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Packages that use DataRow | |
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org.knime.base.data.append.column | |
org.knime.base.data.append.row | |
org.knime.base.data.bitvector | This package contains classes responsible for the representation and conversion of bit vectors. |
org.knime.base.data.filter.column | Contains column filter for DataTable objects which
includes or excludes a certain number of columns from a given data table. |
org.knime.base.data.filter.row | Contains row filter for DataTable objects which uses
the FilterRowGenerator interface to dynamically ask for included or
excluded rows during itertation. |
org.knime.base.data.join | Implementation of a DataTable that joins to underlying tables based on their row key (like a Database join). |
org.knime.base.data.normalize | This package contains the utility classes for normalization of DataRows. |
org.knime.base.data.replace | |
org.knime.base.data.sort | |
org.knime.base.data.statistics | This package holds the StatisticsTable. |
org.knime.base.node.flowvariable.tablerowtovariable | |
org.knime.base.node.io.arffreader | Contains the implementation of a node which reads ARFF files. |
org.knime.base.node.io.def | (Obsolete) Node that produces predefined DataTables. |
org.knime.base.node.io.filereader | Contains a node implementation that reads in data from an ASCII file. |
org.knime.base.node.mine.bayes.naivebayes.datamodel | This package implements the naive bayes algorithm for numerical and nominal attributes. |
org.knime.base.node.mine.bayes.naivebayes.predictor | This package contains the classes of the predictor node. |
org.knime.base.node.mine.bfn | Contains abstract and util classes to train and perform prediction to rule models, also called BasisFunction models. |
org.knime.base.node.mine.bfn.fuzzy | Contains the learner and predictor to train fuzzy rules and apply them to unknown data. |
org.knime.base.node.mine.bfn.radial | Contains the PNN learner and predictor. |
org.knime.base.node.mine.cluster.assign | This package contains the classes for the Cluster Assigner Node. |
org.knime.base.node.mine.cluster.fuzzycmeans | The fuzzycmeans package contains all classes for the Fuzzy c-means node. |
org.knime.base.node.mine.cluster.hierarchical | Contains the implementation of a node performing hierarchical clustering. |
org.knime.base.node.mine.cluster.hierarchical.distfunctions | Contains distance function implementations for the Hierarchical Clustering node. |
org.knime.base.node.mine.decisiontree2 | |
org.knime.base.node.mine.decisiontree2.model | This package contains code for a decision tree model. |
org.knime.base.node.mine.mds | A node that applies multi dimensional scaling (MDS) on high dimensional data. |
org.knime.base.node.mine.mds.distances | The package contains distance classes, computing various kinds of distances between DataRows and DataPoints ect. |
org.knime.base.node.mine.mds.mdsprojection | A node that applies multi dimensional scaling (MDS) projection on high dimensional data. |
org.knime.base.node.mine.pca | This package contains all classes for the PCA (principal component analysis) Node. |
org.knime.base.node.mine.regression | Contains nodes and utility classes for linear and polynomial regression. |
org.knime.base.node.mine.regression.linear | Contains classes for linear regression. |
org.knime.base.node.mine.sota.distances | Contains classes to compute distances for SOTA. |
org.knime.base.node.mine.sota.logic | Contains the logic classes of SOTA. |
org.knime.base.node.mine.sota.predictor | Contains the Sotapredictor node, which can be used for class prediction of incoming data. |
org.knime.base.node.mine.subgroupminer.freqitemset | This package contains the necessary data structures for the subgroup mining. |
org.knime.base.node.mine.svm.predictor | This subpackage contains all classes for the SVM Predictor Node. |
org.knime.base.node.parallel.appender | |
org.knime.base.node.parallel.builder | |
org.knime.base.node.preproc.binner | Contains a node to bin/group a number of numeric columns into a string column by predefined intervals. |
org.knime.base.node.preproc.cellsplit | Contains the implementation of a node that splits (and replaces) a values in a column into multiple new ones. |
org.knime.base.node.preproc.cellsplitbypos | |
org.knime.base.node.preproc.columnTrans | This package contains the classes of the Many2One and One2Many column transformation nodes. |
org.knime.base.node.preproc.filter.row | Contains a node filtering out rows from an input data table, including only those rows into the output/result table that match a certain criteria. |
org.knime.base.node.preproc.filter.row.rowfilter | Contains all filters currently implemented for the row filter node. |
org.knime.base.node.preproc.missingval | Implemenation of the node that treats missing values. |
org.knime.base.node.preproc.sample | Node that samples rows from an input table. |
org.knime.base.node.preproc.setoperator | Contains a node to perform a set operation on two columns from two input tables. |
org.knime.base.node.rules | |
org.knime.base.node.util | |
org.knime.base.node.viz.histogram.datamodel | This package contains all data and visualization models which are used in the different histogram implementations. |
org.knime.base.node.viz.histogram.impl.interactive | This is the interactive implementation of the abstract histogram plotter. |
org.knime.base.node.viz.pie.datamodel.fixed | This package contains the fixed pie chart data model classes. |
org.knime.base.node.viz.pie.datamodel.interactive | This package contains the interactive pie chart data model classes. |
org.knime.base.node.viz.plotter.dendrogram | Contains all classes necessary to visualize a hierachical clustering result
represented by a DendrogramNode . |
org.knime.core.data | Contains the interface definitions of the DataCell
and DataTable and related
classes, used to store and access the actual data. |
org.knime.core.data.collection | Definitions for KNIME collection types and default implementations. |
org.knime.core.data.container |
Implementation of a DataContainer . |
org.knime.core.data.def | Provides default implementations for all (except
DataTable ) abstract
classes in the data package.The default implementations of classes derived from DataCell just store their
values in members of the appropriate java type (like
DoubleCell has a private
member of Java type double, etc.).The default implementation of DataTable
is deprecated and shouldn't be used. |
org.knime.core.node.tableview | Node implementation of a table view. |
Uses of DataRow in org.knime.base.data.append.column |
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Classes in org.knime.base.data.append.column that implement DataRow | |
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class |
AppendedColumnRow
A DataRow that is extended by one or more
cells. |
Methods in org.knime.base.data.append.column that return DataRow | |
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DataRow |
AppendedColumnRowIterator.next()
Returns the next DataRow . |
Methods in org.knime.base.data.append.column with parameters of type DataRow | |
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DataCell[] |
AppendedCellFactory.getAppendedCell(DataRow row)
Get the new cells for a given row. |
DataCell[] |
DefaultAppendedCellFactory.getAppendedCell(DataRow row)
Get the value to row's key. |
Constructors in org.knime.base.data.append.column with parameters of type DataRow | |
