forked from s464965/WMICraft
Merge branch 'master' of https://git.wmi.amu.edu.pl/s464965/WMICraft into cnn
This commit is contained in:
commit
c8f0dc76b6
@ -4,10 +4,11 @@ import heapq
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from dataclasses import dataclass, field
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from typing import Tuple, Optional, List
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from algorithms.genetic.const import MAP_ALIASES
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from common.constants import ROWS, COLUMNS, LEFT, RIGHT, UP, DOWN
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from common.helpers import directions
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EMPTY_FIELDS = ['s', 'g', ' ']
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EMPTY_FIELDS = [MAP_ALIASES.get("SAND"), MAP_ALIASES.get("GRASS"), ' ']
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TURN_LEFT = 'TURN_LEFT'
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TURN_RIGHT = 'TURN_RIGHT'
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142
algorithms/genetic/common.py
Normal file
142
algorithms/genetic/common.py
Normal file
@ -0,0 +1,142 @@
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from dataclasses import dataclass
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import numpy as np
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from const import *
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from typing import List, Dict, Tuple
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import numpy.typing as npt
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@dataclass
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class Position:
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row: int
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col: int
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@dataclass
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class Area:
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position: Position
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width: int
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height: int
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AREAS_TO_CROSS = [
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# up above left knights spawn
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Area(position=Position(row=0, col=0),
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width=KNIGHTS_SPAWN_WIDTH,
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height=LEFT_KNIGHTS_SPAWN_FIRST_ROW),
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# down below left knights spawn
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Area(position=Position(row=LEFT_KNIGHTS_SPAWN_FIRST_ROW + KNIGHTS_SPAWN_HEIGHT, col=0),
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width=KNIGHTS_SPAWN_WIDTH,
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height=ROWS - LEFT_KNIGHTS_SPAWN_FIRST_ROW - KNIGHTS_SPAWN_HEIGHT),
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# between left knights spawn and castle
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Area(position=Position(row=0, col=KNIGHTS_SPAWN_WIDTH),
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width=CASTLE_SPAWN_FIRST_COL - KNIGHTS_SPAWN_WIDTH,
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height=ROWS),
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# up above castle
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Area(position=Position(row=0, col=CASTLE_SPAWN_FIRST_COL),
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width=2,
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height=CASTLE_SPAWN_FIRST_ROW),
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# down below castle
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Area(position=Position(row=CASTLE_SPAWN_FIRST_ROW + 2, col=CASTLE_SPAWN_FIRST_COL),
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width=2,
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height=ROWS - CASTLE_SPAWN_FIRST_ROW - 2),
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# between castle and right knights spawn
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Area(position=Position(row=0, col=CASTLE_SPAWN_FIRST_COL + 2),
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width=RIGHT_KNIGHTS_SPAWN_FIRST_COL - CASTLE_SPAWN_FIRST_COL - 2,
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height=ROWS),
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# up above right knights spawn
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Area(position=Position(row=0, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
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width=KNIGHTS_SPAWN_WIDTH,
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height=RIGHT_KNIGHTS_SPAWN_FIRST_ROW),
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# down below right knights spawn
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Area(position=Position(row=RIGHT_KNIGHTS_SPAWN_FIRST_ROW + KNIGHTS_SPAWN_HEIGHT, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
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width=KNIGHTS_SPAWN_WIDTH,
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height=ROWS - RIGHT_KNIGHTS_SPAWN_FIRST_ROW - KNIGHTS_SPAWN_HEIGHT),
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]
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def dfs(grid: npt.NDArray, visited: Dict[Tuple[int, int], bool], position: Position, rows: int, cols: int) -> None:
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visited[(position.row, position.col)] = True
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row_vector = [0, 0, 1, -1]
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col_vector = [-1, 1, 0, 0]
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neighbours = []
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for i in range(4):
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rr = position.row + row_vector[i]
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cc = position.col + col_vector[i]
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if rr < 0 or rr >= ROWS:
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continue
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elif cc < 0 or cc >= COLUMNS:
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continue
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else:
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p = Position(rr, cc)
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if (p.row, p.col) in visited:
