所有类

类	说明
AgglomerativeClustering	An AlgoOperator that performs a hierarchical clustering using a bottom-up approach.
AgglomerativeClusteringParams<T>	Params of AgglomerativeClustering.
ANOVATest	An AlgoOperator which implements the ANOVA test algorithm.
ANOVATestParams<T>	Params for ANOVATest.
Binarizer	A Transformer that binarizes the columns of continuous features by the given thresholds.
BinarizerParams<T>	Params of Binarizer.
BinaryClassificationEvaluator	An AlgoOperator which calculates the evaluation metrics for binary classification.
BinaryClassificationEvaluator.BinaryMetrics	The evaluation metrics for binary classification.
BinaryClassificationEvaluator.BinarySummary	Binary Summary of data in one worker.
BinaryClassificationEvaluatorParams<T>	Params of BinaryClassificationEvaluator.
BinaryLogisticLoss	The loss function for binary logistic loss.
Bucketizer	A Transformer that maps multiple columns of continuous features to multiple columns of discrete features, i.e., buckets indices.
BucketizerParams<T>	Params for Bucketizer.
BucketizerParams.SplitsArrayValidator	Param validator for splitsArray.
ChiSqTest	An AlgoOperator which implements the Chi-square test algorithm.
ChiSqTestParams<T>	Params for ChiSqTest.
CountVectorizer	An Estimator which converts a collection of text documents to vectors of token counts.
CountVectorizerModel	A Model which transforms data using the model data computed by CountVectorizer.
CountVectorizerModelData	Model data of CountVectorizerModel.
CountVectorizerModelData.ModelDataDecoder	Decoder for CountVectorizerModelData.
CountVectorizerModelData.ModelDataEncoder	Encoder for CountVectorizerModelData.
CountVectorizerModelParams<T>	Params for CountVectorizerModel.
CountVectorizerParams<T>	Params of CountVectorizer.
DCT	A Transformer that takes the 1D discrete cosine transform of a real vector.
DCTParams<T>	Params for DCT.
ElementwiseProduct	A Transformer that multiplies each input vector with a given scaling vector using Hadamard product.
ElementwiseProductParams<T>	Params of ElementwiseProduct.
FeatureHasher	A Transformer that transforms a set of categorical or numerical features into a sparse vector of a specified dimension.
FeatureHasherParams<T>	Params of FeatureHasher.
Functions	Built-in table functions for data transformations.
Functions.ArrayToVectorFunction	A ScalarFunction that converts a column of arrays of numeric type into a column of DenseVector instances.
Functions.VectorToArrayFunction	A ScalarFunction that converts a column of Vectors into a column of double arrays.
FValueTest	An AlgoOperator which implements the F-value test algorithm.
FValueTestParams<T>	Params for FValueTest.
HashingTF	A Transformer that maps a sequence of terms(strings, numbers, booleans) to a sparse vector with a specified dimension using the hashing trick.
HashingTF.HashTFFunction	The main logic of HashingTF, which converts the input to a sparse vector.
HashingTFParams<T>	Params of HashingTF.
HasWindows<T>	Interface for the shared windows param.
HingeLoss	The loss function for hinge loss.
IDF	An Estimator that computes the inverse document frequency (IDF) for the input documents.
IDFModel	A Model which transforms data using the model data computed by IDF.
IDFModelData	Model data of IDFModel.
IDFModelData.ModelDataDecoder	Decoder for IDFModelData.
IDFModelData.ModelDataEncoder	Encoder for IDFModelData.
IDFModelParams<T>	Params for IDFModel.
IDFParams<T>	Params for IDF.
Imputer	The imputer for completing missing values of the input columns.
ImputerModel	A Model which replaces the missing values using the model data computed by Imputer.
ImputerModelData	Model data of ImputerModel.
ImputerModelData.ModelDataDecoder	Decoder for ImputerModelData.
ImputerModelData.ModelDataEncoder	Encoder for ImputerModelData.
ImputerModelParams<T>	Params for ImputerModel.
ImputerParams<T>	Params of Imputer.
IndexToStringModel	A Model which transforms input index column(s) to string column(s) using the model data computed by StringIndexer.
IndexToStringModelParams<T>	Params for IndexToStringModel.
Interaction	A Transformer that takes vector or numerical columns, and generates a single vector column that contains the product of all combinations of one value from each input column.
InteractionParams<T>	Params of Interaction.
KBinsDiscretizer	An Estimator which implements discretization (also known as quantization or binning) to transform continuous features into discrete ones.
KBinsDiscretizerModel	A Model which transforms continuous features into discrete features using the model data computed by KBinsDiscretizer.
