def count_ones(self, stateCount):
stateCount = stateCount
initial = OneMax()
state = self.generate_test_state(stateCount, "CO")
problem = DiscreteOpt(length=stateCount,
fitness_fn=initial,
maximize=True)
problem.set_state(state)
return problem
def knapsack(self, max_weight_percent, stateCount):
state, weights, values = self.generate_test_state(stateCount, "KS")
initial = Knapsack(weights, values, max_weight_percent)
problem = DiscreteOpt(length=stateCount,
fitness_fn=initial,
maximize=True)
problem.set_state(state)
return problem
def four_peaks(self, threshold, stateCount):
initial = FourPeaks(t_pct=threshold)
state = self.generate_test_state(stateCount, "FP")
problem = DiscreteOpt(length=stateCount,
fitness_fn=initial,
maximize=True)
problem.set_state(state)
return problem
def test_random_neighbor_max2():
"""Test random_neighbor method when max_val is equal to 2"""
problem = DiscreteOpt(5, OneMax(), maximize=True)
x = np.array([0, 0, 1, 1, 1])
problem.set_state(x)
neigh = problem.random_neighbor()
sum_diff = np.sum(np.abs(x - neigh))
assert (len(neigh) == 5 and sum_diff == 1)
def test_find_neighbors_max2():
"""Test find_neighbors method when max_val is equal to 2"""
problem = DiscreteOpt(5, OneMax(), maximize=True, max_val=2)
x = np.array([0, 1, 0, 1, 0])
problem.set_state(x)
problem.find_neighbors()
neigh = np.array([[1, 1, 0, 1, 0], [0, 0, 0, 1, 0], [0, 1, 1, 1, 0],
[0, 1, 0, 0, 0], [0, 1, 0, 1, 1]])
assert np.array_equal(np.array(problem.neighbors), neigh)
def test_random_neighbor_max_gt2():
"""Test random_neighbor method when max_val is greater than 2"""
problem = DiscreteOpt(5, OneMax(), maximize=True, max_val=5)
x = np.array([0, 1, 2, 3, 4])
problem.set_state(x)
neigh = problem.random_neighbor()
abs_diff = np.abs(x - neigh)
abs_diff[abs_diff > 0] = 1
sum_diff = np.sum(abs_diff)
assert (len(neigh) == 5 and sum_diff == 1)
