DARJEELING

Machine learning tools for Rust

Contact

elocolburn@comcast.net

Installation

Add the following to your Cargo.toml file darjeeling = "0.2.0"

Example

```rust use core::{panic}; use darjeeling::{ categorize::NeuralNetwork, tests, input::Input} use std::{io::{BufReader, BufRead}, fs};

/// This program  function would read from a file containing all possible inputs to a binary logic gate, and all the correct answers.
/// Then it would train a model with 1 hidden layer. 
/// 2 nodes in its input layer, because there are two inputs.
/// 2 Nodes for its output layer because there are two possible answers(the "brighter" one is selected a the chosen answer)
/// and 2 nodes in its hidden layer, because I like patterns.
/// If this doesn't work, check the tests.ts source code for verified working code.
/// Hint: Try fiddling with the learning rate you're using if things aren't working properly
/// Different problems work differently with different learning rates, although I recommend one of 0.5 to start.m
fn train_test_xor() {
    let learning_rate:f32 = 1.0;
    let categories = NeuralNetwork::categories_format(vec!["0","1"]);
    let data = xor_file();


    let model_name: String = train_network_xor(data.clone(), categories.clone(), learning_rate).unwrap();

    test_network_xor(data, categories, model_name)
}

fn train_network_xor(mut data:Vec<Input>, categories: Vec<String>, learning_rate: f32) -> Option<String> {
    let input_num: i32 = 2;
    let hidden_num: i32 = 2;
    let answer_num: i32 = 2;
    let hidden_layers: i32 = 1;
    let mut net = NeuralNetwork::new(input_num, hidden_num, answer_num, hidden_layers);


    match net.learn(&mut data, categories, learning_rate) {

        Some(name) => Some(name),
        None => None
    }
}

fn test_network_xor(data: Vec<Input>, categories: Vec<String>, model_name: String) {

    NeuralNetwork::test(data, categories, model_name);
}

// This isn't very important, this just reads the file you want to and format it as Inputs
fn xor_file() -> Vec<Input> {
    let file = match fs::File::open("training_data/xor.txt") {
        Ok(file) => file,
        Err(error) => panic!("Panic opening the file: {:?}", error)
    };

    let reader = BufReader::new(file);
    let mut inputs: Vec<Input> = vec![];

    for l in reader.lines() {

        let line = match l {
            Ok(line) => line,
            Err(error) => panic!("{:?}", error)
        };

        let init_inputs: Vec<&str> = line.split(";").collect();
        let float_inputs: Vec<f32> = vec![init_inputs[0].split(" ").collect::<Vec<&str>>()[0].parse().unwrap(), init_inputs[0].split(" ").collect::<Vec<&str>>()[1].parse().unwrap()];

        let input: Input = Input { inputs: float_inputs, answer:init_inputs.get(init_inputs.len()-1).as_ref().unwrap().to_owned().to_string() };
        inputs.push(input);
    }

inputs  
}

```