RANSAC (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.regression.RANSAC

All Implemented Interfaces:

Serializable, Cloneable, Parameterized, Regressor
```
public class RANSAC
extends Object
implements Regressor, Parameterized
```
RANSAC is a randomized meta algorithm that is useful for fitting a model to a data set that has a large amount of outliers that do not represent the true distribution well.
RANSAC has the concept of inliers and outliers. An initial number of seed points is specified. This makes the initial inlier set. The algorithm than iterates several times, randomly selecting the specified number of points. It then regresses on all other points, adding all points within a specified absolute error to the set of inliers. The model is then trained again on the larger set, and the training error becomes the measure of the strength of the model. The model that has the lowest error is then the fit model.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`RANSAC(Regressor baseRegressor, int iterations, int initialTrainSize, int minResultSize, double maxPointError)` Creates a new RANSAC training object.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`RANSAC`	`clone()`
`Regressor`	`getBaseRegressorClone()` Once RANSAC is complete, it maintains its trained version of the finalized regressor.
`boolean[]`	`getConsensusSet()` Returns an boolean array where the indices correspond to data points in the original training set.
`int`	`getInitialTrainSize()` Returns the number of data points to be sampled from the training set to create initial models.
`int`	`getIterations()` Returns the number models that will be tested on the data set.
`double`	`getMaxPointError()` Each data point not in the initial training set will be tested against.
`int`	`getMinResultSize()` RANSAC requires an initial model to be accurate enough to include a minimum number of inliers before being considered as a potentially good model.
`double`	`getModelError()` Returns the model error, which is the average absolute difference between the model and all points in the set of inliers.
`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`	`regress(DataPoint data)`
`void`	`setInitialTrainSize(int initialTrainSize)` Sets the number of data points to be sampled from the training set to create initial models.
`void`	`setIterations(int iterations)` Sets the number models that will be tested on the data set.
`void`	`setMaxPointError(double maxPointError)` Each data point not in the initial training set will be tested against.
`void`	`setMinResultSize(int minResultSize)` RANSAC requires an initial model to be accurate enough to include a minimum number of inliers before being considered as a potentially good model.
`boolean`	`supportsWeightedData()`
`void`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet dataSet, ExecutorService threadPool)`

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

- Constructor Detail
  - RANSAC
```
public RANSAC(Regressor baseRegressor,
              int iterations,
              int initialTrainSize,
              int minResultSize,
              double maxPointError)
```
    Creates a new RANSAC training object. Because RANSAC is sensitive to parameter settings, which are data and model dependent, no default values exist for them.
    
    Parameters:
    
    baseRegressor - the model to fit using RANSAC
    
    iterations - the number of iterations of the algorithm to perform
    
    initialTrainSize - the number of points to seed each iteration of training with
    
    minResultSize - the minimum number of inliers to make it into the model to be considered a possible fit.
    
    maxPointError - the maximum allowed absolute difference in the output of the model and the true value for the data point to be added to the inlier set.
- Method Detail
  - 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.
  - getInitialTrainSize
```
public int getInitialTrainSize()
```
    Returns the number of data points to be sampled from the training set to create initial models.
    
    Returns:
    
    the number of data points used to first create models
  - setInitialTrainSize
```
public void setInitialTrainSize(int initialTrainSize)
```
    Sets the number of data points to be sampled from the training set to create initial models.
    
    Parameters:
    
    initialTrainSize - the number of data points to use to create models
  - getIterations
```
public int getIterations()
```
    Returns the number models that will be tested on the data set.
    
    Returns:
    
    the number of algorithm iterations
  - setIterations
```
public void setIterations(int iterations)
```
    Sets the number models that will be tested on the data set.
    
    Parameters:
    
    iterations - the number of iterations to perform
  - getMaxPointError
```
public double getMaxPointError()
```
    Each data point not in the initial training set will be tested against. If a data points error is sufficiently small, it will be added to the set of inliers.
    
    Returns:
    
    the maximum error any one point may have to be an inliner
  - setMaxPointError
```
public void setMaxPointError(double maxPointError)
```
    Each data point not in the initial training set will be tested against. If a data points error is sufficiently small, it will be added to the set of inliers.
    
    Parameters:
    
    maxPointError - the new maximum error a data point may have to be considered an inlier.
  - getMinResultSize
```
public int getMinResultSize()
```
    RANSAC requires an initial model to be accurate enough to include a minimum number of inliers before being considered as a potentially good model. This is the number of points that must make it into the inlier set for a model to be considered.
    
    Returns:
    
    the minimum number of inliers to be considered
  - setMinResultSize
```
public void setMinResultSize(int minResultSize)
```
    RANSAC requires an initial model to be accurate enough to include a minimum number of inliers before being considered as a potentially good model. This is the number of points that must make it into the inlier set for a model to be considered.
    
    Parameters:
    
    minResultSize - the minimum number of inliers to be considered
  - 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
  - train
```
public void train(RegressionDataSet dataSet)
```
    Specified by:
    
    train in interface Regressor
  - supportsWeightedData
```
public boolean supportsWeightedData()
```
    Specified by:
    
    supportsWeightedData in interface Regressor
  - clone
```
public RANSAC clone()
```
    Specified by:
    
    clone in interface Regressor
    
    Overrides:
    
    clone in class Object
  - getBaseRegressorClone
```
public Regressor getBaseRegressorClone()
```
    Once RANSAC is complete, it maintains its trained version of the finalized regressor. A clone of it may be retrieved from this method.
    
    Returns:
    
    a clone of the learned regressor
  - getConsensusSet
```
public boolean[] getConsensusSet()
```
    Returns an boolean array where the indices correspond to data points in the original training set. true indicates that the data point was apart of the final consensus set. false indicates that it was not.
    
    Returns:
    
    a boolean array indicating which points made it into the consensus set
  - getModelError
```
public double getModelError()
```
    Returns the model error, which is the average absolute difference between the model and all points in the set of inliers.
    
    Returns:
    
    the error for the learned model. Returns Double.POSITIVE_INFINITY if the model has not been trained or failed to fit.

Class RANSAC

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

RANSAC

Method Detail

getParameters

getParameter

getInitialTrainSize

setInitialTrainSize

getIterations

setIterations

getMaxPointError

setMaxPointError

getMinResultSize

setMinResultSize

regress

train

train

supportsWeightedData

clone

getBaseRegressorClone

getConsensusSet

getModelError