def dist_sequence(self, db, smooth):
# create Voronoi
f_ins = []
for per in self.percent_range():
f_ins.append(self.comm_path(per, False))
vertices = set()
for f in f_ins:
fverts = set(part.read(f).keys())
vertices = vertices.union(fverts)
vor = Voronoi(db, vertices)
# create paritions
parts = {}
for per in self.percent_range():
f_in = self.comm_path(per, False)
par = part.Partition(vor)
par.read(f_in)
if smooth:
par.smooth_until_stable()
parts[per] = par
prev = False
for per in self.percent_range():
if prev:
dist = parts[per].distance(parts[prev])
print('%s' % dist)
prev = per
def similarity_matrix(self, db, smooth, optimize):
# create precentile range
pr = self.percent_range()
npers = len(pr)
# save memory
use_disk = optimize == 'memory'
# create Voronoi
f_ins = []
for per in pr:
f_ins.append(self.comm_path(per, False))
vertices = set()
for f in f_ins:
fverts = set(part.read(f).keys())
vertices = vertices.union(fverts)
vor = Voronoi(db, vertices)
# create paritions
parts = {}
for per in pr:
f_in = self.comm_path(per, False)
par = part.Partition(vor)
par.read(f_in)
if smooth:
par.smooth_until_stable()
parts[per] = par
# create distances cache
dists = np.zeros((npers, npers))
# compute distances
for i in range(npers):
per1 = pr[i]
if use_disk and (i > 0):
parts[per1].load_commxcomm('tmp/%s' % per1)
for j in range(npers):
per2 = pr[j]
if per1 < per2:
if use_disk and (i > 0):
parts[per2].load_commxcomm('tmp/%s' % per2)
dist = parts[per1].distance(parts[per2])
if use_disk:
if i > 0:
parts[per2].clean_commxcomm()
else:
parts[per2].save_commxcomm('tmp/%s' % per2)
if j == 1:
parts[per1].save_commxcomm('tmp/%s' % per1)
dists[i][j] = dist
else:
dist = dists[j][i]
if j > 0:
print(',', end="")
print('%s' % dist, end="", flush=True)
print('', flush=True)
if use_disk:
parts[per1].clean_commxcomm()
def metric(self, metric, db, best, smooth, scale):
# create Voronoi
f_ins = []
for per in self.percent_range():
if best:
f_ins.append(self.comm_path(per, False))
else:
dir_in = self.comm_path(per, True)
for (dirpath, dirnames, filenames) in os.walk(dir_in):
f_ins.extend(filenames)
f_ins = ["%s/%s" % (dir_in, f) for f in f_ins]
vertices = set()
for f in f_ins:
fverts = set(part.read(f).keys())
vertices = vertices.union(fverts)
vor = Voronoi(db, vertices)
# compute metrics
print("percentile,distance,metric")
for per in self.percent_range():
f_ins = []
if best:
f_ins = [self.comm_path(per, False)]
else:
dir_in = self.comm_path(per, True)
for (dirpath, dirnames, filenames) in os.walk(dir_in):
f_ins.extend(filenames)
f_ins = ["%s/%s" % (dir_in, f) for f in f_ins]
m = 0.
for f in f_ins:
par = part.Partition(vor)
par.read(f)
if smooth:
par.smooth_until_stable()
m += par.metric(metric)
m /= float(len(f_ins))
print("%s,%s,%s" % (per, self.dist(per, scale), m))
def init_voronoi(self):
vertices = set(part.read(self.in_file).keys())
return Voronoi(self.db, vertices)
def init_voronoi(self):
vertices = set(part.read(self.in_file).keys())
return Voronoi(self.db, vertices)
def init_voronoi(self, files):
vertices = set()
for file in files:
fverts = set(part.read(file).keys())
vertices = vertices.union(fverts)
self.vor = Voronoi(self.db, vertices)
def init_voronoi(self, files):
vertices = set()
for file in files:
fverts = set(part.read(file).keys())
vertices = vertices.union(fverts)
self.vor = Voronoi(self.db, vertices)
