All Classes and Interfaces
Class
Description
Estimates coefficients of a polynomial passing through provided set of x and y points.
Class to estimate the most likely value from a series of samples assumed to
be normally distributed.
Computes barycentric rational interpolation.
Abstract base class used by all interpolation implementations.
Base class for interpolation methods based on Radial Basis Functions to interpolate sparse
points.
Computes bicubic spline interpolation in two dimensions.
Interpolation in two dimensions.
This class searches for a single REAL root on a single dimension function
(i.e.
This class searches for brackets of values containing a minimum in a single
dimension function.
Computes a root for a single dimension function inside a given bracket of
values, in other words, root will only be searched within provided minimum
and maximum evaluation points.
This class uses Brent algorithm to determine a local function minimum for
single dimension functions.
This class estimates the root of a single dimension continuous function using
Brent's method.
Contains build data of this library.
Utility class to evaluate complex polynomials.
This class searches for a multi dimension function local minimum.
Convolves a 1D signal with a 1D kernel.
Interface defining events produced by this class.
This enumerator indicates how edges should be treated during convolution.
Computes cubic spline interpolation.
Computes curve interpolation of multidimensional points using cubic splines.
Class to compute local minimum on single dimension functions using a
modification of Brent's algorithm that takes into account the function's
derivative.
This class searches for a multi dimension function local minimum.
Class to estimate the derivative of a single dimension function at a given
point.
Class to find a minimum on a multidimensional function along a given line
of input values.
Contains an evaluation of the derivative of a given order of a polynomial and
the point where such derivative has been evaluated.
Abstract class to find function roots of a single dimension function using
also its derivative information.
This class evaluates a multidimensional function and obtains its gradient
along a line; such line is defined by an input point and a given direction.
This class evaluates a multidimensional function along a line, such line is
defined by an input point and a given direction.
Contains an evaluation of a polynomial and the point where the polynomial
has been evaluated.
Interface to define how matrix (multivariate) single dimension functions can be evaluated in
Double Exponential Rule Quadrature function integrators.
Implementation of quadrature using double exponential, which allows integration with a variable
transformation.
Implementation of quadrature using double exponential, which allows integration with a variable
transformation.
Computes function integration by using double exponential quadrature.
Computes matrix function integration by using double exponential quadrature.
Interface to define how single dimension functions can be evaluated in Double Exponential Rule
Quadrature function integrators.
Estimates factorial values as double precision floating point values.
Exception raised when function evaluation fails.
Estimates exponential of a square matrix.
This is an exact replacement for MidPointMatrixQuadrature, except that upper limit is assumed to be
infinite.
This is an exact replacement for MidPointQuadrature, except that upper limit is assumed to be
infinite.
Computes a root for a single dimension function inside a given bracket of
values, in other words, root will only be searched within provided minimum
and maximum evaluation points.
This class computes indices of subsets of samples using a uniform randomizer
to pick random samples as fast as possible.
Class to estimate the root of a first degree polynomial along with other
polynomial properties.
Base class for function fitters used to estimate function parameters along
with their covariance matrix and chi square value
Raised when a fitter fails to fit a function to provided data.
Gaussian Radial Basis Function implementation.
This class for a single dimensional function's local minimum.
Class to estimate the gradient of a multidimensional function.
Listener to evaluate/retrieve a multidimensional function's gradient.
Class to estimate the most likely value from a series of samples assumed to
be normally distributed.
This is an exact replacement for MidPointQuadrature i.e., returns the nth stage of refinement of
the integral of a function from "a" to "b", except that the function is evaluated at evenly spaced
points in 1=x rather than in "x".
This is an exact replacement for MidPointQuadrature i.e., returns the nth stage of refinement of
the integral of a function from "a" to "b", except that the function is evaluated at evenly spaced
points in 1=x rather than in x.
Computes function integration by using an infinity mid-point quadrature.
Computes matrix (multivariate) function integration by using an infinity mid-point quadrature.
Base class defining inlier data for a robust estimator.
Contains an evaluation of an interval of the nth-integral of a polynomial.
Contains an evaluation of the nth-integral of a polynomial and the
point where such integral has been evaluated.
