Huge News!Announcing our $40M Series B led by Abstract Ventures.Learn More
Socket
Sign inDemoInstall
Socket

curve-fitting

Package Overview
Dependencies
Maintainers
1
Versions
5
Alerts
File Explorer

Advanced tools

Socket logo

Install Socket

Detect and block malicious and high-risk dependencies

Install

curve-fitting

Levenberg Marquardt curve-fitting: minimize sum of weighted squared residuals

  • 0.0.5
  • latest
  • Source
  • npm
  • Socket score

Version published
Weekly downloads
3
increased by200%
Maintainers
1
Weekly downloads
 
Created
Source

Levenberg Marquardt curve-fitting

minimize sum of weighted squared residuals. Javascript version of matlab library from Henri Gavin.

See example for usage

--------- INPUT VARIABLES -----------

func = function of n independent variables, 't', and m parameters, 'p',

returning the simulated model: y_hat = func(t,p,c)

p = n-vector of initial guess of parameter values

t = m-vectors or matrix of independent variables (used as arg to func)

y_dat = m-vectors or matrix of data to be fit by func(t,p)

weight = weighting vector for least squares fit ( weight >= 0 ) ...

inverse of the standard measurement errors

Default: sqrt(d.o.f. / ( y_dat' * y_dat ))

dp = fractional increment of 'p' for numerical derivatives

dp(j)>0 central differences calculated

dp(j)<0 one sided 'backwards' differences calculated

dp(j)=0 sets corresponding partials to zero; i.e. holds p(j) fixed

Default: 0.001;

p_min = n-vector of lower bounds for parameter values

p_max = n-vector of upper bounds for parameter values

c = an optional matrix of values passed to func(t,p,c)

opts = vector of algorithmic parameters

parameter defaults meaning

opts(1) = prnt 3 >1 intermediate results; >2 plots

opts(2) = MaxIter 10*Npar maximum number of iterations

opts(3) = epsilon_1 1e-3 convergence tolerance for gradient

opts(4) = epsilon_2 1e-3 convergence tolerance for parameters

opts(5) = epsilon_3 1e-3 convergence tolerance for Chi-square

opts(6) = epsilon_4 1e-2 determines acceptance of a L-M step

opts(7) = lambda_0 1e-2 initial value of L-M paramter

opts(8) = lambda_UP_fac 11 factor for increasing lambda

opts(9) = lambda_DN_fac 9 factor for decreasing lambda

opts(10) = Update_Type 1 1: Levenberg-Marquardt lambda update

2: Quadratic update

3: Nielsen's lambda update equations

##---------- OUTPUT VARIABLES -----------

p = least-squares optimal estimate of the parameter values

X2 = Chi squared criteria

Henri Gavin, Dept. Civil & Environ. Engineering, Duke Univ. 22 Sep 2013 modified from: [http://octave.sourceforge.net/optim/function/leasqr.html] using references by

Press, et al., Numerical Recipes, Cambridge Univ. Press, 1992, Chapter 15.

Sam Roweis [http://www.cs.toronto.edu/~roweis/notes/lm.pdf]

Manolis Lourakis [http://www.ics.forth.gr/~lourakis/levmar/levmar.pdf]

Hans Nielson [http://www2.imm.dtu.dk/~hbn/publ/TR9905.ps]

Mathworks optimization toolbox reference manual

K. Madsen, H.B., Nielsen, and O. Tingleff

[http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/3215/pdf/imm3215.pdf]

Keywords

FAQs

Package last updated on 07 Sep 2015

Did you know?

Socket

Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.

Install

Related posts

SocketSocket SOC 2 Logo

Product

  • Package Alerts
  • Integrations
  • Docs
  • Pricing
  • FAQ
  • Roadmap
  • Changelog

Packages

npm

Stay in touch

Get open source security insights delivered straight into your inbox.


  • Terms
  • Privacy
  • Security

Made with ⚡️ by Socket Inc