Uses of Interface jsat.distributions.kernels.KernelTrick (Java Statistical Analysis Tool 0.0.8 API)

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Packages that use KernelTrick
Package	Description
jsat.classifiers.linear.kernelized
jsat.classifiers.svm
jsat.clustering.kmeans
jsat.datatransform.kernel
jsat.distributions.kernels
jsat.linear.distancemetrics
jsat.regression

Uses of KernelTrick in jsat.classifiers.linear.kernelized

Fields in jsat.classifiers.linear.kernelized declared as KernelTrick
Modifier and Type	Field and Description
`protected KernelTrick`	DUOL.`k` Kernel trick to use

Methods in jsat.classifiers.linear.kernelized that return KernelTrick
Modifier and Type	Method and Description
`KernelTrick`	Projectron.`getKernel()` Returns the kernel trick in use
`KernelTrick`	OSKL.`getKernel()` Returns the kernel to use
`KernelTrick`	KernelSGD.`getKernel()` Returns the kernel in use
`KernelTrick`	DUOL.`getKernel()` Returns the kernel trick in use
`KernelTrick`	CSKLR.`getKernel()` Returns the kernel trick in use
`KernelTrick`	BOGD.`getKernel()` Returns the kernel to use
`KernelTrick`	Forgetron.`getKernelTrick()` Returns the current kernel trick
`KernelTrick`	ALMA2K.`getKernelTrick()` Returns the kernel in use

Methods in jsat.classifiers.linear.kernelized with parameters of type KernelTrick
Modifier and Type	Method and Description
`void`	Projectron.`setKernel(KernelTrick k)` Sets the kernel trick to be used
`void`	OSKL.`setKernel(KernelTrick k)` Sets the kernel to use
`void`	KernelSGD.`setKernel(KernelTrick kernel)` Sets the kernel to use
`void`	DUOL.`setKernel(KernelTrick k)` Sets the kernel trick to use
`void`	CSKLR.`setKernel(KernelTrick k)` Set which kernel trick to use
`void`	BOGD.`setKernel(KernelTrick k)` Sets the kernel to use
`void`	Forgetron.`setKernelTrick(KernelTrick K)` Sets the kernel trick to use
`void`	ALMA2K.`setKernelTrick(KernelTrick K)` Sets the kernel to use

Constructors in jsat.classifiers.linear.kernelized with parameters of type KernelTrick
Constructor and Description
`ALMA2K(KernelTrick kernel, double alpha)` Creates a new kernelized ALMA2 object
`BOGD(KernelTrick k, int budget, double eta, double reg, double maxCoeff)` Creates a new BOGD++ learner using the `HingeLoss`
`BOGD(KernelTrick k, int budget, double eta, double reg, double maxCoeff, LossC lossC)` Creates a new BOGD++ learner
`CSKLR(double eta, KernelTrick k, double R, CSKLR.UpdateMode mode)` Creates a new CSKLR object
`CSKLRBatch(double eta, KernelTrick kernel, double R, CSKLR.UpdateMode mode, SupportVectorLearner.CacheMode cacheMode)` Creates a new SCKLR Batch learning object
`DUOL(KernelTrick k)` Creates a new DUOL learner
`Forgetron(KernelTrick kernel, int budget)` Creates a new Forgetron
`KernelSGD(LossFunc loss, KernelTrick kernel, double lambda, KernelPoint.BudgetStrategy budgetStrategy, int budgetSize)` Creates a new Kernel SGD object
`KernelSGD(LossFunc loss, KernelTrick kernel, double lambda, KernelPoint.BudgetStrategy budgetStrategy, int budgetSize, double eta, double errorTolerance)` Creates a new Kernel SGD object
`OSKL(KernelTrick k, double R)` Creates a new OSKL learner using the `LogisticLoss`.
`OSKL(KernelTrick k, double eta, double G, double R)` Creates a new OSKL learner using the `LogisticLoss`
`OSKL(KernelTrick k, double eta, double G, double R, LossC lossC)` Creates a new OSKL learner
`Projectron(KernelTrick k)` Creates a new Projectron++ learner
`Projectron(KernelTrick k, double eta)` Creates a new Projectron++ learner
`Projectron(KernelTrick k, double eta, boolean useMarginUpdates)` Creates a new Projectron learner

