Gaussian Structure |
Namespace: Accord.Statistics.Kernels
[SerializableAttribute] public struct Gaussian : IKernel, IKernel<double[]>, IRadialBasisKernel, IDistance, IDistance<double[]>, IDistance<double[], double[]>, IEstimable, IEstimable<double[]>, ICloneable, IReverseDistance, IKernel<Sparse<double>>, IEstimable<Sparse<double>>, IDistance<Sparse<double>>, IDistance<Sparse<double>, Sparse<double>>
The Gaussian type exposes the following members.
Name | Description | |
---|---|---|
Gaussian |
Constructs a new Gaussian Kernel with a given sigma value. To
construct from a gamma value, use the FromGamma(Double)
named constructor instead.
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Name | Description | |
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Gamma |
Gets or sets the gamma value for the kernel. When setting
gamma, sigma gets updated accordingly (gamma = 0.5/sigma^2).
| |
Sigma |
Gets or sets the sigma value for the kernel. When setting
sigma, gamma gets updated accordingly (gamma = 0.5/sigma^2).
| |
SigmaSquared |
Gets or sets the sigma² value for the kernel. When setting
sigma², gamma gets updated accordingly (gamma = 0.5/sigma²).
|
Name | Description | |
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Clone |
Creates a new object that is a copy of the current instance.
| |
Distance(Double, Double) |
Computes the squared distance in feature space
between two points given in input space.
| |
Distance(SparseDouble, SparseDouble) |
Computes the squared distance in feature space
between two points given in input space.
| |
Distances(Double, Int32) |
Computes the set of all distances between
all points in a random subset of the data.
| |
Distances(SparseDouble, Int32) |
Computes the set of all distances between
all points in a random subset of the data.
| |
DistancesTDistance, TInput(TInput, Int32, TDistance) |
Computes the set of all distances between
all points in a random subset of the data.
| |
Equals | Indicates whether this instance and a specified object are equal. (Inherited from ValueType.) | |
Estimate(Double) |
Estimate appropriate values for sigma given a data set.
| |
Estimate(SparseDouble) |
Estimate appropriate values for sigma given a data set.
| |
Estimate(Double, DoubleRange) |
Estimate appropriate values for sigma given a data set.
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Estimate(Double, Int32) |
Estimates appropriate values for sigma given a data set.
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Estimate(SparseDouble, DoubleRange) |
Estimate appropriate values for sigma given a data set.
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Estimate(SparseDouble, Int32) |
Estimates appropriate values for sigma given a data set.
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Estimate(Double, Int32, DoubleRange) |
Estimates appropriate values for sigma given a data set.
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Estimate(SparseDouble, Int32, DoubleRange) |
Estimates appropriate values for sigma given a data set.
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EstimateT(T, Double) |
Estimate appropriate values for sigma given a data set.
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EstimateT(T, Double, DoubleRange) |
Estimate appropriate values for sigma given a data set.
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EstimateT(T, Double, Int32) |
Estimates appropriate values for sigma given a data set.
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EstimateT(T, Double, Int32, DoubleRange) |
Estimates appropriate values for sigma given a data set.
| |
EstimateTInput, TDistance(TInput, TDistance) |
Estimate appropriate values for sigma given a data set.
| |
EstimateTInput, TDistance(TInput, Int32, TDistance) |
Estimates appropriate values for sigma given a data set.
| |
EstimateTInput, TDistance(TInput, TDistance, DoubleRange) |
Estimate appropriate values for sigma given a data set.
| |
EstimateTInput, TDistance(TInput, Int32, TDistance, DoubleRange) |
Estimates appropriate values for sigma given a data set.
| |
FromGamma |
Constructs a new Gaussian Kernel with a given gamma value. To
construct from a sigma value, use the Gaussian(Double)
constructor instead.
| |
Function(Double) |
Gaussian Kernel function.
| |
Function(Double, Double) |
Gaussian Kernel function.
| |
Function(SparseDouble, SparseDouble) |
Gaussian Kernel function.
| |
GetHashCode | Returns the hash code for this instance. (Inherited from ValueType.) | |
GetType | Gets the Type of the current instance. (Inherited from Object.) | |
ReverseDistance(Double) |
Computes the distance in input space given
a distance computed in feature space.
| |
ReverseDistance(Double, Double) |
Computes the squared distance in input space
between two points given in feature space.
| |
ToString | Returns the fully qualified type name of this instance. (Inherited from ValueType.) |
Name | Description | |
---|---|---|
Distance |
Computes the kernel distance for a kernel function even if it doesn't
implement the IDistance interface. Can be used to check
the proper implementation of the distance function.
(Defined by Tools.) | |
HasMethod |
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.) | |
IsEqual |
Compares two objects for equality, performing an elementwise
comparison if the elements are vectors or matrices.
(Defined by Matrix.) | |
To(Type) | Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) | |
ToT | Overloaded.
Converts an object into another type, irrespective of whether
the conversion can be done at compile time or not. This can be
used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.) |
The Gaussian kernel requires tuning for the proper value of σ. Different approaches to this problem includes the use of brute force (i.e. using a grid-search algorithm) or a gradient ascent optimization.
References: