Apache Commons Math 3.6.1 · org.apache.commons.math3.analysis.interpolation.LoessInterpolator

Modifier and Type	Field and Description
private final double	accuracy If the median residual at a certain robustness iteration is less than this amount, no more iterations are done.
private final double	bandwidth The bandwidth parameter: 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.
public static final double	DEFAULT_ACCURACY Default value for accuracy.
public static final double	DEFAULT_BANDWIDTH Default value of the bandwidth parameter.
public static final int	DEFAULT_ROBUSTNESS_ITERS Default value of the number of robustness iterations.
private final int	robustnessIters The number of robustness iterations parameter: this many robustness iterations are done.
private static final long	serialVersionUID serializable version identifier.

Constructor Summary

Access	Constructor and Description
public	LoessInterpolator() Constructs a new `LoessInterpolator` with a bandwidth of `DEFAULT_BANDWIDTH`, `DEFAULT_ROBUSTNESS_ITERS` robustness iterations and an accuracy of {#link #DEFAULT_ACCURACY}.
public	LoessInterpolator(double 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`. bandwidth, int 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`. robustnessIters) Construct a new `LoessInterpolator` with given bandwidth and number of robustness iterations.
public	LoessInterpolator(double 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`. bandwidth, int 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`. robustnessIters, double If the median residual at a certain robustness iteration is less than this amount, no more iterations are done. accuracy) Construct a new `LoessInterpolator` with given bandwidth, number of robustness iterations and accuracy.

Method Summary

Modifier and Type	Method and Description
private static void	checkAllFiniteReal(final double[] Values array. values) Check that all elements of an array are finite real numbers.
public final PolynomialSplineFunction	Returns: A cubic spline built upon a loess fit to the data at the original abscissae interpolate(final double[] the arguments for the interpolation points xval, final double[] the values for the interpolation points yval) Implements org.apache.commons.math3.analysis.interpolation.UnivariateInterpolator.interpolate. Deprecated as a consequence of `PolynomialSplineFunction` being deprecated. Compute an interpolating function by performing a loess fit on the data at the original abscissae and then building a cubic spline with a `org.apache.commons.math3.analysis.interpolation.SplineInterpolator` on the resulting fit.
private static int	Returns: the smallest compliant index. nextNonzero(final double[] Weights array. weights, final int Index from which to start search. i) Return the smallest index `j` such that `j > i && (j == weights.length \|\| weights[j] != 0)`.
public final double[]	Returns: the values of the loess fit at corresponding original abscissae. smooth(final double[] Arguments for the interpolation points. xval, final double[] Values for the interpolation points. yval, final double[] point weights: coefficients by which the robustness weight of a point is multiplied. weights) Compute a weighted loess fit on the data at the original abscissae.
public final double[]	Returns: values of the loess fit at corresponding original abscissae smooth(final double[] the arguments for the interpolation points xval, final double[] the values for the interpolation points yval) Compute a loess fit on the data at the original abscissae.
private static double	Returns: `(1 - \|x\|³)³` for \|x\| < 1, 0 otherwise. tricube(final double Argument. x) Compute the tricube weight function
private static void	updateBandwidthInterval(final double[] Arguments array. xval, final double[] Weights array. weights, final int Index around which the new interval should be computed. i, final int[] a two-element array {left, right} such that: `(left==0 or xval[i] - xval[left-1] > xval[right] - xval[i])` and `(right==xval.length-1 or xval[right+1] - xval[i] > xval[i] - xval[left])`. The array will be updated. bandwidthInterval) Given an index interval into xval that embraces a certain number of points closest to `xval[i-1]`, update the interval so that it embraces the same number of points closest to `xval[i]`, ignoring zero weights.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Field Detail

accuracy	back to summary
private final double accuracy If the median residual at a certain robustness iteration is less than this amount, no more iterations are done.

bandwidth	back to summary
private final double bandwidth The bandwidth parameter: 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.

bandwidth

back to summary

private final double bandwidth

The bandwidth parameter: 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.

DEFAULT_ACCURACY	back to summary
public static final double DEFAULT_ACCURACY Default value for accuracy. Since 2.1

DEFAULT_ACCURACY

back to summary

public static final double DEFAULT_ACCURACY

Default value for accuracy.

Since: 2.1

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

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

robustnessIters	back to summary
private final int robustnessIters The number of robustness iterations parameter: this many robustness iterations are done. A sensible value is usually 0 (just the initial fit without any robustness iterations) to 4.

robustnessIters

back to summary

private final int robustnessIters

The number of robustness iterations parameter: this many robustness iterations are done.

A sensible value is usually 0 (just the initial fit without any robustness iterations) to 4.

serialVersionUID	back to summary
private static final long serialVersionUID serializable version identifier.

