123 lines
3.9 KiB
ObjectPascal
123 lines
3.9 KiB
ObjectPascal
/// Основной модуль библиотеки машинного обучения.
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/// Объединяет модели, метрики, валидацию и вспомогательные компоненты.
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unit MLABC;
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interface
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uses LinearAlgebraML;
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uses ValidationML;
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uses MLCoreABC;
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uses MLModelsABC;
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uses MetricsABC;
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uses PreprocessorABC;
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uses DataFrameABC;
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uses DataFrameABCCore;
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uses MLExceptions;
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uses InspectionML;
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uses MLPipelineABC;
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uses MLDatasets;
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uses DataAdapters;
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type
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Vector = LinearAlgebraML.Vector;
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Matrix = LinearAlgebraML.Matrix;
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Validation = ValidationML.Validation;
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Metrics = MetricsABC.Metrics;
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ClassificationMetrics = MetricsABC.ClassificationMetrics;
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RegressionMetrics = MetricsABC.RegressionMetrics;
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ClusteringMetrics = MetricsABC.ClusteringMetrics;
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ConfusionMatrix = MetricsABC.ConfusionMatrix;
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DataPipeline = MLPipelineABC.DataPipeline;
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DataFrame = DataFrameABC.DataFrame;
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DataFrameCursor = DataFrameABCCore.DataFrameCursor;
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Statistics = DataFrameABC.Statistics;
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CsvLoader = DataFrameABC.CsvLoader;
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JoinKind = DataFrameABC.JoinKind;
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GroupView = DataFrameABC.GroupView;
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IProbabilisticClassifier = MLCoreABC.IProbabilisticClassifier;
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IRegressor = MLCoreABC.IRegressor;
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StandardScaler = MLModelsABC.StandardScaler;
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PCATransformer = MLModelsABC.PCATransformer;
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MinMaxScaler = MLModelsABC.MinMaxScaler;
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VarianceThreshold = MLModelsABC.VarianceThreshold;
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SelectKBest = MLModelsABC.SelectKBest;
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FeatureScore = MLModelsABC.FeatureScore;
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Normalizer = MLModelsABC.Normalizer;
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NormType = MLModelsABC.NormType;
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Activations = MLModelsABC.Activations;
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Pipeline = MLModelsABC.Pipeline;
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LinearRegression = MLModelsABC.LinearRegression;
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LogisticRegression = MLModelsABC.LogisticRegression;
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RidgeRegression = MLModelsABC.RidgeRegression;
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ElasticNet = MLModelsABC.ElasticNet;
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DecisionTreeClassifier = MLModelsABC.DecisionTreeClassifier;
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DecisionTreeRegressor = MLModelsABC.DecisionTreeRegressor;
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RandomForestRegressor = MLModelsABC.RandomForestRegressor;
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RandomForestClassifier = MLModelsABC.RandomForestClassifier;
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GradientBoostingRegressor = MLModelsABC.GradientBoostingRegressor;
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GradientBoostingClassifier = MLModelsABC.GradientBoostingClassifier;
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KNNClassifier = MLModelsABC.KNNClassifier;
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KNNRegressor = MLModelsABC.KNNRegressor;
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KMeans = MLModelsABC.KMeans;
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DBSCAN = MLModelsABC.DBSCAN;
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KNNWeighting = MLModelsABC.KNNWeighting;
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TGBLoss = MLModelsABC.TGBLoss;
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TMaxFeaturesMode = MLModelsABC.TMaxFeaturesMode;
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MLException = MLExceptions.MLException;
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MLNotFittedException = MLExceptions.MLNotFittedException;
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MLDimensionException = MLExceptions.MLDimensionException;
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Inspection = InspectionML.Inspection;
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IPreprocessor = PreprocessorABC.IPreprocessor;
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LabelEncoder = PreprocessorABC.LabelEncoder;
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OneHotEncoder = PreprocessorABC.OneHotEncoder;
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ImputeStrategy = PreprocessorABC.ImputeStrategy;
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Imputer = PreprocessorABC.Imputer;
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Datasets = MLDatasets.Datasets;
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Dataset = MLDatasets.Dataset;
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IModel = MLCoreABC.IModel;
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ISupervisedModel = MLCoreABC.ISupervisedModel;
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IUnSupervisedModel = MLCoreABC.IUnSupervisedModel;
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UPipeline = MLModelsABC.UPipeline;
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UDataPipeline = MLPipelineABC.UDataPipeline;
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TaskKind = MLPipelineABC.TaskKind;
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AggregationKind = DataFrameABC.AggregationKind;
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const
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akMean = AggregationKind.akMean;
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akMin = AggregationKind.akMin;
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akMax = AggregationKind.akMax;
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akCount = AggregationKind.akCount;
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akSum = AggregationKind.akSum;
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akStd = AggregationKind.akStd;
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function LabelsToInts(y: Vector): array of integer;
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function EncodeLabels(labels: array of string): array of integer;
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implementation
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function LabelsToInts(y: Vector): array of integer;
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begin
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Result := DataAdapters.LabelsToInts(y);
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end;
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function EncodeLabels(labels: array of string): array of integer := DataAdapters.EncodeLabels(labels);
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end. |