PlattSMO (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.svm.SupportVectorLearner
- - jsat.classifiers.svm.PlattSMO

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, WarmClassifier, Parameterized, Regressor, WarmRegressor
```
public class PlattSMO
extends SupportVectorLearner
implements BinaryScoreClassifier, WarmRegressor, Parameterized, WarmClassifier
```
An implementation of SVMs using Platt's Sequential Minimum Optimization (SMO) for both Classification and Regression problems.

This algorithm can be warm started for classification problems by any algorithm implementing the BinaryScoreClassifier interface. For regression any algorithm can be used as a warms start. For best results, warm starts should be from algorithms that will have a similar solution to PlattSMO.

See:
- Platt, J. C. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Advances in kernel methods (pp. 185 – 208). Retrieved from here
- Keerthi, S. S., Shevade, S. K., Bhattacharyya, C.,&Murthy, K. R. K. (2001). Improvements to Platt’s SMO Algorithm for SVM Classifier Design . Neural Computation, 13(3), 637–649. doi:10.1162/089976601300014493
- Smola, A. J.,&Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222. doi:10.1023/B:STCO.0000035301.49549.88
- Shevade, S. K., Keerthi, S. S., Bhattacharyya, C.,&Murthy, K. K. (1999) . Improvements to the SMO algorithm for SVM regression. Control D ivision, Dept. of Mechanical Engineering CD-99–16. Control Division, Dept. of Mechanical Engineering. doi:10.1109/72.870050
- Shevade, S. K., Keerthi, S. S., Bhattacharyya, C.,&Murthy, K. K. (2000) . Improvements to the SMO algorithm for SVM regression. IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council, 11(5), 1188–93. doi:10.1109/72.870050
Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary
- Nested classes/interfaces inherited from class jsat.classifiers.svm.SupportVectorLearner
  SupportVectorLearner.CacheMode

Field Summary

Fields
Modifier and Type	Field and Description
`protected double`	`b` Bias
`protected double`	`b_low` Bias
`protected double`	`b_up` Bias
`protected double[]`	`fcache`
`protected double[]`	`label` Stores the true value of the data point
`protected Vec`	`weights` Weight values to apply to each data point

Fields inherited from class jsat.classifiers.svm.SupportVectorLearner
accelCache, alphas, cacheEvictions, evalCount, vecs

Constructor Summary

Constructors
Constructor and Description
`PlattSMO()` Creates a new SVM object with a `LinearKernel` that uses no cache mode.
`PlattSMO(KernelTrick kf)` Creates a new SVM object that uses no cache mode.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CategoricalResults`	`classify(DataPoint data)` Performs classification on the given data point.
`PlattSMO`	`clone()`
`protected double`	`decisionFunction(int v)` Returns the local decision function for classification training purposes without the bias term
`protected double`	`decisionFunctionR(int v)` Returns the local decision function for regression training purposes without the bias term
`double`	`getC()` Returns the soft margin complexity parameter of the SVM
`double`	`getEpsilon()` Returns the epsilon insensitive loss value
`int`	`getMaxIterations()` Returns the maximum number of iterations
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`double`	`getScore(DataPoint dp)` Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
`double`	`getTolerance()` Returns the solution tolerance
`static Distribution`	`guessC(DataSet d)` Guess the distribution to use for the regularization term `C` in a SVM.
`boolean`	`isModificationOne()` Returns true if modification one is in use
`double`	`regress(DataPoint data)`
`void`	`setC(double C)` Sets the complexity parameter of SVM.
`void`	`setEpsilon(double epsilon)` Sets the epsilon for the epsilon insensitive loss when performing regression.
`void`	`setMaxIterations(int maxIterations)` Sets the maximum number of iterations to perform of the training loop.
`void`	`setModificationOne(boolean modificationOne)` Sets where or not modification one or two should be used when training.
`void`	`setTolerance(double tolerance)` Sets the tolerance for the solution.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`protected boolean`	`takeStep(int i1, int i2)`
`protected boolean`	`takeStepR(int i1, int i2)`
`void`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet dataSet, ExecutorService threadPool)`
`void`	`train(RegressionDataSet dataSet, Regressor warmSolution)` Trains the regressor and constructs a model for regression using the given data set.
`void`	`train(RegressionDataSet dataSet, Regressor warmSolution, ExecutorService threadPool)` Trains the regressor and constructs a model for regression using the given data set.
`void`	`trainC(ClassificationDataSet dataSet)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, Classifier warmSolution)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, Classifier warmSolution, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.
`boolean`	`warmFromSameDataOnly()` Some models can only be warm started from a solution trained on the exact same data set as the model it is warm starting from.