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neighbours.append(p)
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for neighbour in neighbours:
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if not visited[(neighbour.row, neighbour.col)]:
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dfs(grid, visited, neighbour, rows, cols)
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def get_islands(grid: npt.NDArray, positions: List[Position], rows: int = ROWS, cols: int = COLUMNS) -> List[Position]:
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"""it returns list of all islands roots"""
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visited = {}
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for position in positions:
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visited[(position.row, position.col)] = False
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islands = 0
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roots = []
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for position in positions:
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if not visited[(position.row, position.col)]:
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dfs(grid, visited, position, rows, cols)
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roots.append(position)
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islands += 1
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return roots
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def find_neighbours(grid: npt.NDArray, col: int, row: int) -> List[Position]:
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dr = [-1, 1, 0, 0]
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dc = [0, 0, -1, 1]
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neighbours = []
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for i in range(4):
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rr = row + dr[i]
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cc = col + dc[i]
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if 0 <= rr < ROWS and 0 <= cc < COLUMNS and grid[rr][cc] == MAP_ALIASES.get('GRASS'):
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neighbours.append(Position(row=rr, col=cc))
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return neighbours
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def get_tiles_positions(grid: npt.NDArray):
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sands = []
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trees = []
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waters = []
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monsters = []
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for row_num in range(len(grid)):
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for col_num in range(len(grid[row_num])):
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if grid[row_num][col_num] == MAP_ALIASES.get('WATER'):
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waters.append(Position(row=row_num, col=col_num))
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elif grid[row_num][col_num] == MAP_ALIASES.get('TREE'):
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trees.append(Position(row=row_num, col=col_num))
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elif grid[row_num][col_num] == MAP_ALIASES.get('SAND'):
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sands.append(Position(row=row_num, col=col_num))
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elif grid[row_num][col_num] == MAP_ALIASES.get('MONSTER'):
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monsters.append(Position(row=row_num, col=col_num))
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return sands, trees, waters, monsters
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29
algorithms/genetic/const.py
Normal file
29
algorithms/genetic/const.py
Normal file
@ -0,0 +1,29 @@
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# map config
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KNIGHTS_PER_TEAM_COUNT = 4
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SAND_COUNT = 21
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WATER_COUNT = 21
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TREE_COUNT = 37
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MONSTERS_COUNT = 2
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CASTLES_COUNT = 1
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ROWS = 19
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COLUMNS = 24
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KNIGHTS_SPAWN_WIDTH = 4
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KNIGHTS_SPAWN_HEIGHT = 7
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LEFT_KNIGHTS_SPAWN_FIRST_ROW = 6
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LEFT_KNIGHTS_SPAWN_FIRST_COL = 0
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RIGHT_KNIGHTS_SPAWN_FIRST_ROW = 6
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RIGHT_KNIGHTS_SPAWN_FIRST_COL = 20
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CASTLE_SPAWN_FIRST_ROW = 7
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CASTLE_SPAWN_FIRST_COL = 11
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# map aliases
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MAP_ALIASES = {
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"GRASS": 0,
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"SAND": 1,
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"WATER": 2,
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"TREE": 3,
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"MONSTER": 4,
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"CASTLE": 5,
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"KNIGHT_RED": 6,
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"KNIGHT_BLUE": 7,
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}
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166
algorithms/genetic/genome.py
Normal file
166
algorithms/genetic/genome.py
Normal file
@ -0,0 +1,166 @@
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import math
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import random
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from copy import deepcopy
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from random import randrange
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from typing import List
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import numpy as np
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import numpy.typing as npt
