def size_scaling_smallest_eig(natoms):
from pygmin.systems import LJCluster
import time, sys
system = LJCluster(natoms)
pot = system.get_potential()
quencher = system.get_minimizer(tol=10.)
time1 = 0.
time2 = 0.
time3 = 0.
time4 = 0.
for i in range(100):
coords = system.get_random_configuration()
# print "len(coords)", len(coords)
coords = quencher(coords)[0]
e, g, h = pot.getEnergyGradientHessian(coords)
t0 = time.time()
w1, v1 = get_smallest_eig(h)
t1 = time.time()
w, v = get_smallest_eig_arpack(h)
t2 = time.time()
w2, v2 = get_smallest_eig_sparse(h)
t3 = time.time()
w3, v3 = get_smallest_eig_nohess(coords, system, tol=1e-3)
t4 = time.time()
time1 += t1 - t0
time2 += t2 - t1
time3 += t3 - t2
time4 += t4 - t3
wdiff = np.abs(w - w1) / np.max(np.abs([w, w1]))
if wdiff > 5e-3:
sys.stderr.write(
"eigenvalues for dense are different %g %g normalized diff %g\n"
% (w1, w, wdiff))
wdiff = np.abs(w - w2) / np.max(np.abs([w, w2]))
if wdiff > 5e-2:
sys.stderr.write(
"eigenvalues for sparse are different %g %g normalized diff %g\n"
% (w2, w, wdiff))
wdiff = np.abs(w - w3) / np.max(np.abs([w, w3]))
if wdiff > 5e-2:
sys.stderr.write(
"eigenvalues for nohess are different %g %g normalized diff %g\n"
% (w3, w, wdiff))
# print "times", n, t1-t0, t2-t1, w1, w
print "times", n, time1, time2, time3, time4
sys.stdout.flush()
def size_scaling_smallest_eig(natoms):
from pygmin.systems import LJCluster
import time, sys
system = LJCluster(natoms)
pot = system.get_potential()
quencher = system.get_minimizer(tol=10.)
time1 = 0.
time2 = 0.
time3 = 0.
time4 = 0.
for i in range(100):
coords = system.get_random_configuration()
# print "len(coords)", len(coords)
coords = quencher(coords)[0]
e, g, h = pot.getEnergyGradientHessian(coords)
t0 = time.time()
w1, v1 = get_smallest_eig(h)
t1 = time.time()
w, v = get_smallest_eig_arpack(h)
t2 = time.time()
w2, v2 = get_smallest_eig_sparse(h)
t3 = time.time()
w3, v3 = get_smallest_eig_nohess(coords, system, tol=1e-3)
t4 = time.time()
time1 += t1-t0
time2 += t2-t1
time3 += t3-t2
time4 += t4-t3
wdiff = np.abs(w-w1) / np.max(np.abs([w,w1]))
if wdiff > 5e-3:
sys.stderr.write("eigenvalues for dense are different %g %g normalized diff %g\n" % (w1, w, wdiff))
wdiff = np.abs(w-w2) / np.max(np.abs([w,w2]))
if wdiff > 5e-2:
sys.stderr.write("eigenvalues for sparse are different %g %g normalized diff %g\n" % (w2, w, wdiff))
wdiff = np.abs(w-w3) / np.max(np.abs([w,w3]))
if wdiff > 5e-2:
sys.stderr.write("eigenvalues for nohess are different %g %g normalized diff %g\n" % (w3, w, wdiff))
# print "times", n, t1-t0, t2-t1, w1, w
print "times", n, time1, time2, time3, time4
sys.stdout.flush()
pl.show()
if __name__ == "__main__":
from pygmin.systems import LJCluster
natoms = 30
system = LJCluster(natoms)
pot = system.get_potential()
coords = system.get_random_configuration()
xmin = system.get_random_minimized_configuration()[0]
e, g, h = pot.getEnergyGradientHessian(xmin)
evals = get_eigvals(h)
print evals
quencher = system.get_minimizer(tol=10.)
coords = quencher(coords)[0]
e, g, h = pot.getEnergyGradientHessian(coords)
w1, v1 = get_smallest_eig(h)
print w1
w, v = get_smallest_eig_arpack(h)
print w
w2, v2 = get_smallest_eig_sparse(h)
print w2, w2 / w1
w3, v3 = get_smallest_eig_nohess(coords, system)
print w3, w3 / w1
# plot_hist(h)
# exit()
if False:
n = 10
from pygmin.utils.disconnectivity_graph import DisconnectivityGraph, database2graph
# initialize the system class
natoms = 13
system = LJCluster(natoms)
# make a random configuration
coords = system.get_random_configuration()
# compute the energy of that configuration
potential = system.get_potential()
energy = potential.getEnergy(coords)
print "the energy of the random configuration is", energy
# minimize that configuration
quencher = system.get_minimizer()
ret = quencher(coords)
newcoords = ret[0]
newenergy = ret[1]
print "after quenching, the energy is", newenergy
# create a database to store the minimum in
db = system.create_database()
# note: this creates a database in memory, if you want to save the results
# you would use
# db = system.create_database("lj.sqlite")
minimum1 = db.addMinimum(newenergy, newcoords)
# get a second random minimized configuration and add it to the database
ret = system.get_random_minimized_configuration()
print "a second minimum has energy", ret[1]
pl.hist(np.log10(np.abs(hess.reshape(-1))))
pl.show()
if __name__ == "__main__":
from pygmin.systems import LJCluster
natoms = 30
system = LJCluster(natoms)
pot = system.get_potential()
coords = system.get_random_configuration()
xmin = system.get_random_minimized_configuration()[0]
e, g, h = pot.getEnergyGradientHessian(xmin)
evals = get_eigvals(h)
print evals
quencher = system.get_minimizer(tol=10.)
coords = quencher(coords)[0]
e, g, h = pot.getEnergyGradientHessian(coords)
w1, v1 = get_smallest_eig(h)
print w1
w, v = get_smallest_eig_arpack(h)
print w
w2, v2 = get_smallest_eig_sparse(h)
print w2, w2/w1
w3, v3 = get_smallest_eig_nohess(coords, system)
print w3, w3/w1
# plot_hist(h)
# exit()
if False:
n = 10
See the class LJCluster for what the default parameters are for this system
"""
import numpy as np
from pygmin.systems import LJCluster
natoms = 38
system = LJCluster(natoms)
# load the coordinates from disk
coords1 = np.genfromtxt("coords.A").flatten()
coords2 = np.genfromtxt("coords.B").flatten()
# minimize them
quencher = system.get_minimizer()
ret1 = quencher(coords1)
ret2 = quencher(coords2)
# make a database and add the minima
db = system.create_database()
coords, E = ret1[:2]
min1 = db.addMinimum(E, coords)
coords, E = ret2[:2]
min2 = db.addMinimum(E, coords)
#
# here is where we modify the parameters
#
"""