Namespace AiDotNet.Enums
Enums
- AcquisitionFunctionType
Represents different types of acquisition functions used in Bayesian optimization.
- ActivationFunction
Represents different activation functions used in neural networks and deep learning.
- ActivationFunctionRole
Defines the functional roles of activation functions in neural networks.
- AssignmentStrategy
Strategy for assigning requests to model versions during A/B testing.
- AutoMLBudgetPreset
Defines compute budget presets for AutoML runs.
- AutoMLFinalModelSelectionPolicy
Defines how AutoML chooses the final model to return after the search completes.
- AutoMLSearchStrategy
Defines the search strategy used to explore AutoML candidate configurations.
- AutoMLStatus
Represents the current status of an AutoML search process.
- AutoMLTaskFamily
Defines the high-level task family for an AutoML run.
- BenchmarkExecutionStatus
Represents the execution outcome for a benchmark suite run.
- BenchmarkFailurePolicy
Controls how the benchmark runner should behave when one or more suites fail.
- BenchmarkMetric
Defines standardized metrics used in benchmark reports.
- BenchmarkReportDetailLevel
Specifies how much detail should be included in benchmark reports.
- BenchmarkSuite
Defines the supported benchmark suites available through the AiDotNet facade.
- BenchmarkSuiteKind
Categorizes benchmark suites by their evaluation style and infrastructure requirements.
- BetaSchedule
Defines the types of beta (noise variance) schedules available for diffusion models.
- BoundaryHandlingMethod
Specifies how to handle boundaries when processing data that extends beyond the available range.
- CacheEvictionPolicy
Cache eviction policies for managing limited cache memory.
- CalibrationMethod
Calibration methods for quantization - techniques to determine optimal scaling factors when converting high-precision models to low-precision formats.
- ChainType
Represents different types of chains for composing language model operations.
- CheckpointMetricType
Standard metrics for checkpoint selection and early stopping.
- ClassificationTaskType
Specifies the type of classification task being performed.
- CompressionType
Defines the types of model compression strategies available in the AiDotNet library.
- ConditionNumberMethod
Specifies different methods for calculating the condition number of a matrix.
- ConformalPredictionMode
Defines conformal prediction calibration modes.
- ContrastiveLossType
Types of contrastive loss functions for knowledge distillation.
- ConvexSolverType
Types of convex solvers available for MetaOptNet.
- CrossValidationType
Defines the types of cross-validation strategies available.
- DataComplexity
Represents the level of complexity in a dataset, which helps determine appropriate model selection and preprocessing.
- DataSetType
Represents the different types of datasets used in machine learning workflows.
- DecompositionComponentType
Represents the different components that can be extracted when decomposing a time series.
- DenseNetVariant
Specifies the DenseNet model variant.
- DiffusionPredictionType
Defines what the diffusion model predicts during the denoising process.
- DistanceMetricType
Represents different methods for measuring the distance or similarity between data points.
- DistillationStrategyType
Specifies the type of knowledge distillation strategy to use for transferring knowledge from teacher to student models.
- DistributedStrategy
Defines the distributed training strategy to use.
- DistributionType
Represents different probability distributions used in statistical modeling and machine learning.
- EdgeDeviceType
Types of edge devices for optimization targeting.
- EdgeDirection
Specifies the direction of edges to retrieve when querying a knowledge graph.
- EfficientNetVariant
Specifies the EfficientNet model variant.
- EnvelopeType
Specifies whether to use an upper or lower envelope in signal processing and data analysis operations.
- ExpressionNodeType
Defines the different types of nodes that can exist in a computational graph.
- FeatureExtractionStrategy
Defines strategies for extracting features from higher-dimensional tensors.
- FederatedPartitioningStrategy
Defines how a centralized dataset should be partitioned into per-client datasets for federated simulations.
- FewShotSelectionStrategy
Represents strategies for selecting few-shot examples in prompt templates.
- FitType
Represents different types of model fit quality and common issues in machine learning models.
- FitnessCalculatorType
Specifies different loss functions and fitness calculators for evaluating model performance.
- GeneticNodeType
Types of nodes in a genetic programming tree.
- GradientType
Specifies different types of gradient descent optimization algorithms used in machine learning.
- GraphGenerationType
Type of graph generation approach.
- ImportanceThresholdStrategy
Defines strategies for setting the importance threshold in feature selection.
- InitializationMethod
Methods for initializing a population.
- InputType
Specifies the dimensionality of input data for machine learning models.
- Interpolation2DType
Specifies different methods for interpolating 2D data points to create a continuous surface.
- InverseType
Specifies different algorithms for calculating matrix inverses in mathematical operations.
- KernelType
Specifies different kernel functions used in machine learning algorithms like Support Vector Machines (SVMs).
- LLMProvider
Defines the large language model (LLM) providers available for AI agent assistance during model building and inference.
- LanguageModelBackbone
Defines the language model backbone types used in multimodal neural networks.
- LayerType
Specifies different types of layers used in neural networks, particularly in deep learning models.
- MatrixDecompositionType
Specifies different methods for breaking down (decomposing) matrices into simpler components.
- MatrixLayout
Specifies how data is organized in matrices when working with arrays of data.
- MatrixType
Defines the different types of matrices that can be used in mathematical operations.
