Nonlinear Least Squares Algorithm

Statistics::OLS is a Perl module to perform ordinary least squares and associated statistics. SYNOPSIS use Statistics::OLS; my $ls = Statistics::OLS->new(); $ls->setData (@xydataset) or die( $ls->error() ); $ls->setData (@xdataset, @ydataset); $ls->regress(); my ($intercept, $slope)...

Statistics::GaussHelmert is a general weighted least squares estimation module. SYNOPSIS use Statistics::GaussHelmert; # create an empty model my $estimation = new Statistics::GaussHelmert; # setup the model given observations $y, covariance matrices # $Sigma_yy, an initial guess $b0...

DMFitter ActiveX control allows you to add sophisticated regression analysis tools to your software in a few lines of code. Features include: both linear and nonlinear (Levenberg-Marquardt) least squares curve fitting algorithms, arbitrary user models (defined analytically or by string...


Platforms: Windows

% Based on - A simple method for fitting of bounding rectangle to closed regions - D. Chaudhuri a , A. Samal b.% fit_rectangle - Function provides a least squares fit to the% given boundary points of an object of unknown shape.%% Inputs - Boundary elements that must be a Nx2 array. (atleast 3...


Platforms: Matlab

The function computes parameters a, b, and c of the weighted orthogonal least squares fit of line ax+by+c=0 to a set of 2D points with coordinates given by x and y and weights w.n=100;x=1:n;y=2.5*x + (5+300./x).*randn(1,n);plot(x,y,'.k')hold onw=1:n;[a b c] = wols(x,y,w);plot([0,n],[-c/b,-c/b -...


Platforms: Matlab

Solves the linear least squares problem with nonnegative variables using the newton's algorithm in:Portugal, Judice and Vicente, A comparison of block pivoting and interior point algorithms for linear least squares problems with nonnegative variables, Mathematics of Computation, 63(1994), pp....


Platforms: Matlab

Solves the linear least squares problem with nonnegative variables using the predictor-corrector algorithm in:Portugal, Judice and Vicente, A comparison of block pivoting and interior point algorithms for linear least squares problems with nonnegative variables, Mathematics of Computation,...


Platforms: Matlab

Inputs: 1. a 1 dimensional array of drug concentrations 2. the corresponding m x n array of responses Algorithm: generate a set of initial coefficients including the Hill coefficient fit the data to the 4 parameter dose-response curve using nonlinear least squares Output: a matrix of the 4...


Platforms: Matlab

BestCurFit calculates the parameters of 12 curves using the method of Linear Least Squares, fitting each curve to user data. The nonlinear equations are linearized. The obtained parameters are used for following optimization procedure using Simplex and Gauss-Newton algorithm. If it does not...


Platforms: Windows

Motofit co-refines Neutron and X-ray reflectometry data, using the Abeles matrix / Parratt recursion and least squares fitting (Genetic algorithm or Levenberg Marquardt). It works in the IGOR Pro environment (TM Wavemetrics).


Platforms: Windows, Mac, Linux

FitChiTool provides an interface similar to the Curve Fitting Toolbox to the fitChiSquare function.This allows you to perform fits using chi-square when measurement errors are known on linear or nonlinear models. It allows allows you to perform fits with multiple independent (more than one x)...


Platforms: Matlab

Genetic Programming (GP) generates nonlinear input-output models of dynamical systems from input-output measurement data. This GP-OLS toolbox applies Orthogonal Least Squares algorithm (OLS) to improve the search effeciency of GP.It can be used for static equation discovery or structure...


Platforms: Matlab

Demonstrates a way to implement the LSM algorithm.This is done on a example from the book of Astrom and Wittenmark, Adaptive Control, 2nd ed


Platforms: Matlab

This library implements a randomized algorithm for solving the least-squares equation x = arg min norm(A * x - b, 2) or the underdetermined system min(norm(x, 2)) s.t. Ax=b. With high probability the running this o(mn^2) for an m-by-n matrix.For details of the solver see the paper at:Blendenpik:...


Platforms: Matlab

Sake is a PHP implementation of Polynomial Least Squares Regression. The algorithm takes data points as input and returns the resulting polynomial. Graphs can be made using any plotting package. Sake provides the polynomial and will compute as many point


Platforms: Windows, Mac, Linux

This is a wrapper function to solve optimization problems (using FMINCON) of the form:min w.r.t. X of CostFunc(X) = beta*(W_ls*F(X)) + (1 - beta)*(max(W_mm*F(X)))subject to: A*X


Platforms: Matlab

For a system having multiple inputs x and outputs y, the partial coherence is the coherence computed between any individual input and the output when the effect of all other inputs is removed from the output by a linear least squares prediction. This coherence obeys the usual inequality, and will...


Platforms: Matlab

Functions NELSONFUN and NELSONFIT evaluate and fit (with non-linear least-squares) the Nelson-Siegel function, a popular yield-curve approximation device.


Platforms: Matlab

The code implemented a special case of the ellipsoid fitting technique proposed in the paper Least Squares Ellipsoid Specific Fitting, corresponding to case k= 4. Just put the two files in the same directory and run "testFit4". You can change the noise level to observe the fitting accuracy and...


Platforms: Matlab

M-files for unimodality (or monotonically) constrained least squares regression.


Platforms: Matlab