def MetropolisJsampling(J,B,alpha): # Metropolis sampling algorithm for Jars
Jmean=np.array([0]*len(J)) # declare a J mean.
#Jtemp=np.array([0]*len(J))
for i in range(1,1000): # run the iteration for 1000 times.
Jnew=b.Jrandom(J) # Randomize the Jar sequence Value
acceptance_ratio=b.PalphaJB(alpha,Jnew,B)/b.PalphaJB(alpha,J,B) # find Acceptance Ratio, which is division of probabilities with new vs old values
if random.uniform(0,1)<= acceptance_ratio: # choose a random number, if It is less than acceptance ratio
J[:]=Jnew[:] # if it is less than, then assign it to a temp varaibale
Jmean[:]=np.add(J[:],Jmean[:]) # add temp variable to Jmean entries
return [x/1000.0 for x in Jmean[:]] # return mean of all entries for J values.
