Freeware Downloads for "Least Squares Java Linear Solver"

Related: Least Squares Java Source - Least Squares Java Source Code - Least Squares Fit Routine - Algorithm Java Sudoku Solver - Creating A Grid Squares Java

Statistical models with python using numpy and scipy. Currently covers linear regression (with ordinary, generalized and weighted least squares), robust linear regression, and generalized linear model..

This application calculates the angular and linear coefficients of a linear regression considering the Linear Least Squares methodology..

  • Platform: Android 2.x, Android 3.x, Android 4.4, Android 4.x
  • Publisher: Prof. Braga
  • Date: 22-03-2014
  • Size: 282 KB

OLS - Orthogonal Least Squares: Proposed by T. Blumensath, M. E. Davies StOLS - Stagewise OLS: Combining StOMP ideas with OLS ROLS - Regularized OLS: Combining ROMP ideas with OLS.

  • Platform: Matlab, Scripts
  • Publisher: Angshul Majumdar
  • Date: 07-05-2013
  • Size: 10 KB

Least Squares Fit Routine (LuSiFeR) is a program for least squares data analysis. Least Squares Fit Routine (LuSiFeR) is a software application that was designed for least squares data analysis. The first package is in the form of a script for MATLAB, while the second is a standalone program written in C++, with extended capabilities.Both packages perform least-squares regression analysis on a user-supplied dataset, and calculate appropriate fitted parameters (with uncertainties) and an associated X2 value.

The function LMFsolve.m serves for finding optimal solution of an overdetermined system of nonlinear equations in the least-squares sense. The standard Levenberg- Marquardt algorithm was modified by Fletcher and coded in FORTRAN many years ago. LMFsolve is its essentially shortened version implemented in MATLAB and complemented by setting iteration parameters as options. This part of the code has been strongly influenced by Duane Hanselman's function mmfsolve.m. Next to it, a finite difference approximation of Jacobian matrix is appended to it as a nested subfunction as well as a function for dispaying of intermediate results.

  • Platform: Matlab, Scripts
  • Publisher: Miroslav Balda
  • Date: 01-02-2013
  • Size: 10 KB

The function The LMFnlsq.m serves for finding optimal solution of an overdetermined system of nonlinear equations in the least-squares sense. The standard Levenberg- Marquardt algorithm was modified by Fletcher and coded in FORTRAN many years ago (see the Reference). This version of LMFnlsq is its complete MATLAB implementation complemented by setting parameters of iterations as options. This part of the code has been strongly influenced by Duane Hanselman's function mmfsolve.m. Calling of the function is rather simple and is one of the following: LMFnlsq % for help output x = LMFnlsq(Eqns,X0); x = LMFnlsq(Eqns,X0); x = LMFnlsq(Eqns,X0); x = LMFnlsq(Eqns,X0,'Name',Value,.

  • Platform: Matlab, Scripts
  • Publisher: Miroslav Balda
  • Date: 05-06-2013
  • Size: 870 KB

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 reveal the existence of a linear relationship between a particular residual input and the output even when the relationship is not apparent from the ordinary coherence function.

  • nnls
  • License: Freeware
  • Rating

solves the linear least squares problem with nonnegative variables using the block principal pivoting 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. 625-643.

  • Platform: Matlab, Scripts
  • Publisher: Uriel Roque
  • Date: 19-04-2013
  • Size: 10 KB

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 to predict (4) composite.txt - synthetic time series of decadal average sunspot number.

  • Platform: Matlab, Scripts
  • Publisher: Dmitry Volobuev
  • Date: 02-04-2013
  • Size: 10 KB

This project is a maze solver library written in Java.
Java Maze Solver License - GNU General Public License (GPL).

variogramfit performs a least squares fit of various theoretical variograms to an experimental, isotropic variogram. The user can choose between various bounded (e.g. spherical) and unbounded (e.g. exponential) models. A nugget variance can be modelled as well, but higher nested models are not supported. The function works best with the function fminsearchbnd available on the FEX. You should download it from the File Exchange (File ID: #8277). If you don't have fminsearchbnd, variogramfit uses fminsearch.

  • dmgps
  • License: Freeware
  • Rating

The design matrix of a GPS network is established for any kind of least squares adjustment, namely; -free (trace minimum), -minimum constrained, -over-determined. The user can adapt easily dmgps to the adjustment problem of a GPS network whose baselines are taken as observations. Moreover, it can be used for optimization of GPS networks..

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)..

  • Sake
  • License: Freeware
  • Rating

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.

Tidal Analysis Program in PYthon (TAPPY) uses the least squares optimization function from scipy to perform a harmonic analysis (calculate amplitude and phases of a set of sine waves) of a hourly time series of water level values..

zpkfit uses the nonlinear least-squares fitter (lsqnonlin) to fit an analytic model of any (fixed) number of poles and zeroes to numerical (presumably measured) frequency response data. It takes as arguments a frequency vector, a frequency response vector, and vectors of the initial guesses of pole and zero locations. The number of poles and zeroes given in the initial guesses determines the order of the model that will be fit. This works extremely well for models that can be well fit by a few poles and zeroes.

  • Platform: Matlab, Scripts
  • Publisher: Tobin Fricke
  • Date: 13-04-2013
  • Size: 10 KB

Both functions caculate the Lomb normalized periodogram (aka Lomb-Scargle, Gauss-Vanicek or Least-Squares spectrum) of a vector x with coordinates in t, which is essentially a generalization of the DFT for unevenly sampled data. The codes are transcriptions from Fortran of the subroutines found in Section 13.8 (pp. 569-577) of "Numerical recipes in Fortran 77: the art of scientific computing", 2nd ed., vol. 1, Cambridge University Press, NY, USA, 2001 by WH Press, SA Teukolsky, WT Vetterling and BP Flannery, However, Matlab's characteristics have been taken into account in order to make it fast for Matlab.

  • pol
  • License: Freeware
  • Rating

Calculates a topographic polygonal using the least-squares method. Data files for demo are included..

  • Platform: Matlab, Scripts
  • Publisher: Luis Ros
  • Date: 19-04-2013
  • Size: 10 KB

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.

  • Platform: Matlab, Scripts
  • Publisher: Robert Bemis
  • Date: 03-03-2013
  • Size: 51 KB

The goal of "Statistics Tutor" is to make statistics learning easier.

Major Updates:
Chapter 4:
What is least square line?
Linear regression modeling.
Trend, seasonal variations, cyclical variations, and irregularity in time series analysis.
How to get seasonal index in time series analysis.

Chapter 5
Basic rules, rule of addition, rule of multiplication, and their applications (Updated examples and solutions)

Applications of probability
- What is the probability of winning a lottery

Combinations in probability.

  • Platform: Android 1.x, Android 2.x, Android 3.x, Android 4.4, Android 4.x
  • Publisher: Rockwall Harbor Publishing
  • Date: 22-07-2014
  • Size: 782 KB

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