| Modifier and Type | Class and Description |
|---|---|
class |
DoubleValue |
class |
FloatValue |
| Modifier and Type | Method and Description |
|---|---|
abstract Value<V> |
Value.abs() |
abstract Value<V> |
Value.add(Number value) |
abstract Value<V> |
Value.add(Number coefficient,
Number... factors)
Adds
coefficient * product(factors). |
abstract Value<V> |
Value.add(Number coefficient,
Number factor)
Adds
coefficient * factor. |
abstract Value<V> |
Value.add(Number coefficient,
Number factor,
int exponent)
Adds
coefficient * (factor ^ exponent). |
abstract Value<V> |
Value.add(Number coefficient,
Number firstFactor,
Number secondFactor) |
abstract Value<V> |
Value.add(Value<? extends Number> value) |
abstract Value<V> |
Value.arctan() |
Value<V> |
ValueAggregator.average() |
static <V extends Number> |
MeasureUtil.calculateAdjustment(ValueFactory<V> valueFactory,
List<FieldValue> values) |
static <V extends Number> |
MeasureUtil.calculateAdjustment(ValueFactory<V> valueFactory,
List<FieldValue> values,
List<? extends Number> adjustmentValues) |
abstract Value<V> |
Value.ceiling() |
abstract Value<V> |
Value.copy() |
abstract Value<V> |
Value.cos() |
static <V extends Number> |
NormalizationUtil.denormalize(org.dmg.pmml.NormContinuous normContinuous,
Value<V> value) |
abstract Value<V> |
Value.denormalize(Number leftOrig,
Number leftNorm,
Number rightOrig,
Number rightNorm) |
abstract Value<V> |
Value.divide(Number value) |
abstract Value<V> |
Value.divide(Value<? extends Number> value) |
abstract Value<V> |
Value.elliott() |
protected Value<V> |
ValueMap.ensureValue(K key) |
static <V extends Number> |
MeasureUtil.evaluateDistance(ValueFactory<V> valueFactory,
org.dmg.pmml.ComparisonMeasure comparisonMeasure,
List<? extends org.dmg.pmml.ComparisonField<?>> comparisonFields,
List<FieldValue> values,
List<FieldValue> referenceValues,
Value<V> adjustment) |
static <V extends Number> |
TargetUtil.evaluateRegressionInternal(TargetField targetField,
Value<V> value) |
static <V extends Number> |
MeasureUtil.evaluateSimilarity(ValueFactory<V> valueFactory,
org.dmg.pmml.ComparisonMeasure comparisonMeasure,
List<? extends org.dmg.pmml.ComparisonField<?>> comparisonFields,
BitSet flags,
BitSet referenceFlags) |
abstract Value<V> |
Value.exp() |
abstract Value<V> |
Value.floor() |
abstract Value<V> |
Value.gauss() |
abstract Value<V> |
Value.gaussSim(Number value) |
abstract Value<V> |
Vector.get(int index) |
Value<Double> |
DoubleVector.get(int index) |
Value<Float> |
FloatVector.get(int index) |
Value<V> |
Regression.getValue() |
abstract Value<V> |
Value.inverseCauchit() |
abstract Value<V> |
Value.inverseCloglog() |
abstract Value<V> |
Value.inverseLogc() |
abstract Value<V> |
Value.inverseLogit() |
abstract Value<V> |
Value.inverseLoglog() |
abstract Value<V> |
Value.inverseNegbin(Number value) |
abstract Value<V> |
Value.inverseOddspower(Number value) |
abstract Value<V> |
Value.inversePower(Number value) |
abstract Value<V> |
Value.inverseProbit() |
abstract Value<V> |
Value.ln() |
abstract Value<V> |
Vector.max() |
Value<Double> |
DoubleVector.max() |
Value<Float> |
FloatVector.max() |
abstract Value<V> |
Vector.median() |
Value<Double> |
DoubleVector.median() |
Value<V> |
ValueAggregator.median() |
Value<Float> |
FloatVector.median() |
abstract Value<V> |
Value.multiply(Number value) |
abstract Value<V> |
Value.multiply(Number factor,
Number exponent)
Multiplies by
factor ^ exponent. |
abstract Value<V> |
Value.multiply(Value<? extends Number> value) |
Value<Float> |
ValueFactoryFactory.FloatValueFactory.newValue() |
Value<Double> |
ValueFactoryFactory.DoubleValueFactory.newValue() |
abstract Value<V> |
ValueFactory.newValue()
Creates a value, which is "silently" set to the zero value.
