157 lines
6.1 KiB
Markdown
157 lines
6.1 KiB
Markdown
# Documentation for mlpack
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## A fast, flexible machine learning library
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mlpack is an intuitive, fast, and flexible header-only C++ machine learning
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library with bindings to other languages. It aims to provide fast, lightweight
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implementations of both common and cutting-edge machine learning algorithms.
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mlpack's lightweight C++ implementation makes it ideal for deployment, and it
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can also be used for interactive prototyping via C++ notebooks (these can be
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seen in action on mlpack's [homepage](https://www.mlpack.org/)).
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In addition to its [powerful C++ interface](quickstart/cpp.md), mlpack also
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provides [command-line programs](quickstart/cli.md), and bindings to the
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[Python](quickstart/python.md), [R](quickstart/r.md),
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[Julia](quickstart/julia.md), and [Go](quickstart/go.md) languages.
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_If you use mlpack, please [cite the software](citation.md)._
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## mlpack basics
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Installing mlpack can be done using the
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[instructions in the README](README.md#3-installing-and-using-mlpack-in-c);
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or the [Windows build guide](user/build_windows.md).
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The following basic guides are *highly recommended* before using mlpack.
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* ***First steps***:
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- [mlpack C++ quickstart](quickstart/cpp.md): create a couple simple C++
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programs that use mlpack
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- [Sample Windows mlpack C++ application](user/sample_ml_app.md): create a
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working mlpack Windows program using Visual Studio
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* ***Basics of matrices and data in mlpack***:
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- [Matrices and data in mlpack](user/matrices.md)
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- [Loading and saving mlpack objects](user/load_save.md)
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* ***Reference for mlpack core classes***:
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- [Core mlpack documentation](user/core.md)
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* ***Using mlpack natively with our extensions in Python, R, CLI, Julia, and Go***:
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- [Links to quickstarts and references](#bindings-to-other-languages)
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## mlpack algorithm documentation
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Documentation for each machine learning algorithm that mlpack implements is
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detailed in the sections below.
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* [Classification algorithms](#classification-algorithms): classify points as
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discrete labels (`0`, `1`, `2`, ...).
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* [Regression algorithms](#regression-algorithms): predict continuous values.
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* [Clustering algorithms](#clustering-algorithms): group points into clusters.
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* [Geometric algorithms](#geometric-algorithms): computations based on distance
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metrics (nearest neighbors, kernel density estimation, etc.).
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* [Preprocessing utilities](#preprocessing-utilities): prepare data for machine
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learning algorithms.
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* [Transformations](#transformations): transform data from one space to
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another (principal components analysis, etc.).
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* [Modeling utilities](#modeling-utilities): cross-validation, hyperparameter
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tuning, etc.
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### Classification algorithms
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Classify points as discrete labels (`0`, `1`, `2`, ...).
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* [`AdaBoost`](user/methods/adaboost.md): Adaptive Boosting
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* [`DecisionTree`](user/methods/decision_tree.md): ID3-style decision tree
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classifier
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* [`LogisticRegression`](user/methods/logistic_regression.md): L2-regularized
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logistic regression (two-class only)
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* [`NaiveBayesClassifier`](user/methods/naive_bayes_classifier.md): simple
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multi-class naive Bayes classifier
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* [`Perceptron`](user/methods/perceptron.md): simple Perceptron classifier
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* [`SoftmaxRegression`](user/methods/softmax_regression.md): L2-regularized
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softmax regression (i.e. multi-class logistic regression)
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### Regression algorithms
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Predict continuous values.
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* [`DecisionTreeRegressor`](user/methods/decision_tree_regressor.md): ID3-style
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decision tree regressor
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* [`LARS`](user/methods/lars.md): Least Angle Regression (LARS), L1-regularized
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and L2-regularized
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* [`LinearRegression`](user/methods/linear_regression.md): L2-regularized
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linear regression (ridge regression)
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### Clustering algorithms
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Group points into clusters.
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<!-- TODO: add some -->
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### Geometric algorithms
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Computations based on distance metrics.
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<!-- TODO: add some -->
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### Preprocessing utilities
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Prepare data for machine learning algorithms.
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<!-- TODO: add some -->
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### Transformations
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Transform data from one space to another.
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<!-- TODO: add some -->
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### Modeling utilities
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Cross-validation, hyperparameter tuning, etc.
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<!-- TODO: add some -->
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## Bindings to other languages
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mlpack's bindings to other languages have less complete functionality than
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mlpack in C++, but almost all the same algorithms are available.
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| ***Python*** | -- | [quickstart](quickstart/python.md) | -- | [reference](https://www.mlpack.org/doc/python_documentation.html) |
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| ***Julia*** | -- | [quickstart](quickstart/julia.md) | -- | [reference](https://www.mlpack.org/doc/julia_documentation.html) |
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| ***R*** | -- | [quickstart](quickstart/r.md) | -- | [reference](https://www.mlpack.org/doc/r_documentation.html)
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| ***Command-line programs*** | -- | [quickstart](quickstart/cli.md) | -- | [reference](https://www.mlpack.org/doc/cli_documentation.html) |
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| ***Go*** | -- | [quickstart](quickstart/go.md) | -- | [reference](https://www.mlpack.org/doc/go_documentation.html) |
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## Examples and further documentation
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* [mlpack examples repository](https://github.com/mlpack/examples/): numerous
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fully-working example applications of mlpack, in C++ and other languages.
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* [mlpack models repository](https://github.com/mlpack/models/): complex models
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in C++ built with mlpack
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For additional documentation beyond what is covered in all the resources above,
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the source code should be consulted. Each method is fully documented.
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## Developer documentation
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Throughout the codebase, mlpack uses some common template parameter policies.
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These are documented below.
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* [The `ElemType` policy](developer/elemtype.md): element types for data
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* [The `MetricType` policy](developer/metrics.md): distance metrics
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* [The `KernelType` policy](developer/kernels.md): kernel functions
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* [The `TreeType` policy](developer/trees.md): space trees (ball trees,
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KD-trees, etc.)
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In addition, the following documentation may be useful when developing bindings
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for other languages:
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* [Timers](developer/timer.md): timing parts of bindings
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* [Writing an mlpack binding](developer/iodoc.md): simple examples of mlpack
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bindings
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* [Automatic bindings](developer/bindings.md): details on mlpack's automatic
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binding generator system.
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