SVM (encog-java-core 3.4 API)

java.lang.Object
- org.encog.ml.BasicML
- - org.encog.ml.svm.SVM

All Implemented Interfaces:

java.io.Serializable, MLClassification, MLError, MLInput, MLInputOutput, MLMethod, MLOutput, MLProperties, MLRegression
```
public class SVM
extends BasicML
implements MLRegression, MLClassification, MLError
```
This is a network that is backed by one or more Support Vector Machines (SVM). It is designed to function very similarly to an Encog neural network, and is largely interchangeable with an Encog neural network. The support vector machine supports several types. Regression is used when you want the network to predict a value, given the input. Function approximation is a good example of regression. Classification is used when you want the SVM to group the input data into one or more classes. Support Vector Machines typically have a single output. Neural networks can have multiple output neurons. To get around this issue, this class will create multiple SVM's if there is more than one output specified. Because a SVM is trained quite differently from a neural network, none of the neural network training classes will work. This class must be trained using SVMTrain.

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`DEFAULT_C` The default C.
`static int`	`DEFAULT_CACHE_SIZE` The default cache size.
`static int`	`DEFAULT_COEF0` The default COEF0.
`static int`	`DEFAULT_DEGREE` The default degree.
`static double`	`DEFAULT_EPS` The default EPS.
`static double`	`DEFAULT_NU` The default NU.
`static double`	`DEFAULT_P` The default P.

Constructor Summary

Constructors
Constructor and Description
`SVM()` Construct the SVM.
`SVM(int theInputCount, boolean regression)` Construct an SVM network.
`SVM(int theInputCount, SVMType svmType, KernelType kernelType)` Construct a SVM network.
`SVM(svm_model theModel)` Construct a SVM from a model.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double`	`calculateError(MLDataSet data)` Calculate the error for this SVM.
`int`	`classify(MLData input)` Classify the input into a group.
`MLData`	`compute(MLData input)` Compute the output for the given input.
`int`	`getInputCount()`
`KernelType`	`getKernelType()`
`svm_model`	`getModel()`
`int`	`getOutputCount()`
`svm_parameter`	`getParams()`
`SVMType`	`getSVMType()`
`svm_node[]`	`makeSparse(MLData data)` Convert regular Encog MLData into the "sparse" data needed by an SVM.
`void`	`setInputCount(int i)` Set the input count.
`void`	`setModel(svm_model theModel)` Set the model.
`void`	`updateProperties()` Not needed, no properties to update.

Methods inherited from class org.encog.ml.BasicML
getProperties, getPropertyDouble, getPropertyLong, getPropertyString, setProperty, setProperty, setProperty

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - DEFAULT_DEGREE
```
public static final int DEFAULT_DEGREE
```
    The default degree.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_COEF0
```
public static final int DEFAULT_COEF0
```
    The default COEF0.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_NU
```
public static final double DEFAULT_NU
```
    The default NU.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_CACHE_SIZE
```
public static final int DEFAULT_CACHE_SIZE
```
    The default cache size.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_C
```
public static final int DEFAULT_C
```
    The default C.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_EPS
```
public static final double DEFAULT_EPS
```
    The default EPS.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_P
```
public static final double DEFAULT_P
```
    The default P.
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - SVM
```
public SVM()
```
    Construct the SVM.
  - SVM
```
public SVM(int theInputCount,
           boolean regression)
```
    Construct an SVM network. For regression it will use an epsilon support vector. Both types will use an RBF kernel.
    
    Parameters:
    
    theInputCount - The input count.
    
    regression - True if this network is used for regression.
  - SVM
```
public SVM(int theInputCount,
           SVMType svmType,
           KernelType kernelType)
```
    Construct a SVM network.
    
    Parameters:
    
    theInputCount - The input count.
    
    svmType - The type of SVM.
    
    kernelType - The SVM kernal type.
  - SVM
```
public SVM(svm_model theModel)
```
    Construct a SVM from a model.
    
    Parameters:
    
    theModel - The model.
- Method Detail
  - calculateError
```
public double calculateError(MLDataSet data)
```
    Calculate the error for this SVM.
    
    Specified by:
    
    calculateError in interface MLError
    
    Parameters:
    
    data - The training set.
    
    Returns:
    
    The error percentage.
  - classify
```
public int classify(MLData input)
```
    Classify the input into a group.
    
    Specified by:
    
    classify in interface MLClassification
    
    Parameters:
    
    input - The input data to classify.
    
    Returns:
    
    The group that the data was classified into.
  - compute
```
public MLData compute(MLData input)
```
    Compute the output for the given input.
    
    Specified by:
    
    compute in interface MLRegression
    
    Parameters:
    
    input - The input to the SVM.
    
    Returns:
    
    The results from the SVM.
  - getInputCount
```
public int getInputCount()
```
    Specified by:
    
    getInputCount in interface MLInput
    
    Returns:
    
    The input count.
  - getKernelType
```
public KernelType getKernelType()
```
    Returns:
    
    The kernel type.
  - getModel
```
public svm_model getModel()
```
    Returns:
    
    The SVM models for each output.
  - getOutputCount
```
public int getOutputCount()
```
    Specified by:
    
    getOutputCount in interface MLOutput
    
    Returns:
    
    For a SVM, the output count is always one.
  - getParams
```
public final svm_parameter getParams()
```
    Returns:
    
    The SVM params for each of the outputs.
  - getSVMType
```
public SVMType getSVMType()
```
    Returns:
    
    The SVM type.
  - makeSparse
```
public svm_node[] makeSparse(MLData data)
```
    Convert regular Encog MLData into the "sparse" data needed by an SVM.
    
    Parameters:
    
    data - The data to convert.
    
    Returns:
    
    The SVM sparse data.
  - setInputCount
```
public void setInputCount(int i)
```
    Set the input count.
    
    Parameters:
    
    i - The new input count.
  - setModel
```
public void setModel(svm_model theModel)
```
    Set the model.
    
    Parameters:
    
    theModel - The model.
  - updateProperties
```
public void updateProperties()
```
    Not needed, no properties to update.
    
    Specified by:
    
    updateProperties in interface MLProperties
    
    Specified by:
    
    updateProperties in class BasicML

Class SVM

Field Summary

Constructor Summary

Method Summary

Methods inherited from class org.encog.ml.BasicML

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_DEGREE

DEFAULT_COEF0

DEFAULT_NU

DEFAULT_CACHE_SIZE

DEFAULT_C

DEFAULT_EPS

DEFAULT_P

Constructor Detail

SVM

SVM

SVM

SVM

Method Detail

calculateError

classify

compute

getInputCount

getKernelType

getModel

getOutputCount

getParams

getSVMType

makeSparse

setInputCount

setModel

updateProperties