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Cross decomposition algorithms (PLS, CCA)
This module is part of sklears, providing scikit-learn compatible machine learning algorithms in Rust.
Re-exports§
pub use bayesian::BayesianCCA;pub use bayesian::BayesianCCAResults;pub use bayesian::HierarchicalBayesianCCA;pub use bayesian::HierarchicalBayesianCCAResults;pub use bayesian::VariationalPLS;pub use bayesian::VariationalPLSResults;pub use benchmarks::AccuracyResults;pub use benchmarks::BenchmarkResults;pub use benchmarks::BenchmarkSuite;pub use benchmarks::DecompositionResult;pub use benchmarks::MethodBenchmarkResults;pub use benchmarks::ScalabilityResults;pub use benchmarks::SpeedResult;pub use benchmarks::SummaryStats;pub use cca::RidgeCCA;pub use cca::SparseCCA;pub use cca::CCA;pub use consensus_pca::ConsensusPCA;pub use deep_cca::ActivationFunction;pub use deep_cca::DeepCCA;pub use deep_learning::ActivationFunction as DeepActivationFunction;pub use deep_learning::AttentionActivation;pub use deep_learning::AttentionConfig;pub use deep_learning::AttentionLayer;pub use deep_learning::AttentionOutput;pub use deep_learning::AttentionTensorDecomposition;pub use deep_learning::AttentionType;pub use deep_learning::CrossModalAttention;pub use deep_learning::CrossModalAttentionOutput;pub use deep_learning::CrossModalSimilarity;pub use deep_learning::CrossModalVAE;pub use deep_learning::MultiHeadAttention;pub use deep_learning::NeuralActivation;pub use deep_learning::NeuralParafacDecomposition;pub use deep_learning::NeuralTensorConfig;pub use deep_learning::NeuralTensorResults;pub use deep_learning::NeuralTuckerDecomposition;pub use deep_learning::TransformerDecoderBlock;pub use deep_learning::TransformerEncoderBlock;pub use deep_learning::VAEConfig;pub use deep_learning::VAETrainingResults;pub use deep_learning::VariationalTensorNetwork;pub use federated_learning::AggregationStrategy as FederatedAggregationStrategy;pub use federated_learning::ClientId;pub use federated_learning::CommunicationConfig;pub use federated_learning::FederatedCCA;pub use federated_learning::FederatedCCAResults;pub use federated_learning::FederatedClient;pub use federated_learning::FederatedError;pub use federated_learning::FederatedPCA;pub use federated_learning::FederatedPCAResults;pub use federated_learning::FederatedServer;pub use federated_learning::PrivacyBudget;pub use finance::FactorConstrainedOptimization;pub use finance::FactorRotation;pub use finance::FactorStatistics;pub use finance::FinanceError;pub use finance::FinancialFactorAnalysis;pub use finance::FittedFinancialFactorAnalysis;pub use finance::FittedMacroeconomicFactorAnalysis;pub use finance::ForecastingModel;pub use finance::MacroFactorStatistics;pub use finance::MacroeconomicFactorAnalysis;pub use finance::OptimizedPortfolio;pub use finance::RiskDecomposition;pub use generalized_cca::GeneralizedCCA;pub use genomics::ConsensusMethod;pub use genomics::EnhancedPathwayAnalysis;pub use genomics::EnhancedPathwayResults;pub use genomics::EnrichmentMethod;pub use genomics::FittedGeneEnvironmentInteraction;pub use genomics::FittedMultiOmicsIntegration;pub use genomics::FittedSingleCellMultiModal;pub use genomics::FittedTemporalGeneExpression;pub use genomics::GeneEnvironmentInteraction;pub use genomics::GenomicsError;pub use genomics::MLScoringConfig;pub use genomics::MissingDataStrategy;pub use genomics::MultiModalConfig;pub use genomics::MultiOmicsIntegration;pub use genomics::MultipleTestingCorrection;pub use genomics::NetworkAnalysisConfig;pub use genomics::PathwayAnalysis;pub use genomics::PathwayAnalysisConfig;pub use genomics::PathwayDatabase;pub use genomics::SingleCellMultiModal;pub use genomics::TemporalAnalysisConfig;pub use genomics::TemporalGeneExpression;pub use gpu_acceleration::GpuAcceleratedContext;pub use gpu_acceleration::GpuCCA;pub use gpu_acceleration::GpuCCAFitted;pub use gpu_acceleration::GpuMatrixOps;pub use gpu_acceleration::GpuMemoryInfo;pub use graph_regularization::CommunityAlgorithm;pub use graph_regularization::CommunityDetectionConfig;pub use graph_regularization::CommunityDetector;pub use