def test_gradient(self):
gradient = LogisticRegression._gradient_loss(self.x, self.t, self.b, self.w, self.lam)
gradient_act = np.array([0.0, 7 * 3, 7 * -1])
error = LogisticRegression._sigmoid(self.x, self.b, self.w) - self.t
gradient_act[0] = np.sum(error)
gradient_act[1] += np.sum(error * self.x[:, 0])
gradient_act[2] += np.sum(error * self.x[:, 1])
self.assertTrue(np.all(np.abs(gradient - gradient_act) < 1.0e-12))
def test_error(self):
x0 = np.array([self.x[0]])
x1 = np.array([self.x[1]])
error = LogisticRegression._loss(x0, x1, self.b, self.w, self.lam)
# this assumes test_sigmoid pases
err_act = -np.log(LogisticRegression._sigmoid(x1, self.b, self.w)) - np.log(1.0 - LogisticRegression._sigmoid(x0, self.b, self.w)) + 0.5 * 7 * 10
self.assertTrue( abs(float(err_act) - error) < 1.0e-12 )
# weighted case
x00 = np.array([self.x[0], [55, -2]])
error_weighted = LogisticRegression._loss(x00, x1, self.b, self.w, self.lam, [np.array([0.4, 0.75]), np.array(0.35)])
err_weighted_act = -np.log(LogisticRegression._sigmoid(x1, self.b, self.w)) * 0.35 - np.log(1.0 - LogisticRegression._sigmoid(x0, self.b, self.w)) * 0.4 - np.log(1.0 - LogisticRegression._sigmoid([x00[1, :]], self.b, self.w)) * 0.75 + 0.5 * 7 * 10
self.assertTrue( abs(float(err_weighted_act) - error_weighted) < 1.0e-12 )
def test_sigmoid(self):
y = LogisticRegression._sigmoid(self.x, self.b, self.w)
yact = np.array([1.0 / (1.0 + np.exp(-6)), 1.0 / (1.0 + np.exp(-1.5))])
self.assertTrue(np.all(np.abs(y - yact) < 1.0e-12))
