# All Classes

- All Classes Interface Summary Class Summary Enum Summary
  Class | Description
  --- | ---
  AbstractQlt<T extends com.tibco.patterns.learn.jaxb.model.SearchQueryBaseType> | Stores a single querylet in an XML structure.
  AddPairsResult | Contains sets of pairs that were added to each dataset, and collections of pairs that were not added to the project for various reasons.
  COErrorRate | Stops the training when the error rate over validation dataset during the last iteration is below the given threshold.
  COErrorRate.IterationResult | Stores statistics for a full or partial training iteration
  COErrorRateMin | Stops the training after a minimum error rate for the validation dataset was found and this result was not improved during the specified number of subsequent iterations.
  CognateQlt | A Cognate querylet for matching several related text fields where values may be entered into a wrong field.
  COIterationCount | A simple class that counts iterations and stops training after the given maximum number of iterations has been reached.
  ConfDefault | Settings for the default model confidence measure.
  ConfFeature | Feature based confidence measure.
  Confidence | Defines a confidence measure and its options.
  ConfNone | Turns off calculation of a confidence measure.
  ConfQFeature | Extends the ConfFeature class to provide means to configure a NetricsQuery.
  ConfQNone | Extends the ConfNone class to provide means to configure a NetricsQuery.
  ConfQuery | Defines a confidence measure that can be used in a Patterns server RLink query.
  ConfRatio | Weighted true/false ratio confidence measure.
  ConfRatioLabel | Label based confidence measure.
  ConfStats | Confidence measure based on submodel statistics.
  ConvergenceObserver | Defines an object that makes decisions when to stop the training.
  COScoreChange | Stops the training when the maximum change in prediction scores compared with the previous iteration is below the given threshold.
  DataPartition | Defines the type of a dataset: Training, Validation, Test, Reserve, Low Confidence, False Subsets.
  DatasetResult | Stores model training results for one dataset.
  DatasetStats | Stores statistics about existing pairs in a dataset, grouped by subset: the number of pairs with each label and the total number of labeled pairs.
  DateQlt | A Date querylet for comparing the similarity of date values.
  Feature<T extends com.tibco.patterns.learn.jaxb.model.FeatureType> | A user-defined feature composed of one or more querylets.
  FeatureListener | Classes implementing this interface receive notifications when feature query changes.
  FeatureQuery | Supports a feature query that can contain all types of features.
  FeatureVector | Stores a feature vector that can be predicted by a Learn model.
  FieldStats | Stores statistics for one table field.
  GenderFeature | A feature with 2 predicate querylets that mean: both records are male; both records are female.
  GenericFeature | A generic feature that has a single querylet of any type.
  Hint | Any suggestion that is given to the user based on the current state of the project.
  HintAddLabel | Hint to add pairs for subsets that have too few True/False labels.
  HintAddPairs | A hint to select additional record pairs for specified subsets.
  HintAddUnderrepr | Hint to add pairs to subsets that are underrepresented in training dataset.
  HintAddUntrained | Hint to add pairs to subsets that are present in validation dataset, but are not in training dataset.
  HintAddUntrainedInTable | Hint to add pairs to subsets that are not found in either training or validation datasets, but are found in the data table.
  HintReviewContra | A hint to review pairs that have contradicting pairs.
  HintReviewMislabeled | A hint to review possibly mislabeled pairs.
  HintReviewPairs | A hint to review the labels for a list of existing record pairs.
  LowConfPairFinder | Finds new low confidence pairs to be labeled and added to the project.
  ModelConfig | Stores a configuration of an RLink model and builds model(s) with this configuration.
  ModelSettings | Stores all settings for a single Patterns Learn model.xml file.
  ModelTrainConfig | Defines configuration for creating and training a Learn model.
  ModelTrainer | Functionality to communicate with Patterns server, load data table, calculate feature scores, train and evaluate model.
  Partition | Defines the type of a dataset: Training or Validation.
  PersonNameFeature | A feature with 3 querylets: Simple: first name, with thesaurus; Simple: last name; Cognate: first, middle (optional) and last name fields, with thesaurus, non-cognate weight 0.8, empty field penalty 0.1.
  PredicateQlt | A Predicate querylet for evaluating a predicate expression.
  PredictOptions | Holds all options for calculating prediction using a Learn model.
  ProjectFiles | Manages all files for one Patterns Learn project.
  ProjectSettings | Stores all settings for a single Patterns Learn project.xml file.
  Querylet | Defines shared functionality for a single Learn querylet.
  RecPair | Stores information about one record pair, including field values, feature scores and label.
  RecPair.ConfScoreComparator | Provides ordering by a combination (weighted sum) of the prediction confidence (ascending) and the distance of the model score from the 0.5 threshold (ascending).
  RecPair.Label | Defines a label that can be assigned to a record pair: True, False or Unsure.
  RecPairExample | A training example constructed based on a pair of database records.
  RecPairExperiment | Manages a training dataset and a validation dataset.
  RecPairId | Stores a pair of keys.
  RecPairMap | Manages datasets in the XML structure in sync with a map of all record pairs that are stored in these datasets.
  RecPairScorer | Manages calculation of feature scores and communication with Patterns server.
  RecPairSet | A container for storing a set of RecPairExample training examples.
  RLink | Performs communication with rlink_jni library using JNI to create, load, save, train and evaluate RLink models.
  RLink.SubsetTrainMode | Defines methods used to generate training examples for subsets of given training example
  RLink.ThermometerType | Type of thermometers used in RLink model.
  RLinkDataSet<T extends VectorExample> | A container for storing a set of training examples suitable for model training.
  RLinkExperiment<T extends VectorExample> | Manages all data required for a model training experiment: training and validation datasets.
  RLinkOut | Stores the result of RLink model prediction.
  RLinkServerInterface | Provides more convenient communication with the Patterns server in situations that are frequently used in record pair scoring for Learn.
  SimpleQlt | A Simple querylet for matching one field or concatenated string values of several fields.
  STAll | Specifies training of all subsets with generated examples.
  STDynamic | Specifies dynamic training of subsets.
  STFixed | Specifies training up to a fixed depth of subsets, defined by percentage of missing information in the subset.
  STNone | Specifies training with actual examples only, without any generated examples for submodels.
  STTaper | Specifies the use of a random tapering function to generate some examples for subsets.
  SubsetFamily | Stores a family (a set) of RLink subsets.
  SubsetLabelPairMap | Stores a family (a set) of RLink subsets.
  SubsetTrainConfig | Defines configuration parameters for one of the subset training modes.
  TextQlt<T extends com.tibco.patterns.learn.jaxb.model.SearchQueryBaseType> | Provides common functionality for querylets that use approximate text matching (Simple and Cognate querylets).
  ThermoCsvDump | Dumps thermometers to CSV files
  Thesaurus | Represents thesaurus file that can be used in multiple querylets.
  Thesaurus.Type | The type of a thesaurus file.
  Trainer | Implements automated training for a single RLink model.
  TrainingObserver | Defines an object that gathers training statistics for a certain dataset.
  ValueFreq | Stores one field value and its frequency.
  VectorExample | Stores a feature vector and a label that are used for training the model.
  VectorExperiment | Manages a training dataset and a validation dataset.
  VectorSet | A container for storing a set of VectorExample training examples.