[−][src]Crate ndarray_glm

A rust library for performing GLM regression with data represented in ndarrays. The ndarray-linalg crate is used to allow optimization of linear algebra operations with BLAS.

This crate is early alpha and may change rapidly. No guarantees can be made about the accuracy of the fits.

At the moment the docs and CI are blocked by an upstream issue. For more detail see the README.

Examples:

use ndarray_glm::{array, Linear, ModelBuilder, standardize};

let data_y = array![0.3, 1.3, 0.7];
let data_x = array![[0.1, 0.2], [-0.4, 0.1], [0.2, 0.4]];
// The design matrix can optionally be standardized, where the mean of each independent
// variable is subtracted and each is then divided by the standard deviation of that variable.
let data_x = standardize(data_x);
// The interface takes `ArrayView`s to allow for efficient passing of slices.
let model = ModelBuilder::<Linear>::data(data_y.view(), data_x.view()).build().unwrap();
// L2 (ridge) regularization can be applied with l2_reg().
let fit = model.fit_options().l2_reg(1e-5).fit().unwrap();
// The result is a flat array with the first term as the intercept.
println!("Fit result: {}", fit.result);

The canonical link function is used by default. An alternative link function can be specified as a type parameter to the response struct.

use ndarray_glm::{array, Logistic, logistic_link::Cloglog, ModelBuilder};

let data_y = array![true, false, false, true, true];
let data_x = array![[0.5, 0.2], [0.1, 0.3], [0.2, 0.6], [0.6, 0.3], [0.4, 0.4]];
let model = ModelBuilder::<Logistic<Cloglog>>::data(data_y.view(), data_x.view()).build().unwrap();
let fit = model.fit_options().l2_reg(1e-5).fit().unwrap();
println!("Fit result: {}", fit.result);

Feature summary:

Generic over floating-point type
Linear, logistic, Poisson, and binomial regression
L2 (ridge) regularization
Statistical tests of fit result
Alternative and custom link functions

Requirements: See the README for dependency requirements.

Modules

error	define the error enum for the result of regressions
link	Defines traits for link functions
logistic_link	Link functions for logistic regression
model	Collect data for and configure a model
num	numerical trait constraints

Macros

array

Create an Array with one, two or three dimensions.

Structs

Linear	Linear regression with constant variance (Ordinary least squares).
Logistic	Logistic regression
Poisson	Poisson regression over an unsigned integer type.

Returns a standardization of a design matrix where rows are seperate observations and columns are different dependent variables. Each quantity has its mean subtracted and is then divided by the standard deviation.

Type Definitions

Array1	one-dimensional array
Array2	two-dimensional array
ArrayView1	one-dimensional array view
ArrayView2	two-dimensional array view