Class HyperbolicTangentKernel
- All Implemented Interfaces:
Serializable, ToDoubleBiFunction<double[],double[]>, DotProductKernel, MercerKernel<double[]>, Function
k(u, v) = tanh(γ uTv - λ)
where γ is the scale of the used inner product and λ is the offset of the used inner product. If the offset is negative the likelihood of obtaining a kernel matrix that is not positive definite is much higher (since then even some diagonal elements may be negative), hence if this kernel has to be used, the offset should always be positive. Note, however, that this is no guarantee that the kernel will be positive.
The hyperbolic tangent kernel was quite popular for support vector machines due to its origin from neural networks. However, it should be used carefully since the kernel matrix may not be positive semi-definite. Besides, it was reported the hyperbolic tangent kernel is not better than the Gaussian kernel in general.
- See Also:
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Field Summary
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Constructor Summary
ConstructorsConstructorDescriptionConstructor with scale 1.0 and offset 0.0.HyperbolicTangentKernel(double scale, double offset) Constructor.HyperbolicTangentKernel(double scale, double offset, double[] lo, double[] hi) Constructor. -
Method Summary
Modifier and TypeMethodDescriptiondouble[]hi()Returns the upper bound of hyperparameters (in hyperparameter tuning).double[]Returns the hyperparameters of kernel.doublek(double[] x, double[] y) Kernel function.double[]kg(double[] x, double[] y) Computes the kernel and its gradient over hyperparameters.double[]lo()Returns the lower bound of hyperparameters (in hyperparameter tuning).of(double[] params) Returns the same kind kernel with the new hyperparameters.Methods inherited from class Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, waitMethods inherited from interface DotProductKernel
apply, f, KMethods inherited from interface MercerKernel
apply, applyAsDouble, K, K, KG
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Constructor Details
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HyperbolicTangentKernel
public HyperbolicTangentKernel()Constructor with scale 1.0 and offset 0.0. -
HyperbolicTangentKernel
public HyperbolicTangentKernel(double scale, double offset) Constructor.- Parameters:
scale- The scale parameter.offset- The offset parameter.
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HyperbolicTangentKernel
public HyperbolicTangentKernel(double scale, double offset, double[] lo, double[] hi) Constructor.- Parameters:
scale- The scale parameter.offset- The offset parameter.lo- The lower bound of scale and offset for hyperparameter tuning.hi- The upper bound of scale and offset for hyperparameter tuning.
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Method Details
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k
public double k(double[] x, double[] y) Description copied from interface:MercerKernelKernel function.- Specified by:
kin interfaceMercerKernel<double[]>- Parameters:
x- an object.y- an object.- Returns:
- the kernel value.
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kg
public double[] kg(double[] x, double[] y) Description copied from interface:MercerKernelComputes the kernel and its gradient over hyperparameters.- Specified by:
kgin interfaceMercerKernel<double[]>- Parameters:
x- an object.y- an object.- Returns:
- the kernel value and gradient.
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of
Description copied from interface:MercerKernelReturns the same kind kernel with the new hyperparameters.- Specified by:
ofin interfaceMercerKernel<double[]>- Parameters:
params- the hyperparameters.- Returns:
- the same kind kernel with the new hyperparameters.
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hyperparameters
public double[] hyperparameters()Description copied from interface:MercerKernelReturns the hyperparameters of kernel.- Specified by:
hyperparametersin interfaceMercerKernel<double[]>- Returns:
- the hyperparameters of kernel.
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lo
public double[] lo()Description copied from interface:MercerKernelReturns the lower bound of hyperparameters (in hyperparameter tuning).- Specified by:
loin interfaceMercerKernel<double[]>- Returns:
- the lower bound of hyperparameters.
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hi
public double[] hi()Description copied from interface:MercerKernelReturns the upper bound of hyperparameters (in hyperparameter tuning).- Specified by:
hiin interfaceMercerKernel<double[]>- Returns:
- the upper bound of hyperparameters.
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