def build_and_fill2(self, name, dims):
T = ambit.Tensor(ambit.TensorType.kCore, name, dims)
N = [[0 for x in range(dims[1])] for x in range(dims[0])]
data = T.tensor.data()
for r in range(dims[0]):
for c in range(dims[1]):
value = random.random()
data[r * dims[1] + c] = value
N[r][c] = value
return [T, N]
def build_and_fill3(self, name, dims):
T = ambit.Tensor(ambit.TensorType.CoreTensor, name, dims)
N = [[[0 for x in range(dims[2])] for x in range(dims[1])] for x in range(dims[0])]
data = T.tensor.data()
for p in range(dims[0]):
for q in range(dims[1]):
for r in range(dims[2]):
value = random.random()
data[p*dims[1]*dims[2] + q*dims[2] + r] = value
N[p][q][r] = value
return [T, N]
def build(self, name, dims, ttype=tensor_type, fill=None):
# Builds arbitrary Tensors and numpy arrys
# Accepts a list of integers or a string for dimensions
if ttype != ambit.TensorType.kCore:
raise ValueError("Only kCore is currently supported")
if isinstance(dims, str):
dims = [dim_size_dict[i] for i in dims]
T = ambit.Tensor(ttype, name, dims)
# Fill both N and T
N = np.asarray(T)
if fill:
N.flat[:] = fill
else:
N.flat[:] = np.arange(np.prod(dims))
# Copy numpy array so we no longer share memory
N = N.copy()
return [T, N]
def __init__(self, world, dims, syms):
self.factor = 1.0
self.world = world
self.dims = dims
self.syms = syms
self.tensor = ambit.Tensor(world, dims, syms)
