack/actions/workflows/main.yml)
|
||||
|
||||
* Job configuration is found in `.github/workflows/main.yml`
|
||||
|
||||
* The job produces 1 artifact, which is the tarball that can be uploaded to
|
||||
[CRAN](https://cran.r-project.org/).
|
||||
|
||||
* When this job fails, it is usually because of:
|
||||
- An intermittent problem downloading dependencies or setting up the
|
||||
environment.
|
||||
- A test failure which can probably be more easily debugged or reproduced via
|
||||
the main [Azure Pipelines build jobs](#basic-compilation-and-test-jobs).
|
||||
|
||||
## Documentation build and test
|
||||
|
||||
The 'documentation build and test' job builds and tests *all* documentation,
|
||||
checking:
|
||||
|
||||
* that all Markdown pages build and render properly;
|
||||
* that all HTML is valid;
|
||||
* that all links referenced in the documentation are valid;
|
||||
* that all code examples compile and run.
|
||||
|
||||
All of the scripts to perform these builds are located in the `scripts/`
|
||||
directory, so that they can be run locally.
|
||||
|
||||
* `./scripts/build-docs.sh`
|
||||
- Builds all documentation in `doc/` with the output directory `doc/html/`.
|
||||
- If you browse to `doc/html/index.html` you can browse locally-built
|
||||
documentation.
|
||||
- Checks all HTML links and anchors.
|
||||
|
||||
* `./scripts/test-docs.sh doc/`
|
||||
* `./scripts/test-docs.sh doc/path/to/file.md`
|
||||
- Extracts code blocks from documentation and compiles and runs them.
|
||||
- Can be run on either all the documentation (with `doc/` or directory
|
||||
argument), or a single file.
|
||||
- May require `CXX`, `CXXFLAGS`, and `LDFLAGS` environment variables to be
|
||||
set. See the script itself for more details.
|
||||
- If run on an individual file, the output of each compiled code snippet will
|
||||
be printed.
|
||||
|
||||
When writing new documentation, be sure to test it locally---going back and
|
||||
forth with the
|
||||
[job on Jenkins](http://ci.mlpack.org/job/pull-request%20documentation%20build%20and%20test/)
|
||||
can be very tedious.
|
||||
|
||||
## Style checks
|
||||
|
||||
The [style checker job](http://ci.mlpack.org/job/pull-requests%20mlpack%20style%20checks/) runs on Jenkins.
|
||||
|
||||
* The [`lint.sh` script](https://github.com/mlpack/jenkins-conf/blob/master/linter/lint.sh) to check for C++ style issues.
|
||||
|
||||
* If your job failed this check, look at the "Style-Check Warnings" tab in the
|
||||
Jenkins job.
|
||||
|
||||
* See also the
|
||||
[style guidelines for mlpack](https://github.com/mlpack/mlpack/wiki/DesignGuidelines).
|
||||
|
||||
## Cross-compilation checks
|
||||
|
||||
The [cross-compilation checks](http://ci.mlpack.org/job/CrossCompile-mlpack-for-embedded-aarch64/)
|
||||
run on Jenkins.
|
||||
|
||||
* The job builds mlpack in a
|
||||
[cross-compilation environment](../embedded/supported_boards.md).
|
||||
|
||||
* Any failures seen here *that are not seen in other jobs* will probably be
|
||||
failures specific to the cross-compilation environment.
|
||||
|
||||
## Static code analysis checks
|
||||
|
||||
The [static code analysis checks](http://ci.mlpack.org/job/pull-requests-mlpack-static-code-analysis/)
|
||||
use a few C++ code analysis tools to try and report issues with the codebase.
|
||||
|
||||
Currently, most of the output by this job is not actionable---there are too many
|
||||
false positives or spurious issues---and therefore should be used only as
|
||||
informational output.
|
||||
|
||||
Configuration can be found in the
|
||||
[`jenkins-conf` repository](https://github.com/mlpack/jenkins-conf).
|
||||
|
||||
## List of CI infrastructure
|
||||
|
||||
Many physical systems are involved with testing mlpack and are hooked up to
|
||||
Jenkins.
|
||||
|
||||
Link: [***Jenkins (`ci.mlpack.org`)***](http://ci.mlpack.org)
|
||||
|
||||
* The 'specialized' build system.
|
||||
|
||||
* Various Jenkins configuration related resources are found in the
|
||||
[`jenkins-conf` repository](https://github.com/mlpack/jenkins-conf/).
|
||||
|
||||
* The list of workers (individual systems) can be found
|
||||
[here](http://ci.mlpack.org/computer/).
|
||||
|
||||
* Adding or modifying jobs requires privileges; you can either ask an mlpack
|
||||
maintainer to make changes, or if you are on the Contributors team but still
|
||||
don't have access, ask somewhere and someone will give you access. (Probably
|
||||
`#mlpack:matrix.org` is the best bet!)
|
||||
@@ -18,7 +18,7 @@ via issues on GitHub, or via chat:
|
||||
|
||||
## Real-time chat
|
||||
|
||||
* #mlpack:matrix.org on [Matrix](https://www.matrix.org/)
|
||||
* `#mlpack:matrix.org` on [Matrix](https://www.matrix.org/)
|
||||
* [mlpack Slack workspace](https://mlpack.slack.com/)
|
||||
- You will need to request an invite from the
|
||||
[auto-inviter](http://slack-inviter.mlpack.org:4000).
|
||||
@@ -27,11 +27,10 @@ via issues on GitHub, or via chat:
|
||||
|
||||
## Video meetup
|
||||
|
||||
On the first and third Friday of every month, at ***1700 UTC*** on Fridays, we
|
||||
have *casual video meetups* with no particular agenda. Feel free to join up!
|
||||
We often talk about code changes that we are working on, issues that people are
|
||||
having with mlpack, general design direction, and whatever else might be on our
|
||||
mind.
|
||||
On the first Monday of every month, at ***1530 UTC***, we have *casual video
|
||||
meetups* with no particular agenda. Feel free to join up! We often talk about
|
||||
code changes that we are working on, issues that people are having with mlpack,
|
||||
general design direction, and whatever else might be on our mind.
|
||||
|
||||
We use [this Zoom room](https://zoom.us/j/3820896170). For security, we use a
|
||||
password for the meeting to keep malicious bots out. The password is simple:
|
||||
@@ -55,9 +54,8 @@ contains many examples you can build and play around with.
|
||||
|
||||
Once you have an idea of what's included in mlpack and how a user might use it,
|
||||
then a good next step would be to set up a development environment. Once you
|
||||
have that set up, you can
|
||||
[build mlpack from source](../README.md#3-installing-and-using-mlpack-in-c),
|
||||
and explore the codebase to see how it's organized.
|
||||
have that set up, you can [build mlpack from source](../user/install.md), and
|
||||
explore the codebase to see how it's organized.
|
||||
|
||||
Try making small changes to the code, or adding new tests to the test suite, and
|
||||
then rebuild to see how your changes work.
|
||||
|
||||
@@ -0,0 +1,29 @@
|
||||
# Developers
|
||||
|
||||
If you want to contribute to mlpack, or if you already are a regular contributor
|
||||
to mlpack or a maintainer, the following pages may serve as useful documentation
|
||||
about internal development processes, guidelines, and systems:
|
||||
|
||||
* [Community](community.md): details of how the mlpack community operates and
|
||||
communicates, including *how to get involved*.
|
||||
|
||||
* [Google Summer of Code](gsoc.md): advice on applying to mlpack for Google
|
||||
Summer of Code.
|
||||
|
||||
* [CI/CD](ci.md): systems and servers involved in mlpack's continuous
|
||||
integration pipeline.
|
||||
|
||||
* [Timers](timer.md): interface for timing bindings and other mlpack programs.
|
||||
|
||||
* [Automatic binding system](bindings.md): design details and operation of
|
||||
mlpack's automatic binding generator, including how to add a new language.
|
||||
|
||||
* [Writing a binding](iodoc.md): a tutorial on writing an mlpack binding that
|
||||
will automatically be compiled to any language mlpack has bindings for.
|
||||
|
||||
* [Template policies](policies.md): documentation for standardized class
|
||||
interfaces used by mlpack algorithms.
|
||||
- [The ElemType policy](elemtype.md)
|
||||
- [The DistanceType policy](distances.md)
|
||||
- [The KernelType policy](kernels.md)
|
||||
- [The TreeType policy](trees.md)
|
||||
@@ -35,12 +35,11 @@ project. A student should ideally be familiar with
|
||||
|
||||
- *using the development toolchain on your computer*: you should be able to
|
||||
download and compile mlpack, make changes to the code, and recompile with the
|
||||
new changes. There is a
|
||||
[section in the README](../README.md#3-installing-and-using-mlpack-in-c)
|
||||
for how to build mlpack and would be a great place to get started. If you’re
|
||||
on Windows, then the [Windows build guide](../user/build_windows.md) could be
|
||||
very useful. See also the [Community page](community.md) for more information
|
||||
on getting started.
|
||||
new changes. There is a [guide](../user/install.md) for how to build mlpack
|
||||
and that would be a great place to get started. If you’re on Windows, then
|
||||
the [Windows build guide](../user/build_windows.md) could be very useful. See
|
||||
also the [Community page](community.md) for more information on getting
|
||||
started.
|
||||
|
||||
- *at least intermediate C++ knowledge*: mlpack uses lots of different C++
|
||||
paradigms including template metaprogramming, C++ features like rvalue
|
||||
|
||||
@@ -0,0 +1,9 @@
|
||||
# Template policies
|
||||
|
||||
mlpack has a number of common template policy patterns that are used in various
|
||||
algorithms.
|
||||
|
||||
* [The ElemType policy](elemtype.md)
|
||||
* [The DistanceType policy](distances.md)
|
||||
* [The KernelType policy](kernels.md)
|
||||
* [The TreeType policy](trees.md)
|
||||
@@ -87,7 +87,7 @@ the target.
|
||||
In this tutorial we use the autodownloader since it automates the entire
|
||||
process, including the cross-compilation of OpenBLAS. The first step is to
|
||||
create a build directory, just like the
|
||||
[regular build process](../../README.md#3-installing-and-using-mlpack-in-c):
|
||||
[regular build process](../user/install.md#install-from-source):
|
||||
|
||||
```sh
|
||||
cd mlpack/
|
||||
|
||||
@@ -0,0 +1,3 @@
|
||||
<svg width="1024" height="1024" viewBox="0 0 1024 1024" fill="none" xmlns="http://www.w3.org/2000/svg">
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|
||||
</svg>
|
||||
|
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@@ -0,0 +1,36 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
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<svg version="1.1" id="レイヤー_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" width="401.98px" height="559.472px" viewBox="0 0 401.98 559.472" enable-background="new 0 0 401.98 559.472" xml:space="preserve">
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|
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|
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<svg xmlns="http://www.w3.org/2000/svg" class="external-icon" viewBox="0 0 28.57 20" focusable="false" style="pointer-events: none; display: block; width: 100%; height: 100%;">
|
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<svg viewBox="0 0 28.57 20" preserveAspectRatio="xMidYMid meet" xmlns="http://www.w3.org/2000/svg">
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<g>
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|
||||
</svg>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 955 B |
+18
-208
@@ -2,19 +2,19 @@
|
||||
|
||||
<!--
|
||||
This file contains the landing page for mlpack documentation. Note that if you
|
||||
change any section headers, or add any new algorithms, the sidebar in
|
||||
sidebar.html will need to be manually modified!
|
||||
add new documentation, the sidebar in sidebar.html will need to be manually
|
||||
modified! In addition, you should modify the pipeline SVGs (see the comment
|
||||
below.)
|
||||
-->
|
||||
|
||||
## A fast, flexible machine learning library
|
||||
|
||||
mlpack is an intuitive, fast, and flexible header-only C++ machine learning
|
||||
library with bindings to other languages. It aims to provide fast, lightweight
|
||||
implementations of both common and cutting-edge machine learning algorithms.
|
||||
|
||||
mlpack's lightweight C++ implementation makes it ideal for deployment, and it
|
||||
can also be used for interactive prototyping via C++ notebooks (these can be
|
||||
seen in action on mlpack's [homepage](https://www.mlpack.org/)).
|
||||
can also be used for interactive prototyping via C++ notebooks (see
|
||||
[here](https://mybinder.org/v2/gh/mlpack/examples/HEAD) for a BinderHub instance
|
||||
on the [examples repository](https://github.com/mlpack/examples/)).
|
||||
|
||||
In addition to its [powerful C++ interface](quickstart/cpp.md), mlpack also
|
||||
provides [command-line programs](quickstart/cli.md), and bindings to the
|
||||
@@ -23,209 +23,19 @@ provides [command-line programs](quickstart/cli.md), and bindings to the
|
||||
|
||||
_If you use mlpack, please [cite the software](citation.md)._
|
||||
|
||||
## mlpack basics
|
||||
---
|
||||
|
||||
Installing mlpack can be done using the
|
||||
[instructions in the README](README.md#3-installing-and-using-mlpack-in-c);
|
||||
or the [Windows build guide](user/build_windows.md).
|
||||
The following basic guides are *highly recommended* before using mlpack.