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AppendedColumnRow(DataRow baseRow,
DataCell... appendCell)
Creates new Row with baseRow providing the first cells and
appendCell as last cells. |
|
AppendedColumnRow(DataRow baseRow,
DataRow appendedRow,
boolean[] appendColumn)
Create a new row with the baseRow providing the first cells
and appendedRow providing the following cells. |
|
AppendedColumnRow(RowKey rowKey,
DataRow baseRow,
DataCell... appendCell)
Creates new Row with baseRow providing the first cells and
appendCell as last cells. |
|
AppendedColumnRow(RowKey rowKey,
DataRow baseRow,
DataRow appendedRow)
Create a new row with the baseRow providing the first cells
and appendedRow providing the following cells. |
|
AppendedColumnRow(RowKey rowKey,
DataRow baseRow,
DataRow appendedRow,
boolean[] appendColumn)
Create a new row with the baseRow providing the first cells
and appendedRow providing the following cells. |
Uses of DataRow in org.knime.base.data.append.row |
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Classes in org.knime.base.data.append.row that implement DataRow | |
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class |
ResortedCellsRow
A row that takes a base row and re-sorts the cells in it according to an int[] parameter passed in the constructor. |
Methods in org.knime.base.data.append.row that return DataRow | |
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DataRow |
AppendedRowsIterator.next()
Returns the next DataRow . |
Constructors in org.knime.base.data.append.row with parameters of type DataRow | |
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ResortedCellsRow(DataRow row,
int[] sort)
Creates new row with row as underlying base row and
sort the new sorting scheme. |
Uses of DataRow in org.knime.base.data.bitvector |
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Methods in org.knime.base.data.bitvector with parameters of type DataRow | |
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DataCell |
IdString2BitVectorCellFactory.getCell(DataRow row)
Called from getCells. |
DataCell |
Numeric2BitVectorMeanCellFactory.getCell(DataRow row)
Called from getCells. |
DataCell |
Hex2BitVectorCellFactory.getCell(DataRow row)
Called from getCells. |
DataCell |
BitString2BitVectorCellFactory.getCell(DataRow row)
Called from getCells. |
DataCell |
Numeric2BitVectorThresholdCellFactory.getCell(DataRow row)
Called from getCells. |
Uses of DataRow in org.knime.base.data.filter.column |
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Classes in org.knime.base.data.filter.column that implement DataRow | |
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class |
FilterColumnRow
Filter DataRow which extracts particular cells (columns) from an
underlying row. |
Methods in org.knime.base.data.filter.column that return DataRow | |
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DataRow |
CloseableFilterColumnRowIterator.next()
Returns the next DataRow . |
DataRow |
FilterColumnRowIterator.next()
Returns the next DataRow . |
Constructors in org.knime.base.data.filter.column with parameters of type DataRow | |
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FilterColumnRow(DataRow row,
int[] columns)
Inits a new filter column DataRow with the underling row and an
array of indices into this row. |
Uses of DataRow in org.knime.base.data.filter.row |
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Methods in org.knime.base.data.filter.row that return DataRow | |
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DataRow |
FilterRowIterator.next()
Returns the next DataRow . |
Methods in org.knime.base.data.filter.row with parameters of type DataRow | |
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boolean |
DoubleCellFilterRowGenerator.isIn(DataRow row)
Checks if the given row lies within the define interval borders. |
boolean |
FilterRowGenerator.isIn(DataRow row)
|
Uses of DataRow in org.knime.base.data.join |
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Methods in org.knime.base.data.join that return DataRow | |
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DataRow |
JoinedTableRowIterator.next()
Returns the next DataRow . |
DataRow |
InMemoryIterator.next()
Returns the next DataRow . |
Uses of DataRow in org.knime.base.data.normalize |
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Methods in org.knime.base.data.normalize that return DataRow | |
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DataRow |
AffineTransRowIterator.next()
Returns the next DataRow . |
Uses of DataRow in org.knime.base.data.replace |
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Classes in org.knime.base.data.replace that implement DataRow | |
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class |
ReplacedColumnsDataRow
|
Methods in org.knime.base.data.replace that return DataRow | |
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DataRow |
ReplacedColumnsRowIterator.next()
Returns the next DataRow . |
Methods in org.knime.base.data.replace with parameters of type DataRow | |
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abstract DataCell |
ReplacedCellFactory.getReplacement(DataRow row,
int column)
Computes the data cell that should replace the column -th
column in the given row. |
DataCell[] |
ReplacedCellFactory.getReplacement(DataRow row,
int[] columns)
Returns an array of length 1 containing the replacement of the data cell at the position given in the first element of the columns array. |
DataCell[] |
ReplacedCellsFactory.getReplacement(DataRow row,
int[] columns)
Computes the data cells that should replace the column -th
column in the given row. |
Constructors in org.knime.base.data.replace with parameters of type DataRow | |
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ReplacedColumnsDataRow(DataRow row,
DataCell[] newCells,
int[] columns)
Creates a new replaced column row. |
|
ReplacedColumnsDataRow(DataRow row,
DataCell newCell,
int column)
Convenience constructor that replaces one cell only. |
Uses of DataRow in org.knime.base.data.sort |
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Constructor parameters in org.knime.base.data.sort with type arguments of type DataRow | |
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SortedTable(BufferedDataTable dataTable,
Comparator<DataRow> rowComparator,
boolean sortInMemory,
ExecutionContext exec)
Creates a new sorted table. |
Uses of DataRow in org.knime.base.data.statistics |
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Methods in org.knime.base.data.statistics with parameters of type DataRow | |
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protected void |
StatisticsTable.calculateMomentInSubClass(DataRow row)
Deprecated. Derived classes may do additional calculations here. |
Uses of DataRow in org.knime.base.node.flowvariable.tablerowtovariable |
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Methods in org.knime.base.node.flowvariable.tablerowtovariable with parameters of type DataRow | |
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protected void |
TableToVariableNodeModel.pushVariables(DataTableSpec variablesSpec,
DataRow currentVariables)
Pushes the variable as given by the row argument onto the stack. |
Uses of DataRow in org.knime.base.node.io.arffreader |
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Methods in org.knime.base.node.io.arffreader that return DataRow | |
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DataRow |
ARFFRowIterator.next()
Returns the next DataRow . |
Uses of DataRow in org.knime.base.node.io.def |
