Uses of Class
org.apache.commons.math3.exception.DimensionMismatchException
Packages that use DimensionMismatchException
Package
Description
Common classes used throughout the commons-math library.
This package holds the main interfaces and basic building block classes
dealing with differentiation.
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.Gauss family of quadrature schemes.
Univariate real functions interpolation algorithms.
Univariate real polynomials implementations, seen as differentiable
univariate real functions.
Complex number type and implementations of complex transcendental
functions.
Decimal floating point library for Java
Implementations of common discrete and continuous distributions.
Fitting of parameters against distributions.
Implementations of common discrete-time linear filters.
This package provides Genetic Algorithms components and implementations.
This package provides basic 3D geometry components.
This package provides basic 2D geometry components.
Linear algebra support.
Common distance measures.
This package provides classes to solve Ordinary Differential Equations problems.
This package provides classes to solve non-stiff Ordinary Differential Equations problems.
This package provides optimization algorithms that do not require derivatives.
Algorithms for optimizing a vector function.
This package provides optimization algorithms that don't require derivatives.
Random number and random data generators.
Data storage, manipulation and summary routines.
Correlations/Covariance computations.
Generic univariate summary statistic objects.
Summary statistics based on moments.
Classes providing hypothesis testing.
Implementations of transform methods, including Fast Fourier transforms.
Convenience routines and common data structures used throughout the commons-math library.
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Uses of DimensionMismatchException in org.apache.commons.math3
Methods in org.apache.commons.math3 that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionTwo arguments arc tangent operation.Returns the hypotenuse of a triangle with sidesthisandy- sqrt(this2 +y2) avoiding intermediate overflow or underflow.RealFieldElement.linearCombination(double[] a, T[] b) Compute a linear combination.RealFieldElement.linearCombination(T[] a, T[] b) Compute a linear combination.Power operation.IEEE remainder operator. -
Uses of DimensionMismatchException in org.apache.commons.math3.analysis.differentiation
Methods in org.apache.commons.math3.analysis.differentiation that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionDerivativeStructure.add(DerivativeStructure a) Compute this + a.DerivativeStructure.atan2(DerivativeStructure x) Two arguments arc tangent operation.static DerivativeStructureDerivativeStructure.atan2(DerivativeStructure y, DerivativeStructure x) Two arguments arc tangent operation.voidDSCompiler.checkCompatibility(DSCompiler compiler) Check rules set compatibility.DerivativeStructure.compose(double... f) Compute composition of the instance by a univariate function.DerivativeStructure.divide(DerivativeStructure a) Compute this ÷ a.doubleDerivativeStructure.getPartialDerivative(int... orders) Get a partial derivative.intDSCompiler.getPartialDerivativeIndex(int... orders) Get the index of a partial derivative in the array.DerivativeStructure.hypot(DerivativeStructure y) Returns the hypotenuse of a triangle with sidesthisandy- sqrt(this2 +y2) avoiding intermediate overflow or underflow.static DerivativeStructureDerivativeStructure.hypot(DerivativeStructure x, DerivativeStructure y) Returns the hypotenuse of a triangle with sidesxandy- sqrt(x2 +y2) avoiding intermediate overflow or underflow.DerivativeStructure.linearCombination(double[] a, DerivativeStructure[] b) Compute a linear combination.DerivativeStructure.linearCombination(double a1, DerivativeStructure b1, double a2, DerivativeStructure b2) Compute a linear combination.DerivativeStructure.linearCombination(double a1, DerivativeStructure b1, double a2, DerivativeStructure b2, double a3, DerivativeStructure b3) Compute a linear combination.DerivativeStructure.linearCombination(double a1, DerivativeStructure b1, double a2, DerivativeStructure b2, double a3, DerivativeStructure b3, double a4, DerivativeStructure b4) Compute a linear combination.DerivativeStructure.linearCombination(DerivativeStructure[] a, DerivativeStructure[] b) Compute a linear combination.DerivativeStructure.linearCombination(DerivativeStructure a1, DerivativeStructure b1, DerivativeStructure a2, DerivativeStructure b2) Compute a linear combination.DerivativeStructure.linearCombination(DerivativeStructure a1, DerivativeStructure b1, DerivativeStructure a2, DerivativeStructure b2, DerivativeStructure a3, DerivativeStructure b3) Compute a linear combination.DerivativeStructure.linearCombination(DerivativeStructure a1, DerivativeStructure b1, DerivativeStructure a2, DerivativeStructure b2, DerivativeStructure a3, DerivativeStructure b3, DerivativeStructure a4, DerivativeStructure b4) Compute a linear combination.SparseGradient.linearCombination(SparseGradient[] a, SparseGradient[] b) Compute a linear combination.DerivativeStructure.multiply(DerivativeStructure a) Compute this × a.DerivativeStructure.pow(DerivativeStructure e) Power operation.DerivativeStructure.remainder(DerivativeStructure a) IEEE remainder operator.DerivativeStructure.subtract(DerivativeStructure a) Compute this - a.UnivariateDifferentiableFunction.value(DerivativeStructure t) Simple mathematical function.Constructors in org.apache.commons.math3.analysis.differentiation that throw DimensionMismatchExceptionModifierConstructorDescriptionDerivativeStructure(double a1, DerivativeStructure ds1, double a2, DerivativeStructure ds2) Linear combination constructor.DerivativeStructure(double a1, DerivativeStructure ds1, double a2, DerivativeStructure ds2, double a3, DerivativeStructure ds3) Linear combination constructor.DerivativeStructure(double a1, DerivativeStructure ds1, double a2, DerivativeStructure ds2, double a3, DerivativeStructure ds3, double a4, DerivativeStructure ds4) Linear combination constructor.DerivativeStructure(int parameters, int order, double... derivatives) Build an instance from all its derivatives. -
Uses of DimensionMismatchException in org.apache.commons.math3.analysis.function
