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java.lang.Objectorg.apache.commons.math.analysis.interpolation.LoessInterpolator
public class LoessInterpolator
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.
Field Summary | |
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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 | |
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LoessInterpolator()
Constructs a new LoessInterpolator
with a bandwidth of DEFAULT_BANDWIDTH and
DEFAULT_ROBUSTNESS_ITERS robustness iterations. |
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LoessInterpolator(double bandwidth,
int robustnessIters)
Constructs a new LoessInterpolator
with given bandwidth and number of robustness iterations. |
Method Summary | |
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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. |
Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
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public static final double DEFAULT_BANDWIDTH
public static final int DEFAULT_ROBUSTNESS_ITERS
Constructor Detail |
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public LoessInterpolator()
LoessInterpolator
with a bandwidth of DEFAULT_BANDWIDTH
and
DEFAULT_ROBUSTNESS_ITERS
robustness iterations.
See LoessInterpolator(double, int)
for an explanation of
the parameters.
public LoessInterpolator(double bandwidth, int robustnessIters) throws MathException
LoessInterpolator
with given bandwidth and number of robustness iterations.
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
.
MathException
- if bandwidth does not lie in the interval [0,1]
or if robustnessIters is negative.Method Detail |
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public final PolynomialSplineFunction interpolate(double[] xval, double[] yval) throws MathException
SplineInterpolator
on the resulting fit.
interpolate
in interface UnivariateRealInterpolator
xval
- the arguments for the interpolation pointsyval
- the values for the interpolation points
MathException
- if some of the following conditions are false:
public final double[] smooth(double[] xval, double[] yval) throws MathException
xval
- the arguments for the interpolation pointsyval
- the values for the interpolation points
MathException
- if some of the following conditions are false:
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