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Constructors in org.knime.base.node.io.def with parameters of type DataRow | |
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DefaultTableNodeFactory(DataRow[] rows,
DataTableSpec spec)
Also this constructor is available in DefaultTable . |
|
DefaultTableNodeFactory(DataRow[] rows,
String[] columnNames,
DataType[] columnTypes)
We provide the same constructors as the DefaultTable . |
|
DefaultTableNodeModel(DataRow[] rows,
DataTableSpec spec)
Also this constructor is available in DefaultTable . |
|
DefaultTableNodeModel(DataRow[] rows,
String[] columnNames,
DataType[] columnTypes)
|
Uses of DataRow in org.knime.base.node.io.filereader |
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Methods in org.knime.base.node.io.filereader that return DataRow | |
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(package private) DataRow |
FileReaderException.getErrorRow()
|
DataRow |
FileReaderPreviewRowIterator.next()
Returns the next DataRow . |
DataRow |
FileRowIterator.next()
Returns the next DataRow . |
Constructors in org.knime.base.node.io.filereader with parameters of type DataRow | |
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FileReaderException(String msg,
DataRow faultyRow,
int lineNumber)
Constructor for an exception that stores the last (partial) row where things went wrong. |
Uses of DataRow in org.knime.base.node.mine.bayes.naivebayes.datamodel |
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Methods in org.knime.base.node.mine.bayes.naivebayes.datamodel with parameters of type DataRow | |
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double[] |
NaiveBayesModel.getClassProbabilities(String[] attributeNames,
DataRow row,
List<String> classValues,
boolean normalize,
double laplaceCorrector)
|
String |
NaiveBayesModel.getMostLikelyClass(String[] attrNames,
DataRow row,
double laplaceCorrector)
Returns the name of the class with the highest probability for the given row. |
void |
NaiveBayesModel.updateModel(DataRow row,
DataTableSpec tableSpec,
int classColIdx)
Updates the current NaiveBayesModel with the values from the
given DataRow . |
Uses of DataRow in org.knime.base.node.mine.bayes.naivebayes.predictor |
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Methods in org.knime.base.node.mine.bayes.naivebayes.predictor with parameters of type DataRow | |
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DataCell[] |
NaiveBayesCellFactory.getAppendedCell(DataRow row)
Get the new cells for a given row. |
DataCell[] |
NaiveBayesCellFactory.getCells(DataRow row)
Get the new cells for a given row. |
Uses of DataRow in org.knime.base.node.mine.bfn |
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Classes in org.knime.base.node.mine.bfn that implement DataRow | |
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(package private) class |
BasisFunctionFilterRow
Inner class to separate an data input row into a new row which are the first n-1 double cells and returns the class label. |
class |
BasisFunctionLearnerRow
General BasisFunctionLearnerRow prototype which provides
functions to shrink, cover, and reset rules; and to be compared with others
by its coverage. |
Methods in org.knime.base.node.mine.bfn that return DataRow | |
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DataRow |
BasisFunctionLearnerRow.getAnchor()
Returns the basisfunction's anchor vector. |
DataRow |
BasisFunctionIterator.next()
Returns the next DataRow . |
DataRow |
BasisFunctionPredictorRowIterator.next()
Returns the next DataRow . |
Methods in org.knime.base.node.mine.bfn with parameters of type DataRow | |
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void |
BasisFunctionLearnerRow.addCovered(DataRow row,
DataCell classInfo)
If a new instance of this class is covered. |
abstract BasisFunctionLearnerRow |
BasisFunctionFactory.commit(RowKey key,
DataCell classInfo,
DataRow row)
Returns a new row initialised by a DataRow as its initial center vector and a class
label. |
abstract boolean |
BasisFunctionLearnerRow.compareCoverage(BasisFunctionLearnerRow o,
DataRow r)
Compares coverage of this and another row. |
abstract double |
BasisFunctionPredictorRow.compose(DataRow row,
double act)
Composes the activation of the given array and of the calculated one based on the given row. |
double |
Distance.compute(DataRow x,
DataRow y)
Computes the Euclidean distance between two normalized rows. |
double |
Distance.compute(double[] x,
DataRow y)
Computes the Euclidean distance between two normalized vectors. |
double |
Distance.compute(DoubleValue[] x,
DataRow y)
Computes the Euclidean distance between two normalized vectors. |
abstract double |
BasisFunctionLearnerRow.computeActivation(DataRow row)
Computes activation for a given row using this basis function. |
abstract double |
BasisFunctionPredictorRow.computeActivation(DataRow row)
Computes the activation based on the given row for this basisfunction. |
abstract double |
BasisFunctionPredictorRow.computeDistance(DataRow row)
|
abstract void |
BasisFunctionLearnerRow.cover(DataRow row)
Called if a row covers a new DataRow . |
(package private) void |
BasisFunctionPredictorRow.cover(DataRow row,
DataCell classLabel)
If the same class as this basisfunction is assigned to, the number of correctly covered pattern is increased, otherwise the number of wrong covered ones. |
(package private) void |
BasisFunctionLearnerRow.coverIntern(DataRow row)
Covers a new row and decrease covered counter. |
abstract boolean |
BasisFunctionLearnerRow.covers(DataRow row)
Returns true if the input row is covered by this row,
otherwise false . |
abstract boolean |
BasisFunctionLearnerRow.explains(DataRow row)
Returns true if the input row is explained by this row,
otherwise false . |
double |
BasisFunctionAntisymmetricRowOverlap.getAffinityDegree(DataRow row1,
DataRow row2)
|
double |
DegreeOfAffinity.getAffinityDegree(DataRow dr1,
DataRow dr2)
|
double |
BasisFunctionRowInclusion.getAffinityDegree(DataRow row1,
DataRow row2)
|
double |
BasisFunctionSymmetricRowOverlap.getAffinityDegree(DataRow row1,
DataRow row2)
|
DataCell[] |
BasisFunctionPredictorCellFactory.getCells(DataRow row)
Predicts given row using the underlying basis function model. |
abstract boolean |
BasisFunctionLearnerRow.getShrinkValue(DataRow row)
Called if a new row has to be adjusted. |
protected DataCell[] |
BasisFunctionPredictorCellFactory.predict(DataRow row,
Map<DataCell,List<BasisFunctionPredictorRow>> model)
Predicts an unknown row to the given model. |
abstract boolean |
BasisFunctionLearnerRow.shrink(DataRow row)
Called if a new row has to be adjusted, all conflicting rows are shrunken. |
Constructors in org.knime.base.node.mine.bfn with parameters of type DataRow | |
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BasisFunctionFilterRow(BasisFunctionLearnerTable model,
DataRow row,
int[] dataColumns,
int[] classColumns,
String[] classColumnNames,
BasisFunctionLearnerTable.MissingValueReplacementFunction missing)
Create new basisfunction input data row with data and class columns. |
|
BasisFunctionLearnerRow(RowKey key,
DataRow centroid,
DataCell classInfo)
Initialise a new basisfunction rule with one covered pattern since this rule is also covered by itself. |
Uses of DataRow in org.knime.base.node.mine.bfn.fuzzy |
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Classes in org.knime.base.node.mine.bfn.fuzzy that implement DataRow | |
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class |
FuzzyBasisFunctionLearnerRow
