def analyse_psiscaling(J2, size):
h = load_hamiltonian('J1J2', size=size, J2=J2)
configs, indexer = h.get_config_table()
H, e0, v0, configs = analyse_exact(h, do_printsign=False, num_eng=1)
rand = np.random.randn(len(v0))
func_list = [
lambda x: x**3, lambda x: x**5, lambda x: np.sinh(x),
lambda x: np.cosh(x)
]
labels = ['x^3', 'x^5', 'sinh(x)', 'cosh(x)']
x = np.arange(1, len(v0) + 1)
amp = abs(v0)
amp = np.sort(amp)[::-1]
plt.ion()
#plt.plot(np.log(amp))
plt.plot(x, amp)
for func in func_list:
amp2 = abs(func(rand))
amp2 = np.sort(amp2)[::-1] / np.linalg.norm(amp2)
plt.plot(x, amp2)
#plt.xscale('log')
plt.legend(['$v$'] + labels)
pdb.set_trace()
def show_prod(J2, size):
h = load_hamiltonian('J1J2', size=size, J2=J2)
H = h.get_mat()
c0, indexer = h.get_config_table()
e0, v0 = sps.linalg.eigsh(H, which='SA', k=1)
v0 = v0.ravel()
order = np.argsort(abs(v0))[::-1]
v0 = v0[order]
c0 = c0[order]
amp = np.abs(v0) * 100
nn = np.sum(c0 * np.roll(c0, 1, axis=1), axis=1)
nnn = np.sum(c0 * np.roll(c0, 2, axis=1), axis=1)
hndim = len(c0)
#wlist = np.linspace(0,hndim,2000)
#nn = dos(np.arange(hndim), wlist=wlist, weights=nn, eta=2.)*hndim
plt.ion()
plt.plot(np.arange(hndim), amp)
plt.fill_between(np.arange(hndim), nn, 0, alpha=0.5, color='r')
plt.fill_between(np.arange(hndim), nnn, 0, alpha=0.5, color='g')
plt.legend([
'$|\psi|$', r'$\sum_iS^z_i\times S^z_{i+1}$',
r'$\sum_iS^z_i\times S^z_{i+2}$'
])
plt.axhline(0, color='#999999')
pdb.set_trace()
plt.savefig('notes/img/prod_J2%sN%s.png' % (J2, size), dpi=300)
def analyse_poly(configfile, num_iter, bench_id_list):
config = load_config(configfile)
# folder to store data, containing config.py
folder = os.path.dirname(configfile)
e0 = config['hamiltonian']['EG']
labels = [
'polynomial', 'legendre', 'hermite', 'chebyshev', 'laguerre',
'hermiteE'
]
legends = []
sys.path.insert(0, folder)
from config import modifyconfig_and_getnn
plt.ion()
for ib, (bench_id, label) in enumerate(zip(bench_id_list, labels)):
rbm = modifyconfig_and_getnn(config, bench_id)
optimizer, problem = pconfig(config, rbm)
h, sr, rbm, vmc = problem.hamiltonian, problem.sr, problem.rbm, problem.vmc
if ib == 0:
H, e0, v0, configs = analyse_exact(h, do_printsign=False)
# load data
data = np.load(folder + '/variables-%d%d.npy' % (bench_id, num_iter))
rbm.set_variables(data)
# compute overlap
vec = rbm.tovec(mag=h.mag)
vec = vec / np.linalg.norm(vec)
overlap = abs(vec.T.conj().dot(v0))
print('overlap = %s' % overlap)
# show weights in conv layers.
for layer in rbm.layers:
if hasattr(layer, 'kernel_dict'):
polylayer = layer
data = polylayer.get_variables()
plt.plot(np.real(data))
legends.append('%s (%.4f)' % (label, overlap))
plt.axhline(y=0, ls='--', color='k')
plt.legend(legends)
pdb.set_trace()
plt.savefig('notes/img/polyweight.png')
def analyse_polycurve(configfile,
num_iter,
bench_id_list,
show_var=False,
token=''):
config = load_config(configfile)
# folder to store data, containing config.py
folder = os.path.dirname(configfile)
e0 = config['hamiltonian']['EG']
legends = []
sys.path.insert(0, folder)
from config import modifyconfig_and_getnn
x = np.linspace(-2, 2, 200)
plt.ion()
for bench_id in bench_id_list:
rbm = modifyconfig_and_getnn(config, bench_id)
# show weights in conv layers.
for nit in np.atleast_1d(num_iter):
rbm_var = np.load(folder + '/variables-%d%d.npy' % (bench_id, nit))
rbm.set_variables(rbm_var)
for layer in rbm.layers:
if hasattr(layer, 'kernel_dict'):
polylayer = layer
legends.append(layer.kernel + '-%d' % nit)
if show_var:
data = polylayer.get_variables()
plt.plot(data.real)
else:
data = polylayer.forward(x)
plt.plot(x, data.real)
plt.legend(legends)
#plt.ylim(-20,20)
pdb.set_trace()
if show_var:
plt.savefig('notes/img/polyvar-%s.png' % token)
else:
plt.savefig('notes/img/polycurve-%s.png' % token)
def scale_ed_msr(size, J2MIN=0, J2MAX=1, NJ2=51, yscale='log'):
from qstate.classifier.rules import marshall_sign_rule
J2L = np.linspace(J2MIN, J2MAX, NJ2)
e0l, el = [], []
for i, J2 in enumerate(J2L):
h = load_hamiltonian('J1J2', size=size, J2=J2)
H = h.get_mat()
e0, v0 = sps.linalg.eigsh(H, which='SA', k=1)
v0 = v0.ravel()
configs, config_indexer = h.get_config_table()
marshall_signs = marshall_sign_rule(configs, size=size)
v = abs(v0) * marshall_signs
el.append(v.T.conj().dot(H.dot(v)))
e0l.append(e0.item())
print('%s' % i)
np.savetxt('notes/data/scale_msr_%s.dat' % (size, ), list(zip(el, e0l)))
plt.ion()
plt.plot(J2L, np.array(el) - e0l)
plt.xlabel(r'$J_2$')
plt.ylabel(r'$E-E_0$')
plt.yscale(yscale)
pdb.set_trace()