Methods inherited from class jsat.classifiers.svm.SupportVectorLearner
accessingRow, getCacheMode, getCacheValue, getKernel, k, kEval, kEval, kEvalSum, setAlphas, setCacheMode, setCacheSize, setCacheValue, setKernel, sparsify

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

- Field Detail
  - b
```
protected double b
```
    Bias
  - b_low
```
protected double b_low
```
    Bias
  - b_up
```
protected double b_up
```
    Bias
  - fcache
```
protected double[] fcache
```
  - label
```
protected double[] label
```
    Stores the true value of the data point
  - weights
```
protected Vec weights
```
    Weight values to apply to each data point
- Constructor Detail
  - PlattSMO
```
public PlattSMO()
```
    Creates a new SVM object with a LinearKernel that uses no cache mode.
  - PlattSMO
```
public PlattSMO(KernelTrick kf)
```
    Creates a new SVM object that uses no cache mode.
    
    Parameters:
    
    kf - the kernel trick to use
- Method Detail
  - classify
```
public CategoricalResults classify(DataPoint data)
```
    Description copied from interface: Classifier
    
    Performs classification on the given data point.
    
    Specified by:
    
    classify in interface Classifier
    
    Parameters:
    
    data - the data point to classify
    
    Returns:
    
    the results of the classification.
  - getScore
```
public double getScore(DataPoint dp)
```
    Description copied from interface: BinaryScoreClassifier
    
    Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
    
    Specified by:
    
    getScore in interface BinaryScoreClassifier
    
    Parameters:
    
    dp - the data point to predict the class label of
    
    Returns:
    
    the score for the given data point
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   Classifier warmSolution,
                   ExecutorService threadPool)
```
    Description copied from interface: WarmClassifier
    
    Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
    
    Specified by:
    
    trainC in interface WarmClassifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    warmSolution - the solution to use to warm start this model
    
    threadPool - the source of threads to use.
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   ExecutorService threadPool)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    threadPool - the source of threads to use.
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   Classifier warmSolution)
```
    Description copied from interface: WarmClassifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface WarmClassifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    warmSolution - the solution to use to warm start this model
  - trainC
```
public void trainC(ClassificationDataSet dataSet)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
  - takeStep
```
protected boolean takeStep(int i1,
                           int i2)
```
  - takeStepR
```
protected boolean takeStepR(int i1,
                            int i2)
```
  - decisionFunction
```
protected double decisionFunction(int v)
```
    Returns the local decision function for classification training purposes without the bias term
    
    Parameters:
    
    v - the index of the point to select
    
    Returns:
    
    the decision function output sans bias
  - decisionFunctionR
```
protected double decisionFunctionR(int v)
```
    Returns the local decision function for regression training purposes without the bias term
    
    Parameters:
    
    v - the index of the point to select
    
    Returns:
    
    the decision function output sans bias
  - clone
```
public PlattSMO clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface Regressor
    
    Overrides:
    
    clone in class Object
  - supportsWeightedData
```
public boolean supportsWeightedData()
```
    Description copied from interface: Classifier
    
    Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.
    
    Specified by:
    
    supportsWeightedData in interface Classifier
    
    Specified by:
    
    supportsWeightedData in interface Regressor
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - setC
```
public void setC(double C)
```
    Sets the complexity parameter of SVM. The larger the C value the harder the margin SVM will attempt to find. Lower values of C allow for more misclassification errors.
    
    Parameters:
    
    C - the soft margin parameter
  - getC
```
public double getC()
```
    Returns the soft margin complexity parameter of the SVM
    
    Returns:
    
    the complexity parameter of the SVM
  - setMaxIterations
```
public void setMaxIterations(int maxIterations)
```
    Sets the maximum number of iterations to perform of the training loop. This is important for cases with a C value that is to large for a non linear problem, which can result in SVM failing to converge.
    
    Parameters:
    
    maxIterations - the maximum number of main iteration loops
  - getMaxIterations
```
public int getMaxIterations()
```
    Returns the maximum number of iterations
    
    Returns:
    
    the maximum number of iterations
  - setModificationOne
```
public void setModificationOne(boolean modificationOne)
```
    Sets where or not modification one or two should be used when training. Modification two is more aggressive, but often results in less kernel evaluations.
    
