def load_adjacency(self, path, lcc=False):
comment = 'Ontology: {}\nYear: {}\nSubmissionID: {}'.format(
self.name, self.year, self.submission_id)
field = 'integer'
if lcc:
self.lcc_A = read_sparse_matrix(
path, self.get_onto_filename(path, ADJ_EXT, lcc))
else:
self.A = read_sparse_matrix(path,
self.get_onto_filename(path, ADJ_EXT))
def create_distance_matrix(self, path, hopspath, lcc=False):
fn_final = self.get_distance_matrix_fn(lcc)
if os.path.exists(os.path.join(path, fn_final)):
return read_sparse_matrix(path, fn_final)
fname = self.get_khop_matrix_fn(k=None, lcc=lcc)
files = [
fn for fn in os.listdir(hopspath)
if fn.startswith(fname.replace('<k>HOP.{}'.format(ADJ_EXT), ''))
and fn.endswith('HOP.{}'.format(ADJ_EXT))
]
m = None
print('\n'.join(files))
maximun = len(files)
for fn in files:
khop = int(fn.split('_')[-1].split('HOP')[0])
print('file:{}, k:{}'.format(fn, khop))
if m is None:
m = read_sparse_matrix(hopspath, fn) * khop
else:
m += read_sparse_matrix(hopspath, fn) * khop
m.eliminate_zeros()
if m.max() > maximun:
print('>>> weird: {} in k:{} in {}'.format(
m.max(), khop, self.name))
return None
#m = m.tolil()
#m.setdiag(0)
#m = m.tocsr()
m.eliminate_zeros()
comment = 'LCC: {}\nOntology: {}\nYear: {}\nSubmissionID: {}'.format(
lcc, self.name, self.year, self.submission_id)
field = 'integer'
save_sparse_matrix(m, path, fn_final, comment=comment, field=field)
return m
def load_adjacency(self, path):
comment = 'Clickstreams\nOntology: {}\nYear: {}\nNavitype:{}'.format(
self.name, self.year, self.get_navitype())
field = 'integer'
self.T = read_sparse_matrix(path, self.get_cs_filename(path, ADJ_EXT))
def get_khop_matrix(self, path, k, lcc=False):
fn = self.get_khop_matrix_fn(k, lcc)
return read_sparse_matrix(path, fn).tocsr()
def get_khop(self, k):
fn = self.get_khop_matrix_fn(k, lcc=self.lcc)
return read_sparse_matrix(self.path_khop, fn)