def markov_predict_test(simulator, state, solution):
markov_agent = MarkovAgent(GameStateHandler(state).get_valid_positions())
score = simulator.simulate_probability_agent(markov_agent,
markov_agent.predict)
if type(solution) == int:
return score >= solution
return markov_agent.thoughts() == convert_solution(solution)
def markov_listen_test(simulator, state, solution):
markov_agent = MarkovAgent(GameStateHandler(state).get_valid_positions())
score = simulator.simulate_probability_agent(markov_agent,
markov_agent.listen)
if type(solution) == int:
return score >= solution
return roundThoughts(markov_agent.thoughts()) == roundThoughts(
convert_solution(solution))
def particle_listen_test(simulator, state, solution):
particle_agent = ParticleAgent(
GameStateHandler(state).get_valid_positions())
score = simulator.simulate_probability_agent(particle_agent,
particle_agent.listen)
if type(solution) == int:
return score >= solution
return particle_agent.thoughts() == convert_solution(solution)
def particle_reweight_test(simulator, state, solution):
# Don't actually need simulator for this test
# To make our lives simple we will match the maps open tiles (for divisibility purposes) when giving a particle count
valid_positions = GameStateHandler(state).get_valid_positions()
grid = ParticleGrid(valid_positions, len(valid_positions) * SCALE_FACTOR)
# Generate a random distribution
dist = {key: random.random() for key in grid.get_particle_distribution()}
# Reweight the particles
grid.reweight_particles(dist)
return roundThoughts(grid.get_particle_distribution()) == roundThoughts(
convert_solution(solution))
def particle_reset_test(simulator, state, solution):
# Don't actually need simulator for this test
# To make our lives simple we will match the maps open tiles (for divisibility purposes) when giving a particle count
valid_positions = GameStateHandler(state).get_valid_positions()
grid = ParticleGrid(valid_positions, len(valid_positions) * SCALE_FACTOR)
particle_dist = grid.get_particle_distribution()
# Mess up the distribution
for key in particle_dist:
particle_dist[key] = random.random()
# Reset the distribution
grid.reset()
return grid.get_particle_distribution() == convert_solution(solution)