object

gaussian_process_regression

Gaussian process regression regressor supporting continuous and mixed-feature datasets using an exact mixed Gaussian process with posterior uncertainty estimates. Learns from a dataset object implementing the regression_dataset_protocol protocol and returns a regressor term that can be used for prediction, predictive-distribution queries, and export as predicate clauses.

Availability:

logtalk_load(gaussian_process_regression(loader))

Author: Paulo Moura

Version: 1:0:0

Date: 2026-05-05

Compilation flags:

static, context_switching_calls

Imports:

public regressor_common

Uses:

format

linear_algebra

list

numberlist

population

type

Remarks:

(none)

Inherited public predicates:

 check_option/1  check_options/1  check_regressor/1  default_option/1  default_options/1  diagnostic/2  diagnostics/2  export_to_clauses/4  export_to_file/4  learn/2  learn/3  option/2  option/3  predict/3  print_regressor/1  regressor_options/2  valid_option/1  valid_options/1  valid_regressor/1

Public predicates

predict_distribution/3

Predicts the posterior predictive Gaussian distribution for a new instance using the learned regressor. The returned term has the shape gaussian(Mean, Variance) where Variance includes the learned observation noise variance.

Compilation flags:

static

Template:

predict_distribution(Regressor,Instance,Distribution)

Mode and number of proofs:

predict_distribution(+compound,+list,-compound) - one

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Protected predicates

(no local declarations; see entity ancestors if any)

Private predicates

(no local declarations; see entity ancestors if any)

Operators

(none)

See also

linear_regression, ridge_regression, lasso_regression, elastic_net_regression, regression_tree, random_forest_regression