use std::io::*;
use std::time::{SystemTime, UNIX_EPOCH};
pub const INPUT_COUNT: usize = 24;
pub const OUTPUT_COUNT: usize = 12;
pub const LEARNING_RATE: f64 = 0.012;
pub fn activation_function(x: f64) -> f64 {
//1. / (1. + std::f64::consts::E.powf(-x))
//x.tanh()
x.max(0.)
}
pub fn inv_activation_function(x: f64) -> f64 {
//f64::ln(x / (1. - x)) //.max(-10000.).min(10000.)
//x.atanh()
x.max(0.)
}
#[allow(dead_code)]
pub fn pseudo_rand() -> f64 {
let start = SystemTime::now();
let since_the_epoch = start
.duration_since(UNIX_EPOCH)
.expect("Time went backwards");
#[allow(arithmetic_overflow)]
const MAX: u128 = 1000000;
// Randomish enough
let res = ((since_the_epoch.as_nanos() * 23525623626 >> 2) % MAX) as f64 / MAX as f64;
res % 1.
}
#[derive(Debug, Clone)]
pub struct DataPoint<const S: usize>(pub [f64; S]);
impl<const S: usize> DataPoint<S> {
const MIN_VALUE: f64 = -38.;
const MAX_VALUE: f64 = 38.;
pub fn new(values: &[f64]) -> Self {
let mut data_point = Self([0.; S]);
data_point.0.clone_from_slice(values);
let range = Self::MAX_VALUE - Self::MIN_VALUE;
for i in 0..S {
data_point.0[i] = (data_point.0[i] - Self::MIN_VALUE) / range
}
data_point
}
pub fn get(&self, i: usize) -> f64 {
let range = Self::MAX_VALUE - Self::MIN_VALUE;
self.0[i] * range + Self::MIN_VALUE
}
}
// Layer of neural network with input and output count
#[derive(Debug, Clone, Copy)]
pub struct Layer<const I: usize, const O: usize> {
pub weights: [[f64; I]; O],
pub biases: [f64; O],
}
impl<const I: usize, const O: usize> Layer<I, O> {
pub fn new() -> Self {
Self {
weights: [[0.; I]; O],
biases: [0.; O],
}
}
pub fn random() -> Self {
let mut layer = Self::new();
for i in 0..O {
for j in 0..I {
layer.weights[i][j] = (pseudo_rand() * 2. - 1.) / 100.;
}
layer.biases[i] = (pseudo_rand() * 2. - 1.) / 100.;
}
layer
}
pub fn propagate(&self, activation: &[f64; I]) -> [f64; O] {
let mut result = [0.; O];
for i in 0..O {
let mut value = 0.;
for j in 0..I {
value += activation[j] * self.weights[i][j];
}
result[i] = activation_function(value + self.biases[i]);
}
result
}
pub fn back_propagate(
&self,
changes: &mut Self,
activation: &[f64; I],
actual_outcome: &[f64; O],
desired_outcome: &[f64; O],
) -> [f64; I] {
let mut desired_activation = activation.clone();
let mut z = [0.; O];
for i in 0..O {
z[i] = self.biases[i];
for j in 0..I {
z[i] += activation[j] * self.weights[i][j];
}
}
for i in 0..O {
let dca = 2. * (actual_outcome[i] - desired_outcome[i]);
let dzb = 1.;
for j in 0..I {
let daz = inv_activation_function(z[i]);
let dzw = activation[j];
let dza = self.weights[i][j];
let dcw = dca * dzw * daz;
let dcb = dzb * daz * dca;
let dca2 = dza * daz * dca;
changes.weights[i][j] -= dcw;
changes.biases[i] -= dcb / I as f64;
desired_activation[j] -= dca2
}
}
desired_activation
}
pub fn apply_changes(&mut self, changes: Layer<I, O>, rate: f64) {
for i in 0..O {
for j in 0..I {
self.weights[i][j] += changes.weights[i][j] * rate
}
self.biases[i] += changes.biases[i] * rate;
}
}
}
#[derive(Debug, Clone)]
pub struct NeuralNetwork<const I: usize, const O: usize, const H: usize, const L: usize> {
pub input_layer: Layer<I, H>,
pub hidden_layers: [Layer<H, H>; L],
pub output_layer: Layer<H, O>,
}
impl<const I: usize, const O: usize, const H: usize, const L: usize> NeuralNetwork<I, O, H, L> {
#[allow(dead_code)]
pub fn new() -> Self {
Self {
input_layer: Layer::new(),
hidden_layers: [Layer::new(); L],
output_layer: Layer::new(),
}
}
