def test_logstic_cost_function_val(self):
x_train = TEST_DATA['cost']['x_train']
y_train = TEST_DATA['cost']['y_train']
w = TEST_DATA['cost']['w']
val = TEST_DATA['cost']['L']
val_computed, _ = logistic_cost_function(w, x_train, y_train)
self.assertAlmostEqual(val, val_computed, 6)
def test_logstic_cost_function_grad(self):
x_train = TEST_DATA['cost']['x_train']
y_train = TEST_DATA['cost']['y_train']
w = TEST_DATA['cost']['w']
grad = TEST_DATA['cost']['grad']
_, grad_computed = logistic_cost_function(w, x_train, y_train)
max_diff = np.max(np.abs(grad - grad_computed))
self.assertAlmostEqual(max_diff, 0, 6)
def test_logstic_cost_function_grad(self):
w = TEST_DATA['cost']['w']
x_train = TEST_DATA['cost']['x_train']
y_train = TEST_DATA['cost']['y_train']
grad_expected = TEST_DATA['cost']['grad']
_, grad = logistic_cost_function(w, x_train, y_train)
self.assertEqual(np.shape(grad), (20, 1))
np.testing.assert_almost_equal(grad, grad_expected)
def test_logstic_cost_function_val(self):
w = TEST_DATA['cost']['w']
x_train = TEST_DATA['cost']['x_train']
y_train = TEST_DATA['cost']['y_train']
val_expected = TEST_DATA['cost']['L']
val, _ = logistic_cost_function(w, x_train, y_train)
self.assertEqual(np.size(val), 1)
self.assertAlmostEqual(val, val_expected)