Constructor Detail

LoessInterpolator	back to summary
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 back to summary

LoessInterpolator	back to summary
public LoessInterpolator(double bandwidth, int robustnessIters) Construct 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:double 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:int 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`. See Also `LoessInterpolator(double, int, double)`

public LoessInterpolator(double bandwidth, int robustnessIters)

Construct 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:double: 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:int: 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.

LoessInterpolator back to summary

LoessInterpolator	back to summary
public LoessInterpolator(double bandwidth, int robustnessIters, double accuracy) throws OutOfRangeException, NotPositiveException Construct a new `LoessInterpolator` with given bandwidth, number of robustness iterations and accuracy. Parameters bandwidth:double 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:int 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:double If the median residual at a certain robustness iteration is less than this amount, no more iterations are done. Exceptions OutOfRangeException: if bandwidth does not lie in the interval [0,1]. NotPositiveException: if `robustnessIters` is negative. Since 2.1 See Also `LoessInterpolator(double, int)`

public LoessInterpolator(double bandwidth, int robustnessIters, double accuracy) throws OutOfRangeException, NotPositiveException

Construct a new LoessInterpolator with given bandwidth, number of robustness iterations and accuracy.

Parameters

bandwidth:double: 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:int: 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:double: If the median residual at a certain robustness iteration is less than this amount, no more iterations are done.

Exceptions

OutOfRangeException:: if bandwidth does not lie in the interval [0,1].
NotPositiveException:: if robustnessIters is negative.

Since

2.1

See Also

LoessInterpolator(double, int)

Method Detail

checkAllFiniteReal	back to summary
private static void checkAllFiniteReal(final double[] values) Check that all elements of an array are finite real numbers. Parameters values:double[] Values array. Exceptions NotFiniteNumberException: if one of the values is not a finite real number.

checkAllFiniteReal

back to summary

private static void checkAllFiniteReal(final double[] values)

Check that all elements of an array are finite real numbers.

Parameters

values:double[]: Values array.

Exceptions

NotFiniteNumberException:: if one of the values is not a finite real number.

interpolate back to summary

interpolate	back to summary
public final PolynomialSplineFunction interpolate(final double[] xval, final double[] yval) throws NonMonotonicSequenceException, DimensionMismatchException, NoDataException, NotFiniteNumberException, NumberIsTooSmallException Implements org.apache.commons.math3.analysis.interpolation.UnivariateInterpolator.interpolate. Deprecated as a consequence of `PolynomialSplineFunction` being deprecated. See corresponding docs for further information. Compute an interpolating function by performing a loess fit on the data at the original abscissae and then building a cubic spline with a `org.apache.commons.math3.analysis.interpolation.SplineInterpolator` on the resulting fit. Parameters xval:double[] the arguments for the interpolation points yval:double[] the values for the interpolation points Returns:PolynomialSplineFunction A cubic spline built upon a loess fit to the data at the original abscissae Exceptions NonMonotonicSequenceException: if `xval` not sorted in strictly increasing order. DimensionMismatchException: if `xval` and `yval` have different sizes. NoDataException: if `xval` or `yval` has 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).

public final PolynomialSplineFunction interpolate(final double[] xval, final double[] yval) throws NonMonotonicSequenceException, DimensionMismatchException, NoDataException, NotFiniteNumberException, NumberIsTooSmallException

Implements org.apache.commons.math3.analysis.interpolation.UnivariateInterpolator.interpolate.

Deprecated

as a consequence of PolynomialSplineFunction being deprecated. See corresponding docs for further information.

Compute an interpolating function by performing a loess fit on the data at the original abscissae and then building a cubic spline with a org.apache.commons.math3.analysis.interpolation.SplineInterpolator on the resulting fit.

Parameters

xval:double[]: the arguments for the interpolation points
yval:double[]: the values for the interpolation points

Returns:PolynomialSplineFunction

A cubic spline built upon a loess fit to the data at the original abscissae

Exceptions

NonMonotonicSequenceException:: if xval not sorted in strictly increasing order.
DimensionMismatchException:: if xval and yval have different sizes.
NoDataException:: if xval or yval has 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).

nextNonzero back to summary

nextNonzero	back to summary
private static int nextNonzero(final double[] weights, final int i) Return the smallest index `j` such that `j > i && (j == weights.length \|\| weights[j] != 0)`. Parameters weights:double[] Weights array. i:int Index from which to start search. Returns:int the smallest compliant index.

private static int nextNonzero(final double[] weights, final int i)

Return the smallest index j such that j > i && (j == weights.length || weights[j] != 0).

Parameters

weights:double[]: Weights array.
i:int: Index from which to start search.

