NuMojo

NuMojo is a library for numerical computing in Mojo 🔥 similar to NumPy, SciPy in Python.

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Table of Contents

1. About The Project
2. Goals
3. Usage
4. How to install
5. Contributing
6. Warnings
7. License
8. Acknowledgements
9. Contributors

About the project

NuMojo aims to encompass the extensive numerics capabilities found in Python packages such as NumPy, SciPy, and Scikit-learn.

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What NuMojo is

We seek to harness the full potential of Mojo, including vectorization, parallelization, and GPU acceleration (when available). Currently, NuMojo extends most (if not all) standard library math functions to support array inputs.

Our vision for NuMojo is to serve as an essential building block for other Mojo packages needing fast math operations, without the additional weight of a machine learning back-propagation system.

What NuMojo is not

NuMojo is not a machine learning library and will never include back-propagation as part of the base library.

Features and goals

Our primary objective is to develop a fast, comprehensive numerics library in Mojo. Below are some features and long-term goals. Some have already been implemented, either fully or partially.

Core data types:

• Native n-dimensional array (numojo.core.ndarray.NDArray).
• Native 2-dimensional array, i.e., matrix (numojo.core.matrix.Matrix).
• Native n-dimensional complex array (numojo.core.complex_ndarray.ComplexNDArray)
• Native fixed-dimension array (to be implemented when trait parameterization is available).

Routines and objects:

• Array creation routines (numojo.routines.creation)
• Array manipulation routines (numojo.routines.manipulation)
• Input and output (numojo.routines.io)
• Linear algebra (numojo.routines.linalg)
• Logic functions (numojo.routines.logic)
• Mathematical functions (numojo.routines.math)
• Exponents and logarithms (numojo.routines.exponents)
• Extrema finding (numojo.routines.extrema)
• Rounding (numojo.routines.rounding)
• Trigonometric functions (numojo.routines.trig)
• Random sampling (numojo.routines.random)
• Sorting, searching, and counting (numojo.routines.sorting, numojo.routines.searching)
• Statistics (numojo.routines.statistics)
• etc...

Please find all the available functions and objects here.

For a detailed roadmap, please refer to the docs/roadmap.md file.

Usage

An example of n-dimensional array (NDArray type) goes as follows.

import numojo as nm
from numojo.prelude import *


fn main() raises:
    # Generate two 1000x1000 matrices with random float64 values
    var A = nm.random.randn(Shape(1000, 1000))
    var B = nm.random.randn(Shape(1000, 1000))

    # Generate a 3x2 matrix from string representation
    var X = nm.fromstring[f32]("[[1.1, -0.32, 1], [0.1, -3, 2.124]]")

    # Print array
    print(A)

    # Array multiplication
    var C = A @ B

    # Array inversion
    var I = nm.inv(A)

    # Array slicing
    var A_slice = A[1:3, 4:19]

    # Get scalar from array
    var A_item = A[item(291, 141)]
    var A_item_2 = A.item(291, 141)

An example of matrix (Matrix type) goes as follows.

from numojo import Matrix
from numojo.prelude import *


fn main() raises:
    # Generate two 1000x1000 matrices with random float64 values
    var A = Matrix.rand(shape=(1000, 1000))
    var B = Matrix.rand(shape=(1000, 1000))

    # Generate 1000x1 matrix (column vector) with random float64 values
    var C = Matrix.rand(shape=(1000, 1))

    # Generate a 4x3 matrix from string representation
    var F = Matrix.fromstring[i8](
        "[[12,11,10],[9,8,7],[6,5,4],[3,2,1]]", shape=(4, 3)
    )

    # Matrix slicing
    var A_slice = A[1:3, 4:19]
    var B_slice = B[255, 103:241:2]

    # Get scalar from matrix
    var A_item = A[291, 141]

    # Flip the column vector
    print(C[::-1, :])

    # Sort and argsort along axis
    print(nm.sort(A, axis=1))
    print(nm.argsort(A, axis=0))

    # Sum the matrix
    print(nm.sum(B))
    print(nm.sum(B, axis=1))

    # Matrix multiplication
    print(A @ B)

    # Matrix inversion
    print(A.inv())

    # Solve linear algebra
    print(nm.solve(A, B))

    # Least square
    print(nm.lstsq(A, C))

An example of ComplexNDArray is as follows,

import numojo as nm
from numojo.prelude import *


fn main() raises:
    # Create a complexscalar 5 + 5j
    var complexscalar = ComplexSIMD[cf32](re=5, im=5) 
    # Create complex array filled with (5 + 5j)
    var A = nm.full[cf32](Shape(1000, 1000), fill_value=complexscalar)
    # Create complex array filled with (1 + 1j)
    var B = nm.ones[cf32](Shape(1000, 1000))

    # Print array
    print(A)

    # Array slicing
    var A_slice = A[1:3, 4:19]

    # Array multiplication
    var C = A * B

    # Get scalar from array
    var A_item = A[item(291, 141)]
    # Set an element of the array
    A[item(291, 141)] = complexscalar

How to install

There are three approach to install and use the Numojo package.

Use magic CLI

You can use the following command in the terminal to install numojo.

magic add numojo 

Add in toml file

You can add numojo in the dependencies section of your toml file.

[dependencies]
numojo = "==0.5"

Build package

This approach involves building a standalone package file mojopkg.

1. Clone the repository.
2. Build the package using magic run package.
3. Move the numojo.mojopkg into the directory containing the your code.

Include NuMojo's path for compiler and LSP

This approach does not require building a package file. Instead, when you compile your code, you can include the path of NuMojo repository with the following command:

mojo run -I "../NuMojo" example.mojo

This is more flexible as you are able to edit the NuMojo source files when testing your code.

In order to allow VSCode LSP to resolve the imported numojo package, you can:

1. Go to preference page of VSCode.
2. Go to Mojo › Lsp: Include Dirs
3. Click add item and write the path where the Numojo repository is located, e.g. /Users/Name/Programs/NuMojo.
4. Restart the Mojo LSP server.

Now VSCode can show function hints for the Numojo package!

Contributing

Any contributions you make are greatly appreciated. For more details and guidelines on contributions, please check here

Warnings

This library is still very much a work in progress and may change at any time.

License

Distributed under the Apache 2.0 License with LLVM Exceptions. See LICENSE and the LLVM License for more information.

This project includes code from Mojo Standard Library, licensed under the Apache License v2.0 with LLVM Exceptions (see the LLVM License). MAX and Mojo usage and distribution are licensed under the MAX & Mojo Community License.

Acknowledgements

Built in native Mojo which was created by Modular.

Contributors