def predict(model, sols, setting, step):
"""
:param model:
:param sol:
:param setting:
:param step:
:return:
"""
if step <= 0:
return
elif step in [1, 2, 3]:
Amat, fval = init_training(model, setting, step, delForce=0)
else:
print("ERROR: Unknown pred_step: ", step)
exit(-1)
errs = []
for i in range(len(sols)):
err = predict_holdout(Amat, fval[:, 0], sols[i])
errs.append(err[0])
print(" sol# %d: err= (%.2f%%) %f" % (i, err[0], err[1]))
np.savetxt("%s_%d" % (setting['fval_out'], i), np.transpose(err[2:4]))
print("+ Prediction done")
return np.argmin(errs)
def predict(model, sols, setting, step):
"""
:param model:
:param sol:
:param setting:
:param step:
:return:
"""
if step <= 0:
return
elif step in [1, 2, 3]:
Amat, fval = init_training(model, setting, step, delForce=0)
else:
print("ERROR: Unknown pred_step: ", step)
exit(-1)
print('\n')
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print('!!!!! PREDICTING USING FITTED RESULTS !!!!!!')
print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
print('\n')
errs = []
for i in range(len(sols)):
err = predict_holdout(Amat, fval[:, 0], sols[i])
err[2:4] += fval[:, 2:].T
errs.append(err[0])
print(" sol# %d: err= (%.2f%%) %f" % (i, err[0], err[1]))
np.savetxt("%s_%d" % (setting['fval_out'], i), np.transpose(err[2:4]))
if setting.getboolean('save_force_prediction',
True) and setting['corr_type'] == 'f':
supercells = [
y.split() for x, y in setting.items()
if re.match('traindat.*', x)
]
left = 0
f_all = np.reshape(err[3], (-1, 3))
for sc in supercells:
nA = Poscar.from_file(sc[0]).structure.num_sites
for subs in sc[1:]:
for f in sorted(glob.glob(subs)):
np.savetxt(f + '/force.txt_predicted',
f_all[left:left + nA])
left += nA
print("+ Prediction done")
return np.argmin(errs)