#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import interface as api
import utils as u
import algorithms as algo
from time import time
from random import randrange
IAName = "OPT OPTIMISATION"
(mazeWidth, mazeHeight, mazeMap, preparationTime, turnTime, playerLocation,
opponentLocation, coins, gameIsOver) = api.initGame(IAName)
coinsNumber = len(coins) + 1
route = []
old_coins = [playerLocation]
def dists_from_each(locations, maze_map):
""" Return the dists and routes from each locations
TODO :
- Cache routes and dists
"""
dists_matrix = {l: {} for l in locations}
routes_matrix = {l: {} for l in locations}
for i in range(len(locations)):
l1 = locations[i]
routing_table, dists_table = algo.dijkstra(maze_map, l1)
for j in range(i + 1, len(locations)):
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import interface
import utils as u
# import algorithms as algo
import random
# Version 1
# This version is only based on instant distances, doesn't take account
# for the enemy future movements
IAName = "probas"
(mazeWidth, mazeHeight, mazeMap, preparationTime, turnTime, playerLocation, opponentLocation, coins, gameIsOver) = interface.initGame(IAName)
route = []
dists_matrix, route_matrix = [], []
coins_probability = {}
coins_to_get = []
best_weight = float('inf')
best_path = []
def closer_probability(player_location, enemy_location, coins, dists_matrix):
"""Return the probability that the player will get a coin based only on
the player distance to the coin and the enemy distance to the coin
return a dictionary coin : probability """
probabilities = {}
for coin in coins:
coin_value = dists_matrix[enemy_location][coin] / float(dists_matrix[enemy_location][coin] + dists_matrix[player_location][coin])
probabilities[coin] = coin_value
return probabilities
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import interface
import utils as u
# import algorithms as algo
import random
# Version 1
# This version is only based on instant distances, doesn't take account
# for the enemy future movements
IAName = "probas"
(mazeWidth, mazeHeight, mazeMap, preparationTime, turnTime, playerLocation,
opponentLocation, coins, gameIsOver) = interface.initGame(IAName)
route = []
dists_matrix, route_matrix = [], []
coins_probability = {}
coins_to_get = []
best_weight = float('inf')
best_path = []
def closer_probability(player_location, enemy_location, coins, dists_matrix):
"""Return the probability that the player will get a coin based only on
the player distance to the coin and the enemy distance to the coin
return a dictionary coin : probability """
probabilities = {}
for coin in coins:
coin_value = dists_matrix[enemy_location][coin] / float(
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import interface as api
import utils as u
# import algorithms as algo
IAName = "Improved closest"
(mazeWidth, mazeHeight, mazeMap, preparationTime, turnTime, playerLocation, opponentLocation, coins, gameIsOver) = api.initGame(IAName)
coinsNumber = len(coins) + 1
route = []
best_weight = float("inf")
best_path = []
currentcoin = "gfdsf"
def get_n_shortest(n, coins, playerLocation, dist_matrix):
sorted(coins, key=lambda x: dist_matrix[playerLocation][x])
return coins[:n]
def exhaustive(left, node, path, weight, coins_graph):
global best_weight, best_path
if len(left) == 0:
if weight < best_weight:
best_weight = weight
best_path[:] = path
else: