def f(cardNO_1,num):
print("尝试数：",num,)
cardNO_1=list(cardNO_1)
cardNO_1.append(num)
cardNO_1.reverse()
print("尝试数：",num,cardNO_1)
#resvCardNO = cardNO_1[::-1].append(num)
#print(cardNO_1)
jiweisum=0
for i in range(len(cardNO_1)):
if i%2==0:
jiweisum+=int(cardNO_1[i])
#print(cardNO_1[i],jiweisum)
print("奇数和",jiweisum)
oushsum=0
for i in range(len(cardNO_1)):
tmp=0
if i%2==1:
tmp=int(cardNO_1[i])*2
if tmp>10:
tmp=tmp-9
oushsum+=tmp
print("偶数和",oushsum)
if (jiweisum+oushsum)%10==0 :
print(num, "is the key")
return "ok"
else:
print(num, "is not the key")
cardNO_1.reverse()
cardNO_1.pop()
def find_cycle(graph):
# select any node to start cycle, may as well be the first
cycle = []
current_node = find_start_node(graph)
# list(graph.values())[0]
cycle.append(current_node)
# do initial cycle
cycle = get_path_from_node(current_node, graph, cycle)
done = False
while not done:
c = cycle[:]
for i in range(0, len(c)):
if len(c[i].edges) > 0:
cycle_prime = []
cycle_prime.append(c[i])
cycle_prime = get_path_from_node(c[i], graph, cycle_prime)
cycle = cycle[:i] + cycle_prime + cycle[i + 1:]
break
# if we get here an there are no more nodes then we're done
if i == len(c) - 1:
done = True
return cycle
def get_individual_records(db, tables, table_headers, foreign_keys):
cursor = db.cursor()
var_string = ''
table = ''
refs = ''
var_string += table_headers[0] + "."
var_string += table_headers[1] + ", "
var_string = var_string[:-2]
for table_name in tables:
table += table_name + ", "
table = table[:-2]
if table_headers[0].find("discrete", 0) >= 0:
for i in range(len(foreign_keys)):
refs += "discrete_"
refs += foreign_keys[i][2]+ "."
refs += foreign_keys[i][0]+ " = "
refs += "discrete_"
refs += foreign_keys[i][1]
refs += ".id and "
else:
for i in range(len(foreign_keys)):
refs += foreign_keys[i][2]+ "."
refs += foreign_keys[i][0]+ " = "
refs += foreign_keys[i][1]
refs += ".id and "
refs = refs[:-5]
sql = 'SELECT {} FROM {} WHERE {}'.format(var_string, table, refs)
logging.debug("SQL query: %s", sql)
cursor.execute(sql)
return cursor
def get_records(db, tables, headers, foreign_keys):
cursor = db.cursor()
var_string = ''
table = ''
refs = ''
for i, table_headers in enumerate(headers):
for j in range(len(table_headers)):
var_string += tables[i] + "."
var_string += headers[i][j] + ", "
var_string = var_string[:-2]
for i in range(len(tables)):
table += tables[i] + ", "
table = table[:-2]
if tables[0].find("discrete", 0) >= 0:
for i in range(len(foreign_keys)):
refs += "discrete_"
refs += foreign_keys[i][2]+ "."
refs += foreign_keys[i][0]+ " = "
refs += "discrete_"
refs += foreign_keys[i][1]
refs += ".id and "
else:
for i in range(len(foreign_keys)):
refs += foreign_keys[i][2]+ "."