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from common import Position, get_islands, AREAS_TO_CROSS, find_neighbours, get_tiles_positions
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from const import *
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class Genome:
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grid: npt.NDArray
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knights_red: List[Position]
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knights_blue: List[Position]
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waters: List[Position]
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trees: List[Position]
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sands: List[Position]
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monsters: List[Position]
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fitness: int
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sand_islands: List[Position]
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tree_islands: List[Position]
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water_islands: List[Position]
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def __init__(self):
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self.grid = np.zeros((ROWS, COLUMNS), dtype=int)
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self.fitness = 0
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self.knights_red = spawn_objects_in_given_area(
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grid=self.grid,
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object_alias=MAP_ALIASES.get("KNIGHT_RED"),
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objects_count=KNIGHTS_PER_TEAM_COUNT,
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spawn_position_start=Position(row=LEFT_KNIGHTS_SPAWN_FIRST_ROW, col=LEFT_KNIGHTS_SPAWN_FIRST_COL),
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width=KNIGHTS_SPAWN_WIDTH,
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height=KNIGHTS_SPAWN_HEIGHT
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)
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self.knights_blue = spawn_objects_in_given_area(
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grid=self.grid,
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object_alias=MAP_ALIASES.get("KNIGHT_BLUE"),
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objects_count=KNIGHTS_PER_TEAM_COUNT,
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spawn_position_start=Position(row=RIGHT_KNIGHTS_SPAWN_FIRST_ROW, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
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width=KNIGHTS_SPAWN_WIDTH,
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height=KNIGHTS_SPAWN_HEIGHT
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)
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spawn_objects_in_given_area(
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grid=self.grid,
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object_alias=MAP_ALIASES.get("CASTLE"),
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objects_count=4,
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spawn_position_start=Position(row=CASTLE_SPAWN_FIRST_ROW, col=CASTLE_SPAWN_FIRST_COL),
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width=2,
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height=2
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)
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self.waters = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("WATER"),
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objects_count=WATER_COUNT)
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self.trees = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("TREE"),
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objects_count=TREE_COUNT)
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self.sands = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("SAND"),
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objects_count=SAND_COUNT)
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self.monsters = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("MONSTER"),
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objects_count=MONSTERS_COUNT)
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self.sand_islands = get_islands(self.grid, self.sands)
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self.tree_islands = get_islands(self.grid, self.trees)
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self.water_islands = get_islands(self.grid, self.waters)
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def update_map(self):
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self.sands, self.trees, self.waters, self.monsters = get_tiles_positions(self.grid)
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self.sand_islands = get_islands(self.grid, self.sands)
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self.tree_islands = get_islands(self.grid, self.trees)
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self.water_islands = get_islands(self.grid, self.waters)
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def calc_fitness(self):
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score = SAND_COUNT + TREE_COUNT + WATER_COUNT
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score = score - len(self.sand_islands) - len(self.tree_islands) - len(self.water_islands)
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sands, trees, waters, monsters = get_tiles_positions(self.grid)
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||||
if len(monsters) != MONSTERS_COUNT:
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||||
self.fitness = 0
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||||
return
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||||
if len(sands) < SAND_COUNT or len(trees) < TREE_COUNT or len(waters) < WATER_COUNT:
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||||
self.fitness = 5
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return
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||||
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self.fitness = score
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||||
def crossover(self, partner):
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# replace a randomly selected part of the grid with partner's part
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child = Genome()
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||||
child.grid = deepcopy(self.grid)
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area_to_cross = random.choice(AREAS_TO_CROSS)
