InputType
- The input type for supervised learning.public static class VectorFunctionToScalarFunction.Learner<InputType> extends AbstractCloneableSerializable implements SupervisedBatchLearner<InputType,java.lang.Double,VectorFunctionToScalarFunction<InputType>>
VectorFunctionToScalarFunction.Learner
class implements a
simple learner for a VectorFunctionToScalarFunction
that allows
a learning algorithm that outputs a vector function to be adapted to
learn on data whose output are doubles.Modifier and Type | Field and Description |
---|---|
protected BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,? extends Evaluator<? super InputType,? extends Vectorizable>> |
vectorLearner
The supervised learner that learns on vectors as outputs.
|
Constructor and Description |
---|
Learner()
Creates a new
VectorFunctionToScalarFunction.Learner . |
Learner(BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,? extends Evaluator<? super InputType,? extends Vectorizable>> vectorLearner)
Creates a new
VectorFunctionToScalarFunction.Learner . |
Modifier and Type | Method and Description |
---|---|
VectorFunctionToScalarFunction.Learner<InputType> |
clone()
This makes public the clone method on the
Object class and
removes the exception that it throws. |
VectorFunctionToScalarFunction<InputType> |
learn(java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>> data)
The
learn method creates an object of ResultType using
data of type DataType , using some form of "learning" algorithm. |
protected BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,? extends Evaluator<? super InputType,? extends Vectorizable>> vectorLearner
public Learner()
VectorFunctionToScalarFunction.Learner
.public Learner(BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,Vector>>,? extends Evaluator<? super InputType,? extends Vectorizable>> vectorLearner)
VectorFunctionToScalarFunction.Learner
.vectorLearner
- The supervised learner to use that learns on
vectors as outputs.public VectorFunctionToScalarFunction.Learner<InputType> clone()
AbstractCloneableSerializable
Object
class and
removes the exception that it throws. Its default behavior is to
automatically create a clone of the exact type of object that the
clone is called on and to copy all primitives but to keep all references,
which means it is a shallow copy.
Extensions of this class may want to override this method (but call
super.clone()
to implement a "smart copy". That is, to target
the most common use case for creating a copy of the object. Because of
the default behavior being a shallow copy, extending classes only need
to handle fields that need to have a deeper copy (or those that need to
be reset). Some of the methods in ObjectUtil
may be helpful in
implementing a custom clone method.
Note: The contract of this method is that you must use
super.clone()
as the basis for your implementation.clone
in interface CloneableSerializable
clone
in class AbstractCloneableSerializable
public VectorFunctionToScalarFunction<InputType> learn(java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>> data)
BatchLearner
learn
method creates an object of ResultType
using
data of type DataType
, using some form of "learning" algorithm.learn
in interface BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Double>>,VectorFunctionToScalarFunction<InputType>>
data
- The data that the learning algorithm will use to create an
object of ResultType
.