Uses of KernelTrick in jsat.classifiers.svm

Methods in jsat.classifiers.svm that return KernelTrick
Modifier and Type	Method and Description
`KernelTrick`	SupportVectorLearner.`getKernel()`

Methods in jsat.classifiers.svm with parameters of type KernelTrick
Modifier and Type	Method and Description
`void`	SVMnoBias.`setKernel(KernelTrick kernel)`
`void`	SupportVectorLearner.`setKernel(KernelTrick kernel)` Sets the kernel trick to use

Constructors in jsat.classifiers.svm with parameters of type KernelTrick
Constructor and Description
`DCSVM(KernelTrick k)` Creates a new DC-SVM for the given kernel
`LSSVM(KernelTrick kernel)` Creates a new LS-SVM learner that does not use a cache
`LSSVM(KernelTrick kernel, SupportVectorLearner.CacheMode cacheMode)` Creates a new LS-SVM learner
`PegasosK(double regularization, int iterations, KernelTrick kernel)` Creates a new kernelized Pegasos SVM solver
`PegasosK(double regularization, int iterations, KernelTrick kernel, SupportVectorLearner.CacheMode cacheMode)` Creates a new kernelized Pegasos SVM solver
`PlattSMO(KernelTrick kf)` Creates a new SVM object that uses no cache mode.
`SBP(KernelTrick kernel, SupportVectorLearner.CacheMode cacheMode, int iterations, double v)` Creates a new SBP SVM learner
`SupportVectorLearner(KernelTrick kernel, SupportVectorLearner.CacheMode cacheMode)` Creates a new Support Vector Learner
`SVMnoBias(KernelTrick kf)` Creates a new SVM object that uses no cache mode.

Uses of KernelTrick in jsat.clustering.kmeans

Fields in jsat.clustering.kmeans declared as KernelTrick
Modifier and Type	Field and Description
`protected KernelTrick`	KernelKMeans.`kernel` The kernel trick to use

Constructors in jsat.clustering.kmeans with parameters of type KernelTrick
Constructor and Description
`ElkanKernelKMeans(KernelTrick kernel)` Creates a new Kernel K Means object
`KernelKMeans(KernelTrick kernel)`
`LloydKernelKMeans(KernelTrick kernel)` Creates a new Kernel K Means object

Uses of KernelTrick in jsat.datatransform.kernel

Methods in jsat.datatransform.kernel that return KernelTrick
Modifier and Type	Method and Description
`KernelTrick`	Nystrom.`getKernel()`
`KernelTrick`	KernelPCA.`getKernel()`

Methods in jsat.datatransform.kernel with parameters of type KernelTrick
Modifier and Type	Method and Description
`static List<Vec>`	Nystrom.`sampleBasisVectors(KernelTrick k, DataSet dataset, List<Vec> X, Nystrom.SamplingMethod method, int basisSize, boolean sampleWithReplacment, Random rand)` Performs sampling of a data set for a subset of the vectors that make a good set of basis vectors for forming an approximation of a full kernel space.
`void`	Nystrom.`setKernel(KernelTrick k)`
`void`	KernelPCA.`setKernel(KernelTrick k)`

Constructors in jsat.datatransform.kernel with parameters of type KernelTrick
Constructor and Description
`KernelPCA(KernelTrick k, DataSet ds, int dimensions, int basisSize, Nystrom.SamplingMethod samplingMethod)` Creates a new Kernel PCA transform object
`KernelPCA(KernelTrick k, int dimensions)` Creates a new Kernel PCA transform object
`KernelPCA(KernelTrick k, int dimensions, int basisSize, Nystrom.SamplingMethod samplingMethod)` Creates a new Kernel PCA transform object
`Nystrom(KernelTrick k, DataSet dataset, int basisSize, Nystrom.SamplingMethod method)` Creates a new Nystrom approximation object
`Nystrom(KernelTrick k, DataSet dataset, int basisSize, Nystrom.SamplingMethod method, double ridge, boolean sampleWithReplacment)` Creates a new Nystrom approximation object
`Nystrom(KernelTrick k, int basisSize)` Creates a new Nystrom approximation object
`Nystrom(KernelTrick k, int basisSize, Nystrom.SamplingMethod method, double ridge, boolean sampleWithReplacment)` Creates a new Nystrom approximation object

