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    //! NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994.
    //! See "Kohonen neural networks for optimal colour quantization"
    //! in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367.
    //! for a discussion of the algorithm.
    //! See also <http://www.acm.org/~dekker/NEUQUANT.HTML>

    /* NeuQuant Neural-Net Quantization Algorithm
     * ------------------------------------------
     *
     * Copyright (c) 1994 Anthony Dekker
     *
     * NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994.
     * See "Kohonen neural networks for optimal colour quantization"
     * in "Network: Computation in Neural Systems" Vol. 5 (1994) pp 351-367.
     * for a discussion of the algorithm.
     * See also  http://www.acm.org/~dekker/NEUQUANT.HTML
     *
     * Any party obtaining a copy of these files from the author, directly or
     * indirectly, is granted, free of charge, a full and unrestricted irrevocable,
     * world-wide, paid up, royalty-free, nonexclusive right and license to deal
     * in this software and documentation files (the "Software"), including without
     * limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
     * and/or sell copies of the Software, and to permit persons who receive
     * copies from any such party to do so, with the only requirement being
     * that this copyright notice remain intact.
     *
     *
     * Incorporated bugfixes and alpha channel handling from pngnq
     * http://pngnq.sourceforge.net
     *
     */

    use math::utils::clamp;
    use std::cmp::{max, min};

    const CHANNELS: usize = 4;

    const RADIUS_DEC: i32 = 30; // factor of 1/30 each cycle

    const ALPHA_BIASSHIFT: i32 = 10; // alpha starts at 1
    const INIT_ALPHA: i32 = 1 << ALPHA_BIASSHIFT; // biased by 10 bits

    const GAMMA: f64 = 1024.0;
    const BETA: f64 = 1.0 / GAMMA;
    const BETAGAMMA: f64 = BETA * GAMMA;

    // four primes near 500 - assume no image has a length so large
    // that it is divisible by all four primes
    const PRIMES: [usize; 4] = [499, 491, 478, 503];

    #[derive(Clone, Copy)]
    struct Quad<T> {
        r: T,
        g: T,
        b: T,
        a: T,
    }

    type Neuron = Quad<f64>;
    type Color = Quad<i32>;

    /// Neural network color quantizer
    pub struct NeuQuant {
        network: Vec<Neuron>,
        colormap: Vec<Color>,
        netindex: Vec<usize>,
        bias: Vec<f64>, // bias and freq arrays for learning
        freq: Vec<f64>,
        samplefac: i32,
        netsize: usize,
    }

    impl NeuQuant {
        /// Creates a new neural network and trains it with the supplied data
        pub fn new(samplefac: i32, colors: usize, pixels: &[u8]) -> Self {
            let netsize = colors;
            let mut this = NeuQuant {
                network: Vec::with_capacity(netsize),
                colormap: Vec::with_capacity(netsize),
                netindex: vec![0; 256],
                bias: Vec::with_capacity(netsize),
                freq: Vec::with_capacity(netsize),
                samplefac,
                netsize,
            };
            this.init(pixels);
            this
        }

        /// Initializes the neural network and trains it with the supplied data
        pub fn init(&mut self, pixels: &[u8]) {
            self.network.clear();
            self.colormap.clear();
            self.bias.clear();
            self.freq.clear();
            let freq = (self.netsize as f64).recip();
            for i in 0..self.netsize {
                let tmp = (i as f64) * 256.0 / (self.netsize as f64);
                // Sets alpha values at 0 for dark pixels.
                let a = if i < 16 { i as f64 * 16.0 } else { 255.0 };
                self.network.push(Neuron {
                    r: tmp,
                    g: tmp,
                    b: tmp,
                    a,
                });
                self.colormap.push(Color {
                    r: 0,
                    g: 0,
                    b: 0,
                    a: 255,
                });
                self.freq.push(freq);
                self.bias.push(0.0);
            }
            self.learn(pixels);
            self.build_colormap();
            self.build_netindex();
        }

        /// Maps the pixel in-place to the best-matching color in the color map
        #[inline(always)]
        pub fn map_pixel(&self, pixel: &mut [u8]) {
            assert_eq!(pixel.len(), 4);
            match (pixel[0], pixel[1], pixel[2], pixel[3]) {
                (r, g, b, a) => {
                    let i = self.search_netindex(b, g, r, a);
                    pixel[0] = self.colormap[i].r as u8;
                    pixel[1] = self.colormap[i].g as u8;
                    pixel[2] = self.colormap[i].b as u8;
                    pixel[3] = self.colormap[i].a as u8;
                }
            }
        }

