Removed a skipped test.

bob/learn/boosting/tests/test_loss_jes.py

@@ -40,44 +40,3 @@ class TestJesorskyLoss(unittest.TestCase):
     self.assertTrue(grad_sum.shape[0] == num_outputs)
 
 
-  @unittest.skip("Implement me!")
-  def test02_negative_target(self):
-
-    loss_function = bob.learn.boosting.JesorskyLoss()
-    num_samples = 2
-    num_outputs = 4
-    targets = numpy.array([[10, 10, 10, 30], [12, 11, 13, 29]])
-    score = numpy.array([[8, 9, 7, 34], [11, 6, 16, 26]], 'float64')
-    alpha = 0.5
-    weak_scores = numpy.array([[0.2, 0.4, 0.5, 0.6], [0.5, 0.5, 0.5, 0.5]], 'float64')
-    prev_scores = numpy.array([[0.1, 0.2, 0.3, 0.4], [0.5, 0.5, 0.5, 0.5]], 'float64')
-
-    # TODO: implement this test properly
-    # check the loss values
-    loss_value = loss_function.loss(targets, score)
-    val1 = numpy.exp(- targets * score)
-    self.assertTrue((loss_value == val1).all())
-
-    # Check loss gradient
-    loss_grad = loss_function.loss_gradient( targets, score)
-
-    temp = numpy.exp(-targets * score)
-    val2 = -targets * temp
-    self.assertTrue((loss_grad == val2).all())
-
-    # Check loss sum
-    loss_sum_val = loss_function.loss_sum(alpha, targets, prev_scores, weak_scores)
-
-    curr_scores = prev_scores + alpha*weak_scores
-    val3 = sum(numpy.exp(-targets * curr_scores))
-    self.assertTrue((val3 == loss_sum_val).all())
-
-    # Check the gradient sum
-    grad_sum_val = loss_function.loss_grad_sum(alpha, targets, prev_scores, weak_scores)
-
-    curr_scores = prev_scores + alpha*weak_scores
-    temp = numpy.exp(-targets * curr_scores)
-    grad = -targets * temp
-    val4 = numpy.sum(grad * weak_scores,0)
-
-    self.assertTrue((val4 == grad_sum_val).all())