radiance 0.7.1

// Models in this file are based on and ported from the madmom project:
// @inproceedings{madmom,
//    Title = {{madmom: a new Python Audio and Music Signal Processing Library}},
//    Author = {B{\"o}ck, Sebastian and Korzeniowski, Filip and Schl{\"u}ter, Jan and Krebs, Florian and Widmer, Gerhard},
//    Booktitle = {Proceedings of the 24th ACM International Conference on
//    Multimedia},
//    Month = {10},
//    Year = {2016},
//    Pages = {1174--1178},
//    Address = {Amsterdam, The Netherlands},
//    Doi = {10.1145/2964284.2973795}
// }
//
// Models ported to Rust by Eric Van Albert
//
// The content of this file is distributed under the following license:
//
// Creative Commons Attribution-NonCommercial-ShareAlike 4.0
//
// The short version of this license:
//
// You are free to:
//
//   Share: copy and redistribute the material in any medium or format
//   Adapt: remix, transform, and build upon the material
//
// Under the following terms:
//
//   Attribution:   You must give appropriate credit, provide a link to the
//                  license, and indicate if changes were made.
//   NonCommercial: You must not use the material for commercial purposes.
//   ShareAlike:    If you remix, transform, or build upon the material, you must
//                  distribute your contributions under the same license as the
//                  original.
//
// All legal details can be found here:
// http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
//
// If you want to include any of these files (or a variation or modification
// thereof) or technology which utilises them in a commercial product, please
// contact both Gerhard Widmer at http://www.cp.jku.at/people/widmer/.
// and Eric Van Albert at https://eric.van.al/.

use crate::beat_tracking::layers::*;
use nalgebra::{dmatrix, dvector, DVector};

pub struct NeuralNetwork {
    layer1: LSTMLayer, // INPUT_SIZE = N_FILTERS * 2
    layer2: LSTMLayer,
    layer3: LSTMLayer,
    layer4: FeedForwardLayer, // OUTPUT_SIZE = 1
}

impl NeuralNetwork {
    pub fn new(
        layer1: LSTMLayer,
        layer2: LSTMLayer,
        layer3: LSTMLayer,
        layer4: FeedForwardLayer,
    ) -> Self {
        Self {
            layer1,
            layer2,
            layer3,
            layer4,
        }
    }

    pub fn reset(&mut self) {
        self.layer1.reset();
        self.layer2.reset();
        self.layer3.reset();
        // layer4 is a FeedForwardLayer
        // and therefore doesn't have any state to reset()
    }

    pub fn process(
        &mut self,
        data: &DVector<f32>, // size N_FILTERS * 2
    ) -> f32 {
        let layer1_result = self.layer1.process(data);
        let layer2_result = self.layer2.process(&layer1_result);
        let layer3_result = self.layer3.process(&layer2_result);
        let layer4_result = self.layer4.process(&layer3_result);
        assert_eq!(layer4_result.len(), 1); // NN should produce a scalar output
        layer4_result[0]
    }
}

#[allow(clippy::excessive_precision)]
pub fn models() -> Vec<NeuralNetwork> {
    vec![
        NeuralNetwork::new(
            LSTMLayer::new(
                Gate::new(
                    dmatrix!(-0.07287129759788513, -0.10052700340747833, -0.1099419966340065, -0.17807599902153015, -0.1716960072517395, -0.1655929982662201, -0.1017180010676384, -0.2213740050792694, -0.11243099719285965, -0.4135560095310211, -0.04719259962439537, -0.054266199469566345, -0.2727090120315552, -0.058917101472616196, -0.08948460221290588, 0.04178829863667488, -0.17520500719547272, 0.010875700041651726, 0.023337600752711296, -0.18692399561405182, -0.1627569943666458, 0.027395099401474, 0.18118099868297577, -0.22661900520324707, -0.06951259821653366, 0.0036295598838478327, -0.22535599768161774, -0.07063420116901398, -0.1098179966211319, 0.04257329925894737, -0.19176800549030304, 0.08060559630393982, -0.07304219901561737, -0.07142580300569534, -0.13947099447250366, -0.16708000004291534, -0.1734369993209839, -0.10907100141048431, 0.05814829841256142, 0.008937650360167027, 0.07959599792957306, 0.054515399038791656, 0.16463300585746765, 0.1091570034623146, -0.11235000193119049, -0.3660309910774231, 0.03393330052495003, -0.05547650158405304, 0.07991810142993927, 0.072365403175354, -0.04138119891285896, 0.03856559842824936, -0.17589999735355377, 0.30408400297164917, 0.16410499811172485, -0.0013150200247764587, -0.22873400151729584, -0.13943399488925934, -0.14250199496746063, -0.0797123983502388, 0.011498300358653069, -0.07963889837265015, -0.1532360017299652, -0.06338810175657272, -0.10635700076818466, -0.000844269001390785, 0.06636299937963486, 0.0988612025976181, 0.23196199536323547, 0.10905499756336212, 0.09704039990901947, 0.32911399006843567, 0.13040299713611603, 0.013554800301790237, 0.10395999997854233, 0.03894510120153427, -0.06951239705085754, -0.01646129973232746, -0.13452699780464172, 0.0209548007696867, -0.18848100304603577, 0.12780000269412994, 0.27837300300598145, 0.11552999913692474, 0.008221070282161236, -0.10014700144529343, -0.07558959722518921, 0.019257299602031708, -0.152305006980896, -0.20021100342273712, 0.07656480371952057, 0.23433199524879456, 0.16775499284267426, 0.16134199500083923, -0.022472500801086426, 0.04337310045957565, 0.11542300134897232, 0.03047199919819832, -0.04252209886908531, 0.31366801261901855, 0.1267089992761612, 0.05862610042095184, -0.02297309972345829, 0.15418699383735657, 0.08286750316619873, -0.17386500537395477, 0.04494649916887283, -0.06585849821567535, 0.027742499485611916, -0.019243299961090088, 0.17510899901390076, 0.054640501737594604, 0.12472400069236755, 0.03397120162844658, 0.14394700527191162, -0.009537950158119202, -0.0025426300708204508, 0.05554239824414253, -0.1464949995279312, -0.09349360316991806, -0.014752999879419804, -0.033386699855327606, 0.12985800206661224, 0.15970200300216675, -0.040157098323106766, 0.016925200819969177, -0.29719001054763794, 0.17110399901866913, 0.10384999960660934, 0.052418701350688934, -0.006891789846122265, 0.02692429907619953, -0.058017101138830185, 0.20578299462795258, -0.06264530122280121, -0.13369299471378326, 0.008544639684259892, -0.03988479822874069, 0.024375300854444504, 0.014614899642765522, -0.022735700011253357, 0.038739100098609924, 0.1345759928226471, 0.049228500574827194, 0.20833100378513336, 0.1555359959602356, 0.17207999527454376, 0.06237579882144928, 0.025242600589990616, -0.01738560013473034, 0.02896069921553135, 0.18544000387191772, 0.06577560305595398, 0.12359800189733505, 0.1271360069513321, 0.1795710027217865, 0.061908598989248276, 0.0983961969614029, 0.056126199662685394, 0.10483500361442566, 0.07209719717502594, 0.21393699944019318; 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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                ),
                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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                Gate::new(
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