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||||
|
||||
for row in range(area_to_cross.position.row, area_to_cross.position.row + area_to_cross.height):
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||||
for col in range(area_to_cross.position.col, area_to_cross.position.col + area_to_cross.width):
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child.grid[row][col] = partner.grid[row][col]
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||||
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||||
child.update_map()
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||||
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||||
return child
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||||
def mutate(self, mutation_rate: float):
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||||
# remove 1 item from a random island and add a neighbor to another island
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||||
if random.random() < mutation_rate:
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||||
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||||
# select islands of the same, random type
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||||
islands_of_same_type = random.choice([self.sand_islands, self.tree_islands, self.water_islands])
|
||||
random_index = random.randint(0, len(islands_of_same_type) - 1)
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||||
island = islands_of_same_type[random_index]
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||||
next_island = islands_of_same_type[(random_index + 1) % len(islands_of_same_type)]
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||||
|
||||
free_tiles_nearby = find_neighbours(self.grid, next_island.col, next_island.row)
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||||
|
||||
tile_type = self.grid[island.row][island.col]
|
||||
self.grid[island.row][island.col] = MAP_ALIASES.get('GRASS')
|
||||
|
||||
# todo: if there are no free tiles around then randomize another next_island
|
||||
if len(free_tiles_nearby) > 0:
|
||||
random_free_tile = random.choice(free_tiles_nearby)
|
||||
island.row = random_free_tile.row
|
||||
island.col = random_free_tile.col
|
||||
self.grid[island.row][island.col] = tile_type
|
||||
|
||||
self.update_map()
|
||||
|
||||
|
||||
def is_empty(grid: npt.NDArray, position: Position) -> bool:
|
||||
return grid[position.row, position.col] in [MAP_ALIASES.get("GRASS"), MAP_ALIASES.get("SAND")]
|
||||
|
||||
|
||||
def is_invalid_area(spawn_position_start, height, width) -> bool:
|
||||
return spawn_position_start.row + height - 1 < 0 or \
|
||||
spawn_position_start.row + height - 1 >= ROWS or \
|
||||
spawn_position_start.col + width - 1 < 0 or \
|
||||
spawn_position_start.col + width - 1 >= COLUMNS
|
||||
|
||||
|
||||
def spawn_objects_in_given_area(grid: npt.NDArray,
|
||||
object_alias: str,
|
||||
objects_count: int = 1,
|
||||
spawn_position_start: Position = Position(row=0, col=0),
|
||||
width: int = COLUMNS,
|
||||
height: int = ROWS) -> List[Position]:
|
||||
if is_invalid_area(spawn_position_start, height, width):
|
||||
raise ValueError("Invalid spawn area")
|
||||
|
||||
objects_remaining = int(objects_count)
|
||||
positions = []
|
||||
|
||||
while objects_remaining > 0:
|
||||
row = randrange(spawn_position_start.row, spawn_position_start.row + height)
|
||||
col = randrange(spawn_position_start.col, spawn_position_start.col + width)
|
||||
position = Position(row=row, col=col)
|
||||
|
||||
if is_empty(grid=grid, position=position):
|
||||
grid[position.row, position.col] = object_alias
|
||||
positions.append(position)
|
||||
objects_remaining -= 1
|
||||
|
||||
return positions
|
26
algorithms/genetic/map_generator.py
Normal file
26
algorithms/genetic/map_generator.py
Normal file
@ -0,0 +1,26 @@
|
||||
from algorithms.genetic.genome import Genome
|
||||
from algorithms.genetic.map_importer_exporter import export_map
|
||||
from population import Population
|
||||
|
||||
|
||||
def main() -> None:
|
||||
population_size = 500
|
||||
mutation_rate = 0.3
|
||||
|
||||
population = Population(mutation_rate, population_size, 55)
|
||||
|
||||
while not population.evaluate():
|
||||
# create next generation
|
||||
population.generate()
|
||||
|
||||
# calc fitness
|
||||
population.calc_fitness()
|
||||
|
||||
print(population.best_genome.grid)
|
||||
print("Fitness of the best: ", population.best_genome.fitness)
|
||||
|
||||
export_map(population.best_genome.grid)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
42
algorithms/genetic/map_importer_exporter.py
Normal file
42
algorithms/genetic/map_importer_exporter.py
Normal file
@ -0,0 +1,42 @@
|
||||
import json
|
||||
import random
|
||||
import string
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
import numpy
|
||||
import numpy.typing as npt
|
||||
from os import listdir
|
||||
from os.path import isfile, join
|
||||
|
||||
|
||||
# Save map to file
|
||||
def export_map(grid: npt.NDArray):
|
||||
json_data = {"map": grid.tolist()}
|
||||
|
||||
now = datetime.now()
|
||||
file_name = "map_" + now.strftime("%Y_%m_%d_%H_%M_%S") + ".json"
|
||||
path = Path("../../resources/maps/")
|
||||
file_to_open = path / file_name
|
||||
|
||||
with open(file_to_open, "w+") as write_file:
|
||||
json.dump(json_data, write_file)
|
||||
print("Saved map to file " + file_name)
|
||||
|
||||
|
||||
def import_random_map() -> object:
|
||||
path = "resources/maps"
|
||||
files = [f for f in listdir(path) if isfile(join(path, f))]
|
||||
random_map_name = random.choice(files)
|
||||
return import_map(random_map_name)
|
||||
|
||||
|
||||
# Read map from file
|
||||
def import_map(file_name: string) -> object:
|
||||
file_to_open = "resources/maps/" + file_name
|
||||
with open(file_to_open, "r") as read_file:
|
||||
print("Reading map from file " + file_name)
|
||||
decoded_json = json.load(read_file)
|
||||
|
||||
decoded_grid = numpy.asarray(decoded_json["map"])
|
||||
print(decoded_grid)
|
||||
return decoded_grid.tolist()
|
81
algorithms/genetic/population.py
Normal file
81
algorithms/genetic/population.py
Normal file
@ -0,0 +1,81 @@
|
||||
import random
|
||||
from typing import List
|
||||
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
|
||||
from genome import Genome
|
||||
|
||||
|
||||
class Population:
|
||||
population: List[Genome] = [] # array to hold the current population
|
||||
mating_pool: List[Genome] = [] # array which we will use for our "mating pool"
|
||||
generations: int = 0 # number of generations
|
||||
finished: bool = False # are we finished evolving?
|
||||
mutation_rate: float
|
||||
perfect_score: int
|
||||
best_genome: Genome
|
||||
|
||||
def __init__(self, mutation_rate, population_size, perfect_score=20):
|
||||
self.mutation_rate = mutation_rate
|
||||
self.perfect_score = perfect_score
|
||||
|
||||
for i in range(0, population_size):
|
||||
new_genome = Genome()
|
||||
new_genome.calc_fitness()
|
||||
self.population.append(new_genome)
|
||||
|
||||
# create a new generation
|
||||
def generate(self):
|
||||
max_fitness = 0
|
||||
for genome in self.population:
|
||||
if genome.fitness > max_fitness:
|
||||
max_fitness = genome.fitness
|
||||
|
||||
print("Max fitness of generation " + str(self.generations) + " = " + str(max_fitness))
|
||||
|
||||
# refill the population with children from the mating pool
|
||||
new_population = []
|
||||
for genome in self.population:
|
||||
partner_a = self.accept_reject(max_fitness)
|
||||
partner_b = self.accept_reject(max_fitness)
|
||||
child = partner_a.crossover(partner_b)
|
||||
child.mutate(self.mutation_rate)
|
||||
new_population.append(child)
|
||||
|
||||
self.population = new_population
|
||||
self.generations += 1
|
||||
|
||||
# select random with correct probability from population
|
||||
def accept_reject(self, max_fitness: int):
|
||||
safe_flag = 0
|
||||
|
||||
while safe_flag < 10000:
|
||||
partner = random.choice(self.population)
|
||||
r = random.randint(0, max_fitness)
|
||||
|
||||
if r < partner.fitness:
|
||||
return partner
|
||||
|
||||
safe_flag += 1
|
||||
|
||||
# compute the current "most fit" member of the population
|
||||
def evaluate(self):
|
||||
record = 0
|
||||
best_index = 0
|
||||
|
||||
for index in range(len(self.population)):
|
||||
genome = self.population[index]
|
||||
if genome.fitness > record:
|
||||
record = genome.fitness
|
||||
best_index = index
|
||||
|
||||
self.best_genome = self.population[best_index]
|
||||
if record >= self.perfect_score:
|
||||
self.finished = True
|
||||
|
||||
return self.finished
|
||||
|
||||
def calc_fitness(self):
|
||||
for genome in self.population:
|
||||
genome.calc_fitness()
|
0
algorithms/neural_network/__init__.py
Normal file
0
algorithms/neural_network/__init__.py
Normal file
@ -6,7 +6,7 @@ GAME_TITLE = 'WMICraft'
|
||||
WINDOW_HEIGHT = 800
|
||||
WINDOW_WIDTH = 1360
|
||||
FPS_COUNT = 60
|
||||
TURN_INTERVAL = 300
|
||||
TURN_INTERVAL = 500
|
||||
|
||||
GRID_CELL_PADDING = 5
|
||||
GRID_CELL_SIZE = 36
|
||||
|
@ -1,6 +1,8 @@
|
||||
from typing import Tuple, List
|
||||
|
||||
import pygame
|
||||
|
||||
from algorithms.genetic.const import MAP_ALIASES
|
||||
from common.constants import GRID_CELL_PADDING, GRID_CELL_SIZE, COLUMNS, ROWS, CLASSES, CLASS_TO_ID
|
||||
import csv
|
||||
import os
|
||||
@ -99,7 +101,7 @@ def castle_neighbors(map, castle_bottom_right_row, castle_bottom_right_col):
|
||||
return neighbors
|
||||
|
||||
|
||||
def find_neighbours(grid: List[List[str]], col: int, row: int) -> List[Tuple[int, int]]:
|
||||
def find_neighbours(grid: List[List[int]], col: int, row: int) -> List[Tuple[int, int]]:
|
||||
dr = [-1, 1, 0, 0]
|
||||
dc = [0, 0, -1, 1]
|
||||
|
||||
@ -111,7 +113,7 @@ def find_neighbours(grid: List[List[str]], col: int, row: int) -> List[Tuple[int
|
||||
|
||||
if rr < 0 or cc < 0: continue
|
||||
if rr >= ROWS or cc >= COLUMNS: continue
|
||||
if grid[rr][cc] not in ['g', 's', '.']: continue
|
||||
if grid[rr][cc] not in [MAP_ALIASES.get("GRASS"), MAP_ALIASES.get("SAND"), '.']: continue
|
||||
neighbours.append((rr, cc))
|
||||
|
||||
return neighbours
|
||||
|
@ -34,7 +34,7 @@ class DecisionTree:
|
||||
self.model = DecisionTreeClassifier(criterion='entropy')
|
||||
self.model.fit(self.train_set.values, self.goals)
|
||||
|
||||
def predict_move(self, grid: List[List[str]], current_knight: Knight, castle: Castle, monsters: List[Monster],
