Merge pull request 'genetic_alg_angela' (#31) from genetic_alg_angela into genetic_alg

Reviewed-on: s464965/WMICraft#31
This commit is contained in:
Angelika Iskra 2022-06-02 00:52:53 +02:00
commit ee7a324e5d
13 changed files with 250 additions and 23 deletions

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@ -1,4 +1,7 @@
from dataclasses import dataclass from dataclasses import dataclass
import numpy as np
from const import * from const import *
from typing import List, Dict, Tuple from typing import List, Dict, Tuple
@ -11,6 +14,56 @@ class Position:
col: int col: int
@dataclass
class Area:
position: Position
width: int
height: int
AREAS_TO_CROSS = [
# up above left knights spawn
Area(position=Position(row=0, col=0),
width=KNIGHTS_SPAWN_WIDTH,
height=LEFT_KNIGHTS_SPAWN_FIRST_ROW),
# down below left knights spawn
Area(position=Position(row=LEFT_KNIGHTS_SPAWN_FIRST_ROW + KNIGHTS_SPAWN_HEIGHT, col=0),
width=KNIGHTS_SPAWN_WIDTH,
height=ROWS - LEFT_KNIGHTS_SPAWN_FIRST_ROW - KNIGHTS_SPAWN_HEIGHT),
# between left knights spawn and castle
Area(position=Position(row=0, col=KNIGHTS_SPAWN_WIDTH),
width=CASTLE_SPAWN_FIRST_COL - KNIGHTS_SPAWN_WIDTH,
height=ROWS),
# up above castle
Area(position=Position(row=0, col=CASTLE_SPAWN_FIRST_COL),
width=2,
height=CASTLE_SPAWN_FIRST_ROW),
# down below castle
Area(position=Position(row=CASTLE_SPAWN_FIRST_ROW + 2, col=CASTLE_SPAWN_FIRST_COL),
width=2,
height=ROWS - CASTLE_SPAWN_FIRST_ROW - 2),
# between castle and right knights spawn
Area(position=Position(row=0, col=CASTLE_SPAWN_FIRST_COL + 2),
width=RIGHT_KNIGHTS_SPAWN_FIRST_COL - CASTLE_SPAWN_FIRST_COL - 2,
height=ROWS),
# up above right knights spawn
Area(position=Position(row=0, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
width=KNIGHTS_SPAWN_WIDTH,
height=RIGHT_KNIGHTS_SPAWN_FIRST_ROW),
# down below right knights spawn
Area(position=Position(row=RIGHT_KNIGHTS_SPAWN_FIRST_ROW + KNIGHTS_SPAWN_HEIGHT, col=RIGHT_KNIGHTS_SPAWN_FIRST_COL),
width=KNIGHTS_SPAWN_WIDTH,
height=ROWS - RIGHT_KNIGHTS_SPAWN_FIRST_ROW - KNIGHTS_SPAWN_HEIGHT),
]
def dfs(grid: npt.NDArray, visited: Dict[Tuple[int, int], bool], position: Position, rows: int, cols: int) -> None: def dfs(grid: npt.NDArray, visited: Dict[Tuple[int, int], bool], position: Position, rows: int, cols: int) -> None:
visited[(position.row, position.col)] = True visited[(position.row, position.col)] = True
@ -51,3 +104,19 @@ def get_islands(grid: npt.NDArray, positions: List[Position], rows: int = ROWS,
islands += 1 islands += 1
return roots return roots
def find_neighbours(grid: npt.NDArray, col: int, row: int) -> List[Position]:
dr = [-1, 1, 0, 0]
dc = [0, 0, -1, 1]
neighbours = []
for i in range(4):
rr = row + dr[i]
cc = col + dc[i]
if 0 <= rr < ROWS and 0 <= cc < COLUMNS and grid[rr][cc] == MAP_ALIASES.get('GRASS'):
neighbours.append(Position(row=rr, col=cc))
return neighbours

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@ -13,6 +13,8 @@ LEFT_KNIGHTS_SPAWN_FIRST_ROW = 6
LEFT_KNIGHTS_SPAWN_FIRST_COL = 0 LEFT_KNIGHTS_SPAWN_FIRST_COL = 0
RIGHT_KNIGHTS_SPAWN_FIRST_ROW = 6 RIGHT_KNIGHTS_SPAWN_FIRST_ROW = 6
RIGHT_KNIGHTS_SPAWN_FIRST_COL = 20 RIGHT_KNIGHTS_SPAWN_FIRST_COL = 20
CASTLE_SPAWN_FIRST_ROW = 7
CASTLE_SPAWN_FIRST_COL = 11
# map aliases # map aliases
MAP_ALIASES = { MAP_ALIASES = {
@ -24,4 +26,4 @@ MAP_ALIASES = {
"CASTLE": 5, "CASTLE": 5,
"KNIGHT_RED": 6, "KNIGHT_RED": 6,
"KNIGHT_BLUE": 7, "KNIGHT_BLUE": 7,
} }

