Consistent file structure for quad

src/probnum/quad/integration_measures/__init__.py

@@ -1,6 +1,8 @@
 """Integration measures for Bayesian quadrature methods."""
 
-from ._integration_measures import GaussianMeasure, IntegrationMeasure, LebesgueMeasure
+from ._gaussian_measure import GaussianMeasure
+from ._integration_measure import IntegrationMeasure
+from ._lebesgue_measure import LebesgueMeasure
 
 # Public classes and functions. Order is reflected in documentation.
 __all__ = [

src/probnum/quad/integration_measures/_gaussian_measure.py

@@ -0,0 +1,76 @@
+"""The Gaussian measure."""
+
+
+from __future__ import annotations
+
+from typing import Optional, Union
+
+import numpy as np
+
+from probnum.randvars import Normal
+from probnum.typing import IntLike
+
+from ._integration_measure import IntegrationMeasure
+
+
+# pylint: disable=too-few-public-methods
+class GaussianMeasure(IntegrationMeasure):
+    """Gaussian measure on Euclidean space with given mean and covariance.
+
+    If ``mean`` and ``cov`` are scalars but ``input_dim`` is larger than one, ``mean``
+    and ``cov`` are extended to a constant vector and diagonal matrix, respectively,
+    of appropriate dimensions.
+
+    Parameters
+    ----------
+    mean
+        *shape=(input_dim,)* -- Mean of the Gaussian measure.
+    cov
+        *shape=(input_dim, input_dim)* -- Covariance matrix of the Gaussian measure.
+    input_dim
+        Dimension of the integration domain.
+    """
+
+    def __init__(
+        self,
+        mean: Union[float, np.floating, np.ndarray],
+        cov: Union[float, np.floating, np.ndarray],
+        input_dim: Optional[IntLike] = None,
+    ) -> None:
+
+        # Extend scalar mean and covariance to higher dimensions if input_dim has been
+        # supplied by the user
+        if (
+            (np.isscalar(mean) or mean.size == 1)
+            and (np.isscalar(cov) or cov.size == 1)
+            and input_dim is not None
+        ):
+            mean = np.full((input_dim,), mean)
+            cov = cov * np.eye(input_dim)
+
+        # Set dimension based on the mean vector
+        if np.isscalar(mean):
+            input_dim = 1
+        else:
+            input_dim = mean.size
+
+        # Set domain as whole R^n
+        domain = (np.full((input_dim,), -np.Inf), np.full((input_dim,), np.Inf))
+        super().__init__(input_dim=input_dim, domain=domain)
+
+        # Exploit random variables to carry out mean and covariance checks
+        # squeezes are needed due to the way random variables are currently implemented
+        # pylint: disable=no-member
+        self.random_variable = Normal(mean=np.squeeze(mean), cov=np.squeeze(cov))
+        self.mean = np.reshape(self.random_variable.mean, (self.input_dim,))
+        self.cov = np.reshape(
+            self.random_variable.cov, (self.input_dim, self.input_dim)
+        )
+
+        # Set diagonal_covariance flag
+        if input_dim == 1:
+            self.diagonal_covariance = True
+        else:
+            self.diagonal_covariance = (
+                np.count_nonzero(self.cov - np.diag(np.diagonal(self.cov))) == 0
+            )

src/probnum/quad/integration_measures/_integration_measure.py

@@ -0,0 +1,77 @@
+"""Base class of an integration measures for Bayesian quadrature."""
+
+from __future__ import annotations
+
+import abc
+from typing import Optional, Union
+
+import numpy as np
+
+from probnum.quad._utils import as_domain
+from probnum.quad.typing import DomainLike
+from probnum.typing import FloatLike, IntLike
+
+
+class IntegrationMeasure(abc.ABC):
+    """An abstract class for a measure against which a target function is integrated.
+
+    Child classes implement specific integration measures and, if available, make use
+    of random variables for sampling and evaluation of the density function.
+
+    Parameters
+    ----------
+    domain
+        Domain of integration. Contains lower and upper bound as a scalar or
+        ``np.ndarray``.
+    input_dim
+        Dimension of the integration domain.
+    """
+
+    def __init__(
+        self,
+        domain: DomainLike,
+        input_dim: Optional[IntLike],
+    ) -> None:
+
+        self.domain, self.input_dim = as_domain(domain, input_dim)
+
+    def __call__(self, points: Union[FloatLike, np.ndarray]) -> np.ndarray:
+        """Evaluate the density function of the integration measure.
+
+        Parameters
+        ----------
+        points
+            *shape=(n_points, input_dim)* -- Input locations.
+
+        Returns
+        -------
+        density_evals :
+            *shape=(n_points,)* -- Density evaluated at given locations.
+        """
+        # pylint: disable=no-member
+        return self.random_variable.pdf(points).reshape(-1)
+
+    def sample(
+        self,
+        n_sample: IntLike,
+        rng: Optional[np.random.Generator] = np.random.default_rng(),
+    ) -> np.ndarray:
+        """Sample ``n_sample`` points from the integration measure.
+
+        Parameters
+        ----------
+        n_sample
+            Number of points to be sampled
+        rng
+            Random number generator. Optional. Default is `np.random.default_rng()`.
+
+        Returns
+        -------
+        points :
+            *shape=(n_sample,input_dim)* -- Sampled points
+        """
+        # pylint: disable=no-member
+        return np.reshape(
+            self.random_variable.sample(size=n_sample, rng=rng),
+            newshape=(n_sample, self.input_dim),
+        )