#[allow(dead_code)]
pub fn random() -> Self {
Self {
input_layer: Layer::random(),
hidden_layers: [Layer::random(); L],
output_layer: Layer::random(),
}
}
pub fn predict(&self, start_activation: &DataPoint<I>) -> DataPoint<O> {
let mut activation = self.input_layer.propagate(&start_activation.0);
for hidden_layer in self.hidden_layers {
activation = hidden_layer.propagate(&activation);
}
DataPoint(self.output_layer.propagate(&activation))
}
pub fn train(
&self,
changes: &mut Self,
start_activation: &DataPoint<I>,
desired_outcome: &DataPoint<O>,
batch_size: usize,
) -> f64 {
let mut activations = [[0.; H]; L];
for i in 0..L {
let activation = if i == 0 {
self.input_layer.propagate(&start_activation.0)
} else {
activations[i - 1]
};
activations[i] = self.hidden_layers[i].propagate(&activation);
}
let last_activation = if L > 0 {
activations[L - 1]
} else {
self.input_layer.propagate(&start_activation.0)
};
let actual_outcome = self.output_layer.propagate(&last_activation);
let desired_output_activation = self.output_layer.back_propagate(
&mut changes.output_layer,
&last_activation,
&actual_outcome,
&desired_outcome.0,
);
let mut desired_activations = [[0.; H]];
for i in (0..L).rev() {
let prev_desired_activation = if i == L - 1 {
desired_output_activation
} else {
desired_activations[i]
};
let activation = if i == 0 {
self.input_layer.propagate(&start_activation.0)
} else {
activations[i]
};
let desired_activation = self.hidden_layers[i].back_propagate(
&mut changes.hidden_layers[i],
&activation,
&activations[i],
&prev_desired_activation,
);
desired_activations[i] = desired_activation;
}
let _ = self.input_layer.back_propagate(
&mut changes.input_layer,
&start_activation.0,
if L > 0 {
&activations[0]
} else {
&last_activation
},
&(if L > 0 {
desired_activations[0]
} else {
desired_output_activation
}),
);
//println!("outcome: {:#?}", actual_outcome);
//println!("Desired outcome: {:#?}", desired_outcome);
let mut loss = 0.;
for i in 0..O {
let l = (actual_outcome[i] - desired_outcome.0[i]).powi(2);
//println!("i: {} loss {}", i, l);
loss += l;
}
loss / batch_size as f64
}
pub fn apply_changes(&mut self, changes: &Self, batch_size: usize) {
self.input_layer
.apply_changes(changes.input_layer, LEARNING_RATE / batch_size as f64);
for i in 0..L {
self.hidden_layers[i]
.apply_changes(changes.hidden_layers[i], LEARNING_RATE / batch_size as f64)
}
self.output_layer
.apply_changes(changes.output_layer, LEARNING_RATE / batch_size as f64);
}
}
pub fn read_training_data() -> (Vec<DataPoint<INPUT_COUNT>>, Vec<DataPoint<OUTPUT_COUNT>>) {
let mut input = String::new();
stdin()
.read_line(&mut input)
.expect("Unexpected end of input");
let days: i32 = input.trim().parse().unwrap();
let mut input_data = Vec::new();
let mut output_data = Vec::new();
for _ in 0..days {
let mut input = String::new();
stdin()
.read_line(&mut input)
.expect("Unexpected end of input");
let arr = input
.split(" ")
.map(|s| s.trim().parse().unwrap())
.collect::<Vec<f64>>();
input_data.push(DataPoint::new(&arr[..INPUT_COUNT]));
output_data.push(DataPoint::new(&arr[INPUT_COUNT..]));
}
(input_data, output_data)
}
#[allow(dead_code)]
pub fn read_input_data() -> Vec<DataPoint<INPUT_COUNT>> {
let mut input = String::new();
stdin()
.read_line(&mut input)
.expect("Unexpected end of input");
let days: i32 = input.trim().parse().unwrap();
let mut data = Vec::new();
for _ in 0..days {
let mut input = String::new();
stdin()
