DARJEELING

Machine learning and data manipulation tools for Rust

Contact

elocolburn@comcast.net

Installation

Add the following to your Cargo.toml file

darjeeling = "0.3.1"

Examples

Machine Learning Networks

Categorization

This program reads from a file containing all possible inputs to a binary logic gate, and all the correct answers.

Then it trains 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.

    use core::{panic};
    use darjeeling::{ categorize::NeuralNetwork, tests, input::Input, series::Series, dataframe::{DataFrame, Point}};
    use std::{io::{BufReader, BufRead}, fs};

    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).unwrap()
    }

    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  
    }

Generation

This program doesn't have a large enough dataset to get interesting results. All it does is just create a network and

    use darjeeling::{
         generation::NeuralNetwork,
         activation::ActivationFunction,
         input::Input, 
         // This file may not be avaliable
         // Everything found here will be hyper-specific to your project.
         tests::{categories_str_format, file}
    };
     
    // A file with data
    // To make sure the networked is properly trained, make sure it follows some sort of pattern
    // This is just sample data, for accurate results, around 3800 datapoints is needed
    // 1 2 3 4 5 6 7 8
    // 3 2 5 4 7 6 1 8
    // 0 2 5 4 3 6 1 8
    // 7 2 3 4 9 6 1 8
    // You also need to write the file input function
    // Automatic file reading and formatting function coming soon
    let mut data: Vec<Input> = file();
    let mut net = NeuralNetwork::new(2, 2, 2, 1, ActivationFunction::Sigmoid);
    let learning_rate = 1.0;
    let model_name = net.learn(&mut data, categories, learning_rate, "gen").unwrap();
    let new_data: Vec<Input> = net.test(data).unwrap();

TODO:

• Add Support for Polars Dataframes

Dataframes are now deprecated