Methods in org.apache.commons.math3.analysis.function that throw DimensionMismatchExceptionModifier and TypeMethodDescriptiondouble[]Gaussian.Parametric.gradient(double x, double... param) Computes the value of the gradient atx.double[]HarmonicOscillator.Parametric.gradient(double x, double... param) Computes the value of the gradient atx.double[]Logistic.Parametric.gradient(double x, double... param) Computes the value of the gradient atx.double[]Logit.Parametric.gradient(double x, double... param) Computes the value of the gradient atx.double[]Sigmoid.Parametric.gradient(double x, double... param) Computes the value of the gradient atx.doubleGaussian.Parametric.value(double x, double... param) Computes the value of the Gaussian atx.Gaussian.value(DerivativeStructure t) Simple mathematical function.doubleHarmonicOscillator.Parametric.value(double x, double... param) Computes the value of the harmonic oscillator atx.HarmonicOscillator.value(DerivativeStructure t) Simple mathematical function.doubleLogistic.Parametric.value(double x, double... param) Computes the value of the sigmoid atx.doubleLogit.Parametric.value(double x, double... param) Computes the value of the logit atx.doubleSigmoid.Parametric.value(double x, double... param) Computes the value of the sigmoid atx.Sigmoid.value(DerivativeStructure t) Simple mathematical function.Sinc.value(DerivativeStructure t) Simple mathematical function.Constructors in org.apache.commons.math3.analysis.function that throw DimensionMismatchExceptionModifierConstructorDescriptionStepFunction(double[] x, double[] y) Builds a step function from a list of arguments and the corresponding values. -
Uses of DimensionMismatchException in org.apache.commons.math3.analysis.integration.gauss
Methods in org.apache.commons.math3.analysis.integration.gauss that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionprotected voidStores a rule.BaseRuleFactory.computeRule(int numberOfPoints) Computes the rule for the given order.HermiteRuleFactory.computeRule(int numberOfPoints) Computes the rule for the given order.protected Pair<BigDecimal[], BigDecimal[]> LegendreHighPrecisionRuleFactory.computeRule(int numberOfPoints) Computes the rule for the given order.LegendreRuleFactory.computeRule(int numberOfPoints) Computes the rule for the given order.Pair<double[], double[]> BaseRuleFactory.getRule(int numberOfPoints) Gets a copy of the quadrature rule with the given number of integration points.BaseRuleFactory.getRuleInternal(int numberOfPoints) Gets a rule.Constructors in org.apache.commons.math3.analysis.integration.gauss that throw DimensionMismatchExceptionModifierConstructorDescriptionGaussIntegrator(double[] points, double[] weights) Creates an integrator from the givenpointsandweights.SymmetricGaussIntegrator(double[] points, double[] weights) Creates an integrator from the givenpointsandweights. -
Uses of DimensionMismatchException in org.apache.commons.math3.analysis.interpolation
Methods in org.apache.commons.math3.analysis.interpolation that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionvoidFieldHermiteInterpolator.addSamplePoint(T x, T[]... value) Add a sample point.protected static double[]DividedDifferenceInterpolator.computeDividedDifference(double[] x, double[] y) Return a copy of the divided difference array.AkimaSplineInterpolator.interpolate(double[] xvals, double[] yvals) Computes an interpolating function for the data set.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.DividedDifferenceInterpolator.interpolate(double[] x, double[] y) Compute an interpolating function for the dataset.LinearInterpolator.interpolate(double[] x, double[] y) Computes a linear interpolating function for the data set.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.NevilleInterpolator.interpolate(double[] x, double[] y) 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.SplineInterpolator.interpolate(double[] x, double[] y) Computes an interpolating function for the data set.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.UnivariateInterpolator.interpolate(double[] xval, double[] yval) 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.doubleMicrosphereInterpolatingFunction.value(double[] point) Deprecated.Constructors in org.apache.commons.math3.analysis.interpolation that throw DimensionMismatchExceptionModifierConstructorDescriptionBicubicInterpolatingFunction(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 DimensionMismatchException in org.apache.commons.math3.analysis.polynomials
Methods in org.apache.commons.math3.analysis.polynomials that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionstatic doublePolynomialFunctionLagrangeForm.evaluate(double[] x, double[] y, double z) Evaluate the Lagrange polynomial using Neville's Algorithm.static doublePolynomialFunctionNewtonForm.evaluate(double[] a, double[] c, double z) Evaluate the Newton polynomial using nested multiplication.protected static voidPolynomialFunctionNewtonForm.verifyInputArray(double[] a, double[] c) Verifies that the input arrays are valid.static booleanPolynomialFunctionLagrangeForm.verifyInterpolationArray(double[] x, double[] y, boolean abort) Check that the interpolation arrays are valid.Constructors in org.apache.commons.math3.analysis.polynomials that throw DimensionMismatchExceptionModifierConstructorDescriptionPolynomialFunctionLagrangeForm(double[] x, double[] y) Construct a Lagrange polynomial with the given abscissas and function values.PolynomialFunctionNewtonForm(double[] a, double[] c) Construct a Newton polynomial with the given a[] and c[].PolynomialSplineFunction(double[] knots, PolynomialFunction[] polynomials) Construct a polynomial spline function with the given segment delimiters and interpolating polynomials. -
Uses of DimensionMismatchException in org.apache.commons.math3.complex
Constructors in org.apache.commons.math3.complex that throw DimensionMismatchExceptionModifierConstructorDescriptionQuaternion(double scalar, double[] v) Builds a quaternion from scalar and vector parts. -
Uses of DimensionMismatchException in org.apache.commons.math3.dfp
Methods in org.apache.commons.math3.dfp that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionTwo arguments arc tangent operation.Dfp.linearCombination(double[] a, Dfp[] b) Compute a linear combination.Dfp.linearCombination(Dfp[] a, Dfp[] b) Compute a linear combination. -
Uses of DimensionMismatchException in org.apache.commons.math3.distribution
Methods in org.apache.commons.math3.distribution that throw DimensionMismatchExceptionModifier and TypeMethodDescriptiondoubleMultivariateNormalDistribution.density(double[] vals) Returns the probability density function (PDF) of this distribution evaluated at the specified pointx.Constructors in org.apache.commons.math3.distribution that throw DimensionMismatchExceptionModifierConstructorDescriptionEnumeratedIntegerDistribution(int[] singletons, double[] probabilities) Create a discrete distribution using the given probability mass function definition.EnumeratedIntegerDistribution(RandomGenerator rng, int[] singletons, double[] probabilities) Create a discrete distribution using the given random number generator and probability mass function definition.EnumeratedRealDistribution(double[] singletons, double[] probabilities) Create a discrete real-valued distribution using the given probability mass function enumeration.EnumeratedRealDistribution(RandomGenerator rng, double[] singletons, double[] probabilities) Create a discrete real-valued distribution using the given random number generator and probability mass function enumeration.MixtureMultivariateNormalDistribution(RandomGenerator rng, List<Pair<Double, MultivariateNormalDistribution>> components) Creates a mixture model from a list of distributions and their associated weights.MultivariateNormalDistribution(double[] means, double[][] covariances) Creates a multivariate normal distribution with the given mean vector and covariance matrix.MultivariateNormalDistribution(RandomGenerator rng, double[] means, double[][] covariances) Creates a multivariate normal distribution with the given mean vector and covariance matrix. -
