org.apache.commons.math.analysis.interpolation
Class LoessInterpolator

java.lang.Object
  extended by org.apache.commons.math.analysis.interpolation.LoessInterpolator
All Implemented Interfaces:
Serializable, UnivariateRealInterpolator

public class LoessInterpolator
extends Object
implements UnivariateRealInterpolator, Serializable

Implements the Local Regression Algorithm (also Loess, Lowess) for interpolation of real univariate functions.

For reference, see William S. Cleveland - Robust Locally Weighted Regression and Smoothing Scatterplots

This class implements both the loess method and serves as an interpolation adapter to it, allowing to build a spline on the obtained loess fit.

Since:
2.0
Version:
$Revision: 990655 $ $Date: 2010-08-29 23:49:40 +0200 (dim. 29 août 2010) $
See Also:
Serialized Form

Field Summary
static double DEFAULT_ACCURACY
          Default value for accuracy.
static double DEFAULT_BANDWIDTH
          Default value of the bandwidth parameter.
static int DEFAULT_ROBUSTNESS_ITERS
          Default value of the number of robustness iterations.
 
Constructor Summary
LoessInterpolator()
          Constructs a new LoessInterpolator with a bandwidth of DEFAULT_BANDWIDTH, DEFAULT_ROBUSTNESS_ITERS robustness iterations and an accuracy of {#link #DEFAULT_ACCURACY}.
LoessInterpolator(double bandwidth, int robustnessIters)
          Constructs a new LoessInterpolator with given bandwidth and number of robustness iterations.
LoessInterpolator(double bandwidth, int robustnessIters, double accuracy)
          Constructs a new LoessInterpolator with given bandwidth, number of robustness iterations and accuracy.
 
Method Summary
 PolynomialSplineFunction interpolate(double[] xval, double[] yval)
          Compute an interpolating function by performing a loess fit on the data at the original abscissae and then building a cubic spline with a SplineInterpolator on the resulting fit.
 double[] smooth(double[] xval, double[] yval)
          Compute a loess fit on the data at the original abscissae.
 double[] smooth(double[] xval, double[] yval, double[] weights)
          Compute a weighted loess fit on the data at the original abscissae.
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Field Detail

DEFAULT_BANDWIDTH

public static final double DEFAULT_BANDWIDTH
Default value of the bandwidth parameter.

See Also:
Constant Field Values

DEFAULT_ROBUSTNESS_ITERS

public static final int DEFAULT_ROBUSTNESS_ITERS
Default value of the number of robustness iterations.

See Also:
Constant Field Values

DEFAULT_ACCURACY

public static final double DEFAULT_ACCURACY
Default value for accuracy.

Since:
2.1
See Also:
Constant Field Values
Constructor Detail

LoessInterpolator

public LoessInterpolator()
Constructs a new LoessInterpolator with a bandwidth of DEFAULT_BANDWIDTH, DEFAULT_ROBUSTNESS_ITERS robustness iterations and an accuracy of {#link #DEFAULT_ACCURACY}. See LoessInterpolator(double, int, double) for an explanation of the parameters.


LoessInterpolator

public LoessInterpolator(double bandwidth,
                         int robustnessIters)
                  throws MathException
Constructs a new LoessInterpolator with given bandwidth and number of robustness iterations.

Calling this constructor is equivalent to calling {link LoessInterpolator(bandwidth, robustnessIters, LoessInterpolator.DEFAULT_ACCURACY)

Parameters:
bandwidth - when computing the loess fit at a particular point, this fraction of source points closest to the current point is taken into account for computing a least-squares regression.
A sensible value is usually 0.25 to 0.5, the default value is DEFAULT_BANDWIDTH.
robustnessIters - This many robustness iterations are done.
A sensible value is usually 0 (just the initial fit without any robustness iterations) to 4, the default value is DEFAULT_ROBUSTNESS_ITERS.
Throws:
MathException - if bandwidth does not lie in the interval [0,1] or if robustnessIters is negative.
See Also:
LoessInterpolator(double, int, double)

LoessInterpolator

public LoessInterpolator(double bandwidth,
                         int robustnessIters,
                         double accuracy)
                  throws MathException
Constructs a new LoessInterpolator with given bandwidth, number of robustness iterations and accuracy.

Parameters:
bandwidth - when computing the loess fit at a particular point, this fraction of source points closest to the current point is taken into account for computing a least-squares regression.
A sensible value is usually 0.25 to 0.5, the default value is DEFAULT_BANDWIDTH.
robustnessIters - This many robustness iterations are done.
A sensible value is usually 0 (just the initial fit without any robustness iterations) to 4, the default value is DEFAULT_ROBUSTNESS_ITERS.
accuracy - If the median residual at a certain robustness iteration is less than this amount, no more iterations are done.
Throws:
MathException - if bandwidth does not lie in the interval [0,1] or if robustnessIters is negative.
Since:
2.1
See Also:
LoessInterpolator(double, int)
Method Detail

interpolate

public final PolynomialSplineFunction interpolate(double[] xval,
                                                  double[] yval)
                                           throws MathException
Compute an interpolating function by performing a loess fit on the data at the original abscissae and then building a cubic spline with a SplineInterpolator on the resulting fit.

Specified by:
interpolate in interface UnivariateRealInterpolator
Parameters:
xval - the arguments for the interpolation points
yval - the values for the interpolation points
Returns:
A cubic spline built upon a loess fit to the data at the original abscissae
Throws:
MathException - if some of the following conditions are false:
  • Arguments and values are of the same size that is greater than zero
  • The arguments are in a strictly increasing order
  • All arguments and values are finite real numbers

smooth

public final double[] smooth(double[] xval,
                             double[] yval,
                             double[] weights)
                      throws MathException
Compute a weighted loess fit on the data at the original abscissae.

Parameters:
xval - the arguments for the interpolation points
yval - the values for the interpolation points
weights - point weights: coefficients by which the robustness weight of a point is multiplied
Returns:
values of the loess fit at corresponding original abscissae
Throws:
MathException - if some of the following conditions are false:
  • Arguments and values are of the same size that is greater than zero
  • The arguments are in a strictly increasing order
  • All arguments and values are finite real numbers
Since:
2.1

smooth

public final double[] smooth(double[] xval,
                             double[] yval)
                      throws MathException
Compute a loess fit on the data at the original abscissae.

Parameters:
xval - the arguments for the interpolation points
yval - the values for the interpolation points
Returns:
values of the loess fit at corresponding original abscissae
Throws:
MathException - if some of the following conditions are false:
  • Arguments and values are of the same size that is greater than zero
  • The arguments are in a strictly increasing order
  • All arguments and values are finite real numbers


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