def test_spinbitconversion():
a = np.array([0., 1., 0., 1., 0., 1., 0., 1., 0., 1.])
b = tools.bits2spins(a)
c = tools.spins2bits(b)
assert (np.all(a == [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]))
assert (np.all(b == [1, -1, 1, -1, 1, -1, 1, -1, 1, -1]))
assert (np.all(c == a))
def bitstr2spins(vec):
""" Take a bitstring and return a spinvector. """
a = [int(k) for k in vec]
return tools.bits2spins(a)
return reduce(lambda x, y: x + y,
[str(int(k)) for k in tools.spins2bits(vec)])
# Construct Ising matrix
memMat = sp.matrix(memories).T
isingJ = sp.triu(memMat * sp.linalg.pinv(memMat))
isingJ -= sp.diag(sp.diag(isingJ))
isingJ = sp.triu(memMat * sp.linalg.pinv(memMat))
isingJ += inpbias * sp.diag(vinput)
isingJ = sps.dok_matrix(isingJ)
# get energies of all states
results = []
energies = np.zeros(2**nspins)
for b in [bin(x)[2:].rjust(nspins, '0') for x in range(2**nspins)]:
svec = tools.bits2spins(np.array([int(k) for k in b]))
energies[int(b, 2)] = sa.ClassicalIsingEnergy(svec, isingJ)
results.append([energies[int(b, 2)], b])
print("All possible states and their energies:")
for res in sorted(results)[:10]:
print("%s %2.4f" % tuple(res[::-1]))
# Initialize random state
spinVector = np.array([2 * rng.randint(2) - 1 for k in range(nspins)],
dtype=np.float)
confs = np.tile(spinVector, (trotterslices, 1)).T
# Generate list of nearest-neighbors for each spin
neighbors = tools.GenerateNeighbors(nspins, isingJ, 8)
# keep a count of the populations
coinc_sa = np.zeros(2**nspins)
coinc_qa = np.zeros(2**nspins)
def bitstr2spins(vec):
""" Take a bitstring and return a spinvector. """
a = [ int(k) for k in vec ]
return tools.bits2spins(a)
print ("Initial state energy: ",
sorted([ sa.ClassicalIsingEnergy(v, isingJ) for v in svec ]))
print '\n'
# Generate list of nearest-neighbors for each spin
neighbors = tools.GenerateNeighbors(nspins, isingJ, 4)
# SA annealing schedule
tannealingsched = np.linspace(preannealingtemp,
annealingtemp,
annealingsteps)
# Normal annealing routine
t0 = time.time()
for vec in svec:
sa.Anneal(tannealingsched, annealingmcsteps, vec, neighbors, rng)
t1 = time.time()
print ("Final SA energy: ",
sorted([ sa.ClassicalIsingEnergy(v, isingJ) for v in svec ]))
print "SA time (seconds): ", str(t1-t0)
print '\n'
# Now try multispin encoding
t0 = time.time()
sa.Anneal_multispin(tannealingsched, annealingmcsteps, msvec, neighbors, rng)
t1 = time.time()
print ("Final SA-multispin energies: ",
sorted([ sa.ClassicalIsingEnergy(tools.bits2spins(v), isingJ) for v in msvec ]))
print "SA-multispin time (seconds): ", str(t1-t0)
print '\n'
svec = 2 * msvec - 1
print("Initial state energy: ",
sorted([sa.ClassicalIsingEnergy(v, isingJ) for v in svec]))
print '\n'
# Generate list of nearest-neighbors for each spin
neighbors = tools.GenerateNeighbors(nspins, isingJ, 4)
# SA annealing schedule
tannealingsched = np.linspace(preannealingtemp, annealingtemp, annealingsteps)
# Normal annealing routine
t0 = time.time()
for vec in svec:
sa.Anneal(tannealingsched, annealingmcsteps, vec, neighbors, rng)
t1 = time.time()
print("Final SA energy: ",
sorted([sa.ClassicalIsingEnergy(v, isingJ) for v in svec]))
print "SA time (seconds): ", str(t1 - t0)
print '\n'
# Now try multispin encoding
t0 = time.time()
sa.Anneal_multispin(tannealingsched, annealingmcsteps, msvec, neighbors, rng)
t1 = time.time()
print(
"Final SA-multispin energies: ",
sorted(
[sa.ClassicalIsingEnergy(tools.bits2spins(v), isingJ) for v in msvec]))
print "SA-multispin time (seconds): ", str(t1 - t0)
print '\n'
def getbitstr(vec):
""" Return bitstring from spin vector array. """
return reduce(lambda x,y: x+y,
[ str(int(k)) for k in tools.spins2bits(vec) ])
# Construct Ising matrix
memMat = sp.matrix(memories).T
isingJ = sp.triu(memMat * sp.linalg.pinv(memMat))
isingJ -= sp.diag(sp.diag(isingJ))
isingJ = sp.triu(memMat * sp.linalg.pinv(memMat))
isingJ += inpbias*sp.diag(vinput)
isingJ = sps.dok_matrix(isingJ)
# get energies of all states
results = []
energies = np.zeros(2**nspins)
for b in [ bin(x)[2:].rjust(nspins, '0') for x in range(2**nspins) ]:
svec = tools.bits2spins(np.array([ int(k) for k in b ]))
energies[int(b,2)] = sa.ClassicalIsingEnergy(svec, isingJ)
results.append([energies[int(b,2)], b])
print("All possible states and their energies:")
for res in sorted(results)[:10]:
print("%s %2.4f" % tuple(res[::-1]))
# Initialize random state
spinVector = np.array([ 2*rng.randint(2)-1 for k in range(nspins) ],
dtype=np.float)
confs = np.tile(spinVector, (trotterslices, 1)).T
# Generate list of nearest-neighbors for each spin
neighbors = tools.GenerateNeighbors(nspins, isingJ, 8)
# keep a count of the populations
coinc_sa = np.zeros(2**nspins)
coinc_qa = np.zeros(2**nspins)
