1dsvd


Usage: 1dsvd [options] 1Dfile 1Dfile ...
- Computes SVD of the matrix formed by the 1D file(s).
- Output appears on stdout; to save it, use '>' redirection.

OPTIONS:
 -one    = Make 1st vector be all 1's.
 -vmean  = Remove mean from each vector (can't be used with -one).
 -vnorm  = Make L2-norm of each vector = 1 before SVD.
           * The above 2 options mirror those in 3dpc.
 -cond   = Only print condition number (ratio of extremes)
 -sing   = Only print singular values
           * To compare the singular values from 1dsvd with those from
             3dDeconvolve you must use the -vnorm option with 1dsvd.
             For example, try
               3dDeconvolve -nodata 200 1 -polort 5 -num_stimts 1 \
                            -stim_times 1 '1D: 30 130' 'BLOCK(50,1)' -singvals
               1dsvd -sing -vnorm nodata.xmat.1D
 -sort   = Sort singular values (descending) [the default]
 -nosort = Don't bother to sort the singular values
 -asort  = Sort singular values (ascending)
 -1Dleft = Only output left eigenvectors, in a .1D format
           This might be useful for reducing the number of
           columns in a design matrix.  The singular values
           are printed at the top of each vector column,
           as a '#...' comment line.
 -nev n  = If -1Dleft is used, '-nev' specifies to output only
           the first 'n' eigenvectors, rather than all of them.
           * If you are a tricky person, such as Souheil, you can
             put a '%' after the value, and then you are saying
             keep eigenvectors until at least n% of the sum of
             singular values is accounted for.  In this usage,
             'n' must be a number less than 100; for example, to
             reduce a matrix down to a smaller set of columns that
             capture most of its column space, try something like
               1dsvd -1Dleft -nev 99% Xorig.1D > X99.1D
EXAMPLE:
 1dsvd -vmean -vnorm -1Dleft fred.1D'[1..6]' | 1dplot -stdin
NOTES:
* Call the input n X m matrix [A] (n rows, m columns).  The SVD
  is the factorization [A] = [U] [S] [V]' ('=transpose), where
  - [U] is an n x m matrix (whose columns are the 'Left vectors')
  - [S] is a diagonal m x m matrix (the 'singular values')
  - [V] is an m x m matrix (whose columns are the 'Right vectors')
* The default output of the program is
  - An echo of the input [A]
  - The [U] matrix, each column headed by its singular value
  - The [V] matrix, each column headed by its singular value
    (please note that [V] is output, not [V]')
  - The pseudo-inverse of [A]
* This program was written simply for some testing purposes,
  but is distributed with AFNI because it might be useful-ish.
* Recall that you can transpose a .1D file on input by putting
  an escaped ' character after the filename.  For example,
    1dsvd fred.1D\'
  You can use this feature to get around the fact that there
  is no '-1Dright' option.  If you understand.
* For more information on the SVD, you can start at
  http://en.wikipedia.org/wiki/Singular_value_decomposition
* Author: Zhark the Algebraical (Linear).

++ Compile date = Oct 13 2022 {AFNI_22.3.03:linux_ubuntu_16_64}