def load(self, name):
'''
This method will load the matrix from the
database.
'''
if not probability_table_exists():
raise AssertionError
self._bibmap, self._matrix = load_bibmap_and_matrix_from_db(name)
def store(self, name):
'''
This method will store the matrix to the
database.
'''
if not probability_table_exists():
raise AssertionError
save_bibmap_and_matrix_to_db(name, self._bibmap, self._matrix)
def load(self, name):
'''
This method will load the matrix from the
database.
'''
if not probability_table_exists():
raise AssertionError
self._bibmap, self._matrix = load_bibmap_and_matrix_from_db(name)
def store(self, name):
'''
This method will store the matrix to the
database.
'''
if not probability_table_exists():
raise AssertionError
save_bibmap_and_matrix_to_db(name, self._bibmap, self._matrix)
def __init__(self,
cluster_set,
last_name="",
cached=[],
use_cache=False,
save_cache=False):
'''
Constructs probability matrix. If use_cache is true, it will
try to load old computations from the database. If save cache
is true it will save the current results into the database.
@param cluster_set: A cluster set object, used to initialize
the matrix.
@param last_name: A string which defines the current cluster
of names. It is used only if use_cache or save_cache is true.
@param cached: A list with the bibs, which are not touched
since last save.
'''
self._bib_matrix = self.bib_matrix(cluster_set)
old_matrix = self.bib_matrix()
if use_cache and probability_table_exists():
old_matrix.load(last_name)
elif cached:
raise AssertionError("You cannot have cached"
"results and empty table!")
for cl1 in cluster_set.clusters:
for cl2 in cluster_set.clusters:
if id(cl1) != id(cl2) and cl1.hates(cl2) == False:
for bib1 in cl1.bibs:
for bib2 in cl2.bibs:
if bib1 in cached and bib2 in cached:
val = old_matrix[bib1, bib2]
if val == None:
val = compare_bibrefrecs(bib1, bib2)
else:
val = compare_bibrefrecs(bib1, bib2)
self._bib_matrix[bib1, bib2] = val
if save_cache:
if not probability_table_exists():
create_probability_table()
self._bib_matrix.store(last_name)
def __init__(self, cluster_set, last_name="", cached = [],
use_cache = False, save_cache = False):
'''
Constructs probability matrix. If use_cache is true, it will
try to load old computations from the database. If save cache
is true it will save the current results into the database.
@param cluster_set: A cluster set object, used to initialize
the matrix.
@param last_name: A string which defines the current cluster
of names. It is used only if use_cache or save_cache is true.
@param cached: A list with the bibs, which are not touched
since last save.
'''
self._bib_matrix = self.bib_matrix(cluster_set)
old_matrix = self.bib_matrix()
if use_cache and probability_table_exists():
old_matrix.load(last_name)
elif cached:
raise AssertionError("You cannot have cached"
"results and empty table!")
for cl1 in cluster_set.clusters:
for cl2 in cluster_set.clusters:
if id(cl1) != id(cl2) and cl1.hates(cl2) == False:
for bib1 in cl1.bibs:
for bib2 in cl2.bibs:
if bib1 in cached and bib2 in cached:
val = old_matrix[bib1, bib2]
if val == None:
val = compare_bibrefrecs(bib1, bib2)
else:
val = compare_bibrefrecs(bib1, bib2)
self._bib_matrix[bib1, bib2] = val
if save_cache:
if not probability_table_exists():
create_probability_table()
self._bib_matrix.store(last_name)