RANDN Normally distributed pseudorandom numbers.
    R = RANDN(N) returns an N-by-N matrix containing pseudorandom values drawn
    from the standard normal distribution.  RANDN(M,N) or RANDN([M,N]) returns
    an M-by-N matrix. RANDN(M,N,P,...) or RANDN([M,N,P,...]) returns an
    M-by-N-by-P-by-... array. RANDN returns a scalar.  RANDN(SIZE(A)) returns
    an array the same size as A.
 
    Note: The size inputs M, N, P, ... should be nonnegative integers.
    Negative integers are treated as 0.
 
    R = RANDN(..., 'double') or R = RANDN(..., 'single') returns an array of
    normal values of the specified class.
 
    Compatibility Note: In versions of MATLAB prior to 7.7, you controlled
    the internal state of the random number stream used by RANDN by calling
    RANDN directly with the 'seed' or 'state' keywords.  That syntax is
    still supported for backwards compatibility, but is deprecated. Beginning
    in MATLAB 7.7, use the default stream as described in RANDSTREAM.
 
    The sequence of numbers produced by RANDN is determined by the internal
    state of the uniform pseudorandom number generator that underlies RAND,
    RANDI, and RANDN.  RANDN uses one or more uniform values from that
    default stream to generate each normal value.  Control the default
    stream using its properties and methods.  See RANDSTREAM for details
    about the default stream.
 
    Resetting the default stream to the same fixed state allows computations
    to be repeated.  Setting the stream to different states leads to unique
    computations, however, it does not improve any statistical properties.
    Since MATLAB uses the same state each time it starts up, RAND, RANDN, and
    RANDI will generate the same sequence of numbers in each session unless
    the state is changed.
 
    Examples:
 
       Generate values from a normal distribution with mean 1 and standard
       deviation 2.
          r = 1 + 2.*randn(100,1);
 
       Generate values from a bivariate normal distribution with specified mean
       vector and covariance matrix.
          mu = [1 2];
          Sigma = [1 .5; .5 2]; R = chol(Sigma);
          z = repmat(mu,100,1) + randn(100,2)*R;
 
       Save the current state of the default stream, generate 5 values,
       restore the state, and repeat the sequence.
          defaultStream = RandStream.getDefaultStream;
          savedState = defaultStream.State;
          z1 = randn(1,5)
          defaultStream.State = savedState;
          z2 = randn(1,5) % contains exactly the same values as z1
 
       Replace the default stream with a stream whose seed is based on CLOCK, so
       RANDN will return different values in different MATLAB sessions.  NOTE: It
       is usually not desirable to do this more than once per MATLAB session.
          RandStream.setDefaultStream(RandStream('mt19937ar','seed',sum(100*clock)));
          randn(1,5)