Algebraic Operations on Functions

docs/source/api.rst

@@ -13,6 +13,8 @@ API Reference
     +-------------------------------------------------+--------------------------------------------------------------+
     | :mod:`~probnum.filtsmooth`                      | Bayesian filtering and smoothing.                            |
     +-------------------------------------------------+--------------------------------------------------------------+
+    | :mod:`~probnum.functions`                       | Callables with in- and output shape information.             |
+    +-------------------------------------------------+--------------------------------------------------------------+
     | :mod:`~probnum.linalg`                          | Probabilistic numerical linear algebra.                      |
     +-------------------------------------------------+--------------------------------------------------------------+
     | :mod:`~probnum.linops`                          | Finite-dimensional linear operators.                         |
@@ -39,6 +41,7 @@ API Reference
     api/config
     api/diffeq
     api/filtsmooth
+    api/functions
     api/linalg
     api/linops
     api/problems

docs/source/api/functions.rst

@@ -0,0 +1,7 @@
+*****************
+probnum.functions
+*****************
+
+.. automodapi:: probnum.functions
+    :no-heading:
+    :headings: "="

docs/source/api/randprocs.rst

@@ -10,5 +10,4 @@ probnum.randprocs
     :hidden:
 
     randprocs/markov
-    randprocs/mean_fns
     randprocs/kernels

pyproject.toml

@@ -145,6 +145,8 @@ exclude_lines = [
     # Don't complain if non-runnable code isn't run:
     'if 0:',
     'if __name__ == .__main__.:',
+    # Don't complain if operator's are not overloaded
+    'return NotImplemented'
 ]
 
 ################################################################################

src/probnum/__init__.py

@@ -26,6 +26,7 @@
 from . import (
     diffeq,
     filtsmooth,
+    functions,
     linalg,
     linops,
     problems,
@@ -34,23 +35,18 @@
     randvars,
     utils,
 )
-from ._function import Function, LambdaFunction
 from ._version import version as __version__
 from .randvars import asrandvar
 
 # Public classes and functions. Order is reflected in documentation.
 __all__ = [
     "asrandvar",
-    "Function",
-    "LambdaFunction",
     "ProbabilisticNumericalMethod",
     "StoppingCriterion",
     "LambdaStoppingCriterion",
 ]
 
 # Set correct module paths. Corrects links and module paths in documentation.
-Function.__module__ = "probnum"
-LambdaFunction.__module__ = "probnum"
 ProbabilisticNumericalMethod.__module__ = "probnum"
 StoppingCriterion.__module__ = "probnum"
 LambdaStoppingCriterion.__module__ = "probnum"

src/probnum/functions/__init__.py

@@ -0,0 +1,6 @@
+"""Callables with in- and output shape information supporting algebraic operations."""
+
+from . import _algebra
+from ._algebra_fallbacks import ScaledFunction, SumFunction
+from ._function import Function, LambdaFunction
+from ._zero import Zero

src/probnum/functions/_algebra.py

@@ -0,0 +1,69 @@
+r"""Algebraic operations on :class:`Function`\ s."""
+
+from ._algebra_fallbacks import SumFunction
+from ._function import Function
+from ._zero import Zero
+
+############
+# Function #
+############
+
+
+@Function.__add__.register  # pylint: disable=no-member
+def _(self, other: Function) -> SumFunction:
+    return SumFunction(self, other)
+
+
+@Function.__add__.register  # pylint: disable=no-member
+def _(self, other: SumFunction) -> SumFunction:
+    return SumFunction(self, *other.summands)
+
+
+@Function.__add__.register  # pylint: disable=no-member
+def _(self, other: Zero) -> Function:  # pylint: disable=unused-argument
+    return self
+
+
+@Function.__sub__.register  # pylint: disable=no-member
+def _(self, other: Function) -> SumFunction:
+    return SumFunction(self, -other)
+
+
+@Function.__sub__.register  # pylint: disable=no-member
+def _(self, other: Zero) -> Function:  # pylint: disable=unused-argument
+    return self
+
+
+###############
+# SumFunction #
+###############
+
+
+@SumFunction.__add__.register  # pylint: disable=no-member
+def _(self, other: Function) -> SumFunction:
+    return SumFunction(*self.summands, other)
+
+
+@SumFunction.__add__.register  # pylint: disable=no-member
+def _(self, other: SumFunction) -> SumFunction:
+    return SumFunction(*self.summands, *other.summands)
+
+
+@SumFunction.__sub__.register  # pylint: disable=no-member
+def _(self, other: Function) -> SumFunction:
+    return SumFunction(*self.summands, -other)
+
+
+########
+# Zero #
+########
+
+
+@Zero.__add__.register  # pylint: disable=no-member
+def _(self, other: Function) -> Function:  # pylint: disable=unused-argument
+    return other
+
+
+@Zero.__sub__.register  # pylint: disable=no-member
+def _(self, other: Function) -> Function:  # pylint: disable=unused-argument
+    return -other

