def assign(profile_fn, outfile=None):
blast_data = bi.parse_psiblast(profile_fn)
qry = query.Query("test")
qry.set_input(blast_data)
sc1_input = fg.ScorerInput(["profile", "sequence"])
sc1_input.set_data(qry)
scorers1 = []
for i in range(para.num_classes - 1):
scorers1.append(scorer.Scorer(para.lvl1_coef[i], sc1_input))
qry.set_structure_vector(scorers1)
#print "#Lvl 1 complete"
#print qry.profile_vec
#print qry.structure_vec
#sc2_input = fg.ScorerInput(["sequence", "profile"])
sc2_input = fg.ScorerInput(["structure", "profile"])
sc2_input.set_data(qry)
scorers2 = []
for i in range(para.num_classes - 1):
scorers2.append(scorer.Scorer(para.lvl2_coef[i], sc2_input))
qry.set_scores(scorers2)
qry.set_assignment(expected.get_expected(para.num_classes))
qry.set_confidence()
#print "Lvl 2 complete"
out.output(qry, outfile)
return qry
def main_wrapper():
""" Loop main """
words = read_subword_file('data/linux_command.txt')
my_scorer = scorer.Scorer()
while is_continued(my_scorer):
main(words, my_scorer)
print(my_scorer.num_correct)
print(my_scorer.num_incorrect)
print(my_scorer.end_time())
confpath = os.path.join('..', 'conf')
random.seed()
subjectid = ""
sessionid = ""
while subjectid == "" or subjectid == None:
subjectid = raw_input("Subject ID: ")
print subjectid
sessionid = 0
random.seed(subjectid + str(time.time()))
drawer = drawer.Drawer()
scorer = scorer.Scorer()
acceptedkeys = ['s', 'd', 'f', 'j', 'k', 'l']
sessionid = 0
exptname = "skill3"
gensettings = readConfig(os.path.join(confpath, 'configuration.txt'))
colort = readConfig(os.path.join(confpath, 'colors.txt'))
colors = []
for k, v in colort.iteritems():
colors.append(map(int, v))
print colors
letters = [
'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O',
'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'
help='Display result limit')
parser.add_argument('--loc-weight',
default=0.40,
type=float,
help='Location distance weight')
parser.add_argument('--job-weight',
default=0.30,
type=float,
help='Job Title Weight')
parser.add_argument('--industry-weight',
default=0.30,
type=float,
help='Industry Type Weight')
args = parser.parse_args()
scorer = scorer.Scorer(**args.__dict__)
scorer.run()
# items = load_data(args)
# project = load_project(args)
# processed = list()
# for item in items:
# if not is_within_100(project, item):
# print(f"Excluding participant {item['firstname']} > 100 km")
# continue
# score = compute_score(args, project, item)
# item['score'] = score
# processed.append(item)
def __init__(self):
super().__init__()
self.lock = threading.RLock()
self.scorer = scorer.Scorer()
dest_db = get_dest_db()
if dest_db is None:
dest_db = create_dest_db()
else:
source_db = get_source_db()
dest_db = None
# Get the destination DB in loop in case of concurrecy proble,
# that is, slave nodes try to acquire the dest DB before master node creates it.
while dest_db == None:
dest_db = get_dest_db()
alcohols_dict = "./topic analysis/dictionary/alcohols.txt"
fastfood_dict = "./topic analysis/dictionary/fastfood.txt"
smoking_dict = "./topic analysis/dictionary/smoking.txt"
# create Scorer objects for each topic
# so that the resource only needs to be loaded once.
alcohols_scorer = scorer.Scorer(senti_analyzer, alcohols_dict)
fastfood_scorer = scorer.Scorer(senti_analyzer, fastfood_dict)
smoking_scorer = scorer.Scorer(senti_analyzer, smoking_dict)
# index records the number of tweets
index = 0
# records the number of results in the buffer
buffer = 0
# A temporary list to store results.
# if the buffer is full, data in result will be written into dest DB,
# and buffer and result will be reset.
result = []
for ele in source_db:
index += 1
print(index)
# each node only handles the tweets they are responsible for.
if index % comm_size == comm_rank: