SMO

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

All Implemented Interfaces:: TechnicalInformationHandler, WeightedInstancesHandler

Direct Known Subclasses:: PDFC

public class SMO
extends java.lang.Object
implements WeightedInstancesHandler, TechnicalInformationHandler

Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.

This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes by default. (In that case the coefficients in the output are based on the normalized data, not the original data --- this is important for interpreting the classifier.)

Multi-class problems are solved using pairwise classification (1-vs-1 and if logistic models are built pairwise coupling according to Hastie and Tibshirani, 1998).

To obtain proper probability estimates, use the option that fits logistic regression models to the outputs of the support vector machine. In the multi-class case the predicted probabilities are coupled using Hastie and Tibshirani's pairwise coupling method.

Note: for improved speed normalization should be turned off when operating on SparseInstances.

For more information on the SMO algorithm, see

J. Platt: Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning, 1998.

S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy (2001). Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation. 13(3):637-649.

Trevor Hastie, Robert Tibshirani: Classification by Pairwise Coupling. In: Advances in Neural Information Processing Systems, 1998.

BibTeX:

 @incollection{Platt1998,
    author = {J. Platt},
    booktitle = {Advances in Kernel Methods - Support Vector Learning},
    editor = {B. Schoelkopf and C. Burges and A. Smola},
    publisher = {MIT Press},
    title = {Machines using Sequential Minimal Optimization},
    year = {1998},
    URL = {http://research.microsoft.com/~jplatt/smo.html},
    PS = {http://research.microsoft.com/~jplatt/smo-book.ps.gz},
    PDF = {http://research.microsoft.com/~jplatt/smo-book.pdf}
 }
 
 @article{Keerthi2001,
    author = {S.S. Keerthi and S.K. Shevade and C. Bhattacharyya and K.R.K. Murthy},
    journal = {Neural Computation},
    number = {3},
    pages = {637-649},
    title = {Improvements to Platt's SMO Algorithm for SVM Classifier Design},
    volume = {13},
    year = {2001},
    PS = {http://guppy.mpe.nus.edu.sg/~mpessk/svm/smo_mod_nc.ps.gz}
 }
 
 @inproceedings{Hastie1998,
    author = {Trevor Hastie and Robert Tibshirani},
    booktitle = {Advances in Neural Information Processing Systems},
    editor = {Michael I. Jordan and Michael J. Kearns and Sara A. Solla},
    publisher = {MIT Press},
    title = {Classification by Pairwise Coupling},
    volume = {10},
    year = {1998},
    PS = {http://www-stat.stanford.edu/~hastie/Papers/2class.ps}
 }

Valid options are:

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console

 -no-checks
  Turns off all checks - use with caution!
  Turning them off assumes that data is purely numeric, doesn't
  contain any missing values, and has a nominal class. Turning them
  off also means that no header information will be stored if the
  machine is linear. Finally, it also assumes that no instance has
  a weight equal to 0.
  (default: checks on)

 -C <double>
  The complexity constant C. (default 1)

 -N
  Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)

 -L <double>
  The tolerance parameter. (default 1.0e-3)

 -P <double>
  The epsilon for round-off error. (default 1.0e-12)

 -M
  Fit logistic models to SVM outputs.

 -V <double>
  The number of folds for the internal
  cross-validation. (default -1, use training data)

 -W <double>
  The random number seed. (default 1)

 -K <classname and parameters>
  The Kernel to use.
  (default: weka.classifiers.functions.supportVector.PolyKernel)

 
 Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:

 -D
  Enables debugging output (if available) to be printed.
  (default: off)

 -no-checks
  Turns off all checks - use with caution!
  (default: checks on)

 -C <num>
  The size of the cache (a prime number).
  (default: 250007)

 -E <num>
  The Exponent to use.
  (default: 1.0)

 -L
  Use lower-order terms.
  (default: no)

Version:: $Revision: 1.3 $
Author:: Eibe Frank (eibe@cs.waikato.ac.nz), Shane Legg (shane@intelligenesis.net) (sparse vector code), Stuart Inglis (stuart@reeltwo.com) (sparse vector code)

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

class SMO.BinarySMO
Class for building a binary support vector machine.