|
||||
def predict_move(self, grid: List[List[int]], current_knight: Knight, castle: Castle, monsters: List[Monster],
|
||||
opponents: List[Knight]) -> \
|
||||
List[Tuple[int, int]]:
|
||||
distance_to_castle = manhattan_distance(current_knight.position, castle.position)
|
||||
@ -42,14 +42,15 @@ class DecisionTree:
|
||||
monsters_parsed = []
|
||||
for monster in monsters:
|
||||
monsters_parsed.append((manhattan_distance(current_knight.position, monster.position), parse_hp(
|
||||
monster.current_hp)))
|
||||
monster.health_bar.current_hp)))
|
||||
|
||||
opponents_parsed = []
|
||||
for opponent in opponents:
|
||||
opponents_parsed.append(
|
||||
(manhattan_distance(current_knight.position, opponent.position), parse_hp(opponent.health_bar.current_hp)))
|
||||
(manhattan_distance(current_knight.position, opponent.position),
|
||||
parse_hp(opponent.health_bar.current_hp)))
|
||||
|
||||
prediction = self.get_prediction(tower_dist=distance_to_castle, tower_hp=castle.current_hp,
|
||||
prediction = self.get_prediction(tower_dist=distance_to_castle, tower_hp=castle.health_bar.current_hp,
|
||||
mob1_dist=monsters_parsed[0][0], mob1_hp=monsters_parsed[0][1],
|
||||
mob2_dist=monsters_parsed[1][0], mob2_hp=monsters_parsed[1][1],
|
||||
opp1_dist=opponents_parsed[0][0], opp1_hp=opponents_parsed[0][1],
|
||||
@ -57,7 +58,7 @@ class DecisionTree:
|
||||
opp3_dist=opponents_parsed[2][0], opp3_hp=opponents_parsed[2][1],
|
||||
opp4_dist=opponents_parsed[3][0], opp4_hp=opponents_parsed[3][1],
|
||||
agent_hp=current_knight.health_bar.current_hp)
|
||||
print(prediction)
|
||||
print(f'Prediction = {prediction}')
|
||||
if prediction == 'tower': # castle...
|
||||
return castle_neighbors(grid, castle_bottom_right_row=castle.position[0],
|
||||
castle_bottom_right_col=castle.position[1])
|
||||
|
@ -3,10 +3,11 @@ import random
|
||||
import pygame
|
||||
|
||||
from algorithms.a_star import a_star, State, TURN_RIGHT, TURN_LEFT, FORWARD
|
||||
from algorithms.genetic.const import MAP_ALIASES
|
||||
from algorithms.genetic.map_importer_exporter import import_random_map
|
||||
from common.constants import *
|
||||
from learning.decision_tree import DecisionTree
|
||||
from logic.knights_queue import KnightsQueue
|
||||
from logic.spawner import Spawner
|
||||
from models.castle import Castle
|
||||
from models.knight import Knight
|
||||
from models.monster import Monster
|
||||
@ -21,7 +22,7 @@ class Level:
|
||||
# sprite group setup
|
||||
self.sprites = pygame.sprite.LayeredUpdates()
|
||||
|
||||
self.map = [['g' for _ in range(COLUMNS)] for y in range(ROWS)]
|
||||
self.map = []
|
||||
|
||||
self.list_knights_blue = []
|
||||
self.list_knights_red = []
|
||||
@ -31,27 +32,11 @@ class Level:
|
||||
self.knights_queue = None
|
||||
|
||||
def create_map(self):
|
||||
self.generate_map()
|
||||
self.map = import_random_map()
|
||||
self.setup_base_tiles()
|
||||
self.setup_objects()
|
||||
self.knights_queue = KnightsQueue(self.list_knights_blue, self.list_knights_red)
|
||||
|
||||
def generate_map(self):
|
||||
spawner = Spawner(self.map)
|
||||
spawner.spawn_where_possible(['w' for _ in range(NBR_OF_WATER)])
|
||||
spawner.spawn_where_possible(['t' for _ in range(NBR_OF_TREES)])
|
||||
spawner.spawn_where_possible(['s' for _ in range(NBR_OF_SANDS)])
|
||||
|
||||
spawner.spawn_in_area(['k_b' for _ in range(4)], LEFT_KNIGHTS_SPAWN_FIRST_ROW, LEFT_KNIGHTS_SPAWN_FIRST_COL,
|
||||
KNIGHTS_SPAWN_WIDTH, KNIGHTS_SPAWN_HEIGHT)
|
||||
spawner.spawn_in_area(['k_r' for _ in range(4)], RIGHT_KNIGHTS_SPAWN_FIRST_ROW, RIGHT_KNIGHTS_SPAWN_FIRST_COL,
|
||||
KNIGHTS_SPAWN_WIDTH, KNIGHTS_SPAWN_HEIGHT)
|
||||
|
||||
spawner.spawn_in_area(['c'], CASTLE_SPAWN_FIRST_ROW, CASTLE_SPAWN_FIRST_COL, CASTLE_SPAWN_WIDTH,