|
Value<Float> |
ValueFactoryFactory.FloatValueFactory.newValue(Number value) |
Value<Double> |
ValueFactoryFactory.DoubleValueFactory.newValue(Number value) |
abstract Value<V> |
ValueFactory.newValue(Number value)
Creates a value, which is "vocally" set to the specified value.
|
Value<Float> |
ValueFactoryFactory.FloatValueFactory.newValue(String value) |
Value<Double> |
ValueFactoryFactory.DoubleValueFactory.newValue(String value) |
abstract Value<V> |
ValueFactory.newValue(String value)
Creates a value, which is "vocally" set to the specified value.
|
static <V extends Number> |
NormalizationUtil.normalize(org.dmg.pmml.NormContinuous normContinuous,
Value<V> value) |
abstract Value<V> |
Value.normalize(Number leftOrig,
Number leftNorm,
Number rightOrig,
Number rightNorm) |
abstract Value<V> |
Value.power(Number value) |
static <V extends Number> |
TargetUtil.processValue(org.dmg.pmml.Target target,
Value<V> value) |
abstract Value<V> |
Value.reciprocal() |
abstract Value<V> |
Value.relu() |
abstract Value<V> |
Value.residual(Value<? extends Number> value) |
abstract Value<V> |
Value.restrict(Number lowValue,
Number highValue) |
abstract Value<V> |
Value.round() |
abstract Value<V> |
Value.sin() |
abstract Value<V> |
Value.square() |
abstract Value<V> |
Value.subtract(Number value) |
abstract Value<V> |
Value.subtract(Value<? extends Number> value) |
abstract Value<V> |
Vector.sum() |
Value<Double> |
DoubleVector.sum() |
Value<V> |
ValueAggregator.sum() |
Value<Float> |
FloatVector.sum() |
static <V extends Number> |
ValueUtil.sum(Iterable<Value<V>> values) |
abstract Value<V> |
Value.tanh() |
abstract Value<V> |
Value.threshold(Number value) |
Value<V> |
ValueAggregator.weightedAverage() |
Value<V> |
ValueAggregator.weightedMedian() |
Value<V> |
ValueAggregator.weightedSum() |
| Modifier and Type | Method and Description |
|---|---|
protected Map<K,Value<V>> |
KeyValueAggregator.asTransformedMap(com.google.common.base.Function<Vector<V>,Value<V>> function) |
protected static <K,V extends Number> |
Classification.createOrdering(Classification.Type type) |
protected Set<Map.Entry<K,Value<V>>> |
Classification.entrySet() |
protected Map.Entry<K,Value<V>> |
Classification.getWinner() |
static <K,V extends Number> |
Classification.getWinner(Classification.Type type,
Collection<Map.Entry<K,Value<V>>> entries) |
static <K,V extends Number> |
Classification.getWinnerList(Classification.Type type,
Collection<Map.Entry<K,Value<V>>> entries) |
protected List<Map.Entry<K,Value<V>>> |
Classification.getWinnerRanking() |
Iterator<Value<V>> |
ValueMap.iterator() |
| Modifier and Type | Method and Description |
|---|---|
FloatVector |
ComplexFloatVector.add(Value<? extends Number> value) |
abstract Vector<V> |
Vector.add(Value<? extends Number> value) |
FloatValue |
FloatValue.add(Value<? extends Number> value) |
DoubleVector |
ComplexDoubleVector.add(Value<? extends Number> value) |
FloatVector |
SimpleFloatVector.add(Value<? extends Number> value) |
abstract Value<V> |
Value.add(Value<? extends Number> value) |
DoubleValue |
DoubleValue.add(Value<? extends Number> value) |
DoubleVector |
SimpleDoubleVector.add(Value<? extends Number> value) |
int |
DoubleValue.compareTo(Value<Double> that) |
int |
FloatValue.compareTo(Value<Float> that) |
<V extends Number> |
Classification.Type.compareValues(Value<V> left,
Value<V> right) |
<V extends Number> |
Classification.Type.compareValues(Value<V> left,
Value<V> right) |
static <V extends Number> |
NormalizationUtil.denormalize(org.dmg.pmml.NormContinuous normContinuous,