|
||||
|
||||
* ***First steps***:
|
||||
- [mlpack C++ quickstart](quickstart/cpp.md): create a couple simple C++
|
||||
programs that use mlpack
|
||||
- [Sample Windows mlpack C++ application](user/sample_ml_app.md): create a
|
||||
working mlpack Windows program using Visual Studio
|
||||
|
||||
* ***Basics of matrices and data in mlpack***:
|
||||
- [Matrices and data in mlpack](user/matrices.md)
|
||||
- [Loading and saving mlpack objects](user/load_save.md)
|
||||
|
||||
* ***Reference for mlpack core classes***:
|
||||
- [Core mlpack documentation](user/core.md)
|
||||
* [Core math utilities](user/core/math.md)
|
||||
* [Distances](user/core/distances.md)
|
||||
* [Distributions](user/core/distributions.md)
|
||||
* [Kernels](user/core/kernels.md)
|
||||
|
||||
* ***Using mlpack natively with our extensions in Python, R, CLI, Julia, and Go***:
|
||||
- [Links to quickstarts and references](#bindings-to-other-languages)
|
||||
|
||||
## mlpack algorithm documentation
|
||||
|
||||
Documentation for each machine learning algorithm that mlpack implements is
|
||||
detailed in the sections below.
|
||||
|
||||
* [Classification algorithms](#classification-algorithms): classify points as
|
||||
discrete labels (`0`, `1`, `2`, ...).
|
||||
* [Regression algorithms](#regression-algorithms): predict continuous values.
|
||||
* [Clustering algorithms](#clustering-algorithms): group points into clusters.
|
||||
* [Geometric algorithms](#geometric-algorithms): computations based on distance
|
||||
metrics (nearest neighbors, kernel density estimation, etc.).
|
||||
* [Preprocessing utilities](#preprocessing-utilities): prepare data for machine
|
||||
learning algorithms.
|
||||
* [Transformations](#transformations): transform data from one space to
|
||||
another (principal components analysis, etc.).
|
||||
* [Modeling utilities](#modeling-utilities): cross-validation, hyperparameter
|
||||
tuning, etc.
|
||||
|
||||
### Classification algorithms
|
||||
|
||||
Classify points as discrete labels (`0`, `1`, `2`, ...).
|
||||
|
||||
* [`AdaBoost`](user/methods/adaboost.md): Adaptive Boosting
|
||||
* [`DecisionTree`](user/methods/decision_tree.md): ID3-style decision tree
|
||||
classifier
|
||||
* [`HoeffdingTree`](user/methods/hoeffding_tree.md): streaming/incremental
|
||||
decision tree classifier
|
||||
* [`LinearSVM`](user/methods/linear_svm.md): simple linear support vector
|
||||
machine classifier
|
||||
* [`LogisticRegression`](user/methods/logistic_regression.md): L2-regularized
|
||||
logistic regression (two-class only)
|
||||
* [`NaiveBayesClassifier`](user/methods/naive_bayes_classifier.md): simple
|
||||
multi-class naive Bayes classifier
|
||||
* [`Perceptron`](user/methods/perceptron.md): simple Perceptron classifier
|
||||
* [`RandomForest`](user/methods/random_forest.md): parallelized random forest
|
||||
classifier
|
||||
* [`SoftmaxRegression`](user/methods/softmax_regression.md): L2-regularized
|
||||
softmax regression (i.e. multi-class logistic regression)
|
||||
|
||||
### Regression algorithms
|
||||
|
||||
Predict continuous values.
|
||||
|
||||
* [`BayesianLinearRegression`](user/methods/bayesian_linear_regression.md):
|
||||
Bayesian L2-penalized linear regression
|
||||
* [`DecisionTreeRegressor`](user/methods/decision_tree_regressor.md): ID3-style
|
||||
decision tree regressor
|
||||
* [`LARS`](user/methods/lars.md): Least Angle Regression (LARS), L1-regularized
|
||||
and L2-regularized
|
||||
* [`LinearRegression`](user/methods/linear_regression.md): L2-regularized
|
||||
linear regression (ridge regression)
|
||||
|
||||
### Clustering algorithms
|
||||
|
||||
***NOTE:*** this documentation is still under construction and so some
|
||||
algorithms that mlpack implements are not yet listed here. For now, see
|
||||
[the mlpack/methods directory](https://github.com/mlpack/mlpack/tree/master/src/mlpack/methods)
|
||||
for a full list of algorithms.
|
||||
|
||||
Group points into clusters.
|
||||
|
||||
* [`MeanShift`](user/methods/mean_shift.md): clustering with the density-based
|
||||
mean shift algorithm
|
||||
|
||||
### Geometric algorithms
|
||||
|
||||
***NOTE:*** this documentation is still under construction and so no geometric
|
||||
algorithms in mlpack are documented yet. For now, see
|
||||
[the mlpack/methods directory](https://github.com/mlpack/mlpack/tree/master/src/mlpack/methods)
|
||||
for a full list of algorithms.
|
||||
|
||||
Computations based on distance metrics.
|
||||
|
||||
<!-- TODO: add some -->
|
||||
|
||||
### Preprocessing utilities
|
||||
|
||||
Prepare data for machine learning algorithms.
|
||||
|
||||
* [Normalizing labels](user/core/normalizing_labels.md): map labels to and from
|
||||
the range `[0, numClasses - 1]`.
|
||||
* [Dataset splitting](user/core/split.md): split a dataset into a
|
||||
training set and a test set.
|
||||
|
||||
***NOTE:*** this documentation is still under construction and so not all
|
||||
preprocessing utilities in mlpack are documented yet. See also
|
||||
[the mlpack/methods/preprocess directory](https://github.com/mlpack/mlpack/tree/master/src/mlpack/methods)
|
||||
for a full list of algorithms.
|
||||
|
||||
### Transformations
|
||||
|
||||
***NOTE:*** this documentation is still under construction and so some
|
||||
algorithms that mlpack implements are not yet listed here. For now, see
|
||||
[the mlpack/methods directory](https://github.com/mlpack/mlpack/tree/master/src/mlpack/methods)
|
||||
for a full list of algorithms.
|
||||
|
||||
Transform data from one space to another.
|
||||
|
||||
* [`AMF`](user/methods/amf.md): alternating matrix factorization
|
||||
* [`LocalCoordinateCoding`](user/methods/local_coordinate_coding.md): local
|
||||
coordinate coding with dictionary learning
|
||||
* [`LMNN`](user/methods/lmnn.md): large margin nearest neighbor (distance
|
||||
metric learning)
|
||||
* [`NCA`](user/methods/nca.md): neighborhood components analysis (distance
|
||||
metric learning)
|
||||
* [`NMF`](user/methods/nmf.md): non-negative matrix factorization
|
||||
* [`PCA`](user/methods/pca.md): principal components analysis
|
||||
* [`RADICAL`](user/methods/radical.md): robust, accurate, direct independent
|
||||
components analysis (ICA) algorithm
|
||||
* [`SparseCoding`](user/methods/sparse_coding.md): sparse coding with
|
||||
dictionary learning
|
||||
|
||||
### Modeling utilities
|
||||
|
||||
Tools for assembling a full data science pipeline.
|
||||
|
||||
* [Cross-validation](user/cv.md): k-fold cross-validation tools for any mlpack
|
||||
algorithm
|
||||
* [Hyperparameter tuning](user/hpt.md): generic hyperparameter tuner to find
|
||||
good hyperparameters for any mlpack algorithm
|
||||
|
||||
## Bindings to other languages
|
||||
|
||||
mlpack's bindings to other languages have less complete functionality than
|
||||
mlpack in C++, but almost all the same algorithms are available.
|
||||
|
||||
| ***Python*** | -- | [quickstart](quickstart/python.md) | -- | [reference](user/bindings/python.md) |
|
||||
| ***Julia*** | -- | [quickstart](quickstart/julia.md) | -- | [reference](user/bindings/julia.md) |
|
||||
| ***R*** | -- | [quickstart](quickstart/r.md) | -- | [reference](user/bindings/r.md)
|
||||
| ***Command-line programs*** | -- | [quickstart](quickstart/cli.md) | -- | [reference](user/bindings/cli.md) |
|
||||
| ***Go*** | -- | [quickstart](quickstart/go.md) | -- | [reference](user/bindings/go.md) |
|
||||
|
||||
## mlpack on embedded systems
|
||||
|
||||
mlpack is well suited for embedded systems due to the fact that it is written
|
||||
in C++ and it is header-only with minimal dependencies. In the following, we are
|
||||
adding a set of tutorials to allow you to experiment mlpack on various types of
|
||||
these systems.
|
||||
|
||||
* [cross-compile and run k-NN on a Raspberry Pi 2 (armv7)](embedded/crosscompile_armv7.md)
|
||||
|
||||
## Examples and further documentation
|
||||
|
||||
* [mlpack examples repository](https://github.com/mlpack/examples/): numerous
|
||||
fully-working example applications of mlpack, in C++ and other languages.
|
||||
* [mlpack models repository](https://github.com/mlpack/models/): complex models
|
||||
in C++ built with mlpack
|
||||
|
||||
For additional documentation beyond what is covered in all the resources above,
|
||||
the source code should be consulted. Each method is fully documented.
|
||||
|
||||
## Developer documentation
|
||||
|
||||
The following general documentation can be useful if you are interested in
|
||||
contributing to mlpack:
|
||||
|
||||
* [The mlpack community](developer/community.md)
|
||||
* [mlpack and Google Summer of Code](developer/gsoc.md)
|
||||
|
||||
Throughout the codebase, mlpack uses some common template parameter policies.
|
||||
These are documented below.
|
||||
|
||||
* [The `ElemType` policy](developer/elemtype.md): element types for data
|
||||
* [The `DistanceType` policy](developer/distances.md): distance metrics
|
||||
* [The `KernelType` policy](developer/kernels.md): kernel functions
|
||||
* [The `TreeType` policy](developer/trees.md): space trees (ball trees,
|
||||
KD-trees, etc.)
|
||||
|
||||
In addition, the following documentation may be useful when developing bindings
|
||||
for other languages:
|
||||
|
||||
* [Timers](developer/timer.md): timing parts of bindings
|
||||
* [Writing an mlpack binding](developer/iodoc.md): simple examples of mlpack
|
||||
bindings
|
||||
* [Automatic bindings](developer/bindings.md): details on mlpack's automatic
|
||||
binding generator system.
|
||||
<!-- NOTE: If you are updating the pipeline image, make sure to update *both*
|
||||
the wide and narrow versions. Yes, it is tedious, but we are limited
|
||||
by what HTML allows us. -->
|
||||
<object data="img/pipeline-wide.svg" type="image/svg+xml" id="pipeline-wide">
|
||||
(Your browser does not support inline SVG objects. Browse the mlpack
|
||||
pipeline using the navigation sidebar instead.)
|
||||
</object>
|
||||
<object data="img/pipeline-narrow.svg" type="image/svg+xml" id="pipeline-narrow">
|
||||
(Your browser does not support inline SVG objects. Browse the mlpack
|
||||
pipeline using the navigation sidebar instead.)
|
||||
</object>
|
||||
|
||||
## Changelog
|
||||
|
||||
|
||||
@@ -32,8 +32,9 @@ docker run -it mlpack/mlpack /bin/bash
|
||||
This Docker image has mlpack's command-line bindings already built and
|
||||
installed.
|
||||
|
||||
If you prefer to build mlpack from scratch, see the
|
||||
[main README](../../README.md).
|
||||
If you prefer to build the command-line programs from scratch, follow the
|
||||
instructions in the
|
||||
[installation guide](../user/install.md#compile-bindings-manually).
|
||||
|
||||
## Simple quickstart example
|
||||
|
||||
|
||||
@@ -24,9 +24,12 @@ make
|
||||
sudo make install
|
||||
```
|
||||
|
||||
After this, `go run my_code.go` will be able to correctly link against mlpack's
|
||||
Go bindings and run.
|
||||
|
||||
Building the Go bindings from scratch is a little more in-depth, though. For
|
||||
information on that, follow the instructions in the
|
||||
[main README](../../README.md).
|
||||
[installation guide](../user/install.md#compile-bindings-manually).
|
||||
|
||||
## Simple mlpack quickstart example
|
||||
|
||||
|
||||
@@ -18,7 +18,7 @@ Pkg.add("mlpack")
|
||||
|
||||
Building the Julia bindings from scratch is a little more in-depth, though. For
|
||||
information on that, follow the instructions in the
|
||||
[main README](../../README.md).
|
||||
[installation guide](../user/install.md#compile-bindings-manually).
|
||||
|
||||
## Simple quickstart example
|
||||
|
||||
|
||||
@@ -26,8 +26,9 @@ Python bindings pre-installed:
|
||||
docker run -it mlpack/mlpack /bin/bash
|
||||
```
|
||||
|
||||
Otherwise, you can build the Python bindings from scratch using the
|
||||
documentation in the [main README](../../README.md).
|
||||
Building the Python bindings from scratch is a little more in-depth, though.
|
||||
For information on that, follow the instructions in the
|
||||
[installation guide](../user/install.md#compile-bindings-manually).
|
||||
|
||||
## Simple mlpack quickstart example
|
||||
|
||||
|
||||
+1
-1
@@ -17,7 +17,7 @@ install.packages('mlpack')
|
||||
|
||||
Building the R bindings from scratch is a little more in-depth, though. For
|
||||
information on that, follow the instructions in the
|
||||
[main README](../../README.md).
|
||||
[installation guide](../user/install.md#compile-bindings-manually).
|
||||
|
||||
## Simple mlpack quickstart example
|
||||
|
||||
|
||||
+314
-182
@@ -20,15 +20,18 @@ when the sidebar is built for each page.
|
||||
</summary>
|
||||
|
||||
<ul>
|
||||
<!-- General "getting started" documentation -->
|
||||
<!-- The "Prerequisites" section stays expanded at the top level. -->
|
||||
<li>
|
||||
<a href="LINKROOTquickstart/cpp.html">Quickstart</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/matrices.html">Matrices and data</a>
|
||||
<a href="LINKROOTuser/install.html">Installing mlpack</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/load_save.html">Loading and saving</a>
|
||||
<a href="LINKROOTembedded/supported_boards.html">Cross-compilation setup</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/matrices.html">Data representation in mlpack</a>
|
||||
</li>
|
||||
<li class="bottom-space">
|
||||
<a href="LINKROOTcitation.html">Citation</a>
|
||||
@@ -36,16 +39,42 @@ when the sidebar is built for each page.
|
||||
|
||||
<!-- Documentation for library core functions -->
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTuser/tutorials.html">
|
||||
Tutorials and Examples
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="https://github.com/mlpack/examples/">
|
||||
mlpack examples repository
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://www.youtube.com/@mlpack">
|
||||
mlpack Youtube channel
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://github.com/mlpack/models/">
|
||||
mlpack models repository
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
<li class="bottom-space">
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTuser/core.html">
|
||||
Core functionality
|
||||
Utility classes
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/math.html">
|
||||
Core math utilities
|
||||
Math
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
@@ -63,190 +92,105 @@ when the sidebar is built for each page.
|
||||
Kernels
|
||||
</a>
|
||||
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|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTuser/core/trees.html">
|
||||
Trees
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/kdtree.html">
|
||||
<code>KDTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/mean_split_kdtree.html">
|
||||
<code>MeanSplitKDTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/ball_tree.html">
|
||||
<code>BallTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/mean_split_ball_tree.html">
|
||||
<code>MeanSplitBallTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/vptree.html">
|
||||
<code>VPTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/rp_tree.html">
|
||||
<code>RPTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/max_rp_tree.html">
|
||||
<code>MaxRPTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/ub_tree.html">
|
||||
<code>UBTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/binary_space_tree.html">
|
||||
<code>BinarySpaceTree</code>
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
|
||||
<!-- Trees -->
|
||||
<!-- Data loading and I/O -->
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTuser/core/trees.html">
|
||||
Trees
|
||||
<a href="LINKROOTuser/load_save.html">
|
||||
Data loading and I/O
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/kdtree.html">
|
||||
<code>KDTree</code>
|
||||
<a href="LINKROOTuser/load_save.html#numeric-data">
|
||||
Numeric data
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/mean_split_kdtree.html">
|
||||
<code>MeanSplitKDTree</code>
|
||||
<a href="LINKROOTuser/load_save.html#mixed-categorical-data">
|
||||
Mixed categorical data
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/ball_tree.html">
|
||||
<code>BallTree</code>
|
||||
<a href="LINKROOTuser/load_save.html#image-data">
|
||||
Image data
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/mean_split_ball_tree.html">
|
||||
<code>MeanSplitBallTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/vptree.html">
|
||||
<code>VPTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/rp_tree.html">
|
||||
<code>RPTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/max_rp_tree.html">
|
||||
<code>MaxRPTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/ub_tree.html">
|
||||
<code>UBTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/core/trees/binary_space_tree.html">
|
||||
<code>BinarySpaceTree</code>
|
||||
<a href="LINKROOTuser/load_save.html#mlpack-objects">
|
||||
mlpack models and objects
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
|
||||
<!-- Classification algorithms -->
|
||||
<!-- Preprocessing / feature extraction -->
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTindex.html#classification-algorithms">
|
||||
Classification
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/adaboost.html">
|
||||
<code>AdaBoost</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/decision_tree.html">
|
||||
<code>DecisionTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/hoeffding_tree.html">
|
||||
<code>HoeffdingTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/linear_svm.html">
|
||||
<code>LinearSVM</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/logistic_regression.html">
|
||||
<code>LogisticRegression</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/naive_bayes_classifier.html">
|
||||
<code>NaiveBayesClassifier</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/perceptron.html">
|
||||
<code>Perceptron</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/random_forest.html">
|
||||
<code>RandomForest</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/softmax_regression.html">
|
||||
<code>SoftmaxRegression</code>
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
|
||||
<!-- Regression algorithms -->
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTindex.html#regression-algorithms">
|
||||
Regression
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/bayesian_linear_regression.html">
|
||||
<code>BayesianLinearRegression</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/decision_tree_regressor.html">
|
||||
<code>DecisionTreeRegressor</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/lars.html">
|
||||
<code>LARS</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/linear_regression.html">
|
||||
<code>LinearRegression</code>
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
|
||||
<!-- Clustering algorithms -->
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTindex.html#clustering-algorithms">
|
||||
Clustering
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/mean_shift.html">
|
||||
<code>MeanShift</code>
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
|
||||
<!-- Geometric algorithms -->
|
||||
<li>
|
||||
<a href="LINKROOTindex.html#geometric-algorithms">
|
||||
Geometry
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<!-- Preprocessing utilities -->
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTindex.html#preprocessing-utilities">
|
||||
Preprocessing
|
||||
<a href="LINKROOTuser/preprocessing.html">
|
||||
Preprocessing/feature extraction
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
@@ -268,7 +212,7 @@ when the sidebar is built for each page.
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTindex.html#transformations">
|
||||
<a href="LINKROOTuser/transformations.html">
|
||||
Transformations
|
||||
</a>
|
||||
</summary>
|
||||
@@ -317,14 +261,135 @@ when the sidebar is built for each page.
|
||||
</details>
|
||||
</li>
|
||||
|
||||
<!-- Modeling utilities -->
|
||||
<li class="bottom-space">
|
||||
<!-- Classification algorithms -->
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTindex.html#modeling-utilities">
|
||||
<a href="LINKROOTuser/modeling.html">
|
||||
Modeling
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTuser/modeling.html#classification">
|
||||
Classification
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/adaboost.html">
|
||||
<code>AdaBoost</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/decision_tree.html">
|
||||
<code>DecisionTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/hoeffding_tree.html">
|
||||
<code>HoeffdingTree</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/linear_svm.html">
|
||||
<code>LinearSVM</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/logistic_regression.html">
|
||||
<code>LogisticRegression</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/naive_bayes_classifier.html">
|
||||
<code>NaiveBayesClassifier</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/perceptron.html">
|
||||
<code>Perceptron</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/random_forest.html">
|
||||
<code>RandomForest</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/softmax_regression.html">
|
||||
<code>SoftmaxRegression</code>
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTuser/modeling.html#regression">
|
||||
Regression
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/bayesian_linear_regression.html">
|
||||
<code>BayesianLinearRegression</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/decision_tree_regressor.html">
|
||||
<code>DecisionTreeRegressor</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/lars.html">
|
||||
<code>LARS</code>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/linear_regression.html">
|
||||
<code>LinearRegression</code>
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTuser/modeling.html#clustering">
|
||||
Clustering
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTuser/methods/mean_shift.html">
|
||||
<code>MeanShift</code>
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/modeling.html#geometric-algorithms">
|
||||
Geometric algorithms
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
|
||||
<!-- Evaluation -->
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTuser/evaluation.html">
|
||||
Evaluation
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTuser/cv.html">
|
||||
@@ -339,39 +404,106 @@ when the sidebar is built for each page.
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
|
||||
|
||||
<!-- Deployment -->
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTindex.html#mlpack-on-embedded-systems">
|
||||
Embedded Applications
|
||||
<a href="LINKROOTuser/deployment.html">
|
||||
Deployment
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTembedded/crosscompile_armv7.html">
|
||||
cross-compile and run k-NN on a Raspberry Pi 2 (armv7)
|
||||
<a href="LINKROOTuser/compile.html">
|
||||
Compilation
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTembedded/supported_boards.html">
|
||||
Setting up an mlpack cross-compilation environment
|
||||
<a href="LINKROOTembedded/crosscompile_armv7.html">
|
||||
Cross-compile to RPi2
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTuser/deploy_windows.html">
|
||||
Deploying mlpack on Windows
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</details>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a href="LINKROOTindex.html#examples-and-further-documentation">
|
||||
Examples
|
||||
</a>
|
||||
</li>
|
||||
|
||||
<li>
|
||||
<a href="LINKROOTindex.html#developer-documentation">
|
||||
Developers
|
||||
</a>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTdeveloper/developers.html">
|
||||
Developers
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTdeveloper/community.html">
|
||||
Community
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTdeveloper/gsoc.html">
|
||||
GSoC
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTdeveloper/ci.html">
|
||||
CI/CD
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTdeveloper/timer.html">
|
||||
Timers
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTdeveloper/bindings.html">
|
||||
Binding system
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTdeveloper/iodoc.html">
|
||||
Writing a binding
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<details>
|
||||
<summary>
|
||||
<a href="LINKROOTdeveloper/policies.html">
|
||||
Template policies
|
||||
</a>
|
||||
</summary>
|
||||
<ul>
|
||||
<li>
|
||||
<a href="LINKROOTdeveloper/elemtype.html">
|
||||
ElemType
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTdeveloper/distances.html">
|
||||
DistanceType
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTdeveloper/kernels.html">
|
||||
KernelType
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="LINKROOTdeveloper/trees.html">
|
||||
TreeType
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
</li>
|
||||
</ul>
|
||||
</details>
|
||||
@@ -382,7 +514,7 @@ when the sidebar is built for each page.
|
||||
<li>
|
||||
<details> <!-- default closed for non-binding pages -->
|
||||
<summary>
|
||||
<a href="LINKROOTindex.html#bindings-to-other-languages" class="textlink"><b>Binding API</b></a>
|
||||
<a href="LINKROOTuser/bindings.html" class="textlink"><b>Binding API</b></a>
|
||||
</summary>
|
||||
|
||||
<ul>
|
||||
|
||||
@@ -0,0 +1,54 @@
|
||||
# Bindings to Other Languages
|
||||
|
||||
In addition to the [main C++ interface](../index.md), mlpack also provides
|
||||
bindings via a simplified API to a number of other languages. This binding API
|
||||
is consistent across all languages, allowing for easy transition of mlpack code
|
||||
between languages,
|
||||
|
||||
***Note:*** the binding API is not as flexible or general as the C++ interface;
|
||||
to get the most out of mlpack, C++ is likely the better route to go.
|
||||
|
||||
## CLI (Command-line programs)
|
||||
|
||||
<center>
|
||||
<img src="../img/terminal.svg" width="50" alt="a terminal" />
|
||||
</center>
|
||||
|
||||
- [CLI quickstart](../quickstart/cli.md)
|
||||
- [CLI API reference](bindings/cli.md)
|
||||
|
||||
## Python
|
||||
|
||||
<center>
|
||||
<img src="../img/python.svg" width="50" alt="the Python logo" />
|
||||
</center>
|
||||
|
||||
- [Python quickstart](../quickstart/python.md)
|
||||
- [Python API reference](bindings/python.md)
|
||||
|
||||
## Julia
|
||||
|
||||
<center>
|
||||
<img src="../img/julia.svg" width="50" alt="the Julia logo" />
|
||||
</center>
|
||||
|
||||
- [Julia quickstart](../quickstart/julia.md)
|
||||
- [Julia API reference](bindings/julia.md)
|
||||
|
||||
## R
|
||||
|
||||
<center>
|
||||
<img src="../img/r.svg" width="50" alt="the R logo" />
|
||||
</center>
|
||||
|
||||
- [R quickstart](../quickstart/r.md)
|
||||
- [R API reference](bindings/r.md)
|
||||
|
||||
## Go
|
||||
|
||||
<center>
|
||||
<img src="../img/gopher.svg" width="50" alt="the Go gopher" />
|
||||
</center>
|
||||
|
||||
- [Go quickstart](../quickstart/go.md)
|
||||
- [Go API reference](bindings/go.md)
|
||||
@@ -155,8 +155,8 @@ manually download ensmallen from http://ensmallen.org/ and extract it to
|
||||
`C:\mlpack\mlpack\build\Debug` (or `C:\mlpack\mlpack\build\Release` if you
|
||||
changed to Release mode)
|
||||
|
||||
You are ready to create your first application; take a look at the
|
||||
[Sample C++ ML App](sample_ml_app.md).
|
||||
You are ready to create your first application; take a look at
|
||||
[Deploying mlpack on Windows](deploy_windows.md).
|
||||
|
||||
## Building mlpack with Visual Studio's CMake integration
|
||||
|
||||
|
||||
@@ -0,0 +1,154 @@
|
||||
# Compile an mlpack program
|
||||
|
||||
Once an mlpack application has been developed, it is easy to compile it into a
|
||||
standalone program. On this page, compilation is performed via the command-line
|
||||
on a standard Linux or OS X system; if this is not your environment, see also:
|
||||
|
||||
* [Cross-compile to a Raspberry Pi 2](../embedded/crosscompile_armv7.md)
|
||||
* [Deploy mlpack on Windows](deploy_windows.md)
|
||||
|
||||
## Simple command-line compilation
|
||||
|
||||
Assuming that mlpack and its dependencies are [installed on the
|
||||
system](install.md), an mlpack program can be compiled just like any other C++
|
||||
program:
|
||||
|
||||
```sh
|
||||
g++ -std=c++17 -O3 -o mlpack_program mlpack_program.cpp -larmadillo -fopenmp
|
||||
```
|
||||
|
||||
The command above uses [gcc](https://gcc.gnu.org/) to compile the program
|
||||
`mlpack_program.cpp` in C++17 mode with optimizations, using OpenMP for
|
||||
parallelization. It is expected that `mlpack_program.cpp` has the `int main()`
|
||||
function defined.
|
||||
|
||||
For more complex applications that have multiple source files, it can often be
|
||||
easier to develop a simple [`Makefile`](https://www.gnu.org/software/make/manual/html_node/Simple-Makefile.html).
|
||||
|
||||
The [examples repository](https://github.com/mlpack/examples) contains several
|
||||
standalone C++ projects, each of which have `Makefile`s. These can be adapted
|
||||
for any project, and are especially useful if any extra include directories or
|
||||
library directories need to be specified. (This might be the case if, for
|
||||
instance, mlpack or any dependencies are not installed to standard locations.)
|
||||
|
||||
* [Example adaptable `Makefile`](https://github.com/mlpack/examples/blob/master/cpp/neural_networks/mnist_cnn/Makefile)
|
||||
|
||||
A full list of compiler options to configure the build is beyond the scope of
|
||||
this simple documentation, but
|
||||
[this simple list](https://gist.github.com/g-berthiaume/74f0485fbba5cc3249eee458c1d0d386)
|
||||
has a handful of commonly-used gcc/clang options.
|
||||
|
||||
### Configuring mlpack with compile-time definitions
|
||||
|
||||
Several compilation options can control the behavior of an mlpack program.
|
||||
These can be specified directly on the command line, or at the top of the
|
||||
program (before including mlpack or Armadillo!).
|
||||
|
||||
| ***Command-line option*** | ***Code option*** | ***Meaning*** |
|
||||
|---------------------------|-------------------|---------------|
|
||||
|*Speed and debugging.* |||
|
||||
| `-DNDEBUG` | `#define NDEBUG` | Remove all debugging checks. This can result in slightly faster code, but with no error checking! |
|
||||
| `-DARMA_NO_DEBUG` | `#define ARMA_NO_DEBUG` | Remove all Armadillo error checking. *Warning:* if there are errors in your code, you are more likely to get a segfault instead of an exception! |
|
||||
|---------------------------|-------------------|---------------|
|
||||
|*Output.* |||
|
||||
| `-DMLPACK_COUT_STREAM=std::cout` | `#define MLPACK_COUT_STREAM std::cout` | Set the default output stream. (Defaults to `std::cout`.) |
|
||||
| `-DMLPACK_CERR_STREAM=std::cerr` | `#define MLPACK_CERR_STREAM std::cerr` | Set the default error stream. (Defaults to `std::cerr`.) |
|
||||
| `-DMLPACK_PRINT_INFO` | `#define MLPACK_PRINT_INFO` | Print information messages (`[INFO ]`) during program execution. |
|
||||
| `-DMLPACK_PRINT_WARN` | `#define MLPACK_PRINT_WARN` | Print warning messages (`[WARN ]`) during program execution. |
|
||||
| `-DMLPACK_SUPPRESS_FATAL` | `#define MLPACK_PRINT_FATAL` | Do not print `[FATAL]` messages during program execution. |
|
||||
| `-DENS_PRINT_INFO` | `#define ENS_PRINT_INFO` | Print informational messages from [ensmallen](https://www.ensmallen.org/) optimizers. |
|
||||
| `-DENS_PRINT_WARN` | `#define ENS_PRINT_WARN` | Print warning messages from [ensmallen](https://ensmallen.org/) optimizers. |
|
||||
|---------------------------|-------------------|---------------|
|
||||
|*Functionality.* |||
|
||||
| `-DMLPACK_ENABLE_ANN_SERIALIZATION` | `#define MLPACK_ENABLE_ANN_SERIALIZATION` | Allow neural network layers to be serialized. |
|
||||
| `-DMLPACK_DISABLE_STB` | `#define MLPACK_DISABLE_STB` | Disable [STB](https://github.com/nothings/stb)-related [image functionality](load_save.md#image-data). |
|
||||
|
||||
***Note:*** If your code serializes (saves or loads) mlpack neural networks, the
|
||||
`MLPACK_ENABLE_ANN_SERIALIZATION` option must be enabled. This option is not
|
||||
enabled by default because it can cause compilation time to increase
|
||||
significantly, but it is necessary for any code that serializes neural networks.
|
||||
|
||||
## Linking without the Armadillo wrapper
|
||||
|
||||
Armadillo, by default, requires linking against the runtime library
|
||||
`libarmadillo.so` (or `libarmadillo.dylib` or `armadillo.dll` on non-Linux
|
||||
systems). This library is a convenience library that internally contains all of
|
||||
the symbols necessary from lower-level libraries (e.g.
|
||||
[OpenBLAS](https://www.openblas.net/),
|
||||
[SuperLU](https://portal.nersc.gov/project/sparse/superlu/),
|
||||
[ARPACK](https://www.arpack.org/),
|
||||
[HDF5](https://www.hdfgroup.org/solutions/hdf5/), and so on).
|
||||
When the wrapper library is used, linking against Armadillo means simply typing
|
||||
`-larmadillo` instead of linking against all of Armadillo's dependencies.
|
||||
|
||||
In some situations this is not preferable, and it is therefore possible via the
|
||||
[`ARMA_DONT_USE_WRAPPER` macro](https://arma.sourceforge.net/docs.html#config_hpp)
|
||||
to avoid the Armadillo runtime library and link directly against Armadillo's
|
||||
dependencies.
|
||||
|
||||
When the Armadillo wrapper library is not being used, a compilation command will
|
||||
need to be adjusted. For instance, the example of the previous section would
|
||||
need to be changed to:
|
||||
|
||||
```
|
||||
g++ -DARMA_DONT_USE_WRAPPER -std=c++17 -O3 -o mlpack_program mlpack_program.cpp -lopenblas -fopenmp
|
||||
```
|
||||
|
||||
Some notes on the command above:
|
||||
|
||||
* Here, `ARMA_DONT_USE_WRAPPER` is specified on the command line instead of in
|
||||
`mlpack_program.cpp` (or otherwise in the Armadillo
|
||||
[configuration](https://arma.sourceforge.net/docs.html#config_hpp)).
|
||||
|
||||
* OpenBLAS is used for BLAS/LAPACK support. But, other options include ACML,
|
||||
reference LAPACK/BLAS, Intel MKL, and so forth.
|
||||
|
||||
* In some programs, especially if sparse matrix support or HDF5 support is
|
||||
used, it may be necessary to link against other libraries (e.g. `-lSuperLU
|
||||
-lhdf5`, etc.). The precise set of libraries to link against depends on the
|
||||
code being used and the system configuration, but it should be easy enough to
|
||||
use any linker errors to figure out what libraries need to be linked against.
|
||||
|
||||
## Using mlpack in another CMake project
|
||||
|
||||
For complex C++ projects, a build system like CMake may be in use. Adding
|
||||
mlpack as a dependency to a C++ project is straightforward. The following CMake
|
||||
code will require mlpack and its dependencies to be available:
|
||||
|
||||
```cmake
|
||||
# Find mlpack and its dependencies.
|
||||
find_package(Armadillo REQUIRED)
|
||||
find_package(cereal REQUIRED)
|
||||
find_package(ensmallen REQUIRED)
|
||||
find_package(mlpack REQUIRED)
|
||||
|
||||
include_directories("${ARMADILLO_INCLUDE_DIRS}" "${CEREAL_INCLUDE_DIR}"
|
||||
"${ENSMALLEN_INCLUDE_DIR}" "${MLPACK_INCLUDE_DIR}")
|
||||
|
||||
# Targets should link against ${ARMADILLO_LIBRARIES}.
|
||||
```
|
||||
|
||||
If the relevant files are not available on the system to find those four
|
||||
packages, they can be downloaded from the
|
||||
[models](https://github.com/mlpack/models) repository:
|
||||
|
||||
* [`models/CMake` directory](https://github.com/mlpack/models/tree/master/CMake)
|
||||
|
||||
The following files in that directory are necessary (and can be added to the
|
||||
CMake files for the project):
|
||||
|
||||
* [`ARMA_FindACML.cmake`](https://github.com/mlpack/models/blob/master/CMake/ARMA_FindACML.cmake)
|
||||
* [`ARMA_FindACMLMP.cmake`](https://github.com/mlpack/models/blob/master/CMake/ARMA_FindACMLMP.cmake)
|
||||
* [`ARMA_FindARPACK.cmake`](https://github.com/mlpack/models/blob/master/CMake/ARMA_FindARPACK.cmake)
|
||||
* [`ARMA_FindBLAS.cmake`](https://github.com/mlpack/models/blob/master/CMake/ARMA_FindBLAS.cmake)
|
||||
* [`ARMA_FindCBLAS.cmake`](https://github.com/mlpack/models/blob/master/CMake/ARMA_FindCBLAS.cmake)
|
||||
* [`ARMA_FindCLAPACK.cmake`](https://github.com/mlpack/models/blob/master/CMake/ARMA_FindCLAPACK.cmake)
|
||||
* [`ARMA_FindLAPACK.cmake`](https://github.com/mlpack/models/blob/master/CMake/ARMA_FindLAPACK.cmake)
|
||||
* [`ARMA_FindMKL.cmake`](https://github.com/mlpack/models/blob/master/CMake/ARMA_FindMKL.cmake)
|
||||
* [`ARMA_FindOpenBLAS.cmake`](https://github.com/mlpack/models/blob/master/CMake/ARMA_FindOpenBLAS.cmake)
|
||||
* [`FindArmadillo.cmake`](https://github.com/mlpack/models/blob/master/CMake/FindArmadillo.cmake)
|
||||
* [`FindEnsmallen.cmake`](https://github.com/mlpack/models/blob/master/CMake/FindEnsmallen.cmake)
|
||||
* [`Findcereal.cmake`](https://github.com/mlpack/models/blob/master/CMake/Findcereal.cmake)
|
||||
* [`Findmlpack.cmake`](https://github.com/mlpack/models/blob/master/CMake/Findmlpack.cmake)
|
||||
|
||||
<!-- TODO: improve this so that it is simpler in the future! -->
|
||||
+3
-3
@@ -1,8 +1,8 @@
|
||||
# mlpack core class documentation
|
||||
# Utility classes
|
||||
|
||||
Underlying the implementations of [mlpack's machine learning
|
||||
algorithms](../index.md#mlpack-algorithm-documentation) are mlpack core support
|
||||
classes, each of which are documented in the pages below:
|
||||
algorithms](../index.md) are mlpack core support classes, each of which are
|
||||
documented in the pages below:
|
||||
|
||||
* [Core math utilities](core/math.md): utility classes for mathematical
|
||||
purposes
|
||||
|
||||
@@ -31,7 +31,7 @@ including:
|
||||
## `LMetric`
|
||||
|
||||
The `LMetric` template class implements a [generalized
|
||||
L-metric](https://en.wikipedia.org/wiki/Lp_space#Definition)
|
||||
L-metric](https://en.wikipedia.org/wiki/Lp_space#Preliminaries)
|
||||
(L1-metric, L2-metric, etc.). The class has two template parameters:
|
||||
|
||||
```
|
||||
|
||||
@@ -1873,13 +1873,13 @@ class BoundType
|
||||
```
|
||||
|
||||
Behavior of some aspects of the `BinarySpaceTree` depend on the traits of a
|
||||
particular bound. Optionally, you may define a `BoundTraits` specialization for
|
||||
your bound type, of the following form:
|
||||
particular bound. Optionally, you may define an `mlpack::BoundTraits`
|
||||
specialization for your bound type, of the following form:
|
||||
|
||||
```c++
|
||||
// Replace `BoundType` below with the name of the custom class.
|
||||
template<typename DistanceType, typename ElemType>
|
||||
struct BoundTraits<BoundType<DistanceType, ElemType>>
|
||||
struct mlpack::BoundTraits<BoundType<DistanceType, ElemType>>
|
||||
{
|
||||
//! If true, then the bounds for each dimension are tight. If false, then the
|
||||
//! bounds for each dimension may be looser than the range of all points held
|
||||
|
||||
@@ -1,12 +1,12 @@
|
||||
# Sample C++ ML App for Windows
|
||||
# Deploying mlpack on Windows
|
||||
|
||||
*by German Lancioni*
|
||||
|
||||
This tutorial will help you create a sample machine learning app using
|
||||
mlpack/C++. Although this app does not cover all the mlpack capabilities, it
|
||||
will walkthrough several APIs to understand how everything connects. This
|
||||
Windows sample app is created using Visual Studio, but you can easily adapt it
|
||||
to a different platform by following the provided source code.
