I have a data file “a_new.txt” having the inferred links in the network. As a first step, I set the couplings to zero that are extremely close to zero. Now I am left with non-zero couplings. In the process of decimation, I maximize the likelihood(Lmax) and sort the inferred couplings and again move the least couplings from(\rho) from j1s to j0s(set of null couplings). The fraction of decimated couplings should be x = (Nq- \rho)/ Nq. As per the definition the tilted likelihood should decrease once we start to prune the relevant couplings from the network. But in my code, it keeps on ascends..Following is the code I wrote in Python.
# Load the inferred values: a_new = np.loadtxt('anew.txt') #print a_new j_old = 0.01 count = 0 Nq = 2000 ## Decimation algorithm: ## Null couplings: J0 = a_new[np.where(a_new <= -0.03)] #Decimate the small values: J0s = a_new[np.where(a_new <= -0.03)] = 0.0 n1 = (a_new == 0).sum() # count the zero values print n1 # Non-zero couplings: J2 = a_new[np.where(a_new >= -0.03)] j2s = np.count_nonzero(a_new) #print J2,j2s for i in range(500): j0s = 0.0 #maximize: x = np.random.uniform(0.2,0.4) Lmax = np.log(1-x)*(s2[j1])-((1-x)*sum1/1-np.power((1-x), sum1))*(np.log(1-x)*s2[j1]) #print Lmax #infer the couplings: jnew = j_old + (rate*Lmax[:,44]) #sort inferred couplings by absolute values: jabs = np.absolute(jnew) #print jabs j1s = jabs[np.where(jabs < 0.0009)] = 0.0 #print jabs #move the least couplings from j1s to j0s: # jabs1 = np.extract(j1s == 0.0, j1s) #j1s ==0.0 jabs2 = np.extract(jabs >= 0.0009, jabs) #print jabs2 if(np.any(jabs != 0.0)): count = count+1 print count, jabs, j1s #Nq = (100*99)/2 #x1 = (Nq - jj)/ Nq # jj: fraction of non-decimated couplings: #x1 = np.arange(1,0,-0.01) x1 = (Nq-j1s)/Nq xs = (Nq-count)/2 # of decimated couplings: #print x1 #define the tilted likelihood: tplf = L1[:,44]-x1*Lmax[:,44]-(1-x1)*(-(np.log(2))) #print tplf, x1, Nq, j1s #print tplf, xs, x1
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