numd 1.0.4

A rudimentary numerical computing library for Dart, inspired by NumPy

numd

A powerful numerical computing library for Dart that provides efficient N-dimensional array operations and linear algebra capabilities. The library is designed to offer an intuitive interface for mathematical computations while maintaining type safety and performance.

Table of Contents

1. Installation
2. Features
3. Getting Started
4. Core Concepts
5. API Reference
6. Examples
7. Performance Considerations
8. Contributing

Installation

Add this to your package's pubspec.yaml file:

dependencies:
  numd: ^1.0.3

Then run:

dart pub get

Features

• Generic N-dimensional array implementation supporting both integer and floating-point numbers
• Efficient memory management using nested lists
• Built-in array initialization methods (zeros, ones, random)
• Operator overloading for basic arithmetic operations
• Matrix multiplication support through LinearAlgebra utility class
• Type-safe operations with compile-time checking
• Intuitive API design following Dart conventions
• Copy constructor for deep copying arrays

Getting Started

First, import the library:

import 'package:numd/numd.dart';

void main() {
  // Basic array creation
  var array = NDArray.init([
    [1, 2, 3],
    [4, 5, 6]
  ]);
  print(array);

  // Binary array with diagonal ones
  var diagonal = NDArray<int>.binary([3, 3], (i, j) => i == j);
  print(diagonal);

  // Random binary array
  var randomBinary = NDArray<int>.binaryRand([2, 2]);
  print(randomBinary);

  // Array operations
  var a = NDArray.ones([2, 2]);
  var b = NDArray.zeros([2, 2]);
  print(a + b);

  // Copy constructor
  var copy = NDArray.copy(array);
  print('Copy: $copy');
}

Core Concepts

NDArray

The NDArray<T> class is the foundation of the library. It represents a multi-dimensional array where T must be a numeric type (int or double). Key features include:

• Type Safety: The generic type parameter T ensures type safety at compile time
• Shape Information: Maintains array dimensions through the shape property
• Efficient Storage: Uses nested Lists for data storage
• Element Access: Direct element access through the at(i, j) method

Shape and Dimensions

Each NDArray maintains its shape as a List

• shape[0]: Number of rows
• shape[1]: Number of columns

Accessible through properties:

• dimensions: Total number of dimensions
• rows: Number of rows
• cols: Number of columns

Type Checking

The library provides runtime type checking methods:

• isInt(): Returns true if the array contains integers
• isEmpty(): Checks if the array is empty
• isDouble(): Returns true if the array contains floating-point numbers

API Reference

Constructors

1. NDArray.init(List<List

• Creates an NDArray from a nested list structure
  • Optional type parameter defaults to double

2. NDArray.zeros(List

• Creates an NDArray filled with zeros
  • Dimensions specify the shape
  • Optional type parameter

3. NDArray.ones(List

• Creates an NDArray filled with ones
  • Similar parameters to zeros constructor

4. NDArray.random(List

• Creates an NDArray filled with random values
  • Supports custom range through min and max parameters
  • Optional type parameter

5. NDArray.binary(List

• Creates a binary NDArray (0s and 1s) based on a condition function
  • Condition function determines placement of 1s
  • Optional type parameter

6. NDArray.binaryRand(List

   • Creates a random binary NDArray
   • Randomly places 0s and 1s
   • Optional type parameter

7. NDArray.copy(NDArray

   • Creates a deep copy of an existing NDArray
   • Maintains type safety with generic type T
   • Copies both shape and data
   • Returns new independent NDArray instance

Operators

The library provides operator overloading for basic arithmetic operations:

• Addition (+): Element-wise addition of two arrays
• Subtraction (-): Element-wise subtraction
• Multiplication (*): Element-wise multiplication
• Division (/): Element-wise division
• Array access: [], []=

All operators verify shape compatibility and throw ArgumentError if shapes don't match.

Methods

1. at(int i, int j)

   • Returns element at specified indices
   • Type-safe return value matching array type

2. forEach(void Function(T element) action)

   • Iterates through all elements
   • Accepts a callback function for element processing

LinearAlgebra Utilities

The LinearAlgebra class provides additional matrix operations:

1. matmul(NDArray a, NDArray b)
   • Performs matrix multiplication
   • Verifies matrix dimension compatibility
   • Returns new NDArray with result

Ex

Basic Array Operations

// Create arrays
var a = NDArray.init([[1, 2], [3, 4]]);
var b = NDArray.init([[5, 6], [7, 8]]);

// Addition
var sum = a + b;
print(sum.toString());

// Matrix multiplication
var product = LinearAlgebra.matmul(a, b);
print(product.toString());

Random Array Generation

// Create 3x3 random integer array
var randomInts = NDArray.random([3, 3], min: 1, max: 100, type: int);

// Create 2x2 random double array
var randomDoubles = NDArray.random([2, 2], min: 0, max: 1);

Binary Array Generation

// Create a diagonal binary matrix
var diagonal = NDArray<int>.binary([3, 3], (i, j) => i == j);
print(diagonal);
// Output:
// [ 
//  [1, 0, 0],
//  [0, 1, 0],
//  [0, 0, 1]
// ]

// Create a random binary matrix
var randomBinary = NDArray<int>.binaryRand([2, 2]);
print(randomBinary);
// Possible output:
// [ 
//  [1, 0],
//  [0, 1]
// ]

Example

amples

Basic Array Operations

// Create arrays
var a = NDArray.init([[1, 2], [3, 4]]);
var b = NDArray.init([[5, 6], [7, 8]]);

// Addition
var sum = a + b;
print(sum.toString());

// Matrix multiplication
var product = LinearAlgebra.matmul(a, b);
print(product.toString());

Random Array Generation

// Create 3x3 random integer array
var randomInts = NDArray.random([3, 3], min: 1, max: 100, type: int);

// Create 2x2 random double array
var randomDoubles = NDArray.random([2, 2], min: 0, max: 1);

Binary Array Generation

// Create a diagonal binary matrix
var diagonal = NDArray<int>.binary([3, 3], (i, j) => i == j);
print(diagonal);
// Output:
// [ 
//  [1, 0, 0],
//  [0, 1, 0],
//  [0, 0, 1]
// ]

// Create a random binary matrix
var randomBinary = NDArray<int>.binaryRand([2, 2]);
print(randomBinary);
// Possible output:
// [ 
//  [1, 0],
//  [0, 1]
// ]

Example

import 'package:numd/numd.dart';

void main() {
  // Create a 2x3 array
  var array = NDArray.init([
    [1, 2, 3],
    [4, 5, 6]
  ]);
  print(array);

  // Create binary array
  var diagonal = NDArray<int>.binary([3, 3], (i, j) => i == j);
  print(diagonal);

  // Basic operations
  var a = NDArray.ones([2, 2]);
  var b = NDArray.zeros([2, 2]);
  print(a + b);

  // Copy constructor
  var copy = NDArray.copy(array);
  print('Copy: $copy');
}

Performance Considerations

The library is optimized for performance in several ways:

1. Efficient String Building: Uses StringBuffer for string operations
2. Memory Management: Reuses existing arrays where possible
3. Type Safety: Compile-time type checking reduces runtime overhead

Contributing

Contributions are welcome! Please feel free to submit pull requests. When contributing:

1. Follow the Dart style guide
2. Add tests for new functionality
3. Update documentation as needed
4. Maintain type safety
5. Consider performance implications

License

This project is licensed under the MIT License - see the LICENSE file for details.