KBinsDiscretizerModelData	Model data of KBinsDiscretizerModel.
KBinsDiscretizerModelData.ModelDataDecoder	Decoder for KBinsDiscretizerModelData.
KBinsDiscretizerModelData.ModelDataEncoder	Encoder for KBinsDiscretizerModelData.
KBinsDiscretizerModelParams<T>	Params for KBinsDiscretizerModel.
KBinsDiscretizerParams<T>	Params for KBinsDiscretizer.
KMeans	An Estimator which implements the k-means clustering algorithm.
KMeansModel	A Model which clusters data into k clusters using the model data computed by KMeans.
KMeansModelData	Model data of KMeansModel and OnlineKMeansModel.
KMeansModelData.ModelDataDecoder	Data decoder for KMeansModelData.
KMeansModelData.ModelDataEncoder	Data encoder for KMeansModelData.
KMeansModelParams<T>	Params of KMeansModel and OnlineKMeansModel.
KMeansParams<T>	Params of KMeans.
Knn	An Estimator which implements the KNN algorithm.
KnnModel	A Model which classifies data using the model data computed by Knn.
KnnModelData	Model data of KnnModel.
KnnModelData.ModelDataDecoder	Decoder for KnnModelData.
KnnModelData.ModelDataEncoder	Encoder for KnnModelData.
KnnModelParams<T>	Params for KnnModel.
KnnParams<T>	Params for Knn.
LeastSquareLoss	The loss function for least square loss.
LinearRegression	An Estimator which implements the linear regression algorithm.
LinearRegressionModel	A Model which predicts data using the model data computed by LinearRegression.
LinearRegressionModelData	Model data of LinearRegressionModel.
LinearRegressionModelData.ModelDataDecoder	Data decoder for LinearRegressionModel.
LinearRegressionModelData.ModelDataEncoder	Data encoder for LinearRegressionModel.
LinearRegressionModelParams<T>	Params for LinearRegressionModel.
LinearRegressionParams<T>	Params for LinearRegression.
LinearSVC	An Estimator which implements the linear support vector classification.
LinearSVCModel	A Model which classifies data using the model data computed by LinearSVC.
LinearSVCModelData	Model data of LinearSVCModel.
LinearSVCModelData.ModelDataDecoder	Data decoder for LinearSVCModel.
LinearSVCModelData.ModelDataEncoder	Data encoder for LinearSVCModel.
LinearSVCModelParams<T>	Params for LinearSVCModel.
LinearSVCParams<T>	Params for LinearSVC.
LogisticRegression	An Estimator which implements the logistic regression algorithm.
LogisticRegressionModel	A Model which classifies data using the model data computed by LogisticRegression.
LogisticRegressionModelDataUtil	The utility class which provides methods to convert model data from Table to Datastream, and classes to save/load model data.
LogisticRegressionModelDataUtil.ModelDataDecoder	Data decoder for LogisticRegression and OnlineLogisticRegression.
LogisticRegressionModelDataUtil.ModelDataEncoder	Data encoder for LogisticRegression and OnlineLogisticRegression.
LogisticRegressionParams<T>	Params for LogisticRegression.
LossFunc	A loss function is to compute the loss and gradient with the given coefficient and training data.
LSHModelParams<T>	Params for LSHModel.
LSHParams<T>	Params for LSH.
MaxAbsScaler	An Estimator which implements the MaxAbsScaler algorithm.
MaxAbsScalerModel	A Model which transforms data using the model data computed by MaxAbsScaler.
MaxAbsScalerModelData	Model data of MaxAbsScalerModel.
MaxAbsScalerModelData.ModelDataDecoder	Decoder for MaxAbsScalerModelData.
MaxAbsScalerModelData.ModelDataEncoder	Encoder for MaxAbsScalerModelData.
MaxAbsScalerParams<T>	Params for MaxAbsScaler.
MinHashLSH	An Estimator that implements the MinHash LSH algorithm, which supports LSH for Jaccard distance.
MinHashLSHModel	A Model which generates hash values using the model data computed by MinHashLSH.
MinHashLSHModelData	Model data of MinHashLSHModel.
MinHashLSHModelData.ModelDataEncoder	Encoder for MinHashLSHModelData.
MinHashLSHParams<T>	Params for MinHashLSH.
MinMaxScaler	An Estimator which implements the MinMaxScaler algorithm.
MinMaxScaler.MinMaxReduceFunctionOperator	A stream operator to compute the min and max values in each partition of the input bounded data stream.
MinMaxScalerModel	A Model which transforms data using the model data computed by MinMaxScaler.