        /// Finds the best-matching index in the color map for `pixel`
        #[inline(always)]
        pub fn index_of(&self, pixel: &[u8]) -> usize {
            assert_eq!(pixel.len(), 4);
            match (pixel[0], pixel[1], pixel[2], pixel[3]) {
                (r, g, b, a) => self.search_netindex(b, g, r, a),
            }
        }

        /// Move neuron i towards biased (a,b,g,r) by factor alpha
        fn alter_single(&mut self, alpha: f64, i: i32, quad: Quad<f64>) {
            let n = &mut self.network[i as usize];
            n.b -= alpha * (n.b - quad.b);
            n.g -= alpha * (n.g - quad.g);
            n.r -= alpha * (n.r - quad.r);
            n.a -= alpha * (n.a - quad.a);
        }

        /// Move neuron adjacent neurons towards biased (a,b,g,r) by factor alpha
        fn alter_neighbour(&mut self, alpha: f64, rad: i32, i: i32, quad: Quad<f64>) {
            let lo = max(i - rad, 0);
            let hi = min(i + rad, self.netsize as i32);
            let mut j = i + 1;
            let mut k = i - 1;
            let mut q = 0;

            while (j < hi) || (k > lo) {
                let rad_sq = f64::from(rad) * f64::from(rad);
                let alpha = (alpha * (rad_sq - f64::from(q) * f64::from(q))) / rad_sq;
                q += 1;
                if j < hi {
                    let p = &mut self.network[j as usize];
                    p.b -= alpha * (p.b - quad.b);
                    p.g -= alpha * (p.g - quad.g);
                    p.r -= alpha * (p.r - quad.r);
                    p.a -= alpha * (p.a - quad.a);
                    j += 1;
                }
                if k > lo {
                    let p = &mut self.network[k as usize];
                    p.b -= alpha * (p.b - quad.b);
                    p.g -= alpha * (p.g - quad.g);
                    p.r -= alpha * (p.r - quad.r);
                    p.a -= alpha * (p.a - quad.a);
                    k -= 1;
                }
            }
        }

        /// Search for biased BGR values
        /// finds closest neuron (min dist) and updates freq
        /// finds best neuron (min dist-bias) and returns position
        /// for frequently chosen neurons, freq[i] is high and bias[i] is negative
        /// bias[i] = gamma*((1/self.netsize)-freq[i])
        fn contest(&mut self, b: f64, g: f64, r: f64, a: f64) -> i32 {
            use std::f64;

            let mut bestd = f64::MAX;
            let mut bestbiasd: f64 = bestd;
            let mut bestpos = -1;
            let mut bestbiaspos: i32 = bestpos;

            for i in 0..self.netsize {
                let bestbiasd_biased = bestbiasd + self.bias[i];
                let mut dist;
                let n = &self.network[i];
                dist = (n.b - b).abs();
                dist += (n.r - r).abs();
                if dist < bestd || dist < bestbiasd_biased {
                    dist += (n.g - g).abs();
                    dist += (n.a - a).abs();
                    if dist < bestd {
                        bestd = dist;
                        bestpos = i as i32;
                    }
                    let biasdist = dist - self.bias[i];
                    if biasdist < bestbiasd {
                        bestbiasd = biasdist;
                        bestbiaspos = i as i32;
                    }
                }
                self.freq[i] -= BETA * self.freq[i];
                self.bias[i] += BETAGAMMA * self.freq[i];
            }
            self.freq[bestpos as usize] += BETA;
            self.bias[bestpos as usize] -= BETAGAMMA;
            bestbiaspos
        }

        /// Main learning loop
        /// Note: the number of learning cycles is crucial and the parameters are not
        /// optimized for net sizes < 26 or > 256. 1064 colors seems to work fine
        fn learn(&mut self, pixels: &[u8]) {
            let initrad: i32 = self.netsize as i32 / 8; // for 256 cols, radius starts at 32
            let radiusbiasshift: i32 = 6;
            let radiusbias: i32 = 1 << radiusbiasshift;
            let init_bias_radius: i32 = initrad * radiusbias;
            let mut bias_radius = init_bias_radius;
            let alphadec = 30 + ((self.samplefac - 1) / 3);
            let lengthcount = pixels.len() / CHANNELS;
            let samplepixels = lengthcount / self.samplefac as usize;
            // learning cycles
            let n_cycles = match self.netsize >> 1 {
                n if n <= 100 => 100,
                n => n,
            };
            let delta = match samplepixels / n_cycles {
                0 => 1,
                n => n,
            };
            let mut alpha = INIT_ALPHA;

            let mut rad = bias_radius >> radiusbiasshift;
            if rad <= 1 {
                rad = 0
            };