.read_line(&mut input)
.expect("Unexpected end of input");
let arr = input
.split(" ")
.map(|s| s.trim().parse().unwrap())
.collect::<Vec<f64>>();
data.push(DataPoint::new(&arr[..INPUT_COUNT]));
}
data
}
#[allow(dead_code)]
pub fn main() {
#[cfg(feature = "evaluate")]
let (data, output_data) = read_training_data();
#[cfg(not(feature = "evaluate"))]
let data = read_input_data();
let neural_net = get_neural_network();
let question_mark_net = get_question_mark_network();
#[cfg(feature = "evaluate")]
let mut correct: i32 = 0;
#[cfg(feature = "evaluate")]
let mut incorrect: i32 = 0;
#[cfg(feature = "evaluate")]
let mut correct_distribution = [0; OUTPUT_COUNT];
#[cfg(feature = "evaluate")]
let mut error_distribution = [0; OUTPUT_COUNT];
#[cfg(feature = "evaluate")]
let mut correct_diff = 0.;
#[cfg(feature = "evaluate")]
let mut incorrect_diff = 0.;
#[cfg(feature = "evaluate")]
let mut question_marks = 0;
#[cfg(feature = "evaluate")]
let mut correct_q_marks = 0;
#[cfg(feature = "evaluate")]
let mut incorrect_q_marks = 0;
for i in 0..data.len() {
let result = neural_net.predict(&data[i]);
let mut input_buffer = [0.; 36];
input_buffer[..INPUT_COUNT].clone_from_slice(&data[i].0);
input_buffer[INPUT_COUNT..].clone_from_slice(&result.0);
let question_mark_res = question_mark_net.predict(&DataPoint(input_buffer));
for j in 0..OUTPUT_COUNT {
// False to when testing the actual prediction network
let skip = question_mark_res.0[j] > 0.5155;
#[cfg(feature = "evaluate")]
{
let diff = ((result.get(j.max(1) - 1) + result.get((j + 1).min(OUTPUT_COUNT - 1)))
/ 2.
- result.get(j))
.abs();
print!("{} ", (result.get(j) * 100.).round() / 100.);
let dist = (result.get(j) - output_data[i].get(j)).abs();
if skip {
question_marks += 1;
if dist > 2.05 {
correct_q_marks += 1;
} else if dist < 0.75 {
incorrect_q_marks += 1;
}
} else {
if dist < 0.75 {
print!("+ ");
correct += 1;
correct_distribution[j] += 1;
correct_diff += diff;
} else if dist > 2.05 {
print!("! ");
error_distribution[j] += 1;
incorrect += 1;
incorrect_diff += diff;
}
print!("{} ", (output_data[i].get(j) * 1000.).round() / 1000.);
println!("");
}
}
if skip {
print!("? ");
#[cfg(feature = "evaluate")]
{
print!("{} ", (result.get(j) * 100.).round() / 100.);
}
} else {
#[cfg(not(feature = "evaluate"))]
print!("{} ", result.get(j));
}
}
#[cfg(feature = "evaluate")]
println!("");
}
#[cfg(feature = "evaluate")]
println!("correct distribution: {:#?}", correct_distribution);
#[cfg(feature = "evaluate")]
println!("error distribution: {:#?}", incorrect_diff);
#[cfg(feature = "evaluate")]
println!("correct diff: {:#?}", correct_diff / correct as f64);
#[cfg(feature = "evaluate")]
println!("incorrect diff: {:#?}", incorrect_diff / incorrect as f64);
#[cfg(feature = "evaluate")]
println!("question marks: {:#?}", question_marks);
#[cfg(feature = "evaluate")]
println!(
"correct question marks: {:#?}, incorrect question marks: {:#?}, ",
correct_q_marks, incorrect_q_marks
);
#[cfg(feature = "evaluate")]
println!(
"Correct: {} / {}, Incorrect: {} / {}, score: {}",
correct,
data.len() * OUTPUT_COUNT,
incorrect,
data.len() * OUTPUT_COUNT,
25. * ((correct - incorrect) as f64 / data.len() as f64)
);
}
pub fn get_neural_network() -> NeuralNetwork<INPUT_COUNT, OUTPUT_COUNT, 36, 0> {
NeuralNetwork {
input_layer: Layer {
weights: [
[
-0.006746986588669736,
-0.009014266652303917,
-0.0007451666762135255,
0.006342633373192264,
0.0027298735690419814,
0.009817673852109085,