graph_regularization::CommunityStructure;pub use graph_regularization::GraphBuilder;pub use graph_regularization::GraphRegularizationConfig;pub use graph_regularization::GraphRegularizationError;pub use graph_regularization::GraphRegularizedCCA;pub use graph_regularization::GraphStructure;pub use graph_regularization::GraphType;pub use graph_regularization::Hypergraph;pub use graph_regularization::HypergraphCCA;pub use graph_regularization::HypergraphCCAResults;pub use graph_regularization::HypergraphCentrality;pub use graph_regularization::HypergraphConfig;pub use graph_regularization::HypergraphLaplacianType;pub use graph_regularization::MotifType;pub use graph_regularization::MultiGraphCCA;pub use graph_regularization::MultiWayInteractionAnalyzer;pub use graph_regularization::NetworkConstrainedPLS;pub use graph_regularization::RegularizationType;pub use graph_regularization::TemporalAnalysisResults;pub use graph_regularization::TemporalMotif;pub use graph_regularization::TemporalNetwork;pub use graph_regularization::TemporalNetworkAnalyzer;pub use graph_regularization::TemporalNetworkConfig;pub use information_theory::ComponentInterpretation;pub use information_theory::ComponentInterpreter;pub use information_theory::ComponentSelection;pub use information_theory::ComponentSimilarityAnalysis;pub use information_theory::DistanceBasedConfig;pub use information_theory::DistanceBasedMetric;pub use information_theory::DistanceBasedResults;pub use information_theory::DistanceCCA;pub use information_theory::DistanceCovariance;pub use information_theory::EntropyComponentSelection;pub use information_theory::EntropyEstimator;pub use information_theory::FeatureContribution;pub use information_theory::FeatureImportanceAnalyzer;pub use information_theory::FeatureImportanceResults;pub use information_theory::FittedMutualInformationCCA;pub use information_theory::HigherOrderAnalyzer;pub use information_theory::HigherOrderConfig;pub use information_theory::HigherOrderResults;pub use information_theory::ImportanceMethod;pub use information_theory::InformationGeometry;pub use information_theory::InformationMeasure;pub use information_theory::InformationTheoreticRegularization;pub use information_theory::InformationTheoryError;pub use information_theory::KLDivergenceMethods;pub use information_theory::ManifoldStructure;pub use information_theory::MutualInformationCCA;pub use information_theory::NonGaussianComponentAnalysis;pub use information_theory::NonGaussianResults;pub use information_theory::PolyspectralCCA;pub use information_theory::PolyspectralResults;pub use information_theory::RegularizationMethod;pub use information_theory::RiemannianOptimizer;pub use information_theory::SelectionCriteria;pub use information_theory::SingleComponentInterpretation;pub use information_theory::VariableInterpretation;pub use information_theory::HSIC;pub use interactive_visualization::ColorScheme;pub use interactive_visualization::InteractivePlot;pub use interactive_visualization::InteractiveVisualizationConfig;pub use interactive_visualization::InteractiveVisualizer;pub use interactive_visualization::PlotData;pub use interactive_visualization::PlotType;pub use interactive_visualization::VisualizationError;pub use jive::JIVE;pub use kernel_cca::KernelCCA;pub use kernel_cca::KernelType;pub use manifold_learning::AdvancedManifoldLearning;pub use manifold_learning::ConvergenceInfo;pub use manifold_learning::CrossModalAlignment;pub use manifold_learning::DistanceMetric;pub use manifold_learning::EigenSolver;pub use manifold_learning::FittedManifoldAwareCCA;pub use manifold_learning::FittedManifoldCCA as FittedAdvancedManifoldCCA;pub use manifold_learning::GeodesicMethod;pub use manifold_learning::ManifoldAwareCCA;pub use manifold_learning::ManifoldCCA as AdvancedManifoldCCA;pub use manifold_learning::ManifoldError;pub use manifold_learning::ManifoldLearning;pub use manifold_learning::ManifoldLearningResult;pub use manifold_learning::ManifoldMethod;pub use manifold_learning::ManifoldProperties;pub use manifold_learning::ManifoldRegularization;pub use manifold_learning::ManifoldResults;pub use manifold_learning::OptimizationParams;pub