Extends the general BasisFunctionLearnerRow object to act as
rectangular fuzzy prototype. |
Methods in org.knime.base.node.mine.bfn.fuzzy with parameters of type DataRow | |
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BasisFunctionLearnerRow |
FuzzyBasisFunctionFactory.commit(RowKey key,
DataCell classInfo,
DataRow row)
Creates and returns a new row initialised with a class label and a center vector. |
boolean |
FuzzyBasisFunctionLearnerRow.compareCoverage(BasisFunctionLearnerRow o,
DataRow r)
Compares this basisfunction with the another one by the fuzzy rule's number of covered pattern. |
double |
FuzzyBasisFunctionPredictorRow.compose(DataRow row,
double act)
Composes the degree of membership by using the disjunction of the tco-norm operator. |
double |
FuzzyBasisFunctionLearnerRow.computeActivation(DataRow row)
Computes activation for a given row using this basis function. |
double |
FuzzyBasisFunctionPredictorRow.computeActivation(DataRow row)
Returns the compute activation of this input vector. |
double |
FuzzyBasisFunctionPredictorRow.computeDistance(DataRow row)
|
void |
FuzzyBasisFunctionLearnerRow.cover(DataRow row)
This basis function covers the given row. |
boolean |
FuzzyBasisFunctionLearnerRow.covers(DataRow row)
Returns true if the given row is covered by this prototype, that is, if FuzzyBasisFunctionLearnerRow.computeActivation(DataRow) returns a degree greater than
MINACT . |
boolean |
FuzzyBasisFunctionLearnerRow.explains(DataRow row)
Returns true if the given row is covered by this
prototype, that is, if FuzzyBasisFunctionLearnerRow.computeActivation(DataRow) returns a
degree equal 1. |
boolean |
FuzzyBasisFunctionLearnerRow.getShrinkValue(DataRow row)
Called if a new BasisFunctionLearnerRow has to be adjusted. |
boolean |
FuzzyBasisFunctionLearnerRow.shrink(DataRow row)
If a new prototype has to be adjusted. |
Constructors in org.knime.base.node.mine.bfn.fuzzy with parameters of type DataRow | |
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FuzzyBasisFunctionLearnerRow(RowKey key,
DataCell classInfo,
DataRow centroid,
int norm,
int shrink,
MutableDouble[] min,
MutableDouble[] max)
Creates a new learner row for fuzzy rules. |
Uses of DataRow in org.knime.base.node.mine.bfn.radial |
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Classes in org.knime.base.node.mine.bfn.radial that implement DataRow | |
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class |
RadialBasisFunctionLearnerRow
This class extends the general BasisFunctionLearnerRow in order to
use radial basis function prototypes for training. |
Methods in org.knime.base.node.mine.bfn.radial with parameters of type DataRow | |
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BasisFunctionLearnerRow |
RadialBasisFunctionFactory.commit(RowKey key,
DataCell classInfo,
DataRow row)
Creates and returns a new RadialBasisFunctionLearnerRow
initialized with a center vector and a class label. |
boolean |
RadialBasisFunctionLearnerRow.compareCoverage(BasisFunctionLearnerRow best,
DataRow row)
Compares this basis function with the other one by its standard deviation if the number of covered pattern is equal otherwise use this identification. |
double |
RadialBasisFunctionPredictorRow.compose(DataRow row,
double act)
Sum of the given activation plus the newly calculated one for the given row. |
double |
RadialBasisFunctionLearnerRow.computeActivation(DataRow row)
Computes activation for a given row using this basis function. |
double |
RadialBasisFunctionPredictorRow.computeActivation(DataRow row)
Calculates the current activation of this basis function given a input row which is always between 0.0 and 1.0
using the the hereinafter called distance function. |
double |
RadialBasisFunctionPredictorRow.computeDistance(DataRow row)
Computes the distance between this prototype's center vector and the given row. |
void |
RadialBasisFunctionLearnerRow.cover(DataRow row)
Method is empty. |
boolean |
RadialBasisFunctionLearnerRow.covers(DataRow row)
Checks if the given row is covered by this basis function. |
boolean |
RadialBasisFunctionLearnerRow.explains(DataRow row)
Checks if the given row is explained by this basisfunction. |
boolean |
RadialBasisFunctionLearnerRow.getShrinkValue(DataRow row)
Called if a new BasisFunctionLearnerRow has to be adjusted. |
boolean |
RadialBasisFunctionLearnerRow.shrink(DataRow row)
Basis functions need to be adjusted if they conflict with other ones. |
Constructors in org.knime.base.node.mine.bfn.radial with parameters of type DataRow | |
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RadialBasisFunctionLearnerRow(RowKey key,
DataCell classInfo,
DataRow center,
double thetaMinus,
double thetaPlus,
int distance)
Creates a new radial basisfunction using the center vector as the anchor of the Gaussian function and also assigns class label for this prototype. |
|
RadialBasisFunctionPredictorRow(RowKey key,
DataRow center,
DataCell classLabel,
double thetaMinus,
int distance)
Creates a new predictor for PNN rules. |
Uses of DataRow in org.knime.base.node.mine.cluster.assign |
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Methods in org.knime.base.node.mine.cluster.assign with parameters of type DataRow | |
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double |
Prototype.getDistance(DataRow row)
Computes the distance between this prototype and a given DataRow . |
double |
Prototype.getDistance(DataRow row,
int[] indices)
Computes the distance between this prototype and a given DataRow . |
double |
Prototype.getSquaredEuclideanDistance(DataRow row,
int[] indices)
Computes the distance between this prototype and a given DataRow . |
Uses of DataRow in org.knime.base.node.mine.cluster.fuzzycmeans |
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Methods in org.knime.base.node.mine.cluster.fuzzycmeans with parameters of type DataRow | |
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DataCell[] |
ClusterMembershipFactory.getCells(DataRow row)
Get the new cells for a given row. |
Uses of DataRow in org.knime.base.node.mine.cluster.hierarchical |
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Methods in org.knime.base.node.mine.cluster.hierarchical that return DataRow | |
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DataRow[] |
ClusterNode.getAllDataRows()
Returns all data row (leaf nodes) this sub tree. |
DataRow |
ClusterNode.getLeafDataPoint()
Returns the DataRow associated with a leaf node. |
Constructors in org.knime.base.node.mine.cluster.hierarchical with parameters of type DataRow | |
---|---|
ClusterNode(DataRow row,
int rowIdx)
Constructs a new leaf node from a data row. |
Uses of DataRow in org.knime.base.node.mine.cluster.hierarchical.distfunctions |
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Methods in org.knime.base.node.mine.cluster.hierarchical.distfunctions with parameters of type DataRow | |
---|---|
double |
ManhattanDist.calcDistance(DataRow firstDataRow,
DataRow secondDataRow,
int[] includedCols)
Calculates the distance between two data rows based on the Manhatten distance. |
double |
MinkowskiDist.calcDistance(DataRow firstDataRow,
DataRow secondDataRow,
int[] includedCols)
Calculates the distance between two data rows based on the Minkowski distance. |
double |
DistanceFunction.calcDistance(DataRow firstDataRow,
DataRow secondDataRow,
int[] includedCols)
Calculates the distance between two data rows. |
double |
EuclideanDist.calcDistance(DataRow firstDataRow,
DataRow secondDataRow,
int[] includedCols)
Calculates the distance between two data rows based on the Euclidean distance. |
