CSES - Datatähti 2024 alku - Results
Submission details
Task:Säähavainnot
Sender:joonas_lerber
Submission time:2023-11-08 14:36:51 +0200
Language:Rust
Status:COMPILE ERROR

Compiler report

error[E0463]: can't find crate for `neural_network_lib`
 --> input/code.rs:1:1
  |
1 | extern crate neural_network_lib as nnl;
  | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ can't find crate

error[E0432]: unresolved import `bincode`
 --> input/code.rs:2:5
  |
2 | use bincode::deserialize;
  |     ^^^^^^^ use of undeclared crate or module `bincode`

error[E0432]: unresolved import `nalgebra`
 --> input/code.rs:3:5
  |
3 | use nalgebra::DVector;
  |     ^^^^^^^^ use of undeclared crate or module `nalgebra`

error: aborting due to 3 previous errors

Some errors have detailed explanations: E0432, E0463.
For more information about an error, try `rustc --explain E0432`.

Code

extern crate neural_network_lib as nnl;
use bincode::deserialize;
use nalgebra::DVector;
use nnl::{
    files::SaveNeuralNetwork, // files::save_network,
                              // loss_and_activation_functions::{ActivationFunction, LossFunction},
                              // neuralnetwork::{BatchType, Optimizer},
                              // neuralnetwork::{InitializaitonType, NeuralNetwork},
    neuralnetwork::NeuralNetwork,
};
// use std::fs::File;
use std::io::{stdin, BufRead, BufReader, Result};
// fn main() -> Result<()> {
//     let data = File::open("sample-data.txt")?;
//     let data_reader = BufReader::new(data);
//     let mut test_data: Vec<(DVector<f32>, DVector<f32>)> = data_reader
//         .lines()
//         .skip(1)
//         .map(|l| {
//             let temps: Vec<f32> = l
//                 .unwrap()
//                 .split_whitespace()
//                 .map(|num| num.parse::<f32>().unwrap())
//                 .collect();
//             assert_eq!(temps.len(), 36);
//             let known: Vec<f32> = temps.iter().take(24).map(|yaas| yaas.to_owned()).collect();
//             let expected: Vec<f32> = temps.into_iter().skip(24).collect();
//             (DVector::from_vec(known), DVector::from_vec(expected))
//         })
//         .collect();
//     let mut neural_network: NeuralNetwork = NeuralNetwork::new(
//         &[24, 40, 30, 30, 30, 12],
//         InitializaitonType::HeKaiming,
//         LossFunction::MSE,
//         ActivationFunction::Identity,
//         ActivationFunction::LeReLU,
//     )
//     .unwrap();

//     neural_network.train(
//         &mut test_data,
//         0.001,
//         2000,
//         BatchType::MiniBatch(64),
//         Optimizer::Adam(0.99, 0.999),
//     );

//     let measurements: Vec<DVector<f32>> = test_data.iter().map(|(_, x)| x.clone()).collect();
//     let predictions: Vec<DVector<f32>> = test_data
//         .iter()
//         .map(|(x, _)| neural_network.input(x))
//         .collect();

//     dbg!(calculate_score(predictions, measurements));

//     save_network("joo.lol", &neural_network)?;

//     Ok(())
// }
fn main() -> Result<()> {
    let neural_network =
        NeuralNetwork::from(deserialize::<SaveNeuralNetwork>(&NEURALNETWORKBYTES).unwrap());
    let mut data_reader = BufReader::new(stdin()).lines();
    let datapoints_len = data_reader.next().unwrap()?.parse::<u32>().unwrap();

    let datapoints = (0..datapoints_len)
        .map(|_| {
            DVector::from_iterator(
                24,
                data_reader
                    .next()
                    .unwrap()
                    .unwrap()
                    .split_ascii_whitespace()
                    .map(|y| y.parse::<f32>().unwrap()),
            )
        })
        .collect::<Vec<_>>();

    let predictions = datapoints
        .iter()
        .map(|datapoint| neural_network.input(datapoint))
        .collect::<Vec<_>>();

    predictions
        .iter()
        .map(|prediction| {
            prediction
                .iter()
                .map(|x| x.to_string())
                .reduce(|acc, x| [acc, " ".to_owned(), x].concat())
                .unwrap()
        })
        .for_each(|x| println!("{x}"));
    Ok(())
}

// fn calculate_score(predictions: Vec<DVector<f32>>, measurements: Vec<DVector<f32>>) -> f64 {
//     let n = predictions.len();
//     let a: f64 = predictions
//         .iter()
//         .flatten()
//         .zip(measurements.iter().flatten())
//         .map(|(p, m)| f64::from((p - m).abs() < 0.75))
//         .sum();
//     let b: f64 = predictions
//         .iter()
//         .flatten()
//         .zip(measurements.iter().flatten())
//         .map(|(p, m)| f64::from((p - m).abs() >= 2.05))
//         .sum();
//     25.0 * (a - b) / n as f64
// }

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