|
||||
mlpack/C++ on Windows. The tutorial will walk through several APIs to
|
||||
understand how everything connects. This Windows sample app is created using
|
||||
Visual Studio, but you can easily adapt it to a different platform by following
|
||||
the provided source code.
|
||||
|
||||
*Note*: before starting, make sure you have built mlpack for Windows following
|
||||
this [Windows guide](build_windows.md).
|
||||
@@ -0,0 +1,25 @@
|
||||
<object data="../img/pipeline-top-6.svg" type="image/svg+xml" id="pipeline-top">
|
||||
</object>
|
||||
|
||||
# Deployment
|
||||
|
||||
Once a modeling pipeline is ready for deployment, it is easy to deploy mlpack
|
||||
applications to a wide variety of settings due to its simple header-only nature.
|
||||
|
||||
See also the [examples repository](https://github.com/mlpack/examples/),
|
||||
which contains a number of fully-working deployable example applications.
|
||||
|
||||
The pages below provide guidance for how to deploy mlpack to a variety of
|
||||
relatively simple environments.
|
||||
|
||||
* [***Compile an mlpack program***](compile.md): compile a standalone C++ program
|
||||
that uses mlpack.
|
||||
|
||||
* [***Cross-compile to a Raspberry Pi***](../embedded/crosscompile_armv7.md):
|
||||
cross-compile an mlpack C++ application to an embedded or low-resource
|
||||
device.
|
||||
- See also the
|
||||
[cross-compilation setup page](../embedded/supported_boards.md).
|
||||
|
||||
* [***Deploying mlpack on Windows***](deploy_windows.md): build a Windows
|
||||
application that uses mlpack.
|
||||
@@ -0,0 +1,12 @@
|
||||
<object data="../img/pipeline-top-5.svg" type="image/svg+xml" id="pipeline-top">
|
||||
</object>
|
||||
|
||||
# Evaluation
|
||||
|
||||
Once a model is trained, mlpack contains a number of utilities for testing the
|
||||
model.
|
||||
|
||||
* [Cross-validation](cv.md): k-fold cross-validation tools for any mlpack
|
||||
algorithm
|
||||
* [Hyperparameter tuning](hpt.md): generic hyperparameter tuner to find
|
||||
good hyperparameters for any mlpack algorithm
|
||||
@@ -0,0 +1,279 @@
|
||||
# Installing mlpack
|
||||
|
||||
mlpack is available via a wide variety of sources, depending on what you want to
|
||||
do with the library.
|
||||
|
||||
***If you want to use mlpack in a C++ program:***
|
||||
|
||||
* Install via [your system's package manager](#install-via-package-manager)
|
||||
*(easiest)*.
|
||||
|
||||
* [Install from source](#install-from-source) (see also the
|
||||
[dependencies](#dependencies) of mlpack).
|
||||
|
||||
* If you are on Windows, see the
|
||||
[Building mlpack from source on Windows page](build_windows.md).
|
||||
|
||||
* If you intend to cross-compile, see the
|
||||
[cross-compilation setup page](../embedded/supported_boards.md).
|
||||
|
||||
***If you want to use mlpack's bindings to another language:***
|
||||
|
||||
* Install mlpack's bindings to other languages via
|
||||
[language package managers](#install-bindings-via-language-package-managers)
|
||||
*(easiest)*.
|
||||
|
||||
* [Compile and install bindings manually](#compile-bindings-manually).
|
||||
|
||||
***If you want to develop mlpack:***
|
||||
|
||||
* [Configure and compile all of mlpack from source](#compile-from-source).
|
||||
|
||||
* Look at the [CMake configuration options](#cmake-options).
|
||||
|
||||
* [Build the tests](#build-tests).
|
||||
|
||||
Once mlpack is installed, try
|
||||
[compiling a test program](#compiling-a-test-program).
|
||||
|
||||
---
|
||||
|
||||
## Install via package manager
|
||||
|
||||
The easiest way to install the mlpack C++ library is to use your system package
|
||||
manager. This will handle mlpack's dependencies automatically.
|
||||
|
||||
* ***Ubuntu/Debian***: `sudo apt-get install libmlpack-dev`
|
||||
* ***Fedora/RHEL***: `sudo dnf install mlpack-devel`
|
||||
* ***Arch Linux***: `sudo pacman -S mlpack`
|
||||
* ***OS X (Homebrew)***: `brew install mlpack`
|
||||
* ***OS X (MacPorts)***: `sudo port install mlpack`
|
||||
* ***vcpkg (Windows)***: `vcpkg install mlpack:x64-windows`
|
||||
* ***conda***: `conda install conda-forge::mlpack`
|
||||
* ***Conan***: [see here](https://conan.io/center/recipes/mlpack)
|
||||
|
||||
You can also use the
|
||||
[`mlpack/mlpack` image on DockerHub](https://hub.docker.com/r/mlpack/mlpack) for
|
||||
a container with mlpack already installed.
|
||||
|
||||
If you plan to write mlpack programs, make sure you have a C++ compiler that
|
||||
supports C++17 available (this may not be automatically installed by the package
|
||||
manager).
|
||||
|
||||
## Install from source
|
||||
|
||||
If you only intend to use mlpack in a C++ program, it is not necessary to
|
||||
[configure and compile from source](#compile-from-source), because mlpack is a
|
||||
header-only library. This means that you can simply
|
||||
[download mlpack](https://www.mlpack.org/download.html) and unpack it, and when
|
||||
you are [compiling a program](#compiling-a-test-program), you must make sure
|
||||
that the `src/` directory is on the include path.
|
||||
|
||||
With most compilers, this means you simply add the flag `-I/path/to/mlpack/src/`
|
||||
to the compiler command-line (e.g.,
|
||||
`g++ -I/path/to/mlpack/src/ -o program program.cpp -larmadillo`).
|
||||
|
||||
---
|
||||
|
||||
If you wish to install the mlpack headers to your system manually via CMake, you
|
||||
can use the following commands:
|
||||
|
||||
```sh
|
||||
mkdir build && cd build/
|
||||
cmake ..
|
||||
sudo make install
|
||||
```
|
||||
|
||||
Alternately, since CMake v3.14.0, the `cmake` command can create the build
|
||||
folder itself, and so the above commands can be rewritten as follows:
|
||||
|
||||
```sh
|
||||
cmake -S . -B build
|
||||
sudo cmake --build build --target install
|
||||
```
|
||||
|
||||
### Dependencies
|
||||
|
||||
You must also ensure that the dependencies of mlpack are available to the
|
||||
compiler:
|
||||
|
||||
- [Armadillo](https://arma.sourceforge.net)  >= 10.8
|
||||
- [ensmallen](https://ensmallen.org)  >= 2.10.0
|
||||
- [cereal](http://uscilab.github.io/cereal/)     >= 1.1.2
|
||||
|
||||
Dependencies can be installed using the system package manager. For example,
|
||||
on Debian and Ubuntu, all relevant dependencies can be installed with `sudo
|
||||
apt-get install libarmadillo-dev libensmallen-dev libcereal-dev libstb-dev g++
|
||||
cmake`.
|
||||
|
||||
If the STB library headers are available, image loading support will be
|
||||
available.
|
||||
|
||||
If you are compiling Armadillo by hand, ensure that LAPACK and BLAS are enabled.
|
||||
|
||||
If you are configuring mlpack with CMake (as in the code snippets in the
|
||||
previous section), you can use the auto-downloader to obtain mlpack's
|
||||
dependencies with the `-DDOWNLOAD_DEPENDENCIES=ON` option (detailed in the
|
||||
[CMake options section](#cmake-options). The autodownloader is especially
|
||||
useful for [cross-compilation](../embedded/supported_boards.md), as it
|
||||
automatically downloads and compiles OpenBLAS for the target architecture.
|
||||
|
||||
## Install bindings via language package managers
|
||||
|
||||
If you wish to use mlpack's bindings to other languages, see the quickstarts for
|
||||
each language for more information on installation:
|
||||
|
||||
* [Python](../quickstart/python.md)
|
||||
* [Command-line](../quickstart/cli.md)
|
||||
* [Julia](../quickstart/julia.md)
|
||||
* [R](../quickstart/r.md)
|
||||
* [Go](../quickstart/go.md)
|
||||
|
||||
## Compile bindings manually
|
||||
|
||||
It is possible to manually build the bindings from source. However, this is not
|
||||
recommended, as building bindings for a specific language often requires some
|
||||
amount of setup and is not often a user-friendly process. Specifically, after
|
||||
the bindings are built, deploying them to the environment of the target language
|
||||
can be non-trivial and requires knowledge specific to that language (not covered
|
||||
here).
|
||||
|
||||
To compile bindings for a particular language, follow the
|
||||
[Compile from source](#compile-from-source) section below, and enable the
|
||||
appropriate [CMake options](#cmake-options).
|
||||
|
||||
The results of bindings will be built into `build/src/mlpack/bindings/<lang>/`
|
||||
where `build/` is the build directory configured with CMake, and `<lang>` should
|
||||
be replaced with the appropriate language (`python`/`r`/`go`/`julia`). There
|
||||
are two exceptions:
|
||||
|
||||
* Command-line bindings will be built into `build/bin/`.
|
||||
* Markdown bindings will produce Markdown files in `build/doc/`.
|
||||
|
||||
## Compile from source
|
||||
|
||||
If you intend to develop mlpack, or want to build the tests or bindings to
|
||||
another language (including the command-line bindings), you will need to compile
|
||||
from source. Once you have installed [the dependencies](#dependencies) and
|
||||
[downloaded mlpack](https://www.mlpack.org/download.html), unpack the sources
|
||||
and configure with CMake.
|
||||
|
||||
The command below enables building the tests and the command-line programs.
|
||||
More options are detailed in the [CMake options section](#cmake-options).
|
||||
|
||||
```sh
|
||||
mkdir build && cd build/
|
||||
cmake -DBUILD_TESTS=ON -DBUILD_CLI_EXECUTABLES=ON ../
|
||||
make -j4
|
||||
```
|
||||
|
||||
The `-j4` option specifies that 4 cores should be used for the build; if you are
|
||||
running into RAM limitations (or don't have four cores), reduce this. If you
|
||||
have more cores available, you can increase the number of cores for a faster
|
||||
build.
|
||||
|
||||
### CMake options
|
||||
|
||||
The following options can be used when configuring mlpack.
|
||||
|
||||
| ***Option*** | ***Description*** | ***Default*** |
|
||||
|--------------|-------------------|---------------|
|
||||
| ***General configuration*** |||
|
||||
| `-DDOWNLOAD_DEPENDENCIES=ON` | Download all dependencies that are not found on the system. | `OFF` |
|
||||
| `-DDEBUG=ON` | Compile with debugging symbols. | `OFF` |
|
||||
| `-DPROFILE=ON` | Compile with profiling symbols. | `OFF` |
|
||||
| `-DARMA_EXTRA_DEBUG=ON` | Emit extra Armadillo debugging output (warning: *very* verbose). | `OFF` |
|
||||
| `-DTEST_VERBOSE=ON` | Emit verbose output when running tests. | `OFF` |
|
||||
| `-DBUILD_TESTS=ON` | Build `mlpack_test`. | `OFF` |
|
||||
| `-DUSE_OPENMP=ON` | Use OpenMP for parallelization. | `ON` |
|
||||
| `-DUSE_PRECOMPILED_HEADERS=OFF` | Disable precompiled headers during build. |
|
||||
`OFF` |
|
||||
|--------------|-------------------|---------------|
|
||||
| ***Dependency locations*** |||
|
||||
| `-DARMADILLO_INCLUDE_DIR=/path/to/arma/include/` | Path containing `armadillo` header file. ||
|
||||
| `-DARMADILLO_LIBRARY=/path/to/libarmadillo.so` | Path of compiled Armadillo library (if using the Armadillo wrapper library). ||
|
||||
| `-DARMADILLO_LIBRARIES=/path/to/lib1.so;/path/to/lib2.so` | List of libraries to link against for Armadillo (if not using the Armadillo wrapper library). ||
|
||||
| `-DCEREAL_INCLUDE_DIR=/path/to/cereal/include/` | Path containing cereal headers. ||
|
||||
| `-DENSMALLEN_INCLUDE_DIR=/path/to/ens/include/` | Path containing `ensmallen.hpp`. ||
|
||||
| `-DSTB_INCLUDE_DIR=/path/to/stb/include/` | Path containing `stb.h` and `stb_image.h`. ||
|
||||
|--------------|-------------------|---------------|
|
||||
| ***Bindings*** |||
|
||||
| `-DBUILD_CLI_EXECUTABLES=ON` | Enable building command-line programs. | `OFF` |
|
||||
| `-DBUILD_PYTHON_BINDINGS=ON` | Enable building Python bindings. | `OFF` |
|
||||
| `-DPYTHON_EXECUTABLE=/path/to/python` | Location of Python program to use. ||
|
||||
| `-DBUILD_GO_BINDINGS=ON` | Enable building Go bindings. | `OFF` |
|
||||
| `-DBUILD_GO_SHLIB=OFF` | Do not shared library for Go bindings. | `ON` |
|
||||
| `-DBUILD_JULIA_BINDINGS=ON` | Enable building Julia bindings. | `OFF` |
|
||||
| `-DJULIA_EXECUTABLE=/path/to/julia` | Location of Julia interpreter. ||
|
||||
| `-DBUILD_R_BINDINGS=ON` | Enable building R bindings. | `OFF` |
|
||||
| `-DBUILD_MARKDOWN_BINDINGS=ON` | Enable building Markdown bindings (e.g. Markdown documentation for each binding language). | `OFF` |
|
||||
|--------------|-------------------|---------------|
|
||||
|
||||
### Build tests
|
||||
|
||||
If you are developing mlpack or simply want to run the test suite, after you
|
||||
have configured the library with CMake, you can build the tests directly:
|
||||
|
||||
```sh
|
||||
make -j4 mlpack_test
|
||||
```
|
||||
|
||||
Replace the `-j4` with the number of cores desired for building.
