Метод | Описание | |
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CalculateError ( IMLDataSet data ) : double |
Calculate the error for this SVM.
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Classify ( IMLData input ) : int | ||
Compute ( IMLData input ) : IMLData |
Compute the output for the given input.
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MakeSparse ( IMLData data ) : Encog.MathUtil.LIBSVM.svm_node[] |
Convert regular Encog MLData into the "sparse" data needed by an SVM.
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SupportVectorMachine ( ) : System |
Construct the SVM.
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SupportVectorMachine ( int theInputCount, SVMType svmType, KernelType kernelType ) : System |
Construct a SVM network.
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SupportVectorMachine ( int theInputCount, bool regression ) : System |
Construct an SVM network. For regression it will use an epsilon support vector. Both types will use an RBF kernel.
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SupportVectorMachine ( |
Construct a SVM from a model.
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UpdateProperties ( ) : void |
Not needed, no properties to update.
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public CalculateError ( IMLDataSet data ) : double | ||
data | IMLDataSet | The training set. |
Результат | double |
public Compute ( IMLData input ) : IMLData | ||
input | IMLData | The input to the SVM. |
Результат | IMLData |
public MakeSparse ( IMLData data ) : Encog.MathUtil.LIBSVM.svm_node[] | ||
data | IMLData | The data to convert. |
Результат | Encog.MathUtil.LIBSVM.svm_node[] |
public SupportVectorMachine ( int theInputCount, SVMType svmType, KernelType kernelType ) : System | ||
theInputCount | int | The input count. |
svmType | SVMType | The type of SVM. |
kernelType | KernelType | The SVM kernal type. |
Результат | System |
public SupportVectorMachine ( int theInputCount, bool regression ) : System | ||
theInputCount | int | The input count. |
regression | bool | True if this network is used for regression. |
Результат | System |
public SupportVectorMachine ( |
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theModel | The model. | |
Результат | System |