org.tribuo.regression.example

Class NonlinearGaussianDataSource

java.lang.Object

org.tribuo.regression.example.NonlinearGaussianDataSource

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 NonlinearGaussianDataSource
extends Object
implements ConfigurableDataSource<Regressor>

Generates a single dimensional output drawn from N(w_0*x_0 + w_1*x_1 + w_2*x_1*x_0 + w_3*x_1*x_1*x_1 + intercept,variance).

The features are drawn from a uniform distribution over the range.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	NonlinearGaussianDataSource.NonlinearGaussianDataSourceProvenance Provenance for NonlinearGaussianDataSource.

Constructor Summary

Constructors

Constructor and Description
NonlinearGaussianDataSource(int numSamples, float[] weights, float intercept, float variance, float xZeroMin, float xZeroMax, float xOneMin, float xOneMax, long seed) Generates a single dimensional output drawn from N(w_0*x_0 + w_1*x_1 + w_2*x_1*x_0 + w_3*x_1*x_1*x_1 + intercept,variance).

Method Summary

Modifier and Type	Method and Description
static Dataset<Regressor>	generateDataset(int numSamples, float[] weights, float intercept, float variance, float xZeroMin, float xZeroMax, float xOneMin, float xOneMax, long seed) Generates a single dimensional output drawn from N(w_0*x_0 + w_1*x_1 + w_2*x_1*x_0 + w_3*x_1*x_1*x_1 + 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 Detail

NonlinearGaussianDataSource

public NonlinearGaussianDataSource(int numSamples,
                                   float[] weights,
                                   float intercept,
                                   float variance,
                                   float xZeroMin,
                                   float xZeroMax,
                                   float xOneMin,
                                   float xOneMax,
                                   long seed)

Generates a single dimensional output drawn from N(w_0*x_0 + w_1*x_1 + w_2*x_1*x_0 + w_3*x_1*x_1*x_1 + intercept,variance).

The features are drawn from a uniform distribution over the range.

Parameters:

numSamples - The size of the output dataset.

weights - The feature weights.

intercept - The y intercept of the line.

variance - The variance of the gaussian.

xZeroMin - The minimum x_0 value (inclusive).

xZeroMax - The maximum x_0 value (exclusive).

xOneMin - The minimum x_1 value (inclusive).

xOneMax - The maximum x_1 value (exclusive).

seed - The rng seed to use.

Method Detail

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[] weights,
                                                 float intercept,
                                                 float variance,
                                                 float xZeroMin,
                                                 float xZeroMax,
                                                 float xOneMin,
                                                 float xOneMax,
                                                 long seed)

Generates a single dimensional output drawn from N(w_0*x_0 + w_1*x_1 + w_2*x_1*x_0 + w_3*x_1*x_1*x_1 + intercept,variance).

The features are drawn from a uniform distribution over the range.

Parameters:

numSamples - The size of the output dataset.

weights - The feature weights.

intercept - The y intercept of the line.

variance - The variance of the gaussian.

xZeroMin - The minimum x_0 value (inclusive).

xZeroMax - The maximum x_0 value (exclusive).

xOneMin - The minimum x_1 value (inclusive).

xOneMax - The maximum x_1 value (exclusive).

seed - The rng seed to use.

Returns:

A dataset drawn from a gaussian.