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)
