SVM
SVM - GetC
Returns the regularization constant C.
IN
| Name | Type | Description |
|---|---|---|
| InSVM | DataModel_SVM | The input SVM |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutFloat | Float | C. |
| Error | ErrorState | Gets the execution error message |
SVM - GetClasses
Returns the number of classes considered by the model.
IN
| Name | Type | Description |
|---|---|---|
| InSVM | DataModel_SVM | The input SVM |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutInt | Int | Number of classes. |
| Error | ErrorState | Gets the execution error message |
SVM - GetDegree
Returns the degree when a polynomial kernel is used.
IN
| Name | Type | Description |
|---|---|---|
| InSVM | DataModel_SVM | The input SVM |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutFloat | FloatNull | Polynomial degree. |
| Error | ErrorState | Gets the execution error message |
SVM - GetFeatures
Returns the number of features considered by the model.
IN
| Name | Type | Description |
|---|---|---|
| InSVM | DataModel_SVM | The input SVM |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutInt | Int | Number of features. |
| Error | ErrorState | Gets the execution error message |
SVM - GetGamma
Returns the value (if used).
IN
| Name | Type | Description |
|---|---|---|
| InSVM | DataModel_SVM | The input SVM |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutFloat | FloatNull | value. |
| Error | ErrorState | Gets the execution error message |
SVM - GetKernel
Returns the kernel used.
IN
| Name | Type | Description |
|---|---|---|
| InSVM | DataModel_SVM | The input SVM |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutKernelSVM | Enum < KernelSVM > | Kernel type. |
| Error | ErrorState | Gets the execution error message |
SVM - GetNu
Returns the value (if set).
IN
| Name | Type | Description |
|---|---|---|
| InSVM | DataModel_SVM | The input SVM |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutFloat | FloatNull | value. |
| Error | ErrorState | Gets the execution error message |
SVM - IsTrained
Checks if model has been trained.
IN
| Name | Type | Description |
|---|---|---|
| InSVM | DataModel_SVM | The input SVM |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutBool | Bool | true if model was trained, false otherwise. |
| Error | ErrorState | Gets the execution error message |
SVM - Load
Load internal model.
IN
| Name | Type | Description |
|---|---|---|
| InFilename | PathFile | Filename. |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutSVM | DataModel_SVM | Loaded model. |
| Error | ErrorState | Gets the execution error message |
SVM - Predict_Dataset
Classify dataset's features.
IN
| Name | Type | Description |
|---|---|---|
| InSVM | DataModel_SVM | The input SVM |
| InDataset | Dataset | Input dataset. |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutInt | IntArray | Predicted classes. |
| Error | ErrorState | Gets the execution error message |
SVM - Predict_Multiple
Classify multiple feature vectors.
IN
| Name | Type | Description |
|---|---|---|
| InSVM | DataModel_SVM | The input SVM |
| InFeatures | FloatArrayArray | Input features. |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutInt | IntArray | Predicted classes. |
| Error | ErrorState | Gets the execution error message |
SVM - Predict_Single
Classify a single feature vector.
IN
| Name | Type | Description |
|---|---|---|
| InSVM | DataModel_SVM | The input SVM |
| InFeatures | FloatArray | Input features. |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutInt | Int | Predicted class. |
| Error | ErrorState | Gets the execution error message |
SVM - Save
Save internal model.
IN
| Name | Type | Description |
|---|---|---|
| InSVM | DataModel_SVM | The input SVM |
| InFilename | PathFile | Filename. |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| Error | ErrorState | Gets the execution error message |
SVM - Train_FromDataset
Train a new model.
IN
| Name | Type | Description |
|---|---|---|
| InDataset | Dataset | Dataset. |
| InKernel | Enum < KernelSVM > | Type of kernel. Linear is the fastest and simplest kernel, to use when the number of feature is very large or the data seem linearly separable. RBF is the most powerful choice, the general-purpose kernel that can model complex and non-linear relationships. |
| InC | Float | Controls the trade-off against misclassification to prevent overfitting. Range: 〔0, +inf）. Default: 1. A lower C creates a smoother, more tolerant model. A higher C creates a more complex model that penalizes errors more heavily. |
| InEpsilon | Float | Defines the precision of the training: how much the error could change between the iterations to make the algorithm continue. The smaller the value, the more accurate the training and the more time it takes. Range: （-inf, +inf）. A value between 1e-4 and 1e-6 is usually suggested. |
| InValidationRatio | Float | Validation ratio （0 means no validation）. Range: 〔0,1）. |
| InShuffle | Bool | Whether to shuffle the data. |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutSVM | DataModel_SVM | Trained model. |
| Error | ErrorState | Gets the execution error message |
SVM - Train_FromFeaturesAndClasses
Train a new model.
IN
| Name | Type | Description |
|---|---|---|
| InFeatures | FloatArrayArray | Features. |
| InClasses | IntArray | Corresponding classes. |
| InNClasses | Int | Number of classes. |
| InKernel | Enum < KernelSVM > | Type of kernel. Linear is the fastest and simplest kernel, to use when the number of feature is very large or the data seem linearly separable. RBF is the most powerful choice, the general-purpose kernel that can model complex and non-linear relationships. |
| InC | Float | Controls the trade-off against misclassification to prevent overfitting. Range: 〔0, +inf）. Default: 1. A lower C creates a smoother, more tolerant model. A higher C creates a more complex model that penalizes errors more heavily. |
| InEpsilon | Float | Defines the precision of the training: how much the error could change between the iterations to make the algorithm continue. The smaller the value, the more accurate the training and the more time it takes. Range: （-inf, +inf）. A value between 1e-4 and 1e-6 is usually suggested. |
| InValidationRatio | Float | Validation ratio （0 means no validation）. Range: 〔0,1）. |
| InShuffle | Bool | Whether to shuffle the data. |
| Name | String | Set the tool's name |
| Enable | Bool | Sets if current tool is enabled or not |
OUT
| Name | Type | Description |
|---|---|---|
| OutSVM | DataModel_SVM | Trained model. |
| Error | ErrorState | Gets the execution error message |