gistfile1.py

   def discrete_sample(self,num_samples,burn_in=0):
        sample = randint(self.range[0],self.range[1],self.size)
        samples = []
        prop_dist = 1.0/(self.range[1]**2)
        print prop_dist
        print self.dist([0,0])
        print self.dist([0,1])
        print self.dist([1,0])
        print self.dist([1,1])
        proposal = randint(self.range[0],self.range[1],self.size)
        while len(samples) < num_samples:
            proposal_prob = min(1, self.dist(proposal)*1.0/self.dist(sample))
#            print 'self.dist(prop): %f | self.dist(sample): %f' %(self.dist(proposal),self.dist(sample))
#            print self.dist(proposal)*1.0/self.dist(sample)
#            print proposal_prob
            if proposal_prob > np.random.rand():
                print sample
                samples.append(sample)
                sample = proposal
                proposal = randint(self.range[0],self.range[1],self.size)
        return samples

text

input distribution
..[[ 0.05  0.45]
 [ 0.25  0.25]]

output distribution after 10,000 samples
[[ 0.3  0.3]
 [ 0.2  0.2]]