Recent Releases of https://github.com/cool-japan/scirs
https://github.com/cool-japan/scirs - SciRS2 0.1.0 Alpha-6 Release
Full Changelog: https://github.com/cool-japan/scirs/compare/v0.1.0-alpha.4...v0.1.0-alpha.6
- Rust
Published by cool-japan 8 months ago
https://github.com/cool-japan/scirs - SciRS2 0.1.0 Alpha-5 Release
Full Changelog: https://github.com/cool-japan/scirs/compare/v0.1.0-alpha.4...v0.1.0-alpha.5
- Rust
Published by cool-japan 8 months ago
https://github.com/cool-japan/scirs - SciRS2 0.1.0 Alpha-4 Release
Full Changelog: https://github.com/cool-japan/scirs/compare/v0.1.0-alpha.3...v0.1.0-alpha.4
- Rust
Published by cool-japan 9 months ago
https://github.com/cool-japan/scirs - SciRS2 0.1.0 Alpha- Release
Full Changelog: https://github.com/cool-japan/scirs/compare/v0.1.0-alpha.2...v0.1.0-alpha.3
- Rust
Published by cool-japan 9 months ago
https://github.com/cool-japan/scirs - SciRS2 0.1.0 Alpha-2 Release
SciRS2 0.1.0 Alpha-2 Release
Full Changelog: https://github.com/cool-japan/scirs/compare/v0.1.0-alpha...v0.1.0-alpha.2
- Rust
Published by cool-japan 10 months ago
https://github.com/cool-japan/scirs - SciRS2 0.1.0 Alpha Release
# SciRS2 0.1.0-alpha Release
This is the first alpha release of the SciRS2 project, which implements a SciPy-like scientific computing and AI library in Rust. All modules have been published on crates.io.
## Key Features
- 24 Specialized Modules: Covering various scientific computing domains including linear algebra, statistics, signal processing, and more
- Leveraging Rust's Strengths: Memory safety, parallel processing, and zero-cost abstractions
- SciPy Compatibility: Familiar APIs in Rust
- High-Performance Implementation: Optimizations including SIMD, multi-threading, and caching
## Published Modules
### Core Modules - scirs2-core: Core utilities and common functionality - scirs2-linalg: Linear algebra operations - scirs2-stats: Statistical distributions and functions - scirs2-integrate: Numerical integration - scirs2-interpolate: Interpolation algorithms - scirs2-optimize: Optimization algorithms - scirs2-fft: Fast Fourier Transform - scirs2-special: Special functions - scirs2-signal: Signal processing - scirs2-sparse: Sparse matrix operations - scirs2-spatial: Spatial algorithms - scirs2-cluster: Clustering algorithms - scirs2-transform: Data transformation - scirs2-metrics: ML evaluation metrics
### Preview Modules - scirs2-ndimage: N-dimensional image processing - scirs2-neural: Neural network building blocks - scirs2-optim: ML optimization algorithms - scirs2-series: Time series analysis - scirs2-text: Text processing - scirs2-io: Input/output utilities - scirs2-datasets: Dataset utilities - scirs2-graph: Graph processing - scirs2-vision: Computer vision - scirs2-autograd: Automatic differentiation
### Main Package - scirs2: A Rust port of SciPy with AI/ML extensions
## Usage
Add the required modules to your Cargo.toml:
toml
[dependencies]
scirs2 = "0.1.0-alpha.1" # Main package
scirs2-linalg = "0.1.0-alpha.1" # If using specific modules only
Simple example:
``` use scirs2linalg::basic::matrixmultiply; use scirs2_stats::distributions::normal::Normal; use ndarray::Array2;
// Matrix multiplication let a = Array2::fromshapevec((2, 2), vec![1.0, 2.0, 3.0, 4.0]).unwrap(); let b = Array2::fromshapevec((2, 2), vec![5.0, 6.0, 7.0, 8.0]).unwrap(); let c = matrix_multiply(&a, &b).unwrap();
// Normal distribution let normal = Normal::new(0.0, 1.0).unwrap(); let sample = normal.random_sample(100);
``` Important Notes
This is an alpha release, and the API may undergo breaking changes. We welcome feedback and contributions.
Next Steps
- Further enhance documentation
- Build benchmarking suite
- Compare performance with SciPy
- Stabilize API and prepare for beta release
Contributing
Bug reports, feature requests, and pull requests are welcome. Please see CONTRIBUTING.md before contributing to this project.
This release marks the v0.1.0-alpha tag. Feel free to adjust the content as needed.
- Rust
Published by cool-japan 10 months ago
https://github.com/cool-japan/scirs - SciRS2 0.1.0 Alpha Release
# SciRS2 0.1.0-alpha Release
This is the first alpha release of the SciRS2 project, which implements a SciPy-like scientific computing and AI library in Rust. All modules have been published on crates.io.
## Key Features
- 24 Specialized Modules: Covering various scientific computing domains including linear algebra, statistics, signal processing, and more
- Leveraging Rust's Strengths: Memory safety, parallel processing, and zero-cost abstractions
- SciPy Compatibility: Familiar APIs in Rust
- High-Performance Implementation: Optimizations including SIMD, multi-threading, and caching
## Published Modules
### Core Modules - scirs2-core: Core utilities and common functionality - scirs2-linalg: Linear algebra operations - scirs2-stats: Statistical distributions and functions - scirs2-integrate: Numerical integration - scirs2-interpolate: Interpolation algorithms - scirs2-optimize: Optimization algorithms - scirs2-fft: Fast Fourier Transform - scirs2-special: Special functions - scirs2-signal: Signal processing - scirs2-sparse: Sparse matrix operations - scirs2-spatial: Spatial algorithms - scirs2-cluster: Clustering algorithms - scirs2-transform: Data transformation - scirs2-metrics: ML evaluation metrics
### Preview Modules - scirs2-ndimage: N-dimensional image processing - scirs2-neural: Neural network building blocks - scirs2-optim: ML optimization algorithms - scirs2-series: Time series analysis - scirs2-text: Text processing - scirs2-io: Input/output utilities - scirs2-datasets: Dataset utilities - scirs2-graph: Graph processing - scirs2-vision: Computer vision - scirs2-autograd: Automatic differentiation
### Main Package - scirs2: A Rust port of SciPy with AI/ML extensions
## Usage
Add the required modules to your Cargo.toml:
toml
[dependencies]
scirs2 = "0.1.0-alpha.1" # Main package
scirs2-linalg = "0.1.0-alpha.1" # If using specific modules only
Simple example:
``` use scirs2linalg::basic::matrixmultiply; use scirs2_stats::distributions::normal::Normal; use ndarray::Array2;
// Matrix multiplication let a = Array2::fromshapevec((2, 2), vec![1.0, 2.0, 3.0, 4.0]).unwrap(); let b = Array2::fromshapevec((2, 2), vec![5.0, 6.0, 7.0, 8.0]).unwrap(); let c = matrix_multiply(&a, &b).unwrap();
// Normal distribution let normal = Normal::new(0.0, 1.0).unwrap(); let sample = normal.random_sample(100);
``` Important Notes
This is an alpha release, and the API may undergo breaking changes. We welcome feedback and contributions.
Next Steps
- Further enhance documentation
- Build benchmarking suite
- Compare performance with SciPy
- Stabilize API and prepare for beta release
Contributing
Bug reports, feature requests, and pull requests are welcome. Please see CONTRIBUTING.md before contributing to this project.
This release marks the v0.1.0-alpha tag. Feel free to adjust the content as needed.
- Rust
Published by cool-japan 10 months ago