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