def test2(self):
ho = HarmonicOscillator(self.d)
self.visits = ho.make_visits(self.Tlist, self.binenergy, self.N, random=True)
assert self.visits.shape == (len(self.Tlist), len(self.binenergy))
self.reduced_energy = self.binenergy[np.newaxis,:] / (self.Tlist[:,np.newaxis])
wham_utils.estimate_dos(self.visits, self.visits)
def test2(self):
ho = HarmonicOscillator(self.d)
self.visits = ho.make_visits(self.Tlist,
self.binenergy,
self.N,
random=True)
assert self.visits.shape == (len(self.Tlist), len(self.binenergy))
self.reduced_energy = self.binenergy[np.newaxis, :] / (
self.Tlist[:, np.newaxis])
wham_utils.estimate_dos(self.visits, self.visits)
def minimize(self):
"""compute the best estimate for the density of states"""
nreps = self.nrep
nbins = self.nebins
visitsT = (self.visits1d)
#print "min vis", np.min(visitsT)
#print "minlogp", np.min(self.logP)
self.reduced_energy = self.binenergy[np.newaxis, :] / (
self.Tlist[:, np.newaxis] * self.k_B)
self.whampot = WhamPotential(visitsT, self.reduced_energy)
if False:
X = np.random.rand(nreps + nbins)
else:
# estimate an initial guess for the offsets and density of states
# so the minimizer converges more rapidly
offsets_estimate, log_dos_estimate = wham_utils.estimate_dos(
self.visits1d, self.reduced_energy)
X = np.concatenate((offsets_estimate, log_dos_estimate))
E0, grad = self.whampot.getEnergyGradient(X)
rms0 = np.linalg.norm(grad) / np.sqrt(grad.size)
try:
from pele.optimize import lbfgs_cpp as quench
if self.verbose:
print "minimizing with pele lbfgs"
ret = quench(X, self.whampot, tol=1e-3, maxstep=1e4, nsteps=10000)
except ImportError:
from wham_utils import lbfgs_scipy
if self.verbose:
print "minimizing with scipy lbfgs"
ret = lbfgs_scipy(X, self.whampot, tol=1e-3, nsteps=10000)
#print "quench energy", ret.energy
if self.verbose:
print "chi^2 went from %g (rms %g) to %g (rms %g) in %d iterations" % (
E0, rms0, ret.energy, ret.rms, ret.nfev)
X = ret.coords
self.logn_E = X[nreps:]
self.w_i_final = X[:nreps]
def minimize(self):
"""compute the best estimate for the density of states"""
nreps = self.nrep
nbins = self.nebins
visitsT = (self.visits1d)
#print "min vis", np.min(visitsT)
#print "minlogp", np.min(self.logP)
self.reduced_energy = self.binenergy[np.newaxis,:] / (self.Tlist[:,np.newaxis] * self.k_B)
self.whampot = WhamPotential(visitsT, self.reduced_energy)
if False:
X = np.random.rand( nreps + nbins )
else:
# estimate an initial guess for the offsets and density of states
# so the minimizer converges more rapidly
offsets_estimate, log_dos_estimate = wham_utils.estimate_dos(self.visits1d,
self.reduced_energy)
X = np.concatenate((offsets_estimate, log_dos_estimate))
E0, grad = self.whampot.getEnergyGradient(X)
rms0 = np.linalg.norm(grad) / np.sqrt(grad.size)
try:
from pele.optimize import lbfgs_cpp as quench
if self.verbose:
print "minimizing with pele lbfgs"
ret = quench(X, self.whampot, tol=1e-3, maxstep=1e4, nsteps=10000)
except ImportError:
from wham_utils import lbfgs_scipy
if self.verbose:
print "minimizing with scipy lbfgs"
ret = lbfgs_scipy(X, self.whampot, tol=1e-3, nsteps=10000)
#print "quench energy", ret.energy
if self.verbose:
print "chi^2 went from %g (rms %g) to %g (rms %g) in %d iterations" % (
E0, rms0, ret.energy, ret.rms, ret.nfev)
X = ret.coords
self.logn_E = X[nreps:]
self.w_i_final = X[:nreps]