Uses of DataRow in org.knime.base.node.mine.decisiontree2 |
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Methods in org.knime.base.node.mine.decisiontree2 with parameters of type DataRow | |
---|---|
Boolean |
PMMLSimplePredicate.evaluate(DataRow row,
DataTableSpec spec)
Evaluates the predicate for the passed parameters and returns the result. |
Boolean |
PMMLFalsePredicate.evaluate(DataRow row,
DataTableSpec spec)
Evaluates the predicate for the passed parameters and returns the result. |
Boolean |
PMMLSimpleSetPredicate.evaluate(DataRow row,
DataTableSpec spec)
Evaluates the predicate for the passed parameters and returns the result. |
Boolean |
PMMLTruePredicate.evaluate(DataRow row,
DataTableSpec spec)
Evaluates the predicate for the passed parameters and returns the result. |
abstract Boolean |
PMMLPredicate.evaluate(DataRow row,
DataTableSpec spec)
Evaluates the predicate for the passed parameters and returns the result. |
Boolean |
PMMLCompoundPredicate.evaluate(DataRow row,
DataTableSpec spec)
Evaluates the predicate for the passed parameters and returns the result. |
Uses of DataRow in org.knime.base.node.mine.decisiontree2.model |
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Methods in org.knime.base.node.mine.decisiontree2.model with parameters of type DataRow | |
---|---|
abstract void |
DecisionTreeNodeSplit.addCoveredColor(DataCell cell,
DataRow row,
DataTableSpec spec,
double weight)
Add colors for patterns given as a row of values if they fall within a specific node. |
void |
DecisionTreeNodeSplitNominalBinary.addCoveredColor(DataCell cell,
DataRow row,
DataTableSpec spec,
double weight)
Deprecated. Add colors for a pattern given as a row of values. |
void |
DecisionTreeNodeSplitPMML.addCoveredColor(DataCell cell,
DataRow row,
DataTableSpec spec,
double weight)
Add colors for patterns given as a row of values if they fall within a specific node. |
void |
DecisionTreeNodeSplitContinuous.addCoveredColor(DataCell cell,
DataRow row,
DataTableSpec spec,
double weight)
Deprecated. Add colors for a pattern given as a row of values. |
void |
DecisionTreeNodeSplitNominal.addCoveredColor(DataCell cell,
DataRow row,
DataTableSpec spec,
double weight)
Deprecated. Add colors for a pattern given as a row of values. |
void |
DecisionTree.addCoveredColor(DataRow row,
DataTableSpec spec)
Add color of a new pattern to this tree. |
void |
DecisionTreeNodeSplit.addCoveredColor(DataRow row,
DataTableSpec spec,
double weight)
Add colors for a row of values if they fall within a specific node/branch. |
abstract void |
DecisionTreeNode.addCoveredColor(DataRow row,
DataTableSpec spec,
double weight)
Add colors for a row of values if they fall within a specific node/branch. |
void |
DecisionTreeNodeLeaf.addCoveredColor(DataRow row,
DataTableSpec spec,
double weight)
Add colors for a pattern given as a row of values. |
abstract void |
DecisionTreeNodeSplit.addCoveredPattern(DataCell cell,
DataRow row,
DataTableSpec spec,
double weight)
Add patterns given as a row of values if they fall within a specific node. |
void |
DecisionTreeNodeSplitNominalBinary.addCoveredPattern(DataCell cell,
DataRow row,
DataTableSpec spec,
double weight)
Deprecated. Add patterns given as a row of values if they fall within a specific node. |
void |
DecisionTreeNodeSplitPMML.addCoveredPattern(DataCell cell,
DataRow row,
DataTableSpec spec,
double weight)
Add patterns given as a row of values if they fall within a specific node. |
void |
DecisionTreeNodeSplitContinuous.addCoveredPattern(DataCell cell,
DataRow row,
DataTableSpec spec,
double weight)
Deprecated. Add patterns given as a row of values if they fall within a specific node. |
void |
DecisionTreeNodeSplitNominal.addCoveredPattern(DataCell cell,
DataRow row,
DataTableSpec spec,
double weight)
Deprecated. Add patterns given as a row of values if they fall within a specific node. |
void |
DecisionTree.addCoveredPattern(DataRow row,
DataTableSpec spec)
Add a new pattern to this tree for HiLiting purposes. |
void |
DecisionTreeNodeSplit.addCoveredPattern(DataRow row,
DataTableSpec spec,
double weight)
Add patterns given as a row of values if they fall within a specific node. |
abstract void |
DecisionTreeNode.addCoveredPattern(DataRow row,
DataTableSpec spec,
double weight)
Add patterns given as a row of values if they fall within a specific node. |
void |
DecisionTreeNodeLeaf.addCoveredPattern(DataRow row,
DataTableSpec spec,
double weight)
Add patterns given as a row of values. |
DataCell |
DecisionTreeNode.classifyPattern(DataRow row,
DataTableSpec spec)
Classify a new pattern given as a row of values. |
DataCell |
DecisionTree.classifyPattern(DataRow row,
DataTableSpec spec)
Classify a new pattern given as a row of values. |
abstract LinkedHashMap<DataCell,Double> |
DecisionTreeNodeSplit.getClassCounts(DataCell cell,
DataRow row,
DataTableSpec spec)
Determine class counts for a new pattern given as a row of values. |
LinkedHashMap<DataCell,Double> |
DecisionTreeNodeSplitNominalBinary.getClassCounts(DataCell cell,
DataRow row,
DataTableSpec spec)
Deprecated. Determine class counts for a new pattern given as a row of values. |
LinkedHashMap<DataCell,Double> |
DecisionTreeNodeSplitPMML.getClassCounts(DataCell cell,
DataRow row,
DataTableSpec spec)
Determine class counts for a new pattern given as a row of values. |
LinkedHashMap<DataCell,Double> |
DecisionTreeNodeSplitContinuous.getClassCounts(DataCell cell,
DataRow row,
DataTableSpec spec)
Deprecated. Determine class counts for a new pattern given as a row of values. |
LinkedHashMap<DataCell,Double> |
DecisionTreeNodeSplitNominal.getClassCounts(DataCell cell,
DataRow row,
DataTableSpec spec)
Deprecated. Determine class counts for a new pattern given as a row of values. |
LinkedHashMap<DataCell,Double> |
DecisionTreeNodeSplit.getClassCounts(DataRow row,
DataTableSpec spec)
Determine class counts for a new pattern given as a row of values. |
abstract LinkedHashMap<DataCell,Double> |
DecisionTreeNode.getClassCounts(DataRow row,
DataTableSpec spec)
Determine class counts for a new pattern given as a row of values. |
LinkedHashMap<DataCell,Double> |
DecisionTreeNodeSplitPMML.getClassCounts(DataRow row,
DataTableSpec spec)
Determine class counts for a new pattern given as a row of values. |
LinkedHashMap<DataCell,Double> |
DecisionTreeNodeLeaf.getClassCounts(DataRow row,
DataTableSpec spec)
Determine class counts for a new pattern given as a row of values. |
LinkedHashMap<DataCell,Double> |
DecisionTree.getClassCounts(DataRow row,
DataTableSpec spec)
Determine class counts for a new pattern given as a row of values. |
Uses of DataRow in org.knime.base.node.mine.mds |
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Methods in org.knime.base.node.mine.mds with parameters of type DataRow | |
---|---|
DataCell[] |
MDSCellFactory.getCells(DataRow row)
Get the new cells for a given row. |
Uses of DataRow in org.knime.base.node.mine.mds.distances |
---|
Methods in org.knime.base.node.mine.mds.distances with parameters of type DataRow | |
---|---|
static double |
Distances.getCosinusDistance(DataRow row1,
DataRow row2,
double offset,
boolean fuzzy)
Computes the cosinus distance between the given two rows, with given offset. |
double |
EuclideanDistanceManager.getDistance(DataRow row1,
DataRow row2)
Returns the distance between the given DataRow s, row1 and
row2. |
double |
ManhattanDistanceManager.getDistance(DataRow row1,
DataRow row2)
Returns the distance between the given DataRow s, row1 and
row2. |
double |
DistanceManager.getDistance(DataRow row1,
