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//! This module contains definitions for the network models and their constructing algorithms.

use crate::{cn, Network};
use rand::prelude::{Rng, SliceRandom};

/// Decides the algorithm used to connect the nodes during [`Network`]'s initialization. It can
/// also be used for identification purposes when the [`Model::Custom`] variant is used.
#[derive(Debug, Clone, PartialEq)]
pub enum Model {
    /// The Erdos–Renyi random network model.
    ER {
        /// The probability of connecting any given pair of nodes.
        p: f64,
        /// Should the network be in one piece
        whole: bool,
    },
    /// The Barabasi–Albert preferential attachment model.
    BA {
        /// The initial number of clustered nodes.
        m0: usize,
        /// The number of nodes added in each step during network creation.
        /// Keep in mind that this should be strictly less or equal m0 and the Barabasi-Albert
        /// initialization function **will panic** if this is not the case.
        m: usize,
    },
    /// Custom model, can be dynamically assigned to a network using [`Network::set_model`].
    Custom(String),
    /// Placeholder for a network with no specified model. When a network is initialized with this
    /// model no immediate connections will be made.
    None,
}

impl Model {
    /// Clear the network's links and reinitialize them according to the Erdos-Renyi model.
    ///
    /// See [`Model::ER`] for more detailed explanation.
    pub fn init_er(net: &mut Network, p: f64, whole: bool) {
        let net_len = net.size();
        net.disconnect_all();
        if p <= 0. || p > 1. {
            panic!("{}", cn::Err::BadProbability(p));
        }
        for i in 0..net_len {
            for j in i + 1..net_len {
                if net.rng().unwrap().gen::<f64>() <= p {
                    net.link(i, j).unwrap();
                }
            }
        }
        if whole {
            net.stitch_together();
        }
        net.model = Model::ER { p, whole };
    }

    /// Clear the network's links and reinitialize them according to the Barabasi-Albert model.
    ///
    /// See [`Model::BA`] for more detailed model explanation.
    pub fn init_ba(net: &mut Network, m0: usize, m: usize) {
        if m0 < 1 || m == 0 || m > m0 || m0 > net.size() {
            panic!("Incorrect model parameters: m0 = {}, m = {}", m0, m);
        }
        net.disconnect_all();
        // Initial cluster - connect everything to everything
        for i in 0..m0 {
            if m0 == 1 {
                break;
            }
            for j in i + 1..m0 {
                net.link(i, j).unwrap();
            }
        }
        // Attach the other nodes one by one
        for current_idx in m0..net.size() {
            let chosen: Vec<usize> = net
                .nodes()
                .filter(|&node| node < current_idx)
                .map(|node| (node, net.deg_of(node).unwrap()))
                .collect::<Vec<_>>() // <- This is sadly needed
                .choose_multiple_weighted(&mut *net.rng().unwrap(), m, |&(_index, deg)| deg as f64)
                .unwrap()
                .map(|&(idx, _deg)| idx)
                .collect();
            for other in chosen {
                net.link(current_idx, other).unwrap();
            }
        }
    }
}

impl Default for Model {
    fn default() -> Self {
        Model::None
    }

}