def test_RecurrentConnection_forward_pass_single_samples():
rc = RecurrentConnection(4)
theta = 2 * np.eye(4).flatten()
for x in X:
x = np.atleast_2d(x)
# for single samples recurrence should not do anything
assert_equal(rc.forward_pass(theta, x), x)
def test_RecurrentConnection_forward_pass_single_samples():
rc = RecurrentConnection(4)
theta = 2 * np.eye(4).flatten()
for x in X:
x = np.atleast_2d(x)
# for single samples recurrence should not do anything
assert_equal(rc.forward_pass(theta, x), x)
def test_RecurrentConnection_forward_pass_multi_sample():
fc = RecurrentConnection(4)
theta = np.eye(4).flatten()
X_summed = np.array([[0, 0, 0, 1], [1, 0, 0, 2],[1, 1, 0, 3],[1, 1, 1, 4],[2, 2, 1, 5]])
assert_equal(fc.forward_pass(theta, X), X_summed)
def test_RecurrentConnection_dimensions():
rc = RecurrentConnection(5)
assert_equal(rc.input_dim, 5)
assert_equal(rc.output_dim, 5)
assert_equal(rc.get_param_dim(), 5**2)
def test_RecurrentConnection_forward_pass_multi_sample():
fc = RecurrentConnection(4)
theta = np.eye(4).flatten()
X_summed = np.array([[0, 0, 0, 1], [1, 0, 0, 2], [1, 1, 0, 3],
[1, 1, 1, 4], [2, 2, 1, 5]])
assert_equal(fc.forward_pass(theta, X), X_summed)
def test_RecurrentConnection_dimensions():
rc = RecurrentConnection(5)
assert_equal(rc.input_dim, 5)
assert_equal(rc.output_dim, 5)
assert_equal(rc.get_param_dim(), 5**2)
