def test_weighted_sum(self):
"""Tests weighted sum to make sure weights are implemented correctly"""
fitness_params = {
'goal_fos': 1.5,
'critical_nodes': np.array([1]),
'w_fos': 100,
'w_mass': 1,
'w_deflection': 10
}
f = FitnessFunction('weighted_sum', fitness_params)
mass = 5
deflection = np.array([[1, 0, 0, 0, 0, 0],
[np.sqrt(2), np.sqrt(2), 0, 0, 0, 0]])
fos = np.array([.5, 5])
t1 = Truss(0, 0, 0, 0)
t1.mass = mass
t1.deflection = deflection
t1.fos = fos
t1 = f(t1)
self.assertAlmostEqual(t1.fitness_score, 125)
t0 = Truss(0, 0, 0, 0)
fitness_params['critical_nodes'] = np.array([])
fos = np.array([])
t0.mass = mass
t0.deflection = deflection
t0.fos = fos
t0 = f(t0)
self.assertAlmostEqual(t0.fitness_score, 185)
def test_truss_plot(self):
"""Plots a pyramidal truss with loads and deflections."""
user_spec_nodes = np.array([[-1, -1, 0], [-1, 1, 0]])
rand_nodes = np.array([[1, 1, 0], [1, -1, 0], [0, 0, 1]])
edges = np.array([[0, 1], [1, 2], [2, 3], [3, 0], [0, 4], [1, 4],
[2, 4], [3, 4]])
properties = np.zeros(8)
loads = np.concatenate((np.zeros(
(4, 6)), np.array([[-100, 0, -100, 0, 0, 0]])),
axis=0).reshape(5, 6, 1)
fixtures = np.concatenate((np.ones((4, 6)), np.zeros((1, 6))),
axis=0).reshape(5, 6, 1)
load_scale = .005
def_scale = 10
truss = Truss(user_spec_nodes, rand_nodes, edges, properties)
truss.deflection = np.concatenate((np.zeros(
(4, 6)), np.array([[-.01, 0, -.01, 0, 0, 0]])),
axis=0).reshape(5, 6, 1)
domain = np.array([[-1.5, 1.5], [-1.5, 1.5], [0, 1.5]]).T
truss.plot(domain, loads, fixtures, True, load_scale, def_scale)