def EF12(self):
if self.mat is None:
self.mat = matrices(self.xmol)
if self.eF12 is None:
x = self.xmol.no
v = self.xmol.nv
c = self.xmol.nc
eps = np.array(self.xmol._vhf.mo_energy).reshape(x + v, 1)
eij = eps[:x] + eps[:x].T
ef12 = sumdiag(self.mat.B, x * x) + 2.0 * sumdiag(self.mat.V, x * x) - sumdiag(self.mat.V.transpose(1,0,2,3), x * x) + np.einsum('abij,ijab,abij', self.mat.ieps, self.mat.C, self.mat.C.transpose(2,3,0,1).conj() + 2.0 * self.mat.g - self.mat.g.transpose(1,0,2,3)) - np.einsum('ij,ijij', eij, self.mat.X)
self.eF12 = ef12 + self.E1()
return self.eF12
def E1(self):
if self.mat is None:
self.mat = matrices(self.xmol)
Fcx = self.mat.TF.Fcx
Fcv = self.mat.TF.Fca
Fcc = self.mat.TF.Fcc
e1 = 0
for i in xrange(self.xmol.no):
ei = self.xmol._vhf.mo_energy[i]
ie = np.empty((self.xmol.nv, 1))
for a in xrange(self.xmol.nv):
ie[a] = 1.0 / (ei - self.xmol._vhf.mo_energy[a + self.xmol.no])
A = Fcc - ei * np.eye(self.xmol.nc) + np.einsum('a,pa,aq->pq', ie.reshape(-1), Fcv, HerConj(Fcv))
b = -Fcx[:, i]
ti = sp.linalg.solve(A, b, overwrite_a = True, overwrite_b = True)
e1 += np.dot(ti.reshape(-1), Fcx.conj().reshape(-1))
return e1 * 2.0
def EF12(self):
if self.mat is None:
self.mat = matrices(self.xmol)
if self.eF12 is None:
x = self.xmol.no
v = self.xmol.nv
c = self.xmol.nc
eps = np.array(self.xmol._vhf.mo_energy).reshape(x + v, 1)
eij = eps[:x] + eps[:x].T
ef12 = 0
for i in xrange(x):
for j in xrange(x):
# c^ij
tB = self.mat.B - eij[i, j] * self.mat.X + np.einsum('ab,xyab,abvw->xyvw', self.mat.ieps[:, :, i, j], self.mat.C, self.mat.C.transpose(2,3,0,1).conj())
tV = self.mat.V[:, :, i, j] + np.einsum('ab,xyab,ab->xy', self.mat.ieps[:, :, i, j], self.mat.C, self.mat.g[:, :, i, j])
cij = sp.linalg.solve(-tB.reshape(x * x, -1), tV.reshape(-1), overwrite_a = True)
ef12 += np.dot(np.conj(2.0 * tV - tV.T).reshape(-1), cij.reshape(-1))
# TODO
self.eF12 = ef12# + self.E1()
return self.eF12
