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
// }
const NEURALNETWORKBYTES: [u8; 9156] = [
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