用于数值分析的iOS5 Objective-C库还是GNU Octave包装类？

Question

I'm doing some numerical estimation and correction with the Kalman filter , and would like to better estimate my parameters of Q and R, preferably dynamically. http://en.wikipedia.org/wiki/Kalman_filter#Estimation_of_the_noise_covariances_Qk_and_Rk

That article mentions that GNU Octave is currently the best way of determining these parameters from data: http://en.wikipedia.org/wiki/GNU_Octave#C.2B.2B_integration Unfortunately it is written for Matlab, and there's supposedly a C++ implementation. I'm very weak in C++ and would not even know how to import a C++ library and link it properly in XCode. All of my C++ libraries to date have been wrapped in 3rd party Objective-C classes.

Has anyone used the C++ implementation for scientific computing or engineering applications on iPhone? I'd appreciate any pointers or tutorials on how to do this kind of analysis with Objective-C.

Additional keywords: estimating covariance from data

Autocovariance Least-Squares (ALS) technique

noise covariance

Thank you!

Answer 1

I do not know of any such C++ library, if you fancy doing numerical analysis on iOS, the best way to go is the accelerate framework , specifically (from this description ):

Linear Algebra: LAPACK and BLAS

The Basic Linear Algebra Subprograms (BLAS) and Linear Algebra Package (LAPACK) libraries contain—as you would expect—functions to perform linear algebra computations such as solving simultaneous linear equations, least squares solutions of linear equations, and eigenvalue problems. The BLAS library serves as a building block for the LAPACK library. The BLAS and LAPACK libraries are widely distributed and industry standard computational libraries. They are available on a number of different platforms and architectures. So, if you are already using these libraries you should feel right at home, as the APIs are exactly the same on Mac OS X.

You'll need a fairly good grounding in C, pointers, arrays and such though, no way around it I feel. There is a detailed description of how to use these linear algebra primitives to implement kalman filtering (although this is using R, so probably not of mush use to you).

This is a SO post on Kalman Filtering which expressed my opinion quite well. I'm afraid I think the chances of finding a magic Objective-C wrapper for Kalman Filtering are fairly low, though I would be very happy to be proven wrong!