DataRow row2)
Returns the distance between the given DataRow s, row1 and
row2. |
double |
CosinusDistanceManager.getDistance(DataRow row1,
DataRow row2)
Returns the distance between the given DataRow s, row1 and
row2. |
static double |
Distances.getEuclideanDistance(DataRow row1,
DataRow row2)
Calculates the euclidean distance between two DataRow s
using the Minkowski distance with power 2. |
static double |
Distances.getEuclideanDistance(DataRow row1,
DataRow row2,
boolean fuzzy)
Calculates the euclidean distance between two DataRow s
using the Minkowski distance with power 2. |
static double |
Distances.getManhattanDistance(DataRow row1,
DataRow row2)
Calculates the Manhattan distance between two DataRow s
using the Minkowski distance with power 1. |
static double |
Distances.getManhattanDistance(DataRow row1,
DataRow row2,
boolean fuzzy)
Calculates the Manhattan distance between two DataRow s
using the Minkowski distance with power 1. |
static double |
Distances.getMinkowskiDistance(int power,
DataRow row1,
DataRow row2)
Calculates the Minkowski distance between two rows no matter if they contain fuzzy or number values. |
static double |
Distances.getMinkowskiDistance(int power,
DataRow row1,
DataRow row2,
boolean fuzzy)
Calculates the Minkowski distance between two rows. |
Uses of DataRow in org.knime.base.node.mine.mds.mdsprojection |
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Methods in org.knime.base.node.mine.mds.mdsprojection with parameters of type DataRow | |
---|---|
protected void |
MDSProjectionManager.adjustDataPoint(DataPoint p1,
DataPoint p2,
DataRow r1,
DataRow r2)
Adjusts the low dimensional mapping of the first data point according to the second data point and its mapping. |
Uses of DataRow in org.knime.base.node.mine.pca |
---|
Methods in org.knime.base.node.mine.pca with parameters of type DataRow | |
---|---|
protected static DataCell[] |
PCAReverseNodeModel.convertInputRow(Jama.Matrix eigenvectors,
DataRow row,
double[] means,
int[] inputColumnIndices,
int resultDimensions)
reduce a single input row to the principal components. |
protected static DataCell[] |
PCANodeModel.convertInputRow(Jama.Matrix eigenvectors,
DataRow row,
double[] means,
int[] inputColumnIndices,
int resultDimensions,
boolean failOnMissing)
reduce a single input row to the principal components. |
Uses of DataRow in org.knime.base.node.mine.regression |
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Methods in org.knime.base.node.mine.regression with parameters of type DataRow | |
---|---|
DataCell |
RegressionPortObject.predict(DataRow row)
Predict a row, returning the regression value. |
Uses of DataRow in org.knime.base.node.mine.regression.linear |
---|
Methods in org.knime.base.node.mine.regression.linear with parameters of type DataRow | |
---|---|
DataCell |
LinearRegressionContent.predict(DataRow row)
Predicts the target value for the given row. |
Uses of DataRow in org.knime.base.node.mine.sota.distances |
---|
Methods in org.knime.base.node.mine.sota.distances with parameters of type DataRow | |
---|---|
static double |
Distances.getCorrelationDistance(DataRow row1,
DataRow row2,
double offset,
boolean abs,
boolean fuzzy)
Returns the coefficient of correlation distance between the rows with a given offset. |
static double |
Distances.getCorrelationDistance(DataRow row,
SotaTreeCell cell,
double offset,
boolean abs,
boolean fuzzy)
Returns the coefficient of correlation distance between the cells values and the number cells of the given row with a given offset. |
static double |
Distances.getCosinusDistance(DataRow row1,
DataRow row2,
double offset,
boolean fuzzy)
Computes the cosinus distance between the given two rows, with given offset. |
static double |
Distances.getCosinusDistance(DataRow row,
SotaTreeCell cell,
double offset,
boolean fuzzy)
Returns the cosinus distance between the cells values and the number cells of the given row with a given offset. |
double |
EuclideanDistanceManager.getDistance(DataRow row1,
DataRow row2)
Returns the distance between the given row1 and row2. |
double |
ManhattanDistanceManager.getDistance(DataRow row1,
DataRow row2)
Returns the distance between the given row1 and row2. |
double |
DistanceManager.getDistance(DataRow row1,
DataRow row2)
Returns the distance between the given row1 and row2. |
double |
CosinusDistanceManager.getDistance(DataRow row1,
DataRow row2)
Returns the distance between the given row1 and row2. |
double |
EuclideanDistanceManager.getDistance(DataRow row,
SotaTreeCell cell)
Returns the distance between the given cell and row. |
double |
ManhattanDistanceManager.getDistance(DataRow row,
SotaTreeCell cell)
Returns the distance between the given cell and row. |
double |
DistanceManager.getDistance(DataRow row,
SotaTreeCell cell)
Returns the distance between the given cell and row. |
double |
CosinusDistanceManager.getDistance(DataRow row,
SotaTreeCell cell)
Returns the distance between the given cell and row. |
static double |
Distances.getEuclideanDistance(DataRow row1,
DataRow row2,
boolean fuzzy)
Calculates the euclidean distance between two DataRow s
using the Minkowski distance with power 2. |
static double |
Distances.getEuclideanDistance(DataRow row,
SotaTreeCell cell,
boolean fuzzy)
Returns the euclidean distance between a given DataRow
and SotaTreeCell using the Minkowski distance with
power 2. |
static double |
Distances.getManhattanDistance(DataRow row1,
DataRow row2,
boolean fuzzy)
Calculates the manhattan distance between two DataRow s
using the Minkowski distance with power 1. |
static double |
Distances.getManhattanDistance(DataRow row,
SotaTreeCell cell,
boolean fuzzy)
Returns the manhattan distance between a given DataRow
and SotaTreeCell using the Minkowski distance with
power 1. |
static double |
Distances.getMean(DataRow row,
boolean fuzzy)
Returns the mean value of the given row. |
static double |
Distances.getMinkowskiDistance(int power,
DataRow row1,
DataRow row2,
boolean fuzzy)
Calculates the Minkowski distance between two rows. |
static double |
Distances.getMinkowskiDistance(int power,
DataRow row,
SotaTreeCell cell,
boolean fuzzy)
Calculates the Minkowski distance between a regular DataRow
and a SotaTreeCell . |
static double |
Distances.getStandardDeviation(DataRow row,
boolean fuzzy)
Returns the standard deviation of the given row. |
Uses of DataRow in org.knime.base.node.mine.sota.logic |
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Methods in org.knime.base.node.mine.sota.logic that return DataRow | |
---|---|
DataRow |
FuzzyHierarchyFilterRowContainer.getRow(int idx)
Returns the row from the container with index idx . |
Methods in org.knime.base.node.mine.sota.logic with parameters of type DataRow | |
---|---|
abstract void |
SotaHelper.adjustSotaCell(SotaTreeCell cell,
DataRow row,
double learningrate,
String cellClass)
Adjusts the given SotaTreeCell related to the given
DataRow and learningrate and assigns the given class. |
void |
SotaFuzzyHelper.adjustSotaCell(SotaTreeCell cell,
DataRow row,
double learningrate,
String cellClass)
Adjusts the given SotaTreeCell related to the given
DataRow and learningrate and assigns the given class. |
void |
SotaNumberHelper.adjustSotaCell(SotaTreeCell cell,
DataRow row,
double learningrate,
String cellClass)
Adjusts the given SotaTreeCell related to the given
DataRow and learningrate and assigns the given class. |
static double[] |
SotaFuzzyMath.getCenterOfAllCoreRegions(DataRow cells,
DataTableSpec spec)
Computes the center vector of all core regions of the given FuzzyCells as a double array. |