refs += foreign_keys[i][0]+ " = "
refs += foreign_keys[i][1]
refs += ".id and "
refs = refs[:-5]
sql = 'SELECT {} FROM {} WHERE {}'.format(var_string, table, refs)
cursor.execute(sql)
return cursor
def store_db_struct(db, tables, headers, types, user_input, attributes, discretize,
scripts, partition_sizes, offsets, decision, foreign_keys, references):
cursor = db.cursor()
cursor.execute("DROP TABLE IF EXISTS _tables")
cursor.execute("DROP TABLE IF EXISTS _headers")
cursor.execute("DROP TABLE IF EXISTS _foreign_keys")
create_db_struct(db)
for i in range(len(tables)):
cursor.execute("INSERT INTO _tables "
"(name) "
"VALUES (?)",
(tables[i],))
for i, table_headers in enumerate(headers):
for j in range(len(table_headers)):
cursor.execute("INSERT INTO _headers "
"(table_id, name, type, user_input, attributes, discretize, script, partition_size, offset, decision) "
"VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)",
(i + 1, headers[i][j], types[i][j], user_input[i][j],
attributes[i][j], discretize[i][j], scripts[i][j],
partition_sizes[i][j], offsets[i][j], decision[i][j]))
for i, foreign_key in enumerate(foreign_keys):
for j in range(len(foreign_key)):
cursor.execute("INSERT INTO _foreign_keys "
"(table_id, name, reference) "
"VALUES (?, ?, ?)",
(i + 1, foreign_key[j], references[i][j],))
db.commit()
def build_alphabet(spectrum, m):
"""
:param spectrum: an experimental spectrum
:param m: the multiplicity threshold
:return: a convolution spectrum, trimmed to contain only peptide masses appearing m times or more.
"""
convolutions = dict()
for i in range(0, len(spectrum)):
for j in range(i + 1, len(spectrum)):
diff = spectrum[j] - spectrum[i]
if diff > 0 and 57 <= diff <= 200:
if diff in convolutions.keys():
convolutions[diff] += 1
else:
convolutions[diff] = 1
sorted_list = sorted(convolutions.items(), key=operator.itemgetter(1), reverse=True)
score_to_beat = sorted_list[m - 1][1]
result = []
for item in sorted_list:
if item[1] >= score_to_beat:
result.append(item[0])
return result
def create_paths(self):
self.activity_list.sort(key=lambda predecessor: predecessor["Predecessor"])
for i in range(len(self.activity_list)):
if self.activity_list[i]["Predecessor"] is '-':
self.activity_list[i]["Total Time"] += self.activity_list[i]["Time"]
self.paths.append([self.activity_list[i]])
continue
else:
for path in self.paths:
if int(self.activity_list[i]["Predecessor"]) is path[-1]["ID"]:
if i+1 < len(self.activity_list) and self.activity_list[i]["Predecessor"] == self.activity_list[i+1]["Predecessor"]:
self.paths.append(cp.copy(path))
self.activity_list[i]["Total Time"] += path[-1]["Total Time"]+self.activity_list[i]["Time"]
path.append(self.activity_list[i])
break
else:
self.activity_list[i]["Total Time"] += path[-1]["Total Time"]+self.activity_list[i]["Time"]
path.append(self.activity_list[i])
break
else:
for path1 in self.paths:
if int(self.activity_list[i]["Predecessor"]) is path1[-1]["ID"]:
self.activity_list[i]["Total Time"] += path1[-1]["Total Time"]+self.activity_list[i]["Time"]
path1.append(self.activity_list[i])
break
for path1 in self.paths:
if int(self.activity_list[i]["Predecessor"]) is path1[-1]["ID"]:
self.activity_list[i]["Total Time"] += path1[-1]["Total Time"]+self.activity_list[i]["Time"]
path1.append(self.activity_list[i])
break
for i in range(len(self.paths)): print("Path %d: "%(i+1), self.paths[i])
return self.paths
def obtain_reduced_table(db, discrete_tables, tables, headers, foreign_keys,
relevant_attribute_list, dreduct, map_i, map_j):
reduced_table = []
table_header_names = []
records = []
results = []
for attribute in dreduct:
reduced_table.append([])
i = map_i[attribute-1]
j = map_j[attribute-1]
table_header_names.append([discrete_tables[i],headers[i][j]])
i = map_i[len(map_i)-1]
j = map_j[len(map_j)-1]
table_header_names.append([tables[i],headers[i][j]])
logging.debug("Table-header pairs: %s", table_header_names)
for k in range(len(table_header_names)):
if k < len(table_header_names) - 1:
records.append(get_individual_records(db, discrete_tables,
table_header_names[k], foreign_keys))
else:
results_record = get_individual_records(db, tables, table_header_names[k],
foreign_keys)
#records = get_records(db, new_tables, new_headers, foreign_keys)
for record in results_record:
results.append(record[0])
for i, record in enumerate(records):
for j, value in enumerate(record):
if relevant_attribute_list[j].count(dreduct[i]) > 0:
reduced_table[i].append([value[0], results[j]])
logging.debug("i: %s, j:%s Value: %s, Result: %s, Relevant: %s", i, j,
value[0], results[j], relevant_attribute_list[j])
logging.debug("Reduced table: %s", reduced_table)
return reduced_table
def import_records_fromXML(db, tables, headers, types, foreign_keys, user_input):
value_table = []
value_row = []
value = []
filename = "records.xml"
#filename = Console.get_string("Import from", "filename")
if not filename:
return
try:
tree = xml.etree.ElementTree.parse(filename)
except (EnvironmentError,
xml.parsers.expat.ExpatError) as err:
print("ERROR:", err)
return
for i, table_header in enumerate(headers):
reset_tables(db, [tables[i]])
for element in tree.findall(tables[i]):
try:
for j in range(len(table_header)):
if user_input[i][j]==1:
if types[i][j] == "int" or types[i][j] == "float":
instr = 'value.append(' + types[i][j] + '(element.get("' + headers[i][j] + '")))'
else:
instr = 'value.append(element.get("' + headers[i][j] + '"))'
eval(instr)
value_row.append(value)
value = []
except ValueError as err:
db.rollback()
print("ERROR:", err)
break
value_table.append(value_row)
value_row = []
logging.debug("Imported data: %s", value_table)
ref_table_id = 0
main_table_id = 0
if len(foreign_keys) > 0:
for i, current_key in enumerate(foreign_keys):
for p, table in enumerate(tables):
'''Store the index of the table that contains the reference'''
if table == foreign_keys[i][1]:
ref_table_id = p
if table == foreign_keys[i][2]:
main_table_id = p
for k, current_value_row in enumerate(value_table[ref_table_id]):
try:
key_value = get_and_set_foreign(db, tables[ref_table_id],
headers[ref_table_id],
current_key, current_value_row)
value_table[main_table_id][k].append(key_value)
insert_sqlite(db, tables[main_table_id], headers[main_table_id],
value_table[main_table_id][k])
except ValueError as err:
db.rollback()
print("ERROR:", err)
break
else:
db.commit()
def __init__(self, point_map, original, generated):
self.generated = generated
self.original = original
self.point_map = point_map
self.width = len(point_map[0])
self.height = len(point_map)
self.cell_size = int(min(MAX_X / self.width, MAX_Y / self.height))
self.offset = self.cell_size / 2
def score_motifs(motifs):
motif_score = 0
for i in range(0, len(motifs)):
for j in range(i + 1, len(motifs)):
motif_score += gms.get_hamming_distance(motifs[i], motifs[j])
return motif_score
def refresh_discrete_tables(db, tables, headers, types, foreign_keys, discretize,
offsets, decision, partition_sizes, user_input):
value_table = []
value = []
discrete_tables = list_discrete_tables(tables)
reset_tables(db, discrete_tables)
records = get_records(db, tables, headers, foreign_keys)
for i in tables:
value_table.append([])
for record in records:
for i, table_headers in enumerate(headers):
try:
for j in range(len(table_headers)):
if user_input[i][j] == 1:
if discretize[i][j] == 1:
if types[i][j] == "int":
value.append(int(ceil(int(record[i*len(table_headers) + j]/
partition_sizes[i][j]))) + offsets[i][j])
elif types[i][j] == "float":
value.append(int(ceil(float(record[i*len(table_headers) + j]/
partition_sizes[i][j]))) + offsets[i][j])
else:
value.append(record[i*len(table_headers)+j])
value_table[i].append(value)
value = []
except ValueError as err:
db.rollback()
print("ERROR:", err)
break
logging.debug("Imported data: %s", value_table)
ref_table_id = 0
main_table_id = 0
if len(foreign_keys) > 0:
for i, current_key in enumerate(foreign_keys):
for p, table in enumerate(discrete_tables):
'''Store the index of the table that contains the reference'''
if table == "discrete_" + foreign_keys[i][1]:
ref_table_id = p
if table == "discrete_" + foreign_keys[i][2]:
main_table_id = p
