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SparseLinear Class

Note: This API is now obsolete.

Sparse Linear Kernel.
Inheritance Hierarchy
SystemObject
  Accord.Statistics.KernelsKernelBaseDouble
    Accord.Statistics.KernelsKernelBase
      Accord.Statistics.Kernels.SparseSparseLinear

Namespace:  Accord.Statistics.Kernels.Sparse
Assembly:  Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0
Syntax
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[SerializableAttribute]
[ObsoleteAttribute("Please use the Linear kernel with Sparse<double> instead.")]
public sealed class SparseLinear : KernelBase, 
	IKernel, IKernel<double[]>
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The SparseLinear type exposes the following members.

Constructors
  NameDescription
Public methodSparseLinear
Constructs a new Linear Kernel.
Public methodSparseLinear(Double)
Constructs a new Linear kernel.
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Properties
  NameDescription
Public propertyConstant
Gets or sets the kernel's intercept term.
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Methods
  NameDescription
Public methodClone
Creates a new object that is a copy of the current instance.
Public methodDistance
Computes the squared distance in feature space between two points given in input space.
(Overrides KernelBaseTInputDistance(TInput, TInput).)
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Public methodFunction
Sparse Linear kernel function.
(Overrides KernelBaseTInputFunction(TInput, TInput).)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodStatic memberProduct
Computes the product of two vectors given in sparse representation.
Public methodStatic memberSquaredEuclidean
Computes the squared Euclidean distance of two vectors given in sparse representation.
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Extension Methods
  NameDescription
Public Extension MethodDistance
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.)
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodTo(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.)
Public Extension MethodToTOverloaded.
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.)
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Remarks
The Sparse Linear kernel accepts inputs in the libsvm sparse format.
Examples

The following example shows how to teach a kernel support vector machine using the linear sparse kernel to perform the AND classification task using sparse vectors.

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// Example AND problem
double[][] inputs =
{
    new double[] {          }, // 0 and 0: 0 (label -1)
    new double[] {      2,1 }, // 0 and 1: 0 (label -1)
    new double[] { 1,1      }, // 1 and 0: 0 (label -1)
    new double[] { 1,1, 2,1 }  // 1 and 1: 1 (label +1)
};

// Dichotomy SVM outputs should be given as [-1;+1]
int[] labels =
{
    // 0,  0,  0, 1
      -1, -1, -1, 1
};

// Create a Support Vector Machine for the given inputs
// (sparse machines should use 0 as the number of inputs)
var machine = new KernelSupportVectorMachine(new SparseLinear(), inputs: 0); 

// Instantiate a new learning algorithm for SVMs
var smo = new SequentialMinimalOptimization(machine, inputs, labels);

// Set up the learning algorithm
smo.Complexity = 100000.0;

// Run
double error = smo.Run(); // should be zero

double[] predicted = inputs.Apply(machine.Compute).Sign();

// Outputs should be -1, -1, -1, +1
See Also