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] > 0.528;
#[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,
],
[
-0.1374718602771199,
-0.08858414226675812,
-0.046114997481435674,
0.005190541912769447,
0.030116549818844054,
0.08516104312210385,
0.10383948052418127,
0.1363880741491669,
0.10557632143475869,
0.09925183614325775,
0.07988818548323558,
0.05463662352258456,
0.0677658433833196,
0.07649687461438887,
0.13542568111360315,
0.17453923613327807,
0.16757339909079397,
0.16128536134366817,
0.19307176482933172,
0.2920783922561112,
0.47113648616756576,
0.7660533803575258,
1.1635503382596086,
1.7478975342452823,
],
[
5.289888321010628e-5,
0.007170514928142816,
-0.004552689846927485,
0.010629545061255857,
-0.0011977933346659399,
0.005763266176429892,
-0.006122187234556654,
0.0008307759897317522,
0.008956944247097979,
-0.0040512319607136785,
0.004135342221359699,
-0.0007141333716333785,
0.007510213142376712,
-0.005413965034513239,
0.002880649595932594,
0.010051710698282163,
-0.0027462935965422643,
0.005718966931382826,
-0.00690420280742223,
0.009807933196393871,
-0.0027413618705124337,
0.005930219215342435,
0.013488009868925661,
0.0023157531562921514,
],
[
-0.00656447492558349,
0.0017046236180520103,
-0.001908156993489079,
0.005179642690132218,
-0.007732557247015847,
0.0005365432339117237,
0.007624343430597159,
-0.004106556382713482,
0.002981242755577155,
-0.008749656548263113,
-0.0016618560588251717,
-0.005274616108214345,
0.0018131842383333643,
-0.009917714839882288,
-0.0028299152561202653,
0.005439184495836372,
-0.007473015775680165,
0.0007960834289538997,
0.007883882175005184,
0.004271101756941377,
-0.008641099192579091,
-0.0003719992459158372,
0.0067158006114559935,
-0.0050150984050534005,
],
[
-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,
],
[
-0.0002029082512279985,
-0.002290365326734262,
-0.013605286089546429,
-0.004155059222354141,
0.007006721348857359,
0.0021262036215362596,
0.01723036018113653,
0.0333266335859346,
0.027073825007284526,
0.028110619801990026,
0.038273164828516996,
0.04953355730698577,
0.039132695305468944,
0.050309306758476245,
0.04036118248258221,
0.049129935485618385,
0.051511608912173885,
0.04033414197734488,
0.04143638835265009,
0.025471532078279614,
0.03107017728755848,
0.021216608067581168,
0.03269330041536453,
0.048257577105303165,
],
[
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,
],
[
0.010535409232385717,
-0.001168485065072168,
0.00594089365807556,
-0.005778356513573977,
0.0013260137845324748,
0.009623816664717504,
-0.0032696724927337886,
0.005010798347474498,
0.00021243839783772502,
0.008482439598241753,
-0.004436123278902407,
0.0038376695263035642,
0.010931998618316742,
-0.000776848169281625,
0.006353252931142517,
-0.005324466885129352,
0.009911206511008469,
-0.0029736907308684007,
0.0053566156886643845,
-0.0074728243293333135,
0.0009176473885329671,
0.00817282447936262,
-0.0033370526555124985,
0.004034620787441336,
],
[
0.006566534250131607,
-0.005153638765167112,
0.0019463525545225543,
0.010226394292909564,
-0.00266818444488569,
0.005624715133763514,
-0.007268307257922019,
0.009130909078297068,
-0.0037788224865153643,
0.004491560274607485,
-0.008422247564867955,
-0.0001518395871206386,
0.0069383993328964305,
-0.005965410095653773,
0.0023195645786632337,
0.009425189642615572,
0.005818611494811163,
-0.005916705221239537,
0.0011711916279287904,
0.00826423011201781,
-0.003446315010934954,
0.003680883266455842,
-0.007991638280339811,
-0.0008187541419290411,
],
[
0.0029913549010112304,
-0.008739541863955895,
-0.0016517419635732388,
0.006617356657188222,
-0.006294843874650718,
0.001974256499285041,
0.009062054698217166,
0.005449298239523828,
-0.007462900344677824,
-0.00037510141718102955,
0.007893997466235378,
-0.0050182004843813246,
0.0032508987412207743,
-0.00966130001907877,
-0.0013921990981706387,
-0.006186278944140933,
0.0020828217627648165,
0.00917061631471033,
-0.002560285163874821,
0.004527514174283358,
-0.007203386898164219,
-0.00011558695994963821,
0.008153512301644574,
0.0033594521932592443,
],
[
1.8044580598238075e-5,
0.007109139298458513,
-0.0046155456850614646,
0.0036602224285370126,
-0.009224370748912834,
-0.002093491349459803,
-0.005658964572793859,
0.0014713435259032374,
0.009778129654589811,
-0.0031046065055395367,
0.0051861918813647335,
-0.007706350096384323,
0.0005790372483693703,
0.007681950600174347,
0.004084472414835115,
-0.008824507778756796,
-0.0005766087134176793,
0.006468283993108861,
-0.00530883660675738,
0.0017339618205244466,
0.009965950220214508,
-0.002972276932780523,
-0.006605448446033479,
0.0004671637238118375,
],
[
-0.0031964229140423995,
0.0038913980404097818,
-0.007839489924229186,
-0.0007516565699549812,
0.007517442631385474,
-0.005394810069599269,
-0.00900748150604367,
-0.0019196048069806463,
0.006349613093098878,
-0.006562553032330176,
0.0017064607445080747,
0.008794229911186148,
-0.002936722485135858,
0.004151062213090849,
0.0005382563630484383,
0.007626070960220657,
-0.004104801370147264,
0.002983085049113601,
-0.008747752214474009,
-0.0016599396886958918,
0.006609031881951412,
-0.006303139061174779,
-0.009915955855937193,
-0.002828319975854515,
],
[
-0.006209177339239983,
0.0008819453634754734,
0.009155260021351742,
-0.003753413970002377,
0.004522757516680768,
-0.00025981751988471896,
0.008020488478000742,
-0.004882168896120179,
0.003393427424936555,
-0.009513649458893964,
-0.001241466687859861,
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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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