com.emphysic.myriad.core.data.roi

Class PassiveAggressiveROIFinder

java.lang.Object

com.emphysic.myriad.core.data.roi.MLROIConfFinder

com.emphysic.myriad.core.data.roi.PassiveAggressiveROIFinder

All Implemented Interfaces:

MLROIFinder, ROIFinder, ROIProbability, ObjectMap, com.esotericsoftware.kryo.KryoSerializable

public class PassiveAggressiveROIFinder
extends MLROIConfFinder

PassiveAggressiveROIFinder - machine learning ROI detector that uses the Passive-Aggressive algorithm outlined in "Online Passive-Aggressive Algorithms" K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR 7 (2006).

Field Summary

Fields inherited from class com.emphysic.myriad.core.data.roi.MLROIConfFinder

confThr

Constructor Summary

Constructors

Constructor and Description
PassiveAggressiveROIFinder() Creates a new PassiveAggressiveROIFinder with a learning rate of 0.1.
PassiveAggressiveROIFinder(double learningRate) Creates a new PassiveAggressiveROIFinder with the specified learning rate.

Method Summary

Modifier and Type	Method and Description
org.apache.mahout.math.Vector	classify(org.apache.mahout.math.DenseVector d)
double	getLearningRate() Returns the current learning rate.
org.apache.mahout.classifier.sgd.PassiveAggressive	getModel() Returns the current model.
int	getNumCategories() Returns the number of labels known by the current model.
int	getNumFeatures() Get the number of features in the data i.e.
java.util.Map<java.lang.String,java.lang.Object>	getObjectMap() Creates a map of the important fields for the instance, suitable for serialization.
long	getSerializationVersion() Returns the current version of the serialization format.
int	getVersion() Returns the current class version.
void	initCurrentVersion(java.util.Map<java.lang.String,java.lang.Object> objectMap) Initializes an instance with a current-version object graph.
boolean	isROI(Dataset dataset) Predict if a sample contains a region of interest.
boolean	isROI(double[] data) Predict if a sample contains a region of interest.
void	legacyRead(com.esotericsoftware.kryo.Kryo kryo, com.esotericsoftware.kryo.io.Input input)
void	legacyWrite(com.esotericsoftware.kryo.Kryo kryo, com.esotericsoftware.kryo.io.Output output)
double	negativeClass() Returns the numeric value of the negative class of a two-category model.
double	positiveClass() Returns the numeric value of the positive class of a two-category model.
void	setLearningRate(double learningRate) Sets the learning rate.
void	setModel(org.apache.mahout.classifier.sgd.PassiveAggressive model) Sets the ROI model.
void	setNumCategories(int numCategories) Sets the number of categories for the model.
void	setNumFeatures(int numFeatures) Sets the number of features in the sample data.
void	train(double[][] X, int[] y) Incrementally trains the model with new samples, creating the model if required.

Methods inherited from class com.emphysic.myriad.core.data.roi.MLROIConfFinder

getConfidenceThreshold, predict, predict, setConfidenceThreshold, writableWriteToBytes

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface com.emphysic.myriad.core.data.roi.ROIFinder

fromFile

Methods inherited from interface com.emphysic.myriad.core.data.util.ObjectMap

init, initPreviousVersion, initUnknownVersion, load, read, save, write

Methods inherited from interface com.emphysic.myriad.core.data.roi.ROIProbability

predict_proba, predict_proba

Constructor Detail

PassiveAggressiveROIFinder

public PassiveAggressiveROIFinder(double learningRate)

Creates a new PassiveAggressiveROIFinder with the specified learning rate.

Parameters:

learningRate - learning rate

PassiveAggressiveROIFinder

public PassiveAggressiveROIFinder()

Creates a new PassiveAggressiveROIFinder with a learning rate of 0.1.

Method Detail

train

public void train(double[][] X,
                  int[] y)

Incrementally trains the model with new samples, creating the model if required.

Parameters:

X - N examples with M features per example

y - N labels for the N examples in X

positiveClass

public double positiveClass()

Returns the numeric value of the positive class of a two-category model.

Returns:

positive class label

negativeClass

public double negativeClass()

Returns the numeric value of the negative class of a two-category model.

Returns:

negative class label

isROI

public boolean isROI(double[] data)

Predict if a sample contains a region of interest.

Parameters:

data - data to examine

Returns:

true if the sample appears to contain an ROI, false otherwise.

isROI

public boolean isROI(Dataset dataset)

Predict if a sample contains a region of interest.

Parameters:

dataset - data to examine

Returns:

true if the sample appears to contain an ROI, false otherwise.

getSerializationVersion

public long getSerializationVersion()

Description copied from interface: ObjectMap

Returns the current version of the serialization format. Increments when the serialization format changes, i.e. backwards compatibility is broken.

Returns:

serialization version

getVersion

public int getVersion()

Description copied from interface: ObjectMap

Returns the current class version. Class version increments when the class changes but doesn't necessarily break compatibility with serialization.

Returns:

class version

getObjectMap

public java.util.Map<java.lang.String,java.lang.Object> getObjectMap()

Description copied from interface: ObjectMap

Creates a map of the important fields for the instance, suitable for serialization.

Returns:

"object map" of the instance

initCurrentVersion

public void initCurrentVersion(java.util.Map<java.lang.String,java.lang.Object> objectMap)

Description copied from interface: ObjectMap

Initializes an instance with a current-version object graph.

Parameters:

objectMap - object graph for initialization

getNumCategories

public int getNumCategories()

Returns the number of labels known by the current model.

Returns:

number of categories

setNumCategories

public void setNumCategories(int numCategories)

Sets the number of categories for the model. Will require creating a new model.

Parameters:

numCategories - number of categories

getNumFeatures

public int getNumFeatures()

Get the number of features in the data i.e. the size of each Dataset used to train the model.

Returns:

number of features

setNumFeatures

public void setNumFeatures(int numFeatures)

Sets the number of features in the sample data. Changing this number will require training a new model.

Parameters:

numFeatures - number of features in the data

getModel

public org.apache.mahout.classifier.sgd.PassiveAggressive getModel()

Returns the current model.

Returns:

current model

setModel

public void setModel(org.apache.mahout.classifier.sgd.PassiveAggressive model)

Sets the ROI model.

Parameters:

model - model to predict regions of interest

getLearningRate

public double getLearningRate()

Returns the current learning rate.

Returns:

learning rate

setLearningRate

public void setLearningRate(double learningRate)

Sets the learning rate.

Parameters:

learningRate - new learning rate

legacyWrite

public void legacyWrite(com.esotericsoftware.kryo.Kryo kryo,
                        com.esotericsoftware.kryo.io.Output output)

legacyRead

public void legacyRead(com.esotericsoftware.kryo.Kryo kryo,
                       com.esotericsoftware.kryo.io.Input input)

classify

public org.apache.mahout.math.Vector classify(org.apache.mahout.math.DenseVector d)