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 = 0.0001;
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] > 0.51;
//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: [
[
-0.0067487719772802105,
-0.009015941516673115,
-0.0007467274259001574,
0.0063410319221280705,
0.002727979573311874,
0.009815977080198131,
-0.0019152945633162885,
0.005172351429301325,
-0.006559047879979771,
0.0005287509586254338,
0.008797810523665425,
-0.00411441372961224,
-0.007727413303234425,
-0.0006396772707140236,
0.0076296197854085705,
-0.005281995172093803,
0.00298762033529237,
-0.00992401755762791,
-0.00283600375078209,
0.005432390408696109,
-0.007480689547164098,
0.008906371306350845,
-0.004004945004209504,
0.004268130175962042,
],
[
-0.4402116409469384,
-0.2796042138619242,
-0.17578590876494435,
-0.15664367691484946,
-0.145656732403091,
0.024812638980171425,
0.010446766983385512,
0.05145794653273743,
0.027500219239748328,
0.0010958216469275088,
-0.041559675891418936,
-0.05900641134730251,
-0.0777458211935703,
-0.00662436439129592,
0.1203135213262468,
0.23833863561529486,
0.14286029849239398,
0.07380230399370916,
-0.01181485132602488,
-0.22332895209953055,
-0.374491841316699,
-0.2324654134844429,
0.2996782568380241,
2.0095355158048216,
],
[
0.003993144277769503,
0.012083994630252273,
0.0008107275176369847,
0.015402718457167652,
0.0015204617049688572,
0.007243858557731712,
-0.0061779948796893925,
0.0011380235008514883,
0.009466505398456764,
-0.003170285542568127,
0.004951116036548803,
0.00030567782185169403,
0.00838826458765336,
-0.003927089831362108,
0.004689477435268061,
0.011498075726313796,
-0.0036285315078289247,
0.004436494543855681,
-0.006154511817996125,
0.012659004205762589,
0.0033424945787719953,
0.017868026526934404,
0.034615430610340926,
0.0402342600562761,
],
[
-0.006559831209784058,
0.0017088615713023883,
-0.0019040696419069022,
0.005184101510091046,
-0.007728349112989219,
0.0005400312459417156,
0.0076275646421023625,
-0.004103131739530507,
0.0029848078513545644,
-0.00874604819281035,
-0.0016581362650832745,
-0.005271085107613324,
0.0018165410810351416,
-0.009914861915354473,
-0.0028281968589400196,
0.005439492166340288,
-0.007473657542615047,
0.0007950828596733515,
0.007882586108578279,
0.00427010832562409,
-0.008641445649749686,
-0.0003718623299047284,
0.006716266281712858,
-0.005014952068712034,
],
[
-0.009659736803395966,
-0.001390535563748656,
-0.006184403518139252,
0.00208485233419104,
0.009173056142223808,
-0.002557563811057666,
0.00453030888249002,
-0.007200776943671231,
-0.00011311386728052984,
0.008155870266129364,
0.00336169176422297,
-0.008369150661626591,
-0.0012812742400873833,
0.0069880335253331176,
-0.005923728308226437,
0.002345954847201578,
0.00943426493200577,
-0.00229626939756641,
-0.007090220795739443,
0.0011788038673913564,
0.008266352983281348,
-0.003464938101300565,
0.0036222672166223847,
-0.008109662448082753,
],
[
-0.9160979732004472,
-0.6413006894922905,
-0.46062772139142444,
-0.42294831354007756,
-0.26476095535415384,
0.06929436252327437,
0.30182393598872825,
0.49784975483047983,
0.6143037450157585,
0.6386771155475518,
0.600488414713716,
0.6521820518515271,
0.6685181829032788,
0.8508923068067396,
1.0859709529414854,
1.2607960296002032,
1.0021223743017782,
0.5759483534090177,
0.26926338544464273,
-0.04827065045010466,
-0.15013605312501938,
0.1660816982636188,
0.9758542718628944,
2.74447109167583,
],
[
0.004153381416172368,
0.000540738122478393,
0.0076283424723310785,
-0.004103036772047668,
0.0029846408038199457,
-0.008746238225343306,
-0.0016581233306769256,
0.0066111123205606975,
-0.006300625231389966,