|
||||
CASTLE_SPAWN_HEIGHT, 2)
|
||||
|
||||
spawner.spawn_where_possible(['m' for _ in range(NBR_OF_MONSTERS)])
|
||||
|
||||
def setup_base_tiles(self):
|
||||
textures = []
|
||||
for texture_path in TILES:
|
||||
@ -63,15 +48,15 @@ class Level:
|
||||
for col_index, col in enumerate(row):
|
||||
|
||||
# add base tiles, e.g. water, tree, grass
|
||||
if col == "w":
|
||||
if col == MAP_ALIASES.get('WATER'):
|
||||
texture_index = 5
|
||||
texture_surface = textures[texture_index][1]
|
||||
Tile((col_index, row_index), texture_surface, self.sprites, 'w')
|
||||
elif col == "t":
|
||||
elif col == MAP_ALIASES.get('TREE'):
|
||||
texture_index = 6
|
||||
texture_surface = textures[texture_index][1]
|
||||
Tile((col_index, row_index), texture_surface, self.sprites, 't')
|
||||
elif col == "s":
|
||||
elif col == MAP_ALIASES.get('SAND'):
|
||||
texture_index = 4
|
||||
texture_surface = textures[texture_index][1]
|
||||
Tile((col_index, row_index), texture_surface, self.sprites)
|
||||
@ -88,19 +73,19 @@ class Level:
|
||||
for col_index, col in enumerate(row):
|
||||
|
||||
# add objects, e.g. knights, monsters, castle
|
||||
if col == "k_b":
|
||||
if col == MAP_ALIASES.get('KNIGHT_BLUE'):
|
||||
knight = Knight(self.screen, (col_index, row_index), self.sprites, "blue")
|
||||
self.map[row_index][col_index] = knight
|
||||
self.list_knights_blue.append(knight)
|
||||
elif col == "k_r":
|
||||
elif col == MAP_ALIASES.get('KNIGHT_RED'):
|
||||
knight = Knight(self.screen, (col_index, row_index), self.sprites, "red")
|
||||
self.map[row_index][col_index] = knight
|
||||
self.list_knights_red.append(knight)
|
||||
elif col == "m":
|
||||
elif col == MAP_ALIASES.get('MONSTER'):
|
||||
monster = Monster(self.screen, (col_index, row_index), self.sprites)
|
||||
self.map[row_index][col_index] = monster
|
||||
self.list_monsters.append(monster)
|
||||
elif col == "c":
|
||||
elif col == MAP_ALIASES.get('CASTLE'):
|
||||
castle_count += 1
|
||||
if castle_count == 4:
|
||||
castle = Castle(self.screen, (col_index, row_index), self.sprites)
|
||||
@ -108,15 +93,25 @@ class Level:
|
||||
self.list_castles.append(castle)
|
||||
|
||||
def handle_turn(self):
|
||||
print("next turn")
|
||||
current_knight = self.knights_queue.dequeue_knight()
|
||||
knights_list = self.list_knights_red + self.list_knights_blue
|
||||
print("next turn " + current_knight.team)
|
||||
knight_pos_x = current_knight.position[0]
|
||||
knight_pos_y = current_knight.position[1]
|
||||
positions = []
|
||||
op_pos_1 = current_knight.position[0] - 1, current_knight.position[1]
|
||||
positions.append(op_pos_1)
|
||||
op_pos_2 = current_knight.position[0], current_knight.position[1] - 1
|
||||
positions.append(op_pos_2)
|
||||
op_pos_3 = current_knight.position[0] + 1, current_knight.position[1]
|
||||
positions.append(op_pos_3)
|
||||
op_pos_4 = current_knight.position[0], current_knight.position[1] + 1
|
||||
positions.append(op_pos_4)
|
||||
|
||||
goal_list = self.decision_tree.predict_move(grid=self.map, current_knight=current_knight,
|
||||
monsters=self.list_monsters,
|
||||
opponents=self.list_knights_red
|
||||
if current_knight.team_alias == 'k_r' else self.list_knights_blue,
|
||||
opponents=self.list_knights_blue
|
||||
if current_knight.team_alias() == 'k_r' else self.list_knights_red,
|
||||
castle=self.list_castles[0])
|
||||
|
||||
if len(goal_list) == 0:
|
||||
@ -131,6 +126,11 @@ class Level:
|
||||
return
|
||||
|
||||
next_action = action_list.pop(0)
|
||||
for some_knight in knights_list:
|
||||
for some_position in positions:
|
||||
if some_knight.position == some_position:
|
||||
some_knight.health_bar.take_dmg(1)
|
||||
|
||||
if next_action == TURN_LEFT:
|
||||
self.logs.enqueue_log(f'AI {current_knight.team}: Obrót w lewo.')
