Machine learning and data manipulation tools for Rust
elocolburn@comcast.net
Add the following to your Cargo.toml file
darjeeling = "0.3.1"
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. ```rust 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
}
```
This program doesn't have a large enough dataset to get interesting results. All it does is just create a network and ```rust 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::{categoriesstrformat, 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();
```
Dataframes are now deprecated