src/probnum/functions/_algebra_fallbacks.py

@@ -0,0 +1,141 @@
+r"""Fallback implementation for algebraic operations on :class:`Function`\ s."""
+
+from __future__ import annotations
+
+import functools
+import operator
+
+import numpy as np
+
+from probnum import utils
+from probnum.typing import ScalarLike, ScalarType
+
+from ._function import Function
+
+
+class SumFunction(Function):
+    r"""Pointwise sum of :class:`Function`\ s.
+
+    Given functions :math:`f_1, \dotsc, f_n \colon \mathbb{R}^n \to \mathbb{R}^m`, this
+    defines a new function
+
+    .. math::
+        \sum_{i = 1}^n f_i \colon \mathbb{R}^n \to \mathbb{R}^m,
+        x \mapsto \sum_{i = 1}^n f_i(x).
+
+    Parameters
+    ----------
+    *summands
+        The functions :math:`f_1, \dotsc, f_n`.
+    """
+
+    def __init__(self, *summands: Function) -> None:
+        if not all(isinstance(summand, Function) for summand in summands):
+            raise TypeError(
+                "The functions to be added must be objects of type `Function`."
+            )
+
+        if not all(
+            summand.input_shape == summands[0].input_shape for summand in summands
+        ):
+            raise ValueError(
+                "The functions to be added must all have the same input shape."
+            )
+
+        if not all(
+            summand.output_shape == summands[0].output_shape for summand in summands
+        ):
+            raise ValueError(
+                "The functions to be added must all have the same output shape."
+            )
+
+        self._summands = summands
+
+        super().__init__(
+            input_shape=summands[0].input_shape,
+            output_shape=summands[0].output_shape,
+        )
+
+    @property
+    def summands(self) -> tuple[SumFunction, ...]:
+        r"""The functions :math:`f_1, \dotsc, f_n` to be added."""
+        return self._summands
+
+    def _evaluate(self, x: np.ndarray) -> np.ndarray:
+        return functools.reduce(
+            operator.add, (summand(x) for summand in self._summands)
+        )
+
+    @functools.singledispatchmethod
+    def __add__(self, other):
+        return super().__add__(other)
+
+    @functools.singledispatchmethod
+    def __sub__(self, other):
+        return super().__sub__(other)
+
+
+class ScaledFunction(Function):
+    r"""Function multiplied pointwise with a scalar.
+
+    Given a function :math:`f \colon \mathbb{R}^n \to \mathbb{R}^m` and a scalar
+    :math:`\alpha \in \mathbb{R}`, this defines a new function
+
+    .. math::
+        \alpha f \colon \mathbb{R}^n \to \mathbb{R}^m,
+        x \mapsto (\alpha f)(x) = \alpha f(x).
+
+    Parameters
+    ----------
+    function
+        The function :math:`f`.
+    scalar
+        The scalar :math:`\alpha`.
+    """
+
+    def __init__(self, function: Function, scalar: ScalarLike):
+        if not isinstance(function, Function):
+            raise TypeError(
+                "The function to be scaled must be an object of type `Function`."
+            )
+
+        self._function = function
+        self._scalar = utils.as_numpy_scalar(scalar)
+
+        super().__init__(
+            input_shape=self._function.input_shape,
+            output_shape=self._function.output_shape,
+        )
+
+    @property
+    def function(self) -> Function:
+        r"""The function :math:`f`."""
+        return self._function
+
+    @property
+    def scalar(self) -> ScalarType:
+        r"""The scalar :math:`\alpha`."""
+        return self._scalar
+
+    def _evaluate(self, x: np.ndarray) -> np.ndarray:
+        return self._scalar * self._function(x)
+
+    @functools.singledispatchmethod
+    def __mul__(self, other):
+        if np.ndim(other) == 0:
+            return ScaledFunction(
+                function=self._function,
+                scalar=self._scalar * np.asarray(other),
+            )
+
+        return super().__mul__(other)
+
+    @functools.singledispatchmethod
+    def __rmul__(self, other):
+        if np.ndim(other) == 0:
+            return ScaledFunction(
+                function=self._function,
+                scalar=np.asarray(other) * self._scalar,
+            )
+
+        return super().__rmul__(other)

