def check_is_valid_im_various_size(self, nrow, ncol, valid):
     # Tests is_valid_im(R) with linkage matrics of various sizes
     R = np.asarray([[0, 1, 3.0, 2, 5], [3, 2, 4.0, 3, 3]], dtype=np.double)
     R = R[:nrow, :ncol]
     assert_(is_valid_im(R) == valid)
     if not valid:
         assert_raises(ValueError, is_valid_im, R, throw=True)

 def test_is_valid_im_4_and_up(self):
     # Tests is_valid_im(R) on im on observation sets between sizes 4 and 15
     # (step size 3).
     for i in xrange(4, 15, 3):
         y = np.random.rand(i*(i-1)//2)
         Z = linkage(y)
         R = inconsistent(Z)
         assert_(is_valid_im(R) == True)

 def test_is_valid_im_4_and_up_neg_dist(self):
     # Tests is_valid_im(R) on im on observation sets between sizes 4 and 15
     # (step size 3) with negative link counts.
     for i in xrange(4, 15, 3):
         y = np.random.rand(i*(i-1)//2)
         Z = linkage(y)
         R = inconsistent(Z)
         R[i//2,2] = -0.5
         assert_(is_valid_im(R) == False)
         assert_raises(ValueError, is_valid_im, R, throw=True)

 def test_is_valid_im_empty(self):
     # Tests is_valid_im(R) with empty inconsistency matrix.
     R = np.zeros((0, 4), dtype=np.double)
     assert_(is_valid_im(R) == False)
     assert_raises(ValueError, is_valid_im, R, throw=True)

 def test_is_valid_im_int_type(self):
     # Tests is_valid_im(R) with integer type.
     R = np.asarray([[0, 1, 3.0, 2],
                     [3, 2, 4.0, 3]], dtype=np.int)
     assert_(is_valid_im(R) == False)
     assert_raises(TypeError, is_valid_im, R, throw=True)