Laminax Kyaro Programming Language

A dynamically-typed, interpreted programming language with dual implementations in Python and Rust.

Implementation Overview

This repository contains two complete implementations of the Laminax-KPL interpreter:

• Python Implementation (src/python/): Original version with extensive built-in functions and AI/ML capabilities
• Rust Implementation (src/rust/): High-performance version with memory safety and type safety guarantees

Both implementations provide compatible core language features.

Features

• Dynamic typing
• First-class functions
• Control flow structures (if/elif/else, while loops, for loops)
• Comprehensive built-in function library
• Dynamic arrays with indexing and iteration
• String manipulation and formatting
• Interactive REPL shell

Quick Start

Python Version

# Clone the repository
git clone https://github.com/hnrie/Laminax-KPL
cd Laminax-KPL

# Run Python interpreter (requires Python 3.6+)
cd src/python
python main.py                    # REPL mode
python main.py program.kyaro      # Run file

Rust Version

# Install Rust (if not already installed)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Build and run Rust interpreter
cd src/rust
cargo run                         # REPL mode
cargo run program.kyaro           # Run file
cargo build --release            # Build optimized binary

Performance Comparison

• Python: Instant startup, interpreted execution, extensive libraries
• Rust: Fast compilation, compiled execution (5-10x faster), memory safe

Installation

Python Version

No installation required! Just Python 3.6+ is needed.

Rust Version

Requires Rust toolchain (automatically installs dependencies via Cargo).

git clone https://github.com/hnrie/Laminax-KPL
cd Laminax-KPL
python main.py

Usage

Interactive REPL

Run without arguments to start the interactive shell:

python main.py

Execute a File

Run a Kyaro program file:

python main.py program.kyaro

Language Syntax

Variables

let x = 10
let name = "Kyaro"
let is_active = true

Functions

func greet(name) {
    print("Hello, " + name + "!")
}

greet("World")

Control Flow

let age = 18

if age >= 18 {
    print("Adult")
} elif age >= 13 {
    print("Teenager")
} else {
    print("Child")
}

Loops

let i = 0
while i < 5 {
    print(i)
    i += 1
}

for item in [1, 2, 3, 4, 5] {
    print(item)
}

for char in "Kyaro" {
    print(char)
}

Lists

let numbers = [1, 2, 3, 4, 5]
print(numbers[0])
append(numbers, 6)
print(len(numbers))

Operators

Arithmetic: +, -, *, /, %, ** (power) Comparison: ==, !=, <, >, <=, >= Logical: and, or, not Assignment: =, +=, -=, *=, /=

Built-in Functions

136 built-in functions across multiple categories:

• I/O: print(), input()
• Type conversion: str(), int(), float(), type()
• Collections: len(), range(), append(), pop(), push(), reverse(), sort(), sorted(), count(), index(), insert(), remove(), clear(), copy(), extend(), unique(), flatten()
• Basic math: abs(), min(), max(), sum(), sqrt(), pow(), exp(), floor(), ceil(), round(), trunc(), factorial(), gcd(), lcm()
• Logarithms: log(), log10(), log2(), ln()
• Trigonometry: sin(), cos(), tan(), asin(), acos(), atan(), atan2(), sinh(), cosh(), tanh(), asinh(), acosh(), atanh(), degrees(), radians(), hypot()
• Special functions: isnan(), isinf(), isfinite(), copysign(), fmod(), remainder(), modf(), frexp(), ldexp(), erf(), erfc(), gamma(), lgamma()
• Statistics: mean(), median(), median_low(), median_high(), mode(), stdev(), variance(), pstdev(), pvariance(), quantiles(), covariance(), correlation(), linear_regression(), harmonic_mean(), geometric_mean(), fmean()
• Random: random(), randint(), uniform(), choice(), shuffle(), sample(), gauss(), normalvariate(), lognormvariate(), expovariate(), vonmisesvariate(), gammavariate(), betavariate(), paretovariate(), weibullvariate(), seed()
• Functional: zip(), enumerate(), filter(), map(), reduce(), all(), any()
• Data analysis: product(), cumsum(), cumprod(), diff(), transpose(), dot(), norm(), normalize()
• Machine learning: sigmoid(), relu(), softmax(), clamp(), lerp()
• Constants: pi(), e(), tau(), inf(), nan()
• Utility: exit(), time(), sleep()

