def random(sites,
bonds=None,
cut=None,
nmax=None,
ttype='D',
dtype=np.float64):
'''
Generate a random mps.
Parameters
----------
sites : list of Label/int/QuantumNumbers
The labels/number-of-degrees-of-freedom/quantum-numbers of the physical legs.
bonds : optional
* list of Label/str
The labels/identifiers of the virtual legs.
* 2-list of QuantumNumber
The quantum number of the first and last virtual legs.
cut : int, optional
The index of the connecting link.
nmax : int, optional
The maximum number of singular values to be kept.
ttype : 'D'/'S', optional
Tensor type. 'D' for dense and 'S' for sparse.
dtype : np.float64, np.complex128, optional
The data type of the random mps.
Returns
-------
MPS
The random mixed-canonical mps.
'''
np.random.seed()
sites = [
site if isinstance(site, Label) else Label(
'__MPS_RANDOM_S_%s__' % i, qns=site)
for i, site in enumerate(sites)
]
if bonds is None or not isinstance(bonds[+0], Label) or not isinstance(
bonds[-1], Label):
if bonds is not None:
iqns = bonds[+0].qns if isinstance(
bonds[+0], Label) else bonds[+0] if isinstance(
bonds[+0], QNS) else QNS.mono(bonds[+0]) if isinstance(
bonds[+0], QN) else 1
oqns = bonds[-1].qns if isinstance(
bonds[-1], Label) else bonds[-1] if isinstance(
bonds[-1], QNS) else QNS.mono(bonds[-1]) if isinstance(
bonds[-1], QN) else 1
else:
iqns, oqns = 1, 1
bonds = [
Label('__MPS_RANDOM_B_%s__' % i, None, None)
for i in range(len(sites) + 1)
]
bonds[+0] = bonds[+0].replace(qns=iqns)
bonds[-1] = bonds[-1].replace(qns=oqns)
else:
assert len(bonds) == len(sites) + 1
bonds = [
bond if isinstance(bond, Label) else Label(bond, None, None)
for bond in bonds
]
qnon, shape = next(iter(sites)).qnon, tuple(
[site.dim for site in sites])
if ttype == 'S': assert qnon
if qnon:
result = 0
if dtype in (np.float32, np.float64):
coeffs = np.random.random(nmax)
else:
coeffs = np.random.random(nmax) + 1j * np.random.random(nmax)
for k, indices in enumerate(
QNS.decomposition([site.qns for site in sites],
bonds[-1].qns[0] - bonds[+0].qns[0],
method='monte carlo',
nmax=nmax)):
ms = [
np.array(
[1.0 if i == index else 0.0 for i in range(site.dim)],
dtype=dtype) for site, index in zip(sites, indices)
]
result += MPS.productstate(ms, sites, copy(bonds)) * coeffs[k]
if ttype == 'S':
for m in result:
m.qnsort()
result = result.tosparse()
else:
ms = []
for i in range(len(sites)):
if dtype in (np.float32, np.float64):
ms.append(np.random.random((nmax, shape[i], nmax)))
else:
ms.append(
np.random.random((nmax, shape[i], nmax)) +
1j * np.random.random((nmax, shape[i], nmax)))
result = MPS.compose(ms=ms, sites=sites, bonds=bonds)
if cut is None:
result.canonicalize(cut=len(sites) / 2, nmax=nmax)
result._merge_ABL_()
else:
result.canonicalize(cut=cut, nmax=nmax)
return result
def random(sites, bonds=None, cut=None, nmax=None, dtype=np.float64):
'''
Generate a random mps.
Parameters
----------
sites : list of Label/int/QuantumNumbers
The labels/number-of-degrees-of-freedom/quantum-numbers of the physical legs.
bonds : optional
* list of Label
The labels of the virtual legs.
* 2-list of QuantumNumber
The quantum number of the first and last virtual legs.
cut : int, optional
The index of the connecting link.
nmax : int, optional
The maximum number of singular values to be kept.
dtype : np.float64, np.complex128, optional
The data type of the random mps.
Returns
-------
MPS
The random mixed-canonical mps.
