Tenso-rs

The tenso-rs crate provides functionality to work with N dimensional tensors, similar to NumPy, PyTorch, TensorFlow, the ndarray crate, etc.

This is a toy project, for me to explore how these amazing libraries work under the hood. This is by no means ready for use aside from exploration or being a laughing stock.

Highlights

• N dimensional Tensors with support for custom types (0 != N for now, 0 dimensional tensor coming soon)
• Common tensor creation methods like zeros, linespace, arange, etc.
• Tensor Views
• Basic Tensor manipulation methods like cat, reshape, permute, transpose, etc.
• Views of Tensors; Tensor Slicing; Tensor Iterators
• Common math operations like cos, arctanh, sqrt, clamp
  • Few "Advanced" functions like erfc, sinc, log_gamma

TODOs

Stuff I want to do soon: - "Good" Macro for tensor creation with user data - Zero Dimensional Tensors - Inplace Tensor operations - Broadcasting - More Tensor manipulation methods (from numpy API docs) - More math operations, (from scipy special functions) - Save and Load using serde - Optimize this slow code (source code not even bench-marked) - Linear Algebra and Tensor multiplication - Integration with BLAS and matrixmultiply crate / custom code - And more.

Examples

You can create new tensors from the (few) creations methods like so: ```rust // The following represents the tensor: [1, 2, 3, 4, 5, 6, 7, 8, 9] let t1 = Tensor::::arange(1, 10, 1).unwrap();

// The following represents the tensor: [-10.0, -5.0, 0.0, 5.0, 10.0] let t2 = Tensor::::linespace(-10.0, 10.0, 5).unwrap();

// The following represents the tensor: [[1, 0, 0], [0, 1, 0], [0, 0, 0]] let t3 = Tensor::::eye(2, 3).unwrap();

// The following represents the tensor: [[1, 2, 3], [4, 5, 6]]. // As the TODOS mention, a nice macro for tensor from user data does not exist right now. let t4 = Tensor::fromsliceand_dims(&[1, 2, 3, 4, 5, 6], &[2, 3]).unwrap();

// There are (a few) more! ```

Modification of tensors: ```rust let t1 = Tensor::fromsliceanddims(&[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], &[3, 4]).unwrap(); let t2 = Tensor::fromsliceanddims(&[13, 14, 15, 16, 17, 18], &[3, 2]).unwrap(); let res = t1.cat(&t2, 1).unwrap(); // res represents [[1, 2, 3, 4, 13, 14], [5, 6, 7, 8, 15, 16], [9, 10, 11, 12, 17, 18]]

let t = Tensor::::arange(0, 9, 1).unwrap(); let res = t.reshape(&[3, 3]).unwrap(); // res represents [[0, 1, 2], [3, 4, 5], [6, 7, 8]]

let t = Tensor::arange(0, 24, 1).unwrap().reshape(&[2, 3, 4]).unwrap(); let res = t.permute(&[2, 0, 1]).unwrap(); // res represents [[[0, 4, 8], [12, 16, 20]], [[1, 5, 9], [13, 17, 21]], [[2, 6, 10], [14, 18, 22]], [[3, 7, 11], [15, 19, 23]]]

// There are (a few) more! ```

Math Operations: ```rust let t = Tensor::logspace(f64::consts::E, 0.0, 5.0, 6).unwrap(); let res = t.cos(); // res represents [cos(0), cos(e), cos(e^2), cos(e^3), cos(e^4), cos(e^5)]

let t = Tensor::fromsliceand_dims(&[1.0, 4.0, 9.0, 16.0, 25.0], &[5]).unwrap(); let res = t.rsqrt(); // res represents [1, 1 / 2, 1 / 3, 1 / 4, 1 / 5]

let t = Tensor::arange(1.0, 6.0, 1.0).unwrap(); let res = t.gammaf(); // res represents [1.0, 1.0, 2.0, 6.0, 24.0, 120.0]

// There are (a few) more! ```

Notes

• Untested: Arbitrary Precision is possible using num_bigint
  • Note: "Advanced" Math operations like erf and gamma are not arbitrary precision yet.
• New issues, pull requests, or ideas are more than welcome!