Nested Classes
Modifier and Type	Class and Description
`class`	`SMO.BinarySMO` Class for building a binary support vector machine.

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`FILTER_NONE` filter: No normalization/standardization
`static int`	`FILTER_NORMALIZE` filter: Normalize training data
`static int`	`FILTER_STANDARDIZE` filter: Standardize training data
`protected java.lang.String`	`input_test_name` Test dataset filename.
`protected java.lang.String`	`input_train_name` Training dataset filename.
`protected java.lang.String`	`input_validation_name` Validation dataset filename.
`protected double`	`m_C` The complexity parameter.
`protected boolean`	`m_checksTurnedOff` Turn off all checks and conversions?
`protected Attribute`	`m_classAttribute` The class attribute
`protected SMO.BinarySMO[][]`	`m_classifiers` The binary classifier(s)
`protected int`	`m_classIndex` The class index from the training data
`protected static double`	`m_Del` Precision constant for updating sets
`protected double`	`m_eps` Epsilon for rounding.
`protected int`	`m_filterType` Whether to normalize/standardize/neither
`protected boolean`	`m_fitLogisticModels` Whether logistic models are to be fit
`protected Kernel`	`m_kernel` the kernel to use
`protected boolean`	`m_KernelIsLinear` whether the kernel is a linear one
`protected boolean`	`m_nominalToBinary` Whether to convert nominal attributes into binary values
`protected int`	`m_NumClasses` The number of the class labels
`protected int`	`m_numFolds` The number of folds for the internal cross-validation
`protected int`	`m_randomSeed` The random number seed
`protected int`	`m_seed` The seed used for of the class labels
`protected double`	`m_tol` Tolerance for accuracy of result.
`protected double[]`	`mean` Variable with the mean of each attribute.
`protected java.lang.String`	`method_output` Model output filename.
`protected java.lang.String`	`output_test_name` Test output filename.
`protected java.lang.String`	`output_train_name` Training output filename.
`double[][]`	`probabilities` SMO probabilities.
`protected double[]`	`std_dev` Variable with the std deviation of each attribute.
`static Tag[]`	`TAGS_FILTER` The filter to apply to the training data

Constructor Summary

Constructors
Constructor and Description

SMO()
Default constructor

SMO(java.lang.String fileParam)
Creates a new instance of SMO with a file parameter of KEEL format

Constructors
Constructor and Description
`SMO()` Default constructor
`SMO(java.lang.String fileParam)` Creates a new instance of SMO with a file parameter of KEEL format

