def minimize(self): #shape(visits2d) is now (nqbins, nebins, nreps) #we need it to be (nreps, nqbins*nebins) #first reorder indices nreps = self.nrep nebins = self.nebins nqbins = self.nqbins nbins = self.nebins * self.nqbins #visits = np.zeros([nreps, nebins, nqbins], np.integer) reduced_energy = np.zeros([nreps, nebins, nqbins]) # for k in range(self.nrep): # for j in range(self.nqbins): # for i in range(self.nebins): # #visits[k,i,j] = self.visits2d[i,j,k] # reduced_energy[k,i,j] = self.binenergy[i] / (self.Tlist[k]*self.k_B) for j in range(self.nqbins): reduced_energy[:,:,j] = self.binenergy[np.newaxis,:] / (self.Tlist[:,np.newaxis]*self.k_B) visits = self.visits2d visits = np.reshape( visits, [nreps, nbins ]) reduced_energy = np.reshape( reduced_energy, [nreps, nbins]) self.logP = np.where( visits != 0, np.log( visits.astype(float) ), 0 ) from wham_potential import WhamPotential whampot = WhamPotential( self.logP, reduced_energy ) nvar = nbins + nreps X = np.random.rand(nvar) print "initial energy", whampot.getEnergy(X) try: from pygmin.optimize import mylbfgs as quench ret = quench(X, whampot, iprint=10, maxstep = 1e4) except ImportError: from pygmin.optimize import lbfgs_scipy as quench ret = quench(X, whampot) print "quenched energy", ret.energy global_min = False if global_min: from pygmin.basinhopping import BasinHopping from pygmin.takestep.displace import RandomDisplacement takestep = RandomDisplacement(stepsize=10.) takestep.useAdaptiveStep() takestep.adaptive_class.f = 2. bh = BasinHopping(X, whampot, takestep) bh.run(1000) #self.logn_Eq = zeros([nebins,nqbins], float64) X = ret.coords self.logn_Eq = X[nreps:] self.w_i_final = X[:nreps] self.logn_Eq = np.reshape(self.logn_Eq, [nebins, nqbins]) self.logn_Eq = np.where( self.visits2d.sum(0) == 0, self.LOGMIN, self.logn_Eq )
def globalMinimization(self): """ in experimentation i've never been able to find more than one minimum """ nreps = self.nrep nbins = self.nebins visitsT = (self.visits1d) #print "min vis", np.min(visitsT) self.logP = np.where( visitsT != 0, np.log( visitsT ), 0 ) #print "minlogp", np.min(self.logP) self.reduced_energy = self.binenergy[np.newaxis,:] / (self.Tlist[:,np.newaxis] * self.k_B) self.whampot = WhamPotential(self.logP, self.reduced_energy) X = np.random.rand( nreps + nbins ) E = self.whampot.getEnergy(X) print "energy", E print "quenching" from pygmin.optimize import lbfgs_scipy as quench ret = quench(X, self.whampot) print "quench energy", ret.energy from pygmin.basinhopping import BasinHopping from pygmin.takestep.displace import RandomDisplacement takestep = RandomDisplacement(stepsize=10) takestep.useAdaptiveStep() takestep.adaptive_class.f = 1.5 #i have no idea what a good stepsize should be bh = BasinHopping(X, self.whampot, takestep ) import matplotlib.pyplot as plt for i in range(10): bh.run(2000) self.logn_E = bh.coords[nreps:] cvdata = self.calc_Cv(400) plt.plot(cvdata[:,0], cvdata[:,5], '-') plt.show() X = bh.coords self.logn_E = X[nreps:] self.w_i_final = X[:nreps]
def globalMinimization(self): """ in experimentation i've never been able to find more than one minimum """ nreps = self.nrep nbins = self.nebins visitsT = (self.visits1d) #print "min vis", np.min(visitsT) self.logP = np.where(visitsT != 0, np.log(visitsT), 0) #print "minlogp", np.min(self.logP) self.reduced_energy = self.binenergy[np.newaxis, :] / ( self.Tlist[:, np.newaxis] * self.k_B) self.whampot = WhamPotential(self.logP, self.reduced_energy) X = np.random.rand(nreps + nbins) E = self.whampot.getEnergy(X) print "energy", E print "quenching" from pygmin.optimize import lbfgs_scipy as quench ret = quench(X, self.whampot) print "quench energy", ret.energy from pygmin.basinhopping import BasinHopping from pygmin.takestep.displace import RandomDisplacement takestep = RandomDisplacement(stepsize=10) takestep.useAdaptiveStep() takestep.adaptive_class.f = 1.5 #i have no idea what a good stepsize should be bh = BasinHopping(X, self.whampot, takestep) import matplotlib.pyplot as plt for i in range(10): bh.run(2000) self.logn_E = bh.coords[nreps:] cvdata = self.calc_Cv(400) plt.plot(cvdata[:, 0], cvdata[:, 5], '-') plt.show() X = bh.coords self.logn_E = X[nreps:] self.w_i_final = X[:nreps]