ubic.basecode.math

Class Smooth

java.lang.Object

ubic.basecode.math.Smooth

public class Smooth
extends Object

Methods for moving averages, loess

Author:

paul, ptan

Constructor Summary

Constructors

Constructor and Description
Smooth()

Method Summary

Modifier and Type	Method and Description
static double[]	interpolate(double[] x, double[] y, double[] xInterpolate) Linearlly interpolate values from a given data set Similar implementation of R's stats.approxfun(..., rule = 2) where values outside the interval ['min(x)', 'max(x)'] get the value at the closest data extreme.
static cern.colt.matrix.DoubleMatrix2D	loessFit(cern.colt.matrix.DoubleMatrix2D xy)
static cern.colt.matrix.DoubleMatrix2D	loessFit(cern.colt.matrix.DoubleMatrix2D xy, double bandwidth) Computes a loess regression line to fit the data
static cern.colt.matrix.DoubleMatrix1D	movingAverage(cern.colt.matrix.DoubleMatrix1D m, int windowSize) Simple moving average that sums the points "backwards".

Methods inherited from class java.lang.Object

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

Constructor Detail

Smooth

public Smooth()

Method Detail

movingAverage

public static cern.colt.matrix.DoubleMatrix1D movingAverage(cern.colt.matrix.DoubleMatrix1D m,
                                                            int windowSize)

Simple moving average that sums the points "backwards".

Parameters:

m -

windowSize -

Returns:

loessFit

public static cern.colt.matrix.DoubleMatrix2D loessFit(cern.colt.matrix.DoubleMatrix2D xy)

Parameters:

xy -

Returns:

loessFit with default bandwitdh

loessFit

public static cern.colt.matrix.DoubleMatrix2D loessFit(cern.colt.matrix.DoubleMatrix2D xy,
                                                       double bandwidth)

Computes a loess regression line to fit the data

Parameters:

xy - data to be fit

bandwidth - the span of the smoother (from 2/n to 1 where n is the number of points in xy)

Returns:

loessFit (same dimensions as xy) or null if there are less than 3 data points

interpolate

public static double[] interpolate(double[] x,
                                   double[] y,
                                   double[] xInterpolate)

Linearlly interpolate values from a given data set Similar implementation of R's stats.approxfun(..., rule = 2) where values outside the interval ['min(x)', 'max(x)'] get the value at the closest data extreme. Also performs sorting based on xTrain.

Parameters:

x - the training set of x values

y - the training set of y values

xInterpolate - the set of x values to interpolate

Returns:

yInterpolate the interpolated set of y values