|
||||
current_knight.rotate_left()
|
||||
@ -139,7 +139,7 @@ class Level:
|
||||
current_knight.rotate_right()
|
||||
elif next_action == FORWARD:
|
||||
current_knight.step_forward()
|
||||
self.map[knight_pos_y][knight_pos_x] = 'g'
|
||||
self.map[knight_pos_y][knight_pos_x] = MAP_ALIASES.get("GRASS")
|
||||
|
||||
# update knight on map
|
||||
if current_knight.direction.name == UP:
|
||||
|
@ -37,7 +37,7 @@ class Knight(pygame.sprite.Sprite):
|
||||
self.attack = random.randint(4, 7)
|
||||
self.defense = random.randint(1, 4)
|
||||
self.points = 1
|
||||
self.health_bar = HealthBar(screen, self.rect, current_hp=random.randint(1, self.max_hp), max_hp=self.max_hp, calculate_xy=True, calculate_size=True)
|
||||
self.health_bar = HealthBar(screen, self.rect, current_hp=self.max_hp, max_hp=self.max_hp, calculate_xy=True, calculate_size=True)
|
||||
|
||||
def rotate_left(self):
|
||||
self.direction = self.direction.left()
|
||||
|
BIN
requirements.txt
BIN
requirements.txt
Binary file not shown.
1
resources/maps/map_2022_06_02_13_27_18.json
Normal file
1
resources/maps/map_2022_06_02_13_27_18.json
Normal file
@ -0,0 +1 @@
|
||||
{"map": [[0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], [0, 0, 0, 0, 3, 3, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3], [0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 3], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 4, 0, 0, 0, 2, 0, 3], [0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2], [0, 0, 3, 3, 3, 0, 0, 0, 3, 3, 3, 3, 0, 2, 2, 2, 0, 0, 0, 0, 0, 7, 2, 0], [0, 0, 0, 6, 0, 0, 0, 2, 2, 2, 0, 5, 5, 0, 2, 0, 0, 2, 2, 2, 2, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 5, 5, 0, 2, 0, 0, 0, 0, 2, 2, 1, 1, 7], [0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 3, 1, 7], [6, 0, 0, 6, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 1, 1, 0], [6, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 3, 3, 0, 0, 0, 0, 3, 3, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 0, 1, 1, 7, 0], [0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 3, 1, 1, 1, 0], [0, 3, 3, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 1, 0, 0], [0, 3, 3, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 4, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
|
1
resources/maps/map_2022_06_06_14_55_49.json
Normal file
1
resources/maps/map_2022_06_06_14_55_49.json
Normal file
@ -0,0 +1 @@
|
||||
{"map": [[0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0], [0, 3, 3, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 3, 0, 0, 0, 2, 0, 0, 0, 0, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 2, 2, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 2, 2, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 3, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 6, 3, 3, 0, 0, 0, 0, 0, 3, 0, 4, 0, 3, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 3, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 7, 7, 0, 0], [6, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 3, 7, 0, 0, 0], [0, 0, 6, 6, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0], [0, 2, 2, 2, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0], [2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 2, 0, 0, 0, 0, 0, 4, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
|
1
resources/maps/map_2022_06_06_14_58_11.json
Normal file
1
resources/maps/map_2022_06_06_14_58_11.json
Normal file
@ -0,0 +1 @@
|
||||
{"map": [[0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 3], [0, 0, 0, 0, 2, 0, 0, 0, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 3, 0], [0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 3, 0, 0, 0, 2, 0], [0, 0, 3, 3, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 2, 0], [0, 0, 3, 0, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 3, 0], [0, 0, 3, 3, 3, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 6, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0], [0, 6, 0, 0, 0, 0, 3, 0, 0, 0, 0, 5, 5, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 5, 5, 1, 2, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 4, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 3, 3, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 4], [6, 0, 0, 6, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 1, 1, 0, 7, 0, 7, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 3, 3, 2, 0, 0, 0, 0, 0, 0], [0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0], [0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
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