nuget
Encapsulates an SVM Model.
Model
01 Syntax
Microsoft.VisualBasic.MachineLearning.SVM.Model
02 Methods
| Name | Overloads | Summary |
|---|---|---|
| .ctor | 1 | Create a new empty model object. |
| Equals | 1 | Compares this model with another object. |
| GetHashCode | 1 | Gets the hash code of this model, which is combined by the hash code of all of its coefficients and labels. |
03 Properties
| Name | Overloads | Summary |
|---|---|---|
| parameter | 1 | Parameter object. |
| numberOfClasses | 1 | Number of classes in the model. |
| supportVectorCount | 1 | Total number of support vectors. |
| supportVectors | 1 | The support vectors. |
| supportVectorCoefficients | 1 | The coefficients for the support vectors. |
| supportVectorIndices | 1 | Values in [1,...,num_training_data] to indicate SVs in the training set |
| rho | 1 | Constants in decision functions |
| pairwiseProbabilityA | 1 | First pairwise probability. |
| pairwiseProbabilityB | 1 | Second pairwise probability. |
| classLabels | 1 | Class labels. |
| numberOfSVPerClass | 1 | Number of support vectors per class. |
| dimensionNames | 1 | The names of the feature dimensions of the training data. |
| trainingSize | 1 | The number of the samples which was used for training this model. |
04 Members
#ctor
Create a new empty model object.
Equals(
Object)Compares this model with another object.
Parameters
| Name | Type | Description |
|---|---|---|
obj | Object | The object that will be compared with this model. |
Returns
True when the obj is a Model object which has the same class labels, support vectors, coefficients and parameters, otherwise False.
GetHashCode
Gets the hash code of this model, which is combined by the hash code of all of its coefficients and labels.
Returns
An Int32 hash code value.
parameter
Parameter object.
numberOfClasses
Number of classes in the model.
supportVectorCount
Total number of support vectors.
supportVectors
The support vectors.
supportVectorCoefficients
The coefficients for the support vectors.
supportVectorIndices
Values in [1,...,num_training_data] to indicate SVs in the training set
rho
Constants in decision functions
pairwiseProbabilityA
First pairwise probability.
pairwiseProbabilityB
Second pairwise probability.
classLabels
Class labels.
numberOfSVPerClass
Number of support vectors per class.
dimensionNames
The names of the feature dimensions of the training data.
Returns
An array of the dimension names.
trainingSize
The number of the samples which was used for training this model.
Returns
An Int32 value.