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 = 0.00012;
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.)
if x < 0. {
-1.
} else {
x
} // */
}
#[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
}),
);
let mut loss = 0.;
for i in 0..O {
let l = (actual_outcome[i] - desired_outcome.0[i]).powi(2);
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 {
let skip = question_mark_res.0[j] > 0.515;
//let skip = question_mark_res.0[j] > 0.298;
if skip {
print!("? ");
} else {
#[cfg(not(feature = "evaluate"))]
print!("{} ", result.get(j));
}
#[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!("");
}
}
#[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: [
[
-0.0067469945635585645,
-0.009014273624850664,
-0.0007451726401320445,
0.006342626998283716,
0.0027298644794807726,
0.009817666462690952,
-0.001913236599957226,
0.005174562338775926,
-0.006556341823967464,
0.0005314583845047529,
0.008800558201636204,
-0.004111641897092092,
-0.007724423788174554,
-0.0006366243215900512,
0.007632477393498744,
-0.005279717542632965,
0.0029893867665776192,
-0.009922808595563094,
-0.0028350071877341555,
0.0054340858865643575,
-0.007478122634487486,
0.008909115162358245,
-0.0040030774911414095,
0.004266057759360017,
],
[
-0.44036940849335793,
-0.27974355422735314,
-0.17592136247681864,
-0.15680775068496652,
-0.1458489325284339,
0.024638897216043693,
0.010254976532894254,
0.051260374681194404,
0.027287980766187395,
0.000892543376297171,
-0.04175854086824354,
-0.05919394449571728,
-0.07793725706714519,
-0.006799768439266844,
0.12017895916692707,
0.23826393837771787,
0.14281943713209416,
0.07379202788261388,
-0.011796528652727279,
-0.22334603207593556,
-0.3745649400458233,
-0.23255211199075218,
0.29964448457492304,
2.0097478257206087,
],
[
0.003993037641798773,
0.012083808905498207,
0.0008105848740836571,
0.015402781843203497,
0.0015208347402323184,
0.007244249928404337,
-0.006177554838380353,
0.0011381166815055798,
0.009466236963015809,
-0.003170834435404459,
0.004950354166523103,
0.00030474187886221464,
0.008387296672610338,
-0.0039282030527051275,
0.004688362920873142,
0.011497134204087881,
-0.003628907452036827,
0.004436459851042806,
-0.006154372624528332,
0.012659611214385354,
0.003343613002804507,
0.017869357768354455,
0.03461648172814431,
0.040233395965520224,
],
[
-0.006564404664349155,
0.001704682013755864,
-0.0019081030307225551,
0.005179697582748459,
-0.007732507920977642,
0.0005365855926177947,
0.007624381641572513,
-0.004106515825949281,
0.0029812766766172784,
-0.008749617911423743,
-0.0016618147115374036,
-0.0052745770183947206,
0.001813223864150538,
-0.009917673325299732,
-0.00282989013156121,
0.005439193254622039,
-0.0074730176393838985,
0.000796075692890322,
0.00788386457159295,
0.004271085797377822,
-0.008641113710748178,
-0.00037200946552984777,
0.006715789850266375,
-0.005015105980759569,
],
[
-0.009658212568844252,
-0.0013891116343624325,
-0.006183169911175131,
0.002085931451400781,
0.009173734978444297,
-0.0025571626741105547,
0.004530637752021315,
-0.007200264172036218,
-0.0001124656358577975,
0.008156633158416816,
0.0033625521475422583,
-0.008368347422490818,
-0.0012805468007304165,
0.006988555029038953,
-0.005923641011442066,
0.0023454643574112766,
0.009433269188727184,
-0.0022976272145730932,
-0.007091685901582452,
0.0011774137449256036,
0.008265211769174862,
-0.003465691520151781,
0.0036221033312650174,
-0.00810880577949996,
],
[
-0.9162275134677947,
-0.6414611667322134,
-0.4607997915048585,
-0.42311172267459424,
-0.2649493782487188,
0.06905345376239022,
0.3015473094446488,
0.4975416453445914,
0.6139586990856436,
0.6383201893631021,
0.6001237743709757,
0.6518013217983676,
0.6681322914483352,
0.8504813673004313,
1.0855357513455899,
1.2603452570778402,
1.0017011068101338,
0.5755838647490358,
0.2689727708814948,
-0.048459280434168694,
-0.1502180136999326,