Uses of DimensionMismatchException in org.apache.commons.math3.distribution.fitting
Methods in org.apache.commons.math3.distribution.fitting that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionMultivariateNormalMixtureExpectationMaximization.estimate(double[][] data, int numComponents) Helper method to create a multivariate normal mixture model which can be used to initializeMultivariateNormalMixtureExpectationMaximization.fit(MixtureMultivariateNormalDistribution).voidMultivariateNormalMixtureExpectationMaximization.fit(MixtureMultivariateNormalDistribution initialMixture, int maxIterations, double threshold) Fit a mixture model to the data supplied to the constructor.Constructors in org.apache.commons.math3.distribution.fitting that throw DimensionMismatchExceptionModifierConstructorDescriptionMultivariateNormalMixtureExpectationMaximization(double[][] data) Creates an object to fit a multivariate normal mixture model to data. -
Uses of DimensionMismatchException in org.apache.commons.math3.filter
Methods in org.apache.commons.math3.filter that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionvoidKalmanFilter.correct(double[] z) Correct the current state estimate with an actual measurement.voidKalmanFilter.correct(RealVector z) Correct the current state estimate with an actual measurement.voidKalmanFilter.predict(double[] u) Predict the internal state estimation one time step ahead.voidKalmanFilter.predict(RealVector u) Predict the internal state estimation one time step ahead.Constructors in org.apache.commons.math3.filter that throw DimensionMismatchExceptionModifierConstructorDescriptionDefaultMeasurementModel(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.KalmanFilter(ProcessModel process, MeasurementModel measurement) Creates a new Kalman filter with the given process and measurement models. -
Uses of DimensionMismatchException in org.apache.commons.math3.genetics
Methods in org.apache.commons.math3.genetics that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionCycleCrossover.crossover(Chromosome first, Chromosome second) Perform a crossover operation on the given chromosomes.NPointCrossover.crossover(Chromosome first, Chromosome second) Performs a N-point crossover.OnePointCrossover.crossover(Chromosome first, Chromosome second) Performs one point crossover.OrderedCrossover.crossover(Chromosome first, Chromosome second) Perform a crossover operation on the given chromosomes.UniformCrossover.crossover(Chromosome first, Chromosome second) Perform a crossover operation on the given chromosomes.RandomKey.inducedPermutation(List<S> originalData, List<S> permutedData) Generates a representation of a permutation corresponding to a permutation which yieldspermutedDatawhen applied tooriginalData.protected ChromosomePairCycleCrossover.mate(AbstractListChromosome<T> first, AbstractListChromosome<T> second) Helper forCycleCrossover.crossover(Chromosome, Chromosome).protected ChromosomePairOrderedCrossover.mate(AbstractListChromosome<T> first, AbstractListChromosome<T> second) -
Uses of DimensionMismatchException in org.apache.commons.math3.geometry.euclidean.threed
Constructors in org.apache.commons.math3.geometry.euclidean.threed that throw DimensionMismatchExceptionModifierConstructorDescriptionFieldVector3D(T[] v) Simple constructor.Vector3D(double[] v) Simple constructor. -
Uses of DimensionMismatchException in org.apache.commons.math3.geometry.euclidean.twod
Constructors in org.apache.commons.math3.geometry.euclidean.twod that throw DimensionMismatchException -
Uses of DimensionMismatchException in org.apache.commons.math3.linear
Subclasses of DimensionMismatchException in org.apache.commons.math3.linearModifier and TypeClassDescriptionclassException to be thrown when a square matrix is expected.classException to be thrown when a square linear operator is expected.Methods in org.apache.commons.math3.linear that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionArrayFieldVector.add(ArrayFieldVector<T> v) Compute the sum ofthisandv.ArrayFieldVector.add(FieldVector<T> v) Compute the sum ofthisandv.ArrayRealVector.add(RealVector v) Compute the sum of this vector andv.FieldVector.add(FieldVector<T> v) Compute the sum ofthisandv.OpenMapRealVector.add(OpenMapRealVector v) Optimized method to add two OpenMapRealVectors.OpenMapRealVector.add(RealVector v) Compute the sum of this vector andv.RealVector.add(RealVector v) Compute the sum of this vector andv.SparseFieldVector.add(FieldVector<T> v) Compute the sum ofthisandv.SparseFieldVector.add(SparseFieldVector<T> v) Optimized method to add sparse vectors.protected voidAbstractFieldMatrix.checkMultiplicationCompatible(FieldMatrix<T> m) Check if a matrix is multiplication compatible with the instance.static voidMatrixUtils.checkMultiplicationCompatible(AnyMatrix left, AnyMatrix right) Check if matrices are multiplication compatibleprotected static voidIterativeLinearSolver.checkParameters(RealLinearOperator a, RealVector b, RealVector x0) Performs all dimension checks on the parameters ofsolveandsolveInPlace, and throws an exception if one of the checks fails.protected static voidPreconditionedIterativeLinearSolver.checkParameters(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0) Performs all dimension checks on the parameters ofsolveandsolveInPlace, and throws an exception if one of the checks fails.protected voidArrayFieldVector.checkVectorDimensions(int n) Check if instance dimension is equal to some expected value.protected voidArrayFieldVector.checkVectorDimensions(FieldVector<T> v) Check if instance and specified vectors have the same dimension.protected voidArrayRealVector.checkVectorDimensions(int n) Check if instance dimension is equal to some expected value.protected voidArrayRealVector.checkVectorDimensions(RealVector v) Check if instance and specified vectors have the same dimension.protected voidRealVector.checkVectorDimensions(int n) Check if instance dimension is equal to some expected value.protected voidRealVector.checkVectorDimensions(RealVector v) Check if instance and specified vectors have the same dimension.protected voidSparseFieldVector.checkVectorDimensions(int n) Check if instance dimension is equal to some expected value.ArrayRealVector.combine(double a, double b, RealVector y) Returns a new vector representinga * this + b * y, the linear combination ofthisandy.RealVector.combine(double a, double b, RealVector y) Returns a new vector representinga * this + b * y, the linear combination ofthisandy.ArrayRealVector.combineToSelf(double a, double b, RealVector y) Updatesthiswith the linear combination ofthisandy.RealVector.combineToSelf(double a, double b, RealVector y) Updatesthiswith the linear combination ofthisandy.doubleRealVector.cosine(RealVector v) Computes the cosine of the angle between this vector and the argument.static <T extends FieldElement<T>>
