Least

This script implements data structures for solving the following two problems: - Range minimization: given an array X of data, quickly find min(X[i:j]) for different ranges i:j. - Least common ancestors: given a tree, quickly find the lowest tree node that is an ancestor of all of a given set... Platforms: Windows, Mac, *nix, Python, BSD Solaris

The Kalman filter can be interpreted as a feedback approach to minimize the least equare error. It can be applied to solve a nonlinear least square optimization problem. This function provides a way using the unscented Kalman filter to solve nonlinear least square optimization problems. Three... Platforms: Matlab

This package contains a set of tools that allows to deform in real-time points and images using the Moving Least Squares algorithms. This is a fast technique to get good image deformations without using the computational expansive techniques provided by the thin-plates splines algorithms. The... Platforms: Matlab

OLS - Orthogonal Least Squares: Proposed by T. Blumensath, M. E. DaviesStOLS - Stagewise OLS: Combining StOMP ideas with OLSROLS - Regularized OLS: Combining ROMP ideas with OLS Platforms: Matlab

The problem of fitting a straight line to data with uncertainties in both coordinates is solved using a weighted total least-squares algorithm. The parameters are transformed from the usual slope/y-axis intersection pair to slope angle and distance to the origin. The advantages of this are that... Platforms: Matlab

The problem of fitting a straight line to data with uncertainties in both coordinates is solved using a weighted total least-squares algorithm. The parameters are transformed from the usual slope/y-axis intersection pair to slope angle and distance to the origin. The advantages of this are that... Platforms: Matlab

Very limited application of least squares. It gives you the plotted curves of original and plotted data with the equation in polynomial form. you can also evaluate that polynomial at different points for prediction.note:1.it would be best viewed on 1024x768.2. if you copy and paste data in data... Platforms: Matlab

We present a Matlab toolbox which can solve basic problems related to the Total Least Squares (TLS) method in the modeling. By illustrative examples we show how to use the TLS method for solution of:- linear regression model- nonlinear regression model- fitting data in 3D space- identification of... Platforms: Matlab

Constructs discrete least-squares rational approximations to data using the full-Newton algorithm for solving separable non-linear least-squares problems that was developed in:Carlos F. Borges, A Full-Newton Approach to Separable Nonlinear Least Squares Problems and its Application to Discrete... Platforms: Matlab

ORTHLLS2D returns the Orthogonal Linear Least Squares estimate for parameters of line a x + b y + c = 0function f = OrthLLS2D(x, y)Inputs x and y must be real vectors of equal size.Output f is the real vector [a b c] where a, b and c are the estimated parameters of the linear equation.Since a... Platforms: Matlab

This function calculates the minimum-norm solution of the least squares problem A*X = B. Where A is low-rank matrix. The function LSMIN is faster then the matlab alternative X = pinv(A)*B. Uses the LAPACK functions (S,C,D,Z)EGLSS or (S,C,D,Z)EGLSD. Platforms: Matlab

Least Square method is common curve fitting approch. But for special some function, the tradiational Least Square method is hard to get enough accurancy dueo to gobal approximation. Moving Least Square(MLS) can fit any differentiable function through local approximation. Platforms: Matlab

Like the finite difference method, the Taylor Series Least Squares method can be used to estimate derivatives. The TLS technique can be used to estimate derivatives from scattered or unstructured data. The Hermite Taylor Series Least Squares technique augments the TLS approach with information... Platforms: Matlab

Least Square method is common curve fitting approch. But for special some function, the tradiational Least Square method is hard to get enough accurancy dueo to gobal approximation. Moving Least Square(MLS) can fit any differentiable function through local approximation.Here I share MLS2D... Platforms: Matlab

Moving Least Square(MLS) for 3-dimension Least Square method is common curve fitting approch. But for special some function, the tradiational Least Square method is hard to get enough accurancy dueo to gobal approximation. Moving Least Square(MLS) can fit any differentiable function through local... Platforms: Matlab

Demonstration of least squares data fitting using both inverse and backslash operators.This example was developed for use in teaching modeling, simulation, and optimization in graduate engineering courses. A corresponding video is available at: Platforms: Matlab

"Most And Least Read Posts Widget" is a free plugin for WordPress, developed by the Whiletrue.it staff to generate lists of the most and least read posts.The following options are customizable: * number of posts to show * exclude post whose title contains certain wordsThe plugin starts counting... Platforms: PHP

Statistics::LineFit module least squares line fit, weighted or unweighted. SYNOPSIS use Statistics::LineFit; $lineFit = Statistics::LineFit->new(); $lineFit->setData (@xValues, @yValues) or die "Invalid data"; ($intercept, $slope) = $lineFit->coefficients(); defined $intercept or die... Platforms: *nix

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)... Platforms: *nix

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... Platforms: *nix