@PublicationReference(author="Wikipedia",title="Linear regression",type=WebPage,year=2008,url="http://en.wikipedia.org/wiki/Linear_regression") @PublicationReference(author="Wikipedia",title="Tikhonov regularization",type=WebPage,year=2011,url="http://en.wikipedia.org/wiki/Tikhonov_regularization",notes="Despite what Wikipedia says, this is always called Ridge Regression") public class MultivariateLinearRegression extends AbstractCloneableSerializable implements SupervisedBatchLearner<Vector,Vector,MultivariateDiscriminantWithBias>
Modifier and Type | Field and Description |
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static double |
DEFAULT_PSEUDO_INVERSE_TOLERANCE
Tolerance for the pseudo inverse in the learn method, 1.0E-10.
|
static double |
DEFAULT_REGULARIZATION
Default regularization, 0.0.
|
Constructor and Description |
---|
MultivariateLinearRegression()
Creates a new instance of MultivariateLinearRegression
|
Modifier and Type | Method and Description |
---|---|
MultivariateLinearRegression |
clone()
This makes public the clone method on the
Object class and
removes the exception that it throws. |
double |
getRegularization()
Getter for regularization
|
boolean |
getUsePseudoInverse()
Getter for usePseudoInverse
|
MultivariateDiscriminantWithBias |
learn(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> data)
The
learn method creates an object of ResultType using
data of type DataType , using some form of "learning" algorithm. |
void |
setRegularization(double regularization)
Setter for regularization
|
void |
setUsePseudoInverse(boolean usePseudoInverse)
Setter for usePseudoInverse
|
public static final double DEFAULT_REGULARIZATION
public static final double DEFAULT_PSEUDO_INVERSE_TOLERANCE
public MultivariateLinearRegression()
public MultivariateLinearRegression 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 MultivariateDiscriminantWithBias learn(java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>> 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 Vector,Vector>>,MultivariateDiscriminantWithBias>
data
- The data that the learning algorithm will use to create an
object of ResultType
.public boolean getUsePseudoInverse()
public void setUsePseudoInverse(boolean usePseudoInverse)
usePseudoInverse
- Flag to use a pseudoinverse. True to use the expensive, but more
accurate, pseudoinverse routine. False uses a very fast, but
numerically less stable LU solver. Default value is "true".public double getRegularization()
public void setRegularization(double regularization)
regularization
- L2 ridge regularization term, must be nonnegative, a value of zero is
equivalent to unregularized regression.