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.lang.String[][][]`	`attributeNames()` Returns the attribute names.
`double[][]`	`bias()` Returns the bias of each binary SMO.
`void`	`buildClassifier(Instances insts)` Method for building the classifier.
`java.lang.String`	`buildLogisticModelsTipText()` Returns the tip text for this property
`java.lang.String`	`checksTurnedOffTipText()` Returns the tip text for this property
`java.lang.String[]`	`classAttributeNames()` Returns the names of the class attributes.
`protected void`	`computeStats(InstanceSet IS)` Compute the mean and std. deviation of each attribute of Attributes.
`java.lang.String`	`cTipText()` Returns the tip text for this property
`double[]`	`distributionForInstance(Instance inst)` Estimates class probabilities for given instance.
`java.lang.String`	`epsilonTipText()` Returns the tip text for this property
`java.lang.String`	`filterTypeTipText()` Returns the tip text for this property
`boolean`	`getBuildLogisticModels()` Get the value of buildLogisticModels.
`double`	`getC()` Get the value of C.
`boolean`	`getChecksTurnedOff()` Returns whether the checks are turned off or not.
`double`	`getEpsilon()` Get the value of epsilon.
`SelectedTag`	`getFilterType()` Gets how the training data will be transformed.
`Kernel`	`getKernel()` Returns the kernel to use
`int`	`getNumFolds()` Get the value of numFolds.
`int`	`getRandomSeed()` Get the value of randomSeed.
`TechnicalInformation`	`getTechnicalInformation()` Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
`double`	`getToleranceParameter()` Get the value of tolerance parameter.
`java.lang.String`	`globalInfo()` Returns a string describing classifier
`protected Instances`	`InstancesKEEL2Weka(InstanceSet is, int preprocessType, boolean nominal2binary)` Creates a new allocated WEKA's set of Instances (i.e.
`java.lang.String`	`kernelTipText()` Returns the tip text for this property
`protected double`	`normalize(double value, Attribute a)` Normalize the input value according to the provided attribute
`int`	`numClassAttributeValues()` Returns the number of values of the class attribute.
`java.lang.String`	`numFoldsTipText()` Returns the tip text for this property
`int[]`	`obtainVotes(Instance inst)` Returns an array of votes for the given instance.
`double[]`	`pairwiseCoupling(double[][] n, double[][] r)` Implements pairwise coupling.
`protected void`	`printSVs()` Prints the Support vectors to file
`java.lang.String`	`randomSeedTipText()` Returns the tip text for this property
`void`	`runModel()` Run the model once the parameters have been set by the method config_read()
`void`	`runModel(InstanceSet train, InstanceSet test)` Run the model once the parameters have been set by the method config_read()
`void`	`setBuildLogisticModels(boolean newbuildLogisticModels)` Set the value of buildLogisticModels.
`void`	`setC(double v)` Set the value of C.
`void`	`setChecksTurnedOff(boolean value)` Disables or enables the checks (which could be time-consuming).
`void`	`setEpsilon(double v)` Set the value of epsilon.
`void`	`setFilterType(SelectedTag newType)` Sets how the training data will be transformed.
`void`	`setKernel(Kernel value)` sets the kernel to use
`void`	`setNumFolds(int newnumFolds)` Set the value of numFolds.
`void`	`setRandomSeed(int newrandomSeed)` Set the value of randomSeed.
`void`	`setToleranceParameter(double v)` Set the value of tolerance parameter.
`int[][][]`	`sparseIndices()` Returns the indices in sparse format.
`double[][][]`	`sparseWeights()` Returns the weights in sparse format.
`protected double`	`standardize(double value, int j)` Standardize the provided value, converting it to a new double value from a normal distribution with mean = 0 and std. deviation = 1
`java.lang.String`	`toleranceParameterTipText()` Returns the tip text for this property
`void`	`turnChecksOff()` Turns off checks for missing values, etc.
`void`	`turnChecksOn()` Turns on checks for missing values, etc.
`static void`	`writeOutput(java.lang.String fileName, java.lang.String[] instancesIN, java.lang.String[] instancesOUT, Attribute[] inputs, Attribute output, int nInputs, java.lang.String relation)` Creates the output file in KEEL format of this method

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

- Field Detail
  - probabilities
```
public double[][] probabilities
```
    SMO probabilities.
  - FILTER_NORMALIZE
```
public static final int FILTER_NORMALIZE
```
    filter: Normalize training data
    
    See Also:
    
    Constant Field Values
  - FILTER_STANDARDIZE
```
public static final int FILTER_STANDARDIZE
```
    filter: Standardize training data
    
    See Also:
    
    Constant Field Values
  - FILTER_NONE
```
public static final int FILTER_NONE
```
    filter: No normalization/standardization
    
    See Also:
    