Exception raised when function integration fails.
Integrates single dimension functions over a specified interval.
Indicates type of integrator.
Base class for interpolating polynomial estimators.
Exception raised when function interpolation fails.
Exception raised when provided bracket of values is not valid.
Raised if provided range of samples to pick subsets from is invalid.
Raised if an invalid subset size is requested on a subset selector
Inverse Multi-quadric Radial Function Basis implementation.
Class to estimate the Jacobian of a multi variate and multidimensional
function.
Implementation of a Kalman filter.
Interpolates sparsely defined points using D.G.
Variogram function.
This class estimates the roots of a polynomial of degree n.
Fits provided data (x, y) to a generic non-linear function using
Levenberg-Marquardt iterative algorithm.
Interface to evaluate non-linear multidimensional functions.
Fits provided data (x, y) to a generic non-linear function using
Levenberg-Marquardt iterative algorithm.
Interface to evaluate non-linear multi variate and multidimensional
functions.
Fits provided data (x,y) to a generic non-linear function using
Levenberg-Marquardt iterative algorithm.
Interface to evaluate non-linear single dimensional functions.
Interface to evaluate linear multidimensional functions
f(x1, x2, ...) = a * f0(x1, x2, ...) + b * f1(x1, x2, ...) + ...
Interface to evaluate linear single dimensional functions
f(x) = a * f0(x) + b * f1(x) + ...
Computes linear interpolation.
Abstract class to search for a local minimum on a multidimensional function
along a given line of input parameters.
Finds the best polynomial using LMedS algorithm.
This class implements LMedS (Least Median of Squares) algorithm to robustly
estimate a data model.
Contains data related to inliers estimated in one iteration.
Listener to get data samples and residuals for LMedS method.
This class defines an LMSE (Least Mean Square Error) estimator of a
polynomial of a given degree using points where polynomials (or its
derivatives or integrals) are evaluated.
Exception raised when an instance is locked.
Estimates factorial values as long integer values.
This is an exact replacement for MidPointMatrixQuadrature, except that it allows for an inverse
square-root singularity in the integrand at the lower limit "a".
This is an exact replacement for MidPointQuadrature, except that it allows for an inverse
square-root singularity in the integrand at the lower limit "a".
Computes function integration by using Lower Square Root mid-point quadrature
when lower bound integration bound lies at a function singularity.
Computes function integration by using Lower Square Root mid-point quadrature
when lower bound integration bound lies at a function singularity.
Integrates single dimension matrix (multivariate) functions over a specified interval.
Abstract base class for elementary matrix quadrature algorithms used for matrix (multivariate)
single dimension function integration.
Interface to define how matrix (multivariate) single dimension functions can be evaluated.
Abstract class to estimate the most likely value from a series of data
assumed to be normally distributed.
Types of maximum likelihood estimation to determine the real value
corresponding to a set of values.
Estimates noise covariance matrix for a given set of measures.
Implementation of matrix quadrature using mid-point algorithm.
Implementation of quadrature using mid-point algorithm.
Computes function integration by using Mid-Point quadrature up to desired accuracy.
Computes single dimension matrix (multivariate) function integration by using Mid-Point
quadrature up to desired accuracy.
Finds the best polynomial using RANSAC algorithm.
This class implements MSAC (Median SAmple Consensus) algorithm to robustly
estimate a data model.
Contains data related to inliers estimated in one iteration.
Listener to get data samples and residuals for MSAC method
Base class to fit a multi dimension function y = f(x1, x2, ...) by using
provided data (x, y)
Interface to define how multi dimension functions can be evaluated.
Base class to fit provided multidimensional data (x1, x2, ..., y1, y2, ...)
to a function made of a linear combination of functions used as a basis
(i.e.
Abstract class to search for minima on multidimensional classes.
Multi-quadric Radial Function Basis implementation.
Base class to fit a multi variate function [y1, y2, ...] = f([x1, x2, ...])
by using provided data (x, y).
Interface to define how multivariate functions can be evaluated.
Finds a single dimensional function's root within a bracket of values using
Newton-Raphson's method.