static double |
SotaFuzzyMath.getCoreDilatationToOtherCore(DataRow cells1,
DataRow cells2,
DataTableSpec spec)
Computes the core dilatation of a core region to another core region. |
static double |
SotaFuzzyMath.getMaxCoreDilatation(DataRow cells,
DataTableSpec spec)
Approximates dilatation of Core region, by using Pythagoras. |
static int |
SotaFuzzyMath.getNumberOfFuzzyCells(DataRow cells,
DataTableSpec spec)
Counts the number of FuzzyIntervalValues of given row and returns it. |
static boolean |
SotaUtil.hasMissingValues(DataRow row)
Returns true if there are missing values in given row and
false if not. |
Uses of DataRow in org.knime.base.node.mine.sota.predictor |
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Methods in org.knime.base.node.mine.sota.predictor with parameters of type DataRow | |
---|---|
DataCell[] |
SotaPredictorCellFactory.getCells(DataRow row)
Get the new cells for a given row. |
Uses of DataRow in org.knime.base.node.mine.subgroupminer.freqitemset |
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Classes in org.knime.base.node.mine.subgroupminer.freqitemset that implement DataRow | |
---|---|
class |
FrequentItemSetRow
This class implements one row of a FrequentItemSetTable. |
Uses of DataRow in org.knime.base.node.mine.svm.predictor |
---|
Methods in org.knime.base.node.mine.svm.predictor with parameters of type DataRow | |
---|---|
DataCell[] |
SVMPredictor.getCells(DataRow row)
Get the new cells for a given row. |
Uses of DataRow in org.knime.base.node.parallel.appender |
---|
Methods in org.knime.base.node.parallel.appender with parameters of type DataRow | |
---|---|
DataCell[] |
ExtendedCellFactory.getCells(DataRow row)
Get the new cells for a given row. |
Uses of DataRow in org.knime.base.node.parallel.builder |
---|
Methods in org.knime.base.node.parallel.builder with parameters of type DataRow | |
---|---|
protected abstract void |
ThreadedTableBuilderNodeModel.processRow(DataRow inRow,
BufferedDataTable[] additionalData,
RowAppender[] outputTables)
This method is called once for each row in the first input table. |
Uses of DataRow in org.knime.base.node.preproc.binner |
---|
Methods in org.knime.base.node.preproc.binner with parameters of type DataRow | |
---|---|
DataCell[] |
BinnerColumnFactory.getCells(DataRow row)
Get the new cells for a given row. |
Uses of DataRow in org.knime.base.node.preproc.cellsplit |
---|
Methods in org.knime.base.node.preproc.cellsplit with parameters of type DataRow | |
---|---|
DataCell[] |
CellSplitterCellFactory.getCells(DataRow row)
Get the new cells for a given row. |
Uses of DataRow in org.knime.base.node.preproc.cellsplitbypos |
---|
Methods in org.knime.base.node.preproc.cellsplitbypos with parameters of type DataRow | |
---|---|
DataCell[] |
CellSplitterByPosCellFactory.getCells(DataRow row)
Get the new cells for a given row. |
Uses of DataRow in org.knime.base.node.preproc.columnTrans |
---|
Methods in org.knime.base.node.preproc.columnTrans with parameters of type DataRow | |
---|---|
abstract int |
AbstractMany2OneCellFactory.findColumnIndex(DataRow row)
Find the column names to put in the condensed column. |
int |
BinaryCellFactory.findColumnIndex(DataRow row)
Find the column names to put in the condensed column. |
int |
MinMaxCellFactory.findColumnIndex(DataRow row)
Find the column names to put in the condensed column. |
int |
RegExpCellFactory.findColumnIndex(DataRow row)
Find the column names to put in the condensed column. |
DataCell[] |
AbstractMany2OneCellFactory.getCells(DataRow row)
Get the new cells for a given row. |
DataCell[] |
One2ManyCellFactory.getCells(DataRow row)
Get the new cells for a given row. |
Uses of DataRow in org.knime.base.node.preproc.filter.row |
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Methods in org.knime.base.node.preproc.filter.row that return DataRow | |
---|---|
DataRow |
RowFilterIterator.next()
This implementation may throw an RuntimeCanceledExecutionException if this class has been initialized with a non-null execution monitor. |
Uses of DataRow in org.knime.base.node.preproc.filter.row.rowfilter |
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Methods in org.knime.base.node.preproc.filter.row.rowfilter with parameters of type DataRow | |
---|---|
static boolean |
MissingCellRowFilter.hasMissingCells(DataRow row)
Checks if given row contains missing cells and returns true ,
otherwise false . |
boolean |
AndRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
RowNoRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
MissingValueRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
NegRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
MissingCellRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
StringCompareRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
abstract boolean |
RowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
TrueRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
OrRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
RowIDRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
ColValFilterOldObsolete.matches(DataRow row,
int rowIndex)
Deprecated. Return true if the specified row matches the criteria set
in the filter. |
boolean |
FalseRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
RangeRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
Uses of DataRow in org.knime.base.node.preproc.missingval |
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Methods in org.knime.base.node.preproc.missingval that return DataRow | |
---|---|
DataRow |
MissingValueHandlingTableIterator.next()
Returns the next DataRow . |
Uses of DataRow in org.knime.base.node.preproc.sample |
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Methods in org.knime.base.node.preproc.sample with parameters of type DataRow | |
---|---|
boolean |
LinearSamplingRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
RandomFractionRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
RandomNumberRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
boolean |
StratifiedSamplingRowFilter.matches(DataRow row,
int rowIndex)
Return true if the specified row matches the criteria set
in the filter. |
Uses of DataRow in org.knime.base.node.preproc.setoperator |
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Methods in org.knime.base.node.preproc.setoperator with parameters of type DataRow | |
---|---|
int |
SingleColRowComparator.compare(DataRow r1,
DataRow r2)
|
Uses of DataRow in org.knime.base.node.rules |
---|
Methods in org.knime.base.node.rules with parameters of type DataRow | |
---|---|
boolean |
RuleNode.evaluate(DataRow row)
Evaluates this rule node. |
boolean |
Rule.matches(DataRow row)
Returns if this rules matches the given row. |
Uses of DataRow in org.knime.base.node.util |
---|
Methods in org.knime.base.node.util that return DataRow | |
---|---|
DataRow |
DataArray.getRow(int idx)
Returns the row from the container with index idx . |
DataRow |
DefaultDataArray.getRow(int idx)
Returns the row from the container with index idx . |
Uses of DataRow in org.knime.base.node.viz.histogram.datamodel |
---|
Methods in org.knime.base.node.viz.histogram.datamodel that return types with arguments of type DataRow | |
---|---|
List<DataRow> |
InteractiveHistogramDataModel.getDataRows()
|
Iterator<DataRow> |
InteractiveHistogramDataModel.iterator()
|
Constructor parameters in org.knime.base.node.viz.histogram.datamodel with type arguments of type DataRow | |
---|---|
InteractiveHistogramVizModel(List<Color> rowColors,
AggregationMethod aggrMethod,
HistogramLayout layout,