    Constant Field Values
  - TAGS_FILTER
```
public static final Tag[] TAGS_FILTER
```
    The filter to apply to the training data
  - m_classifiers
```
protected SMO.BinarySMO[][] m_classifiers
```
    The binary classifier(s)
  - m_C
```
protected double m_C
```
    The complexity parameter.
  - m_eps
```
protected double m_eps
```
    Epsilon for rounding.
  - m_tol
```
protected double m_tol
```
    Tolerance for accuracy of result.
  - m_filterType
```
protected int m_filterType
```
    Whether to normalize/standardize/neither
  - m_classIndex
```
protected int m_classIndex
```
    The class index from the training data
  - m_classAttribute
```
protected Attribute m_classAttribute
```
    The class attribute
  - m_KernelIsLinear
```
protected boolean m_KernelIsLinear
```
    whether the kernel is a linear one
  - m_checksTurnedOff
```
protected boolean m_checksTurnedOff
```
    Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a nominal class. Turning them off also means that no header information will be stored if the machine is linear. Finally, it also assumes that no instance has a weight equal to 0.
  - m_Del
```
protected static double m_Del
```
    Precision constant for updating sets
  - m_fitLogisticModels
```
protected boolean m_fitLogisticModels
```
    Whether logistic models are to be fit
  - m_numFolds
```
protected int m_numFolds
```
    The number of folds for the internal cross-validation
  - m_randomSeed
```
protected int m_randomSeed
```
    The random number seed
  - m_kernel
```
protected Kernel m_kernel
```
    the kernel to use
  - m_NumClasses
```
protected int m_NumClasses
```
    The number of the class labels
  - m_seed
```
protected int m_seed
```
    The seed used for of the class labels
  - m_nominalToBinary
```
protected boolean m_nominalToBinary
```
    Whether to convert nominal attributes into binary values
  - input_train_name
```
protected java.lang.String input_train_name
```
    Training dataset filename.
  - input_validation_name
```
protected java.lang.String input_validation_name
```
    Validation dataset filename.
  - input_test_name
```
protected java.lang.String input_test_name
```
    Test dataset filename.
  - output_train_name
```
protected java.lang.String output_train_name
```
    Training output filename.
  - output_test_name
```
protected java.lang.String output_test_name
```
    Test output filename.
  - method_output
```
protected java.lang.String method_output
```
    Model output filename.
  - mean
```
protected double[] mean
```
    Variable with the mean of each attribute.
  - std_dev
```
protected double[] std_dev
```
    Variable with the std deviation of each attribute.
- Constructor Detail
  - SMO
```
public SMO(java.lang.String fileParam)
```
    Creates a new instance of SMO with a file parameter of KEEL format
    
    Parameters:
    
    fileParam - The path to the configuration file with all the parameters in KEEL format
  - SMO
```
public SMO()
```
    Default constructor
- Method Detail
  - globalInfo
```
public java.lang.String globalInfo()
```
    Returns a string describing classifier
    
    Returns:
    
    a description suitable for displaying in the explorer/experimenter gui
  - getTechnicalInformation
```
public TechnicalInformation getTechnicalInformation()
```
    Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
    
    Specified by:
    
    getTechnicalInformation in interface TechnicalInformationHandler
    
    Returns:
    
    the technical information about this class
  - turnChecksOff
```
public void turnChecksOff()
```
    Turns off checks for missing values, etc. Use with caution.
  - turnChecksOn
```
public void turnChecksOn()
```
    Turns on checks for missing values, etc.
  - buildClassifier
```
public void buildClassifier(Instances insts)
                     throws java.lang.Exception
```
    Method for building the classifier. Implements a one-against-one wrapper for multi-class problems.
    
    Parameters:
    
    insts - the set of training instances
    
    Throws:
    
    java.lang.Exception - if the classifier can't be built successfully
  - distributionForInstance
```
public double[] distributionForInstance(Instance inst)
                                 throws java.lang.Exception
```
    Estimates class probabilities for given instance.
    
    Parameters:
    
    inst - the instance to compute the probabilities for
    
    Returns:
    
    class probabilities for given instance.
    
    Throws:
    
    java.lang.Exception - in case of an error
  - pairwiseCoupling
```
public double[] pairwiseCoupling(double[][] n,
                                 double[][] r)
```
    Implements pairwise coupling.
    
    Parameters:
    
    n - the sum of weights used to train each model
    
    r - the probability estimate from each model
    
    Returns:
    
    the coupled estimates
  - obtainVotes
```
public int[] obtainVotes(Instance inst)
                  throws java.lang.Exception
```
    Returns an array of votes for the given instance.
    
    Parameters:
    
    inst - the instance
    
    Returns:
    
    array of votex
    
    Throws:
    
    java.lang.Exception - if something goes wrong
  - sparseWeights
```
public double[][][] sparseWeights()
```
    Returns the weights in sparse format.
    
    Returns:
    
    the weights in sparse format.
  - sparseIndices
```
public int[][][] sparseIndices()
```
    Returns the indices in sparse format.
    
