85 lines
2.6 KiB
ObjectPascal
85 lines
2.6 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 MLExceptions;
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uses InspectionML;
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uses MLPipelineABC;
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uses MLDatasets;
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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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ConfusionMatrix = MetricsABC.ConfusionMatrix;
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Metrics = MetricsABC.Metrics;
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DataPipeline = MLPipelineABC.DataPipeline;
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DataFrame = DataFrameABC.DataFrame;
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Statistics = DataFrameABC.Statistics;
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CsvLoader = DataFrameABC.CsvLoader;
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IProbabilisticClassifier = MLCoreABC.IProbabilisticClassifier;
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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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implementation
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end. |