Package | Description |
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org.tribuo.regression |
Provides classes and infrastructure for regression problems with single or multiple output dimensions.
|
org.tribuo.regression.impl | |
org.tribuo.regression.slm |
Provides implementations of sparse linear regression using various forms of regularisation penalty.
|
Modifier and Type | Method and Description |
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Regressor.DimensionTuple |
Regressor.DimensionTuple.copy() |
Regressor.DimensionTuple |
Regressor.getDimension(int idx)
Returns a dimension tuple for the requested dimension index.
|
Modifier and Type | Method and Description |
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Optional<Regressor.DimensionTuple> |
Regressor.getDimension(String name)
Returns a dimension tuple for the requested dimension, or optional empty if
it's not valid.
|
Optional<Regressor.DimensionTuple> |
Regressor.DimensionTuple.getDimension(String name) |
Iterator<Regressor.DimensionTuple> |
Regressor.iterator() |
Iterator<Regressor.DimensionTuple> |
Regressor.DimensionTuple.iterator() |
Constructor and Description |
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Regressor(Regressor.DimensionTuple[] dimensions)
Constructs a regressor from the supplied dimension tuples.
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Modifier and Type | Method and Description |
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protected abstract Regressor.DimensionTuple |
SkeletalIndependentRegressionSparseModel.scoreDimension(int dimensionIdx,
SparseVector features)
Makes a prediction for a single dimension.
|
protected abstract Regressor.DimensionTuple |
SkeletalIndependentRegressionModel.scoreDimension(int dimensionIdx,
SparseVector features)
Makes a prediction for a single dimension.
|
Modifier and Type | Method and Description |
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protected Regressor.DimensionTuple |
SparseLinearModel.scoreDimension(int dimensionIdx,
SparseVector features) |
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