FieldMatrix<T> MatrixUtils.createFieldMatrix(T[][] data) Returns aFieldMatrixwhose entries are the the values in the the input array.DiagonalMatrix.createMatrix(int rowDimension, int columnDimension) Create a new RealMatrix of the same type as the instance with the supplied row and column dimensions.static RealMatrixMatrixUtils.createRealMatrix(double[][] data) Returns aRealMatrixwhose entries are the the values in the the input array.ArrayFieldVector.dotProduct(ArrayFieldVector<T> v) Compute the dot product.ArrayFieldVector.dotProduct(FieldVector<T> v) Compute the dot product.doubleArrayRealVector.dotProduct(RealVector v) Compute the dot product of this vector withv.FieldVector.dotProduct(FieldVector<T> v) Compute the dot product.doubleOpenMapRealVector.dotProduct(OpenMapRealVector v) Deprecated.as of 3.1 (to be removed in 4.0).doubleRealVector.dotProduct(RealVector v) Compute the dot product of this vector withv.SparseFieldVector.dotProduct(FieldVector<T> v) Compute the dot product.ArrayFieldVector.ebeDivide(ArrayFieldVector<T> v) Element-by-element division.ArrayFieldVector.ebeDivide(FieldVector<T> v) Element-by-element division.ArrayRealVector.ebeDivide(RealVector v) Element-by-element division.FieldVector.ebeDivide(FieldVector<T> v) Element-by-element division.OpenMapRealVector.ebeDivide(RealVector v) Element-by-element division.abstract RealVectorRealVector.ebeDivide(RealVector v) Element-by-element division.SparseFieldVector.ebeDivide(FieldVector<T> v) Element-by-element division.ArrayFieldVector.ebeMultiply(ArrayFieldVector<T> v) Element-by-element multiplication.ArrayFieldVector.ebeMultiply(FieldVector<T> v) Element-by-element multiplication.ArrayRealVector.ebeMultiply(RealVector v) Element-by-element multiplication.FieldVector.ebeMultiply(FieldVector<T> v) Element-by-element multiplication.OpenMapRealVector.ebeMultiply(RealVector v) Element-by-element multiplication.abstract RealVectorRealVector.ebeMultiply(RealVector v) Element-by-element multiplication.SparseFieldVector.ebeMultiply(FieldVector<T> v) Element-by-element multiplication.doubleArrayRealVector.getDistance(RealVector v) Distance between two vectors.doubleOpenMapRealVector.getDistance(OpenMapRealVector v) Optimized method to compute distance.doubleOpenMapRealVector.getDistance(RealVector v) Distance between two vectors.doubleRealVector.getDistance(RealVector v) Distance between two vectors.doubleArrayRealVector.getL1Distance(RealVector v) Distance between two vectors.doubleOpenMapRealVector.getL1Distance(OpenMapRealVector v) Distance between two vectors.doubleOpenMapRealVector.getL1Distance(RealVector v) Distance between two vectors.doubleRealVector.getL1Distance(RealVector v) Distance between two vectors.doubleArrayRealVector.getLInfDistance(RealVector v) Distance between two vectors.doubleOpenMapRealVector.getLInfDistance(RealVector v) Distance between two vectors.doubleRealVector.getLInfDistance(RealVector v) Distance between two vectors.AbstractFieldMatrix.multiply(FieldMatrix<T> m) Postmultiply this matrix bym.AbstractRealMatrix.multiply(RealMatrix m) Returns the result of postmultiplyingthisbym.Array2DRowFieldMatrix.multiply(Array2DRowFieldMatrix<T> m) Postmultiplying this matrix bym.Array2DRowRealMatrix.multiply(Array2DRowRealMatrix m) Returns the result of postmultiplyingthisbym.BlockFieldMatrix.multiply(BlockFieldMatrix<T> m) Returns the result of postmultiplyingthisbym.BlockFieldMatrix.multiply(FieldMatrix<T> m) Postmultiply this matrix bym.BlockRealMatrix.multiply(BlockRealMatrix m) Returns the result of postmultiplying this bym.BlockRealMatrix.multiply(RealMatrix m) Returns the result of postmultiplyingthisbym.DiagonalMatrix.multiply(DiagonalMatrix m) Returns the result of postmultiplyingthisbym.DiagonalMatrix.multiply(RealMatrix m) Returns the result of postmultiplyingthisbym.FieldMatrix.multiply(FieldMatrix<T> m) Postmultiply this matrix bym.OpenMapRealMatrix.multiply(OpenMapRealMatrix m) Postmultiply this matrix bym.OpenMapRealMatrix.multiply(RealMatrix m) Returns the result of postmultiplyingthisbym.RealMatrix.multiply(RealMatrix m) Returns the result of postmultiplyingthisbym.AbstractFieldMatrix.operate(FieldVector<T> v) Returns the result of multiplying this by the vectorv.T[]Returns the result of multiplying this by the vectorv.double[]AbstractRealMatrix.operate(double[] v) Returns the result of multiplying this by the vectorv.AbstractRealMatrix.operate(RealVector v) Returns the result of multiplyingthisby the vectorx.T[]Returns the result of multiplying this by the vectorv.double[]Array2DRowRealMatrix.operate(double[] v) Returns the result of multiplying this by the vectorv.T[]Returns the result of multiplying this by the vectorv.double[]BlockRealMatrix.operate(double[] v) Returns the result of multiplying this by the vectorv.double[]DiagonalMatrix.operate(double[] v) Returns the result of multiplying this by the vectorv.FieldMatrix.operate(FieldVector<T> v) Returns the result of multiplying this by the vectorv.T[]Returns the result of multiplying this by the vectorv.abstract RealVectorRealLinearOperator.operate(RealVector x) Returns the result of multiplyingthisby the vectorx.double[]RealMatrix.operate(double[] v) Returns the result of multiplying this by the vectorv.RealMatrix.operate(RealVector v) Returns the result of multiplying this by the vectorv.RealLinearOperator.operateTranspose(RealVector x) Returns the result of multiplying the transpose ofthisoperator by the vectorx(optional operation).AbstractFieldMatrix.preMultiply(FieldMatrix<T> m) Premultiply this matrix bym.AbstractFieldMatrix.preMultiply(FieldVector<T> v) Returns the (row) vector result of premultiplying this by the vectorv.T[]AbstractFieldMatrix.preMultiply(T[] v) Returns the (row) vector result of premultiplying this by the vectorv.double[]AbstractRealMatrix.preMultiply(double[] v) Returns the (row) vector result of premultiplying this by the vectorv.AbstractRealMatrix.preMultiply(RealMatrix m) Returns the result of premultiplyingthisbym.AbstractRealMatrix.preMultiply(RealVector v) Returns the (row) vector result of premultiplying this by the vectorv.T[]Array2DRowFieldMatrix.preMultiply(T[] v) Returns the (row) vector result of premultiplying this by the vectorv.double[]Array2DRowRealMatrix.preMultiply(double[] v) Returns the (row) vector result of premultiplying this by the vectorv.T[]BlockFieldMatrix.preMultiply(T[] v) Returns the (row) vector result of premultiplying this by the vectorv.double[]BlockRealMatrix.preMultiply(double[] v) Returns the (row) vector result of premultiplying this by the vectorv.double[]DiagonalMatrix.preMultiply(double[] v) Returns the (row) vector result of premultiplying this by the vectorv.DiagonalMatrix.preMultiply(RealVector v) Returns the (row) vector result of premultiplying this by the vectorv.FieldMatrix.preMultiply(FieldMatrix<T> m) Premultiply this matrix bym.FieldMatrix.preMultiply(FieldVector<T> v) Returns the (row) vector result of premultiplying this by the vectorv.T[]FieldMatrix.preMultiply(T[] v) Returns the (row) vector result of premultiplying this by the vectorv.double[]RealMatrix.preMultiply(double[] v) Returns the (row) vector result of premultiplying this by the vectorv.RealMatrix.preMultiply(RealMatrix m) Returns the result of premultiplyingthisbym.RealMatrix.preMultiply(RealVector v) Returns the (row) vector result of premultiplying this by the vectorv.ArrayFieldVector.projection(ArrayFieldVector<T> v) Find the orthogonal projection of this vector onto another vector.ArrayFieldVector.projection(FieldVector<T> v) Find the orthogonal projection of this vector onto another vector.FieldVector.projection(FieldVector<T> v) Find the orthogonal projection of this vector onto another vector.RealVector.projection(RealVector v) Find the orthogonal projection of this vector onto another vector.SparseFieldVector.projection(FieldVector<T> v) Find the orthogonal projection of this vector onto another vector.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.IterativeLinearSolver.solve(RealLinearOperator a, RealVector b) Returns an estimate of the solution to the linear system A · x = b.IterativeLinearSolver.solve(RealLinearOperator a, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealLinearOperator m, RealVector b) Returns an estimate of the solution to the linear system A · x = b.PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealVector b) Returns an estimate of the solution to the linear system A · x = b.PreconditionedIterativeLinearSolver.solve(RealLinearOperator a, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.SymmLQ.solve(RealLinearOperator a, RealLinearOperator m, RealVector b) Returns an estimate of the solution to the linear system A · x = b.SymmLQ.solve(RealLinearOperator a, RealLinearOperator m, RealVector b, boolean goodb, double shift) Returns an estimate of the solution to the linear system (A - shift · I) · x = b.SymmLQ.solve(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x) Returns an estimate of the solution to the linear system A · x = b.SymmLQ.solve(RealLinearOperator a, RealVector b) Returns an estimate of the solution to the linear system A · x = b.SymmLQ.solve(RealLinearOperator a, RealVector b, boolean goodb, double shift) Returns the solution to the system (A - shift · I) · x = b.SymmLQ.solve(RealLinearOperator a, RealVector b, RealVector x) Returns an estimate of the solution to the linear system A · x = b.ConjugateGradient.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.abstract RealVectorIterativeLinearSolver.solveInPlace(RealLinearOperator a, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.abstract RealVectorPreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.PreconditionedIterativeLinearSolver.solveInPlace(RealLinearOperator a, RealVector b, RealVector x0) Returns an estimate of the solution to the linear system A · x = b.SymmLQ.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x) Returns an estimate of the solution to the linear system A · x = b.SymmLQ.solveInPlace(RealLinearOperator a, RealLinearOperator m, RealVector b, RealVector x, boolean goodb, double shift) Returns an estimate of the solution to the linear system (A - shift · I) · x = b.SymmLQ.solveInPlace(RealLinearOperator a, RealVector b, RealVector x) Returns an estimate of the solution to the linear system A · x = b.static voidMatrixUtils.solveLowerTriangularSystem(RealMatrix rm, RealVector b) Solve a system of composed of a Lower Triangular MatrixRealMatrix.static voidMatrixUtils.solveUpperTriangularSystem(RealMatrix rm, RealVector b) Solver a system composed of an Upper Triangular MatrixRealMatrix.ArrayFieldVector.subtract(ArrayFieldVector<T> v) Computethisminusv.ArrayFieldVector.subtract(FieldVector<T> v) Computethisminusv.ArrayRealVector.subtract(RealVector v) Subtractvfrom this vector.FieldVector.subtract(FieldVector<T> v) Computethisminusv.OpenMapRealVector.subtract(OpenMapRealVector v) Optimized method to subtract OpenMapRealVectors.OpenMapRealVector.subtract(RealVector v) Subtractvfrom this vector.RealVector.subtract(RealVector v) Subtractvfrom this vector.SparseFieldVector.subtract(FieldVector<T> v) Computethisminusv.SparseFieldVector.subtract(SparseFieldVector<T> v) Optimized method to computethisminusv.static <T extends FieldElement<T>>
T[][]BlockFieldMatrix.toBlocksLayout(T[][] rawData) Convert a data array from raw layout to blocks layout.static double[][]BlockRealMatrix.toBlocksLayout(double[][] rawData) Convert a data array from raw layout to blocks layout.Constructors in org.apache.commons.math3.linear that throw DimensionMismatchExceptionModifierConstructorDescriptionArray2DRowFieldMatrix(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[][] 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.BlockFieldMatrix(int rows, int columns, T[][] blockData, boolean copyArray) Create a new dense matrix copying entries from block layout data.BlockFieldMatrix(T[][] rawData) Create a new dense matrix copying entries from raw layout data.BlockRealMatrix(double[][] rawData) Create a new dense matrix copying entries from raw layout data.BlockRealMatrix(int rows, int columns, double[][] blockData, boolean copyArray) Create a new dense matrix copying entries from block layout data. -
Uses of DimensionMismatchException in org.apache.commons.math3.ml.distance
Methods in org.apache.commons.math3.ml.distance that throw DimensionMismatchExceptionModifier and TypeMethodDescriptiondoubleCanberraDistance.compute(double[] a, double[] b) Compute the distance between two n-dimensional vectors.doubleChebyshevDistance.compute(double[] a, double[] b) Compute the distance between two n-dimensional vectors.doubleDistanceMeasure.compute(double[] a, double[] b) Compute the distance between two n-dimensional vectors.doubleEarthMoversDistance.compute(double[] a, double[] b) Compute the distance between two n-dimensional vectors.doubleEuclideanDistance.compute(double[] a, double[] b) Compute the distance between two n-dimensional vectors.doubleManhattanDistance.compute(double[] a, double[] b) Compute the distance between two n-dimensional vectors. -