Exception raised when some value cannot be retrieved, usually because it has
not yet been provided or computed.
Raised if there aren't enough samples to make a computation.
Raised when attempting to do a certain operation and not all parameters have
been provided or are correctly set.
Base class for all the exceptions in this package.
Notifies of events generated by an optimizer.
Raised when an optimizer cannot find a minimum on a function, usually because
of lack of convergence.
Abstract class to find function minima.
Estimates the Padé approximant rational function by using a number of coefficients
of a Taylor series.
Contains result of Padé approximant.
Contains a polynomial and common operations done with polynomials.
Interpolation in two dimensions.
Exception raised if polynomial estimation fails.
This class defines the interface for an estimator of a polynomial of a given
degree using points where polynomials are evaluated.
Listener to be notified when estimation starts, finishes or any progress
changes.
Polynomial estimator types.
Contains an evaluation of a polynomial and the point where the
polynomial has been evaluated.
Determines different types of polynomial evaluations that can be used
to estimate a polynomial.
Utility class to evaluate polynomials having either real or complex
coefficients.
Computes polynomial interpolation.
This is an abstract class for algorithms to robustly find the best
Polynomial for provided collection of evaluations.
Listener to be notified of events such as when estimation starts, ends or
when progress changes.
Abstract class to estimate the roots of a polynomial.
Base exception for polynomials.
This class searches for a multi dimension function local minimum.
Finds the best polynomial using PROMedS algorithm.
This class implements PROMedS (PROgressive least Median Sample) algorithm
to robustly estimate a data model.
Contains data related to inliers estimated in one iteration.
Listener to get data samples and residuals for PROMedS method.
Finds the best polynomial using PROSAC algorithm.
This class implements PROSAC (PROgressive random SAmple Consensus) algorithm
to robustly estimate a data model.
Contains data related to estimated inliers.
Listener to get data samples and residuals for PROSAC method
Abstract base class for elementary quadrature algorithms used for function integration.
Integrates functions given a quadrature implementation up to desired accuracy.
Integrates matrix (multivariate) single dimension functions given a quadrature implementation up
to desired accuracy.
Indicates type of quadrature.
This class searches for a multi dimension function local minimum.
Interface defining a Radial Basis Function (RBF) to be used for interpolation.
Interpolates sparsely defined points of dimension "dim" using a Radial Basis Function.
Finds the best polynomial using RANSAC algorithm.
This class implements RANSAC (RANdom SAmple Consensus) algorithm to robustly
estimate a data model.
Contains data related to estimated inliers.
Listener to get data samples and residuals for RANSAC method
Computes rational interpolation.
Utility class to evaluate real polynomials.
Computes a root for a single dimension function inside a given bracket of
values, in other words, root will only be searched within provided minimum
and maximum evaluation points.
Robust estimator to estimate some object in a robust manner
Raised if estimation on a RobustEstimator fails.
Listener to be notified of events on a robust estimator such as when
estimation starts, ends or when progress changes.
Enumerator containing different robust estimation algorithms.
Computes function integration by using Romberg's method and double exponential quadrature.
Computes function integration by using Romberg's method and double exponential quadrature.
Computes function integration by using Romberg's method and exponential quadrature.
Computes function integration by using Romberg's method and exponential quadrature.
Computes function integration by using Romberg's method and Infinity mid-point quadrature.
Computes function integration by using Romberg's method and Infinity mid-point quadrature.
Base integrator for implementations based on Romberg's method.
Computes function integration by using Romberg's method and Lower Square Root Mid-Point
Quadrature when lower integration bound lies at a function singularity.
Computes function integration by using Romberg's method and Lower Square Root Mid-Point
Quadrature when lower integration bound lies at a function singularity.
Base integrator for implementations based on Romberg's method.
Computes function integration by using Romberg's method and Mid-Point Quadrature.
Computes function integration by using Romberg's method and Mid-Point Quadrature.
Computes function integration by using Romberg integration.
Computes matrix function integration by using Romberg integration.
Computes function integration by using Romberg's method and Upper Square Root Mid-Point
Quadrature when upper integration bound lies at a function singularity.
