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]
def testpot1():
from pygmin.potentials.lj import LJ
import itertools
pot = LJ()
a = 1.12 #2.**(1./6.)
theta = 60. / 360 * np.pi
coords = [ 0., 0., 0., \
-a, 0., 0., \
-a/2, a*np.cos(theta), 0., \
-a/2, -a*np.cos(theta), 0.1 \
]
natoms = len(coords) / 3
c = np.reshape(coords, [-1, 3])
for i, j in itertools.combinations(range(natoms), 2):
r = np.linalg.norm(c[i, :] - c[j, :])
print i, j, r
e, g = pot.getEnergyGradient(coords)
print "initial E", e
print "initial G", g, np.linalg.norm(g)
eigpot = LowestEigPot(coords, pot)
vec = np.random.rand(len(coords))
e, g = eigpot.getEnergyGradient(vec)
print "eigenvalue", e
print "eigenvector", g
if True:
e, g, hess = pot.getEnergyGradientHessian(coords)
print "shape hess", np.shape(hess)
print "hessian", hess
u, v = np.linalg.eig(hess)
print "max imag value", np.max(np.abs(u.imag))
print "max imag vector", np.max(np.abs(v.imag))
u = u.real
v = v.real
print "eigenvalues", u
for i in range(len(u)):
print "eigenvalue", u[i], "eigenvector", v[:, i]
#find minimum eigenvalue, vector
imin = 0
umin = 10.
for i in range(len(u)):
if np.abs(u[i]) < 1e-10: continue
if u[i] < umin:
umin = u[i]
imin = i
print "lowest eigenvalue ", umin, imin
print "lowest eigenvector", v[:, imin]
from pygmin.optimize import lbfgs_py as quench
ret = quench(vec, eigpot.getEnergyGradient, iprint=10, tol = 1e-5, maxstep = 1e-3, \
rel_energy = True)
print ret
print "lowest eigenvalue "
print umin, imin
print "lowest eigenvector"
print v[:, imin]
print "now the estimate"
print ret[1]
print ret[0]
def testpot1():
from pygmin.potentials.lj import LJ
import itertools
pot = LJ()
a = 1.12 #2.**(1./6.)
theta = 60./360*np.pi
coords = [ 0., 0., 0., \
-a, 0., 0., \
-a/2, a*np.cos(theta), 0., \
-a/2, -a*np.cos(theta), 0.1 \
]
natoms = len(coords)/3
c = np.reshape(coords, [-1,3])
for i, j in itertools.combinations(range(natoms), 2):
r = np.linalg.norm(c[i,:] - c[j,:])
print i, j, r
e, g = pot.getEnergyGradient(coords)
print "initial E", e
print "initial G", g, np.linalg.norm(g)
eigpot = LowestEigPot(coords, pot)
vec = np.random.rand(len(coords))
e, g = eigpot.getEnergyGradient(vec)
print "eigenvalue", e
print "eigenvector", g
if True:
e, g, hess = pot.getEnergyGradientHessian(coords)
print "shape hess", np.shape(hess)
print "hessian", hess
u, v = np.linalg.eig(hess)
print "max imag value", np.max(np.abs(u.imag))
print "max imag vector", np.max(np.abs(v.imag))
u = u.real
v = v.real
print "eigenvalues", u
for i in range(len(u)):
print "eigenvalue", u[i], "eigenvector", v[:,i]
#find minimum eigenvalue, vector
imin = 0
umin = 10.
for i in range(len(u)):
if np.abs(u[i]) < 1e-10: continue
if u[i] < umin:
umin = u[i]
imin = i
print "lowest eigenvalue ", umin, imin
print "lowest eigenvector", v[:,imin]
from pygmin.optimize import lbfgs_py as quench
ret = quench(vec, eigpot.getEnergyGradient, iprint=10, tol = 1e-5, maxstep = 1e-3, \
rel_energy = True)
print ret
print "lowest eigenvalue "
print umin, imin
print "lowest eigenvector"
print v[:,imin]
print "now the estimate"
print ret[1]
print ret[0]
