def test_load_del(self):
db = PersistentDB(schema, 'pk', dbname='testdb', overwrite=True)
n_add = 50
mus = np.random.uniform(low=0.0, high=1.0, size=n_add)
sigs = np.random.uniform(low=0.05, high=0.4, size=n_add)
jits = np.random.uniform(low=0.05, high=0.2, size=n_add)
saveinfo = {}
for i, m, s, j in zip(range(n_add), mus, sigs, jits):
new_ts = tsmaker(m, s, j)
db.insert_ts("ts-{}".format(i), tsmaker(m, s, j))
db.upsert_meta("ts-{}".format(i), {
'mean': new_ts.mean(),
'std': new_ts.std()
})
saveinfo["ts-{}".format(i)] = new_ts.mean()
db.add_vp("ts-4")
db.add_vp()
db.delete_ts("ts-4")
pks, fields = db.select(meta={'vp': True}, fields=None)
self.assertEqual(len(pks), 1)
newdb = PersistentDB(schema, 'pk', dbname='testdb', load=True)
pks, fields = db.select(meta={}, fields=['mean'])
self.assertEqual(len(pks), n_add - 1)
self.assertTrue("ts-4" not in pks)
for i in range(0, n_add - 1):
self.assertEqual(fields[i]['mean'], saveinfo[pks[i]])
def test_load_del(self):
db = PersistentDB(schema, 'pk', dbname='testdb', overwrite=True)
n_add = 50
mus = np.random.uniform(low=0.0, high=1.0, size=n_add)
sigs = np.random.uniform(low=0.05, high=0.4, size=n_add)
jits = np.random.uniform(low=0.05, high=0.2, size=n_add)
saveinfo = {}
for i, m, s, j in zip(range(n_add), mus, sigs, jits):
new_ts = tsmaker(m, s, j)
db.insert_ts("ts-{}".format(i), tsmaker(m, s, j))
db.upsert_meta("ts-{}".format(i), {'mean':new_ts.mean(), 'std':new_ts.std()})
saveinfo["ts-{}".format(i)] = new_ts.mean()
db.add_vp("ts-4")
db.add_vp()
db.delete_ts("ts-4")
pks, fields = db.select(meta={'vp':True}, fields=None)
self.assertEqual(len(pks),1)
newdb = PersistentDB(schema, 'pk', dbname='testdb', load=True)
pks, fields = db.select(meta={}, fields=['mean'])
self.assertEqual(len(pks), n_add-1)
self.assertTrue("ts-4" not in pks)
for i in range(0,n_add-1):
self.assertEqual(fields[i]['mean'], saveinfo[pks[i]])
def test_simsearch(self):
db = PersistentDB(schema, 'pk', dbname='testdb', overwrite=True)
n_add = 50
mus = np.random.uniform(low=0.0, high=1.0, size=n_add)
sigs = np.random.uniform(low=0.05, high=0.4, size=n_add)
jits = np.random.uniform(low=0.05, high=0.2, size=n_add)
for i, m, s, j in zip(range(n_add), mus, sigs, jits):
db.insert_ts("ts-{}".format(i), tsmaker(m, s, j))
m = np.random.uniform(low=0.0, high=1.0)
s = np.random.uniform(low=0.05, high=0.4)
j = np.random.uniform(low=0.05, high=0.2)
query = tsmaker(m, s, j)
with self.assertRaises(ValueError): # No similarity search w/o vantage points
closest = db.simsearch(query)
for i in range(5):
db.add_vp()
closest = db.simsearch(query)
def test_simsearch(self):
db = PersistentDB(schema, 'pk', dbname='testdb', overwrite=True)
n_add = 50
mus = np.random.uniform(low=0.0, high=1.0, size=n_add)
sigs = np.random.uniform(low=0.05, high=0.4, size=n_add)
jits = np.random.uniform(low=0.05, high=0.2, size=n_add)
for i, m, s, j in zip(range(n_add), mus, sigs, jits):
db.insert_ts("ts-{}".format(i), tsmaker(m, s, j))
m = np.random.uniform(low=0.0, high=1.0)
s = np.random.uniform(low=0.05, high=0.4)
j = np.random.uniform(low=0.05, high=0.2)
query = tsmaker(m, s, j)
with self.assertRaises(
ValueError): # No similarity search w/o vantage points
closest = db.simsearch(query)
for i in range(5):
db.add_vp()
closest = db.simsearch(query)