Value<V> value) |
FloatValue |
FloatValue.divide(Value<? extends Number> value) |
abstract Value<V> |
Value.divide(Value<? extends Number> value) |
DoubleValue |
DoubleValue.divide(Value<? extends Number> value) |
static <V extends Number> |
MeasureUtil.evaluateDistance(ValueFactory<V> valueFactory,
org.dmg.pmml.ComparisonMeasure comparisonMeasure,
List<? extends org.dmg.pmml.ComparisonField<?>> comparisonFields,
List<FieldValue> values,
List<FieldValue> referenceValues,
Value<V> adjustment) |
static <V extends Number> |
TargetUtil.evaluateRegression(TargetField targetField,
Value<V> value) |
static <V extends Number> |
TargetUtil.evaluateRegressionInternal(TargetField targetField,
Value<V> value) |
static Report |
ReportUtil.getReport(Value<?> value) |
<V extends Number> |
Classification.Type.getValue(Value<V> value) |
<V extends Number> |
Classification.Type.isValidValue(Value<V> value) |
FloatValue |
FloatValue.multiply(Value<? extends Number> value) |
abstract Value<V> |
Value.multiply(Value<? extends Number> value) |
DoubleValue |
DoubleValue.multiply(Value<? extends Number> value) |
static <V extends Number> |
NormalizationUtil.normalize(org.dmg.pmml.NormContinuous normContinuous,
Value<V> value) |
static <V extends Number> |
TargetUtil.processValue(org.dmg.pmml.Target target,
Value<V> value) |
void |
EntityClassification.put(E entity,
K key,
Value<V> value) |
void |
Classification.put(K key,
Value<V> value) |
FloatValue |
FloatValue.residual(Value<? extends Number> value) |
abstract Value<V> |
Value.residual(Value<? extends Number> value) |
DoubleValue |
DoubleValue.residual(Value<? extends Number> value) |
FloatValue |
FloatValue.subtract(Value<? extends Number> value) |
abstract Value<V> |
Value.subtract(Value<? extends Number> value) |
DoubleValue |
DoubleValue.subtract(Value<? extends Number> value) |
| Modifier and Type | Method and Description |
|---|---|
protected Map<K,Value<V>> |
KeyValueAggregator.asTransformedMap(com.google.common.base.Function<Vector<V>,Value<V>> function) |
static <K,V extends Number> |
Classification.getWinner(Classification.Type type,
Collection<Map.Entry<K,Value<V>>> entries) |
static <K,V extends Number> |
Classification.getWinnerList(Classification.Type type,
Collection<Map.Entry<K,Value<V>>> entries) |
static <V extends Number> |
ValueUtil.normalizeSimpleMax(Iterable<Value<V>> values) |
static <V extends Number> |
ValueUtil.normalizeSoftMax(Iterable<Value<V>> values) |
static <V extends Number> |
ValueUtil.sum(Iterable<Value<V>> values) |
| Constructor and Description |
|---|
Regression(Value<V> value) |
| Constructor and Description |
|---|
ValueMap(Map<K,Value<V>> map) |
| Modifier and Type | Method and Description |
|---|---|
void |
ClusterAffinityDistribution.put(org.dmg.pmml.clustering.Cluster entity,
Value<V> value) |
| Modifier and Type | Method and Description |
|---|---|
static <V extends Number> |
GeneralRegressionModelUtil.computeCumulativeLink(org.dmg.pmml.general_regression.GeneralRegressionModel.CumulativeLinkFunction cumulativeLinkFunction,
Value<V> value) |
static <V extends Number> |
GeneralRegressionModelUtil.computeLink(org.dmg.pmml.general_regression.GeneralRegressionModel.LinkFunction linkFunction,
Number distParameter,
Number linkParameter,
Value<V> value) |
| Modifier and Type | Method and Description |
|---|---|
static <V extends Number> |
GeneralRegressionModelUtil.computeCumulativeLink(org.dmg.pmml.general_regression.GeneralRegressionModel.CumulativeLinkFunction cumulativeLinkFunction,
Value<V> value) |