src/probnum/_function.py → src/probnum/functions/_function.py

@@ -3,12 +3,13 @@
 from __future__ import annotations
 
 import abc
+import functools
 from typing import Callable
 
 import numpy as np
 
-from . import utils
-from .typing import ArrayLike, ShapeLike, ShapeType
+from probnum import utils
+from probnum.typing import ArrayLike, ShapeLike, ShapeType
 
 
 class Function(abc.ABC):
@@ -17,6 +18,8 @@ class Function(abc.ABC):
     This class represents a, uni- or multivariate, scalar- or tensor-valued,
     mathematical function. Hence, the call method should not have any observable
     side-effects.
+    Instances of this class can be added and multiplied by a scalar, which means that
+    they are elements of a vector space.
 
     Parameters
     ----------
@@ -29,7 +32,7 @@ class Function(abc.ABC):
     See Also
     --------
     LambdaFunction : Define a :class:`Function` from an anonymous function.
-    ~probnum.randprocs.mean_fns.Zero : Zero mean function of a random process.
+    ~probnum.functions.Zero : Zero function.
     """
 
     def __init__(self, input_shape: ShapeLike, output_shape: ShapeLike = ()) -> None:
@@ -112,6 +115,39 @@ def __call__(self, x: ArrayLike) -> np.ndarray:
     def _evaluate(self, x: np.ndarray) -> np.ndarray:
         pass
 
+    def __neg__(self):
+        return -1.0 * self
+
+    @functools.singledispatchmethod
+    def __add__(self, other):
+        return NotImplemented
+
+    @functools.singledispatchmethod
+    def __sub__(self, other):
+        return NotImplemented
+
+    @functools.singledispatchmethod
+    def __mul__(self, other):
+        if np.ndim(other) == 0:
+            from ._algebra_fallbacks import (  # pylint: disable=import-outside-toplevel
+                ScaledFunction,
+            )
+
+            return ScaledFunction(function=self, scalar=other)
+
+        return NotImplemented
+
+    @functools.singledispatchmethod
+    def __rmul__(self, other):
+        if np.ndim(other) == 0:
+            from ._algebra_fallbacks import (  # pylint: disable=import-outside-toplevel
+                ScaledFunction,
+            )
+
+            return ScaledFunction(function=self, scalar=other)
+
+        return NotImplemented
+
 
 class LambdaFunction(Function):
     """Define a :class:`Function` from a given :class:`callable`.
@@ -131,7 +167,7 @@ class LambdaFunction(Function):
     Examples
     --------
     >>> import numpy as np
-    >>> from probnum import LambdaFunction
+    >>> from probnum.functions import LambdaFunction
     >>> fn = LambdaFunction(fn=lambda x: 2 * x + 1, input_shape=(2,), output_shape=(2,))
     >>> fn(np.array([[1, 2], [4, 5]]))
     array([[ 3,  5],