def fit(self):
# Kill off some not as good terms
# self.termwise_Rsquared()
# Load up the residual Helmholtz term with parameters
n = helmholtz.vectord(self.N0)
d = helmholtz.vectord(self.D0)
t = helmholtz.vectord(self.T0)
l = helmholtz.vectord(self.L0)
self.phir = helmholtz.phir_power(n, d, t, l, 1, len(self.N0) - 1)
# Solve for the coefficients
Nbounds = [(-10, 10) for _ in range(len(self.N0))]
tbounds = [(-1, 30) for _ in range(len(self.T0))]
print(self.OBJECTIVE(np.array(list(self.N0))))
#self.N = self.N0
#self.N = scipy.optimize.minimize(self.OBJECTIVE, np.array(list(self.N0)), bounds = Nbounds, options = dict(maxiter = 5)).x
self.N = scipy.optimize.minimize(self.OBJECTIVE, np.array(list(self.N0)), method='L-BFGS-B', bounds=Nbounds, options=dict(maxiter=100)).x
# Write the coefficients to HDF5 file
h = h5py.File('fit_coeffs.h5', 'w')
grp = h.create_group(self.Ref)
grp.create_dataset("n", data=np.array(self.N), compression="gzip")
print(self.N)
#grp.create_dataset("t", data = np.array(self.N[len(self.N)//2::]), compression = "gzip")
h.close()
def fit(self):
# Kill off some not as good terms
# self.termwise_Rsquared()
# Load up the residual Helmholtz term with parameters
n = helmholtz.vectord(self.N0)
d = helmholtz.vectord(self.D0)
t = helmholtz.vectord(self.T0)
l = helmholtz.vectord(self.L0)
self.phir = helmholtz.phir_power(n, d, t, l, 1, len(self.N0) - 1)
# Solve for the coefficients
Nbounds = [(-10, 10) for _ in range(len(self.N0))]
tbounds = [(-1, 30) for _ in range(len(self.T0))]
print(self.OBJECTIVE(np.array(list(self.N0))))
#self.N = self.N0
#self.N = scipy.optimize.minimize(self.OBJECTIVE, np.array(list(self.N0)), bounds = Nbounds, options = dict(maxiter = 5)).x
self.N = scipy.optimize.minimize(self.OBJECTIVE,
np.array(list(self.N0)),
method='L-BFGS-B',
bounds=Nbounds,
options=dict(maxiter=100)).x
# Write the coefficients to HDF5 file
h = h5py.File('fit_coeffs.h5', 'w')
grp = h.create_group(self.Ref)
grp.create_dataset("n", data=np.array(self.N), compression="gzip")
print(self.N)
#grp.create_dataset("t", data = np.array(self.N[len(self.N)//2::]), compression = "gzip")
h.close()
def termwise_Rsquared(self):
keepers = []
values = []
print('%s terms at start' % len(self.N0))
for i in range(len(self.N0)):
n = helmholtz.vectord([float(1)])
d = helmholtz.vectord([self.D0[i]])
t = helmholtz.vectord([self.T0[i]])
l = helmholtz.vectord([self.L0[i]])
self.phir = helmholtz.phir_power(n, d, t, l, 0, 0)
PPF = self.evaluate_EOS(np.array(list(n)))
R2 = rsquared(PPF.p, self.phir.dDeltaV(self.tauV, self.deltaV))
values.append((R2, i))
if R2 > 0.9:
keepers.append(i)
values, indices = zip(*reversed(sorted(values)))
keepers = list(indices[0:30])
self.N0 = self.N0[keepers]
self.T0 = self.T0[keepers]
self.D0 = self.D0[keepers]
self.L0 = self.L0[keepers]
print('%s terms at end' % len(self.N0))
def termwise_Rsquared(self):
keepers = []
values = []
print('%s terms at start' % len(self.N0))
for i in range(len(self.N0)):
n = helmholtz.vectord([float(1)])
d = helmholtz.vectord([self.D0[i]])
t = helmholtz.vectord([self.T0[i]])
l = helmholtz.vectord([self.L0[i]])
self.phir = helmholtz.phir_power(n, d, t, l, 0, 0)
PPF = self.evaluate_EOS(np.array(list(n)))
R2 = rsquared(PPF.p, self.phir.dDeltaV(self.tauV, self.deltaV))
values.append((R2, i))
if R2 > 0.9:
keepers.append(i)
values, indices = zip(*reversed(sorted(values)))
keepers = list(indices[0:30])
self.N0 = self.N0[keepers]
self.T0 = self.T0[keepers]
self.D0 = self.D0[keepers]
self.L0 = self.L0[keepers]
print('%s terms at end' % len(self.N0))