Class GaussianDataSource

java.lang.Object

org.tribuo.regression.example.GaussianDataSource

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.config.Configurable, com.oracle.labs.mlrg.olcut.provenance.Provenancable<DataSourceProvenance>, Iterable<Example<Regressor>>, ConfigurableDataSource<Regressor>, DataSource<Regressor>

public class GaussianDataSource
extends Object
implements ConfigurableDataSource<Regressor>

Generates a single dimensional output drawn from N(slope*x + intercept,variance).

The single feature is drawn from a uniform distribution over the range.

Set slope to zero to draw from a gaussian.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	GaussianDataSource.GaussianDataSourceProvenance	Provenance for GaussianDataSource.

Constructor Summary

Constructors

Constructor	Description
GaussianDataSource(int numSamples, float slope, float intercept, float variance, float xMin, float xMax, long seed)	Generates a single dimensional output drawn from N(slope*x + intercept,variance).

Method Summary

Modifier and Type	Method	Description
static Dataset<Regressor>	generateDataset(int numSamples, float slope, float intercept, float variance, float xMin, float xMax, long seed)	Generates a single dimensional output drawn from N(slope*x + intercept,variance).
OutputFactory<Regressor>	getOutputFactory()	Returns the OutputFactory associated with this Output subclass.
DataSourceProvenance	getProvenance()
Iterator<Example<Regressor>>	iterator()
void	postConfig()	Used by the OLCUT configuration system, and should not be called by external code.

Methods inherited from class java.lang.Object

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

Methods inherited from interface java.lang.Iterable

forEach, spliterator

Constructor Details

GaussianDataSource

public GaussianDataSource(int numSamples,
 float slope,
 float intercept,
 float variance,
 float xMin,
 float xMax,
 long seed)

Generates a single dimensional output drawn from N(slope*x + intercept,variance).

The single feature is drawn from a uniform distribution over the range.

Set slope to zero to draw from a gaussian.

Parameters:

numSamples - The size of the output dataset.

slope - The slope of the line.

intercept - The y intercept of the line.

variance - The variance of the gaussian.

xMin - The minimum x value (inclusive).

xMax - The maximum x value (exclusive).

seed - The rng seed to use.

Method Details

postConfig

public void postConfig()

Used by the OLCUT configuration system, and should not be called by external code.

Specified by:

postConfig in interface com.oracle.labs.mlrg.olcut.config.Configurable

getOutputFactory

public OutputFactory<Regressor> getOutputFactory()

Description copied from interface: DataSource

Returns the OutputFactory associated with this Output subclass.

Specified by:

getOutputFactory in interface DataSource<Regressor>

Returns:

The output factory.

getProvenance

public DataSourceProvenance getProvenance()

Specified by:

getProvenance in interface com.oracle.labs.mlrg.olcut.provenance.Provenancable<DataSourceProvenance>

iterator

public Iterator<Example<Regressor>> iterator()

Specified by:

iterator in interface Iterable<Example<Regressor>>

generateDataset

public static Dataset<Regressor> generateDataset(int numSamples,
 float slope,
 float intercept,
 float variance,
 float xMin,
 float xMax,
 long seed)

Generates a single dimensional output drawn from N(slope*x + intercept,variance).

The single feature is drawn from a uniform distribution over the range.

Set slope to zero to draw from a gaussian.

Parameters:

numSamples - The size of the output dataset.

slope - The slope of the line.

intercept - The y intercept of the line.

variance - The variance of the gaussian.

xMin - The minimum x value (inclusive).

xMax - The maximum x value (exclusive).

seed - The rng seed to use.

Returns:

A dataset drawn from a gaussian.