Least Squares Regressio Contact Php

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...

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

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

This example solves a complex curve fitting problem that involves parameter pooling and stratification using a nonlinear least-squares approach. This example also takes advantage of some new language features with MATLAB 7. * Anonymous functions * Nested functions


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

Among the considerations in the use of analysis of regression, outliers or bad values can seriously disturb the least-squares fit. They falls far from the line implied by the rest of the data. If these points are really outliers, then the estimate of the intercept may be incorrect and the...


Platforms: Matlab

POLYFIT_ROOTS Least-squares polynomial fit to data.[R,K] = POLYFIT_ROOTS(X, Y, N, TOL) finds the roots R and constant K so that the polynomial P(s) = K*(s-R(1))*(s-R(2))* ... *(s-R(N)) is the best least-squares fit to the data Y at points X.Argument TOL bounds the accuracy of the fit, and if...


Platforms: Matlab

This toolbox contains a set of functions which can be used to compute the Least Median of Squares regression, the Reweighted Least Squares regression, the accociated location and scale estiamtors, and the Minimum Volume Ellipsoid. The concept is the minimization of the median of the squared...


Platforms: Matlab

Add charts to your Java application or website (can be used with ASP, Php, JSP[TM] ...).RChart is a popular product due to the fact that it is very simple but powerfull and useful product.Create more than 18 different types of charts and combine them:- Linechart- Areachart- Piechart- Max.Min...


Platforms: Java

Function: Savitzky-Golay Smoothing and Differentiation Filter The Savitzky-Golay smoothing/differentiation filter (i.e., the polynomial smoothing/differentiation filter, or the least-squares smoothing/differentiation filters) optimally fit a set of data points to polynomials of different degrees....


Platforms: Matlab

Group and Block Sparse Signal reconstruction viaMatching Pursuit - BMP, GMPGradient Pursuit - block_gp, group_gpNearly Orthogonal Matching Pursuit - block_nomp, group_nompPartial Conjugate Gradient Pursuit - block_pcgp, group_pcgpOrthogonal Least Squares - BOLS, GOLS


Platforms: Matlab

It is known that there is no sufficient Matlab program about neuro-fuzzy classifiers. Generally, ANFIS is used as classifier. ANFIS is a function approximator program. But, the usage of ANFIS for classifications is unfavorable. For example, there are three classes, and labeled as 1, 2 and 3. The...


Platforms: Matlab

rilt Regularized Inverse Laplace Transform [g,yfit,cfg] = rilt(t,y,s,g0,alpha) Array g(s) is the Inverse Laplace Transform of the array y(t), calculated by a regularized least squares method.This script is an emulation of S. Provencher CONTIN program, written in Fortran. See...


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

Group and Block Sparse Signal reconstruction viaMatching Pursuit - BMP, GMPGradient Pursuit - block_gp, group_gpNearly Orthogonal Matching Pursuit - block_nomp, group_nompPartial Conjugate Gradient Pursuit - block_pcgp, group_pcgpOrthogonal Least Squares - BOLS, GOLS


Platforms: Matlab

4 files are included in the package:(1) fs01.m - the 1st-order local linear approximation (LLA) very similar to algorithm by Farmer and Sidorowich (1987). Uses constrained linear least squares (lsqlin) and(2) ts1.m - to reconstruct the vectors of the state space.(3) Example.m - runs fs01 and ts1...


Platforms: Matlab

Implementation of Levy et al's LSCM algorithm [1]. Also includes a basic Wavefront OBJ file format reader, and an implementation taking advantage of speed-ups available using JACKET from Accelereyes to perform some computations on an NVIDIA GPU.[1] B. LdoTevy, S. Petitjean, N. Ray, and J....


Platforms: Matlab

I have about a hundred MATLAB routines related to econometrics, statistics and introductory economics teaching, which are written mostly in my spare time in the past years. Most of these MATLAB functions / scripts are supplied with a graphic user interface so that so you may estimate the model...


Platforms: Matlab

The main advantages of the Hausdorff regression are that one should not specify the direction of the errors (vertical, horizontal, orthogonal ... with the ordinary least squares) and that it is less sensitive with respect to the square of the distance.This is the original idea. Any contribution...


Platforms: Matlab