            let mut pos = 0;
            let step = *PRIMES
                .iter()
                .find(|&&prime| lengthcount % prime != 0)
                .unwrap_or(&PRIMES[3]);

            let mut i = 0;
            while i < samplepixels {
                let (r, g, b, a) = {
                    let p = &pixels[CHANNELS * pos..][..CHANNELS];
                    (
                        f64::from(p[0]),
                        f64::from(p[1]),
                        f64::from(p[2]),
                        f64::from(p[3]),
                    )
                };

                let j = self.contest(b, g, r, a);

                let alpha_ = (1.0 * f64::from(alpha)) / f64::from(INIT_ALPHA);
                self.alter_single(alpha_, j, Quad { b, g, r, a });
                if rad > 0 {
                    self.alter_neighbour(alpha_, rad, j, Quad { b, g, r, a })
                };

                pos += step;
                while pos >= lengthcount {
                    pos -= lengthcount
                }

                i += 1;
                if i % delta == 0 {
                    alpha -= alpha / alphadec;
                    bias_radius -= bias_radius / RADIUS_DEC;
                    rad = bias_radius >> radiusbiasshift;
                    if rad <= 1 {
                        rad = 0
                    };
                }
            }
        }

        /// initializes the color map
        fn build_colormap(&mut self) {
            for i in 0usize..self.netsize {
                self.colormap[i].b = clamp(self.network[i].b.round() as i32, 0, 255);
                self.colormap[i].g = clamp(self.network[i].g.round() as i32, 0, 255);
                self.colormap[i].r = clamp(self.network[i].r.round() as i32, 0, 255);
                self.colormap[i].a = clamp(self.network[i].a.round() as i32, 0, 255);
            }
        }

        /// Insertion sort of network and building of netindex[0..255]
        fn build_netindex(&mut self) {
            let mut previouscol = 0;
            let mut startpos = 0;

            for i in 0..self.netsize {
                let mut p = self.colormap[i];
                let mut q;
                let mut smallpos = i;
                let mut smallval = p.g as usize; // index on g
                                                 // find smallest in i..netsize-1
                for j in (i + 1)..self.netsize {
                    q = self.colormap[j];
                    if (q.g as usize) < smallval {
                        // index on g
                        smallpos = j;
                        smallval = q.g as usize; // index on g
                    }
                }
                q = self.colormap[smallpos];
                // swap p (i) and q (smallpos) entries
                if i != smallpos {
                    ::std::mem::swap(&mut p, &mut q);
                    self.colormap[i] = p;
                    self.colormap[smallpos] = q;
                }
                // smallval entry is now in position i
                if smallval != previouscol {
                    self.netindex[previouscol] = (startpos + i) >> 1;
                    for j in (previouscol + 1)..smallval {
                        self.netindex[j] = i
                    }
                    previouscol = smallval;
                    startpos = i;
                }
            }
            let max_netpos = self.netsize - 1;
            self.netindex[previouscol] = (startpos + max_netpos) >> 1;
            for j in (previouscol + 1)..256 {
                self.netindex[j] = max_netpos
            } // really 256
        }

        /// Search for best matching color
        fn search_netindex(&self, b: u8, g: u8, r: u8, a: u8) -> usize {
            let mut bestd = 1 << 30; // ~ 1_000_000
            let mut best = 0;
            // start at netindex[g] and work outwards
            let mut i = self.netindex[g as usize];
            let mut j = if i > 0 { i - 1 } else { 0 };

            while (i < self.netsize) || (j > 0) {
                if i < self.netsize {
                    let p = self.colormap[i];
                    let mut e = p.g - i32::from(g);
                    let mut dist = e * e; // index key
                    if dist >= bestd {
                        break;
                    } else {
                        e = p.b - i32::from(b);
                        dist += e * e;
                        if dist < bestd {
                            e = p.r - i32::from(r);
                            dist += e * e;
                            if dist < bestd {
                                e = p.a - i32::from(a);
                                dist += e * e;
                                if dist < bestd {
                                    bestd = dist;
                                    best = i;
                                }
                            }
                        }
                        i += 1;
                    }
                }
                if j > 0 {
                    let p = self.colormap[j];
                    let mut e = p.g - i32::from(g);
                    let mut dist = e * e; // index key
                    if dist >= bestd {
                        break;
                    } else {
                        e = p.b - i32::from(b);
                        dist += e * e;
                        if dist < bestd {
                            e = p.r - i32::from(r);
                            dist += e * e;
                            if dist < bestd {
                                e = p.a - i32::from(a);
                                dist += e * e;
                                if dist < bestd {
                                    bestd = dist;
                                    best = j;
                                }
                            }
                        }
                        j -= 1;
                    }
                }
            }
            best
        }
    }

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