for k, current_value_row in enumerate(value_table[ref_table_id]):
try:
key_value = get_and_set_foreign(db, discrete_tables[ref_table_id],
headers[ref_table_id],
current_key, current_value_row)
value_table[main_table_id][k].append(key_value)
insert_sqlite(db, discrete_tables[main_table_id], headers[main_table_id],
value_table[main_table_id][k])
except ValueError as err:
db.rollback()
print("ERROR:", err)
break
else:
db.commit()
def _read_entity(self, data):
import builtins
upack = {
5 : { 1 : '>b', 2 : '>h', 4 : '>i', 8 : '>q' }, # int
6 : { 1 : '>B', 2 : '>H', 4 : '>I', 8 : '>Q' } # uint
}
result = None
tlf = data[self._dataoffset]
type = (tlf & 112) >> 4
more = tlf & 128
len = tlf & 15
self._dataoffset += 1
if more > 0:
tlf = data[self._dataoffset]
len = (len << 4) + (tlf & 15)
self._dataoffset += 1
len -= 1
if len == 0: # skip empty optional value
return result
if self._dataoffset + len >= builtins.len(data):
raise Exception("Try to read {} bytes, but only have {}".format(len, builtins.len(data) - self._dataoffset))
if type == 0: # octet string
result = data[self._dataoffset:self._dataoffset+len]
elif type == 5 or type == 6: # int or uint
d = data[self._dataoffset:self._dataoffset+len]
ulen = len
if ulen not in upack[type]: # extend to next greather unpack unit
while ulen not in upack[type]:
d = b'\x00' + d
ulen += 1
result = struct.unpack(upack[type][ulen], d)[0]
elif type == 7: # list
result = []
self._dataoffset += 1
for i in range(0, len + 1):
result.append(self._read_entity(data))
return result
else:
logger.warning('Skipping unkown field {}'.format(hex(tlf)))
self._dataoffset += len
return result
def get_relative_discernibility(m, target):
'''construct discernibility matrix (collection) relative to current row'''
collection = [[] for i in enumerate(m)]
for idx in range(len(m)):
v = m[idx]
for i, row in enumerate(m):
if i <= idx:
continue
collection[i].append(set([(j + 1) for j in range(len(v)-1) if (v[j] != row[j] and v[target] != row[target])]))
del collection[0]
return collection
def discretize_record(headers, types, discretize, offsets, decision,
partition_sizes, user_input, new_record):
idx = 0
discrete_record = []
value = []
for i, table_headers in enumerate(headers):
for j in range(len(table_headers)):
if user_input[i][j] == 1:
if discretize[i][j] == 1:
if decision[i][j] == 0:
if types[i][j] == "int":
value.append(int(ceil(int(new_record[i][idx]/
partition_sizes[i][j]))) + offsets[i][j])
idx += 1
elif types[i][j] == "float":
value.append(int(ceil(float(new_record[i][idx]/
partition_sizes[i][j]))) + offsets[i][j])
idx += 1
else:
value.append(new_record[i][idx])
idx += 1
discrete_record.append(value)
value = []
idx = 0
logging.debug("Discrete record: %s", discrete_record)
return discrete_record
def leaderboard_sequence(spectrum, n, alphabet):
spectrum = sorted(spectrum)
parent_mass = max(spectrum)
leader_board = [[]]
leader_peptide = []
while len(leader_board) > 0:
leader_board = expand(leader_board, alphabet)
# copy for loop
# leader_score = score(leader_peptide, spectrum)
leader_score = 0
temp = leader_board[:]
for peptide in temp:
mass = sum(peptide)
if mass == parent_mass:
s = cyc_score(peptide, spectrum)
if s > leader_score:
leader_peptide = peptide
leader_score = s
elif mass > parent_mass:
leader_board.remove(peptide)
leader_board = trim(leader_board, spectrum, n)
return leader_peptide
def get_profile_score(kmer, profile):
profile_score = 1
for i in range(0, len(kmer)):
profile_score *= profile[kmer[i]][i]
return profile_score
def store_discrete_record(db, discrete_tables, headers, foreign_keys,
discrete_record):
logging.debug("Discrete record to be added: %s", discrete_record)
ref_table_id = 0
main_table_id = 0
if len(foreign_keys) > 0:
for i, current_key in enumerate(foreign_keys):
for p, table in enumerate(discrete_tables):
'''Store the index of the table that contains the reference'''
if table == "discrete_" + foreign_keys[i][1]:
ref_table_id = p
if table == "discrete_" + foreign_keys[i][2]:
main_table_id = p
try:
key_value = get_and_set_foreign(db, discrete_tables[ref_table_id],
headers[ref_table_id],
