def makegp(params):
kernel_time = lgp.ExpQuad(scale=params['time_scale'], dim='time')
kernel_label = lgp.ExpQuad(scale=label_scale, dim='label')
gp = lgp.GP(kernel_time * kernel_label)
gp.addx(x, 'data', deriv=(data_deriv, 'time'))
gp.addx(np.array([(0, 0)], dtype=x.dtype), 'fixed_point')
return gp
def test_incompatible_dtypes():
gp = lgp.GP(lgp.ExpQuad())
gp.addx(0, 0)
with pytest.raises(TypeError):
gp.addx(np.zeros(1, 'd,d'), 1)
gp = lgp.GP(lgp.ExpQuad())
gp.addx(np.zeros(1, 'd,d'), 0)
# gp.addx(np.zeros(1, 'i,i'), 1) # succeeds only if numpy >= 1.23
with pytest.raises(TypeError):
gp.addx(np.zeros(1, 'd,d,d'), 2)
def test_none_key():
gp = lgp.GP(lgp.ExpQuad())
with pytest.raises(ValueError):
gp.addx({None: 0})
gp = lgp.GP(lgp.ExpQuad())
gp.addx(0, 0)
with pytest.raises(ValueError):
gp.addtransf({0: 1}, None)
gp = lgp.GP(lgp.ExpQuad())
with pytest.raises(ValueError):
gp.addcov({None: 1})
def test_kernelop():
gp = lgp.GP()
gp.addproc(lgp.ExpQuad(), 'a')
f = lambda x: x
gp.addproc(lgp.ExpQuad() * lgp.Rescaling(stdfun=f), 'b1')
gp.addkernelop('rescale', f, 'b2', 'a')
x = np.arange(20)
gp.addx(x, 'x1', proc='b1')
gp.addx(x, 'x2', proc='b2')
prior = gp.prior(['x1', 'x2'], raw=True)
util.assert_equal(prior['x1', 'x1'], prior['x2', 'x2'])
util.assert_equal(prior['x2', 'x1'], prior['x1', 'x2'].T)
util.assert_equal(prior['x1', 'x2'], np.zeros(2 * x.shape))
def test_key_already_used():
gp = lgp.GP(lgp.ExpQuad())
gp.addx(0, 0)
with pytest.raises(KeyError):
gp.addx(0, 0)
gp = lgp.GP(lgp.ExpQuad())
gp.addx(0, 0)
with pytest.raises(KeyError):
gp.addtransf({0: 1}, 0)
gp = lgp.GP(lgp.ExpQuad())
gp.addx(0, 0)
with pytest.raises(KeyError):
gp.addcov(1, 0)
def test_proclintransf_checks():
gp = lgp.GP()
gp.addproc(lgp.ExpQuad(), 0)
gp.addproc(lgp.ExpQuad(), 1)
with pytest.raises(KeyError):
gp.addproclintransf(lambda f, g: lambda x: f(x) + g(x), [0, 1], 0)
with pytest.raises(KeyError):
gp.addproclintransf(lambda f, g: lambda x: f(x) + g(x), [0, 2], 2)
with pytest.raises(RuntimeError):
gp.addproclintransf(lambda f, g: lambda x: 1, [0, 1], 2, checklin=True)
with pytest.raises(RuntimeError):
gp.addproclintransf(lambda f, g: lambda x: 1 + f(x) + g(x), [0, 1],
2,
checklin=True)
gp.addproclintransf(lambda f, g: lambda x: 1 + f(x) + g(x), [0, 1], 2)
def test_addcov_wrong_blocks():
gp = lgp.GP(lgp.ExpQuad())
with pytest.raises(ValueError):
gp.addcov(np.zeros((1, 1, 1)), 0)
with pytest.raises(ValueError):
gp.addcov(np.zeros((1, 2, 2, 1)), 0)
with pytest.raises(ValueError):
gp.addcov(np.random.randn(10, 10), 0)
with pytest.raises(KeyError):
gp.addcov({
(0, 0): 1,
(0, 1): 0,
})
with pytest.raises(ValueError):
gp.addcov({
(0, 0): np.ones((2, 2)),
(1, 1): np.ones((3, 3)),
(0, 1): np.ones((3, 2)),
})
with pytest.raises(ValueError):