    Returns:
    
    the indices in sparse format.
  - bias
```
public double[][] bias()
```
    Returns the bias of each binary SMO.
    
    Returns:
    
    the bias of each binary SMO.
  - numClassAttributeValues
```
public int numClassAttributeValues()
```
    Returns the number of values of the class attribute.
    
    Returns:
    
    the number of values of the class attribute.
  - classAttributeNames
```
public java.lang.String[] classAttributeNames()
```
    Returns the names of the class attributes.
    
    Returns:
    
    the names of the class attributes.
  - attributeNames
```
public java.lang.String[][][] attributeNames()
```
    Returns the attribute names.
    
    Returns:
    
    the attribute names.
  - setChecksTurnedOff
```
public void setChecksTurnedOff(boolean value)
```
    Disables or enables the checks (which could be time-consuming). Use with caution!
    
    Parameters:
    
    value - if true turns off all checks
  - getChecksTurnedOff
```
public boolean getChecksTurnedOff()
```
    Returns whether the checks are turned off or not.
    
    Returns:
    
    true if the checks are turned off
  - checksTurnedOffTipText
```
public java.lang.String checksTurnedOffTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - kernelTipText
```
public java.lang.String kernelTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - 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
  - cTipText
```
public java.lang.String cTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getC
```
public double getC()
```
    Get the value of C.
    
    Returns:
    
    Value of C.
  - setC
```
public void setC(double v)
```
    Set the value of C.
    
    Parameters:
    
    v - Value to assign to C.
  - toleranceParameterTipText
```
public java.lang.String toleranceParameterTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getToleranceParameter
```
public double getToleranceParameter()
```
    Get the value of tolerance parameter.
    
    Returns:
    
    Value of tolerance parameter.
  - setToleranceParameter
```
public void setToleranceParameter(double v)
```
    Set the value of tolerance parameter.
    
    Parameters:
    
    v - Value to assign to tolerance parameter.
  - epsilonTipText
```
public java.lang.String epsilonTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getEpsilon
```
public double getEpsilon()
```
    Get the value of epsilon.
    
    Returns:
    
    Value of epsilon.
  - setEpsilon
```
public void setEpsilon(double v)
```
    Set the value of epsilon.
    
    Parameters:
    
    v - Value to assign to epsilon.
  - filterTypeTipText
```
public java.lang.String filterTypeTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getFilterType
```
public SelectedTag getFilterType()
```
    Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
    
    Returns:
    
    the filtering mode
  - setFilterType
```
public void setFilterType(SelectedTag newType)
```
    Sets how the training data will be transformed. Should be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
    
    Parameters:
    
    newType - the new filtering mode
  - buildLogisticModelsTipText
```
public java.lang.String buildLogisticModelsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getBuildLogisticModels
```
public boolean getBuildLogisticModels()
```
    Get the value of buildLogisticModels.
    
    Returns:
    
    Value of buildLogisticModels.
  - setBuildLogisticModels
```
public void setBuildLogisticModels(boolean newbuildLogisticModels)
```
    Set the value of buildLogisticModels.
    
    Parameters:
    
    newbuildLogisticModels - Value to assign to buildLogisticModels.
  - numFoldsTipText
```
public java.lang.String numFoldsTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getNumFolds
```
public int getNumFolds()
```
    Get the value of numFolds.
    
    Returns:
    
    Value of numFolds.
  - setNumFolds
```
public void setNumFolds(int newnumFolds)
```
    Set the value of numFolds.
    
    Parameters:
    
    newnumFolds - Value to assign to numFolds.
  - randomSeedTipText
```
public java.lang.String randomSeedTipText()
```
    Returns the tip text for this property
    
    Returns:
    
    tip text for this property suitable for displaying in the explorer/experimenter gui
  - getRandomSeed
```
public int getRandomSeed()
```
    Get the value of randomSeed.
    
    Returns:
    
    Value of randomSeed.
  - setRandomSeed
```
public void setRandomSeed(int newrandomSeed)
```
    Set the value of randomSeed.
    