current_key, discrete_record[ref_table_id])
discrete_record[main_table_id].append(key_value)
insert_sqlite(db, discrete_tables[main_table_id], headers[main_table_id],
discrete_record[main_table_id])
except ValueError as err:
db.rollback()
print("ERROR:", err)
break
else:
db.commit()
def score_motifs(motifs):
motif_score = 0
for i in range(1, len(motifs)):
motif_score += get_hamming_distance(motifs[0], motifs[i])
return motif_score
def read_record_from_scripts(headers, user_input, modules, decision):
idx_script = 0
idx_decision = 0
record = []
value = []
for i, table_headers in enumerate(headers):
for j in range(len(table_headers)):
if user_input[i][j] == 1:
if decision[i][j] == 0:
try:
value.append(modules[idx_script].run())
idx_script += 1
idx_decision += 1
except ValueError as err:
print("ERROR:", err)
break
else:
decision_script = idx_script
decision_i = i
decision_j = idx_decision
real_j = j
idx_script += 1
idx_decision += 1
idx_decision = 0
record.append(value)
value = []
logging.debug("Record added from scripts: %s", record)
logging.debug("i: %s, j: %s", decision_i, decision_j)
logging.debug("Decision script: %s", modules[decision_script])
return record, decision_script, decision_i, decision_j, real_j
def reconstruct(kmers):
reconstructed = ""
for i in range(0, len(kmers) - 1):
reconstructed += (kmers[i][0])
reconstructed += (kmers[-1])
return reconstructed
def insert_sqlite(db, tablename, headers, values):
cursor = db.cursor()
columns = ', '.join(headers)
columns = columns[4:]
placeholders = ', '.join('?' * len(values))
sql = 'INSERT INTO {} ({}) VALUES ({})'.format(tablename, columns, placeholders)
logging.debug("SQL: %s", sql)
cursor.execute(sql, values)
def trim(leader_board, spectrum, n):
if len(leader_board) > n:
leader_scores = []
for peptide in leader_board:
leader_scores.append((lin_score(peptide, spectrum), peptide))
leader_scores = sorted(leader_scores, key=operator.itemgetter(0), reverse=True)
nth_score = leader_scores[n - 1][0]
temp = []
for i in range(0, len(leader_scores)):
if leader_scores[i][0] >= nth_score:
temp.append(leader_scores[i][1])
leader_board = temp
return leader_board
def initialize_random_motifs(dna, k):
result = []
l = len(dna[0])
i = random.randint(0, l - k)
for row in dna:
result.append(row[i:i + k])
return result
def matrix_segment_absorption(main_row, i, k, min_matrix):
for j, row in enumerate(min_matrix, start = i):
for l in range(len(row)):
if i == j and k >= l or main_row[k] == set() or row[l] == set() or main_row[k].issuperset(row[l]):
continue
if main_row[k].issubset(row[l]):
row[l] = main_row[k]
return min_matrix
def test_each_kmer(self):
result = []
for kmer in gms.each_kmer("ACGTTT", 3):
result.append(kmer)
self.assertEqual(len(result), 4)
self.assertEqual(result[0], "ACG")
self.assertEqual(result[3], "TTT")
def linear_spectrum(peptide):
"""
:param peptide: string representation of a peptide
:return: integer linear spectrum as a list of int
"""
prefix_mass = [0]
for i in range(0, len(peptide)):
prefix_mass.append(prefix_mass[i] + peptide[i])
linear_spectrum = []
for i in range(0, len(peptide)):
for j in range(i + 1, len(peptide) + 1):
linear_spectrum.append(prefix_mass[j] - prefix_mass[i])
return sorted(linear_spectrum)
def read_problem(data):
first_line = data[0]
try:
circles = None
lines = None
if first_line.startswith("/ Meta information:"):
data.pop(0)
circles, lines = parse_meta_information(data)
filtered_input = filter_input(data)
width = int(filtered_input.pop(0).strip())
height = int(filtered_input.pop(0).strip())
map = []
for x in range(0, height):
line = filtered_input[x].strip()
row = []
for char in line:
if char in '_':
row.append(False)
elif char in '#':
row.append(True)
else:
error("Invalid character: %s" % char)
if not len(row) == width:
error("Invalid row length %s expected %s" % (len(row), width))
error(row)
error(line)
sys.exit(-1)
map.append(row)
if not len(row) == width:
error("Invalid number of lines %s expected %s" % (len(map), height))
error(row)
error(line)
sys.exit(-1)
return circles, lines, map
except ValueError:
error("Invalid input!")
