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use super::search_error::SearchError;
use super::search_instance::SearchInstance;
use crate::model::network::edge_id::EdgeId;
use crate::model::state::StateVariable;
use crate::model::unit::Cost;
use allocative::Allocative;
use serde::{Deserialize, Serialize};
use std::fmt::Display;
#[derive(Clone, Debug, Serialize, Deserialize, Allocative)]
pub struct EdgeTraversal {
pub edge_id: EdgeId,
pub access_cost: Cost,
pub traversal_cost: Cost,
pub result_state: Vec<StateVariable>,
}
impl EdgeTraversal {
pub fn total_cost(&self) -> Cost {
self.access_cost + self.traversal_cost
}
}
impl Display for EdgeTraversal {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
write!(
f,
"edge {} acost:{} tcost:{} state:{:?}",
self.edge_id, self.access_cost, self.traversal_cost, self.result_state
)
}
}
impl EdgeTraversal {
/// traverses an edge, possibly after traversing some previous edge,
/// collecting the access and traversal costs. returns the
/// accumulated cost and updated search state.
///
/// # Arguments
///
/// * `next_edge_id` - the edge to traverse
/// * `prev_edge_id_opt` - the previously traversed edge, if exists, for access costs
/// * `prev_state` - the state before traversal, positioned closer to the destination
/// * `si` - the search assets for this query
///
/// # Returns
///
/// An edge traversal summarizing the costs and result state of accessing and traversing the next edge.
pub fn forward_traversal(
next_edge_id: EdgeId,
prev_edge_id_opt: Option<EdgeId>,
prev_state: &[StateVariable],
si: &SearchInstance,
) -> Result<EdgeTraversal, SearchError> {
let mut result_state = prev_state.to_vec();
let mut access_cost = Cost::ZERO;
// find this traversal in the graph
let traversal_trajectory = si.graph.edge_triplet(&next_edge_id)?;
// perform access traversal for (v2)-[next]->(v3)
// access cost for (v1)-[prev]->(v2)-[next]->(v3)
if let Some(prev_edge_id) = prev_edge_id_opt {
let e1 = si.graph.get_edge(&prev_edge_id)?;
let v1 = si.graph.get_vertex(&e1.src_vertex_id)?;
let (v2, e2, v3) = traversal_trajectory;
let access_trajectory = (v1, e1, v2, e2, v3);
si.access_model
.access_edge(access_trajectory, &mut result_state, &si.state_model)?;
let ac = si
.cost_model
.access_cost(e1, e2, prev_state, &result_state)?;
access_cost = access_cost + ac;
}
si.traversal_model.traverse_edge(
traversal_trajectory,
&mut result_state,
&si.state_model,
)?;
let (_, edge, _) = traversal_trajectory;
let total_cost = si
.cost_model
.traversal_cost(edge, prev_state, &result_state)?;
let traversal_cost = total_cost - access_cost;
let result = EdgeTraversal {
edge_id: next_edge_id,
access_cost,
traversal_cost,
result_state,
};
Ok(result)
}
/// traverses an edge, possibly after traversing some next edge,
/// collecting the access and traversal costs in a reverse-oriented
/// tree building process. returns the accumulated cost and updated search state.
/// used in bi-directional search algorithms. definition of previous and next
/// edges is the same as the forward traversal: (v1)-[prev]->(v2)-[next]->(v3)
/// but the "next" edge is now the Optional edge.
///
/// # Arguments
///
/// * `prev_edge_id` - the edge to traverse
/// * `next_edge_id_opt` - the edge previously traversed that appears closer to the origin
/// of this reverse search
/// * `prev_state` - the state before traversal, positioned closer to the destination
/// * `si` - the search assets for this query
///
/// # Returns
///
/// An edge traversal summarizing the costs and result state of accessing and traversing the previous edge.
pub fn reverse_traversal(
prev_edge_id: EdgeId,
next_edge_id_opt: Option<EdgeId>,
prev_state: &[StateVariable],
si: &SearchInstance,
) -> Result<EdgeTraversal, SearchError> {
let mut result_state = prev_state.to_vec();
let mut access_cost = Cost::ZERO;
// find this traversal in the graph
let traversal_trajectory = si.graph.edge_triplet(&prev_edge_id)?;
// perform access traversal for (v1)-[prev]->(v2)
// access cost for (v1)-[prev]->(v2)-[next]->(v3)
if let Some(next_edge_id) = next_edge_id_opt {
let e2 = si.graph.get_edge(&next_edge_id)?;
let v3 = si.graph.get_vertex(&e2.dst_vertex_id)?;
let (v1, e1, v2) = traversal_trajectory;
let access_trajectory = (v1, e1, v2, e2, v3);
si.access_model
.access_edge(access_trajectory, &mut result_state, &si.state_model)?;
let ac = si
.cost_model
.access_cost(e1, e2, prev_state, &result_state)?;
access_cost = access_cost + ac;
}
si.traversal_model.traverse_edge(
traversal_trajectory,
&mut result_state,
&si.state_model,
)?;
let (_, edge, _) = traversal_trajectory;
let total_cost = si
.cost_model
.traversal_cost(edge, prev_state, &result_state)?;
let traversal_cost = total_cost - access_cost;
let result = EdgeTraversal {
edge_id: prev_edge_id,
access_cost,
traversal_cost,
result_state,
};
Ok(result)
}
}