def runtest(X, pot, natoms = 100, iprint=-1):
from _lbfgs_py import PrintEvent
tol = 1e-5
maxstep = 0.005
Xinit = np.copy(X)
e, g = pot.getEnergyGradient(X)
print "energy", e
lbfgs = LBFGS(X, pot, maxstep = 0.1, nsteps=10000, tol=tol,
iprint=iprint, H0=2.)
printevent = PrintEvent( "debugout.xyz")
lbfgs.attachEvent(printevent)
ret = lbfgs.run()
print ret
print ""
print "now do the same with scipy lbfgs"
from pygmin.optimize import lbfgs_scipy as quench
ret = quench(Xinit, pot, tol = tol)
print ret
#print ret[1], ret[2], ret[3]
if False:
print "now do the same with scipy bfgs"
from pygmin.optimize import bfgs as oldbfgs
ret = oldbfgs(Xinit, pot, tol = tol)
print ret
if False:
print "now do the same with gradient + linesearch"
import _bfgs
gpl = _bfgs.GradientPlusLinesearch(Xinit, pot, maxstep = 0.1)
ret = gpl.run(1000, tol = 1e-6)
print ret
if False:
print "calling from wrapper function"
from pygmin.optimize import lbfgs_py
ret = lbfgs_py(Xinit, pot, tol = tol)
print ret
if True:
print ""
print "now do the same with lbfgs_py"
from pygmin.optimize import lbfgs_py
ret = lbfgs_py(Xinit, pot, tol = tol)
print ret
try:
import pygmin.utils.pymolwrapper as pym
pym.start()
for n, coords in enumerate(printevent.coordslist):
coords=coords.reshape(natoms, 3)
pym.draw_spheres(coords, "A", n)
except ImportError:
print "error loading pymol"
def runtest(X, pot, natoms=100, iprint=-1):
from lbfgs_py import PrintEvent
tol = 1e-5
maxstep = 0.005
Xinit = np.copy(X)
e, g = pot.getEnergyGradient(X)
print "energy", e
lbfgs = LBFGS(X, pot, maxstep=0.1)
printevent = PrintEvent("debugout.xyz")
#lbfgs.attachEvent(printevent)
ret = lbfgs.run(10000, tol=tol, iprint=iprint)
print "done", ret[1], ret[2], ret[3], ret[5]
print ""
print "now do the same with scipy lbfgs"
from pygmin.optimize import lbfgs_scipy as quench
ret = quench(Xinit, pot.getEnergyGradient, tol=tol)
print ret[1], ret[2], ret[3]
if False:
print "now do the same with scipy bfgs"
from pygmin.optimize import bfgs as oldbfgs
ret = oldbfgs(Xinit, pot.getEnergyGradient, tol=tol)
print ret[1], ret[2], ret[3]
if False:
print "now do the same with gradient + linesearch"
import bfgs
gpl = bfgs.GradientPlusLinesearch(Xinit, pot, maxstep=0.1)
ret = gpl.run(1000, tol=1e-6)
print ret[1], ret[2], ret[3]
if True:
print ""
print "calling from wrapper function"
from pygmin.optimize import mylbfgs as quench
ret = quench(Xinit, pot.getEnergyGradient, tol=tol)
print ret[1], ret[2], ret[3]
if True:
print ""
print "now do the same with lbfgs_py"
from pygmin.optimize import lbfgs_py
ret = lbfgs_py(Xinit, pot.getEnergyGradient, tol=tol)
print ret[1], ret[2], ret[3]
if False:
import pygmin.utils.pymolwrapper as pym
pym.start()
for n, coords in enumerate(printevent.coordslist):
coords = coords.reshape(natoms, 3)
pym.draw_spheres(coords, "A", n)
def test():
natoms = 100
tol = 1e-6
from pygmin.potentials.lj import LJ
pot = LJ()
X = getInitialCoords(natoms, pot)
X += np.random.uniform(-1,1,[3*natoms]) * 0.3
#do some steepest descent steps so we don't start with a crazy structure
#from optimize.quench import _steepest_descent as steepestDescent
#ret = steepestDescent(X, pot.getEnergyGradient, iprint = 1, dx = 1e-4, nsteps = 100, gtol = 1e-3, maxstep = .5)
#X = ret[0]
#print X
Xinit = np.copy(X)
e, g = pot.getEnergyGradient(X)
print "energy", e
lbfgs = BFGS(X, pot, maxstep = 0.1)
ret = lbfgs.run(100, tol = tol, iprint=1)
print "done", ret[1], ret[2], ret[3]
print "now do the same with scipy lbfgs"
from pygmin.optimize import lbfgs_scipy as quench
ret = quench(Xinit, pot.getEnergyGradient, tol = tol)
print ret[1], ret[2], ret[3]
print "now do the same with scipy bfgs"
from pygmin.optimize import bfgs as oldbfgs
ret = oldbfgs(Xinit, pot.getEnergyGradient, tol = tol)
print ret[1], ret[2], ret[3]
print "now do the same with old gradient + linesearch"
gpl = GradientPlusLinesearch(Xinit, pot, maxstep = 0.1)
ret = gpl.run(100, tol = 1e-6)
print ret[1], ret[2], ret[3]
def test():
natoms = 100
tol = 1e-6
from pygmin.potentials.lj import LJ
pot = LJ()
X = getInitialCoords(natoms, pot)
X += np.random.uniform(-1, 1, [3 * natoms]) * 0.3
#do some steepest descent steps so we don't start with a crazy structure
#from optimize.quench import _steepest_descent as steepestDescent
#ret = steepestDescent(X, pot.getEnergyGradient, iprint = 1, dx = 1e-4, nsteps = 100, gtol = 1e-3, maxstep = .5)
#X = ret[0]
#print X
Xinit = np.copy(X)
e, g = pot.getEnergyGradient(X)
print "energy", e
lbfgs = BFGS(X, pot, maxstep=0.1)
ret = lbfgs.run(100, tol=tol, iprint=1)
print "done", ret[1], ret[2], ret[3]
print "now do the same with scipy lbfgs"
from pygmin.optimize import lbfgs_scipy as quench
ret = quench(Xinit, pot.getEnergyGradient, tol=tol)
print ret[1], ret[2], ret[3]
print "now do the same with scipy bfgs"
from pygmin.optimize import bfgs as oldbfgs
ret = oldbfgs(Xinit, pot.getEnergyGradient, tol=tol)
print ret[1], ret[2], ret[3]
print "now do the same with old gradient + linesearch"
gpl = GradientPlusLinesearch(Xinit, pot, maxstep=0.1)
ret = gpl.run(100, tol=1e-6)
print ret[1], ret[2], ret[3]