def __init__(self, name=None, parityCheckMatrix=None, q=None):
if parityCheckMatrix is not None:
if utils.isStr(parityCheckMatrix):
self.filename = os.path.expanduser(parityCheckMatrix)
self._parityCheckMatrix = matrices.getNonbinaryMatrix(
self.filename)
if name is None:
name = os.path.basename(self.filename)
elif not isinstance(parityCheckMatrix, np.ndarray):
self._parityCheckMatrix = matrices.getNonbinaryMatrix(
parityCheckMatrix)
else:
self._parityCheckMatrix = parityCheckMatrix
if q is None:
q = int(np.max(self.parityCheckMatrix) +
1) # avoid having q stored as numpy int
self.blocklength = self.parityCheckMatrix.shape[1]
rank = gfqla.rank(self.parityCheckMatrix, q)
self.infolength = self.blocklength - rank
cols = np.hstack(
(np.arange(self.infolength,
self.blocklength), np.arange(self.infolength)))
self._generatorMatrix = gfqla.orthogonalComplement(
self.parityCheckMatrix, columns=cols, q=q)
LinearBlockCode.__init__(self, q, name)
def getBinaryMatrix(source):
"""Creates a binary matrix of type :class:`np.ndarray` from either a file or a
two-dimensional python list.
If `source` is a file path, the file must either contain an explicit representation of the
matrix (by means of whitespace-separated '0' and '1' characters) or be in the AList format
(see alistToNumpy docstring).
The file may be bzip2'ed, in which case it will be decompressed automatically.
Returns
-------
np.ndarray[dtype=int]
Numpy ndarray representation of the given matrix.
"""
if isinstance(source, np.ndarray):
return source.astype(np.int)
if utils.isStr(source):
source = os.path.expanduser(source)
fileObj = bz2.BZ2File(source, 'r') if source.endswith('bz2') else open(source, 'rt')
with fileObj as f:
lines = [[int(x) for x in l.strip().split()]
for l in f.readlines()
if len(l.strip()) > 0]
else:
assert hasattr(source, '__iter__') and hasattr(source[0], '__iter__')
lines = source
if lines[0][0] in (0, 1): # explicit 0/1 representation
return np.array(lines, dtype=np.int)
return alistToNumpy(lines)
def __init__(self, code, gurobiParams=None, gurobiVersion=None, name=None):
if name is None:
name = 'GurobiIPDecoder'
if utils.isStr(gurobiParams):
# allow to specify tuning set parameters using gurobiParams='tuning1' or similar
for i in ('1', '2', '3'):
if i in gurobiParams:
gurobiParams = getattr(self, 'tuningSet' + i)
break
elif gurobiParams is None:
gurobiParams = self.tuningSet1
gurobihelpers.GurobiDecoder.__init__(self, code, name, gurobiParams, gurobiVersion,
integer=True)
from gurobimh import GRB, quicksum
matrix = code.parityCheckMatrix
for i in range(code.blocklength):
self.model.addConstr(quicksum(self.x[i, k] for k in range(1, code.q)),
GRB.LESS_EQUAL, 1)
self.z = []
for i in range(matrix.shape[0]):
ub = np.sum(matrix[i]) * (code.q - 1) // code.q
self.z.append(self.model.addVar(0, ub, vtype=GRB.INTEGER, name='z{}'.format(i)))
self.model.update()
for z, row in zip(self.z, matrix):
self.model.addConstr(quicksum(row[i]*k*self.x[i, k] for k in range(1, code.q) for i
in np.flatnonzero(row)) - code.q * z, GRB.EQUAL, 0)
self.model.update()
self.mlCertificate = self.foundCodeword = True
def getBinaryMatrix(source):
"""Creates a binary matrix of type :class:`np.ndarray` from either a file or a
two-dimensional python list.
If `source` is a file path, the file must either contain an explicit representation of the
matrix (by means of whitespace-separated '0' and '1' characters) or be in the AList format
(see alistToNumpy docstring).
The file may be bzip2'ed, in which case it will be decompressed automatically.
Returns
-------
np.ndarray[dtype=int]
Numpy ndarray representation of the given matrix.