DataTableSpec spec,
List<DataRow> rows,
DataColumnSpec xColSpec,
Collection<ColorColumn> aggrColumns,
int noOfBins)
Constructor for class InteractiveHistogramVizModel. |
Uses of DataRow in org.knime.base.node.viz.histogram.impl.interactive |
---|
Methods in org.knime.base.node.viz.histogram.impl.interactive with parameters of type DataRow | |
---|---|
int |
RowByColumnComparator.compare(DataRow o1,
DataRow o2)
|
Uses of DataRow in org.knime.base.node.viz.pie.datamodel.fixed |
---|
Methods in org.knime.base.node.viz.pie.datamodel.fixed with parameters of type DataRow | |
---|---|
void |
FixedPieDataModel.addDataRow(DataRow row,
Color rowColor,
DataCell pieCell,
DataCell aggrCell)
|
Uses of DataRow in org.knime.base.node.viz.pie.datamodel.interactive |
---|
Methods in org.knime.base.node.viz.pie.datamodel.interactive that return types with arguments of type DataRow | |
---|---|
Iterator<DataRow> |
InteractivePieDataModel.getDataRows()
|
Iterator<DataRow> |
InteractivePieDataModel.iterator()
|
Methods in org.knime.base.node.viz.pie.datamodel.interactive with parameters of type DataRow | |
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Color |
InteractivePieDataModel.getRowColor(DataRow row)
|
Uses of DataRow in org.knime.base.node.viz.plotter.dendrogram |
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Methods in org.knime.base.node.viz.plotter.dendrogram that return DataRow | |
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DataRow |
DendrogramNode.getLeafDataPoint()
Returns the DataRow associated with a leaf node. |
Uses of DataRow in org.knime.core.data |
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Methods in org.knime.core.data that return DataRow | |
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abstract DataRow |
RowIterator.next()
Returns the next DataRow . |
Methods in org.knime.core.data with parameters of type DataRow | |
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ColorAttr |
DataTableSpec.getRowColor(DataRow row)
Returns the color that an object should have when displaying information concerning this row (for instance in a scatterplot). |
ShapeFactory.Shape |
DataTableSpec.getRowShape(DataRow row)
Return the shape that an object should have when displaying information concerning this row (for instance in a scatterplot). |
double |
DataTableSpec.getRowSize(DataRow row)
Deprecated. use row size factor instead |
double |
DataTableSpec.getRowSizeFactor(DataRow row)
Return the size (as a scaling factor) that an object should have when displaying information concerning this row (for instance in a scatterplot). |
Uses of DataRow in org.knime.core.data.collection |
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Methods in org.knime.core.data.collection with parameters of type DataRow | |
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static BlobSupportDataCellList |
BlobSupportDataCellList.create(DataRow row,
int[] cols)
Create new list based on selected cell from a DataRow . |
static BlobSupportDataCellSet |
BlobSupportDataCellSet.create(DataRow row,
int[] cols)
Create new set containing selected cells from a DataRow . |
static ListCell |
CollectionCellFactory.createListCell(DataRow row,
int[] cols)
Creates a new ListCell based on selected cells from a
DataRow . |
static SetCell |
CollectionCellFactory.createSetCell(DataRow row,
int[] cols)
Create new SetCell containing a set based on selected cell from a
DataRow . |
Uses of DataRow in org.knime.core.data.container |
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Classes in org.knime.core.data.container that implement DataRow | |
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class |
BlobSupportDataRow
Special row implementation that supports to access the wrapper cells of BlobDataCell . |
Methods in org.knime.core.data.container that return DataRow | |
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DataRow |
JoinTableIterator.next()
Returns the next DataRow . |
Methods in org.knime.core.data.container with parameters of type DataRow | |
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(package private) void |
Buffer.addRow(DataRow r,
boolean isCopyOfExisting,
boolean forceCopyOfBlobs)
Adds a row to the buffer. |
void |
DataContainer.addRowToTable(DataRow row)
Appends a row to the end of a container. |
void |
RowAppender.addRowToTable(DataRow row)
Appends a row to the end of a container. |
abstract DataCell |
SingleCellFactory.getCell(DataRow row)
Called from getCells. |
DataCell[] |
CellFactory.getCells(DataRow row)
Get the new cells for a given row. |
DataCell[] |
SingleCellFactory.getCells(DataRow row)
Get the new cells for a given row. |
Constructors in org.knime.core.data.container with parameters of type DataRow | |
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BlobSupportDataRow(RowKey key,
DataRow oldRow)
Creates a new data row with a new row ID. |
|
BlobSupportDataRow(String id,
DataRow oldRow)
Creates a new data row with a new row ID. |
Uses of DataRow in org.knime.core.data.def |
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Classes in org.knime.core.data.def that implement DataRow | |
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class |
DefaultRow
Default row for DataCell s which keeps a row identifier
and an array of DataCell objects. |
class |
JoinedRow
Row that concatenates two given rows. |
Methods in org.knime.core.data.def that return DataRow | |
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DataRow |
JoinedRow.getLeftRow()
|
DataRow |
JoinedRow.getRightRow()
|
DataRow |
DefaultRowIterator.next()
Returns the next DataRow . |
Methods in org.knime.core.data.def that return types with arguments of type DataRow | |
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protected List<DataRow> |
DefaultTable.getRowsInList()
Deprecated. Get a reference to underlying data container. |
Constructors in org.knime.core.data.def with parameters of type DataRow | |
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DefaultCellIterator(DataRow row)
Creates a new iterator over a given DataRow . |
|
DefaultRow(RowKey key,
DataRow row)
Creates an new row, using the data of the specified row, and overwrites the row key with the given new one. |
|
DefaultRow(String rowId,
DataRow row)
Creates an new row, using the data of the specified row, and overwrites the row key with the given new one. |
|
DefaultRowIterator(DataRow... rows)
Constructs a new iterator that traverses an array of DataRow . |
|
DefaultTable(DataRow[] rows,
DataTableSpec spec)
Deprecated. Creates a new table object from an array of DataRow
objects, and an array of column names and types. |
|
DefaultTable(DataRow[] rows,
String[] columnNames,
DataType[] columnTypes)
Deprecated. Creates a new table object from an array of DataRow
objects, and an array of column names and types. |
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JoinedRow(DataRow left,
DataRow right)
Creates a new row based on two given rows. |
Constructor parameters in org.knime.core.data.def with type arguments of type DataRow | |
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DefaultRowIterator(Iterable<DataRow> iterable)
Constructs a new iterator based on an Iterable . |
Uses of DataRow in org.knime.core.node.tableview |
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Methods in org.knime.core.node.tableview that return DataRow | |
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protected DataRow |
TableContentModel.getRow(int row)
Gets a row with a specified index. |
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