def spawn(dets_p, dets_c, key_p, tau): """Spawning of all parents on the determinant indexed by $key_p$.""" # Spawning. p_sign = (dets_p[key_p].value > 0) p_u_num = abs(dets_p[key_p].value) count = 0 for count in range(p_u_num): # Single or double excitation. rand_val = random.random() if rand_val < gl_consts.single_prob: # Single excitation. orbs_p, key_c, sign, p_gen, orbs_diff = key_ops.single_excite(key_p) p_sing_or_doub = gl_consts.single_prob else: # Double excitation. orbs_p, key_c, sign, p_gen, orbs_diff = key_ops.double_excite(key_p) p_sing_or_doub = 1 - gl_consts.single_prob mat_element = black_box.sandwich(orbs_p, orbs_diff) if not sign: # Accounts for a possible negative sign from permuting the spawned determinant. mat_element = -mat_element prob = tau * mat_element / p_gen / p_sing_or_doub # Gets the sign of children. if prob < 0: c_sign = p_sign prob = -prob else: c_sign = not p_sign c_num = int(prob) # At least we have $c_num$ children. prob_frac = prob - c_num rand_val = random.random() if rand_val < prob_frac: # One more child. c_num += 1 if c_num != 0: # Add $c_num$ children to the list. if not c_sign: c_num = -c_num if key_c in dets_c: dets_c[key_c].value += c_num if dets_c[key_c].value == 0: del dets_c[key_c] else: dets_c[key_c].flag = dets_p[key_p].flag else: dets_c[key_c] = det.Det(c_num, dets_p[key_p].flag)
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 corr_by_proj(dets_p, ref_key): """Calculates correlation energy by projection.""" # E_corr = sum_j(<D_j|H|D_0>*(<N_j>/<N_0>)) - E_0 ref_orbs = key_ops.key_2_orbs(ref_key) numer = 0 for key in dets_p: if key != ref_key: orbs_gnd, sign_exc, orbs_diff = key_ops.difference(ref_key, key) if orbs_gnd != None: term = black_box.sandwich(orbs_gnd, orbs_diff) * dets_p[key].value if not sign_exc: term = -term numer += term denom = float(dets_p[ref_key].value) return numer, denom
def set_diag_entry(self, key): """Sets diagonal matrix element of this determinant.""" orbs = key_ops.key_2_orbs(key) self.diag_entry = black_box.sandwich(orbs, ())