- MetricOptimizationDirection
Specifies the direction for metric optimization (whether lower or higher values are better).
- MetricType
Defines the types of metrics used to evaluate machine learning models.
- MixedPrecisionType
Types of mixed precision training data types.
- MobileNetV2WidthMultiplier
Specifies the width multiplier for MobileNetV2.
- MobileNetV3Variant
Specifies the MobileNetV3 model variant.
- MobileNetV3WidthMultiplier
Specifies the width multiplier for MobileNetV3.
- ModelCompressionMode
Defines the mode of model compression to apply during serialization.
- ModelPerformance
Represents the overall performance quality of a machine learning model.
- ModelType
Defines the types of machine learning models available in the AiDotNet library.
- ModificationType
Represents the types of modifications that can be applied to a model structure.
- NetworkComplexity
Defines the complexity level of a neural network architecture.
- NeuralArchitectureSearchStrategy
Specifies the strategy used for neural architecture search.
- NeuralNetworkTaskType
Defines the different types of tasks that a neural network can be designed to perform.
- NormalizationMethod
Defines different methods for normalizing data before processing in machine learning algorithms.
- OperationType
Represents different operation types in computation graphs for JIT compilation and automatic differentiation.
- OptimizationMode
Specifies the mode of optimization for an optimizer.
- OptimizationPassType
Represents the type of optimization pass applied to the computation graph.
- OptimizerType
Defines different optimization algorithms used to train machine learning models.
- OutlierDetectionMethod
Defines different methods for detecting outliers in datasets.
- OutputDistribution
Specifies the target distribution for quantile transformation.
- PNAAggregator
Aggregation function types for Principal Neighbourhood Aggregation (PNA).
- PNAScaler
Scaler function types for Principal Neighbourhood Aggregation (PNA).
- ParameterType
Defines the types of parameters that can be used in hyperparameter search
- PartitionStrategy
Strategies for partitioning models between cloud and edge devices.
- PoolingType
Defines different methods for pooling (downsampling) data in neural networks, particularly in convolutional neural networks.
- PrecisionMode
Defines the numeric precision mode for neural network training and computation.
- PredictionType
Specifies the type of prediction task that a machine learning model performs.
- PreferenceLossType
Types of loss functions for preference optimization methods.
- ProbabilityCalibrationMethod
Defines probability calibration strategies for classification-like outputs.
- PromptOptimizationStrategy
Represents strategies for optimizing prompts to improve language model performance.
- PromptTemplateType
Represents different types of prompt templates for language model interactions.
- QualityLevel
Quality levels for adaptive inference on resource-constrained devices.
- QuantizationMode
Specifies the quantization mode for model optimization and export.
- RLAutoMLAgentType
Defines which reinforcement learning agent families can be explored by AutoML.
- RegularizationType
Specifies the type of regularization to apply to a machine learning model.
- RelationAggregationMethod
Methods for aggregating multiple relation scores in Relation Networks.
- RelationModuleType
Types of relation module architectures for Relation Networks.
- ResNetVariant
Defines the available ResNet (Residual Network) architecture variants.
- SAGEAggregatorType
Aggregation function type for GraphSAGE.
- SSLMethodCategory
Categorizes self-supervised learning methods by their learning paradigm.
- SSLMethodType
Specifies the type of self-supervised learning method to use for representation learning.
- SamplingType
Specifies the method used to sample or combine values when reducing data dimensions.
- SelectionMethod
Methods for selecting individuals for reproduction.
- SequentialFeatureSelectionDirection
Defines the direction of sequential feature selection.
- SimCSEType
Defines the training paradigms for SimCSE (Simple Contrastive Learning of Sentence Embeddings).
- SpikingNeuronType
Specifies the type of spiking neuron model to use in neuromorphic computing simulations.
- SplitCriterion
Specifies the criterion used to determine the best way to split data in decision trees and other tree-based models.
- StepwiseMethod
Specifies the direction of feature selection in stepwise regression and other statistical models.
- SyntheticTabularTaskType
Defines the task type for the synthetic federated tabular benchmark suite.
- TargetPlatform
Target hardware platforms for model deployment and optimization.
- TeacherModelType
Specifies the type of teacher model to use for knowledge distillation.
- TestStatisticType
Represents different types of statistical tests used to evaluate hypotheses and determine significance in data analysis.
- TimeSeriesModelType
Represents different types of time series forecasting models used for analyzing and predicting sequential data over time.
- TrainingStageType
Types of training stages in a multi-stage training pipeline.
- TransformerTaskType
Defines the different types of tasks that transformer-based AI models can perform.
- TreeSearchStrategy
Tree search strategies for exploring the reasoning space in Tree-of-Thoughts.
- UncertaintyQuantificationMethod
Defines the supported uncertainty quantification strategies for inference.
- UnivariateScoringFunction
Defines the scoring functions available for univariate feature selection.
- VGGVariant
Defines the available VGG network architecture variants.
- WaveletType
Defines the different types of biorthogonal wavelets that can be used for signal processing and analysis.
- WeightFunction
Defines different weight functions used in robust statistical methods and machine learning algorithms.
- WindowFunctionType
Defines different window functions used in signal processing and data analysis.
- Word2VecType
Specifies the architecture type for Word2Vec models.