Uses of Class
org.apache.commons.math3.exception.NoDataException
Packages that use NoDataException
Package
Description
The
function package contains function objects that wrap the
methods contained in Math, as well as common
mathematical functions such as the gaussian and sinc functions.Univariate real functions interpolation algorithms.
Univariate real polynomials implementations, seen as differentiable
univariate real functions.
Root finding algorithms, for univariate real functions.
Complex number type and implementations of complex transcendental
functions.
Implementations of common discrete-time linear filters.
Linear algebra support.
Data storage, manipulation and summary routines.
Classes providing hypothesis testing.
Statistical routines involving multivariate data.
Convenience routines and common data structures used throughout the commons-math library.
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Uses of NoDataException in org.apache.commons.math3.analysis.function
Constructors in org.apache.commons.math3.analysis.function that throw NoDataExceptionModifierConstructorDescriptionStepFunction(double[] x, double[] y) Builds a step function from a list of arguments and the corresponding values. -
Uses of NoDataException in org.apache.commons.math3.analysis.interpolation
Methods in org.apache.commons.math3.analysis.interpolation that throw NoDataExceptionModifier and TypeMethodDescriptionT[][]FieldHermiteInterpolator.derivatives(T x, int order) Interpolate value and first derivatives at a specified abscissa.HermiteInterpolator.getPolynomials()Compute the interpolation polynomials.BicubicInterpolator.interpolate(double[] xval, double[] yval, double[][] fval) Compute an interpolating function for the dataset.BicubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[][] fval) Deprecated.Compute an interpolating function for the dataset.BivariateGridInterpolator.interpolate(double[] xval, double[] yval, double[][] fval) Compute an interpolating function for the dataset.final PolynomialSplineFunctionLoessInterpolator.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 aSplineInterpolatoron the resulting fit.MicrosphereInterpolator.interpolate(double[][] xval, double[] yval) Deprecated.Computes an interpolating function for the data set.MicrosphereProjectionInterpolator.interpolate(double[][] xval, double[] yval) Computes an interpolating function for the data set.MultivariateInterpolator.interpolate(double[][] xval, double[] yval) Computes an interpolating function for the data set.PiecewiseBicubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[][] fval) Compute an interpolating function for the dataset.SmoothingPolynomialBicubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[][] fval) Deprecated.Compute an interpolating function for the dataset.TricubicInterpolator.interpolate(double[] xval, double[] yval, double[] zval, double[][][] fval) Compute an interpolating function for the dataset.TricubicSplineInterpolator.interpolate(double[] xval, double[] yval, double[] zval, double[][][] fval) Deprecated.Compute an interpolating function for the dataset.TrivariateGridInterpolator.interpolate(double[] xval, double[] yval, double[] zval, double[][][] fval) Compute an interpolating function for the dataset.final double[]LoessInterpolator.smooth(double[] xval, double[] yval) Compute a loess fit on the data at the original abscissae.final double[]LoessInterpolator.smooth(double[] xval, double[] yval, double[] weights) Compute a weighted loess fit on the data at the original abscissae.T[]Interpolate value at a specified abscissa.double[]HermiteInterpolator.value(double x) Interpolate value at a specified abscissa.HermiteInterpolator.value(DerivativeStructure x) Interpolate value at a specified abscissa.Constructors in org.apache.commons.math3.analysis.interpolation that throw NoDataExceptionModifierConstructorDescriptionBicubicInterpolatingFunction(double[] x, double[] y, double[][] f, double[][] dFdX, double[][] dFdY, double[][] d2FdXdY) BicubicSplineInterpolatingFunction(double[] x, double[] y, double[][] f, double[][] dFdX, double[][] dFdY, double[][] d2FdXdY) Deprecated.BicubicSplineInterpolatingFunction(double[] x, double[] y, double[][] f, double[][] dFdX, double[][] dFdY, double[][] d2FdXdY, boolean