Subject: w/out change to your routine

From: Defy The Scale <[email-masked]>
Date: Fri, 18 Jul 2014 12:37:45 -0700

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 read in the answers that all of those functions are available without additionally importing Numpy because the intention is for users not to have to know the distinction between the scipy and numpy namespaces. Now I wonder, because I follow the posts about numpy and scipy a bit and use it myself. And I almost always see numpy being there's some differences between scipy and numpy is in FFT stuff, i once got bitten by an issue that eventually tracked down to scipy and numpy's version of rfft defined differently The FFTs of SciPy and NumPy are different. SciPy uses the Fortran library FFTPACK, hence the name scipyfftpack. NumPy uses a C library called fftpack_lite; it has fewer functions and only supports double precision in NumPy. NumPy was originally named scipycore. NumPy and SciPy are closely related projects. The main reason for the separation is to ensure that the array library (NumPy) is lean and mean, as the bulk of SciPy is not always needed. Also, there was as decision among
  scientists to retire the array packages numeric (MIT) and numarray (NASA) in favor of scipycore, and thus it got the name NumPy. SciPy has still not reached 1.0, whereas NumPy is currently released as 1.8.1. NumPy has some facilities for FFT and linear algebra, but not as extensive as SciPshape[0] != b1.shape[0]:
        raise ValueError, 'incompatible dimensions'
    overwrite_a = overwrite_a or (a1 is not a and not hasattr(a,'__array__'))
    overwrite_b = overwrite_b or (b1 is not b and not hasattr(b,'__array__'))
    if debug:
        print 'solve:overwrite_a=',overwrite_a
        print 'solve:overwrite_b=',overwrite_b
    if sym_pos:
        posv, = get_lapack_funcs(('posv',),(a1,b1))
        c,x,info = posv(a1,b1,
                        lower = lower,
                        overwrite_a=overwrite_a,
                        overwrite_b=overwrite_b)You should never use eig() on a covariance matrix when you can simply use svd(). Depending on how many components you plan on using and the size of your data matrix, the numerical error introduced by the former (it does more floating point operations) can become significant. For the same reason you should never explicitly invert a matrix with inv() if what you're really interested in is the inverse times a vector or matrix; you should use solve() instead. tried deleting the app from simulator and also using Reset Contents but neither did anything. I had to physically delete the contents of this hone Simulator and also the derived data to get it to work again. I also have to close and restart Xcode as well. Very annoying because I have to do this each time I want to run the app in the "Resent Content Settings" does the trick but for large applications where lots of data is created/downloaded inside app, its always a headache, you have to
  recreate/re-download the data again and again to continue testing the app. Hope there is another solution exists which does not say "Reset Content Settings".   dev gr Feb 6 at 11:46