def test_gauss_measure_invalid_mean_raises():
mean_wrong_shape = np.ones([3, 1])
with pytest.raises(ValueError):
GaussianMeasure(mean=mean_wrong_shape, variance=1.0)
mean_wrong_type = 0.0
with pytest.raises(TypeError):
GaussianMeasure(mean=mean_wrong_type, variance=1.0)
class DataGaussMeasure:
D = 2
mean = np.array([0, 0.8])
variance = np.array([0.2, 1.4])
measure = GaussianMeasure(mean=mean, variance=variance)
dat_bounds = [(m - 2 * np.sqrt(v), m + 2 * np.sqrt(v)) for m, v in zip(mean, variance)]
reasonable_box_bounds = [(m - 10 * np.sqrt(v), m + 10 * np.sqrt(v)) for m, v in zip(mean, variance)]
class DataGaussIsoMeasure:
D = 2
mean = np.array([0, 0.8])
variance = 0.6
measure = GaussianMeasure(mean=mean, variance=variance)
dat_bounds = [(m - 2 * np.sqrt(0.6), m + 2 * np.sqrt(0.6)) for m in mean]
reasonable_box_bounds = [(m - 10 * np.sqrt(0.6), m + 10 * np.sqrt(0.6)) for m in mean]
def model_gaussian(gpy_model):
X, Y = gpy_model.X, gpy_model.Y
measure = GaussianMeasure(mean=np.arange(gpy_model.X.shape[1]),
variance=np.linspace(0.2, 1.5, X.shape[1]))
qrbf = QuadratureRBFGaussianMeasure(RBFGPy(gpy_model.kern),
measure=measure)
basegp = BaseGaussianProcessGPy(kern=qrbf, gpy_model=gpy_model)
return VanillaBayesianQuadrature(base_gp=basegp,
X=gpy_model.X,
Y=gpy_model.Y)
def base_gp_data():
X = np.array([[-1, 1], [0, 0], [-2, 0.1]])
Y = np.array([[1], [2], [3]])
gpy_model = GPy.models.GPRegression(
X=X, Y=Y, kernel=GPy.kern.RBF(input_dim=X.shape[1]))
measure = GaussianMeasure(mean=np.array([0.1, 1.8]), variance=0.8)
qrbf = QuadratureRBFGaussianMeasure(RBFGPy(gpy_model.kern),
measure=measure)
base_gp = BaseGaussianProcessGPy(kern=qrbf, gpy_model=gpy_model)
return base_gp, X, Y
def test_gauss_measure_invalid_variance_raises(wrong_input):
mean, var_wrong_value = wrong_input
with pytest.raises(ValueError):
GaussianMeasure(mean=mean, variance=var_wrong_value)
def get_gaussian_qrbf():
dat = DataGaussianSpread()
measure = GaussianMeasure(mean=dat.measure_mean, variance=dat.measure_var)
qkern = QuadratureRBFGaussianMeasure(EmukitRBF().kern, measure=measure)
return qkern, dat
def get_base_gp():
gpy_model, dat = get_gpy_model()
measure = GaussianMeasure(mean=dat.measure_mean, variance=dat.measure_var)
qrbf = QuadratureRBFGaussianMeasure(RBFGPy(gpy_model.kern),
measure=measure)
return BaseGaussianProcessGPy(kern=qrbf, gpy_model=gpy_model), dat
def measure_gaussian(n_dim: int):
return GaussianMeasure(mean=np.ones(n_dim), variance=1.0)