MinMaxScalerModelData	Model data of MinMaxScalerModel.
MinMaxScalerModelData.ModelDataDecoder	Decoder for MinMaxScalerModelData.
MinMaxScalerModelData.ModelDataEncoder	Encoder for MinMaxScalerModelData.
MinMaxScalerParams<T>	Params for MinMaxScaler.
NaiveBayes	An Estimator which implements the naive bayes classification algorithm.
NaiveBayesModel	A Model which classifies data using the model data computed by NaiveBayes.
NaiveBayesModelData	Model data of NaiveBayesModel.
NaiveBayesModelData.ModelDataDecoder	Data decoder for the NaiveBayesModelData.
NaiveBayesModelData.ModelDataEncoder	Data encoder for the NaiveBayesModelData.
NaiveBayesModelParams<T>	Params of NaiveBayesModel.
NaiveBayesParams<T>	Params of NaiveBayes.
NGram	A Transformer that converts the input string array into an array of n-grams, where each n-gram is represented by a space-separated string of words.
NGram.NGramUdf	The main logic of NGram, which converts the input string array to an array of n-grams.
NGramParams<T>	Params of NGram.
Normalizer	A Transformer that normalizes a vector to have unit norm using the given p-norm.
NormalizerParams<T>	Params of Normalizer.
OneHotEncoder	An Estimator which implements the one-hot encoding algorithm.
OneHotEncoderModel	A Model which encodes data into one-hot format using the model data computed by OneHotEncoder.
OneHotEncoderModelData	Model data of OneHotEncoderModel.
OneHotEncoderModelData.ModelDataEncoder	Data encoder for the OneHotEncoder model data.
OneHotEncoderModelData.ModelDataStreamFormat	Data decoder for the OneHotEncoder model data.
OneHotEncoderParams<T>	Params of OneHotEncoderModel.
OnlineKMeans	OnlineKMeans extends the function of KMeans, supporting to train a K-Means model continuously according to an unbounded stream of train data.
OnlineKMeansModel	OnlineKMeansModel can be regarded as an advanced KMeansModel operator which can update model data in a streaming format, using the model data provided by OnlineKMeans.
OnlineKMeansParams<T>	Params of OnlineKMeans.
OnlineLogisticRegression	An Estimator which implements the online logistic regression algorithm.
OnlineLogisticRegressionModel	A Model which classifies data using the model data computed by OnlineLogisticRegression.
OnlineLogisticRegressionModelParams<T>	Params for OnlineLogisticRegressionModel.
OnlineLogisticRegressionParams<T>	Params of OnlineLogisticRegression.
OnlineStandardScaler	An Estimator which implements the online standard scaling algorithm, which is the online version of StandardScaler.
OnlineStandardScalerModel	A Model which transforms data using the model data computed by OnlineStandardScaler.
OnlineStandardScalerModelParams<T>	Params for OnlineStandardScalerModel.
OnlineStandardScalerParams<T>	Params for OnlineStandardScaler.
Optimizer	An optimizer is a function to modify the weight of a machine learning model, which aims to find the optimal parameter configuration for a machine learning model.
PolynomialExpansion	A Transformer that expands the input vectors in polynomial space.
PolynomialExpansionParams<T>	Params of PolynomialExpansion.
PriorityQueueSerializer<T>	TypeSerializer for PriorityQueue.
PriorityQueueTypeInfo<T>	TypeInformation for PriorityQueue.
QuantileSummary	Helper class to compute an approximate quantile summary.
QuantileSummary.StatsTuple	Wrapper class to hold all statistics from the Greenwald-Khanna paper.
QuantileSummaryTypeInfoFactory	Used by TypeExtractor to create a TypeInformation for implementations of QuantileSummary.
RandomSplitter	An AlgoOperator which splits a Table into N Tables according to the given weights.
RandomSplitterParams<T>	Params of RandomSplitter.
RegexTokenizer	A Transformer which converts the input string to lowercase and then splits it by white spaces based on regex.
RegexTokenizer.RegexTokenizerUdf	The main logic of $RegexTokenizer, which converts the input string to an array of tokens.
RegexTokenizerParams<T>	Params for RegexTokenizer.
RobustScaler	An Estimator which scales features using statistics that are robust to outliers.
RobustScalerModel	A Model which transforms data using the model data computed by RobustScaler.
RobustScalerModelData	Model data of RobustScalerModel.
RobustScalerModelData.ModelDataDecoder	Data decoder for the RobustScalerModel model data.
RobustScalerModelData.ModelDataEncoder	Data encoder for the RobustScalerModel model data.
