multivariate_linear_regression 0.1.1+2

Multivariate linear regression for Dart with support for multiple outputs and optional intercept.

Multivariate Linear Regression

Multivariate linear regression for Dart with support for multiple outputs and optional intercept, implemented using Golub-Reinsch Singular Value Decomposition.

Inspired by ml-matrix and regression-multivariate-linear Node.js libraries.

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Installation

In order to start using Multivariate Linear Regression you must have the Dart SDK installed on your machine.

Install via dart pub add:

dart pub add multivariate_linear_regression

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Usage

import 'package:multivariate_linear_regression/multivariate_linear_regression.dart';

void main() {
  final x = [
    [0.0, 0.0],
    [1.0, 2.0],
    [2.0, 3.0],
    [3.0, 4.0],
  ];

  final y = [
    [0.0, 0.0, 0.0],
    [2.0, 4.0, 3.0],
    [4.0, 6.0, 5.0],
    [6.0, 8.0, 7.0],
  ];

  final mlr = MultivariateLinearRegression(
    x: x,
    y: y,
  );

  print(mlr.predict([3.0, 3.0]));
  print(mlr.predictBatch([[1.0, 2.0], [2.0, 3.0]]));
  print(mlr.weights);
  print(mlr.stdError);
  print(mlr.stdErrors);
  print(mlr.tStats);
  print(mlr.toJson());
}

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API Overview

Constructor

MultivariateLinearRegression({
  required List<List<double>> x,
  required List<List<double>> y,
  bool intercept = true,
  bool statistics = true,
})

Creates a multivariate linear regression model.

• x - Input feature matrix (rows = samples, columns = features)
• y — Output matrix (rows = samples, columns = targets)
• intercept — Includes a bias (intercept) term when set to true
• statistics — Enables computation of additional metrics (standard errors, t-stats, etc.)

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Load Existing Model

factory MultivariateLinearRegression.load(MultivariateLinearRegression model)

Reconstructs a trained model from previously trained model

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Prediction

List<double> predict(List<double> input)

Returns predicted outputs for a single input vector.

List<List<double>> predictBatch(List<List<double>> inputs)

Returns predictions for multiple input rows.

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Coefficients & Metrics

List<List<double>> get weights

Matrix of regression coefficients (includes intercept if enabled).

double get stdError

Overall standard error of the model.

List<List<double>> get stdErrors

Standard error for each coefficient.

List<List<double>> get tStats

T-statistics corresponding to each coefficient.

List<List<double>> get stdErrorMatrix

Covariance matrix of the coefficients.

Available only when statistics = true

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Serialization

Map<String, dynamic> toJson()

Serializes the model into a JSON-compatible format, including statistics when enabled.

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Continuous Integration

Multivariate Linear Regression comes with a built-in GitHub Actions workflow powered by Very Good Workflows.

On each pull request and push, the CI formats, lints, and tests the code. The project uses Very Good Analysis for a strict set of analysis rules. Code coverage is enforced using Very Good Coverage.

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Running Tests

To run all unit tests and generate coverage:

dart pub global activate coverage 1.15.0
dart test --coverage=coverage
dart pub global run coverage:format_coverage --lcov --in=coverage --out=coverage/lcov.info

To view the coverage report using lcov:

genhtml coverage/lcov.info -o coverage/
open coverage/index.html

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Support

If you find this package useful, please consider supporting it:

• Like the package on pub.dev
• Star the GitHub repository

Your support helps improve the project and keeps it actively maintained 😊