-0.0019132257273602965,
0.0051745747281944955,
-0.006556324799306717,
0.0005314755032777222,
0.008800575752433806,
-0.004111624073289646,
-0.007724403929998394,
-0.0006366038540770546,
0.00763249607248482,
-0.005279704217124032,
0.002989395313221047,
-0.009922805272174274,
-0.002835005931779126,
0.005434093416031036,
-0.007478107236401326,
0.008909132095923724,
-0.004003068640332424,
0.004266030512601896,
],
[
-0.44036724125208454,
-0.2797412490083827,
-0.17591880536467722,
-0.15680552202416997,
-0.14584703692606665,
0.02464105996179666,
0.010256968198738017,
0.05126235401433671,
0.027289903767786762,
0.0008946043614722157,
-0.04175623884993678,
-0.05919158990423597,
-0.07793502371257117,
-0.0067974942440659405,
0.12018122192140689,
0.2382665665362191,
0.14282239234088148,
0.0737950118568948,
-0.011793228818122758,
-0.22334299215258022,
-0.3745624229285746,
-0.2325499649225885,
0.29964705905847244,
2.0097535603000223,
],
[
0.003988214065538124,
0.012076959958384908,
0.0008048062031606355,
0.015402194229029385,
0.0015280427434335919,
0.007252085474908894,
-0.0061682366081676505,
0.0011391596287868276,
0.00945858253206404,
-0.003185193755728234,
0.004930925680954261,
0.00028117428371370853,
0.008363104621317058,
-0.003955871416988509,
0.0046607342743555415,
0.011473787934980734,
-0.0036382742305343684,
0.0044355096643065445,
-0.006150980476692547,
0.012674736914579028,
0.0033716150884663637,
0.017902798316798453,
0.034643072976146855,
0.04021241116996772,
],
[
-0.006564445565697799,
0.0017046447011766312,
-0.0019081390220672427,
0.005179658194080698,
-0.007732545055587314,
0.0005365549842782301,
0.00762435342663522,
-0.004106545908796469,
0.002981245236070128,
-0.008749649726378724,
-0.0016618475261966435,
-0.00527460814141795,
0.0018131943278634184,
-0.009917698247384886,
-0.0028299048038565038,
0.005439191359419188,
-0.007473010922716006,
0.0007960856473408172,
0.007883877161485224,
0.004271095647663451,
-0.008641109762082155,
-0.00037200988823940707,
0.00671578644448233,
-0.005015106427413544,
],
[
-0.00965819643234242,
-0.0013890964159235552,
-0.006183156440969015,
0.0020859434970898717,
0.00917374334601237,
-0.002557156869449866,
0.004530642867693577,
-0.007200257403056705,
-0.0001124576537122779,
0.008156642169794396,
0.0033625620354106383,
-0.008368338069744328,
-0.0012805381514468372,
0.0069885617876349945,
-0.005923638224191524,
0.0023454618712013316,
0.009433262106518858,
-0.0022976375779393786,
-0.007091697214534005,
0.0011774031495636083,
0.008265203479079159,
-0.003465696239455321,
0.0036221040235637656,
-0.008108795736298791,
],
[
-0.9190209159516507,
-0.6450395417181168,
-0.4646820542282987,
-0.4267987216056822,
-0.2692516573176664,
0.06342531527245934,
0.2950392007379275,
0.49024773981032566,
0.6057870601377741,
0.6298741551282641,
0.5914753454367228,
0.6427730415252573,
0.6589633467374223,
0.8407172150718845,
1.0751432150136353,
1.2495257165557745,
0.9916042818481361,
0.5668724345444398,
0.26205868892787526,
-0.05281890973729224,
-0.15191025732642593,
0.1661967967739054,
0.9767834652962147,
2.7424827828980747,
],
[
0.004154380759685555,
0.00054162076473123,
0.007629420762916333,
-0.004101479256544262,
0.0029863206175647907,
-0.008744579588318344,
-0.0016567798640266436,
0.006612319866047457,
-0.006299880406252018,
-0.009912660597061175,
-0.0028248607533275056,
0.005444239123623493,
-0.0074679609765623415,
0.000801138946429314,
0.00788893891917154,
-0.0038419610036987068,