Computes function integration by using Romberg's method and Upper Square Root Mid-Point
Quadrature when upper integration bound lies at a function singularity.
Raised when a root estimator cannot determine a root of a polynomial, usually
because of lack of convergence
Abstract class to find roots of functions.
Computes a root for a single dimension function inside a given bracket of
values, in other words, root will only be searched within provided minimum
and maximum evaluation points.
Class to estimate the derivative of a single dimension function at a given
point.
Class to estimate the gradient of a multidimensional function.
Computes a root for a single dimension function inside a given bracket of
values, in other words, root will only be searched within provided minimum
and maximum evaluation points.
Class to estimate the roots of a second degree polynomial along with other
polynomial properties.
Interpolates sparsely defined points of dimension "dim" using Shepard interpolation, which is
a simplification of Radial Basis Function interpolation that achieves less accurate results but
having less computational cost.
Raised when something fails during signal processing.
Estimates coefficients of a polynomial passing through provided set of x and y points.
Fits provided data (x,y) to a function made of a linear combination of
functions used as a basis (i.e.
This class searches for a multi dimension function local minimum.
Computes function integration by using Simpson's method and double exponential quadrature.
Computes function integration by using Simpson's method and double exponential quadrature.
Computes function integration by using Simpson's rule and infinity mid-point quadrature.
Computes function integration by using Simpson's rule and infinity mid-point quadrature.
Base integrator for implementations based on Simpson's method.
Computes function integration by using Simpson's rule and Lower Square Root mid-point quadrature
when lower integration bound lies at a function singularity.
Computes function integration by using Simpson's rule and Lower Square Root mid-point quadrature
when lower integration bound lies at a function singularity.
Base integrator for implementations based on Simpson's method.
Computes function integration by using Simpson's rule and mid-point quadrature.
Computes function integration by using Simpson's rule and mid-point quadrature.
Computes function integration by using Simpson's rule and trapezoidal quadrature.
Computes matrix function integration by using Simpson's rule and trapezoidal quadrature.
Computes function integration by using Simpson's rule an Upper Square Root mid-point quadrature
when upper integration bound lies at a function singularity.
Computes function integration by using Simpson's rule an Upper Square Root mid-point quadrature
when upper integration bound lies at a function singularity.
Base class to fit a single dimension function y = f(x) by using provided
data (x, y)
Interface to define how single dimension functions can be evaluated.
Base class to fit provided data (x,y) to a function made of a linear
combination of functions used as a basis (i.e.
Abstract class to find minima on single dimension functions.
Abstract class to find roots of single dimension functions.
Fits provided data (x,y) to a straight line following equation y = a + b*x,
estimates parameters a and b their variances, covariance and their chi square
value.
Base class to pick subsets of samples.
Raised if subset selection of samples fails.
Enumerator containing supported types of subset selectors to pick random
samples for robust estimators.
Fits provided data (x,y) to a function made of a linear combination of
functions used as a basis (i.e.
Fits provided data (x,y) to a function made of a linear combination of
functions used as a basis (i.e.
Class to estimate the derivative of a single dimension function at a given
point.
Class to estimate the gradient of a multidimensional function.
Thin-plate spline Radial Basis Function implementation.
Class to estimate the roots of a third degree polynomial along with other
polynomial properties.
Implementation of matrix quadrature using trapezoidal algorithm.
Implementation of quadrature using trapezoidal algorithm.
Computes function integration by using Trapezoidal quadrature up to desired accuracy.
Computes single dimension matrix (multivariate) function integration by using Trapezoidal
quadrature up to desired accuracy.
This is an exact replacement for MidPointMatrixQuadrature, except that it allows for an inverse
square-root singularity in the integrand at the upper limit b.
This is an exact replacement for MidPointQuadrature, except that it allows for an inverse
square-root singularity in the integrand at the upper limit b.
Computes function integration by using an Upper Square Root mid-point quadrature
when upper integration bound lies at a function singularity.
Computes function integration by using an Upper Square Root mid-point quadrature
when upper integration bound lies at a function singularity.
This class implements a polynomial estimator using weighted evaluations.
Class containing the selection that was made on a weighted algorithm.