class TestSumCov(unittest.TestCase):
def setUp(self):
np.random.seed(1)
self._X1 = np.random.randn(10, 5)
self._X2 = np.random.randn(10, 8)
self._cov1 = SQExpCov(self._X1)
self._cov2 = SQExpCov(self._X2)
self._cov = SumCov(self._cov1, self._cov2)
def test_sum_combination(self):
K1 = self._cov1.K() + self._cov2.K()
K2 = self._cov.K()
np.testing.assert_almost_equal(K1, K2)
def test_Kgrad(self):
cov = self._cov
def func(x, i):
cov.setParams(x)
return cov.K()
def grad(x, i):
cov.setParams(x)
return cov.K_grad_i(i)
x0 = cov.getParams()
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.)
def test_use_to_predict_exception(self):
with self.assertRaises(NotImplementedError):
self._cov.use_to_predict = 1.
class TestProd(unittest.TestCase):
def setUp(self):
# np.random.seed(1)
self._X1 = np.random.randn(10, 5)
self._X2 = np.random.randn(10, 8)
self._X3 = np.random.randn(10, 7)
self._cov1 = SQExpCov(self._X1)
self._cov2 = SQExpCov(self._X2)
self._cov3 = SQExpCov(self._X3)
self._cov = ProdCov(self._cov1, self._cov2, self._cov3)
def test_sum_combination(self):
K1 = self._cov1.K() * self._cov2.K() * self._cov3.K()
K2 = self._cov.K()
np.testing.assert_almost_equal(K1, K2)
def test_Kgrad(self):
cov = self._cov
def func(x, i):
cov.setParams(x)
return cov.K()
def grad(x, i):
cov.setParams(x)
return cov.K_grad_i(i)
x0 = cov.getParams()
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.)
def test_Khess(self):
cov = self._cov
for j in range(cov.getNumberParams()):
def func(x, i):
cov.setParams(x)
return cov.K_grad_i(j)
def grad(x, i):
cov.setParams(x)
return cov.K_hess_i_j(j, i)
x0 = cov.getParams()
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.)
def test_use_to_predict_exception(self):
with self.assertRaises(NotImplementedError):
self._cov.use_to_predict = 1.
def simulate_local(self):
tmp = SQExpCov(self.X)
tmp.length = self.l2
k = tmp.K()
k *= covar_rescaling_factor_efficient(k)
self.covar += k
class TestSQExp(unittest.TestCase):
def setUp(self):
np.random.seed(1)
self._X = np.random.randn(10, 5)
self._cov = SQExpCov(self._X)
def test_setX_retE(self):
X1 = self._X
np.random.seed(2)
X2 = np.random.randn(10, 5)
E1 = sp.spatial.distance.pdist(X1,'euclidean')**2
E1 = sp.spatial.distance.squareform(E1)
E2 = sp.spatial.distance.pdist(X2,'euclidean')**2
E2 = sp.spatial.distance.squareform(E2)
np.testing.assert_almost_equal(E1, self._cov.E())
self._cov.X = X2
np.testing.assert_almost_equal(E2, self._cov.E())
def test_param_activation(self):
self._cov.act_scale = False
self._cov.act_length = False
self.assertEqual(len(self._cov.getParams()), 0)
self._cov.act_scale = False
self._cov.act_length = True
self.assertEqual(len(self._cov.getParams()), 1)
self._cov.act_scale = True
self._cov.act_length = False
self.assertEqual(len(self._cov.getParams()), 1)
self._cov.act_scale = True
self._cov.act_length = True
self.assertEqual(len(self._cov.getParams()), 2)
self._cov.act_scale = False
self._cov.act_length = False
self._cov.setParams(np.array([]))
with self.assertRaises(ValueError):
self._cov.setParams(np.array([0]))
with self.assertRaises(ValueError):
self._cov.K_grad_i(0)
with self.assertRaises(ValueError):
self._cov.K_grad_i(1)
def test_Kgrad(self):
def func(x, i):
self._cov.scale = x[i]
return self._cov.K()
def grad(x, i):
self._cov.scale = x[i]
return self._cov.K_grad_i(0)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.)
def func(x, i):
self._cov.length = x[i]
return self._cov.K()
def grad(x, i):
self._cov.scale = x[i]
return self._cov.K_grad_i(1)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
def test_Kgrad_activation(self):
self._cov.act_length = False
def func(x, i):
self._cov.scale = x[i]
return self._cov.K()
def grad(x, i):
self._cov.scale = x[i]
return self._cov.K_grad_i(0)
x0 = np.array([self._cov.scale])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.)
self._cov.act_scale = False
self._cov.act_length = True
def func(x, i):
self._cov.length = x[i]
return self._cov.K()
def grad(x, i):
self._cov.length = x[i]
return self._cov.K_grad_i(0)
x0 = np.array([self._cov.length])
err = mcheck_grad(func, grad, x0)
np.testing.assert_almost_equal(err, 0.)
def test_input(self):
with self.assertRaises(ValueError):
SQExpCov(np.array([[np.inf]]))
with self.assertRaises(ValueError):
SQExpCov(np.array([[np.nan]]))
with self.assertRaises(NotArrayConvertibleError):
SQExpCov("Ola meu querido.")
