Package | Description |
---|---|
org.tribuo.classification.sgd |
Provides infrastructure for Stochastic Gradient Descent for classification problems.
|
org.tribuo.classification.sgd.objectives |
Provides classification loss functions for Stochastic Gradient Descent.
|
org.tribuo.math.la |
Provides a linear algebra system used for numerical operations in Tribuo.
|
org.tribuo.math.util |
Provides math related util classes.
|
org.tribuo.multilabel.sgd |
Provides infrastructure for Stochastic Gradient Descent for multi-label classification problems.
|
org.tribuo.multilabel.sgd.objectives |
Provides multi-label classification loss functions for Stochastic Gradient Descent.
|
Modifier and Type | Method and Description |
---|---|
VectorNormalizer |
LabelObjective.getNormalizer()
Generates a new
VectorNormalizer which normalizes the predictions into [0,1]. |
Modifier and Type | Method and Description |
---|---|
VectorNormalizer |
LogMulticlass.getNormalizer() |
VectorNormalizer |
Hinge.getNormalizer()
Returns a new
NoopNormalizer . |
Modifier and Type | Method and Description |
---|---|
void |
SparseVector.normalize(VectorNormalizer normalizer) |
void |
SGDVector.normalize(VectorNormalizer normalizer)
Normalizes the vector using the supplied vector normalizer.
|
void |
DenseVector.normalize(VectorNormalizer normalizer) |
void |
DenseMatrix.normalizeRows(VectorNormalizer normalizer)
Normalizes each row using the supplied normalizer in place.
|
Modifier and Type | Class and Description |
---|---|
class |
ExpNormalizer
Normalizes the exponential values of the input array.
|
class |
NoopNormalizer
NoopNormalizer returns a copy in
NoopNormalizer.normalize(double[]) and is a no-op in place. |
class |
Normalizer
Normalizes, but first subtracts the minimum value (to ensure positivity).
|
class |
SigmoidNormalizer
Normalizes the input by applying a logistic sigmoid to each element.
|
Modifier and Type | Method and Description |
---|---|
VectorNormalizer |
MultiLabelObjective.getNormalizer()
Generates a new
VectorNormalizer which normalizes the predictions into a suitable format. |
Modifier and Type | Method and Description |
---|---|
VectorNormalizer |
Hinge.getNormalizer()
Returns a new
NoopNormalizer . |
VectorNormalizer |
BinaryCrossEntropy.getNormalizer() |
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