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java.lang.Objectde.jstacs.algorithms.optimization.DifferentiableFunction
de.jstacs.classifier.scoringFunctionBased.OptimizableFunction
de.jstacs.classifier.scoringFunctionBased.AbstractOptimizableFunction
public abstract class AbstractOptimizableFunction
This class extends OptimizableFunction
and implements some common
methods.
Nested Class Summary |
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Nested classes/interfaces inherited from class de.jstacs.classifier.scoringFunctionBased.OptimizableFunction |
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OptimizableFunction.KindOfParameter |
Field Summary | |
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protected int |
cl
The number of different classes. |
protected double[] |
clazz
The class parameters. |
protected Sample[] |
data
The data that is used to evaluate this function. |
protected boolean |
freeParams
Indicates whether only the free parameters or all should be used. |
protected double[] |
logClazz
The logarithm of the class parameters. |
protected boolean |
norm
Indicates whether a normalization should be done or not. |
protected int[] |
shortcut
These shortcuts indicate the beginning of a new part in the parameter vector. |
protected double[] |
sum
The sums of the weighted data per class and additional the total weight sum. |
protected double[][] |
weights
The weights for the data. |
Constructor Summary | |
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protected |
AbstractOptimizableFunction(Sample[] data,
double[][] weights,
boolean norm,
boolean freeParams)
The constructor creates an instance using the given weighted data. |
Method Summary | |
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void |
addTermToClassParameter(int classIndex,
double term)
This method adds the term to the class parameter of the
class with index classIndex . |
double[] |
getClassParams(double[] params)
Returns from the complete vector of parameters those that are for the classes. |
Sample[] |
getData()
Returns the data for each class used in this OptimizableFunction . |
int |
getDimensionOfScope()
Returns the dimension of the scope of the Function . |
protected int |
getNumberOfStarts(ScoringFunction[] score)
Returns the number of recommended starts. |
double[] |
getParameters(OptimizableFunction.KindOfParameter kind)
Returns some parameters that can be used for instance as start parameters. |
abstract void |
getParameters(OptimizableFunction.KindOfParameter kind,
double[] erg)
This method enables the user to get the parameters without creating a new array. |
double[][] |
getSequenceWeights()
Returns the weights for each Sequence for each
class used in this OptimizableFunction . |
void |
setParams(double[] params)
Checks the dimension and sets the class parameters. |
Methods inherited from class de.jstacs.classifier.scoringFunctionBased.OptimizableFunction |
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getNumberOfStarts, reset |
Methods inherited from class de.jstacs.algorithms.optimization.DifferentiableFunction |
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evaluateGradientOfFunction, findOneDimensionalMin |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Methods inherited from interface de.jstacs.algorithms.optimization.Function |
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evaluateFunction |
Field Detail |
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protected int[] shortcut
protected Sample[] data
protected double[][] weights
data
protected double[] clazz
protected double[] logClazz
clazz
protected double[] sum
data
,
weights
protected int cl
protected boolean norm
protected boolean freeParams
Constructor Detail |
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protected AbstractOptimizableFunction(Sample[] data, double[][] weights, boolean norm, boolean freeParams) throws IllegalArgumentException, WrongAlphabetException
data
- the dataweights
- the weightsnorm
- the switch for using the normalization (division by the number
of sequences)freeParams
- the switch for using only the free parameters
IllegalArgumentException
- if the number of classes is not correct
WrongAlphabetException
- if different alphabets are usedMethod Detail |
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public final int getDimensionOfScope()
Function
Function
.
n
with
f: R^n -> R
public abstract void getParameters(OptimizableFunction.KindOfParameter kind, double[] erg) throws Exception
kind
- the kind of the class parameters to be returned in
erg
erg
- the array for the start parameters
Exception
- if the array is null
or does not have the
correct lengthOptimizableFunction.getParameters(KindOfParameter)
public final double[] getParameters(OptimizableFunction.KindOfParameter kind) throws Exception
OptimizableFunction
getParameters
in class OptimizableFunction
kind
- the kind of the class parameters that will be returned
Exception
- if something went wrongpublic void setParams(double[] params) throws DimensionException
setParams
in class OptimizableFunction
params
- the current values
DimensionException
- if the dimension of the current values does not match with
the dimension of the internal parameterspublic final double[] getClassParams(double[] params)
OptimizableFunction
getClassParams
in class OptimizableFunction
params
- the current parameters
protected final int getNumberOfStarts(ScoringFunction[] score)
score
- the underlying scoring functions
OptimizableFunction.getNumberOfStarts()
,
ScoringFunction.getNumberOfRecommendedStarts()
public final void addTermToClassParameter(int classIndex, double term)
OptimizableFunction
term
to the class parameter of the
class with index classIndex
.
addTermToClassParameter
in class OptimizableFunction
classIndex
- the index of the classterm
- the term to be added to the class parameterpublic Sample[] getData()
OptimizableFunction
OptimizableFunction
.
getData
in class OptimizableFunction
OptimizableFunction.getSequenceWeights()
public double[][] getSequenceWeights()
OptimizableFunction
Sequence
for each
class used in this OptimizableFunction
.
getSequenceWeights
in class OptimizableFunction
Sequence
and each
classOptimizableFunction.getData()
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