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)
#!/usr/bin/python
import sys
import numpy as np
import matrixFunctions2d
from scipy.linalg import expm, logm
matrix = matrixFunctions2d.read2dMatrix(sys.argv[1])
Norm = matrixFunctions2d.normalize2dMatrix(matrix, 0)
logd = logm(Norm)
#mat = expm(matrix)
#matrixFunctions2d.printDetailedBalanceftxt(mat, "quickout.txt")
matrixFunctions2d.printDetailedBalanceftxt(
logd, "detailedBalance_%s.dat" % sys.argv[1])
#matrixFunctions2d.printDetailedBalanceftxt(matrix, "quickout.txt")
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
log = logm(Norm)
adjusted = matrixFunctions2d.weightedAdjustment2d(log, k)
exp = expm(adjusted)
matrixFunctions2d.printDetailedBalanceftxt(adjusted,
"weighted_db_%s.dat" % sys.argv[1])
matrixFunctions2d.write2dMatrix(adjusted,
"weighted_Matrix_%s.dat" % sys.argv[1])
#matrixFunctions2d.print2dMatrix(adjusted)
norm = matrixFunctions2d.normalize2dMatrix(mat, 1)#along row
log = logm(norm)
Q = matrixFunctions2d.diagonalAdjustment2d(log) #this is our Qnaught
#Q = matrixFunctions2d.weightedAdjustment2d(log)
def func(x, i, j, Q):
P = expm(Q)
n = Q.shape[0]
copy = np.copy(Q)
for k in range(n):
diff = Q[i,j] - x#initially zero for first iteration, should change as x changes
for u in range(n):
if(u!=j):
copy[i,u] += float(diff/n)
copy[i,i] = -1 * (np.sum(copy[i,:]) - copy[i,i]) #set diagonal to neg sum
return distance(copy, Q)
n = norm.shape[0]
q = np.copy(Q)
for i in range(n):
for j in range(n):
if(i!=j):
calculations+=1
print calculations
q[i,j] = optimize.fmin(func, Q[i,j], args=(i,j,Q))#argmin(i, j, Q, c)
print q[i,j]
Q = np.copy(q) #put updated matrix in place of Q
matrixFunctions2d.printDetailedBalanceftxt(np.transpose(Q), "componentwise_%s.dat"%sys.argv[1])
elif(j==0):
Sort[j,i] = Sort[j,i] + l[i]
else:
Sort[j,i] = Sort[j,i] + l[i]
#matrixFunctions2d.print2dMatrix(Sort)
#use Unsort Matrix to get final output
final = np.zeros((n,n))
if(k==1):
for i in range(n):
for j in range(n):
final[i,j] = Sort[i,int(Unsort[i,j])-1]
else:
for i in range(n):
for j in range(n):
final[j,i] = Sort[int(Unsort[j,i])-1,i]
#print
#print
#matrixFunctions2d.print2dMatrix(Unsort)
#print
#print
#matrixFunctions2d.print2dMatrix(final)
matrixFunctions2d.printDetailedBalanceftxt(final, "QOG_DB_%s.dat"%sys.argv[1])
matrixFunctions2d.write2dMatrix(final, "QOG_mat_%s.dat"%sys.argv[1])
xval = 4
niters = 1000
deltaT = 1 #number of times to jump through prior before moving to next step
repeats = 100
while (nGibbs > 0):
print "Gibbs Iteration: ", totalIters - nGibbs
nGibbs -= 1
samples, accepted, totalCount = metropolis.metropolisHastings(
niters, repeats, n, distribution, deltaT,
metropolis.transitionProbSimple, metropolis.stepGenerator,
Q) #Draw Samples Using Q
N = metropolis.samplesToN(samples, n)
N = matrixFunctions2d.normalize2dMatrix(N, 0) #normalize along row
for i in range(n):
for j in range(n):
if (N[i, j] == 0):
N[i, j] = val
Q[i, j] = gammaFunction(N[i, i], N[i, j],
alpha[i, j]) #Use Samples to make new Q
for i in range(n):
Q[i, i] = -1.0 * (np.sum(Q[i, :]) - Q[i, i])
#repeat
matrixFunctions2d.write2dMatrix(
N, "gibbs_matrix_%s_%s.dat" % (sys.argv[1], totalIters - nGibbs))
matrixFunctions2d.printDetailedBalanceftxt(
N, "gibbs_db_%s_%s.dat" % (sys.argv[1], totalIters - nGibbs),
"#Number of Iterations : %s" % sys.argv[2])
# counts = metropolis.countSamples(samples, n)
# metropolis.printCounts(fname, totalCount, accepted, n, counts, xval)
from scipy.linalg import expm, logm
mat = matrixFunctions2d.read2dMatrix(sys.argv[1])
tau = int(sys.argv[2])
kb = 0.0019872041
T = 298
kbT = kb * T
#print kbT
beta = 1 / kbT
#print beta
binWidth = 0.083333
#edit, first calculate DB statistics, then D(x), then double sum for an answer
#calculate DB
f = matrixFunctions2d.printDetailedBalanceftxt(mat, "temp")
#print f[11], f[35] Our -1, 1 are at 11/35 respectivelyi
lower = 11
upper = 35
adjustment = 1 #the F index is off by one
lower = 12
upper = 36
adjustment = 0
P = expm(0.000001 * mat)
f = np.zeros(P.shape[0])
sumd = 0
for i in range(P.shape[0]):
sumd += P[i, i]
for i in range(P.shape[0]):
f[i] = P[i, i] / sumd
for i in range(40):