Uses of DimensionMismatchException in org.apache.commons.math3.ode
Methods in org.apache.commons.math3.ode that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionprotected FieldODEStateAndDerivative<T> AbstractFieldIntegrator.acceptStep(AbstractFieldStepInterpolator<T> interpolator, T tEnd) Accept a step, triggering events and step handlers.protected doubleAbstractIntegrator.acceptStep(AbstractStepInterpolator interpolator, double[] y, double[] yDot, double tEnd) Accept a step, triggering events and step handlers.T[]AbstractFieldIntegrator.computeDerivatives(T t, T[] y) Compute the derivatives and check the number of evaluations.voidAbstractIntegrator.computeDerivatives(double t, double[] y, double[] yDot) Compute the derivatives and check the number of evaluations.voidExpandableStatefulODE.computeDerivatives(double t, double[] y, double[] yDot) Get the current time derivative of the complete state vector.T[]FieldExpandableODE.computeDerivatives(T t, T[] y) Get the current time derivative of the complete state vector.T[]FieldSecondaryEquations.computeDerivatives(T t, T[] primary, T[] primaryDot, T[] secondary) Compute the derivatives related to the secondary state parameters.voidFirstOrderDifferentialEquations.computeDerivatives(double t, double[] y, double[] yDot) Get the current time derivative of the state vector.voidSecondaryEquations.computeDerivatives(double t, double[] primary, double[] primaryDot, double[] secondary, double[] secondaryDot) Compute the derivatives related to the secondary state parameters.voidMainStateJacobianProvider.computeMainStateJacobian(double t, double[] y, double[] yDot, double[][] dFdY) Compute the jacobian matrix of ODE with respect to main state.voidParameterJacobianProvider.computeParameterJacobian(double t, double[] y, double[] yDot, String paramName, double[] dFdP) Compute the Jacobian matrix of ODE with respect to one parameter.voidEquationsMapper.extractEquationData(double[] complete, double[] equationData) Extract equation data from a complete state or derivative array.T[]FieldEquationsMapper.extractEquationData(int index, T[] complete) Extract equation data from a complete state or derivative array.double[]ExpandableStatefulODE.getCompleteState()Get the complete current state.voidEquationsMapper.insertEquationData(double[] equationData, double[] complete) Insert equation data into a complete state or derivative array.voidFieldEquationsMapper.insertEquationData(int index, T[] equationData, T[] complete) Insert equation data into a complete state or derivative array.abstract voidAbstractIntegrator.integrate(ExpandableStatefulODE equations, double t) Integrate a set of differential equations up to the given time.doubleAbstractIntegrator.integrate(FirstOrderDifferentialEquations equations, double t0, double[] y0, double t, double[] y) Integrate the differential equations up to the given time.doubleFirstOrderIntegrator.integrate(FirstOrderDifferentialEquations equations, double t0, double[] y0, double t, double[] y) Integrate the differential equations up to the given time.FieldEquationsMapper.mapStateAndDerivative(T t, T[] y, T[] yDot) Map flat arrays to a state and derivative.voidJacobianMatrices.registerVariationalEquations(ExpandableStatefulODE expandable) Register the variational equations for the Jacobians matrices to the expandable set.protected voidAbstractFieldIntegrator.sanityChecks(FieldODEState<T> eqn, T t) Check the integration span.protected voidAbstractIntegrator.sanityChecks(ExpandableStatefulODE equations, double t) Check the integration span.voidExpandableStatefulODE.setCompleteState(double[] completeState) Set the complete current state.voidJacobianMatrices.setInitialMainStateJacobian(double[][] dYdY0) Set the initial value of the Jacobian matrix with respect to state.voidJacobianMatrices.setInitialParameterJacobian(String pName, double[] dYdP) Set the initial value of a column of the Jacobian matrix with respect to one parameter.voidExpandableStatefulODE.setPrimaryState(double[] primaryState) Set primary part of the current state.voidExpandableStatefulODE.setSecondaryState(int index, double[] secondaryState) Set secondary part of the current state.protected voidMultistepFieldIntegrator.start(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T t) Start the integration.protected voidMultistepIntegrator.start(double t0, double[] y0, double t) Start the integration.Constructors in org.apache.commons.math3.ode that throw DimensionMismatchExceptionModifierConstructorDescriptionJacobianMatrices(FirstOrderDifferentialEquations fode, double[] hY, String... parameters) Simple constructor for a secondary equations set computing Jacobian matrices. -
Uses of DimensionMismatchException in org.apache.commons.math3.ode.nonstiff
Methods in org.apache.commons.math3.ode.nonstiff that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionAdaptiveStepsizeFieldIntegrator.initializeStep(boolean forward, int order, T[] scale, FieldODEStateAndDerivative<T> state0, FieldEquationsMapper<T> mapper) Initialize the integration step.doubleAdaptiveStepsizeIntegrator.initializeStep(boolean forward, int order, double[] scale, double t0, double[] y0, double[] yDot0, double[] y1, double[] yDot1) Initialize the integration step.AdamsBashforthFieldIntegrator.integrate(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T finalTime) Integrate the differential equations up to the given time.voidAdamsBashforthIntegrator.integrate(ExpandableStatefulODE equations, double t) Integrate a set of differential equations up to the given time.abstract FieldODEStateAndDerivative<T> AdamsFieldIntegrator.integrate(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T finalTime) Integrate the differential equations up to the given time.abstract voidAdamsIntegrator.integrate(ExpandableStatefulODE equations, double t) Integrate a set of differential equations up to the given time.AdamsMoultonFieldIntegrator.integrate(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T finalTime) Integrate the differential equations up to the given time.voidAdamsMoultonIntegrator.integrate(ExpandableStatefulODE equations, double t) Integrate a set of differential equations up to the given time.abstract voidAdaptiveStepsizeIntegrator.integrate(ExpandableStatefulODE equations, double t) Integrate a set of differential equations up to the given time.EmbeddedRungeKuttaFieldIntegrator.integrate(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T finalTime) Integrate the differential equations up to the given time.voidEmbeddedRungeKuttaIntegrator.integrate(ExpandableStatefulODE equations, double t) Integrate a set of differential equations up to the given time.voidGraggBulirschStoerIntegrator.integrate(ExpandableStatefulODE equations, double t) Integrate a set of differential equations up to the given time.RungeKuttaFieldIntegrator.integrate(FieldExpandableODE<T> equations, FieldODEState<T> initialState, T finalTime) Integrate the differential equations up to the given time.voidRungeKuttaIntegrator.integrate(ExpandableStatefulODE equations, double t) Integrate a set of differential equations up to the given time.protected voidAdaptiveStepsizeFieldIntegrator.sanityChecks(FieldODEState<T> eqn, T t) Check the integration span.protected voidAdaptiveStepsizeIntegrator.sanityChecks(ExpandableStatefulODE equations, double t) Check the integration span. -
