def nextTimeStep(
Q, epsilon
): #returns the next timestep and a 0 to continue loop, or the same value and a 1 to stop the loop
n = Q.shape[0]
NextQ = np.zeros((n, n))
for i in range(n):
for j in range(n):
NextQ[i, j] = expectation(
Q, i, j) #as defined in paper, see above functions
NextQN = matrixFunctions2d.normalize2dMatrix(NextQ)
#Cannot be weighted adjust, investigate why later NextQ = matrixFunctions2d.weightedAdjustment2d(NextQN)
NextQ = matrixFunctions2d.diagonalAdjustment2d(NextQN)
# print NextQ
dist = frobenius(NextQ, Q)
if (dist < epsilon):
return Q, dist, 1
else:
return NextQ, dist, 0
z = hops[index - 1]
switch = -1
if (iters > maxIters):
value = 0
iters += 1
# print index, value, iters, x, y
return hops[index]
mat = matrixFunctions2d.read2dMatrix(sys.argv[1])
mat = matrixFunctions2d.normalize2dMatrix(mat, 0)
P = np.copy(mat) #initial Probability Matrix
mat = logm(mat)
Q = matrixFunctions2d.diagonalAdjustment2d(mat, 0) #this is our Qnaught
#Q = matrixFunctions2d.weightedAdjustment2d(log)
def func(x, i, j, Q, P): #x is exp(Q(q_{i,j}))
# P = expm(Q)
Q_e = expm(Q)
n = Q.shape[0]
copy = np.copy(Q)
#diff = np.real(Q[i,j] - x)#initially zero for first iteration, should change as x changes
diff = np.real(
Q_e[i, j] -
P[i,
j]) #initially zero for first iteration, should change as x changes
for k in range(n):
for u in range(n):
print "Opened %s" % sys.argv[1]
out = open(sys.argv[2], "w+")
print "Output will be written to %s" % sys.argv[2]
if (k == 0):
print "Currently Configured to sum along the COLUMN"
else:
print "Currently Configured to sum along the ROW"
n = matrix.shape[0]
print "Normalizing Input Matrix along ",
if (k == 0):
print "each column"
else:
print "each row"
Norm = matrixFunctions2d.normalize2dMatrix(matrix, k)
#print Norm
logd = logm(Norm)
print logd
diag = matrixFunctions2d.diagonalAdjustment2d(logd)
#print free energy to f.txt
matrixFunctions2d.printDetailedBalanceftxt(diag,
"diagonal_db_%s.dat" % sys.argv[1])
matrixFunctions2d.write2dMatrix(diag, "diagonal_Matrix_%s.dat" % sys.argv[1])
#detailed balance
#matrixFunctions2d.print2dMatrix(diag)