org.openimaj.ml.timeseries.processor

Class LinearRegressionProcessor

java.lang.Object

org.openimaj.ml.timeseries.processor.LinearRegressionProcessor

All Implemented Interfaces:

TimeSeriesProcessor<double[],Double,DoubleTimeSeries>

public class LinearRegressionProcessor
extends Object
implements TimeSeriesProcessor<double[],Double,DoubleTimeSeries>

Using a LinearRegression model, a time series is used as input to calculate the coefficients of a linear regression such that value = b * time + c This is the simplest kind of model that can be applied to a time series

Author:

Sina Samangooei (ss@ecs.soton.ac.uk)

Constructor Summary

Constructors

Constructor and Description
LinearRegressionProcessor() Calculate the regression from the same time series inputed
LinearRegressionProcessor(LinearRegression reg) Use reg as the linear regression to predict.

Method Summary

Methods

Modifier and Type	Method and Description
LinearRegression	getRegression()
void	holdreg(boolean regdefined)
void	process(DoubleTimeSeries series)

Methods inherited from class java.lang.Object

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

Constructor Detail

LinearRegressionProcessor

public LinearRegressionProcessor()

Calculate the regression from the same time series inputed

LinearRegressionProcessor

public LinearRegressionProcessor(LinearRegression reg)

Use reg as the linear regression to predict. The process(DoubleTimeSeries) function simply calls LinearRegression.predict(Matrix) with the times in the series as input

Parameters:

reg -

Method Detail

process

public void process(DoubleTimeSeries series)

Specified by:

process in interface TimeSeriesProcessor<double[],Double,DoubleTimeSeries>

Parameters:

series - alter this time series in place

holdreg

public void holdreg(boolean regdefined)

Parameters:

regdefined - if true, process holds its last LinearRegression

getRegression

public LinearRegression getRegression()

Returns:

the LinearRegressionProcessor's underlying LinearRegression model