backend/app/algorithms/ids.py

"""Iterative Deepening Search algorithm."""

from typing import Tuple, Optional, List, TYPE_CHECKING

if TYPE_CHECKING:
    from ..core.generic_search import GenericSearch

from ..core.node import SearchNode
from ..models.state import PathResult, SearchStep


# Sentinel values for DLS results
CUTOFF = "cutoff"
FAILURE = "failure"


def depth_limited_search(
    problem: "GenericSearch",
    limit: int,
    visualize: bool = False,
    steps: Optional[List[SearchStep]] = None,
    base_expanded: int = 0,
) -> Tuple[Optional[PathResult], str, int, Optional[List[SearchStep]]]:
    """
    Depth-limited search - DFS with depth limit.

    Args:
        problem: The search problem
        limit: Maximum depth to search
        visualize: If True, collect visualization steps
        steps: Existing steps list to append to
        base_expanded: Starting count for nodes expanded

    Returns:
        Tuple of (PathResult or None, status, nodes_expanded, steps)
        status is CUTOFF if limit reached, FAILURE if no solution exists
    """
    start = problem.initial_state()
    start_node = SearchNode(state=start, path_cost=0, depth=0)

    return _recursive_dls(
        problem,
        start_node,
        limit,
        set(),
        visualize,
        steps if steps is not None else ([] if visualize else None),
        base_expanded,
    )


def _recursive_dls(
    problem: "GenericSearch",
    node: SearchNode,
    limit: int,
    explored: set,
    visualize: bool,
    steps: Optional[List[SearchStep]],
    nodes_expanded: int,
) -> Tuple[Optional[PathResult], str, int, Optional[List[SearchStep]]]:
    """Recursive helper for depth-limited search."""

    # Record step for visualization
    if visualize and steps is not None:
        steps.append(
            SearchStep(
                step_number=nodes_expanded,
                current_node=node.state,
                action=node.action,
                frontier=[],  # DLS doesn't maintain explicit frontier
                explored=list(explored),
                current_path=node.get_path(),
                path_cost=node.path_cost,
            )
        )

    # Goal test
    if problem.goal_test(node.state):
        return (
            PathResult(
                plan=node.get_solution(),
                cost=node.path_cost,
                nodes_expanded=nodes_expanded,
                path=node.get_path(),
            ),
            "success",
            nodes_expanded,
            steps,
        )

    # Depth limit reached
    if node.depth >= limit:
        return None, CUTOFF, nodes_expanded, steps

    # Mark as explored for this path
    explored.add(node.state)
    nodes_expanded += 1

    cutoff_occurred = False

    # Expand node
    for action in problem.actions(node.state):
        child_state = problem.result(node.state, action)

        if child_state not in explored:
            step_cost = problem.step_cost(node.state, action, child_state)
            child = SearchNode(
                state=child_state,
                parent=node,
                action=action,
                path_cost=node.path_cost + step_cost,
                depth=node.depth + 1,
            )

            result, status, nodes_expanded, steps = _recursive_dls(
                problem, child, limit, explored.copy(), visualize, steps, nodes_expanded
            )

            if status == "success":
                return result, status, nodes_expanded, steps
            elif status == CUTOFF:
                cutoff_occurred = True

    if cutoff_occurred:
        return None, CUTOFF, nodes_expanded, steps
    else:
        return None, FAILURE, nodes_expanded, steps


def ids_search(
    problem: "GenericSearch", visualize: bool = False, max_depth: int = 1000
) -> Tuple[PathResult, Optional[List[SearchStep]]]:
    """
    Iterative deepening search - repeated DLS with increasing depth.

    Combines BFS's completeness and optimality (for unweighted)
    with DFS's space efficiency.

    Args:
        problem: The search problem to solve
        visualize: If True, collect visualization steps
        max_depth: Maximum depth to search (prevents infinite loops)

    Returns:
        Tuple of (PathResult, Optional[List[SearchStep]])
    """
    total_expanded = 0
    all_steps: List[SearchStep] = [] if visualize else None

    for depth in range(max_depth):
        result, status, expanded, steps = depth_limited_search(
            problem, depth, visualize, all_steps, total_expanded
        )
        total_expanded = expanded

        if visualize and steps:
            all_steps = steps

        if status == "success" and result is not None:
            result.nodes_expanded = total_expanded
            return result, all_steps
        elif status == FAILURE:
            # No solution exists
            break

    # No solution found within max_depth
    return (
        PathResult(plan="", cost=float("inf"), nodes_expanded=total_expanded, path=[]),
        all_steps,
    )