Image Processing Functions

70 image manipulation functions:

• I/O: image_load(), image_save(), image_new()
• Transformations: image_resize(), image_crop(), image_rotate(), image_flip_horizontal(), image_flip_vertical()
• Filters: image_blur(), image_sharpen(), image_edge_enhance(), image_find_edges(), image_emboss(), image_contour()
• Enhancements: image_brightness(), image_contrast(), image_color(), image_sharpness()
• Effects: image_grayscale(), image_invert(), image_posterize(), image_solarize(), image_equalize()
• Composition: image_blend(), image_add(), image_subtract(), image_multiply(), image_composite()
• Drawing: image_draw(), draw_line(), draw_rectangle(), draw_circle(), draw_ellipse(), draw_polygon(), draw_text()
• Pixel operations: image_get_pixel(), image_put_pixel()
• Channels: image_split(), image_merge(), image_convert()

AI and Machine Learning Functions

37 AI/ML functions for data science and neural networks:

• Data preprocessing: ml_train_test_split(), ml_standardize(), ml_min_max_scale(), ml_one_hot_encode()
• Algorithms: ml_knn_predict(), ml_kmeans()
• Distance metrics: ml_euclidean_distance(), ml_manhattan_distance(), ml_cosine_similarity()
• Regression metrics: ml_mse(), ml_mae(), ml_rmse(), ml_r2_score()
• Classification metrics: ml_accuracy(), ml_precision(), ml_recall(), ml_f1_score(), ml_confusion_matrix()
• Neural network activations: nn_tanh(), nn_leaky_relu(), nn_elu(), nn_softplus()
• Loss functions: nn_mse_loss(), nn_binary_crossentropy(), nn_categorical_crossentropy()
• NN utilities: nn_dropout(), nn_batch_norm()
• Matrix operations: matrix_multiply(), matrix_transpose(), matrix_add(), matrix_subtract(), matrix_identity(), matrix_determinant()
• Optimization: gradient_descent_step(), adam_step()

File System Functions

52 file system functions for comprehensive file and directory operations:

• File I/O: fs_read_file(), fs_write_file(), fs_read_lines(), fs_write_lines(), fs_read_bytes(), fs_write_bytes(), fs_append_file()
• File/Dir checks: fs_exists(), fs_is_file(), fs_is_dir(), fs_is_link()
• File/Dir operations: fs_delete_file(), fs_delete_dir(), fs_create_dir(), fs_copy_file(), fs_copy_dir(), fs_move(), fs_rename(), fs_touch()
• Directory listing: fs_list_dir(), fs_walk(), fs_glob(), fs_find_files()
• File metadata: fs_get_size(), fs_get_mtime(), fs_get_ctime(), fs_get_atime(), fs_stat()
• Path manipulation: path_join(), path_split(), path_dirname(), path_basename(), path_splitext(), path_abspath(), path_realpath(), path_normpath()
• Working directory: fs_get_cwd(), fs_change_dir(), fs_get_home(), fs_get_temp()
• Advanced: fs_symlink(), fs_readlink(), fs_chmod(), fs_get_extension(), fs_get_stem(), fs_with_suffix()

Example Programs

Hello World

print("Hello, World!")

Fibonacci Sequence

func fibonacci(n) {
    if n <= 1 {
        return n
    }
    return fibonacci(n - 1) + fibonacci(n - 2)
}

for i in range(10) {
    print(fibonacci(i))
}

Factorial

func factorial(n) {
    if n <= 1 {
        return 1
    }
    return n * factorial(n - 1)
}

print(factorial(5))

FizzBuzz

for i in range(1, 101) {
    if i % 15 == 0 {
        print("FizzBuzz")
    } elif i % 3 == 0 {
        print("Fizz")
    } elif i % 5 == 0 {
        print("Buzz")
    } else {
        print(i)
    }
}

License

LAMINAX CO