sys.exit(0)
def create_tables(db, tables, headers, attributes, foreign_keys, references):
'''This function is only executed the first time the DB is created'''
cursor = db.cursor()
drop_tables(db, tables)
for i, table_headers in enumerate(headers):
elements = ""
for j in range(len(table_headers)):
elements += headers[i][j] + " "
elements += attributes[i][j] + ", "
if(i < len(foreign_keys)):
for k in range(len(foreign_keys[i])):
elements += ' FOREIGN KEY ('
elements += foreign_keys[i][k]
elements += ') REFERENCES '
elements += references[i][k] + ", "
elements = elements[:-2]
sql = 'CREATE TABLE IF NOT EXISTS {} ({})'.format(tables[i], elements)
cursor.execute(sql)
def deletion_no_empty_sets(A, i, k, min_matrix):
for j, row in enumerate(min_matrix, start = i):
for l in range(len(row)):
current_value = row[l].difference(A)
if (i == j and k >= l) or row[l] == set():
continue
elif current_value == set():
return False
return True
def _read_entity(self, data):
upack = {
5: {
1: '>b',
2: '>h',
4: '>i',
8: '>q'
}, # int
6: {
1: '>B',
2: '>H',
4: '>I',
8: '>Q'
} # uint
}
result = None
packetstart = self._dataoffset
tlf = data[self._dataoffset]
type = (tlf & 112) >> 4
more = tlf & 128
len = tlf & 15
self._dataoffset += 1
if more > 0:
tlf = data[self._dataoffset]
len = (len << 4) + (tlf & 15)
self._dataoffset += 1
len -= 1
if len == 0: # skip empty optional value
return result
if self._dataoffset + len >= builtins.len(data):
self._parse_error('Tried to read {} bytes, but only have {}',
[len, builtins.len(data) - self._dataoffset],
data, self._dataoffset, packetstart)
elif type == 0: # octet string
result = data[self._dataoffset:self._dataoffset + len]
elif type == 5 or type == 6: # int or uint
d = data[self._dataoffset:self._dataoffset + len]
ulen = len
if ulen not in upack[type]: # extend to next greather unpack unit
while ulen not in upack[type]:
d = b'\x00' + d
ulen += 1
result = struct.unpack(upack[type][ulen], d)[0]
elif type == 7: # list
result = []
self._dataoffset += 1
for i in range(0, len + 1):
result.append(self._read_entity(data))
return result
else:
self._parse_error('Skipping unkown field {}', [hex(tlf)], data,
self._dataoffset, packetstart)
self._dataoffset += len
return result
def extract_type(my_sample, play_feature_dic_new, feature_orders):
# print (feature_weights)
my_vote = {"Achiever": 0, "Explorer": 0, "Careless": 0, "other": 0}
my_lst_key = ["Id_Name", "Achiever", "Explorer", "Careless", "other"]
my_vote_id = {
"Id_Name": my_sample[0],
"Achiever": [],
"Explorer": [],
"Careless": [],
"other": []
}
my_vote_id_con = {
"Id_Name": my_sample[0],
"Achiever": 0,
"Achiever_Conf": 0,
"Explorer": 0,
"Explorer_Conf": 0,
"Careless": 0,
"Careless_Conf": 0,
"other": 0,
"other_Conf": 0
}
for key, item in play_feature_dic_new.items():
# print(key)
if my_sample[key] == 'medium':
# there is no effect with this medium vaue
#print('this feature is medium')
pass
else:
feature_weight = feature_orders[key]
my_ordertest = {}
my_ordertest = sorted(feature_orders.values(), reverse=True)
feature_order = my_ordertest.index(feature_orders[key])
feature_weight = feature_orders[key]
#feature_weight=feature_weights[key]
for key1 in item.keys():
if (item[key1] == my_sample[key]):
my_vote[key1] += feature_weight * (1 / (feature_order + 1))
# my_vote[key1]+=feature_weight
else:
my_vote[key1] -= feature_weight * (1 / (feature_order + 1))
#my_vote[key1]-=feature_weight
#print(my_vote)
total_weight = sum(feature_orders.values())
for cokey in my_vote.keys():
my_confidence = round((my_vote[cokey] / total_weight) * 100)
my_vote_id_con.update(my_vote)
my_vote_id_con[cokey + str('_Conf')] = my_confidence
#my_vote_id[cokey].append(my_vote[cokey])
#my_vote_id[cokey].append(my_confidence)
if len(set(my_vote.values())) == 2:
print('they are similar')
print(my_vote)
#return(max(my_vote,key=my_vote.get)) #returns only labels
return (sorted(my_vote, key=my_vote.get, reverse=True)[0],
my_vote_id_con[sorted(my_vote, key=my_vote.get, reverse=True)[0] +
str('_Conf')], my_vote_id_con)
sum += temp
return sum
strings = open("x_yurchik", "r").read().split("\n")
x = [float(x) for x in strings]
strings = open("y_yurchik", "r").read().split("\n")
y = [float(y) for y in strings]
strings = open("points2_yurchik").read().split("\n")
points = [float(points) for points in strings]
table = coef(x, y)
for list in table:
print(len(list), list)
values = []
for point in points:
values.append(Eval(table, x, point))
for value in values:
print("%.2f" % value)
plt.subplot(211)
plt.plot(x, y)
plt.subplot(212)
#plt.axis([x[0], x[-1], -100, y[-1]])
plt.plot(points, values)
plt.show()
def length(self):
return len(self.accuracies)
def pad_it(word):
return word + '*' * (n - len(word))
from builtins import len
from selenium import webdriver
from BotFunction import Main_Program
# Instagram_Info - Edit this for personal info
username = "******"
password = "******"
hash_tags = "HASH_TAGS,SEPARATED,BY,COMMAS"
web_driver = webdriver.Chrome('/web/driver/path')
hash_tags_list = hash_tags.split(',')
hash_tags_number = len(hash_tags_list)
def main():
Main_Program.main_program(username, password, hash_tags_number,
hash_tags_list, web_driver)
main()
def len(obj): # @ReservedAssignment
return builtins.len(obj)
def xml_parser(data, project_id, scan_id):
"""
:param data:
:param project_id:
:param scan_id:
:return:
"""
fileName = 'Na'
filePath = 'Na'
evidenceCollected = 'Na'
name = 'Na'
cvssScore = 'Na'
cvssAccessVector = 'Na'
cvssAccessComplexity = 'Na'
cvssAuthenticationr = 'Na'
cvssConfidentialImpact = 'Na'
cvssIntegrityImpact = 'Na'
cvssAvailabilityImpact = 'Na'
severity = 'Na'
cwe = 'Na'
description = 'Na'
references = 'Na'
vulnerableSoftware = 'Na'
vul_col = 'Na'
pt = data.xpath('namespace-uri(.)')
# root = data.getroot()
inst = []
for scan in data:
for dependencies in scan:
for dependency in dependencies:
if dependency.tag == '{%s}fileName' % pt:
fileName = dependency.text
if dependency.tag == '{%s}filePath' % pt:
filePath = dependency.text
if dependency.tag == '{%s}evidenceCollected' % pt:
evidenceCollected = dependency.text
for vuln in dependency:
if vuln.tag == '{%s}vulnerability' % pt:
if pt == 'https://jeremylong.github.io/DependencyCheck/dependency-check.2.0.xsd':
for vulner in vuln:
if vulner.tag == '{%s}name' % pt:
name = vulner.text
if vulner.tag == '{%s}description' % pt:
description = vulner.text
if vulner.tag == '{%s}references' % pt:
references = vulner.text
if vulner.tag == '{%s}vulnerableSoftware' % pt:
vulnerableSoftware = vulner.text
for vuln_dat in vulner:
if vuln_dat.tag == '{%s}cwe' % pt:
cwe = vuln_dat.text
if vuln_dat.tag == '{%s}severity' % pt:
severity_dat = vuln_dat.text
if severity_dat == 'HIGH':
