AMI computes and plots average mutual information (ami) and correlation of univariate or bivariate time series for different values of time lag.  
 
USAGE:  
   [amis corrs] = ami(xy,nBins,nLags)  
 
INPUT:  
  xy: either univariate (x) or bivariate ([x y]) time series data. If bivariate time series are given then x should be independent variable and y should be dependent variable. If univariate time series is given then autocorrelation is calculated instead of cross correlation.  
 
  nBins: number of bins for time series data to compute distribution which is required to compute ami. nBins should be either vector of 2 elements (for bivariate) or scalar (univariate).  
 
  nLags: number of time lags to compute ami and correlation. Computation is done for lags values of 0:nLags.  
   
OUTPUT:  
  amis: vector of average mutual information for time lags of 0:nLags  
 
  corrs: vector of correlation (or autocorrelation for univariate time seris) for time lags of 0:nLags  
 
EXAMPLES:  
  xy = rand(1000,2);  
  nBins = [15 10];  
  nLags = 25;  
  [amis corrs]= ami(xy,nBins,nLags);