static <V extends Number> |
GeneralRegressionModelUtil.computeLink(org.dmg.pmml.general_regression.GeneralRegressionModel.LinkFunction linkFunction,
Number distParameter,
Number linkParameter,
Value<V> value) |
| Modifier and Type | Method and Description |
|---|---|
static <V extends Number> |
MiningModelUtil.aggregateValues(ValueFactory<V> valueFactory,
org.dmg.pmml.mining.Segmentation.MultipleModelMethod multipleModelMethod,
org.dmg.pmml.mining.Segmentation.MissingPredictionTreatment missingPredictionTreatment,
Number missingThreshold,
List<SegmentResult> segmentResults) |
| Modifier and Type | Method and Description |
|---|---|
static <V extends Number> |
NeuralNetworkUtil.activateNeuronOutput(org.dmg.pmml.neural_network.NeuralNetwork.ActivationFunction activationFunction,
Number threshold,
Number leakage,
Value<V> value) |
| Modifier and Type | Method and Description |
|---|---|
static <V extends Number> |
NeuralNetworkUtil.normalizeNeuralLayerOutputs(org.dmg.pmml.neural_network.NeuralNetwork.NormalizationMethod normalizationMethod,
Collection<Value<V>> values) |
| Modifier and Type | Method and Description |
|---|---|
static <V extends Number> |
NeuralNetworkUtil.activateNeuronOutput(org.dmg.pmml.neural_network.NeuralNetwork.ActivationFunction activationFunction,
Number threshold,
Number leakage,
Value<V> value) |
| Modifier and Type | Method and Description |
|---|---|
static <V extends Number> |
NeuralNetworkUtil.normalizeNeuralLayerOutputs(org.dmg.pmml.neural_network.NeuralNetwork.NormalizationMethod normalizationMethod,
Collection<Value<V>> values) |
| Modifier and Type | Method and Description |
|---|---|
static <V extends Number> |
RegressionModelUtil.normalizeBinaryLogisticClassificationResult(org.dmg.pmml.regression.RegressionModel.NormalizationMethod normalizationMethod,
Value<V> value) |
static <V extends Number> |
RegressionModelUtil.normalizeRegressionResult(org.dmg.pmml.regression.RegressionModel.NormalizationMethod normalizationMethod,
Value<V> value) |
| Modifier and Type | Method and Description |
|---|---|
static <V extends Number> |
RegressionModelUtil.normalizeBinaryLogisticClassificationResult(org.dmg.pmml.regression.RegressionModel.NormalizationMethod normalizationMethod,
Value<V> value) |
static <V extends Number> |
RegressionModelUtil.normalizeRegressionResult(org.dmg.pmml.regression.RegressionModel.NormalizationMethod normalizationMethod,
Value<V> value) |
| Constructor and Description |
|---|
ScorecardScore(Value<V> value,
List<PartialScore> partialScores) |
| Modifier and Type | Method and Description |
|---|---|
static <V extends Number> |
KernelUtil.evaluate(org.dmg.pmml.support_vector_machine.Kernel kernel,
ValueFactory<V> valueFactory,
Object input,
Object vector) |
static <V extends Number> |
KernelUtil.evaluateLinearKernel(org.dmg.pmml.support_vector_machine.LinearKernel linearKernel,
ValueFactory<V> valueFactory,
Object input,
Object vector) |
static <V extends Number> |
KernelUtil.evaluatePolynomialKernel(org.dmg.pmml.support_vector_machine.PolynomialKernel polynomialKernel,
ValueFactory<V> valueFactory,
Object input,
Object vector) |
static <V extends Number> |
KernelUtil.evaluateRadialBasisKernel(org.dmg.pmml.support_vector_machine.RadialBasisKernel radialBasisKernel,
ValueFactory<V> valueFactory,
Object input,
Object vector) |
static <V extends Number> |
KernelUtil.evaluateSigmoidKernel(org.dmg.pmml.support_vector_machine.SigmoidKernel sigmoidKernel,
ValueFactory<V> valueFactory,
Object input,
Object vector) |
Copyright © 2022. All rights reserved.