use manifold_learning::PathMethod;pub use multi_omics::GenomicsError as MultiOmicsGenomicsError;pub use multiblock_pls::BlockScaling;pub use multiblock_pls::MultiBlockPLS;pub use multitask::DomainAdaptationCCA;pub use multitask::FewShotCCA;pub use multitask::MultiTaskCCA;pub use multitask::TransferLearningCCA;pub use multiview_cca::MultiViewCCA;pub use multiview_clustering::DistanceMetric as MultiViewDistanceMetric;pub use multiview_clustering::InitMethod;pub use multiview_clustering::MultiViewClustering;pub use neuroimaging::BrainBehaviorCorrelation;pub use neuroimaging::BrainBehaviorResults;pub use neuroimaging::ConnectivityType;pub use neuroimaging::CorrelationMethod;pub use neuroimaging::FunctionalConnectivity;pub use neuroimaging::FunctionalConnectivityResults;pub use neuroimaging::NetworkMeasures;pub use opls::OPLS;pub use out_of_core::OOCAlgorithm;pub use out_of_core::OutOfCoreCCA;pub use out_of_core::OutOfCoreCCAResults;pub use out_of_core::OutOfCorePLS;pub use out_of_core::OutOfCorePLSResults;pub use parallel::EigenMethod;pub use parallel::OptimizedMatrixOps;pub use parallel::ParallelEigenSolver;pub use parallel::ParallelMatrixOps;pub use parallel::ParallelSVD;pub use parallel::SVDAlgorithm;pub use parallel::WorkStealingThreadPool;pub use pls::PLSRegression;pub use pls_canonical::PLSCanonical;pub use pls_da::PLSDA;pub use pls_svd::PLSSVD;pub use quantum_methods::QuantumCCA;pub use quantum_methods::QuantumCCAResults;pub use quantum_methods::QuantumCircuit;pub use quantum_methods::QuantumError;pub use quantum_methods::QuantumFeatureSelection;pub use quantum_methods::QuantumGate;pub use quantum_methods::QuantumMethod;pub use quantum_methods::QuantumPCA;pub use quantum_methods::QuantumPCAResults;pub use quantum_methods::QuantumState;pub use regularization::AdaptiveLasso;pub use regularization::ElasticNet;pub use regularization::FusedLasso;pub use regularization::GroupLasso;pub use regularization::MCP;pub use regularization::SCAD;pub use riemannian_optimization::CCAObjective;pub use riemannian_optimization::GrassmannManifold;pub use riemannian_optimization::LineSearchParams;pub use riemannian_optimization::ManifoldType;pub use riemannian_optimization::RiemannianAlgorithm;pub use riemannian_optimization::RiemannianConfig;pub use riemannian_optimization::RiemannianError;pub use riemannian_optimization::RiemannianManifold;pub use riemannian_optimization::RiemannianObjective;pub use riemannian_optimization::RiemannianOptimizer as RiemannianOptimizerAdvanced;pub use riemannian_optimization::RiemannianResults;pub use riemannian_optimization::SPDManifold;pub use riemannian_optimization::StiefelManifold;pub use riemannian_optimization::TrustRegionParams;pub use robust_methods::MEstimatorType;pub use robust_methods::RobustCCA;pub use robust_methods::RobustPLS;pub use scalability::AggregationStrategy;pub use scalability::DistributedCCA;pub use scalability::DistributedCCAResults;pub use scalability::MemoryEfficientCCA;pub use simd_acceleration::AdvancedSimdConfig;pub use simd_acceleration::AdvancedSimdOps;pub use simd_acceleration::SimdBenchmarkResults;pub use simd_acceleration::SimdCCA;pub use simd_acceleration::SimdCCAFitted;pub use simd_acceleration::SimdMatrixOps;pub use sparse_pls::SparsePLS;pub use tensor_methods::BayesianParafac;pub use tensor_methods::ParafacDecomposition;pub use tensor_methods::ProbabilisticConfig;pub use tensor_methods::ProbabilisticTensorResults;pub use tensor_methods::ProbabilisticTucker;pub use tensor_methods::RobustProbabilisticTensor;pub use tensor_methods::SparseTensorDecomposition;pub use tensor_methods::TensorCCA;pub use tensor_methods::TensorCompletion;pub use tensor_methods::TensorInitMethod;pub use tensor_methods::TuckerDecomposition;pub use time_series::DynamicCCA;pub use time_series::DynamicCCAResults;pub use time_series::DynamicCCASummary;pub use time_series::FittedRegimeSwitchingModel;pub use time_series::FittedStateSpaceModel;pub use time_series::FittedVAR;pub use time_series::GrangerCausalityTest;pub use time_series::GrangerTestResult;pub use time_series::InformationCriterion;pub use time_series::RegimeSwitchingModel;pub use