    Parameters:
    
    newrandomSeed - Value to assign to randomSeed.
  - runModel
```
public void runModel()
```
    Run the model once the parameters have been set by the method config_read()
  - runModel
```
public void runModel(InstanceSet train,
                     InstanceSet test)
```
    Run the model once the parameters have been set by the method config_read()
    
    Parameters:
    
    train - training instances set.
    
    test - test instances set.
  - writeOutput
```
public static void writeOutput(java.lang.String fileName,
                               java.lang.String[] instancesIN,
                               java.lang.String[] instancesOUT,
                               Attribute[] inputs,
                               Attribute output,
                               int nInputs,
                               java.lang.String relation)
```
    Creates the output file in KEEL format of this method
    
    Parameters:
    
    fileName - Name of the content file
    
    instancesIN - Vector with the original output values
    
    instancesOUT - Vector the predicted output values
    
    inputs - Input Attributes
    
    output - Output Attribute
    
    nInputs - Number of Inputs Attributes
    
    relation - Name of the data set
  - printSVs
```
protected void printSVs()
```
    Prints the Support vectors to file
  - InstancesKEEL2Weka
```
protected Instances InstancesKEEL2Weka(InstanceSet is,
                                       int preprocessType,
                                       boolean nominal2binary)
```
    Creates a new allocated WEKA's set of Instances (i.e. Instances) from a KEEL's set of instances (i.e. InstanceSet).
    
    Parameters:
    
    is - The KEEL Instance set
    
    preprocessType - An integer with the type of preprocess done before exporting data to Weka format (0 = normalize, 1 = standardize, 2 = do nothing).
    
    nominal2binary - True if the nominal values must be converted in a set of binary ones (one bit per value of the nominal attribute).
    
    Returns:
    
    A new allocated WEKA formatted Instance set
  - normalize
```
protected double normalize(double value,
                           Attribute a)
```
    Normalize the input value according to the provided attribute
    
    Parameters:
    
    value - The value to be normalized
    
    a - The attribute to which value belongs
    
    Returns:
    
    A new double value in the range [0,1]
  - standardize
```
protected double standardize(double value,
                             int j)
```
    Standardize the provided value, converting it to a new double value from a normal distribution with mean = 0 and std. deviation = 1
    
    Parameters:
    
    value - The value to be standardized
    
    j - The INDEX of the attribute in the mean and std_dev members arrays (previously filled)
    
    Returns:
    
    A new value from a normal distribution
  - computeStats
```
protected void computeStats(InstanceSet IS)
```
    Compute the mean and std. deviation of each attribute of Attributes. Then we fill the mean[] and std_dev[] arrays
    
    Parameters:
    
    IS - The InstanceSet with the instanced from we compute the statistics

Class SMO

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

probabilities

FILTER_NORMALIZE

FILTER_STANDARDIZE

FILTER_NONE

TAGS_FILTER

m_classifiers

m_C

m_eps

m_tol

m_filterType

m_classIndex

m_classAttribute

m_KernelIsLinear

m_checksTurnedOff

m_Del

m_fitLogisticModels

m_numFolds

m_randomSeed

m_kernel

m_NumClasses

m_seed

m_nominalToBinary

input_train_name

input_validation_name

input_test_name

output_train_name

output_test_name

method_output

mean

std_dev

Constructor Detail

SMO

SMO

Method Detail

globalInfo

getTechnicalInformation

turnChecksOff

turnChecksOn

buildClassifier

distributionForInstance

pairwiseCoupling

obtainVotes

sparseWeights

sparseIndices

bias

numClassAttributeValues

classAttributeNames

attributeNames

setChecksTurnedOff

getChecksTurnedOff

checksTurnedOffTipText

kernelTipText

setKernel

getKernel

cTipText

getC

setC

toleranceParameterTipText

getToleranceParameter

setToleranceParameter

epsilonTipText

getEpsilon

setEpsilon

filterTypeTipText

getFilterType

setFilterType

buildLogisticModelsTipText

getBuildLogisticModels

setBuildLogisticModels

numFoldsTipText

getNumFolds

setNumFolds

randomSeedTipText