A new, blazing-fast learning engine for Continuous Time Bayesian Networks. Written in pure Rust. 🦀
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use ndarray::prelude::*;
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use petgraph::prelude::*;
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use crate::network;
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use crate::node;
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use crate::params;
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use crate::params::Params;
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pub struct Trajectory {
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time: Array1<f64>,
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events: Array2<u32>
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}
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pub struct Dataset {
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trajectories: Vec<Trajectory>
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}
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pub fn trajectory_generator(net: &Box<dyn network::Network>, n_trajectories: u64, t_end: f64) -> Dataset {
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let mut dataset = Dataset{
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trajectories: Vec::new()
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};
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let node_idx: Vec<_> = net.get_node_indices().collect();
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for _ in 0..n_trajectories {
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let t = 0.0;
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let mut time: Vec<f64> = Vec::new();
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let mut events: Vec<Vec<params::StateType>> = Vec::new();
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let mut current_state: Vec<params::StateType> = node_idx.iter().map(|x| {
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match net.get_node(&x).get_params() {
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node::NodeType::DiscreteStatesContinousTime(params) =>
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params.get_random_state_uniform()
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}
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}).collect();
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let next_transitions: Vec<Option<f64>> = (0..node_idx.len()).map(|_| Option::None).collect();
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events.push(current_state.clone());
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time.push(t.clone());
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while t < t_end {
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}
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}
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dataset
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}
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