-0.009913242090435592,
-0.0028252693784943965,
0.00544403838377329,
-0.00746793302319607,
0.0008014545092430185,
0.00788952855233675,
-0.003841314607141887,
0.0032462551462218454,
-0.00036691444098307897,
0.006720907462657883,
-0.005009654330318323,
0.0020785006175982265,
-0.009652305848751572,
-0.002564687906647745,
0.004521898740442495,
],
[
0.010768567728855375,
-0.0006196912211620042,
0.006611546120372727,
-0.005252205509828025,
0.0017340042748780324,
0.010145435778146555,
-0.002750029062347827,
0.005675629836162811,
0.0010667956826283754,
0.009436039417937477,
-0.003498066774209596,
0.00486807311331311,
0.011986641049309316,
0.00047694123879255603,
0.00781951132139723,
-0.0037573563040879067,
0.011150316304029665,
-0.0019320684719269184,
0.0065672080809764674,
-0.00612880575638734,
0.002513062382187099,
0.010425586394656093,
7.920110547386066e-5,
0.009849095326820233,
],
[
0.007166959846137656,
-0.004373743228908672,
0.002885273489074079,
0.011239047840907101,
-0.0013780259256782283,
0.0072796761666094854,
-0.005463495778758068,
0.01080916620434565,
-0.0022924345223240454,
0.005809510209853396,
-0.007267565498442925,
0.0009260620285794829,
0.007959458175826804,
-0.004896110552224374,
0.00362922874088847,
0.011125783816389347,
0.007868422708192711,
-0.0038397595566182024,
0.0030706335576314945,
0.00983929118580395,
-0.0022109885662241796,
0.00468949885756715,
-0.007122494211243207,
0.0002117672475165636,
],
[
0.0029893448728019233,
-0.008741382922515532,
-0.0016537258261974587,
0.006614957215267292,
-0.006297353098024231,
0.00197204847828953,
0.009060133448906793,
0.005447524504308856,
-0.007464754025186615,
-0.0003768972119963152,
0.007892100488948046,
-0.005020049699900585,
0.0032491470399957684,
-0.00966290005189263,
-0.0013933583257512226,
-0.006186861455735419,
0.0020825111521618417,
0.009170036568056032,
-0.002560915288454482,
0.00452683093820335,
-0.007204203065642291,
-0.00011641810030086697,
0.00815285720501962,
0.0033595327541565763,
],
[
0.0005093803415705951,
0.007655496130170697,
-0.004108724009658425,
0.004011290578637168,
-0.008844532653869013,
-0.0016018977829202982,
-0.0050199526495577455,
0.0021893053777092683,
0.01064478158180916,
-0.002193797521471199,
0.006110711851272066,
-0.00673646134545876,
0.0015985831279196397,
0.008786179849232044,
0.005311151510519923,
-0.007531101339339931,
0.0006336856567146904,
0.0074441138312307965,
-0.004487325906020422,
0.0024383093571762795,
0.010509446665538397,
-0.002655405743454857,
-0.006637810608593419,
-0.00016492139703391253,
],
[
-0.0032005254212043102,
0.0038875276109758402,
-0.007843388331960568,
-0.0007558828331490123,
0.007513556010839206,
-0.005398114957713327,
-0.009009289449323882,
-0.0019213273769157996,
0.006348218107712107,
-0.00656377254495488,
0.001704024797374833,
0.008791406694788232,
-0.0029401429704019842,
0.00414830791263843,
0.0005367786456880137,
0.007627028222275071,
-0.004101418684292886,
0.0029881472496888105,
-0.008742097626011152,
-0.001654961956437761,
0.006610982800513007,
-0.006301551560396029,
-0.009914835958220961,
-0.0028270611040024187,
],
[
-0.006101531292000163,
0.0010238737058036627,
0.009332307837806494,
-0.0035615283428752143,
0.004759429921061856,
4.0991989127277176e-5,
0.008333816822122146,
-0.004604174772649349,
0.003627615320018973,
-0.009313206185989025,
-0.0010707826806460488,
0.006007791069432291,
-0.00573519434387467,
0.009486559918130383,
-0.002185809055490457,
0.004988802593874447,
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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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