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 range(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(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 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_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 range(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_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_4_and_up_neg_index_right(self):
# Tests is_valid_im(R) on im on observation sets between sizes 4 and 15
# (step size 3) with negative link height standard deviations.
for i in xrange(4, 15, 3):
y = np.random.rand(i * (i - 1) // 2)
Z = linkage(y)
R = inconsistent(Z)
R[i // 2, 1] = -2.0
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)
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)