nncorr
Cross correlation between neural network time series
Syntax
nncorr(a,b,maxlag,'
flag
')
Description
nncorr(a,b,maxlag,'
takes these
arguments, flag
')
a | Matrix or cell array, with columns interpreted as timesteps, and having a total
number of matrix rows of |
b | Matrix or cell array, with columns interpreted as timesteps, and having a total
number of matrix rows of |
maxlag | Maximum number of time lags |
flag | Type of normalization (default = |
and returns an N
-by-M
cell array where each
{i,j}
element is a 2*maxlag+1
length row vector formed
from the correlations of a
elements (i.e., matrix row) i
and b
elements (i.e., matrix column) j
.
If a
and b
are specified with row vectors, the result
is returned in matrix form.
The options for the normalization flag
are:
'biased'
— scales the raw cross-correlation by 1/N.'unbiased'
— scales the raw correlation by1/(N-abs(k))
, wherek
is the index into the result.'coeff'
— normalizes the sequence so that the correlations at zero lag are 1.0.'none'
— no scaling. This is the default.
Examples
Here the autocorrelation of a random 1-element, 1-sample, 20-timestep signal is calculated with a maximum lag of 10.
a = nndata(1,1,20) aa = nncorr(a,a,10)
Here the cross-correlation of the first signal with another random 2-element signal are found, with a maximum lag of 8.
b = nndata(2,1,20) ab = nncorr(a,b,8)
Version History
Introduced in R2010b