Package | Description |
---|---|
org.apache.flink.ml.optimization | |
org.apache.flink.ml.regression |
Modifier and Type | Method and Description |
---|---|
WeightVector |
LossFunction.gradient(LabeledVector dataPoint,
WeightVector weightVector)
Calculates the gradient of the loss function given a data point and weight vector
|
static WeightVector |
LinearPrediction.gradient(Vector features,
WeightVector weights) |
abstract WeightVector |
PredictionFunction.gradient(Vector features,
WeightVector weights) |
WeightVector |
LinearPrediction$.gradient(Vector features,
WeightVector weights) |
Modifier and Type | Method and Description |
---|---|
DataSet<WeightVector> |
Solver.createInitialWeightsDS(scala.Option<DataSet<WeightVector>> initialWeights,
DataSet<LabeledVector> data)
Creates initial weights vector, creating a DataSet with a WeightVector element
|
DataSet<WeightVector> |
Solver.createInitialWeightVector(DataSet<Object> dimensionDS)
Creates a DataSet with one zero vector.
|
scala.Tuple2<Object,WeightVector> |
LossFunction.lossGradient(LabeledVector dataPoint,
WeightVector weightVector)
Calculates the gradient as well as the loss given a data point and the weight vector
|
scala.Tuple2<Object,WeightVector> |
GenericLossFunction.lossGradient(LabeledVector dataPoint,
WeightVector weightVector)
Calculates the gradient as well as the loss given a data point and the weight vector
|
abstract DataSet<WeightVector> |
Solver.optimize(DataSet<LabeledVector> data,
scala.Option<DataSet<WeightVector>> initialWeights)
Provides a solution for the given optimization problem
|
DataSet<WeightVector> |
GradientDescent.optimize(DataSet<LabeledVector> data,
scala.Option<DataSet<WeightVector>> initialWeights)
Provides a solution for the given optimization problem
|
DataSet<WeightVector> |
GradientDescent.optimizeWithConvergenceCriterion(DataSet<LabeledVector> dataPoints,
DataSet<WeightVector> initialWeightsDS,
int numberOfIterations,
double regularizationConstant,
double learningRate,
double convergenceThreshold,
LossFunction lossFunction,
LearningRateMethod.LearningRateMethodTrait learningRateMethod) |
DataSet<WeightVector> |
GradientDescent.optimizeWithoutConvergenceCriterion(DataSet<LabeledVector> data,
DataSet<WeightVector> initialWeightsDS,
int numberOfIterations,
double regularizationConstant,
double learningRate,
LossFunction lossFunction,
LearningRateMethod.LearningRateMethodTrait optimizationMethod) |
Modifier and Type | Method and Description |
---|---|
WeightVector |
LossFunction.gradient(LabeledVector dataPoint,
WeightVector weightVector)
Calculates the gradient of the loss function given a data point and weight vector
|
static WeightVector |
LinearPrediction.gradient(Vector features,
WeightVector weights) |
abstract WeightVector |
PredictionFunction.gradient(Vector features,
WeightVector weights) |
WeightVector |
LinearPrediction$.gradient(Vector features,
WeightVector weights) |
double |
LossFunction.loss(LabeledVector dataPoint,
WeightVector weightVector)
Calculates the loss given the prediction and label value
|
scala.Tuple2<Object,WeightVector> |
LossFunction.lossGradient(LabeledVector dataPoint,
WeightVector weightVector)
Calculates the gradient as well as the loss given a data point and the weight vector
|
scala.Tuple2<Object,WeightVector> |
GenericLossFunction.lossGradient(LabeledVector dataPoint,
WeightVector weightVector)
Calculates the gradient as well as the loss given a data point and the weight vector
|
static double |
LinearPrediction.predict(Vector features,
WeightVector weightVector) |
abstract double |
PredictionFunction.predict(Vector features,
WeightVector weights) |
double |
LinearPrediction$.predict(Vector features,
WeightVector weightVector) |
Modifier and Type | Method and Description |
---|---|
DataSet<WeightVector> |
Solver.createInitialWeightsDS(scala.Option<DataSet<WeightVector>> initialWeights,
DataSet<LabeledVector> data)
Creates initial weights vector, creating a DataSet with a WeightVector element
|
abstract DataSet<WeightVector> |
Solver.optimize(DataSet<LabeledVector> data,
scala.Option<DataSet<WeightVector>> initialWeights)
Provides a solution for the given optimization problem
|
DataSet<WeightVector> |
GradientDescent.optimize(DataSet<LabeledVector> data,
scala.Option<DataSet<WeightVector>> initialWeights)
Provides a solution for the given optimization problem
|
DataSet<WeightVector> |
GradientDescent.optimizeWithConvergenceCriterion(DataSet<LabeledVector> dataPoints,
DataSet<WeightVector> initialWeightsDS,
int numberOfIterations,
double regularizationConstant,
double learningRate,
double convergenceThreshold,
LossFunction lossFunction,
LearningRateMethod.LearningRateMethodTrait learningRateMethod) |
DataSet<WeightVector> |
GradientDescent.optimizeWithoutConvergenceCriterion(DataSet<LabeledVector> data,
DataSet<WeightVector> initialWeightsDS,
int numberOfIterations,
double regularizationConstant,
double learningRate,
LossFunction lossFunction,
LearningRateMethod.LearningRateMethodTrait optimizationMethod) |
Modifier and Type | Method and Description |
---|---|
scala.Option<DataSet<WeightVector>> |
MultipleLinearRegression.weightsOption() |
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