initializeDerivatives) Deprecated.MicrosphereInterpolatingFunction(double[][] xval, double[] yval, int brightnessExponent, int microsphereElements, UnitSphereRandomVectorGenerator rand) Deprecated.PiecewiseBicubicSplineInterpolatingFunction(double[] x, double[] y, double[][] f) TricubicInterpolatingFunction(double[] x, double[] y, double[] z, double[][][] f, double[][][] dFdX, double[][][] dFdY, double[][][] dFdZ, double[][][] d2FdXdY, double[][][] d2FdXdZ, double[][][] d2FdYdZ, double[][][] d3FdXdYdZ) TricubicSplineInterpolatingFunction(double[] x, double[] y, double[] z, double[][][] f, double[][][] dFdX, double[][][] dFdY, double[][][] dFdZ, double[][][] d2FdXdY, double[][][] d2FdXdZ, double[][][] d2FdYdZ, double[][][] d3FdXdYdZ) Deprecated. -
Uses of NoDataException in org.apache.commons.math3.analysis.polynomials
Methods in org.apache.commons.math3.analysis.polynomials that throw NoDataExceptionModifier and TypeMethodDescriptionprotected static double[]PolynomialFunction.differentiate(double[] coefficients) Returns the coefficients of the derivative of the polynomial with the given coefficients.protected static doublePolynomialFunction.evaluate(double[] coefficients, double argument) Uses Horner's Method to evaluate the polynomial with the given coefficients at the argument.static doublePolynomialFunctionNewtonForm.evaluate(double[] a, double[] c, double z) Evaluate the Newton polynomial using nested multiplication.doublePolynomialFunction.Parametric.value(double x, double... parameters) Compute the value of the function.PolynomialFunction.value(DerivativeStructure t) Simple mathematical function.protected static voidPolynomialFunctionNewtonForm.verifyInputArray(double[] a, double[] c) Verifies that the input arrays are valid.Constructors in org.apache.commons.math3.analysis.polynomials that throw NoDataExceptionModifierConstructorDescriptionPolynomialFunction(double[] c) Construct a polynomial with the given coefficients.PolynomialFunctionNewtonForm(double[] a, double[] c) Construct a Newton polynomial with the given a[] and c[]. -
Uses of NoDataException in org.apache.commons.math3.analysis.solvers
Methods in org.apache.commons.math3.analysis.solvers that throw NoDataExceptionModifier and TypeMethodDescriptionComplex[]LaguerreSolver.solveAllComplex(double[] coefficients, double initial) Find all complex roots for the polynomial with the given coefficients, starting from the given initial value.Complex[]LaguerreSolver.solveAllComplex(double[] coefficients, double initial, int maxEval) Find all complex roots for the polynomial with the given coefficients, starting from the given initial value.LaguerreSolver.solveComplex(double[] coefficients, double initial) Find a complex root for the polynomial with the given coefficients, starting from the given initial value.LaguerreSolver.solveComplex(double[] coefficients, double initial, int maxEval) Find a complex root for the polynomial with the given coefficients, starting from the given initial value. -
Uses of NoDataException in org.apache.commons.math3.complex
Methods in org.apache.commons.math3.complex that throw NoDataExceptionModifier and TypeMethodDescriptionstatic ComplexFormatComplexFormat.getInstance(String imaginaryCharacter, Locale locale) Returns the default complex format for the given locale.Constructors in org.apache.commons.math3.complex that throw NoDataExceptionModifierConstructorDescriptionComplexFormat(String imaginaryCharacter) Create an instance with a custom imaginary character, and the default number format for both real and imaginary parts.ComplexFormat(String imaginaryCharacter, NumberFormat format) Create an instance with a custom imaginary character, and a custom number format for both real and imaginary parts.ComplexFormat(String imaginaryCharacter, NumberFormat realFormat, NumberFormat imaginaryFormat) Create an instance with a custom imaginary character, a custom number format for the real part, and a custom number format for the imaginary part. -
Uses of NoDataException in org.apache.commons.math3.filter
Constructors in org.apache.commons.math3.filter that throw NoDataExceptionModifierConstructorDescriptionDefaultMeasurementModel(double[][] measMatrix, double[][] measNoise) Create a newMeasurementModel, taking double arrays as input parameters for the respective measurement matrix and noise.DefaultProcessModel(double[][] stateTransition, double[][] control, double[][] processNoise) Create a newProcessModel, taking double arrays as input parameters.DefaultProcessModel(double[][] stateTransition, double[][] control, double[][] processNoise, double[] initialStateEstimate, double[][] initialErrorCovariance) Create a newProcessModel, taking double arrays as input parameters. -
Uses of NoDataException in org.apache.commons.math3.linear
Methods in org.apache.commons.math3.linear that throw NoDataExceptionModifier and TypeMethodDescriptionprotected voidAbstractFieldMatrix.checkSubMatrixIndex(int[] selectedRows, int[] selectedColumns) Check if submatrix ranges indices are valid.static voidMatrixUtils.checkSubMatrixIndex(AnyMatrix m, int[] selectedRows, int[] selectedColumns) Check if submatrix ranges indices are valid.voidAbstractFieldMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, T[][] destination) Copy a submatrix.voidAbstractRealMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, double[][] destination) Copy a submatrix.voidFieldMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, T[][] destination) Copy a submatrix.voidRealMatrix.copySubMatrix(int[] selectedRows, int[] selectedColumns, double[][] destination) Copy a submatrix.static <T extends FieldElement<T>>
FieldMatrix<T> MatrixUtils.createColumnFieldMatrix(T[] columnData) Creates a columnFieldMatrixusing the data from the input array.static RealMatrixMatrixUtils.createColumnRealMatrix(double[] columnData) Creates a columnRealMatrixusing the data from the input array.static <T extends FieldElement<T>>
FieldMatrix<T> MatrixUtils.createFieldMatrix(T[][] data) Returns aFieldMatrixwhose entries are the the values in the the input array.static <T extends FieldElement<T>>
FieldVector<T> MatrixUtils.createFieldVector(T[] data) Creates aFieldVectorusing the data from the input array.static RealMatrixMatrixUtils.createRealMatrix(double[][] data) Returns aRealMatrixwhose entries are the the values in the the input array.static RealVectorMatrixUtils.createRealVector(double[] data) Creates aRealVectorusing the data from the input array.static <T extends FieldElement<T>>
FieldMatrix<T> MatrixUtils.createRowFieldMatrix(T[] rowData) Create a rowFieldMatrixusing the data from the input array.static RealMatrixMatrixUtils.createRowRealMatrix(double[] rowData) Create a rowRealMatrixusing the data from the input array.protected static <T extends FieldElement<T>>
Field<T> AbstractFieldMatrix.extractField(T[] d) Get the elements type from an array.protected static <T extends FieldElement<T>>
Field<T> AbstractFieldMatrix.extractField(T[][] d) Get the elements type from an array.AbstractFieldMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns) Get a submatrix.AbstractRealMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns) Gets a submatrix.FieldMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns) Get a submatrix.RealMatrix.getSubMatrix(int[] selectedRows, int[] selectedColumns) Gets a submatrix.voidAbstractFieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column) Replace the submatrix starting at(row, column)using data in the inputsubMatrixarray.voidAbstractRealMatrix.setSubMatrix(double[][] subMatrix, int row, int column) Replace the submatrix starting atrow, columnusing data in the inputsubMatrixarray.voidArray2DRowFieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column) Replace the submatrix starting at(row, column)using data in the inputsubMatrixarray.voidArray2DRowRealMatrix.setSubMatrix(double[][] subMatrix, int row, int column) Replace the submatrix starting atrow, columnusing data in the inputsubMatrixarray.voidBlockFieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column) Replace the submatrix starting at(row, column)using data in the inputsubMatrixarray.voidBlockRealMatrix.setSubMatrix(double[][] subMatrix, int row, int column) Replace the submatrix starting atrow, columnusing data in the inputsubMatrixarray.voidFieldMatrix.setSubMatrix(T[][] subMatrix, int row, int column) Replace the submatrix starting at(row, column)using data in the inputsubMatrixarray.voidRealMatrix.setSubMatrix(double[][] subMatrix, int row, int column) Replace the submatrix starting atrow, columnusing data in the inputsubMatrixarray.Constructors in org.apache.commons.math3.linear that throw NoDataExceptionModifierConstructorDescriptionArray2DRowFieldMatrix(Field<T> field, T[][] d) Create a newFieldMatrix<T>using the input array as the underlying data array.Array2DRowFieldMatrix(Field<T> field, T[][] d, boolean copyArray) Create a newFieldMatrix<T>using the input array as the underlying data array.Array2DRowFieldMatrix(T[] v) Create a new (column)FieldMatrix<T>usingvas the data for the unique column of the created matrix.Array2DRowFieldMatrix(T[][] d) Create a newFieldMatrix<T>using the input array as the underlying data array.Array2DRowFieldMatrix(T[][] d, boolean copyArray) Create a newFieldMatrix<T>using the input array as the underlying data array.Array2DRowRealMatrix(double[][] d) Create a newRealMatrixusing the input array as the underlying data array.Array2DRowRealMatrix(double[][] d, boolean copyArray) Create a new RealMatrix using the input array as the underlying data array. -
Uses of NoDataException in org.apache.commons.math3.stat
Methods in org.apache.commons.math3.stat that throw NoDataExceptionModifier and TypeMethodDescriptionstatic doubleStatUtils.meanDifference(double[] sample1, double[] sample2) Returns the mean of the (signed) differences between corresponding elements of the input arrays -- i.e., sum(sample1[i] - sample2[i]) / sample1.length.static doubleStatUtils.sumDifference(double[] sample1, double[] sample2) Returns the sum of the (signed) differences between corresponding elements of the input arrays -- i.e., sum(sample1[i] - sample2[i]). -
Uses of NoDataException in org.apache.commons.math3.stat.inference
Methods in org.apache.commons.math3.stat.inference that throw NoDataExceptionModifier and TypeMethodDescriptiondoubleMannWhitneyUTest.mannWhitneyU(double[] x, double[] y) Computes the Mann-Whitney U statistic comparing mean for two independent samples possibly of different length.doubleMannWhitneyUTest.mannWhitneyUTest(double[] x, double[] y) Returns the asymptotic observed significance level, or p-value, associated with a Mann-Whitney U statistic comparing mean for two independent samples.static doubleTestUtils.pairedT(double[] sample1, double[] sample2) doubleTTest.pairedT(double[] sample1, double[] sample2) Computes a paired, 2-sample t-statistic based on the data in the input arrays.static doubleTestUtils.pairedTTest(double[] sample1, double[] sample2) static booleanTestUtils.pairedTTest(double[] sample1, double[] sample2, double alpha) doubleTTest.pairedTTest(double[] sample1, double[] sample2) Returns the observed significance level, or p-value, associated with a paired, two-sample, two-tailed t-test based on the data in the input arrays.booleanTTest.pairedTTest(double[] sample1, double[] sample2, double alpha) Performs a paired t-test evaluating the null hypothesis that the mean of the paired differences betweensample1andsample2is 0 in favor of the two-sided alternative that the mean paired difference is not equal to 0, with significance levelalpha.doubleWilcoxonSignedRankTest.wilcoxonSignedRank(double[] x, double[] y) Computes the Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample.doubleWilcoxonSignedRankTest.wilcoxonSignedRankTest(double[] x, double[] y, boolean exactPValue) Returns the observed significance level, or p-value, associated with a Wilcoxon signed ranked statistic comparing mean for two related samples or repeated measurements on a single sample. -
Uses of NoDataException in org.apache.commons.math3.stat.regression
Methods in org.apache.commons.math3.stat.regression that throw NoDataException -
Uses of NoDataException in org.apache.commons.math3.util
Methods in org.apache.commons.math3.util that throw NoDataExceptionModifier and TypeMethodDescriptionstatic double[]MathArrays.convolve(double[] x, double[] h) Calculates the convolution between two sequences.