from numpy import *
hit_idx = (0,4)
# Define a graph by edge list
edges = [[0,1],[1,2],[2,3],[2,4]]
# Create adj. matrix
A = zeros((5,5))
A[zip(*edges)] = 1
# Undirected condition
A += A.T
# Make the final state an absorbing condition
A[hit_idx[1],:] = 0
A[hit_idx[1],hit_idx[1]] = 1
# Make a proper Markov matrix by row normalizing
A = (A.T/A.sum(axis=1)).T
B = A.copy()
Z = []
for n in xrange(100):
Z.append( B[hit_idx] )
B = dot(B,A)
from pylab import *
plot(Z)
xlabel("steps")
ylabel("hit probability")
show()