'''
np.random.seed()
sites = [
site if isinstance(site, Label) else Label(
'__MPS_RANDOM_S_%s__' % i, qns=site)
for i, site in enumerate(sites)
]
if bonds is not None and all(
isinstance(bond, Label) for bond in bonds):
assert len(bonds) == len(sites) + 1
else:
if bonds is not None:
assert len(bonds) == 2 and isinstance(
bonds[0], QN) and isinstance(bonds[1], QN)
iqns, oqns = (QNS.mono(bonds[0]),
QNS.mono(bonds[1])) if bonds is not None else (1, 1)
bonds = [
Label(
'__MPS_RANDOM_B_%s__' % i,
qns=iqns if i == 0 else oqns if i == len(sites) else None)
for i in xrange(len(sites) + 1)
]
mode, shape = 'QN' if next(iter(sites)).qnon else 'NB', tuple(
[site.dim for site in sites])
if mode == 'QN':
result = 0
if dtype in (np.float32, np.float64):
coeffs = np.random.random(nmax)
else:
coeffs = np.random.random(nmax) + 1j * np.random.random(nmax)
for k, indices in enumerate(
QNS.decomposition([site.qns for site in sites],
bonds[-1].qns[0] - bonds[0].qns[0],
method='monte carlo',
nmax=nmax)):
ms = [
np.array(
[1.0 if i == index else 0.0 for i in xrange(site.dim)],
dtype=dtype) for site, index in zip(sites, indices)
]
result += MPS.productstate(ms, sites, copy(bonds)) * coeffs[k]
else:
ms = []
for i in xrange(len(sites)):
if dtype in (np.float32, np.float64):
ms.append(np.random.random((nmax, shape[i], nmax)))
else:
ms.append(
np.random.random((nmax, shape[i], nmax)) +
1j * np.random.random((nmax, shape[i], nmax)))
result = MPS(mode=mode, ms=ms, sites=sites, bonds=bonds)
if cut is None:
result.canonicalize(cut=len(sites) / 2, nmax=nmax)
result._merge_ABL_()
else:
result.canonicalize(cut=cut, nmax=nmax)
return result
def random(sites,bonds=None,cut=None,nmax=None,ttype='D',dtype=np.float64):
'''
Generate a random mps.
Parameters
----------
sites : list of Label/int/QuantumNumbers
The labels/number-of-degrees-of-freedom/quantum-numbers of the physical legs.
bonds : optional
* list of Label/str
The labels/identifiers of the virtual legs.
* 2-list of QuantumNumber
The quantum number of the first and last virtual legs.
cut : int, optional
The index of the connecting link.
nmax : int, optional
The maximum number of singular values to be kept.
ttype : 'D'/'S', optional
Tensor type. 'D' for dense and 'S' for sparse.
dtype : np.float64, np.complex128, optional
The data type of the random mps.
Returns
-------
MPS
The random mixed-canonical mps.
'''
np.random.seed()
sites=[site if isinstance(site,Label) else Label('__MPS_RANDOM_S_%s__'%i,qns=site) for i,site in enumerate(sites)]
if bonds is None or not isinstance(bonds[+0],Label) or not isinstance(bonds[-1],Label):
if bonds is not None:
iqns=bonds[+0].qns if isinstance(bonds[+0],Label) else bonds[+0] if isinstance(bonds[+0],QNS) else QNS.mono(bonds[+0]) if isinstance(bonds[+0],QN) else 1
oqns=bonds[-1].qns if isinstance(bonds[-1],Label) else bonds[-1] if isinstance(bonds[-1],QNS) else QNS.mono(bonds[-1]) if isinstance(bonds[-1],QN) else 1
else:
iqns,oqns=1,1
bonds=[Label('__MPS_RANDOM_B_%s__'%i,None,None) for i in range(len(sites)+1)]
bonds[+0]=bonds[+0].replace(qns=iqns)
bonds[-1]=bonds[-1].replace(qns=oqns)
else:
assert len(bonds)==len(sites)+1
bonds=[bond if isinstance(bond,Label) else Label(bond,None,None) for bond in bonds]
qnon,shape=next(iter(sites)).qnon,tuple([site.dim for site in sites])
if ttype=='S': assert qnon
if qnon:
result=0
if dtype in (np.float32,np.float64):
coeffs=np.random.random(nmax)
else:
coeffs=np.random.random(nmax)+1j*np.random.random(nmax)
for k,indices in enumerate(QNS.decomposition([site.qns for site in sites],bonds[-1].qns[0]-bonds[+0].qns[0],method='monte carlo',nmax=nmax)):
ms=[np.array([1.0 if i==index else 0.0 for i in range(site.dim)],dtype=dtype) for site,index in zip(sites,indices)]
result+=MPS.productstate(ms,sites,copy(bonds))*coeffs[k]
if ttype=='S':
for m in result: m.qnsort()
result=result.tosparse()
else:
ms=[]
for i in range(len(sites)):
if dtype in (np.float32,np.float64):
ms.append(np.random.random((nmax,shape[i],nmax)))
else:
ms.append(np.random.random((nmax,shape[i],nmax))+1j*np.random.random((nmax,shape[i],nmax)))
result=MPS.compose(ms=ms,sites=sites,bonds=bonds)
if cut is None:
result.canonicalize(cut=len(sites)/2,nmax=nmax)
result._merge_ABL_()
else:
result.canonicalize(cut=cut,nmax=nmax)
return result