|
||||
|
||||
Once the build is complete (it may take a while!), you can run the tests from
|
||||
the build directory, selecting either all of them or an individual test suite.
|
||||
|
||||
```sh
|
||||
bin/mlpack_test
|
||||
bin/mlpack_test [LARSTest]
|
||||
```
|
||||
|
||||
The `mlpack_test` program uses the [Catch2](https://github.com/catchorg/Catch2)
|
||||
library for unit testing; this supports many options---you can see them with
|
||||
`mlpack_test -h`.
|
||||
|
||||
## Compiling a test program
|
||||
|
||||
Once mlpack is installed and available on the system, it is easy to compile a
|
||||
program using mlpack. For instance, consider the trivial program below:
|
||||
|
||||
```c++
|
||||
#include <mlpack.hpp>
|
||||
|
||||
using namespace mlpack;
|
||||
|
||||
int main()
|
||||
{
|
||||
// Sample a point from a 3-dimensional Gaussian distribution.
|
||||
GaussianDistribution g(3);
|
||||
std::cout << "Random sample from 3D Gaussian: " << std::endl
|
||||
<< g.Random();
|
||||
}
|
||||
```
|
||||
|
||||
This can be compiled with the command:
|
||||
|
||||
```
|
||||
g++ -O3 -std=c++17 -o my_program my_program.cpp -larmadillo -fopenmp
|
||||
```
|
||||
|
||||
The command may need slight adaptation if you are using a different compiler or
|
||||
prefer different compilation options.
|
||||
|
||||
***Notes***:
|
||||
|
||||
- If you want to serialize (save or load) neural networks, you should add
|
||||
`#define MLPACK_ENABLE_ANN_SERIALIZATION` before including `<mlpack.hpp>`.
|
||||
|
||||
- When the autodownloader is used to download Armadillo
|
||||
(`-DDOWNLOAD_DEPENDENCIES=ON`), the Armadillo runtime library is not built
|
||||
and Armadillo must be used in header-only mode. Instead, you must link
|
||||
directly with the dependencies of Armadillo. For example, on a system that
|
||||
has OpenBLAS available, compilation can be done like this:
|
||||
|
||||
```sh
|
||||
g++ -O3 -std=c++17 -o my_program my_program.cpp -lopenblas -fopenmp
|
||||
```
|
||||
|
||||
See [the Armadillo documentation](https://arma.sourceforge.net/faq.html#linking)
|
||||
for more information on linking Armadillo programs.
|
||||
@@ -1,4 +1,7 @@
|
||||
# Loading and saving in mlpack
|
||||
<object data="../img/pipeline-top-1.svg" type="image/svg+xml" id="pipeline-top">
|
||||
</object>
|
||||
|
||||
# Data loading and I/O
|
||||
|
||||
mlpack provides the `data::Load()` and `data::Save()` functions to load and save
|
||||
[Armadillo matrices](matrices.md) (e.g. numeric and categorical datasets) and
|
||||
|
||||
@@ -49,7 +49,7 @@ std::cout << arma::accu(predictions == 3) << " test points classified as class "
|
||||
|
||||
#### See also:
|
||||
|
||||
* [mlpack classifiers](../../index.md#classification-algorithms)
|
||||
* [mlpack classifiers](../modeling.md#classification)
|
||||
* [`Perceptron`](perceptron.md)
|
||||
* [`DecisionTree`](decision_tree.md)
|
||||
* [AdaBoost on Wikipedia](https://en.wikipedia.org/wiki/AdaBoost)
|
||||
|
||||
@@ -68,7 +68,7 @@ std::cout << "RMSE of reconstructed matrix: "
|
||||
|
||||
* [`NMF`](nmf.md): non-negative matrix factorization (a version of `AMF`)
|
||||
* [`SparseCoding`](sparse_coding.md)
|
||||
* [mlpack transformations](../../index.md#transformations)
|
||||
* [mlpack transformations](../transformations.md)
|
||||
* [Matrix factorization on Wikipedia](https://en.wikipedia.org/wiki/Matrix_factorization_(recommender_systems))
|
||||
|
||||
### Template parameter overview
|
||||
|
||||
@@ -43,7 +43,7 @@ std::cout << arma::accu(predictions < 0) << " test points predicted to have "
|
||||
|
||||
#### See also:
|
||||
|
||||
* [mlpack regression techniques](../../index.md#regression-algorithms)
|
||||
* [mlpack regression techniques](../modeling.md#regression)
|
||||
* [`LinearRegression`](linear_regression.md)
|
||||
* [`LARS`](lars.md)
|
||||
* [Bayesian linear regression on Wikipedia](https://en.wikipedia.org/wiki/Bayesian_linear_regression)
|
||||
|
||||
@@ -48,7 +48,7 @@ std::cout << arma::accu(predictions == 2) << " test points classified as class "
|
||||
|
||||
* [`DecisionTreeRegressor`](decision_tree_regressor.md)
|
||||
* [Random forests](random_forest.md)
|
||||
* [mlpack classifiers](../../index.md#classification-algorithms)
|
||||
* [mlpack classifiers](../modeling.md#classification)
|
||||
* [Decision tree on Wikipedia](https://en.wikipedia.org/wiki/Decision_tree)
|
||||
* [Decision tree learning on Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning)
|
||||
|
||||
|
||||
@@ -53,7 +53,7 @@ std::cout << arma::accu(predictions < 0) << " test points predicted to have "
|
||||
|
||||
* [`DecisionTree`](decision_tree.md)
|
||||
* [Random forests](random_forest.md)
|
||||
* [mlpack regression techniques](../../index.md#regression-algorithms)
|
||||
* [mlpack regression techniques](../modeling.md#regression)
|
||||
* [Decision tree on Wikipedia](https://en.wikipedia.org/wiki/Decision_tree)
|
||||
* [Decision tree learning on Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning)
|
||||
|
||||
|
||||
@@ -49,7 +49,7 @@ std::cout << arma::accu(predictions == 2) << " test points classified as class "
|
||||
|
||||
* [`DecisionTree`](decision_tree.md)
|
||||
* [Random forests](random_forest.md)
|
||||
* [mlpack classifiers](../../index.md#classification-algorithms)
|
||||
* [mlpack classifiers](../modeling.md#classification)
|
||||
* [Incremental decision tree on Wikipedia](https://en.wikipedia.org/wiki/Incremental_decision_tree)
|
||||
* [Mining High-Speed Data Streams (pdf)](https://dl.acm.org/doi/pdf/10.1145/347090.347107)
|
||||
|
||||
|
||||
@@ -47,7 +47,7 @@ std::cout << arma::accu(predictions < 0) << " test points predicted to have "
|
||||
#### See also:
|
||||
|
||||
* [`LinearRegression`](linear_regression.md)
|
||||
* [mlpack regression techniques](../../index.md#regression-algorithms)
|
||||
* [mlpack regression techniques](../modeling.md#regression)
|
||||
* [Least-angle Regression on Wikipedia](https://en.wikipedia.org/wiki/Least-angle_regression)
|
||||
|
||||
### Constructors
|
||||
|
||||
@@ -43,7 +43,7 @@ std::cout << arma::accu(predictions < 0) << " test points predicted to have "
|
||||
|
||||
#### See also:
|
||||
|
||||
* [mlpack regression techniques](../../index.md#regression-algorithms)
|
||||
* [mlpack regression techniques](../modeling.md#regression)
|
||||
* [`LARS`](lars.md)
|
||||
* [Linear Regression on Wikipedia](https://en.wikipedia.org/wiki/Linear_regression)
|
||||
|
||||
|
||||
@@ -42,7 +42,7 @@ std::cout << arma::accu(predictions == 1) << " test points classified as class "
|
||||
|
||||
#### See also:
|
||||
|
||||
* [mlpack classifiers](../../index.md#classification-algorithms)
|
||||
* [mlpack classifiers](../modeling.md#classification)
|
||||
* [`GaussianDistribution`](../core/distributions.md#gaussiandistribution)
|
||||
* [Naive Bayes classifier on Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
|
||||
|
||||
|
||||
@@ -44,7 +44,7 @@ std::cout << "Average density of encoded test data: "
|
||||
|
||||
* [`SparseCoding`](sparse_coding.md)
|
||||
* [`LARS`](lars.md) (used internally by `LocalCoordinateCoding`)
|
||||
* [mlpack transformations](../../index.md#transformations)
|
||||
* [mlpack transformations](../transformations.md)
|
||||
* [Sparse dictionary learning on Wikipedia](https://en.wikipedia.org/wiki/Sparse_dictionary_learning)
|
||||
* [Nonlinear learning using local coordinate coding (pdf)](https://proceedings.neurips.cc/paper_files/paper/2009/file/2afe4567e1bf64d32a5527244d104cea-Paper.pdf)
|
||||
|
||||
|
||||
@@ -47,7 +47,7 @@ std::cout << arma::accu(predictions == 0) << " test points classified as class "
|
||||
#### See also:
|
||||
|
||||
* [`SoftmaxRegression`](softmax_regression.md)
|
||||
* [mlpack classifiers](../../index.md#classification-algorithms)
|
||||
* [mlpack classifiers](../modeling.md#classification)
|
||||
* [Logistic regression on Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression)
|
||||
|
||||
### Constructors
|
||||
|
||||
@@ -50,7 +50,7 @@ for (size_t c = 0; c < centroids.n_cols; ++c)
|
||||
|
||||
#### See also:
|
||||
|
||||
* [mlpack clustering algorithms](../../index.md#clustering-algorithms)
|
||||
* [mlpack clustering algorithms](../modeling.md#clustering)
|
||||
* [mlpack kernels](../core/kernels.md)
|
||||
* [Mean shift on Wikipedia](https://en.wikipedia.org/wiki/Mean_shift)
|
||||
* [Mean Shift, Mode Seeking, and Clustering (pdf)](http://users.isr.ist.utl.pt/~alex/Resources/meanshift.pdf)
|
||||
|
||||
@@ -42,7 +42,7 @@ std::cout << arma::accu(predictions == 2) << " test points classified as class "
|
||||
|
||||
#### See also:
|
||||
|
||||
* [mlpack classifiers](../../index.md#classification-algorithms)
|
||||
* [mlpack classifiers](../modeling.md#classification)
|
||||
* [`GaussianDistribution`](../core/distributions.md#gaussiandistribution)
|
||||
* [Naive Bayes classifier on Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier)
|
||||
|
||||
|
||||
@@ -52,7 +52,7 @@ std::cout << "RMSE of reconstructed matrix: "
|
||||
|
||||
* [`AMF`](amf.md): alternating matrix factorization
|
||||
* [`SparseCoding`](sparse_coding.md)
|
||||
* [mlpack transformations](../../index.md#transformations)
|
||||
* [mlpack transformations](../transformations.md)
|
||||
* [Non-negative matrix factorization on Wikipedia](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization)
|
||||
* [Learning the parts of objects by non-negative matrix factorization](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=29bae9472203546847ec1352a604566d0f602728) (original NMF paper, pdf)
|
||||
|
||||
@@ -332,7 +332,7 @@ class CustomTerminationPolicy
|
||||
// Note that W and H may have different types than V (i.e. V may be sparse,
|
||||
// and W and H must be dense.)
|
||||
template<typename WHMatType>
|
||||
bool IsConverged(const MatType& H, const MatType& W);
|
||||
bool IsConverged(const WHMatType& H, const WHMatType& W);
|
||||
|
||||
// Return the value that should be returned for the `nmf.Apply()` function
|
||||
// when convergence has been reached. This is called at the end of
|
||||
|
||||
@@ -39,8 +39,8 @@ std::cout << "The transformed data matrix has size " << dataset.n_rows /* 5 */
|
||||
#### See also:
|
||||
|
||||
* [`Radical`](radical.md): independent components analysis
|
||||
* [mlpack preprocessing utilities](../../index.md#preprocessing-utilities)
|
||||
* [mlpack transformations](../../index.md#transformations)
|
||||
* [mlpack preprocessing utilities](../preprocessing.md)
|
||||
* [mlpack transformations](../transformations.md)
|
||||
* [Principal component analysis on Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis)
|
||||
|
||||
### Constructors
|
||||
@@ -314,7 +314,7 @@ class CustomDecompositionPolicy
|
||||
// etc.).
