GaussianProcess¶

class probnum.randprocs.GaussianProcess(mean, cov)

Bases: RandomProcess[Union[_SupportsArray[dtype], _NestedSequence[_SupportsArray[dtype]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]], ndarray]

Gaussian processes.

A Gaussian process is a continuous stochastic process which if evaluated at a finite set of inputs returns a random variable with a normal distribution. Gaussian processes are fully characterized by their mean and covariance function.

Parameters
• mean – Mean function.

• cov – Covariance function or kernel.

RandomProcess

Random processes.

MarkovProcess

Random processes with the Markov property.

Examples

Define a Gaussian process with a zero mean function and RBF kernel.

>>> import numpy as np
>>> from probnum.randprocs.mean_fns import Zero
>>> from probnum.randprocs import GaussianProcess
>>> mu = Zero(input_shape=())  # zero-mean function
>>> k = ExpQuad(input_shape=())  # RBF kernel
>>> gp = GaussianProcess(mu, k)


Sample from the Gaussian process.

>>> x = np.linspace(-1, 1, 5)
>>> rng = np.random.default_rng(seed=42)
>>> gp.sample(rng, x)
array([-0.7539949 , -0.6658092 , -0.52972512,  0.0674298 ,  0.72066223])
>>> gp.cov.matrix(x)
array([[1.        , 0.8824969 , 0.60653066, 0.32465247, 0.13533528],
[0.8824969 , 1.        , 0.8824969 , 0.60653066, 0.32465247],
[0.60653066, 0.8824969 , 1.        , 0.8824969 , 0.60653066],
[0.32465247, 0.60653066, 0.8824969 , 1.        , 0.8824969 ],
[0.13533528, 0.32465247, 0.60653066, 0.8824969 , 1.        ]])


Attributes Summary

 cov Covariance function $$k(x_0, x_1)$$ of the random process. dtype Data type of (elements of) the random process evaluated at an input. input_ndim Syntactic sugar for len(input_shape). input_shape Shape of inputs to the random process. mean Mean function $$m(x) := \mathbb{E}[f(x)]$$ of the random process. output_ndim Syntactic sugar for len(output_shape). output_shape Shape of the random process evaluated at an input.

Methods Summary

 __call__(args) Evaluate the random process at a set of input arguments. marginal(args) Batch of random variables defining the marginal distributions at the inputs. push_forward(args, base_measure, sample) Transform samples from a base measure into samples from the random process. sample(rng[, args, size]) Sample paths from the random process. std(args) Standard deviation function. var(args) Variance function.

Attributes Documentation

cov

Covariance function $$k(x_0, x_1)$$ of the random process.

$$k(x_0, x_1) := \mathbb{E} \left[ (f(x_0) - \mathbb{E}[f(x_0)]) (f(x_1) - \mathbb{E}[f(x_1)])^\top \right]$$
dtype

Data type of (elements of) the random process evaluated at an input.

input_ndim

Syntactic sugar for len(input_shape).

input_shape

Shape of inputs to the random process.

mean

Mean function $$m(x) := \mathbb{E}[f(x)]$$ of the random process.

output_ndim

Syntactic sugar for len(output_shape).

output_shape

Shape of the random process evaluated at an input.

Methods Documentation

__call__(args)[source]

Evaluate the random process at a set of input arguments.

Parameters

args (ArrayLike) – shape= batch_shape + input_shape – (Batch of) input(s) at which to evaluate the random process. Currently, we require batch_shape to have at most one dimension.

Returns

shape= batch_shape + output_shape – Random process evaluated at the input(s).

Return type

randvars.RandomVariable

marginal(args)

Batch of random variables defining the marginal distributions at the inputs.

Parameters

args (InputType) – shape= batch_shape + input_shape – (Batch of) input(s) at which to evaluate the random process. Currently, we require batch_shape to have at most one dimension.

Return type

_RandomVariableList

push_forward(args, base_measure, sample)

Transform samples from a base measure into samples from the random process.

This function can be used to control sampling from the random process by explicitly passing samples from a base measure evaluated at the input arguments.

Parameters
• args (InputType) – Input arguments.

• base_measure (Type[RandomVariable]) – Base measure. Given as a type of random variable.

• sample (ndarray) – shape= sample_shape + input_shape – (Batch of) input(s) at which to evaluate the random process. Currently, we require sample_shape to have at most one dimension.

Return type

ndarray

sample(rng, args=None, size=())

Sample paths from the random process.

If no inputs are provided this function returns sample paths which are callables, otherwise random variables corresponding to the input locations are returned.

Parameters
• rng (Generator) – Random number generator.

• args (Optional[InputType]) – shape= size + input_shape – (Batch of) input(s) at which the sample paths will be evaluated. Currently, we require size to have at most one dimension. If None, sample paths, i.e. callables are returned.

• size (ShapeLike) – Size of the sample.

Return type

Union[Callable[[InputType], OutputType], OutputType]

std(args)

Standard deviation function.

Parameters

args (InputType) – shape= batch_shape + input_shape – (Batch of) input(s) at which to evaluate the standard deviation function.

Returns

shape= batch_shape + output_shape – Standard deviation of the process at args.

Return type

OutputType

var(args)

Variance function.

Returns the variance function which is the value of the covariance or kernel evaluated elementwise at args for each output dimension separately.

Parameters

args (InputType) – shape= batch_shape + input_shape – (Batch of) input(s) at which to evaluate the variance function.

Returns

shape= batch_shape + output_shape – Variance of the process at args.

Return type

OutputType