def gen_matrix():
"""Creates a MATLAB file of the full Hamiltonian as a sparse matrix."""
dets_p = {}
dets_c = {}
spatial_orbs_list = []
key_list = []
for i in range(0, 8):
for j in range(i+1, 8):
for k in range(j+1, 8):
spatial_orbs_list.append([k, j, i])
for sp_orbs_i in spatial_orbs_list:
for sp_orbs_j in spatial_orbs_list:
sp_orbs_temp = sp_orbs_j[:]
for k in range(3):
sp_orbs_temp[k] += 8
orbs = tuple(sp_orbs_temp + sp_orbs_i)
key = key_ops.orbs_2_key(orbs)
key_list.append(key)
for key in key_list:
dets_c[key] = det.Det(1, True)
det_ops.merge(dets_p, dets_c)
f = open('matrix_m8n6.m', 'w')
f.write('data = [')
for i in range(len(key_list)):
key_i = key_list[i]
for j in range(len(key_list)):
key_j = key_list[j]
orbs_i, sign, orbs_diff = key_ops.difference(key_i, key_j)
if orbs_i != None:
entry = black_box.sandwich(orbs_i, orbs_diff)
if entry == 0:
continue
elif not sign:
entry = -entry
f.write(str(i+1))
f.write(' ')
f.write(str(j+1))
f.write(' ')
f.write('{0:.4f}'.format(entry))
f.write(' ')
f.write('];\r\ndata = reshape(data, 3, length(data)/3);\r\ni = data(1,:);\r\nj = data(2,:);\r\n')
f.write('s = data(3,:);\r\nH = sparse(i,j,s);\r\neig_vals = eigs(H);\r\ngnd = min(eig_vals);')
f.close()
return dets_p
def run(old_vec = []):
"""Main FCIQMC function."""
### Initialization.
# Intializes walker distribution as D0 and puts 10 walkers on it.
ref_key = (8, 10, 6, 4)
tau = gl_consts.tau
shift = gl_consts.shift
max_iter_num = gl_consts.max_iter_num
bit_num = gl_consts.para_list[0]
chunk_num = gl_consts.para_list[1]
shift = -6
old_shift = shift
gl_consts.init_crit_w_num = 0
change_shift_step = 5
update_plots_step = 20
damping = 0.05
change_shift_flag = False
aver_flag = False
change_shift_crit_num = 50000
old_w_num = change_shift_crit_num
tau = 0.002
exact_gnd = -8.892162249
aver_times = 0
aver_shift = 0
aver_numer = 0
aver_denom = 1
max_iter_num = 100000
init_walker_num = 50
init_walker = det.Det(init_walker_num, True)
w_num = init_walker_num
dets_p = {}
det_ops.merge(dets_p, dict({ref_key : init_walker}))
ref_energy = dets_p[ref_key].diag_entry
w_num_list = []
log_w_num_list = []
iter_num_list = []
aver_iter_num_list = []
shift_list = []
aver_shift_list = []
proj_list = []
aver_proj_list = []
dets_p_old = {}
for key in dets_p:
dets_p_old[key] = det.Det(dets_p[key].value, True)
# Figure 0: walker number.
f0 = plt.figure(0)
# plt.axis([0, max_iter_num, 0, 20000])
plt.axis([0, max_iter_num, 3, 11])
plt.ion()
plt.xlabel('Iteration')
# plt.ylabel('Walker number')
plt.ylabel('Log of walker number')
plt.show()
# Figure 1: shift.
f2 = plt.figure(1)
plt.axis([0, max_iter_num, -12, 0])
plt.ion()
plt.xlabel('Iteration')
plt.ylabel('Energy')
plt.show()
""""""
# Figure 2: |Psi(t)>.
f2 = plt.figure(2)
plt.axis([0, (2**bit_num)**chunk_num - 1, -1, 1])
plt.ion()
plt.xlabel('Dimension')
plt.ylabel('Value')
plt.show()
""""""
for iter_num in range(1, max_iter_num+1):
det_ops.single_step(dets_p, tau, shift)
w_num = det_ops.count_u_num(dets_p)
if iter_num % update_plots_step == 0:
iter_num_list.append(iter_num)
log_w_num_list.append(math.log(w_num))
# Figure 0: walker number.
plt.figure(0)
plt.clf()
plt.axis([0, max_iter_num, 3, 11])
# Draws walker number.
draw_curve(iter_num_list, log_w_num_list, 'b')
plt.xlabel('Iteration')
plt.ylabel('Log of walker number')
plt.pause(0.01)
shift_list.append(shift)
numer, denom = det_ops.corr_by_proj(dets_p, ref_key)
proj_energy = numer / denom + ref_energy
proj_list.append(proj_energy)
if aver_flag:
aver_iter_num_list.append(iter_num)
aver_shift_list.append(aver_shift)
aver_proj_list.append(aver_proj)
# Figure 1: shift.
plt.figure(1)
plt.clf()
plt.axis([0, max_iter_num, -12, 0])
# Draws shift.
draw_curve([0, max_iter_num], [exact_gnd, exact_gnd], 'k')
draw_curve(iter_num_list, shift_list, 'b')
draw_curve(iter_num_list, proj_list, 'r')
draw_curve(aver_iter_num_list, aver_shift_list, 'c')
draw_curve(aver_iter_num_list, aver_proj_list, 'm')
plt.xlabel('Iteration')
plt.ylabel('Energy')
plt.pause(0.01)
""""""
vec = det_ops.dets_2_vec(dets_p)
# Figure 2: |Psi(t)>.
plt.figure(2)
plt.clf()
plt.axis([0, len(vec)-1, -1, 1])
# Draws eigenvector.
if len(old_vec) == len(vec):
draw_curve(range(len(old_vec)), old_vec, 'r')
draw_curve(range(len(vec)), vec, 'b')
plt.xlabel('Dimension')
plt.ylabel('Value')
plt.pause(0.01)
""""""
ref_key_cand = det_ops.find_ref(dets_p);
print iter_num, w_num, dets_p[ref_key].value, shift, proj_energy, ref_key_cand
if (not change_shift_flag) and w_num > change_shift_crit_num:
change_shift_flag = True
shift = proj_energy
crit_iter_num = iter_num
if change_shift_flag:
if iter_num % change_shift_step == 0:
correction = -damping / change_shift_step / tau \
* math.log(w_num / float(old_w_num))
shift += correction
old_w_num = w_num
if (not aver_flag) and (iter_num - crit_iter_num) > 500:
if abs(shift - old_shift) < 0.02:
aver_flag = True
if aver_flag:
aver_shift = (aver_shift*aver_times+shift) / (aver_times+1)
aver_numer = (aver_numer*aver_times+numer) / (aver_times+1)
aver_denom = (aver_denom*aver_times+denom) / (aver_times+1)
aver_proj = aver_numer / aver_denom + ref_energy
aver_times += 1
old_shift = shift
"""
# Figure 3: Derivative of log(w_num).
s_log_w_num_list = smooth(log_w_num_list, 2)
d_log_w_num_list = derivative(s_log_w_num_list, 2)
f3 = plt.figure(3)
plt.axis([0, max_iter_num, -0.1, 0.2])
plt.ion()
plt.clf()
draw_curve(iter_num_list[:len(s_log_w_num_list)], s_log_w_num_list, 'b')
draw_curve(iter_num_list[:len(d_log_w_num_list)], d_log_w_num_list, 'r')
plt.xlabel('Iteration')
plt.ylabel('Derivative of log(w_num)')
plt.show()
"""
return aver_shift, dets_p, vec
