def test_update_inputs():
prior = Measure()
f = GP(EQ(), measure=prior)
x = np.array([[1], [2], [3]])
y = np.array([[4], [5], [6]], dtype=float)
res = B.concat(x, y, axis=1)
x_ind = np.array([[6], [7]])
res_ind = np.array([[6, 0], [7, 0]])
# Check vanilla case.
gpar = GPAR(x_ind=x_ind)
approx(gpar._update_inputs(x, x_ind, y, f, None), (res, res_ind))
# Check imputation with prior.
gpar = GPAR(impute=True, x_ind=x_ind)
this_y = y.copy()
this_y[1] = np.nan
this_res = res.copy()
this_res[1, 1] = 0
approx(gpar._update_inputs(x, x_ind, this_y, f, None), (this_res, res_ind))
# Check replacing with prior.
gpar = GPAR(replace=True, x_ind=x_ind)
this_y = y.copy()
this_y[1] = np.nan
this_res = res.copy()
this_res[0, 1] = 0
this_res[1, 1] = np.nan
this_res[2, 1] = 0
approx(gpar._update_inputs(x, x_ind, this_y, f, None), (this_res, res_ind))
# Check imputation and replacing with prior.
gpar = GPAR(impute=True, replace=True, x_ind=x_ind)
this_res = res.copy()
this_res[:, 1] = 0
approx(gpar._update_inputs(x, x_ind, y, f, None), (this_res, res_ind))
# Construct observations and update result for inducing points.
obs = Obs(f(np.array([1, 2, 3, 6, 7])), np.array([9, 10, 11, 12, 13]))
res_ind = np.array([[6, 12], [7, 13]])
# Check imputation with posterior.
gpar = GPAR(impute=True, x_ind=x_ind)
this_y = y.copy()
this_y[1] = np.nan
this_res = res.copy()
this_res[1, 1] = 10
approx(gpar._update_inputs(x, x_ind, this_y, f, obs), (this_res, res_ind))
# Check replacing with posterior.
gpar = GPAR(replace=True, x_ind=x_ind)
this_y = y.copy()
this_y[1] = np.nan
this_res = res.copy()
this_res[0, 1] = 9
this_res[1, 1] = np.nan
this_res[2, 1] = 11
approx(gpar._update_inputs(x, x_ind, this_y, f, obs), (this_res, res_ind))
# Check imputation and replacing with posterior.
gpar = GPAR(impute=True, replace=True, x_ind=x_ind)
this_res = res.copy()
this_res[0, 1] = 9
this_res[1, 1] = 10
this_res[2, 1] = 11
approx(gpar._update_inputs(x, x_ind, y, f, obs), (this_res, res_ind))
def test_update_inputs():
graph = Graph()
f = GP(EQ(), graph=graph)
x = array([[1], [2], [3]])
y = array([[4], [5], [6]])
res = B.concat([x, y], axis=1)
x_ind = array([[6], [7]])
res_ind = array([[6, 0], [7, 0]])
# Check vanilla case.
gpar = GPAR(x_ind=x_ind)
yield allclose, gpar._update_inputs(x, x_ind, y, f, None), (res, res_ind)
# Check imputation with prior.
gpar = GPAR(impute=True, x_ind=x_ind)
this_y = y.clone()
this_y[1] = np.nan
this_res = res.clone()
this_res[1, 1] = 0
yield allclose, \
gpar._update_inputs(x, x_ind, this_y, f, None), \
(this_res, res_ind)
# Check replacing with prior.
gpar = GPAR(replace=True, x_ind=x_ind)
this_y = y.clone()
this_y[1] = np.nan
this_res = res.clone()
this_res[0, 1] = 0
this_res[1, 1] = np.nan
this_res[2, 1] = 0
yield allclose, \
gpar._update_inputs(x, x_ind, this_y, f, None), \
(this_res, res_ind)
# Check imputation and replacing with prior.
gpar = GPAR(impute=True, replace=True, x_ind=x_ind)
this_res = res.clone()
this_res[:, 1] = 0
yield allclose, \
gpar._update_inputs(x, x_ind, y, f, None), \
(this_res, res_ind)
# Construct observations and update result for inducing points.
obs = Obs(f(array([1, 2, 3, 6, 7])), array([9, 10, 11, 12, 13]))
res_ind = array([[6, 12], [7, 13]])
# Check imputation with posterior.
gpar = GPAR(impute=True, x_ind=x_ind)
this_y = y.clone()
this_y[1] = np.nan
this_res = res.clone()
this_res[1, 1] = 10
yield allclose, \
gpar._update_inputs(x, x_ind, this_y, f, obs), \
(this_res, res_ind)
# Check replacing with posterior.
gpar = GPAR(replace=True, x_ind=x_ind)
this_y = y.clone()
this_y[1] = np.nan
this_res = res.clone()
this_res[0, 1] = 9
this_res[1, 1] = np.nan
this_res[2, 1] = 11
yield allclose, \
gpar._update_inputs(x, x_ind, this_y, f, obs), \
(this_res, res_ind)
# Check imputation and replacing with posterior.
gpar = GPAR(impute=True, replace=True, x_ind=x_ind)
this_res = res.clone()
this_res[0, 1] = 9
this_res[1, 1] = 10
this_res[2, 1] = 11
yield allclose, \
gpar._update_inputs(x, x_ind, y, f, obs), \
(this_res, res_ind)
