def test_dot(self):
# Note: theta^T . X === X^T . theta
dot = np.dot(
self.theta, self.X.T
) # need to use np dot function as pandas only allows you to do the dot if columns have same name
print(dot)
sig = Classification._sigmoid(dot)
print(sig)
def test_sigmoid_vector(self):
small_sig = Classification._sigmoid(np.array([-10000, -10000, -10000]))
large_sig = Classification._sigmoid(np.array([10000, 10000, 10000]))
for s_val, l_val in zip(small_sig, large_sig):
self.assertAlmostEqual(s_val, 0)
self.assertAlmostEqual(l_val, 1)
def test_sigmoid_scalar(self):
# for very negative numbers, sigmoid should equal zero, for very large, it should be 1
self.assertAlmostEqual(Classification._sigmoid(-1000000), 0)
self.assertAlmostEqual(Classification._sigmoid(1000000), 1)
