Compare `3ae7949 ... +0 ... 58c3023`

        Returns
        -------
        updated_belief :
            Gaussian integral belief after conditioning on the new nodes and evaluations.
            Gaussian integral belief after conditioning on the new nodes and
            evaluations.
        updated_state :
            Updated version of ``bq_state`` that contains all updated quantities.
        """

@@ -110,7 +111,7 @@


        Returns
        -------
        x:
        x :
            The solution to the linear system :math:`K x = b`
        """
        jitter = 1.0e-6

@@ -9,7 +9,7 @@

from probnum.randprocs.kernels import Kernel
from probnum.randvars import Normal

# pylint: disable=too-few-public-methods, too-many-instance-attributes, too-many-arguments
# pylint: disable=too-few-public-methods,too-many-instance-attributes,too-many-arguments


class BQInfo:

@@ -128,6 +128,11 @@

            The Gram matrix of the given nodes.
        kernel_means :
            The kernel means at the given nodes.

        Returns
        -------
        bq_state :
            An instance of this class.
        """
        return cls(
            measure=prev_state.measure,

@@ -10,7 +10,8 @@

class BQStoppingCriterion(StoppingCriterion):
    r"""Stopping criterion of a Bayesian quadrature method.

    Checks whether quantities tracked by the :class:`~probnum.quad.solvers.BQState` meet a desired terminal condition.
    Checks whether quantities tracked by the :class:`~probnum.quad.solvers.BQState`
    meet a desired terminal condition.

    See Also
    --------

@@ -45,7 +45,8 @@

    ----------
    sample_func :
        The sample function. Needs to have the following interface:
        `sample_func(batch_size: int, rng: np.random.Generator)` and return an array of shape (batch_size, n_dim).
        `sample_func(batch_size: int, rng: np.random.Generator)` and return an array of
        shape (batch_size, n_dim).
    batch_size :
        Size of batch of nodes when calling the policy once.
    """

@@ -8,8 +8,6 @@

from probnum.quad._integration_measures import GaussianMeasure
from probnum.randprocs.kernels import ExpQuad

# pylint: disable=invalid-name


def _kernel_mean_expquad_gauss(
    x: np.ndarray, kernel: ExpQuad, measure: GaussianMeasure

@@ -20,17 +18,18 @@

    Parameters
    ----------
    x :
        *shape=(n_eval, input_dim)* -- n_eval locations where to evaluate the kernel mean.
        *shape=(n_eval, input_dim)* -- n_eval locations where to evaluate the kernel
        mean.
    kernel :
        Instance of an ExpQuad kernel.
    measure :
        Instance of a GaussianMeasure.

    Returns
    -------
    k_mean :
    kernel_mean :
        *shape (n_eval,)* -- The kernel integrated w.r.t. its first argument,
        evaluated at locations x.
        evaluated at locations ``x``.
    """
    input_dim = kernel.input_dim


@@ -66,7 +65,7 @@


    Returns
    -------
    k_var :
    kernel_variance :
        The kernel integrated w.r.t. both arguments.
    """
    input_dim = kernel.input_dim

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89.40% 100.00% +<.01%

pylint cleanup quad (#610)

mmahsereci

58c3023

CI Passed

89.40% ø ø

Filter warning for importlib (#613)

pnkraemer

3ae7949

CI Passed

Files		•	•	•	Coverage
src/probnum	+1	+1	ø	ø	^+<.01% 89.40%
Project Totals (181 files)	6,678	5,970	229	479	89.40%

110	111
111	112		Returns
112	113		-------
113		-	x:
	114	+	x :
114	115		The solution to the linear system :math:`K x = b`
115	116		"""
116	117		jitter = 1.0e-6

128	128		The Gram matrix of the given nodes.
129	129		kernel_means :
130	130		The kernel means at the given nodes.
	131	+
	132	+	Returns
	133	+	-------
	134	+	bq_state :
	135	+	An instance of this class.
131	136		"""
132	137		return cls(
133	138		measure=prev_state.measure,

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45	45		Returns
46	46		-------
47	47		updated_belief :
48		-	Gaussian integral belief after conditioning on the new nodes and evaluations.
	48	+	Gaussian integral belief after conditioning on the new nodes and
	49	+	evaluations.
49	50		updated_state :
50	51		Updated version of ``bq_state`` that contains all updated quantities.
51	52		"""

9	9		from probnum.randprocs.kernels import Kernel
10	10		from probnum.randvars import Normal
11	11
12		-	# pylint: disable=too-few-public-methods, too-many-instance-attributes, too-many-arguments
	12	+	# pylint: disable=too-few-public-methods,too-many-instance-attributes,too-many-arguments
13	13
14	14
15	15		class BQInfo:

10	10		class BQStoppingCriterion(StoppingCriterion):
11	11		r"""Stopping criterion of a Bayesian quadrature method.
12	12
13		-	Checks whether quantities tracked by the :class:`~probnum.quad.solvers.BQState` meet a desired terminal condition.
	13	+	Checks whether quantities tracked by the :class:`~probnum.quad.solvers.BQState`
	14	+	meet a desired terminal condition.
14	15
15	16		See Also
16	17		--------

45	45		----------
46	46		sample_func :
47	47		The sample function. Needs to have the following interface:
48		-	`sample_func(batch_size: int, rng: np.random.Generator)` and return an array of shape (batch_size, n_dim).
	48	+	`sample_func(batch_size: int, rng: np.random.Generator)` and return an array of
	49	+	shape (batch_size, n_dim).
49	50		batch_size :
50	51		Size of batch of nodes when calling the policy once.
51	52		"""

8	8		from probnum.quad._integration_measures import GaussianMeasure
9	9		from probnum.randprocs.kernels import ExpQuad
10	10
11		-	# pylint: disable=invalid-name
12		-
13	11
14	12		def _kernel_mean_expquad_gauss(
15	13		x: np.ndarray, kernel: ExpQuad, measure: GaussianMeasure

20	18		Parameters
21	19		----------
22	20		x :
23		-	shape=(n_eval, input_dim) -- n_eval locations where to evaluate the kernel mean.
	21	+	shape=(n_eval, input_dim) -- n_eval locations where to evaluate the kernel
	22	+	mean.
24	23		kernel :
25	24		Instance of an ExpQuad kernel.
26	25		measure :
27	26		Instance of a GaussianMeasure.
28	27
29	28		Returns
30	29		-------
31		-	k_mean :
	30	+	kernel_mean :
32	31		shape (n_eval,) -- The kernel integrated w.r.t. its first argument,
33		-	evaluated at locations x.
	32	+	evaluated at locations ``x``.
34	33		"""
35	34		input_dim = kernel.input_dim
36	35

66	65
67	66		Returns
68	67		-------
69		-	k_var :
	68	+	kernel_variance :
70	69		The kernel integrated w.r.t. both arguments.
71	70		"""
72	71		input_dim = kernel.input_dim

Compare 3ae7949 ... +0 ... 58c3023

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Compare `3ae7949 ... +0 ... 58c3023`