SwiftNP

What's Changed

• Created protocol NDArray and moved implementations to NDArrayImpl: NDArray by @k-arindam in https://github.com/k-arindam/SwiftNP/pull/12

Full Changelog: https://github.com/k-arindam/SwiftNP/compare/0.0.6...0.0.7

What's Changed

• Completed arithmetic ops like dot product, multiplication and division by @k-arindam in https://github.com/k-arindam/SwiftNP/pull/10
• Latest doc generated by @k-arindam in https://github.com/k-arindam/SwiftNP/pull/11

Full Changelog: https://github.com/k-arindam/SwiftNP/compare/0.0.5...0.0.6

What's Changed

• Implemented transpose() & scalar operations on NDArray in https://github.com/k-arindam/SwiftNP/pull/9

Full Changelog: https://github.com/k-arindam/SwiftNP/compare/0.0.4...0.0.5

What's Changed

• Added external type conversion for MLMultiArray & UIImage from/to NDArray by @k-arindam in https://github.com/k-arindam/SwiftNP/pull/7

Full Changelog: https://github.com/k-arindam/SwiftNP/compare/0.0.3...0.0.4

What's Changed

• Critical bug fixes, feature implementation and project restructure by @k-arindam in https://github.com/k-arindam/SwiftNP/pull/6

Full Changelog: https://github.com/k-arindam/SwiftNP/compare/0.0.2...0.0.3

We are thrilled to introduce the first stable release of SwiftNP, a Swift-based numerical computing library designed for developers who want to leverage Swift for multi-dimensional array operations, inspired by libraries like NumPy.

Key Features

•	NDArray (N-Dimensional Array): Provides an efficient data structure for handling multi-dimensional arrays with custom shapes and data types.
•	Comprehensive DType Support: Includes common data types such as Int8, Int16, Int32, Int64, UInt8, UInt16, UInt32, UInt64, Float16, Float32, Float64, and Double.
•	Flexible Initializers: Supports creating arrays with custom shapes, filled with default values, and more.
•	Dynamic Type Casting: Automatically casts input values to match the specified data type (DType).
•	Shape Validation: Ensures all arrays have valid shapes and throws errors for invalid configurations.

Contribution

• @k-arindam made major contribution in https://github.com/k-arindam/SwiftNP/pull/5

Full Changelog: https://github.com/k-arindam/SwiftNP/compare/0.0.1...0.0.2

Improvements Coming in Future Releases

•	Performance Enhancements: Optimizing memory management and computation speed.
•	Error Handling: Improving robustness around invalid data and type casting.
•	Additional Operations: Adding more mathematical and array manipulation functions.
•	Support for Additional Data Types: Expanding the list of supported numerical data types.

Known Issues

•	The current version focuses on core functionality, and performance may not yet be optimal.
•	Some features and operations are still under development and may not be fully tested in complex scenarios.

Feedback and Contributions

This is an early release, and we are actively looking for feedback and contributions from the community. Feel free to submit issues or pull requests on GitHub.

We are thrilled to introduce the alpha release of SwiftNP, a Swift-based numerical computing library designed for developers who want to leverage Swift for multi-dimensional array operations, inspired by libraries like NumPy.

Key Features

•	NDArray (N-Dimensional Array): Provides an efficient data structure for handling multi-dimensional arrays with custom shapes and data types.
•	Comprehensive DType Support: Includes common data types such as Int8, Int16, Int32, Int64, UInt8, UInt16, UInt32, UInt64, Float16, Float32, Float64, and Double.
•	Flexible Initializers: Supports creating arrays with custom shapes, filled with default values, and more.
•	Dynamic Type Casting: Automatically casts input values to match the specified data type (DType).
•	Shape Validation: Ensures all arrays have valid shapes and throws errors for invalid configurations.

Improvements Coming in Future Releases

•	Performance Enhancements: Optimizing memory management and computation speed.
•	Error Handling: Improving robustness around invalid data and type casting.
•	Additional Operations: Adding more mathematical and array manipulation functions.
•	Support for Additional Data Types: Expanding the list of supported numerical data types.

Known Issues

•	The current version focuses on core functionality, and performance may not yet be optimal.
•	Some features and operations are still under development and may not be fully tested in complex scenarios.

Feedback and Contributions

This is an alpha release, and we are actively looking for feedback and contributions from the community. Feel free to submit issues or pull requests on GitHub.