Simulating a Markov Chain

I would like to simulate a Markov chain to construct predictive model based probabilities. So I have a Markov chain for transformation sequences as:

P(S)=P(s1)∏(from i=2:L)P(s(i)|s(i-1))

This states the probability of a transformation applied in the sequence depends upon the transformation that have been applied before. Since the probability doesn't change among the sequence, this is a stationery markov chain. So the

P(s1)

is the probability at the first position of the sequence, and

P(s(i)|s(i-1))

the transition matrix with i=1,...,L which I have already learnt by counting. So my question is when I have a matrix n such as :

n =

       0     1     0     0     1     1     0
       1     1     0     1     1     1     0
       1     1     0     0     0     1     1
       1     1     1     1     1     1     0
       0     0     1     1     1     1     0
       1     0     0     0     1     1     0
       1     0     0     1     0     1     0
       0     0     1     0     0     1     1
       1     0     1     1     0     0     1
       0     0     1     0     0     1     0
       1     0     0     1     0     0     1
       0     0     0     1     0     0     1
       0     1     1     0     0     1     0
       1     1     1     1     1     1     1
       0     1     1     0     0     1     1
       0     1     0     1     1     0     1
       1     1     0     1     1     1     1
       0     1     1     0     0     0     0
       0     0     0     1     1     0     0
       0     1     0     0     0     1     0
       0     1     1     0     1     0     0
       1     0     0     0     0     1     1
       1     0     1     1     0     1     1
       1     0     0     1     0     0     0
       1     1     0     0     1     1     1

using the formula I can calculate the probability of having second variable 1 when first variable had been 1:

>> sum(n(find(n(:,1)),2))/sum(n(find(n(:,1)))) % which is the P(s(2)|s(1))

ans =

      0.4615

and I can carry on to calculate

P(s(i)|s(i-1)) *...*P(s(7)|s(6))

to come up with the product and assuming P(s1)=0.5, I can calculate the final P(S). Is that correct? My understanding is that this way we will have one value for P(S), while as stated in the textbook, we should have a probability matrix of (7*7). Thus apparently I am doing something right here, can anyone help me out on this?

-Amir