"""
if isinstance(source, np.ndarray):
return source.astype(np.int)
if utils.isStr(source):
source = os.path.expanduser(source)
fileObj = bz2.BZ2File(source, 'r') if source.endswith('bz2') else open(
source, 'rt')
with fileObj as f:
lines = [[int(x) for x in l.strip().split()]
for l in f.readlines() if len(l.strip()) > 0]
else:
assert hasattr(source, '__iter__') and hasattr(source[0], '__iter__')
lines = source
if lines[0][0] in (0, 1): # explicit 0/1 representation
return np.array(lines, dtype=np.int)
return alistToNumpy(lines)
def getNonbinaryMatrix(source):
"""Reads a nonbinary matrix (no AList support)."""
if utils.isStr(source):
with open(source, 'rt') as f:
lines = [[int(x) for x in l.strip().split()]
for l in f.readlines() if len(l.strip()) > 0]
return np.array(lines, dtype=np.int)
else:
return np.array(source, dtype=np.int)
def getNonbinaryMatrix(source):
"""Reads a nonbinary matrix (no AList support)."""
if utils.isStr(source):
with open(source, 'rt') as f:
lines = [[int(x) for x in l.strip().split()]
for l in f.readlines() if len(l.strip()) > 0]
return np.array(lines, dtype=np.int)
else:
return np.array(source, dtype=np.int)
def __init__(self, name=None, parityCheckMatrix=None, generatorMatrix=None):
self._parityCheckMatrix = self._generatorMatrix = None
if parityCheckMatrix is not None:
assert generatorMatrix is None
if utils.isStr(parityCheckMatrix) and name is None:
name = os.path.basename(parityCheckMatrix)
hmatrix = matrices.getBinaryMatrix(parityCheckMatrix)
self._parityCheckMatrix = hmatrix
cols = hmatrix.shape[1]
rank = gfqla.rank(hmatrix)
self.blocklength = cols
self.infolength = cols - rank
elif generatorMatrix is not None:
assert name is not None
gmatrix = matrices.getBinaryMatrix(generatorMatrix)
self._generatorMatrix = gmatrix
self.blocklength = gmatrix.shape[1]
self.infolength = gmatrix.shape[0]
assert gfqla.rank(gmatrix, 2) == self.infolength
LinearBlockCode.__init__(self, 2, name)
def __init__(self, name=None, parityCheckMatrix=None, q=None):
if parityCheckMatrix is not None:
if utils.isStr(parityCheckMatrix):
self.filename = os.path.expanduser(parityCheckMatrix)
self._parityCheckMatrix = matrices.getNonbinaryMatrix(self.filename)
if name is None:
name = os.path.basename(self.filename)
elif not isinstance(parityCheckMatrix, np.ndarray):
self._parityCheckMatrix = matrices.getNonbinaryMatrix(parityCheckMatrix)
else:
self._parityCheckMatrix = parityCheckMatrix
if q is None:
q = int(np.max(self.parityCheckMatrix) + 1) # avoid having q stored as numpy int
self.blocklength = self.parityCheckMatrix.shape[1]
rank = gfqla.rank(self.parityCheckMatrix, q)
self.infolength = self.blocklength - rank
cols = np.hstack((np.arange(self.infolength, self.blocklength),
np.arange(self.infolength)))
self._generatorMatrix = gfqla.orthogonalComplement(self.parityCheckMatrix,
columns=cols, q=q)
LinearBlockCode.__init__(self, q, name)
def __init__(self,
name=None,
parityCheckMatrix=None,
generatorMatrix=None):
self._parityCheckMatrix = self._generatorMatrix = None
if parityCheckMatrix is not None:
assert generatorMatrix is None
if utils.isStr(parityCheckMatrix) and name is None:
name = os.path.basename(parityCheckMatrix)
hmatrix = matrices.getBinaryMatrix(parityCheckMatrix)
self._parityCheckMatrix = hmatrix
cols = hmatrix.shape[1]
rank = gfqla.rank(hmatrix)
self.blocklength = cols
self.infolength = cols - rank
elif generatorMatrix is not None:
assert name is not None
gmatrix = matrices.getBinaryMatrix(generatorMatrix)
self._generatorMatrix = gmatrix
self.blocklength = gmatrix.shape[1]
self.infolength = gmatrix.shape[0]
assert gfqla.rank(gmatrix, 2) == self.infolength
LinearBlockCode.__init__(self, 2, name)