com.emphysic.myriad.core.experimental.roi

Class GradMachineROIFinder

java.lang.Object

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

com.emphysic.myriad.core.experimental.roi.GradMachineROIFinder

All Implemented Interfaces:

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

public class GradMachineROIFinder
extends MLROIConfFinder

Region Of Interest (ROI) finder based on Mahout's GradientMachine, an online gradient machine with one hidden sigmoid layer that minimizes the hinge loss. Created by ccoughlin on 11/2/2016.

Field Summary

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

confThr

Constructor Summary

Constructors

Constructor and Description
GradMachineROIFinder() Default no-arg constructor for serialization
GradMachineROIFinder(int numCats) Constructor.
GradMachineROIFinder(int numCats, int numHidden, double learningRate, double sparsity, double regularization) Constructor.

Method Summary

Modifier and Type	Method and Description
org.apache.mahout.math.Vector	classify(org.apache.mahout.math.DenseVector d)
double	getLearningRate()
org.apache.mahout.classifier.sgd.GradientMachine	getModel()
int	getNumCategories()
int	getNumFeatures()
int	getNumHidden()
java.util.Map<java.lang.String,java.lang.Object>	getObjectMap() Creates a map of the important fields for the instance, suitable for serialization.
double	getRegularization()
long	getSerializationVersion() Returns the current version of the serialization format.
double	getSparsity()
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) Examine a dataset and return whether or not it seems to contain a region of interest (ROI)
boolean	isROI(double[] data) Examine an array of data and report whether it appears to contain a region of interest (ROI)
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() The numeric label assigned to negative samples i.e.
double	positiveClass() The numeric label assigned to positive samples i.e.
void	setModel(org.apache.mahout.classifier.sgd.GradientMachine model)
void	train(double[][] X, int[] y) Trains the flaw finder on new data.

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

GradMachineROIFinder

public GradMachineROIFinder()

Default no-arg constructor for serialization

GradMachineROIFinder

public GradMachineROIFinder(int numCats,
                            int numHidden,
                            double learningRate,
                            double sparsity,
                            double regularization)

Constructor.

Parameters:

numCats - number of categories in the data

numHidden - number of nodes in the hidden layer

learningRate - learning rate (0-1)

sparsity - sparsity of hidden layer (0-1)

regularization - reqularization of weight vector

GradMachineROIFinder

public GradMachineROIFinder(int numCats)

Constructor.

Parameters:

numCats - number of categories in the data

Method Detail

train

public void train(double[][] X,
                  int[] y)
           throws java.lang.Exception

Trains the flaw finder on new data. Initializes the model if required and sets the number of features.

Parameters:

X - N examples with M features per example

y - N labels for the N examples in X

Throws:

java.lang.Exception - if an error occurs

positiveClass

public double positiveClass()

The numeric label assigned to positive samples i.e. samples that contain ROI

Returns:

the value assigned to positive samples

negativeClass

public double negativeClass()

The numeric label assigned to negative samples i.e. samples that do not contain ROI

Returns:

the value assigned to negative samples

isROI

public boolean isROI(double[] data)

Examine an array of data and report whether it appears to contain a region of interest (ROI)

Parameters:

data - raw data to examine

Returns:

true if data appears to contain an ROI, false otherwise

isROI

public boolean isROI(Dataset dataset)

Examine a dataset and return whether or not it seems to contain a region of interest (ROI)

Parameters:

dataset - data to examine

Returns:

true if data 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

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)

getModel

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

setModel

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

getNumFeatures

public int getNumFeatures()

getNumCategories

public int getNumCategories()

getNumHidden

public int getNumHidden()

getLearningRate

public double getLearningRate()

getSparsity

public double getSparsity()

getRegularization

public double getRegularization()