    Parameters:
    
    modificationOne - true to us modificaiotn one, false to use modification two.
  - isModificationOne
```
public boolean isModificationOne()
```
    Returns true if modification one is in use
    
    Returns:
    
    true if modification one is in use
  - setTolerance
```
public void setTolerance(double tolerance)
```
    Sets the tolerance for the solution. Higher values converge to worse solutions, but do so faster
    
    Parameters:
    
    tolerance - the tolerance for the solution
  - getTolerance
```
public double getTolerance()
```
    Returns the solution tolerance
    
    Returns:
    
    the solution tolerance
  - getParameters
```
public List<Parameter> getParameters()
```
    Description copied from interface: Parameterized
    
    Returns the list of parameters that can be altered for this learner.
    
    Specified by:
    
    getParameters in interface Parameterized
    
    Returns:
    
    the list of parameters that can be altered for this learner.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.
  - regress
```
public double regress(DataPoint data)
```
    Specified by:
    
    regress in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet,
                  ExecutorService threadPool)
```
    Specified by:
    
    train in interface Regressor
  - setEpsilon
```
public void setEpsilon(double epsilon)
```
    Sets the epsilon for the epsilon insensitive loss when performing regression. This variable has no impact during classification problems. For regression problems, any predicated value that is within the epsilon of the target will be treated as "correct". Increasing epsilon usually decreases the number of support vectors, but may reduce the accuracy of the model
    
    Parameters:
    
    epsilon - the positive value for the acceptable error when doing regression
  - getEpsilon
```
public double getEpsilon()
```
    Returns the epsilon insensitive loss value
    
    Returns:
    
    the epsilon insensitive loss value
  - train
```
public void train(RegressionDataSet dataSet,
                  Regressor warmSolution,
                  ExecutorService threadPool)
```
    Description copied from interface: WarmRegressor
    
    Trains the regressor and constructs a model for regression using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
    
    Specified by:
    
    train in interface WarmRegressor
    
    Parameters:
    
    dataSet - the data set to train on
    
    warmSolution - the solution to use to warm start this model
    
    threadPool - the source of threads to use.
  - train
```
public void train(RegressionDataSet dataSet)
```
    Specified by:
    
    train in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet,
                  Regressor warmSolution)
```
    Description copied from interface: WarmRegressor
    
    Trains the regressor and constructs a model for regression using the given data set.
    
    Specified by:
    
    train in interface WarmRegressor
    
    Parameters:
    
    dataSet - the data set to train on
    
    warmSolution - the solution to use to warm start this model
  - warmFromSameDataOnly
```
public boolean warmFromSameDataOnly()
```
    Description copied from interface: WarmRegressor
    
    Some models can only be warm started from a solution trained on the exact same data set as the model it is warm starting from. If this is the case true will be returned. The behavior for training on a different data set when this is defined is undefined. It may cause an error, or it may cause the algorithm to take longer or reach a worse solution.
    When true, it is important that the data set be unaltered - this includes mutating the values stored or re-arranging the data points within the data set.
    
    Specified by:
    
    warmFromSameDataOnly in interface WarmClassifier
    
    Specified by:
    
    warmFromSameDataOnly in interface WarmRegressor
    
    Returns:
    
    true if the algorithm can only be warm started from the model trained on the exact same data set.
  - guessC
```
public static Distribution guessC(DataSet d)
```
    Guess the distribution to use for the regularization term C in a SVM.
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the C parameter in the SVM

Class PlattSMO

Nested Class Summary

Nested classes/interfaces inherited from class jsat.classifiers.svm.SupportVectorLearner

Field Summary

Fields inherited from class jsat.classifiers.svm.SupportVectorLearner

Constructor Summary

Method Summary

Methods inherited from class jsat.classifiers.svm.SupportVectorLearner

Methods inherited from class java.lang.Object

Field Detail

b

b_low

b_up

fcache

label

weights

Constructor Detail

PlattSMO

PlattSMO

Method Detail

classify

getScore

trainC

trainC

trainC

trainC

takeStep

takeStepR

decisionFunction

decisionFunctionR

clone

supportsWeightedData

setC

getC

setMaxIterations

getMaxIterations

setModificationOne

isModificationOne

setTolerance

getTolerance

getParameters

getParameter

regress

train

setEpsilon

getEpsilon

train

train

train

warmFromSameDataOnly

guessC