regression

WebJar for regression

License	MIT
GroupId	org.webjars.npm
ArtifactId	regression
Last Version	2.0.1
Release Date	May 20, 2020
Type	jar
Description	regression WebJar for regression
Project URL	https://www.webjars.org
Source Code Management	https://github.com/tom-alexander/regression-js

Download regression

Filename	Size
regression-2.0.1.pom
regression-2.0.1.jar	45 KB
regression-2.0.1-sources.jar	22 bytes
regression-2.0.1-javadoc.jar	22 bytes

How to add to project

Apache Maven

<!-- https://jarcasting.com/artifacts/org.webjars.npm/regression/ -->
<dependency>
    <groupId>org.webjars.npm</groupId>
    <artifactId>regression</artifactId>
    <version>2.0.1</version>
</dependency>

Gradle Groovy

// https://jarcasting.com/artifacts/org.webjars.npm/regression/
implementation 'org.webjars.npm:regression:2.0.1'

Gradle Kotlin

// https://jarcasting.com/artifacts/org.webjars.npm/regression/
implementation ("org.webjars.npm:regression:2.0.1")

Apache Buildr

'org.webjars.npm:regression:jar:2.0.1'

Apache Ivy

<dependency org="org.webjars.npm" name="regression" rev="2.0.1">
  <artifact name="regression" type="jar" />
</dependency>

Groovy Grape

@Grapes(
@Grab(group='org.webjars.npm', module='regression', version='2.0.1')
)

Scala SBT

libraryDependencies += "org.webjars.npm" % "regression" % "2.0.1"

Leiningen

[org.webjars.npm/regression "2.0.1"]

Dependencies

There are no dependencies for this project. It is a standalone project that does not depend on any other jars.

Project Modules

There are no modules declared in this project.

regression-js

regression-js is a JavaScript module containing a collection of linear least-squares fitting methods for simple data analysis.

Installation

This module works on node and in the browser. It is available as the 'regression' package on npm. It is also available on a CDN.

npm

npm install --save regression

Usage

import regression from 'regression';
const result = regression.linear([[0, 1], [32, 67], [12, 79]]);
const gradient = result.equation[0];
const yIntercept = result.equation[1];

Data is passed into the model as an array. A second parameter can be used to configure the model. The configuration parameter is optional. null values are ignored. The precision option will set the number of significant figures the output is rounded to.

Configuration options

Below are the default values for the configuration parameter.

{
  order: 2,
  precision: 2,
}

Properties

• equation: an array containing the coefficients of the equation
• string: A string representation of the equation
• points: an array containing the predicted data in the domain of the input
• r2: the coefficient of determination (R²)
• predict(x): This function will return the predicted value

API

regression.linear(data[, options])

Fits the input data to a straight line with the equation . It returns the coefficients in the form [m, c].

regression.exponential(data[, options])

Fits the input data to a exponential curve with the equation . It returns the coefficients in the form [a, b].

regression.logarithmic(data[, options])

Fits the input data to a logarithmic curve with the equation . It returns the coefficients in the form [a, b].

regression.power(data[, options])

Fits the input data to a power law curve with the equation . It returns the coefficients in the form [a, b].

regression.polynomial(data[, options])

Fits the input data to a polynomial curve with the equation . It returns the coefficients in the form [an..., a1, a0]. The order can be configure with the order option.

Example

const data = [[0,1],[32, 67] .... [12, 79]];
const result = regression.polynomial(data, { order: 3 });

Development

• Install the dependencies with npm install
• To build the assets in the dist directory, use npm run build
• You can run the tests with: npm run test.

Versions

Version
2.0.1 May 20, 2020