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@ -1,10 +1,13 @@
import math
import random
from copy import deepcopy
from random import randrange from random import randrange
from typing import List from typing import List
import numpy as np import numpy as np
import numpy.typing as npt import numpy.typing as npt
from common import Position from common import Position, get_islands, AREAS_TO_CROSS, find_neighbours
from const import * from const import *
@ -16,11 +19,15 @@ class Genome:
trees: List[Position] trees: List[Position]
sands: List[Position] sands: List[Position]
monsters: List[Position] monsters: List[Position]
fitness: int
sand_islands: List[Position]
tree_islands: List[Position]
water_islands: List[Position]
def __init__(self): def __init__(self):
self.grid = np.zeros((ROWS, COLUMNS), dtype=int) self.grid = np.zeros((ROWS, COLUMNS), dtype=int)
self.fitness = 0
# todo: spawning castle, monsters, sand, trees in the same way as bellow
self.knights_red = spawn_objects_in_given_area( self.knights_red = spawn_objects_in_given_area(
grid=self.grid, grid=self.grid,
object_alias=MAP_ALIASES.get("KNIGHT_RED"), object_alias=MAP_ALIASES.get("KNIGHT_RED"),
@ -43,7 +50,7 @@ class Genome:
grid=self.grid, grid=self.grid,
object_alias=MAP_ALIASES.get("CASTLE"), object_alias=MAP_ALIASES.get("CASTLE"),
objects_count=4, objects_count=4,
spawn_position_start=Position(row=7, col=9), spawn_position_start=Position(row=CASTLE_SPAWN_FIRST_ROW, col=CASTLE_SPAWN_FIRST_COL),
width=2, width=2,
height=2 height=2
) )
@ -57,6 +64,62 @@ class Genome:
self.monsters = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("MONSTER"), self.monsters = spawn_objects_in_given_area(grid=self.grid, object_alias=MAP_ALIASES.get("MONSTER"),
objects_count=MONSTERS_COUNT) objects_count=MONSTERS_COUNT)
self.sand_islands = get_islands(self.grid, self.sands)
self.tree_islands = get_islands(self.grid, self.trees)
self.water_islands = get_islands(self.grid, self.waters)
def calc_fitness(self):
score = SAND_COUNT + TREE_COUNT + WATER_COUNT
score = score - len(self.sand_islands) - len(self.tree_islands) - len(self.water_islands)
# todo: odległość manhattan od każdej wyspy
self.fitness = score
def crossover(self, partner):
# replace a randomly selected part of the grid with partner's part
child = Genome()
child.grid = deepcopy(self.grid)
area_to_cross = random.choice(AREAS_TO_CROSS)
for row in range(area_to_cross.position.row, area_to_cross.position.row + area_to_cross.height):
for col in range(area_to_cross.position.col, area_to_cross.position.col + area_to_cross.width):
child.grid[row][col] = partner.grid[row][col]
child.sand_islands = get_islands(self.grid, self.sands)
child.tree_islands = get_islands(self.grid, self.trees)
child.water_islands = get_islands(self.grid, self.waters)
return child
def mutate(self, mutation_rate: float):
# remove 1 item from a random island and add a neighbor to another island
if random.random() < mutation_rate:
# select islands of the same, random type
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)
island = islands_of_same_type[random_index]
next_island = islands_of_same_type[(random_index + 1) % len(islands_of_same_type)]
free_tiles_nearby = find_neighbours(self.grid, next_island.col, next_island.row)
tile_type = self.grid[island.row][island.col]
self.grid[island.row][island.col] = MAP_ALIASES.get('GRASS')
islands_of_same_type.remove(island)
# 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.sand_islands = get_islands(self.grid, self.sands)
self.tree_islands = get_islands(self.grid, self.trees)
self.water_islands = get_islands(self.grid, self.waters)
def is_empty(grid: npt.NDArray, position: Position) -> bool: def is_empty(grid: npt.NDArray, position: Position) -> bool:
return grid[position.row, position.col] in [MAP_ALIASES.get("GRASS"), MAP_ALIASES.get("SAND")] return grid[position.row, position.col] in [MAP_ALIASES.get("GRASS"), MAP_ALIASES.get("SAND")]