Returns:int

the smallest compliant index.

smooth back to summary

smooth	back to summary
public final double[] smooth(final double[] xval, final double[] yval, final double[] weights) throws NonMonotonicSequenceException, DimensionMismatchException, NoDataException, NotFiniteNumberException, NumberIsTooSmallException Compute a weighted loess fit on the data at the original abscissae. Parameters xval:double[] Arguments for the interpolation points. yval:double[] Values for the interpolation points. weights:double[] point weights: coefficients by which the robustness weight of a point is multiplied. Returns:double[] the values of the loess fit at corresponding original abscissae. Exceptions NonMonotonicSequenceException: if `xval` not sorted in strictly increasing order. DimensionMismatchException: if `xval` and `yval` have different sizes. NoDataException: if `xval` or `yval` has 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

public final double[] smooth(final double[] xval, final double[] yval, final double[] weights) throws NonMonotonicSequenceException, DimensionMismatchException, NoDataException, NotFiniteNumberException, NumberIsTooSmallException

Compute a weighted loess fit on the data at the original abscissae.

Parameters

xval:double[]: Arguments for the interpolation points.
yval:double[]: Values for the interpolation points.
weights:double[]: point weights: coefficients by which the robustness weight of a point is multiplied.

Returns:double[]

the values of the loess fit at corresponding original abscissae.

Exceptions

NonMonotonicSequenceException:: if xval not sorted in strictly increasing order.
DimensionMismatchException:: if xval and yval have different sizes.
NoDataException:: if xval or yval has 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

smooth back to summary

smooth	back to summary
public final double[] smooth(final double[] xval, final double[] yval) throws NonMonotonicSequenceException, DimensionMismatchException, NoDataException, NotFiniteNumberException, NumberIsTooSmallException Compute a loess fit on the data at the original abscissae. Parameters xval:double[] the arguments for the interpolation points yval:double[] the values for the interpolation points Returns:double[] values of the loess fit at corresponding original abscissae Exceptions NonMonotonicSequenceException: if `xval` not sorted in strictly increasing order. DimensionMismatchException: if `xval` and `yval` have different sizes. NoDataException: if `xval` or `yval` has 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).

public final double[] smooth(final double[] xval, final double[] yval) throws NonMonotonicSequenceException, DimensionMismatchException, NoDataException, NotFiniteNumberException, NumberIsTooSmallException

Compute a loess fit on the data at the original abscissae.

Parameters

xval:double[]: the arguments for the interpolation points
yval:double[]: the values for the interpolation points

Returns:double[]

values of the loess fit at corresponding original abscissae

Exceptions

NonMonotonicSequenceException:: if xval not sorted in strictly increasing order.
DimensionMismatchException:: if xval and yval have different sizes.
NoDataException:: if xval or yval has 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).

tricube back to summary

tricube	back to summary
private static double tricube(final double x) Compute the tricube weight function Parameters x:double Argument. Returns:double `(1 - \|x\|³)³` for \|x\| < 1, 0 otherwise.

private static double tricube(final double x)

Compute the tricube weight function

Parameters

x:double: Argument.

Returns:double

(1 - |x|³)³ for |x| < 1, 0 otherwise.

updateBandwidthInterval back to summary

updateBandwidthInterval	back to summary
private static void updateBandwidthInterval(final double[] xval, final double[] weights, final int i, final int[] bandwidthInterval) Given an index interval into xval that embraces a certain number of points closest to `xval[i-1]`, update the interval so that it embraces the same number of points closest to `xval[i]`, ignoring zero weights. Parameters xval:double[] Arguments array. weights:double[] Weights array. i:int Index around which the new interval should be computed. bandwidthInterval:int[] a two-element array {left, right} such that: `(left==0 or xval[i] - xval[left-1] > xval[right] - xval[i])` and `(right==xval.length-1 or xval[right+1] - xval[i] > xval[i] - xval[left])`. The array will be updated.

private static void updateBandwidthInterval(final double[] xval, final double[] weights, final int i, final int[] bandwidthInterval)

Given an index interval into xval that embraces a certain number of points closest to xval[i-1], update the interval so that it embraces the same number of points closest to xval[i], ignoring zero weights.

Parameters

xval:double[]: Arguments array.
weights:double[]: Weights array.
i:int: Index around which the new interval should be computed.
bandwidthInterval:int[]: a two-element array {left, right} such that: (left==0 or xval[i] - xval[left-1] > xval[right] - xval[i]) and (right==xval.length-1 or xval[right+1] - xval[i] > xval[i] - xval[left]). The array will be updated.

public Class LoessInterpolator

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Method Summary

Field Detail

Constructor Detail

Method Detail