SMO.BinarySMO

java.lang.Object
- keel.Algorithms.SVM.SMO.SMO.BinarySMO

All Implemented Interfaces:

java.io.Serializable

Enclosing class:

SMO
```
public class SMO.BinarySMO
extends java.lang.Object
implements java.io.Serializable
```
Class for building a binary support vector machine.

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected double[]`	`m_alpha` The Lagrange multipliers.
`protected double`	`m_b` The thresholds.
`protected double`	`m_bLow` The thresholds.
`protected double`	`m_bUp` The thresholds.
`protected double[]`	`m_class` The transformed class values.
`protected Instances`	`m_data` The training data.
`protected double[]`	`m_errors` The current set of errors for all non-bound examples.
`protected SMOset`	`m_I0` {i: 0 < m_alpha[i] < C}
`protected SMOset`	`m_I1` {i: m_class[i] = 1, m_alpha[i] = 0}
`protected SMOset`	`m_I2` {i: m_class[i] = -1, m_alpha[i] =C}
`protected SMOset`	`m_I3` {i: m_class[i] = 1, m_alpha[i] = C}
`protected SMOset`	`m_I4` {i: m_class[i] = -1, m_alpha[i] = 0}
`protected int`	`m_iLow` The indices for m_bLow and m_bUp
`protected int`	`m_iUp` The indices for m_bLow and m_bUp
`protected Kernel`	`m_kernel` Kernel to use
`protected Logistic`	`m_logistic` Stores logistic regression model for probability estimate
`protected int[]`	`m_sparseIndices` Variables to hold indices vector in sparse form.
`protected double[]`	`m_sparseWeights` Variables to hold weight vector in sparse form.
`protected double`	`m_sumOfWeights` Stores the weight of the training instances
`protected SMOset`	`m_supportVectors` The set of support vectors
`protected double[]`	`m_weights` Weight vector for linear machine.

Constructor Summary

Constructors
Constructor and Description

BinarySMO()

Constructors
Constructor and Description
`BinarySMO()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`buildClassifier(Instances insts, int cl1, int cl2, boolean fitLogistic, int numFolds, int randomSeed)` Method for building the binary classifier.
`protected void`	`checkClassifier()` Quick and dirty check whether the quadratic programming problem is solved.
`protected boolean`	`examineExample(int i2)` Examines instance.
`protected void`	`fitLogistic(Instances insts, int cl1, int cl2, int numFolds, java.util.Random random)` Fits logistic regression model to SVM outputs analogue to John Platt's method.
`double`	`getB()` This method returns the 'b' threshold value of this SMO
`double[]`	`getClasses()` This method returns the transformed class values of each instance
`Kernel`	`getKernel()` Returns the kernel to use
`double[]`	`getLagrangeMultipliers()` This method returns the Lagrange Multipliers obtained for each instance.
`SMOset`	`getSupportVectors()` This method returns the support vectors
`void`	`setKernel(Kernel value)` sets the kernel to use
`double`	`SVMOutput(int index, Instance inst)` Computes SVM output for given instance.
`protected boolean`	`takeStep(int i1, int i2, double F2)` Method solving for the Lagrange multipliers for two instances.

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

- Field Detail
  - m_alpha
```
protected double[] m_alpha
```
    The Lagrange multipliers.
  - m_b
```
protected double m_b
```
    The thresholds.
  - m_bLow
```
protected double m_bLow
```
    The thresholds.
  - m_bUp
```
protected double m_bUp
```
    The thresholds.
  - m_iLow
```
protected int m_iLow
```
    The indices for m_bLow and m_bUp
  - m_iUp
```
protected int m_iUp
```
    The indices for m_bLow and m_bUp
  - m_data
```
protected Instances m_data
```
    The training data.
  - m_weights
```
protected double[] m_weights
```
    Weight vector for linear machine.
  - m_sparseWeights
```
protected double[] m_sparseWeights
```
    Variables to hold weight vector in sparse form. (To reduce storage requirements.)
  - m_sparseIndices
```
protected int[] m_sparseIndices
```
    Variables to hold indices vector in sparse form. (To reduce storage requirements.)
  - m_kernel
```
protected Kernel m_kernel
```
    Kernel to use
  - m_class
```
protected double[] m_class
```
    The transformed class values.
  - m_errors
```
protected double[] m_errors
```
    The current set of errors for all non-bound examples.
  - m_I0
```
protected SMOset m_I0
```
    {i: 0 < m_alpha[i] < C}
  - m_I1
```
protected SMOset m_I1
```
    {i: m_class[i] = 1, m_alpha[i] = 0}
  - m_I2
```
protected SMOset m_I2
```
    {i: m_class[i] = -1, m_alpha[i] =C}
  - m_I3
```
protected SMOset m_I3
```
    {i: m_class[i] = 1, m_alpha[i] = C}
  - m_I4
```
protected SMOset m_I4
```
    {i: m_class[i] = -1, m_alpha[i] = 0}
  - m_supportVectors
```
protected SMOset m_supportVectors
```
    The set of support vectors
  - m_logistic
```
protected Logistic m_logistic
```
    Stores logistic regression model for probability estimate
  - m_sumOfWeights
```
protected double m_sumOfWeights
```
    Stores the weight of the training instances
- Constructor Detail
  - BinarySMO
```
public BinarySMO()
```
- Method Detail
  - fitLogistic
```
protected void fitLogistic(Instances insts,
                           int cl1,
                           int cl2,
                           int numFolds,
                           java.util.Random random)
                    throws java.lang.Exception
```
    Fits logistic regression model to SVM outputs analogue to John Platt's method.
    
    Parameters:
    
    insts - the set of training instances
    
    cl1 - the first class' index
    
    cl2 - the second class' index
    
    numFolds - the number of folds for cross-validation
    
    random - for randomizing the data
    
    Throws:
    
    java.lang.Exception - if the sigmoid can't be fit successfully
  - setKernel
```
public void setKernel(Kernel value)
```
    sets the kernel to use
    
    Parameters:
    
    value - the kernel to use
  - getKernel
```
public Kernel getKernel()
```
    Returns the kernel to use
    
    Returns:
    
    the current kernel
  - buildClassifier
```
public void buildClassifier(Instances insts,
                            int cl1,
                            int cl2,
                            boolean fitLogistic,
                            int numFolds,
                            int randomSeed)
                     throws java.lang.Exception
```
    Method for building the binary classifier.
    
    Parameters:
    
    insts - the set of training instances
    
    cl1 - the first class' index
    
    cl2 - the second class' index
    
    fitLogistic - true if logistic model is to be fit
    
    numFolds - number of folds for internal cross-validation
    
    randomSeed - random number generator for cross-validation
    
    Throws:
    
    java.lang.Exception - if the classifier can't be built successfully
  - SVMOutput
```
public double SVMOutput(int index,
                        Instance inst)
                 throws java.lang.Exception
```
    Computes SVM output for given instance.
    
    Parameters:
    
    index - the instance for which output is to be computed
    
    inst - the instance
    
    Returns:
    
    the output of the SVM for the given instance
    
    Throws:
    
    java.lang.Exception - in case of an error
  - examineExample
```
protected boolean examineExample(int i2)
                          throws java.lang.Exception
```
    Examines instance.
    
    Parameters:
    
    i2 - index of instance to examine
    
    Returns:
    
    true if examination was successfull
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - takeStep
```
protected boolean takeStep(int i1,
                           int i2,
                           double F2)
                    throws java.lang.Exception
```
    Method solving for the Lagrange multipliers for two instances.
    
    Parameters:
    
    i1 - index of the first instance
    
    i2 - index of the second instance
    
    F2 -
    
    Returns:
    
    true if multipliers could be found
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - checkClassifier
```
protected void checkClassifier()
                        throws java.lang.Exception
```
    Quick and dirty check whether the quadratic programming problem is solved.
    
    Throws:
    
    java.lang.Exception - if checking fails
  - getB
```
public double getB()
```
    This method returns the 'b' threshold value of this SMO
    
    Returns:
    
    The obtained b value of the hyperplane
  - getSupportVectors
```
public SMOset getSupportVectors()
```
    This method returns the support vectors
    
    Returns:
    
    The selected support vectors from the data
  - getLagrangeMultipliers
```
public double[] getLagrangeMultipliers()
```
    This method returns the Lagrange Multipliers obtained for each instance. Those instances with non-zero value are the support vectors.
    
    Returns:
    
    The alpha sub-i values for this SMO.
  - getClasses
```
public double[] getClasses()
```
    This method returns the transformed class values of each instance
    
    Returns:
    
    A reference to the internal array with the class values.

Class SMO.BinarySMO

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

m_alpha

m_b

m_bLow

m_bUp

m_iLow

m_iUp

m_data

m_weights

m_sparseWeights

m_sparseIndices

m_kernel

m_class

m_errors

m_I0

m_I1

m_I2

m_I3

m_I4

m_supportVectors

m_logistic

m_sumOfWeights

Constructor Detail

BinarySMO

Method Detail

fitLogistic

setKernel

getKernel

buildClassifier

SVMOutput

examineExample

takeStep

checkClassifier

getB

getSupportVectors

getLagrangeMultipliers

getClasses