T
- public class GradMKL<T> extends java.lang.Object implements Classifier<T>, KernelSVM<T>, MKL<T>
Constructor and Description |
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GradMKL() |
Modifier and Type | Method and Description |
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void |
addKernel(Kernel<T> k)
Adds a kernel to the MKL problem
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GradMKL<T> |
copy()
Creates and returns a copy of this object.
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double[] |
getAlphas()
Tells the weights of training samples
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double |
getC()
Tells the hyperparameter C
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KernelSVM<T> |
getClassifier()
Returns the classifier used by this MKL algorithm
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java.util.ArrayList<java.lang.Double> |
getExampleWeights()
Tells the weights on examples
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Kernel<T> |
getKernel()
Tells the current Kernel.
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java.util.List<Kernel<T>> |
getKernels()
Returns the list of kernels
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java.util.Map<Kernel<T>,java.lang.Double> |
getKernelWeightMap()
Gets a mapping of pairs
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double[] |
getKernelWeights()
Gets an array containing the weights of the different kernels, in the
same order as getKernels()
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double |
getMKLNorm()
Return the norm used for the regularization on the kernels
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double |
getStopGap()
Returns the stopping criterion
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java.util.Hashtable<Kernel<T>,java.lang.Double> |
getWeights()
Tells training samples and associated weights
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void |
setC(double c)
Sets the hyperparameter C
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void |
setClassifier(KernelSVM<T> cls)
Sets the default training algorithm for the underlying svm calls (default LASVM).
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void |
setKernel(Kernel<T> k)
Sets the kernel to use as similarity measure
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void |
setMKLNorm(double p)
Sets the norm used for kernel weights (real)
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void |
setStopGap(double w)
Sets stopping criterion threshold
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void |
train(java.util.List<TrainingSample<T>> l)
Replace the current training list and train the classifier
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void |
train(TrainingSample<T> t)
Add a single example to the current training set and train the classifier
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double |
valueOf(T e)
Computes the category of the provided example
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public void addKernel(Kernel<T> k)
MKL
public void train(TrainingSample<T> t)
Classifier
train
in interface Classifier<T>
t
- the training samplepublic void train(java.util.List<TrainingSample<T>> l)
Classifier
train
in interface Classifier<T>
l
- list of training samplespublic double valueOf(T e)
Classifier
valueOf
in interface Classifier<T>
e
- examplepublic double getC()
public void setC(double c)
public void setMKLNorm(double p)
p
- value of the normpublic void setStopGap(double w)
w
- public java.util.ArrayList<java.lang.Double> getExampleWeights()
public double[] getKernelWeights()
MKL
getKernelWeights
in interface MKL<T>
public java.util.Hashtable<Kernel<T>,java.lang.Double> getWeights()
public void setClassifier(KernelSVM<T> cls)
cls
- the algorithm used to solve the svm problempublic GradMKL<T> copy() throws java.lang.CloneNotSupportedException
copy
in interface Classifier<T>
java.lang.CloneNotSupportedException
Object.clone()
public java.util.List<Kernel<T>> getKernels()
getKernels
in interface MKL<T>
public double getMKLNorm()
public double getStopGap()
public KernelSVM<T> getClassifier()
public java.util.Map<Kernel<T>,java.lang.Double> getKernelWeightMap()
MKL
getKernelWeightMap
in interface MKL<T>
public void setKernel(Kernel<T> k)
KernelSVM
public double[] getAlphas()
KernelSVM