52eb6ce023
* First draft of the new n-dimensional arrays + NB use case * Improves default implementation of multiple Array methods * Refactors tree methods * Adds matrix decomposition routines * Adds matrix decomposition methods to ndarray and nalgebra bindings * Refactoring + linear regression now uses array2 * Ridge & Linear regression * LBFGS optimizer & logistic regression * LBFGS optimizer & logistic regression * Changes linear methods, metrics and model selection methods to new n-dimensional arrays * Switches KNN and clustering algorithms to new n-d array layer * Refactors distance metrics * Optimizes knn and clustering methods * Refactors metrics module * Switches decomposition methods to n-dimensional arrays * Linalg refactoring - cleanup rng merge (#172) * Remove legacy DenseMatrix and BaseMatrix implementation. Port the new Number, FloatNumber and Array implementation into module structure. * Exclude AUC metrics. Needs reimplementation * Improve developers walkthrough New traits system in place at `src/numbers` and `src/linalg` Co-authored-by: Lorenzo <tunedconsulting@gmail.com> * Provide SupervisedEstimator with a constructor to avoid explicit dynamical box allocation in 'cross_validate' and 'cross_validate_predict' as required by the use of 'dyn' as per Rust 2021 * Implement getters to use as_ref() in src/neighbors * Implement getters to use as_ref() in src/naive_bayes * Implement getters to use as_ref() in src/linear * Add Clone to src/naive_bayes * Change signature for cross_validate and other model_selection functions to abide to use of dyn in Rust 2021 * Implement ndarray-bindings. Remove FloatNumber from implementations * Drop nalgebra-bindings support (as decided in conf-call to go for ndarray) * Remove benches. Benches will have their own repo at smartcore-benches * Implement SVC * Implement SVC serialization. Move search parameters in dedicated module * Implement SVR. Definitely too slow * Fix compilation issues for wasm (#202) Co-authored-by: Luis Moreno <morenol@users.noreply.github.com> * Fix tests (#203) * Port linalg/traits/stats.rs * Improve methods naming * Improve Display for DenseMatrix Co-authored-by: Montana Low <montanalow@users.noreply.github.com> Co-authored-by: VolodymyrOrlov <volodymyr.orlov@gmail.com>
30 lines
1.7 KiB
Rust
30 lines
1.7 KiB
Rust
//! # Linear Models
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//! Linear models describe a continuous response variable as a function of one or more predictor variables.
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//! The model describes the relationship between a dependent variable y (also called the response) as a function of one or more independent, or explanatory variables \\(X_i\\). The general equation for a linear model is:
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//! \\[y = \beta_0 + \sum_{i=1}^n \beta_iX_i + \epsilon\\]
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//!
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//! where \\(\beta_0 \\) is the intercept term (the expected value of Y when X = 0), \\(\epsilon \\) is an error term that is is independent of X and \\(\beta_i \\)
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//! is the average increase in y associated with a one-unit increase in \\(X_i\\)
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//!
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//! Model assumptions:
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//! * _Linearity_. The relationship between X and the mean of y is linear.
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//! * _Constant variance_. The variance of residual is the same for any value of X.
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//! * _Normality_. For any fixed value of X, Y is normally distributed.
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//! * _Independence_. Observations are independent of each other.
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//!
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//! ## References:
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//!
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//! * ["An Introduction to Statistical Learning", James G., Witten D., Hastie T., Tibshirani R., 3. Linear Regression](http://faculty.marshall.usc.edu/gareth-james/ISL/)
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//! * ["The Statistical Sleuth, A Course in Methods of Data Analysis", Ramsey F.L., Schafer D.W., Ch 7, 8, 3rd edition, 2013](http://www.statisticalsleuth.com/)
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//!
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//! <script src="https://polyfill.io/v3/polyfill.min.js?features=es6"></script>
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//! <script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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pub mod bg_solver;
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pub mod elastic_net;
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pub mod lasso;
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pub mod lasso_optimizer;
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pub mod linear_regression;
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pub mod logistic_regression;
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pub mod ridge_regression;
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