WMICraft/algorithms/a_star.py

from __future__ import annotations

from dataclasses import dataclass, field
from typing import Tuple, Optional, List

from common.constants import ROWS, COLUMNS

FREE_FIELD = ' '
LEFT = 'LEFT'
RIGHT = 'RIGHT'
UP = 'UP'
DOWN = 'DOWN'

TURN_LEFT = 'TURN_LEFT'
TURN_RIGHT = 'TURN_RIGHT'
FORWARD = 'FORWARD'

directions = {
    LEFT: (0, -1),
    RIGHT: (0, 1),
    UP: (-1, 0),
    DOWN: (1, 0)
}


@dataclass
class State:
    position: Tuple[int, int]
    direction: str


@dataclass
class Node:
    state: State
    parent: Optional[Node]
    action: str
    cost: int = field(init=False)
    depth: int = field(init=False)

    def __post_init__(self) -> None:
        self.cost = 0 if not self.parent else self.parent.cost + 1
        self.depth = self.cost

    def __eq__(self, other: Node) -> bool:
        return self.state == other.state

    def __hash__(self) -> int:
        return hash(self.state)


def expand(node: Node) -> List[Node]:
    return [child_node(node=node, action=action) for action in actions(node.state)]


def child_node(node: Node, action: str) -> Node:
    next_state = result(state=node.state, action=action)
    return Node(state=next_state, parent=node, action=action)


def next_position(current_position: Tuple[int, int], direction: str) -> Tuple[int, int]:
    next_row, next_col = directions[direction]
    row, col = current_position
    return next_row + row, next_col + col


def valid_move(position: Tuple[int, int], grid: List[List[str]]) -> bool:
    row, col = position
    return grid[row][col] == FREE_FIELD


def actions(state: State, grid: List[List[str]]) -> List[str]:
    possible_actions = [FORWARD, TURN_LEFT, TURN_RIGHT]
    row, col = state.position
    direction = state.direction

    if direction == UP and row == 0:
        remove_forward(possible_actions)
    if direction == DOWN and row == ROWS - 1:
        remove_forward(possible_actions)
    if direction == LEFT and col == 0:
        remove_forward(possible_actions)
    if direction == RIGHT and col == COLUMNS - 1:
        remove_forward(possible_actions)

    if FORWARD not in possible_actions and not valid_move(next_position(state.position, direction), grid):
        remove_forward(possible_actions)

    return possible_actions


def remove_forward(possible_actions: List[str]) -> None:
    if FORWARD in possible_actions:
        possible_actions.remove(FORWARD)


def result(state: State, action: str) -> State:
    next_state = State(state.position, state.direction)

    if state.direction == UP:
        if action == TURN_LEFT:
            next_state.direction = LEFT
        elif action == TURN_RIGHT:
            next_state.direction = RIGHT
        elif action == FORWARD:
            next_state.position = next_position(state.position, UP)

    elif state.direction == DOWN:
        if action == TURN_LEFT:
            next_state.direction = RIGHT
        elif action == TURN_RIGHT:
            next_state.direction = LEFT
        elif action == FORWARD:
            next_state.position = next_position(state.position, DOWN)

    elif state.direction == LEFT:
        if action == TURN_LEFT:
            next_state.direction = DOWN
        elif action == TURN_RIGHT:
            next_state.direction = UP
        elif action == FORWARD:
            next_state.position = next_position(state.position, LEFT)

    elif state.direction == RIGHT:
        if action == TURN_LEFT:
            next_state.direction = UP
        elif action == TURN_RIGHT:
            next_state.direction = DOWN
        elif action == FORWARD:
            next_state.position = next_position(state.position, RIGHT)

    return next_state


def goal_test(state: State, goal_list: List[Tuple[int, int]]) -> bool:
    return state.position in goal_list


def h(state: State, goal: Tuple[int, int]) -> int:
    """heuristics that calculates Manhattan distance between current position and goal"""
    x1, y1 = state.position
    x2, y2 = goal
    return abs(x1 - x2) + abs(y1 - y2)


def f(current_node: Node, goal: Tuple[int, int]) -> int:
    """f(n) = g(n) + h(n), g stands for current cost, h for heuristics"""
    return current_node.cost + h(state=current_node.state, goal=goal)