def test_get_collision_normal(self): p1 = Particle(1, [0, 0, 0], [0, 0, 0]) p2 = Particle(2, [1, 1, 1], [0, 0, 0]) col = Collision(p1, p2) TestCase.assertAlmostEquals(self, vect.mag( col.get_collision_normal() - (np.array([1, 1, 1]) / vect.mag([1, 1, 1]))), 0)
[1., -1.]] W2 = [[ 1., 2., 1.], [-1., 2., -1.]] W = [W1, W2] loss = 0. for x in xs: W, b, loss = train_on_batch(x, W, b) W1, W2 = W b1, b2 = b # test loss expected_loss = 20.5 TestCase.assertAlmostEquals(TestCase, first=loss, second=expected_loss) # test W2 expected_W2 = [[ 1., 1.95, 0.75], [-1., 2.04, -0.8]] for column in range(len(W2)): for row in range(len(W2[column])): TestCase.assertAlmostEquals(TestCase, first=W2[column][row], second=expected_W2[column][row]) # test W1 expected_W1 = [[ 0.91, 2.09], [-0.02, 1.02], [ 0.91, -0.91]] for column in range(len(W1)): for row in range(len(W1[column])): TestCase.assertAlmostEquals(TestCase, first=W1[column][row], second=expected_W1[column][row])
W1 = [[1., 2.], [0., 1.], [1., -1.]] W2 = [[1., 2., 1.], [-1., 2., -1.]] W = [W1, W2] loss = 0. for x in xs: W, b, loss = train_on_batch(x, W, b) W1, W2 = W b1, b2 = b # test loss expected_loss = 20.5 TestCase.assertAlmostEquals(TestCase, first=loss, second=expected_loss) # test W2 expected_W2 = [[1., 1.95, 0.75], [-1., 2.04, -0.8]] for column in range(len(W2)): for row in range(len(W2[column])): TestCase.assertAlmostEquals(TestCase, first=W2[column][row], second=expected_W2[column][row]) # test W1 expected_W1 = [[0.91, 2.09], [-0.02, 1.02], [0.91, -0.91]] for column in range(len(W1)): for row in range(len(W1[column])): TestCase.assertAlmostEquals(TestCase, first=W1[column][row],
def test_normalize(self): TestCase.assertAlmostEquals( self, mag(normalize([1, 1, 1]) - np.array([3**-0.5, 3**-0.5, 3**-0.5])), 0)