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@ -1,16 +1,25 @@
from map_importer_exporter import export_map from algorithms.genetic.genome import Genome
from genome import Genome from algorithms.genetic.map_importer_exporter import export_map
from common import get_islands from population import Population
def main() -> None: def main() -> None:
example_genome = Genome() population_size = 500
print(example_genome.knights_red) mutation_rate = 0.4
print(example_genome.knights_blue)
print(example_genome.grid) population = Population(mutation_rate, population_size, 27)
islands = get_islands(example_genome.grid, example_genome.knights_red)
print(f'Roots {islands} and islands count {len(islands)}') while not population.evaluate():
# export_map(example_genome.grid) fixme: FileNotFoundError # 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__': if __name__ == '__main__':

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@ -2,22 +2,24 @@ import json
import random import random
import string import string
from datetime import datetime from datetime import datetime
from pathlib import Path
import numpy import numpy
import numpy.typing as npt import numpy.typing as npt
from os import listdir from os import listdir
from os.path import isfile, join from os.path import isfile, join
# Save map to file # Save map to file
def export_map(grid: npt.NDArray): def export_map(grid: npt.NDArray):
json_data = {"map": grid.tolist()} json_data = {"map": grid.tolist()}
now = datetime.now() now = datetime.now()
file_name = "map_" + now.strftime("%Y_%m_%d_%H_%M_%S") + ".json" file_name = "map_" + now.strftime("%Y_%m_%d_%H_%M_%S") + ".json"
path = Path("../../resources/maps/")
file_to_open = path / file_name
with open("resources/maps/" + file_name, "w") as write_file: print(file_to_open)
with open(file_to_open, "w+") as write_file:
json.dump(json_data, write_file) json.dump(json_data, write_file)
print("Saved map to file " + file_name) print("Saved map to file " + file_name)
@ -30,9 +32,10 @@ def import_random_map() -> object:
# Read map from file # Read map from file
def import_map(file: string) -> object: def import_map(file_name: string) -> object:
with open("resources/maps/" + file, "r") as read_file: file_to_open = "resources/maps/" + file_name
print("Reading map from file " + file) with open(file_to_open, "r") as read_file:
print("Reading map from file " + file_name)
decoded_json = json.load(read_file) decoded_json = json.load(read_file)
decoded_grid = numpy.asarray(decoded_json["map"]) decoded_grid = numpy.asarray(decoded_json["map"])

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@ -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()

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@ -1 +0,0 @@
{"map": [[0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 4, 2, 3, 0, 0, 3, 3, 0, 3, 4, 0, 0], [0, 3, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0], [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 1, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 1, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0, 0, 3, 5, 5, 0, 0, 0, 0, 1, 0, 1, 3, 0, 0, 7, 0, 0], [2, 0, 3, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0], [6, 6, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 6, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 3, 0, 2, 2, 0, 2, 0, 3, 0, 0, 7, 0], [0, 0, 3, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0], [1, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 2, 0, 1, 0, 0, 0, 0, 0, 0], [0, 2, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 0, 0, 0, 0, 0]]}

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@ -1 +0,0 @@
{"map": [[0, 0, 0, 2, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 3, 0, 0, 0, 3, 0, 0, 3, 0, 0], [0, 0, 0, 1, 0, 0, 0, 1, 0, 4, 0, 0, 0, 0, 2, 1, 2, 3, 0, 3, 0, 0, 0, 0], [3, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0], [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0], [0, 6, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 3, 0, 0, 7, 7], [0, 0, 3, 2, 0, 0, 0, 0, 0, 5, 5, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 6, 0, 0, 0, 0, 1, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0], [0, 0, 6, 1, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 3, 0, 7, 0], [0, 6, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 1, 0, 0, 0], [0, 3, 0, 0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], [3, 0, 0, 0, 0, 0, 0, 0, 3, 0, 2, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 7, 0, 0], [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 3, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0], [0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 0, 2, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 3, 3, 0, 3, 0, 0, 0, 0, 0, 0, 2, 0, 0, 3, 0, 0, 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, 0, 0]]}

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@ -1 +0,0 @@
{"map": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 0], [1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 3, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0], [2, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [1, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 1, 3, 0, 0, 0, 0, 3, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 2, 0], [0, 1, 0, 0, 3, 0, 0, 1, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 0], [6, 0, 0, 0, 1, 2, 0, 0, 0, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 3, 0, 0, 0, 5, 5, 0, 0, 0, 0, 3, 0, 3, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 2, 2, 0, 2, 0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 7, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 0, 0, 0], [6, 2, 6, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 7, 0, 7], [0, 3, 6, 2, 0, 3, 0, 0, 0, 1, 0, 0, 3, 0, 0, 0, 2, 0, 0, 3, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0, 2, 0, 0, 3, 3, 0, 0, 0, 4, 0, 0, 0, 3, 1, 0, 0, 0], [0, 3, 2, 3, 0, 2, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0], [0, 0, 0, 0, 0, 0, 3, 3, 0, 0, 3, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}

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