gp.addcov({
(0, 0): np.ones((2, 2)),
(1, 1): np.ones((3, 3)),
(0, 1): np.ones((2, 3)),
(1, 0): np.zeros((3, 2)),
})
def test_no_checksym():
covs = []
for checksym in [False, True]:
gp = lgp.GP(lgp.ExpQuad(), checksym=checksym)
gp.addx({'x': np.arange(20)})
covs.append(gp.prior('x', raw=True))
util.assert_equal(*covs)
def makegp(params):
kernel = lgp.Cauchy(scale=params['time_scale'], dim='time', beta=2)
kernel *= lgp.ExpQuad(scale=params['label_scale'], dim='label')
gp = lgp.GP(kernel)
x['time'] = jnp.array([time, time - params['delay']])
gp.addx(x, 'A')
return gp
def test_prior_raw_shape():
gp = lgp.GP(lgp.ExpQuad())
gp.addx(np.arange(20).reshape(2, 10), 'x')
cov = gp.prior(raw=True)
assert cov['x', 'x'].shape == (2, 10, 2, 10)
cov = gp.prior('x', raw=True)
assert cov.shape == (2, 10, 2, 10)
def test_addprocderiv():
gp = lgp.GP(lgp.ExpQuad())
gp.addprocderiv(1, 'a')
x = np.arange(20)
gp.addx(x, 0, proc='a')
gp.addx(x, 1, deriv=1)
prior = gp.prior(raw=True)
util.assert_equal(prior[0, 0], prior[1, 1])
util.assert_equal(prior[0, 0], prior[0, 1])
def test_default_kernel():
gp = lgp.GP(lgp.ExpQuad())
gp.addproc(key='a')
x = np.arange(20)
gp.addx(x, 'x1')
gp.addx(x, 'x2', proc='a')
prior = gp.prior(raw=True)
util.assert_equal(prior['x1', 'x1'], prior['x2', 'x2'])
util.assert_equal(prior['x1', 'x2'], np.zeros(2 * x.shape))
def test_nonsense_tensors():
gp = lgp.GP(lgp.ExpQuad())
gp.addx(0, 0)
with pytest.raises(TypeError):
gp.addtransf({0: 'a'}, 1)
with pytest.raises(ValueError):
gp.addtransf({0: np.inf}, 1)
gp.addx([0, 1], 1)
with pytest.raises(ValueError):
gp.addtransf({1: [1, 2, 3]}, 2)
def test_lintransf_matmul():
gp = lgp.GP(lgp.ExpQuad())
x = np.arange(20)
gp.addx(x, 0)
m = np.random.randn(30, len(x))
gp.addlintransf(lambda x: m @ x, [0], 1)
prior = gp.prior([0, 1], raw=True)
np.testing.assert_allclose(m @ prior[0, 0] @ m.T, prior[1, 1])
np.testing.assert_allclose(m @ prior[0, 1], prior[1, 1])
np.testing.assert_allclose(prior[1, 0] @ m.T, prior[1, 1])
def test_double_pred():
n = 50
gp = lgp.GP(lgp.ExpQuad())
ax, bx = np.random.randn(2, n)
gp.addx(ax, 'a')
gp.addx(bx, 'b')
m = np.random.randn(n, n)
ay = gvar.gvar(np.random.randn(n), m.T @ m)
m1, cov1 = gp.predfromdata({'a': ay}, 'b', raw=True)
m2, cov2 = gp.predfromfit(gp.predfromdata({'a': ay}, ['a']), 'b', raw=True)
assert_close(m2, m1)
assert_close_cov(cov2, cov1, 1e-3, 1e-6)
def test_addprocxtransf():
gp = lgp.GP()
f = lambda x: x**2
gp.addproc(lgp.ExpQuad(), 0)
gp.addprocxtransf(f, 'a', 0)
gp.addproclintransf(lambda g: lambda x: g(f(x)), [0], 'b')
x = np.linspace(0, 4, 20)
gp.addx(x, 0, proc='a')
gp.addx(x, 1, proc='b')
gp.addx(f(x), 2, proc=0)
prior = gp.prior(raw=True)
for i in range(3):
for j in range(3):
util.assert_equal(prior[0, 0], prior[i, j])