Class LoessInterpolator
java.lang.Object
org.apache.commons.math3.analysis.interpolation.LoessInterpolator
- All Implemented Interfaces:
Serializable,UnivariateInterpolator
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 one to build a spline on the obtained loess fit.- Since:
- 2.0
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Field Summary
FieldsModifier and TypeFieldDescriptionstatic final doubleDefault value for accuracy.static final doubleDefault value of the bandwidth parameter.static final intDefault value of the number of robustness iterations. -
Constructor Summary
ConstructorsConstructorDescriptionConstructs a newLoessInterpolatorwith a bandwidth ofDEFAULT_BANDWIDTH,DEFAULT_ROBUSTNESS_ITERSrobustness iterations and an accuracy of {#link #DEFAULT_ACCURACY}.LoessInterpolator(double bandwidth, int robustnessIters) Construct a newLoessInterpolatorwith given bandwidth and number of robustness iterations.LoessInterpolator(double bandwidth, int robustnessIters, double accuracy) Construct a newLoessInterpolatorwith given bandwidth, number of robustness iterations and accuracy. -
Method Summary
Modifier and TypeMethodDescriptionfinal PolynomialSplineFunctioninterpolate(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 aSplineInterpolatoron the resulting fit.final double[]smooth(double[] xval, double[] yval) Compute a loess fit on the data at the original abscissae.final double[]smooth(double[] xval, double[] yval, double[] weights) Compute a weighted loess fit on the data at the original abscissae.
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Field Details
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DEFAULT_BANDWIDTH
public static final double DEFAULT_BANDWIDTHDefault value of the bandwidth parameter.- See Also:
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DEFAULT_ROBUSTNESS_ITERS
public static final int DEFAULT_ROBUSTNESS_ITERSDefault value of the number of robustness iterations.- See Also:
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DEFAULT_ACCURACY
public static final double DEFAULT_ACCURACYDefault value for accuracy.- Since:
- 2.1
- See Also:
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Constructor Details
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LoessInterpolator
public LoessInterpolator()Constructs a newLoessInterpolatorwith a bandwidth ofDEFAULT_BANDWIDTH,DEFAULT_ROBUSTNESS_ITERSrobustness iterations and an accuracy of {#link #DEFAULT_ACCURACY}. SeeLoessInterpolator(double, int, double)for an explanation of the parameters. -
LoessInterpolator
public LoessInterpolator(double bandwidth, int robustnessIters) Construct a newLoessInterpolatorwith 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 isDEFAULT_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 isDEFAULT_ROBUSTNESS_ITERS.- See Also:
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LoessInterpolator
public LoessInterpolator(double bandwidth, int robustnessIters, double accuracy) throws OutOfRangeException, NotPositiveException Construct a newLoessInterpolatorwith 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 isDEFAULT_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 isDEFAULT_ROBUSTNESS_ITERS.accuracy- If the median residual at a certain robustness iteration is less than this amount, no more iterations are done.- Throws:
OutOfRangeException- if bandwidth does not lie in the interval [0,1].NotPositiveException- ifrobustnessItersis negative.- Since:
- 2.1
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Method Details
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interpolate
public final PolynomialSplineFunction interpolate(double[] xval, double[] yval) throws NonMonotonicSequenceException, DimensionMismatchException, NoDataException, NotFiniteNumberException, NumberIsTooSmallException Compute an interpolating function by performing a loess fit on the data at the original abscissae and then building a cubic spline with aSplineInterpolatoron the resulting fit.- Specified by:
interpolatein interfaceUnivariateInterpolator- Parameters:
xval- the arguments for the interpolation pointsyval- the values for the interpolation points- Returns:
- A cubic spline built upon a loess fit to the data at the original abscissae
- Throws:
NonMonotonicSequenceException- ifxvalnot sorted in strictly increasing order.DimensionMismatchException- ifxvalandyvalhave different sizes.NoDataException- ifxvaloryvalhas zero size.NotFiniteNumberException- if any of the arguments and values are not finite real numbers.NumberIsTooSmallException- if the bandwidth is too small to accomodate the size of the input data (i.e. the bandwidth must be larger than 2/n).
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smooth
public final double[] smooth(double[] xval, double[] yval, double[] weights) throws NonMonotonicSequenceException, DimensionMismatchException, NoDataException, NotFiniteNumberException, NumberIsTooSmallException Compute a weighted loess fit on the data at the original abscissae.- Parameters:
xval- Arguments for the interpolation points.yval- Values for the interpolation points.weights- point weights: coefficients by which the robustness weight of a point is multiplied.- Returns:
- the values of the loess fit at corresponding original abscissae.
- Throws:
NonMonotonicSequenceException- ifxvalnot sorted in strictly increasing order.DimensionMismatchException- ifxvalandyvalhave different sizes.NoDataException- ifxvaloryvalhas zero size.NotFiniteNumberException- if any of the arguments and values are not finite real numbers.NumberIsTooSmallException- if the bandwidth is too small to accomodate the size of the input data (i.e. the bandwidth must be larger than 2/n).- Since:
- 2.1
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smooth
public final double[] smooth(double[] xval, double[] yval) throws NonMonotonicSequenceException, DimensionMismatchException, NoDataException, NotFiniteNumberException, NumberIsTooSmallException Compute a loess fit on the data at the original abscissae.- Parameters:
xval- the arguments for the interpolation pointsyval- the values for the interpolation points- Returns:
- values of the loess fit at corresponding original abscissae
- Throws:
NonMonotonicSequenceException- ifxvalnot sorted in strictly increasing order.DimensionMismatchException- ifxvalandyvalhave different sizes.NoDataException- ifxvaloryvalhas zero size.NotFiniteNumberException- if any of the arguments and values are not finite real numbers.NumberIsTooSmallException- if the bandwidth is too small to accomodate the size of the input data (i.e. the bandwidth must be larger than 2/n).
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