time_series::StateSpaceForecast;pub use time_series::StateSpaceModel;pub use time_series::StateSpaceModelDiagnostics;pub use time_series::StreamingCCA;pub use time_series::TrendType;pub use time_series::VARMethod;pub use time_series::VectorAutoregression;pub use type_safe_linalg::decomp;pub use type_safe_linalg::ops;pub use type_safe_linalg::Dim;pub use type_safe_linalg::MatrixDimension;pub use type_safe_linalg::SquareMatrix;pub use type_safe_linalg::TypeSafeMatrix;pub use type_safe_linalg::TypeSafeVector;pub use validation_framework::BenchmarkDataset;pub use validation_framework::CaseStudy;pub use validation_framework::ComputationalBenchmarks;pub use validation_framework::CorrelationStructure;pub use validation_framework::CriterionType;pub use validation_framework::CrossValidationResult;pub use validation_framework::CrossValidationSettings;pub use validation_framework::DatasetCharacteristics;pub use validation_framework::DatasetValidationResult;pub use validation_framework::DistributionType;pub use validation_framework::PerformanceMetric;pub use validation_framework::PerformanceRange;pub use validation_framework::PerformanceSummary;pub use validation_framework::RobustnessAnalysis;pub use validation_framework::ScalabilityAnalysis;pub use validation_framework::SignificanceTest;pub use validation_framework::StatisticalTestResult;pub use validation_framework::ValidationError;pub use validation_framework::ValidationFramework;pub use validation_framework::ValidationResults;
Modules§
- bayesian
- Bayesian approaches for cross-decomposition
- benchmarks
- Benchmarking utilities for cross-decomposition methods
- cca
- Canonical Correlation Analysis
- consensus_
pca - Consensus Principal Component Analysis (Consensus PCA)
- deep_
cca - Deep Canonical Correlation Analysis
- deep_
learning - Deep Learning Integration for Cross-Decomposition
- differential_
geometry - Differential Geometry Methods for Cross-Decomposition
- federated_
learning - Federated Learning for Cross-Decomposition Methods
- finance
- Finance and Economics Applications
- generalized_
cca - Generalized Canonical Correlation Analysis (GCCA)
- genomics
- Genomics and Multi-Omics Integration
- gpu_
acceleration - GPU Acceleration Module for Cross-Decomposition Methods
- graph_
regularization - Graph-Regularized Cross-Decomposition Methods
- information_
theory - Information Theory Approaches for Cross-Decomposition
- interactive_
visualization - Interactive Visualization for Cross-Decomposition Methods
- jive
- Joint and Individual Variation Explained (JIVE)
- kernel_
cca - Kernel Canonical Correlation Analysis
- manifold_
learning - Manifold Learning Integration for Cross-Decomposition Methods
- multi_
omics - Multi-Omics Integration
- multiblock_
pls - Multi-block Partial Least Squares
- multitask
- Multi-task learning methods for cross-decomposition
- multiview_
cca - Multi-view Canonical Correlation Analysis (Multi-view CCA)
- multiview_
clustering - Multi-view Clustering
- neuroimaging
- Neuroimaging applications for cross-decomposition methods
- opls
- Orthogonal Partial Least Squares (OPLS)
- out_
of_ core - Out-of-core processing for large-scale cross-decomposition
- parallel
- Parallel computing enhancements for cross-decomposition methods
- pls
- Partial Least Squares regression
- pls_
canonical - PLS in canonical mode
- pls_da
- PLS Discriminant Analysis
- pls_svd
- PLS using SVD
- quantum_
methods - Quantum-Inspired Methods for Cross-Decomposition
- regularization
- Regularization techniques for cross-decomposition methods
- riemannian_
optimization - Riemannian Optimization for Cross-Decomposition Methods
- robust_
methods - Robust cross-decomposition methods
- scalability
- Scalability and performance optimizations for cross-decomposition
- simd_
acceleration - SIMD Acceleration Module for Cross-Decomposition Methods
- sparse_
pls - Sparse Partial Least Squares
- tensor_
methods - Tensor-based cross-decomposition methods
- time_
series - Time series extensions for cross-decomposition
- type_
safe_ linalg - Type-safe linear algebra operations for cross-decomposition
- validation_
framework - Comprehensive Validation Framework