Uses of DimensionMismatchException in org.apache.commons.math3.optim.nonlinear.scalar.noderiv
Methods in org.apache.commons.math3.optim.nonlinear.scalar.noderiv that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionCMAESOptimizer.optimize(OptimizationData... optData) Stores data and performs the optimization. -
Uses of DimensionMismatchException in org.apache.commons.math3.optim.nonlinear.vector
Methods in org.apache.commons.math3.optim.nonlinear.vector that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionJacobianMultivariateVectorOptimizer.optimize(OptimizationData... optData) Deprecated.Stores data and performs the optimization.MultivariateVectorOptimizer.optimize(OptimizationData... optData) Deprecated.Stores data and performs the optimization. -
Uses of DimensionMismatchException in org.apache.commons.math3.optimization.direct
Methods in org.apache.commons.math3.optimization.direct that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionprotected PointVectorValuePairBaseAbstractMultivariateVectorOptimizer.optimize(int maxEval, FUNC f, OptimizationData... optData) Deprecated.Optimize an objective function.protected PointVectorValuePairBaseAbstractMultivariateVectorOptimizer.optimizeInternal(int maxEval, FUNC f, OptimizationData... optData) Deprecated.Optimize an objective function. -
Uses of DimensionMismatchException in org.apache.commons.math3.random
Constructors in org.apache.commons.math3.random that throw DimensionMismatchExceptionModifierConstructorDescriptionHaltonSequenceGenerator(int dimension, int[] bases, int[] weights) Construct a new Halton sequence generator with the given base numbers and weights for each dimension. -
Uses of DimensionMismatchException in org.apache.commons.math3.stat
Methods in org.apache.commons.math3.stat that throw DimensionMismatchExceptionModifier 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]).static doubleStatUtils.varianceDifference(double[] sample1, double[] sample2, double meanDifference) Returns the variance of the (signed) differences between corresponding elements of the input arrays -- i.e., var(sample1[i] - sample2[i]). -
Uses of DimensionMismatchException in org.apache.commons.math3.stat.correlation
Methods in org.apache.commons.math3.stat.correlation that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionvoidStorelessCovariance.append(StorelessCovariance sc) Appendsscto this, effectively aggregating the computations inscwith this.doubleKendallsCorrelation.correlation(double[] xArray, double[] yArray) Computes the Kendall's Tau rank correlation coefficient between the two arrays.voidStorelessCovariance.increment(double[] data) Increment the covariance matrix with one row of data. -
Uses of DimensionMismatchException in org.apache.commons.math3.stat.descriptive
Methods in org.apache.commons.math3.stat.descriptive that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionvoidMultivariateSummaryStatistics.addValue(double[] value) Add an n-tuple to the datavoidSynchronizedMultivariateSummaryStatistics.addValue(double[] value) Add an n-tuple to the datavoidMultivariateSummaryStatistics.setGeoMeanImpl(StorelessUnivariateStatistic[] geoMeanImpl) Sets the implementation for the geometric mean.voidSynchronizedMultivariateSummaryStatistics.setGeoMeanImpl(StorelessUnivariateStatistic[] geoMeanImpl) Sets the implementation for the geometric mean.voidMultivariateSummaryStatistics.setMaxImpl(StorelessUnivariateStatistic[] maxImpl) Sets the implementation for the maximum.voidSynchronizedMultivariateSummaryStatistics.setMaxImpl(StorelessUnivariateStatistic[] maxImpl) Sets the implementation for the maximum.voidMultivariateSummaryStatistics.setMeanImpl(StorelessUnivariateStatistic[] meanImpl) Sets the implementation for the mean.voidSynchronizedMultivariateSummaryStatistics.setMeanImpl(StorelessUnivariateStatistic[] meanImpl) Sets the implementation for the mean.voidMultivariateSummaryStatistics.setMinImpl(StorelessUnivariateStatistic[] minImpl) Sets the implementation for the minimum.voidSynchronizedMultivariateSummaryStatistics.setMinImpl(StorelessUnivariateStatistic[] minImpl) Sets the implementation for the minimum.voidMultivariateSummaryStatistics.setSumImpl(StorelessUnivariateStatistic[] sumImpl) Sets the implementation for the Sum.voidSynchronizedMultivariateSummaryStatistics.setSumImpl(StorelessUnivariateStatistic[] sumImpl) Sets the implementation for the Sum.voidMultivariateSummaryStatistics.setSumLogImpl(StorelessUnivariateStatistic[] sumLogImpl) Sets the implementation for the sum of logs.voidSynchronizedMultivariateSummaryStatistics.setSumLogImpl(StorelessUnivariateStatistic[] sumLogImpl) Sets the implementation for the sum of logs.voidMultivariateSummaryStatistics.setSumsqImpl(StorelessUnivariateStatistic[] sumsqImpl) Sets the implementation for the sum of squares.voidSynchronizedMultivariateSummaryStatistics.setSumsqImpl(StorelessUnivariateStatistic[] sumsqImpl) Sets the implementation for the sum of squares. -
Uses of DimensionMismatchException in org.apache.commons.math3.stat.descriptive.moment
Methods in org.apache.commons.math3.stat.descriptive.moment that throw DimensionMismatchException -
Uses of DimensionMismatchException in org.apache.commons.math3.stat.inference
Methods in org.apache.commons.math3.stat.inference that throw DimensionMismatchExceptionModifier and TypeMethodDescriptiondoubleOneWayAnova.anovaFValue(Collection<double[]> categoryData) Computes the ANOVA F-value for a collection ofdouble[]arrays.doubleOneWayAnova.anovaPValue(Collection<double[]> categoryData) Computes the ANOVA P-value for a collection ofdouble[]arrays.doubleOneWayAnova.anovaPValue(Collection<SummaryStatistics> categoryData, boolean allowOneElementData) Computes the ANOVA P-value for a collection ofSummaryStatistics.booleanOneWayAnova.anovaTest(Collection<double[]> categoryData, double alpha) Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.doubleChiSquareTest.chiSquare(double[] expected, long[] observed) doubleChiSquareTest.chiSquare(long[][] counts) Computes the Chi-Square statistic associated with a chi-square test of independence based on the inputcountsarray, viewed as a two-way table.static doubleTestUtils.chiSquare(double[] expected, long[] observed) static doubleTestUtils.chiSquare(long[][] counts) doubleChiSquareTest.chiSquareDataSetsComparison(long[] observed1, long[] observed2) Computes a Chi-Square two