sklears-gaussian-process 0.2.0

Gaussian Process models for regression and classification
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138

//! Gaussian Process models for regression and classification
//!
//! This module is part of sklears, providing scikit-learn compatible
//! machine learning algorithms in Rust.

// #![warn(missing_docs)]

// Module declarations
pub mod automatic_kernel;
// pub mod batch_bayesian_optimization;
pub mod bayesian_optimization;
pub mod classification;
// pub mod constrained_bayesian_optimization;
pub mod convolution_processes;
pub mod deep_gp;
pub mod features;
pub mod fitc;
pub mod gpr;
pub mod heteroscedastic;
pub mod hierarchical;
pub mod intrinsic_coregionalization;
pub mod kernel_optimization;
pub mod kernel_selection;
pub mod kernel_structure_learning;
pub mod kernel_trait;
pub mod kernels;
pub mod linear_model_coregionalization;
pub mod marginal_likelihood;
// pub mod multi_objective;
pub mod multi_task;
pub mod noise_function_learning;
pub mod nystrom;
pub mod regression;
pub mod robust;
pub mod sparse_gpr;
pub mod sparse_spectrum;
pub mod spatial;
pub mod structured_kernel_interpolation;
// pub mod temporal;
pub mod utils;
pub mod variational;
pub mod variational_deep_gp;

// Re-exports for convenient access
pub use automatic_kernel::{
    AutomaticKernelConstructor, DataCharacteristics, KernelConstructionResult,
};
// pub use batch_bayesian_optimization::{
//     BatchAcquisition, BatchBayesianOptimizer, BatchBayesianOptimizerBuilder, BatchConfig,
//     BatchOptimizationResult, DistanceMetric,
// };
pub use bayesian_optimization::{AcquisitionFunction, BayesianOptimizer, BayesianOptimizerFitted};
pub use classification::{
    sigmoid, sigmoid_derivative, EpGpcTrained, ExpectationPropagationGaussianProcessClassifier,
    GaussianProcessClassifier, GpcConfig, GpcTrained, McGpcTrained,
    MultiClassGaussianProcessClassifier,
};
// pub use constrained_bayesian_optimization::{
//     ConstrainedAcquisition, ConstrainedBayesianOptimizer, ConstrainedBayesianOptimizerBuilder,
//     ConstraintApproximation, ConstraintConfig, ConstraintEvaluation, ConstraintFunction,
//     FeasibilityAnalysis,
// };
pub use convolution_processes::{ConvolutionProcess, ConvolutionProcessTrained};
pub use deep_gp::{DeepGPConfig, DeepGPLayer, DeepGaussianProcessRegressor};
pub use features::{RandomFourierFeatures, RandomFourierFeaturesGPR, RffGprTrained};
pub use fitc::{
    FitcGaussianProcessRegressor, FitcGaussianProcessRegressorConfig, FitcGprTrained,
    InducingPointInit as FitcInducingPointInit,
};
pub use gpr::{GaussianProcessRegressor, GprTrained};
pub use heteroscedastic::{
    ConstantNoise, HeteroscedasticGaussianProcessRegressor, LearnableNoiseFunction, LinearNoise,
    NeuralNetworkNoise, NoiseFunction, PolynomialNoise, Trained as HeteroscedasticGprTrained,
};
pub use hierarchical::{HierarchicalGPConfig, HierarchicalGaussianProcessRegressor};
pub use intrinsic_coregionalization::{IcmTrained, IntrinsicCoregionalizationModel};
pub use kernel_optimization::OptimizationResult as KernelOptimizationResult;
pub use kernel_optimization::{optimize_kernel_parameters, KernelOptimizer};
pub use kernel_selection::{
    select_best_kernel, select_kernel_aic, select_kernel_bic, select_kernel_cv,
    KernelSelectionConfig, KernelSelectionResult, KernelSelector, SelectionCriterion,
};
pub use kernel_structure_learning::{
    ConvergenceInfo, KernelGrammar, KernelStructureLearner, NonTerminalOperation, SearchStrategy,
    StructureLearningResult, TerminalKernel,
};
pub use linear_model_coregionalization::{LinearModelCoregionalization, LmcTrained};
pub use marginal_likelihood::OptimizationResult as MarginalLikelihoodOptimizationResult;
pub use marginal_likelihood::{
    cross_validate_hyperparameters, log_marginal_likelihood, log_marginal_likelihood_stable,
    optimize_hyperparameters, MarginalLikelihoodOptimizer,
};
// pub use multi_objective::{
//     MultiObjectiveAcquisition, MultiObjectiveBayesianOptimizer, MultiObjectiveConfig,
//     ParetoFrontier, ScalarizationMethod, Trained as MultiObjectiveTrained,
// };
pub use multi_task::{MtgpTrained, MultiTaskGaussianProcessRegressor};
pub use noise_function_learning::{
    AdaptiveRegularization, AutomaticNoiseFunctionSelector, CombinationMethod,
    EnsembleNoiseFunction, InformationCriterion, NoiseFunctionEvaluation,
};
pub use nystrom::{LandmarkSelection, NystromGaussianProcessRegressor, NystromGprTrained};
pub use regression::{
    MogprTrained, MultiOutputGaussianProcessRegressor, VariationalOptimizer,
    VariationalSparseGaussianProcessRegressor, VsgprTrained,
};
pub use robust::{
    OutlierDetectionMethod, RobustGPConfig, RobustGaussianProcessRegressor, RobustLikelihood,
    RobustnessMetrics, Trained as RobustGprTrained,
};
pub use sparse_gpr::{InducingPointInit, SgprTrained, SparseGaussianProcessRegressor};
pub use sparse_spectrum::{
    SparseSpectrumGaussianProcessRegressor, SparseSpectrumGprTrained, SpectralApproximationInfo,
    SpectralSelectionMethod,
};
pub use spatial::{
    KrigingType, SpatialGPConfig, SpatialGaussianProcessRegressor, SpatialKernel,
    Trained as SpatialGprTrained, Variogram,
};
pub use structured_kernel_interpolation::{
    GridBoundsMethod, InterpolationMethod, SkiApproximationInfo, SkiGprTrained,
    StructuredKernelInterpolationGPR,
};
// pub use temporal::{
//     SeasonalDecomposition, StateSpaceModel, TemporalGPConfig, TemporalGaussianProcessRegressor,
//     TemporalKernel, Trained as TemporalGprTrained,
// };
pub use variational::{
    SgpcTrained, SparseGaussianProcessClassifier, VariationalGaussianProcessClassifier,
    VariationalGpcConfig, VariationalGpcTrained,
};
pub use variational_deep_gp::{
    VariationalDeepGPBuilder, VariationalDeepGPConfig, VariationalDeepGaussianProcess,
    VariationalLayerConfig, VariationalLayerParameters, VariationalLikelihood,
};

// Re-export common kernel types
pub use kernels::{Kernel, ARDRBF, RBF};