def test_sparse_by_dense():
vec = divisi2.SparseVector.from_dict(dict(A=-1, B=1))
mul = divisi2.matrixmultiply(vec, mat1)
mul2 = divisi2.matrixmultiply(mat1.T, vec)
assert mul.equals(mul2)
assert np.allclose(mul, np.array([2, 2]))
cursor.execute(str % args)
ret = cursor.fetchall()
cursor.close()
return ret
f = open('../includes/config.yaml')
config = yaml.load(f)
f.close()
conn = MySQLdb.connect(config['dbhost'],config['dbuser'],config['dbpass'] or '',config['dbname'])
courses = db_query(conn, "SELECT * FROM comments WHERE parent='1'", ())
data = []
for c in courses:
topics = db_query(conn, "SELECT * FROM comments WHERE parent='%d'", (c[0]))
for t in topics:
data.append( (1, c[0], t[1]) )
mat = divisi2.make_sparse(data)
mat = mat.normalize_rows()
mat_t = mat.T
mult = divisi2.matrixmultiply(mat, mat_t)
print mult
similarities = mult.named_entries()
for s in similarities:
v,c1,c2 = s
if c1 != c2:
db_query(conn, "REPLACE INTO similarities (cid1,cid2,val) VALUE (%d,%d,%f)", (c1,c2,v));
conn.close()