sample test statistic comparing bin frequency counts inobserved1andobserved2.static doubleTestUtils.chiSquareDataSetsComparison(long[] observed1, long[] observed2) doubleChiSquareTest.chiSquareTest(double[] expected, long[] observed) Returns the observed significance level, or p-value, associated with a Chi-square goodness of fit test comparing theobservedfrequency counts to those in theexpectedarray.booleanChiSquareTest.chiSquareTest(double[] expected, long[] observed, double alpha) Performs a Chi-square goodness of fit test evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance levelalpha.doubleChiSquareTest.chiSquareTest(long[][] counts) Returns the observed significance level, or p-value, associated with a chi-square test of independence based on the inputcountsarray, viewed as a two-way table.booleanChiSquareTest.chiSquareTest(long[][] counts, double alpha) Performs a chi-square test of independence evaluating the null hypothesis that the classifications represented by the counts in the columns of the input 2-way table are independent of the rows, with significance levelalpha.static doubleTestUtils.chiSquareTest(double[] expected, long[] observed) static booleanTestUtils.chiSquareTest(double[] expected, long[] observed, double alpha) static doubleTestUtils.chiSquareTest(long[][] counts) static booleanTestUtils.chiSquareTest(long[][] counts, double alpha) doubleChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2) Returns the observed significance level, or p-value, associated with a Chi-Square two sample test comparing bin frequency counts inobserved1andobserved2.booleanChiSquareTest.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha) Performs a Chi-Square two sample test comparing two binned data sets.static doubleTestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2) static booleanTestUtils.chiSquareTestDataSetsComparison(long[] observed1, long[] observed2, double alpha) doubleGTest.g(double[] expected, long[] observed) static doubleTestUtils.g(double[] expected, long[] observed) doubleGTest.gDataSetsComparison(long[] observed1, long[] observed2) Computes a G (Log-Likelihood Ratio) two sample test statistic for independence comparing frequency counts inobserved1andobserved2.static doubleTestUtils.gDataSetsComparison(long[] observed1, long[] observed2) doubleGTest.gTest(double[] expected, long[] observed) Returns the observed significance level, or p-value, associated with a G-Test for goodness of fit comparing theobservedfrequency counts to those in theexpectedarray.booleanGTest.gTest(double[] expected, long[] observed, double alpha) Performs a G-Test (Log-Likelihood Ratio Test) for goodness of fit evaluating the null hypothesis that the observed counts conform to the frequency distribution described by the expected counts, with significance levelalpha.static doubleTestUtils.gTest(double[] expected, long[] observed) static booleanTestUtils.gTest(double[] expected, long[] observed, double alpha) doubleGTest.gTestDataSetsComparison(long[] observed1, long[] observed2) Returns the observed significance level, or p-value, associated with a G-Value (Log-Likelihood Ratio) for two sample test comparing bin frequency counts inobserved1andobserved2.booleanGTest.gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha) Performs a G-Test (Log-Likelihood Ratio Test) comparing two binned data sets.static doubleTestUtils.gTestDataSetsComparison(long[] observed1, long[] observed2) static booleanTestUtils.gTestDataSetsComparison(long[] observed1, long[] observed2, double alpha) doubleGTest.gTestIntrinsic(double[] expected, long[] observed) Returns the intrinsic (Hardy-Weinberg proportions) p-Value, as described in p64-69 of McDonald, J.H.static doubleTestUtils.gTestIntrinsic(double[] expected, long[] observed) static doubleTestUtils.oneWayAnovaFValue(Collection<double[]> categoryData) static doubleTestUtils.oneWayAnovaPValue(Collection<double[]> categoryData) static booleanTestUtils.oneWayAnovaTest(Collection<double[]> categoryData, double alpha) 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.static doubleTestUtils.rootLogLikelihoodRatio(long k11, long k12, long k21, long k22) 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 DimensionMismatchException in org.apache.commons.math3.transform
Methods in org.apache.commons.math3.transform that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionstatic Complex[]TransformUtils.createComplexArray(double[][] dataRI) Builds a new array ofComplexfrom the specified two dimensional array of real and imaginary parts. -
Uses of DimensionMismatchException in org.apache.commons.math3.util
Methods in org.apache.commons.math3.util that throw DimensionMismatchExceptionModifier and TypeMethodDescriptionstatic voidMathArrays.checkRectangular(long[][] in) Throws DimensionMismatchException if the input array is not rectangular.static doubleMathArrays.distance(double[] p1, double[] p2) Calculates the L2 (Euclidean) distance between two points.static doubleMathArrays.distance(int[] p1, int[] p2) Calculates the L2 (Euclidean) distance between two points.static doubleMathArrays.distance1(double[] p1, double[] p2) Calculates the L1 (sum of abs) distance between two points.static intMathArrays.distance1(int[] p1, int[] p2) Calculates the L1 (sum of abs) distance between two points.static doubleMathArrays.distanceInf(double[] p1, double[] p2) Calculates the L∞ (max of abs) distance between two points.static intMathArrays.distanceInf(int[] p1, int[] p2) Calculates the L∞ (max of abs) distance between two points.static double[]MathArrays.ebeAdd(double[] a, double[] b) Creates an array whose contents will be the element-by-element addition of the arguments.static double[]MathArrays.ebeDivide(double[] a, double[] b) Creates an array whose contents will be the element-by-element division of the first argument by the second.static double[]MathArrays.ebeMultiply(double[] a, double[] b) Creates an array whose contents will be the element-by-element multiplication of the arguments.static double[]MathArrays.ebeSubtract(double[] a, double[] b) Creates an array whose contents will be the element-by-element subtraction of the second argument from the first.intMultidimensionalCounter.getCount(int... c) Convert to unidimensional counter.Decimal64.linearCombination(double[] a, Decimal64[] b) Compute a linear combination.Decimal64.linearCombination(Decimal64[] a, Decimal64[] b) Compute a linear combination.static doubleMathArrays.linearCombination(double[] a, double[] b) Compute a linear combination accurately.static voidMathArrays.sortInPlace(double[] x, double[]... yList) Sort an array in ascending order in place and perform the same reordering of entries on other arrays.static voidMathArrays.sortInPlace(double[] x, MathArrays.OrderDirection dir, double[]... yList) Sort an array in place and perform the same reordering of entries on other arrays.