def test_mrfo_hyperparams(): hyperparams = { 'S': 2.0 } new_mrfo = mrfo.MRFO(hyperparams=hyperparams) assert new_mrfo.S == 2.0
def test_mrfo_chain_foraging(): new_mrfo = mrfo.MRFO() search_space = search.SearchSpace(n_agents=5, n_iterations=20, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) chain = new_mrfo._chain_foraging(search_space.agents, search_space.best_agent.position, 1) assert chain[0] != 0
def test_mrfo_update(): def square(x): return np.sum(x**2) search_space = search.SearchSpace( n_agents=5, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10] ) new_mrfo = mrfo.MRFO() new_mrfo.update(search_space, square, 1, 10)
def test_mrfo_cyclone_foraging(): new_mrfo = mrfo.MRFO() search_space = search.SearchSpace( n_agents=5, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10] ) cyclone = new_mrfo._cyclone_foraging( search_space.agents, search_space.best_agent.position, 1, 1, 20 ) assert cyclone[0] != 0
def test_mrfo_params_setter(): new_mrfo = mrfo.MRFO() try: new_mrfo.S = 'a' except: new_mrfo.S = 2.0 try: new_mrfo.S = -1 except: new_mrfo.S = 2.0 assert new_mrfo.S == 2.0
def test_mrfo_run(): def square(x): return np.sum(x**2) def hook(optimizer, space, function): return new_function = function.Function(pointer=square) new_mrfo = mrfo.MRFO() search_space = search.SearchSpace(n_agents=5, n_iterations=20, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) history = new_mrfo.run(search_space, new_function, pre_evaluation=hook) assert len(history.agents) > 0 assert len(history.best_agent) > 0 best_fitness = history.best_agent[-1][1] assert best_fitness <= constants.TEST_EPSILON, 'The algorithm mrfo failed to converge.'
def test_mrfo_params(): params = {'S': 2.0} new_mrfo = mrfo.MRFO(params=params) assert new_mrfo.S == 2.0
def test_mrfo_somersault_foraging(): new_mrfo = mrfo.MRFO() somersault = new_mrfo._somersault_foraging(1, 1) assert somersault != 0
def test_mrfo_build(): new_mrfo = mrfo.MRFO() assert new_mrfo.built == True