InputType - Type of input using the regressionOutputType - Type of outputs from the regressionFunctionType - TYpe of Evaluator that maps the InputType to the OutputTypepublic abstract class AbstractLogisticRegression<InputType,OutputType,FunctionType extends Evaluator<? super InputType,OutputType>> extends AbstractAnytimeSupervisedBatchLearner<InputType,OutputType,FunctionType>
| Modifier and Type | Field and Description |
|---|---|
static int |
DEFAULT_MAX_ITERATIONS
Default number of iterations before stopping, 100
|
static double |
DEFAULT_REGULARIZATION
Default regularization, 0.0.
|
static double |
DEFAULT_TOLERANCE
Default tolerance change in weights before stopping, 1.0E-10
|
protected FunctionType |
objectToOptimize
The object to optimize, used as a factory on successive runs of the
algorithm.
|
protected double |
regularization
L2 ridge regularization term, must be nonnegative, a value of zero is
equivalent to unregularized regression.
|
protected FunctionType |
result
Return value from the algorithm
|
protected double |
tolerance
Tolerance change in weights before stopping
|
data, keepGoingmaxIterationsDEFAULT_ITERATION, iteration| Constructor and Description |
|---|
AbstractLogisticRegression(double regularization,
double tolerance,
int maxIterations)
Creates a new instance of AbstractLogisticRegression
|
| Modifier and Type | Method and Description |
|---|---|
AbstractLogisticRegression<InputType,OutputType,FunctionType> |
clone()
This makes public the clone method on the
Object class and
removes the exception that it throws. |
FunctionType |
getObjectToOptimize()
Getter for objectToOptimize
|
double |
getRegularization()
Getter for regularization
|
FunctionType |
getResult()
Gets the current result of the algorithm.
|
double |
getTolerance()
Getter for tolerance
|
void |
setObjectToOptimize(FunctionType objectToOptimize)
Setter for objectToOptimize
|
void |
setRegularization(double regularization)
Setter for regularization
|
void |
setTolerance(double tolerance)
Setter for tolerance
|
cleanupAlgorithm, getData, getKeepGoing, initializeAlgorithm, learn, setData, setKeepGoing, step, stopgetMaxIterations, isResultValid, setMaxIterationsaddIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, getListeners, removeIterativeAlgorithmListener, setIteration, setListenersequals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, waitlearngetMaxIterations, setMaxIterationsaddIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListenerisResultValidpublic static final int DEFAULT_MAX_ITERATIONS
public static final double DEFAULT_TOLERANCE
public static final double DEFAULT_REGULARIZATION
protected FunctionType extends Evaluator<? super InputType,OutputType> objectToOptimize
protected FunctionType extends Evaluator<? super InputType,OutputType> result
protected double tolerance
protected double regularization
public AbstractLogisticRegression(double regularization,
double tolerance,
int maxIterations)
regularization - L2 ridge regularization term, must be nonnegative, a value of zero is
equivalent to unregularized regression.tolerance - Tolerance change in weights before stoppingmaxIterations - Maximum number of iterations before stoppingpublic AbstractLogisticRegression<InputType,OutputType,FunctionType> clone()
AbstractCloneableSerializableObject 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 CloneableSerializableclone in class AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,FunctionType extends Evaluator<? super InputType,OutputType>>public FunctionType getObjectToOptimize()
public void setObjectToOptimize(FunctionType objectToOptimize)
objectToOptimize - The object to optimize, used as a factory on successive runs of the
algorithm.public FunctionType getResult()
AnytimeAlgorithmpublic double getTolerance()
public void setTolerance(double tolerance)
tolerance - Tolerance change in weights before stopping, must be nonnegative.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.