Uses of KernelTrick in jsat.distributions.kernels

Classes in jsat.distributions.kernels that implement KernelTrick
Modifier and Type	Class and Description
`class`	`BaseKernelTrick` This provides a simple base implementation for the cache related methods in Kernel Trick.
`class`	`BaseL2Kernel` Many Kernels can be described in terms the L2 norm with some operations performed on it.
`class`	`DistanceMetricBasedKernel` This abstract class provides the means of implementing a Kernel based off some `DistanceMetric`.
`class`	`GeneralRBFKernel` This class provides a generalization of the `RBFKernel` to arbitrary `distance metrics`, and is of the form exp(-d(x, y)²/(2 `σ`² )).
`class`	`LinearKernel` Provides a linear kernel function, which computes the normal dot product.
`class`	`NormalizedKernel` This provides a wrapper kernel that produces a normalized kernel trick from any input kernel trick.
`class`	`PolynomialKernel` Provides a Polynomial Kernel of the form k(x,y) = (alpha * x.y + c)^d
`class`	`PukKernel` The PUK kernel is an alternative to the RBF Kernel.
`class`	`RationalQuadraticKernel` Provides an implementation of the Rational Quadratic Kernel, which is of the form: k(x, y) = 1 - \|\|x-y\|\|² / (\|\|x-y\|\|² + c)
`class`	`RBFKernel` Provides a kernel for the Radial Basis Function, which is of the form k(x, y) = exp(-\|\|x-y\|\|²/(2*σ²))
`class`	`SigmoidKernel` Provides an implementation of the Sigmoid (Hyperbolic Tangent) Kernel, which is of the form: k(x, y) = tanh(alpha * < x, y > +c) Technically, this kernel is not positive definite.

Fields in jsat.distributions.kernels declared as KernelTrick
Modifier and Type	Field and Description
`protected KernelTrick`	KernelPoint.`k`

Methods in jsat.distributions.kernels that return KernelTrick
Modifier and Type	Method and Description
`KernelTrick`	KernelTrick.`clone()`
`KernelTrick`	GeneralRBFKernel.`clone()`
`abstract KernelTrick`	DistanceMetricBasedKernel.`clone()`
`abstract KernelTrick`	BaseL2Kernel.`clone()`
`abstract KernelTrick`	BaseKernelTrick.`clone()`
`KernelTrick`	KernelPoints.`getKernel()`

Constructors in jsat.distributions.kernels with parameters of type KernelTrick
Constructor and Description
`KernelPoint(KernelTrick k, double errorTolerance)` Creates a new Kernel Point, which is a point in the kernel space represented by an accumulation of vectors and uses the `KernelPoint.BudgetStrategy.PROJECTION` strategy with an unbounded maximum budget
`KernelPoints(KernelTrick k, int points, double errorTolerance)` Creates a new set of kernel points that uses one unified gram matrix for each KernelPoint
`KernelPoints(KernelTrick k, int points, double errorTolerance, boolean mergeGrams)` Creates a new set of kernel points
`NormalizedKernel(KernelTrick source_kernel)`

Uses of KernelTrick in jsat.linear.distancemetrics

Constructors in jsat.linear.distancemetrics with parameters of type KernelTrick
Constructor and Description

KernelDistance(KernelTrick kf)
Creates a distane metric from the given kernel.

Uses of KernelTrick in jsat.regression

Methods in jsat.regression that return KernelTrick
Modifier and Type	Method and Description
`KernelTrick`	KernelRidgeRegression.`getKernel()` Returns the kernel in use

Methods in jsat.regression with parameters of type KernelTrick
Modifier and Type	Method and Description
`void`	KernelRidgeRegression.`setKernel(KernelTrick k)` Sets the kernel trick to use

Constructors in jsat.regression with parameters of type KernelTrick
Constructor and Description
`KernelRidgeRegression(double lambda, KernelTrick kernel)` Creates a new Kernel Ridge Regression learner
`KernelRLS(KernelTrick k, double errorTolerance)` Creates a new Kernel RLS learner

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