|
||||
// * `VecType` is the corresponding vector type to `MatType` (e.g., a
|
||||
// `MatType` of `arma::mat` would mean a `VecType` of `arma::vec`, etc.).
|
||||
template<typename MatType, typename MatType, typename VecType>
|
||||
template<typename InMatType, typename MatType, typename VecType>
|
||||
static void Apply(const InMatType& data,
|
||||
const MatType& centeredData,
|
||||
MatType& transformedData,
|
||||
|
||||
@@ -53,7 +53,7 @@ std::cout << arma::accu(predictions == 1) << " test points classified as class "
|
||||
* [`NaiveBayesClassifier`](naive_bayes_classifier.md), another simple classifier
|
||||
* [`AdaBoost`](adaboost.md)
|
||||
* [`FFN`](/src/mlpack/methods/ann/ffn.hpp)
|
||||
* [mlpack classifiers](../../index.md#classification-algorithms)
|
||||
* [mlpack classifiers](../modeling.md#classification)
|
||||
* [Perceptron on Wikipedia](https://en.wikipedia.org/wiki/Perceptron)
|
||||
|
||||
### Constructors
|
||||
|
||||
@@ -42,8 +42,8 @@ std::cout << "Independent components matrix size: " << y.n_rows << " x "
|
||||
#### See also:
|
||||
|
||||
* [`PCA`](pca.md): principal components analysis
|
||||
* [mlpack preprocessing utilities](../../index.md#preprocessing-utilities)
|
||||
* [mlpack transformations](../../index.md#transformations)
|
||||
* [mlpack preprocessing utilities](../preprocessing.md)
|
||||
* [mlpack transformations](../transformations.md)
|
||||
* [ICA Using Spacings Estimates of Entropy (pdf)](https://www.jmlr.org/papers/volume4/learned-miller03a/learned-miller03a.pdf)
|
||||
* [Independent components analysis on Wikipedia](https://en.wikipedia.org/wiki/Independent_component_analysis)
|
||||
|
||||
|
||||
@@ -56,7 +56,7 @@ std::cout << arma::accu(predictions == 3) << " test points classified as class "
|
||||
|
||||
* [`DecisionTree`](decision_tree.md)
|
||||
* [`DecisionTreeRegressor`](decision_tree_regressor.md)
|
||||
* [mlpack classifiers](../../index.md#classification-algorithms)
|
||||
* [mlpack classifiers](../modeling.md#classification)
|
||||
* [Random forest on Wikipedia](https://en.wikipedia.org/wiki/Random_forest)
|
||||
* [Decision tree on Wikipedia](https://en.wikipedia.org/wiki/Decision_tree)
|
||||
* [Leo Breiman's Random Forests page](https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm)
|
||||
|
||||
@@ -48,7 +48,7 @@ std::cout << arma::accu(predictions == 2) << " test points classified as class "
|
||||
#### See also:
|
||||
|
||||
* [`LogisticRegression`](logistic_regression.md)
|
||||
* [mlpack classifiers](../../index.md#classification-algorithms)
|
||||
* [mlpack classifiers](../modeling.md#classification)
|
||||
* [UFLDL Softmax Regression Tutorial](http://deeplearning.stanford.edu/tutorial/supervised/SoftmaxRegression/)
|
||||
|
||||
### Constructors
|
||||
|
||||
@@ -43,7 +43,7 @@ std::cout << "Average density of encoded test data: "
|
||||
|
||||
* [`LocalCoordinateCoding`](local_coordinate_coding.md)
|
||||
* [`LARS`](lars.md) (used internally by `SparseCoding`)
|
||||
* [mlpack transformations](../../index.md#transformations)
|
||||
* [mlpack transformations](../transformations.md)
|
||||
* [Sparse dictionary learning on Wikipedia](https://en.wikipedia.org/wiki/Sparse_dictionary_learning)
|
||||
* [Efficient sparse coding algorithms (pdf)](https://proceedings.neurips.cc/paper/2006/file/2d71b2ae158c7c5912cc0bbde2bb9d95-Paper.pdf)
|
||||
|
||||
|
||||
@@ -0,0 +1,67 @@
|
||||
<object data="../img/pipeline-top-4.svg" type="image/svg+xml" id="pipeline-top">
|
||||
</object>
|
||||
|
||||
# Modeling
|
||||
|
||||
mlpack contains numerous different machine learning algorithms that can be used
|
||||
for modeling.
|
||||
|
||||
*Note: this section is under construction and not all functionality is
|
||||
documented yet.*
|
||||
|
||||
## Classification
|
||||
|
||||
Classify points as discrete labels (`0`, `1`, `2`, ...).
|
||||
|
||||
* [`AdaBoost`](methods/adaboost.md): Adaptive Boosting
|
||||
* [`DecisionTree`](methods/decision_tree.md): ID3-style decision tree
|
||||
classifier
|
||||
* [`HoeffdingTree`](methods/hoeffding_tree.md): streaming/incremental decision
|
||||
tree classifier
|
||||
* [`LinearSVM`](methods/linear_svm.md): simple linear support vector machine
|
||||
classifier
|
||||
* [`LogisticRegression`](methods/logistic_regression.md): L2-regularized
|
||||
logistic regression (two-class only)
|
||||
* [`NaiveBayesClassifier`](methods/naive_bayes_classifier.md): simple
|
||||
multi-class naive Bayes classifier
|
||||
* [`Perceptron`](methods/perceptron.md): simple Perceptron classifier
|
||||
* [`RandomForest`](methods/random_forest.md): parallelized random forest
|
||||
classifier
|
||||
* [`SoftmaxRegression`](methods/softmax_regression.md): L2-regularized
|
||||
softmax regression (i.e. multi-class logistic regression)
|
||||
|
||||
## Regression
|
||||
|
||||
Predict continuous values.
|
||||
|
||||
* [`BayesianLinearRegression`](methods/bayesian_linear_regression.md):
|
||||
Bayesian L2-penalized linear regression
|
||||
* [`DecisionTreeRegressor`](methods/decision_tree_regressor.md): ID3-style
|
||||
decision tree regressor
|
||||
* [`LARS`](methods/lars.md): Least Angle Regression (LARS), L1-regularized and
|
||||
L2-regularized
|
||||
* [`LinearRegression`](methods/linear_regression.md): L2-regularized linear
|
||||
regression (ridge regression)
|
||||
|
||||
## Clustering
|
||||
|
||||
***NOTE:*** this documentation is still under construction and so some
|
||||
algorithms that mlpack implements are not yet listed here. For now, see
|
||||
[the mlpack/methods directory](https://github.com/mlpack/mlpack/tree/master/src/mlpack/methods)
|
||||
for a full list of algorithms.
|
||||
|
||||
Group points into clusters.
|
||||
|
||||
* [`MeanShift`](methods/mean_shift.md): clustering with the density-based mean
|
||||
shift algorithm
|
||||
|
||||
## Geometric algorithms
|
||||
|
||||
***NOTE:*** this documentation is still under construction and so no geometric
|
||||
algorithms in mlpack are documented yet. For now, see
|
||||
[the mlpack/methods directory](https://github.com/mlpack/mlpack/tree/master/src/mlpack/methods)
|
||||
for a full list of algorithms.
|
||||
|
||||
Computations based on distance metrics.
|
||||
|
||||
<!-- TODO: add some -->
|
||||
@@ -0,0 +1,19 @@
|
||||
<object data="../img/pipeline-top-2.svg" type="image/svg+xml" id="pipeline-top">
|
||||
</object>
|
||||
|
||||
# Preprocessing / feature extraction
|
||||
|
||||
mlpack provides a number of utilities for data preparation and feature
|
||||
extraction. These utilities are generally used just before actually applying
|
||||
any machine learning [transformations](transformations.md) or
|
||||
[modeling](modeling.md).
|
||||
|
||||
*Note: this section is under construction and not all functionality is
|
||||
documented yet.*
|
||||
|
||||
* [Normalizing labels](core/normalizing_labels.md): convert labels to/from an
|
||||
arbitrary range to `[0, numClasses - 1]`, which is the range that mlpack
|
||||
classifiers require.
|
||||
|
||||
* [Dataset splitting](core/split.md): split a dataset into a training and test
|
||||
set, optionally including labels.
|
||||
@@ -0,0 +1,19 @@
|
||||
# Prerequisites
|
||||
|
||||
Before using mlpack in an application, it must be installed on the system.
|
||||
|
||||
* [Installing mlpack](install.md): a guide to install mlpack.
|
||||
|
||||
* [Cross-compilation setup](../embedded/supported_boards.md): use this guide if
|
||||
you plan to cross-compile mlpack C++ programs for another device.
|
||||
|
||||
Once mlpack is set up, you should start with the following resources:
|
||||
|
||||
* [Quickstart](../quickstart/cpp.md): guide to get simple mlpack programs running
|
||||
in C++.
|
||||
|
||||
* [Matrices and data](matrices.md): details about how data is expected to
|
||||
be represented in mlpack (via the Armadillo library).
|
||||
|
||||
If you wish to use mlpack's bindings to other languages, see the
|
||||
[Bindings](bindings.md) page.
|
||||
@@ -0,0 +1,43 @@
|
||||
<object data="../img/pipeline-top-3.svg" type="image/svg+xml" id="pipeline-top">
|
||||
</object>
|
||||
|
||||
# Transformations
|
||||
|
||||
Once data is [loaded](load_save.html) and any necessary
|
||||
[preprocessing and feature extraction](preprocessing.md) is done,
|
||||
one of mlpack's transformations can be used to transform data into a
|
||||
new space.
|
||||
|
||||
*Note: this section is under construction and not all functionality is
|
||||
documented yet.*
|
||||
|
||||
## Matrix decompositions
|
||||
|
||||
Decompose a matrix into two or more components.
|
||||
|
||||
* [AMF](methods/amf.md): alternating matrix factorization
|
||||
* [NMF](methods/nmf.md): non-negative matrix factorization
|
||||
|
||||
## Linear transformations
|
||||
|
||||
Linearly map a matrix onto a new basis, optionally performing dimensionality
|
||||
reduction.
|
||||
|
||||
* [PCA](methods/pca.md): principal components analysis
|
||||
* [RADICAL](methods/radical.md): an independent components analysis technique
|
||||
|
||||
## Metric learning techniques
|
||||
|
||||
Learn a [distance metric](core/distances.md) based on a data matrix.
|
||||
|
||||
* [LMNN](methods/lmnn.md): large margin nearest neighbor
|
||||
* [NCA](methods/nca.md): neighborhood components analysis
|
||||
|
||||
## Coding techniques
|
||||
|
||||
Encode data points in a matrix as a combination of points in a dictionary.
|
||||
|
||||
* [LocalCoordinateCoding](methods/local_coordinate_coding.md): local coordinate
|
||||
coding with dictionary learning
|
||||
* [SparseCoding](methods/sparse_coding.md): sparse coding with dictionary
|
||||
learning
|
||||
@@ -0,0 +1,41 @@
|
||||
# Tutorials and Examples
|
||||
|
||||
mlpack has a number of examples, video tutorials, and other resources showing
|
||||
usage of the library.
|
||||
|
||||
* [mlpack examples repository](https://github.com/mlpack/examples/): contains
|
||||
simple examples of mlpack usage for various machine learning tasks, in C++
|
||||
and other languages. Both notebooks and standalone programs are available.
|
||||
|
||||
---
|
||||
|
||||
* [mlpack Youtube channel](https://www.youtube.com/@mlpack): tutorial videos
|
||||
for getting started with mlpack.
|
||||
- [Installing mlpack for use in C++](https://www.youtube.com/watch?v=wcEFce7IaS8):
|
||||
a step-by-step tutorial for installing and using mlpack from C++.
|
||||
* [Ubuntu/Debian](https://www.youtube.com/watch?v=wcEFce7IaS8&t=46s)
|
||||
* [Fedora/RHEL](https://www.youtube.com/watch?v=wcEFce7IaS8&t=188s)
|
||||
* [MacOS (via Homebrew)](https://www.youtube.com/watch?v=wcEFce7IaS8&t=303s)
|
||||
* [Installing from source](https://www.youtube.com/watch?v=wcEFce7IaS8&t=440s)
|
||||
* [Installing from source with the autodownloader](https://www.youtube.com/watch?v=wcEFce7IaS8&t=712s)
|
||||
|
||||
- [Using mlpack for command-line data science](https://www.youtube.com/watch?v=M0DLrUVSyrE):
|
||||
a demonstration of mlpack's command-line bindings.
|
||||
|
||||
- [Simple data science workflow in C++ with mlpack](https://www.youtube.com/watch?v=PD9AqGdkPl8):
|
||||
a tutorial using random forests and softmax regression in C++ to solve a
|
||||
simple data science problem.
|
||||
|
||||
- [Development workflow tutorial: VSCode](https://www.youtube.com/watch?v=7DOrMQ2HhBY):
|
||||
set up an mlpack development environment in VSCode. *This is useful if you
|
||||
are interested in contributing to mlpack.*
|
||||
|
||||
- [Development workflow tutorial: command-line](https://www.youtube.com/watch?v=3PgFzA5duwc):
|
||||
set up an mlpack development environment from the command-line. *This is
|
||||
useful if you are interested in contributing to mlpack.*
|
||||
|
||||
---
|
||||
|
||||
* [mlpack models repository](https://github.com/mlpack/models/): contains
|
||||
implementations of specific deep learning models that are too large or
|
||||
complex for inclusion in the main mlpack library.
