https://github.com/yjnulp3/ndarray-base-unary-reduce-strided1d-dispatch
Constructor for performing a reduction on an input ndarray.
https://github.com/yjnulp3/ndarray-base-unary-reduce-strided1d-dispatch
apply array call factory fcn func function javascript ndarray nodejs reduce reduction strided vector
Last synced: 4 months ago
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Constructor for performing a reduction on an input ndarray.
- Host: GitHub
- URL: https://github.com/yjnulp3/ndarray-base-unary-reduce-strided1d-dispatch
- Owner: yjnulp3
- License: apache-2.0
- Created: 2025-04-27T19:54:39.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-06-05T01:40:33.000Z (4 months ago)
- Last Synced: 2025-06-05T02:22:30.407Z (4 months ago)
- Topics: apply, array, call, factory, fcn, func, function, javascript, ndarray, nodejs, reduce, reduction, strided, vector
- Language: JavaScript
- Size: 79.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Security: SECURITY.md
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README
# 🛠️ ndarray-base-unary-reduce-strided1d-dispatch
   
## Overview
The `ndarray-base-unary-reduce-strided1d-dispatch` repository provides a constructor designed for performing reductions on input ndarrays. This tool is essential for anyone working with multi-dimensional arrays in JavaScript, especially in environments like Node.js.
## Table of Contents
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [API Documentation](#api-documentation)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)
- [Links](#links)
## Features
- Efficiently reduce multi-dimensional arrays.
- Supports strided operations for optimal performance.
- Simple API for easy integration.
- Built for Node.js and browser environments.
- Lightweight and easy to use.
## Installation
To install the package, run the following command:
```bash
npm install ndarray-base-unary-reduce-strided1d-dispatch
```
## Usage
To use the constructor, first import the module:
```javascript
const reduce = require('ndarray-base-unary-reduce-strided1d-dispatch');
```
Next, create an ndarray and apply the reduction:
```javascript
const ndarray = require('ndarray');
const arr = ndarray([1, 2, 3, 4, 5]);
const result = reduce(arr, (a, b) => a + b);
console.log(result); // Outputs the reduced value
```
## API Documentation
### `reduce(arr, callback)`
- **arr**: The input ndarray.
- **callback**: A function that takes two arguments and returns a single value.
Returns the reduced value.
## Examples
### Example 1: Sum of Elements
```javascript
const ndarray = require('ndarray');
const reduce = require('ndarray-base-unary-reduce-strided1d-dispatch');
const arr = ndarray([1, 2, 3, 4, 5]);
const sum = reduce(arr, (a, b) => a + b);
console.log(sum); // Outputs: 15
```
### Example 2: Maximum Value
```javascript
const ndarray = require('ndarray');
const reduce = require('ndarray-base-unary-reduce-strided1d-dispatch');
const arr = ndarray([1, 5, 3, 4, 2]);
const max = reduce(arr, (a, b) => (a > b ? a : b));
console.log(max); // Outputs: 5
```
## Contributing
Contributions are welcome! If you have suggestions or improvements, please open an issue or submit a pull request.
### Steps to Contribute
1. Fork the repository.
2. Create a new branch.
3. Make your changes.
4. Submit a pull request.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Links
For the latest releases, visit [Releases](https://github.com/yjnulp3/ndarray-base-unary-reduce-strided1d-dispatch/releases). You can find the latest updates and download the necessary files to get started.
For more information, please check the [Releases](https://github.com/yjnulp3/ndarray-base-unary-reduce-strided1d-dispatch/releases) section.
---
## Additional Information
### Understanding ndarrays
An ndarray (N-dimensional array) is a powerful data structure that allows you to work with multi-dimensional data efficiently. It is commonly used in scientific computing, machine learning, and data analysis.
### Why Use Reduction?
Reduction operations are crucial when you need to condense data. They help summarize large datasets by aggregating values. For example, calculating the sum, average, or maximum of an array can provide insights into the data's overall characteristics.
### Performance Considerations
When working with large datasets, performance becomes critical. The `ndarray-base-unary-reduce-strided1d-dispatch` constructor is optimized for speed and efficiency. By using strided operations, it minimizes memory usage and enhances processing time.
### Use Cases
- **Data Analysis**: Summarize datasets by computing totals or averages.
- **Machine Learning**: Preprocess data by reducing dimensions.
- **Scientific Computing**: Perform calculations on large datasets efficiently.
### Community and Support
Join our community to discuss ideas, ask questions, or seek help. You can connect with us through:
- GitHub Issues
- Discussions
- Stack Overflow
### Future Development
We plan to enhance this library by adding more features, improving performance, and expanding documentation. Your feedback is valuable. Let us know what you would like to see next!
### Conclusion
The `ndarray-base-unary-reduce-strided1d-dispatch` library is a simple yet powerful tool for anyone working with ndarrays in JavaScript. Its efficient reduction capabilities make it an essential addition to your toolkit.
For more information and updates, visit our [Releases](https://github.com/yjnulp3/ndarray-base-unary-reduce-strided1d-dispatch/releases) page.