0.003245839200074572,
-0.0003669205010032827,
0.006720879815267916,
-0.0050100198893737805,
0.0020777803762484864,
-0.009653119375202617,
-0.0025653191299702146,
0.004522481146826952,
],
[
0.01077492893834531,
-0.0006141462663861991,
0.006617174123611476,
-0.005245580518658208,
0.0017406833443151827,
0.010151319863438908,
-0.0027450396492642846,
0.005679425311850294,
0.001068848454732439,
0.009437203426075727,
-0.003497425082581157,
0.004867945237019645,
0.011986006857191618,
0.0004753569198278802,
0.007817077082489882,
-0.0037599933366358664,
0.011148862581737917,
-0.0019320186542260928,
0.006568030582052675,
-0.006126898205689061,
0.002516376967793874,
0.01042991823992817,
8.412358017826641e-5,
0.00985294935308545,
],
[
0.007171864000869709,
-0.004369639883476952,
0.0028886401426411676,
0.01124201596450612,
-0.001376743830558149,
0.007279037571421264,
-0.005465059746200939,
0.010808425022912828,
-0.0022921055915413506,
0.0058107126760006435,
-0.007265559816609989,
0.0009283674316772118,
0.00796185735017895,
-0.004894034631845421,
0.0036298155110653257,
0.011123956958291216,
0.007863987640764394,
-0.003844912363512655,
0.003066008781404618,
0.00983584226684394,
-0.002212942941205049,
0.0046888815307830475,
-0.007121579903392917,
0.00021459365316136404,
],
[
0.002990969592409866,
-0.008739901399119948,
-0.0016520993516704451,
0.006616990038368151,
-0.006295213912081825,
0.001973892528255417,
0.009061683592380684,
0.005448976479244203,
-0.007463192484393455,
-0.00037539534519997044,
0.00789370113303428,
-0.005018477666532173,
0.0032506200865403062,
-0.00966156929340918,
-0.0013924625122187415,
-0.006186549284001895,
0.002082540554058747,
0.009170277448622808,
-0.0025606428496542737,
0.004527144521699135,
-0.007203768376607199,
-0.00011597054446241068,
0.00815311893458282,
0.0033590598389911506,
],
[
0.0005118405501814713,
0.007657675975396126,
-0.004106356618283827,
0.004014357072117953,
-0.00884151083961016,
-0.0015990336567200062,
-0.005017679196983098,
0.0021911936756424263,
0.010645856335411617,
-0.00219297544913757,
0.006111433182215262,
-0.006736041724475694,
0.0015985863849948539,
0.008785666278165134,
0.00531002244261735,
-0.00753245653047694,
0.0006326621612768924,
0.007443868176498684,
-0.004487582815850676,
0.002437390317857727,
0.010507969201414937,
-0.002656993241660425,
-0.0066386244192202645,
-0.00016255476510562637,
],
[
-0.0031947821591558783,
0.003892700081891743,
-0.007838386647559797,
-0.000750404066132315,
0.007518655120342532,
-0.005394393816312422,
-0.009006046782136895,
-0.001917483491298318,
0.006352779908057694,
-0.006558886243799412,
0.0017093090981980838,
0.008796656445398562,
-0.0029349115443550867,
0.004152923876378857,
0.0005396596613471365,
0.0076273380000804105,
-0.0041032301889766375,
0.0029855695560526857,
-0.008744864409658175,
-0.001656822040488682,
0.006610531527993006,
-0.006301262438888728,
-0.009914397418556391,
-0.002828320389113701,
],
[
-0.006101051110949496,
0.001024250317447652,
0.009332547257612345,
-0.0035613976492035442,
0.004759288450790724,
4.055939761189432e-5,
0.008333298018738413,
-0.004604515963808923,
0.0036274815047196538,
-0.009313191929442282,
-0.0010706563556029875,
0.006007960094434147,
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},
}
}
pub fn get_question_mark_network() -> NeuralNetwork<36, OUTPUT_COUNT, 18, 0> {
NeuralNetwork {
input_layer: Layer {
weights: [
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