add-pickle #3
12
src/const.py
12
src/const.py
@ -1,4 +1,5 @@
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import os
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import os
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import pickle
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import pygame as pg
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import pygame as pg
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@ -54,7 +55,7 @@ for name, val in {
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val,
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val,
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name,
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name,
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pg.image.load(
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pg.image.load(
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main_path + '/images/Tiles/' + folder_name + ' Tiles/' + name + ".png"
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f'{main_path}/images/Tiles/{folder_name} Tiles/{name}.png'
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),
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),
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None if 'mine' not in name else (
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None if 'mine' not in name else (
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'AP' if 'AP' in name else (
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'AP' if 'AP' in name else (
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@ -64,7 +65,7 @@ for name, val in {
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)
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)
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)
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)
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ICON = main_path + '/images/mine_icon.png'
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ICON = f'{main_path}/images/mine_icon.png'
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SAPPER_IDLE = []
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SAPPER_IDLE = []
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for name in [
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for name in [
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@ -74,7 +75,7 @@ for name in [
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'agent3',
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'agent3',
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]:
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]:
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SAPPER_IDLE.append(pg.image.load(
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SAPPER_IDLE.append(pg.image.load(
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main_path + '/images/agent/agent_idle/' + name + '.png'
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f'{main_path}/images/agent/agent_idle/{name}.png'
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))
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))
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SAPPER_KABOOM = []
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SAPPER_KABOOM = []
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@ -85,7 +86,10 @@ for name in [
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'agent_kaboom_3',
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'agent_kaboom_3',
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]:
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]:
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SAPPER_KABOOM.append(pg.image.load(
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SAPPER_KABOOM.append(pg.image.load(
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main_path + '/images/agent/agent_kaboom/' + name + '.png'
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f'{main_path}/images/agent/agent_kaboom/{name}.png'
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))
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))
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ROCK_INDEXES = (5, 6, 23, 24)
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ROCK_INDEXES = (5, 6, 23, 24)
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with open(f'{main_path}/src/machine_learning/decision_tree.pkl', 'rb') as file:
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TREE_ROOT = pickle.load(file)
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BIN
src/machine_learning/decision_tree.pkl
Normal file
BIN
src/machine_learning/decision_tree.pkl
Normal file
Binary file not shown.
@ -1,3 +1,4 @@
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import pickle
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from math import log
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from math import log
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from typing import List, Dict
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from typing import List, Dict
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from collections import Counter
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from collections import Counter
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@ -70,6 +71,7 @@ def get_decision(data: dict, root: Node) -> str:
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def main():
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def main():
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tree_root = tree_learn(training_set, attribs, 'detonation')
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print(RenderTree(tree_root))
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print(RenderTree(tree_root))
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print('-' * 150)
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print('-' * 150)
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@ -82,9 +84,8 @@ def main():
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score += 1
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score += 1
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print(f'Accuracy: {score/len(test_set)}')
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print(f'Accuracy: {score/len(test_set)}')
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with open('decision_tree.pkl', 'wb') as file:
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pickle.dump(tree_root, file, pickle.HIGHEST_PROTOCOL)
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tree_root = tree_learn(training_set, attribs, 'detonation')
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if __name__ == "__main__":
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if __name__ == "__main__":
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@ -2,13 +2,13 @@ import pygame as pg
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from agent import Agent
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from agent import Agent
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from game_ui import GameUi
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from game_ui import GameUi
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from const import ICON, IMAGES
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from environment import Environment
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from environment import Environment
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from tilesFactory import TilesFactory
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from tilesFactory import TilesFactory
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from const import ICON, IMAGES, TREE_ROOT
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from src.search_algoritms.a_star import a_star
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from src.search_algoritms.a_star import a_star
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from search_algoritms.BFS import breadth_first_search
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from search_algoritms.BFS import breadth_first_search
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from machine_learning.decision_tree import get_decision
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from machine_learning.helpers import get_dataset_from_tile
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from machine_learning.helpers import get_dataset_from_tile
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from machine_learning.decision_tree import get_decision, tree_root
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def handle_keys(env: Environment, agent: Agent, game_ui: GameUi, factory: TilesFactory):
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def handle_keys(env: Environment, agent: Agent, game_ui: GameUi, factory: TilesFactory):
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@ -33,7 +33,7 @@ def handle_keys(env: Environment, agent: Agent, game_ui: GameUi, factory: TilesF
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tile = env.field[agent.y][agent.x]
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tile = env.field[agent.y][agent.x]
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dataset = get_dataset_from_tile(tile)
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dataset = get_dataset_from_tile(tile)
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decision = get_decision(dataset, tree_root)
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decision = get_decision(dataset, TREE_ROOT)
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print(f'Data: {dataset}')
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print(f'Data: {dataset}')
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print(f'Decision: {decision}')
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print(f'Decision: {decision}')
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