|
||||
+76
-81
@@ -77,70 +77,90 @@ extract_code_blocks()
|
||||
# preceding fence close above it.
|
||||
last_line_fence=1;
|
||||
|
||||
# Track whether or not the entire last file corresponded to a class
|
||||
# declaration.
|
||||
class_decl=0;
|
||||
|
||||
while IFS= read -r line;
|
||||
do
|
||||
if [[ $last_line_fence == 1 ]];
|
||||
then
|
||||
# Skip this line---it will be a fence opening.
|
||||
last_line_fence=0;
|
||||
|
||||
# Create main() function to wrap the code in.
|
||||
echo "#include <mlpack.hpp>" > $output_prefix$output_file_display.cpp;
|
||||
echo "" >> $output_prefix$output_file_display.cpp;
|
||||
|
||||
# If we have a class declaration from the previous file, insert it.
|
||||
if [[ $class_decl == 1 ]];
|
||||
then
|
||||
class_decl=0;
|
||||
last_output_file_id=$(($output_file_id - 1));
|
||||
last_output_file_display=$(printf "%02d" $last_output_file_id);
|
||||
|
||||
cat $output_prefix$last_output_file_display.cpp | awk '
|
||||
BEGIN { p=0 }
|
||||
/int main()/ { p=1 }
|
||||
/^{/ { if(p == 1) { p=2; o=1 } }
|
||||
/^}/ { p=0; }
|
||||
// { if (p == 2 && o == 0) { print substr($0, 3) } o=0 }' >> $output_prefix$output_file_display.cpp;
|
||||
echo "" >> $output_prefix$output_file_display.cpp;
|
||||
rm -f $output_prefix$last_output_file_display.cpp;
|
||||
fi
|
||||
|
||||
echo "int main()" >> $output_prefix$output_file_display.cpp;
|
||||
echo "{" >> $output_prefix$output_file_display.cpp;
|
||||
continue;
|
||||
fi
|
||||
|
||||
if [[ $line == '```'* ]];
|
||||
if [[ $line == '```' ]];
|
||||
then
|
||||
last_line_fence=1;
|
||||
|
||||
# Close main() function.
|
||||
echo "}" >> $output_prefix$output_file_display.cpp;
|
||||
|
||||
# Check after the fact: was this file only a class declaration? If so, we
|
||||
# want to put it instead into the next file.
|
||||
has_class1=`grep '^ class\|^ struct' $output_prefix$output_file_display.cpp | wc -l`;
|
||||
has_class2=`grep '^ };' $output_prefix$output_file_display.cpp | wc -l`;
|
||||
if [[ "$has_class1" != "0" && "$has_class2" != "0" ]];
|
||||
if [ -f $output_prefix$output_file_display.body.cpp ];
|
||||
then
|
||||
class_decl=1;
|
||||
fi;
|
||||
# Determine whether we need a main() function for the code. Also check
|
||||
# whether the file is simply a class definition, in which case we don't
|
||||
# need to do anything except prepare it to be inserted into the next
|
||||
# example.
|
||||
has_main=`grep 'int main(' $output_prefix$output_file_display.body.cpp | wc -l`;
|
||||
has_class1=`grep '^ class\|^ struct' $output_prefix$output_file_display.body.cpp | wc -l`;
|
||||
has_class2=`grep '^ };' $output_prefix$output_file_display.body.cpp | wc -l`;
|
||||
class_decl=0;
|
||||
if [ $has_class1 -ne 0 -a $has_class2 -ne 0 ];
|
||||
then
|
||||
class_decl=1;
|
||||
fi;
|
||||
|
||||
# Detect if we need any to add any special headers. We have to do this
|
||||
# when we finish with the file...
|
||||
if [[ `grep 'Eigen::' $output_prefix$output_file_display.cpp | wc -l` -gt 0 ]];
|
||||
then
|
||||
sed -i '1s/^/#include <Eigen\/Dense>\n/' $output_prefix$output_file_display.cpp;
|
||||
fi
|
||||
if [ $has_main -eq 0 -a $class_decl -eq 0 ];
|
||||
then
|
||||
# Create main() function to wrap the code in.
|
||||
echo "#include <mlpack.hpp>" > $output_prefix$output_file_display.cpp;
|
||||
echo "" >> $output_prefix$output_file_display.cpp;
|
||||
|
||||
if [[ `grep 'xt::' $output_prefix$output_file_display.cpp | wc -l` -gt 0 ]];
|
||||
then
|
||||
sed -i '1s/^/#include <xtensor\/xrandom.hpp>\n/' $output_prefix$output_file_display.cpp;
|
||||
sed -i '1s/^/#include <xtensor\/xarray.hpp>\n/' $output_prefix$output_file_display.cpp;
|
||||
# Insert any class definitions.
|
||||
if [ -f $output_prefix$output_file_display.defn.cpp ];
|
||||
then
|
||||
cat $output_prefix$output_file_display.defn.cpp >> $output_prefix$output_file_display.cpp;
|
||||
rm -f $output_prefix$output_file_display.defn.cpp;
|
||||
fi
|
||||
|
||||
echo "int main()" >> $output_prefix$output_file_display.cpp;
|
||||
echo "{" >> $output_prefix$output_file_display.cpp;
|
||||
|
||||
# Insert the code itself.
|
||||
cat $output_prefix$output_file_display.body.cpp >> $output_prefix$output_file_display.cpp;
|
||||
rm -f $output_prefix$output_file_display.body.cpp;
|
||||
|
||||
# Close main() function.
|
||||
echo "}" >> $output_prefix$output_file_display.cpp;
|
||||
elif [[ "$class_decl" == "1" ]];
|
||||
then
|
||||
# If the function is only a class declaration, set it aside, along
|
||||
# with any other declarations, for the next program.
|
||||
next_id=$(($output_file_id + 1));
|
||||
next_display=$(printf "%02d" $next_id);
|
||||
if [ -f $output_prefix$output_file_display.defn.cpp ];
|
||||
then
|
||||
mv $output_prefix$output_file_display.defn.cpp $output_prefix$next_display.defn.cpp;
|
||||
cat $output_prefix$output_file_display.body.cpp >> $output_prefix$next_display.defn.cpp;
|
||||
rm -f $output_prefix$output_file_display.body.cpp;
|
||||
else
|
||||
mv $output_prefix$output_file_display.body.cpp $output_prefix$next_display.defn.cpp;
|
||||
fi
|
||||
else
|
||||
# The file should be able to compile on its own.
|
||||
mv $output_prefix$output_file_display.body.cpp $output_prefix$output_file_display.cpp;
|
||||
fi
|
||||
|
||||
# Detect if we need any to add any special headers. We have to do this
|
||||
# when we finish with the file...
|
||||
if [ -f $output_prefix$output_file_display.cpp ];
|
||||
then
|
||||
if [[ `grep 'Eigen::' $output_prefix$output_file_display.cpp | wc -l` -gt 0 ]];
|
||||
then
|
||||
sed -i '1s/^/#include <Eigen\/Dense>\n/' $output_prefix$output_file_display.cpp;
|
||||
fi
|
||||
|
||||
if [[ `grep 'xt::' $output_prefix$output_file_display.cpp | wc -l` -gt 0 ]];
|
||||
then
|
||||
sed -i '1s/^/#include <xtensor\/xrandom.hpp>\n/' $output_prefix$output_file_display.cpp;
|
||||
sed -i '1s/^/#include <xtensor\/xarray.hpp>\n/' $output_prefix$output_file_display.cpp;
|
||||
fi
|
||||
fi
|
||||
fi
|
||||
|
||||
output_file_id=$(($output_file_id + 1));
|
||||
@@ -150,35 +170,10 @@ extract_code_blocks()
|
||||
fi
|
||||
|
||||
# Include indentation (two spaces).
|
||||
echo " $line" >> $output_prefix$output_file_display.cpp;
|
||||
echo " $line" >> $output_prefix$output_file_display.body.cpp;
|
||||
done < $input_file.tmp;
|
||||
|
||||
# The last file is always invalid---we opened it without knowing whether
|
||||
# anything would be in it.
|
||||
rm -f $output_prefix$output_file_display.cpp;
|
||||
|
||||
# Check the "true" last file: if it's only class declarations, no need to
|
||||
# compile it.
|
||||
output_file_id=$(($output_file_id - 1));
|
||||
output_file_display=$(printf "%02d" $output_file_id);
|
||||
if [ -f $output_prefix$output_file_display.cpp ];
|
||||
then
|
||||
cat $output_prefix$output_file_display.cpp | awk '
|
||||
BEGIN { p=0 }
|
||||
/int main()/ { p=1 }
|
||||
/^{/ { if(p == 1) { p=2; o=1 } }
|
||||
/^}/ { p=0 }
|
||||
// { if (p == 2 && o == 0) { print substr($0, 3) } o=0 }' >> $output_prefix$output_file_display.cpp.tmp;
|
||||
has_class1=`grep '^class' $output_prefix$output_file_display.cpp.tmp | wc -l`;
|
||||
has_class2=`grep '^};' $output_prefix$output_file_display.cpp.tmp | wc -l`;
|
||||
if [[ "$has_class1" != "0" && "$has_class2" != "0" ]];
|
||||
then
|
||||
# The file's main() function is just a class declaration. Nuke it.
|
||||
rm -f $output_prefix$output_file_display.cpp;
|
||||
fi
|
||||
rm -f $output_prefix$output_file_display.cpp.tmp;
|
||||
fi
|
||||
|
||||
rm -f $output_prefix*.defn.cpp; # Remove any unused definitions.
|
||||
rm -f $input_file.tmp;
|
||||
}
|
||||
|
||||
@@ -338,16 +333,18 @@ do
|
||||
declare -a files_to_skip=(
|
||||
# These files have small incomplete snippets that can't compile into
|
||||
# standalone programs.
|
||||
"sample_ml_app.md"
|
||||
"deploy_windows.md"
|
||||
"hpt.md"
|
||||
"cv.md"
|
||||
"timer.md"
|
||||
"bindings.md"
|
||||
"elemtype.md"
|
||||
"iodoc.md"
|
||||
"distances.md"
|
||||
"elemtype.md"
|
||||
"kernels.md"
|
||||
"metrics.md"
|
||||
"trees.md"
|
||||
# Skip the quickstart, since it depends on some specific data.
|
||||
"cpp.md"
|
||||
# The tutorials are old and are likely to be replaced, so let's not test
|
||||
# them.
|
||||
"amf.md"
|
||||
@@ -369,8 +366,6 @@ do
|
||||
"q_learning.md"
|
||||
"sac.md"
|
||||
"td3.md"
|
||||
# Skip quickstarts, although we should eventually test them.
|
||||
"cpp.md"
|
||||
);
|
||||
|
||||
skip=0;
|
||||
|
||||
@@ -36,7 +36,7 @@ LinearType<MatType, RegularizerType>::LinearType(
|
||||
outSize(outSize),
|
||||
regularizer(regularizer)
|
||||
{
|
||||
weights.set_size(WeightSize(), 1);
|
||||
// Nothing to do here.
|
||||
}
|
||||
|
||||
// Copy constructor.
|
||||
@@ -58,7 +58,9 @@ LinearType<MatType, RegularizerType>::LinearType(LinearType&& layer) :
|
||||
outSize(std::move(layer.outSize)),
|
||||
regularizer(std::move(layer.regularizer))
|
||||
{
|
||||
// Nothing else to do.
|
||||
// Reset parameters of other layer.
|
||||
layer.inSize = 0;
|
||||
layer.outSize = 0;
|
||||
}
|
||||
|
||||
template<typename MatType, typename RegularizerType>
|
||||
@@ -87,6 +89,10 @@ LinearType<MatType, RegularizerType>::operator=(
|
||||
inSize = std::move(layer.inSize);
|
||||
outSize = std::move(layer.outSize);
|
||||
regularizer = std::move(layer.regularizer);
|
||||
|
||||
// Reset parameters of other layer.
|
||||
layer.inSize = 0;
|
||||
layer.outSize = 0;
|
||||
}
|
||||
|
||||
return *this;
|
||||
|
||||
@@ -58,7 +58,9 @@ LinearNoBiasType<MatType, RegularizerType>::LinearNoBiasType(
|
||||
outSize(0),
|
||||
regularizer(std::move(layer.regularizer))
|
||||
{
|
||||
// Nothing to do here.
|
||||
// Reset parameters of other layer.
|
||||
layer.inSize = 0;
|
||||
layer.outSize = 0;
|
||||
}
|
||||
|
||||
template<typename MatType, typename RegularizerType>
|
||||
@@ -88,6 +90,10 @@ LinearNoBiasType<MatType, RegularizerType>::operator=(
|
||||
inSize = std::move(layer.inSize);
|
||||
outSize = std::move(layer.outSize);
|
||||
regularizer = std::move(layer.regularizer);
|
||||
|
||||
// Reset parameters of other layer.
|
||||
layer.inSize = 0;
|
||||
layer.outSize = 0;
|
||||
}
|
||||
|
||||
return *this;
|
||||
|
||||
Reference in New Issue
Block a user