RobustScalerModelParams<T>	Params for RobustScalerModel.
RobustScalerParams<T>	Params for RobustScaler.
SGD	Stochastic Gradient Descent (SGD) is the mostly wide-used optimizer for optimizing machine learning models.
SQLTransformer	SQLTransformer implements the transformations that are defined by SQL statement.
SQLTransformerParams<T>	Params for SQLTransformer.
SQLTransformerParams.SQLStatementValidator	Param validator for SQL statements.
StandardScaler	An Estimator which implements the standard scaling algorithm.
StandardScalerModel	A Model which transforms data using the model data computed by StandardScaler.
StandardScalerModelData	Model data of StandardScalerModel.
StandardScalerModelData.ModelDataDecoder	Data decoder for the StandardScalerModel model data.
StandardScalerModelData.ModelDataEncoder	Data encoder for the StandardScalerModel model data.
StandardScalerParams<T>	Params for StandardScaler.
StopWordsRemover	A feature transformer that filters out stop words from input.
StopWordsRemover.RemoveStopWordsFunction	A Scalar Function that removes stop words from input string array.
StopWordsRemoverParams<T>	Params of StopWordsRemover.
StringIndexer	An Estimator which implements the string indexing algorithm.
StringIndexerModel	A Model which transforms input string/numeric column(s) to double column(s) using the model data computed by StringIndexer.
StringIndexerModelData	Model data of StringIndexerModel and IndexToStringModel.
StringIndexerModelData.ModelDataDecoder	Data decoder for StringIndexerModel and IndexToStringModel.
StringIndexerModelData.ModelDataEncoder	Data encoder for StringIndexerModel and IndexToStringModel.
StringIndexerModelParams<T>	Params of StringIndexerModel.
StringIndexerParams<T>	Params of StringIndexer.
Swing	An AlgoOperator which implements the Swing algorithm.
SwingParams<T>	Params for Swing.
Tokenizer	A Transformer which converts the input string to lowercase and then splits it by white spaces.
Tokenizer.TokenizerUdf	The main logic of Tokenizer, which converts the input string to an array of tokens.
TokenizerParams<T>	Params of Tokenizer.
UnivariateFeatureSelector	An Estimator which selects features based on univariate statistical tests against labels.
UnivariateFeatureSelectorModel	A Model which transforms data using the model data computed by UnivariateFeatureSelector.
UnivariateFeatureSelectorModelData	Model data of UnivariateFeatureSelectorModel.
UnivariateFeatureSelectorModelData.ModelDataDecoder	Decoder for UnivariateFeatureSelectorModelData.
UnivariateFeatureSelectorModelData.ModelDataEncoder	Encoder for UnivariateFeatureSelectorModelData.
UnivariateFeatureSelectorModelParams<T>	Params for UnivariateFeatureSelectorModel.
UnivariateFeatureSelectorParams<T>	Params for UnivariateFeatureSelector.
VarianceThresholdSelector	An Estimator which implements the VarianceThresholdSelector algorithm.
VarianceThresholdSelectorModel	A Model which removes low-variance data using the model data computed by VarianceThresholdSelector.
VarianceThresholdSelectorModelData	Model data of VarianceThresholdSelectorModel.
VarianceThresholdSelectorModelData.ModelDataDecoder	Decoder for VarianceThresholdSelectorModelData.
VarianceThresholdSelectorModelData.ModelDataEncoder	Encoder for VarianceThresholdSelectorModelData.
VarianceThresholdSelectorModelParams<T>	Params for VarianceThresholdSelectorModel.
VarianceThresholdSelectorParams<T>	Params of VarianceThresholdSelectorModel.
VectorAssembler	A Transformer which combines a given list of input columns into a vector column.
VectorAssemblerParams<T>	Params of VectorAssembler.
VectorIndexer	An Estimator which implements the vector indexing algorithm.
VectorIndexerModel	A Model which encodes input vector to an output vector using the model data computed by VectorIndexer.
VectorIndexerModelData	Model data of VectorIndexerModel.
VectorIndexerModelData.ModelDataDecoder	Data decoder for VectorIndexerModel.
VectorIndexerModelData.ModelDataEncoder	Data encoder for VectorIndexerModel.
VectorIndexerModelParams<T>	Params for VectorIndexerModel.
VectorIndexerParams<T>	Params of VectorIndexer.
VectorSlicer	A Transformer that transforms a vector to a new feature, which is a sub-array of the original feature.
VectorSlicerParams<T>	Params of VectorSlicer.
VectorUtils	Provides utility functions for Vector.