def test_tensordot________________01(): larray = np.random.rand(5,5) rarray = np.random.rand(5,5) tarray = np.tensordot(larray, rarray, axes=([1],[0])) a1 = ax.PartitionedAxis((5,5)) L = tn.TiledTensor((a1,a1)) R = tn.TiledTensor((a1,a1)) L[0:5,0:5] = L[5:10,5:10] = larray R[0:5,0:5] = R[5:10,5:10] = rarray T = tn.tensordot(L, R, axis_keys=([1],[0])) assert (T[0: 5,0: 5] == tarray).all() assert (T[5:10,5:10] == tarray).all()
def water_mp2_script(scfwfn, mints):
nirrep = scfwfn.nirrep()
nsopi = tuple(scfwfn.nsopi()[h] for h in range(nirrep))
nso = scfwfn.nso()
import axis as ax
c1_axis = ax.PartitionedAxis(nso)
c2v_axis = ax.PartitionedAxis(nsopi)
import tensor as tn
U = tn.TiledTensor((c2v_axis, c1_axis))
symmetrizer = mints.petite_list().sotoao()
for h in range(nirrep):
offset = c2v_axis.get_partition_start(h)
for i in range(symmetrizer.rows(h)):
for j in range(symmetrizer.cols(h)):
U[i+offset, j] = symmetrizer.get(h, i, j)
C = tn.TiledTensor((c2v_axis, c2v_axis))
mo_coeffs = scfwfn.Ca()
for h in range(nirrep):
offset = c2v_axis.get_partition_start(h)
for i in range(mo_coeffs.rows(h)):
for j in range(mo_coeffs.cols(h)):
C[i+offset, j+offset] = mo_coeffs.get(h, i, j)
import numpy as np
c2v_g = tn.TiledTensor((c2v_axis, c2v_axis, c2v_axis, c2v_axis))
c1_g = tn.TiledTensor(( c1_axis, c1_axis, c1_axis, c1_axis))
c1_g[0:7,0:7,0:7,0:7] = np.array(mints.ao_eri())
g1 = tn.tensordot(U, c1_g, axis_keys=([1],[3]))
g2 = tn.tensordot(U, g1, axis_keys=([1],[3]))
g1 = tn.tensordot(U, g2, axis_keys=([1],[3]))
c2v_g = tn.tensordot(U, g1, axis_keys=([1],[3]))
import numpy.linalg as la
sh = SymmetryHelper(BINARY_IRREPS["C2v"], [0, 1, 2, 3])
def test_tensordot________________02():
a2 = ax.PartitionedAxis(2)
a3 = ax.PartitionedAxis((1,2))
a4 = ax.PartitionedAxis((2,1,1))
a5 = ax.PartitionedAxis((3,2))
a6 = ax.PartitionedAxis((3,0,1,2))
larray = np.random.rand(2,3,4,5,6)
rarray = np.random.rand(6,4,5,3,3)
tarray = np.tensordot(larray, rarray, axes=([1,2,3,4],[3,1,2,0]))
L = tn.TiledTensor((a2,a3,a4,a5,a6))
R = tn.TiledTensor((a6,a4,a5,a3,a3))
for coord in it.product(*(range(dim) for dim in larray.shape)):
L[coord] = larray[coord]
for coord in it.product(*(range(dim) for dim in rarray.shape)):
R[coord] = rarray[coord]
T = tn.tensordot(L, R, axis_keys=([1,2,3,4],[3,1,2,0]))
for coord in it.product(*(range(dim) for dim in tarray.shape)):
assert abs(T[coord] - tarray[coord]) < 1e-13
a5 = ax.PartitionedAxis((3,2))
a6 = ax.PartitionedAxis((3,0,1,2))
larray = np.random.rand(2,3,4,5,6)
rarray = np.random.rand(6,4,5,3,3)
tarray = np.tensordot(larray, rarray, axes=([1,2,3,4],[3,1,2,0]))
L = tn.TiledTensor((a2,a3,a4,a5,a6))
R = tn.TiledTensor((a6,a4,a5,a3,a3))
for coord in it.product(*(range(dim) for dim in larray.shape)):
L[coord] = larray[coord]
for coord in it.product(*(range(dim) for dim in rarray.shape)):
R[coord] = rarray[coord]
T = tn.tensordot(L, R, axis_keys=([1,2,3,4],[3,1,2,0]))
for coord in it.product(*(range(dim) for dim in tarray.shape)):
assert abs(T[coord] - tarray[coord]) < 1e-13
if __name__ == "__main__":
larray = np.random.rand(5,5)
rarray = np.random.rand(5,5)
tarray = np.tensordot(larray, rarray, axes=([1],[0]))
print tarray
a1 = ax.PartitionedAxis((5,5))
L = tn.TiledTensor((a1,a1))
R = tn.TiledTensor((a1,a1))
L[0:5,0:5] = L[5:10,5:10] = larray
R[0:5,0:5] = R[5:10,5:10] = rarray
T = tn.tensordot(L, R, axis_keys=([1],[0]))
print T