0.16607168418845075,
0.9758754047326584,
2.744379994693718,
],
[
0.004154369429069256,
0.0005416104921153137,
0.007629408723824818,
-0.0041014956286710485,
0.0029863031539097535,
-0.008744596835831281,
-0.0016567942239386559,
0.006612306770423994,
-0.0062998892779391625,
-0.009912667965992354,
-0.0028248665379782887,
0.0054442352373066825,
-0.007467962778454392,
0.0008011397592175816,
0.007888942215491041,
-0.003841957206766617,
0.0032458408781953895,
-0.000366922574186884,
0.006720877902038703,
-0.005010018774134799,
0.0020777846764411917,
-0.009653114254064756,
-0.002565315681239765,
0.004522473577938569,
],
[
0.010768820609841825,
-0.0006194696542230251,
0.006611770942181558,
-0.005251942403872755,
0.0017342692389536918,
0.010145669787609883,
-0.002749830218663649,
0.005675781699660878,
0.0010668794631829735,
0.009436088151122068,
-0.003498038833253398,
0.004868070793950015,
0.011986618634520222,
0.0004768817212787045,
0.007819418753802264,
-0.003757456696820218,
0.011150261617033825,
-0.0019320652100387123,
0.006567240747492849,
-0.006128731974413792,
0.002513189544066578,
0.010425752025231829,
7.938896532631668e-5,
0.009849242166865961,
],
[
0.007167218960349592,
-0.004373523637652121,
0.002885455493455475,
0.011239204523488058,
-0.0013779406099604342,
0.007279685577105507,
-0.005463526715494681,
0.01080916520524209,
-0.0022924000441759368,
0.00580957737657714,
-0.007267467366263758,
0.0009261684025420932,
0.007959569539928492,
-0.004896007758574339,
0.003629270344932244,
0.011125731696590374,
0.007868269243318222,
-0.003839943853463443,
0.0030704663446605622,
0.009839164968844473,
-0.002211061609255724,
0.004689483139493269,
-0.007122446015651336,
0.00021190014867676665,
],
[
0.0029909562041167733,
-0.008739913428684652,
-0.0016521126910536287,
0.006616972980137506,
-0.006295231958866798,
0.001973877150251455,
0.009061670822685133,
0.005448964683926552,
-0.007463205269559643,
-0.00037540763392037373,
0.007893687914532793,
-0.005018490596103924,
0.003250608034624191,
-0.009661580044101161,
-0.0013924692948844534,
-0.006186550761484148,
0.0020825416750734353,
0.009170276548117192,
-0.002560644046627951,
0.004527142957166429,
-0.007203771037339538,
-0.00011597329698247484,
0.008153117860095758,
0.003359065443301994,
],
[
0.0005094740792671589,
0.00765557896807648,
-0.004108633892752312,
0.004011407822571981,
-0.008844417009650518,
-0.001601787924070415,
-0.005019865291086862,
0.0021893782469104843,
0.010644823292480596,
-0.002193765277138893,
0.006110740452152973,
-0.006736444229251859,
0.0015985842649378968,
0.008786161086578788,
0.0053111089599731905,
-0.0075311527107239365,
0.0006336469215403894,
0.007444104996767568,
-0.0044873353394364775,
0.002438274136400396,
0.010509389716477314,
-0.0026554670269602317,
-0.006637841975152661,
-0.00016482942632487735,
],
[
-0.0031947851689688575,
0.0038926992183741286,
-0.007838388818989525,
-0.0007504116153924202,
0.00751865021176766,
-0.005394390443621449,
-0.009006037192140768,
-0.0019174796190575213,
0.006352779031355442,
-0.006558889273108437,
0.0017093001506217377,
0.00879664521881662,
-0.0029349252188537235,
0.0041529164047173495,
0.0005396660680173455,
0.007627366112389978,
-0.004103181955606385,
0.0029856274608355436,
-0.008744804519551895,
-0.0016567684552247885,
0.0066105681794204315,
-0.0063012302566726255,
-0.00991436451364553,
-0.002828274263444122,
],
[
-0.006101512359853787,
0.0010238886331969883,
0.009332317442402883,
-0.0035615229761557865,
0.00475942471596466,
4.0975490971763956e-5,
0.008333796969147057,
-0.004604187747311776,
0.003627610384736385,
-0.009313205390670405,
-0.0010707775521872137,
0.006007797841631447,
-0.005735186591993762,
0.009486565830491999,
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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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