severity = 'High'
elif severity_dat == 'MEDIUM':
severity = 'Medium'
elif severity_dat == 'LOW':
severity = 'Low'
elif pt == 'https://jeremylong.github.io/DependencyCheck/dependency-check.2.2.xsd':
for dc22 in vuln:
if dc22.tag == '{%s}name' % pt:
name = dc22.text
if dc22.tag == '{%s}description' % pt:
description = dc22.text
if dc22.tag == '{%s}vulnerableSoftware' % pt:
vulnerableSoftware = dc22.text
for vuln_dat in dc22:
for d in vuln_dat:
if d.tag == '{%s}url' % pt:
references = d.text
if vuln_dat.tag == '{%s}cwe' % pt:
cwe = vuln_dat.text
if vuln_dat.tag == '{%s}severity' % pt:
severity_dat = vuln_dat.text
if severity_dat == 'HIGH':
severity = 'High'
elif severity_dat == 'MEDIUM':
severity = 'Medium'
elif severity_dat == 'LOW':
severity = 'Low'
elif pt == 'https://jeremylong.github.io/DependencyCheck/dependency-check.2.3.xsd':
for dc22 in vuln:
if dc22.tag == '{%s}name' % pt:
name = dc22.text
if dc22.tag == '{%s}description' % pt:
description = dc22.text
if dc22.tag == '{%s}vulnerableSoftware' % pt:
vulnerableSoftware = dc22.text
for vuln_dat in dc22:
for d in vuln_dat:
if d.tag == '{%s}url' % pt:
references = d.text
if vuln_dat.tag == '{%s}cwe' % pt:
cwe = vuln_dat.text
if vuln_dat.tag == '{%s}severity' % pt:
severity_dat = vuln_dat.text
if severity_dat == 'HIGH':
severity = 'High'
elif severity_dat == 'MEDIUM':
severity = 'Medium'
elif severity_dat == 'LOW':
severity = 'Low'
else:
for vulner in vuln:
if vulner.tag == '{%s}name' % pt:
name = vulner.text
if vulner.tag == '{%s}cvssScore' % pt:
cvssScore = vulner.text
if vulner.tag == '{%s}cvssAccessVector' % pt:
cvssAccessVector = vulner.text
if vulner.tag == '{%s}cvssAccessComplexity' % pt:
cvssAccessComplexity = vulner.text
if vulner.tag == '{%s}cvssAuthenticationr' % pt:
cvssAuthenticationr = vulner.text
if vulner.tag == '{%s}cvssConfidentialImpact' % pt:
cvssConfidentialImpact = vulner.text
if vulner.tag == '{%s}cvssIntegrityImpact' % pt:
cvssIntegrityImpact = vulner.text
if vulner.tag == '{%s}cvssAvailabilityImpact' % pt:
cvssAvailabilityImpact = vulner.text
if vulner.tag == '{%s}severity' % pt:
severity = vulner.text
if vulner.tag == '{%s}cwe' % pt:
cwe = vulner.text
if vulner.tag == '{%s}description' % pt:
description = vulner.text
if vulner.tag == '{%s}references' % pt:
references = vulner.text
if vulner.tag == '{%s}vulnerableSoftware' % pt:
vulnerableSoftware = vulner.text
date_time = datetime.now()
vul_id = uuid.uuid4()
if severity == "High":
vul_col = "danger"
elif severity == 'Medium':
vul_col = "warning"
elif severity == 'Low':
vul_col = "info"
dup_data = name + fileName + severity
duplicate_hash = hashlib.sha256(dup_data.encode('utf-8')).hexdigest()
match_dup = dependencycheck_scan_results_db.objects.filter(
dup_hash=duplicate_hash).values('dup_hash')
lenth_match = len(match_dup)
if lenth_match == 1:
duplicate_vuln = 'Yes'
elif lenth_match == 0:
duplicate_vuln = 'No'
else:
duplicate_vuln = 'None'
false_p = dependencycheck_scan_results_db.objects.filter(
false_positive_hash=duplicate_hash)
fp_lenth_match = len(false_p)
if fp_lenth_match == 1:
false_positive = 'Yes'
else:
false_positive = 'No'
if cwe == 'Na':
cwe = name
save_all = dependencycheck_scan_results_db(
# date_time=date_time,
vuln_id=vul_id,
scan_id=scan_id,
project_id=project_id,
fileName=fileName,
filePath=filePath,
evidenceCollected=evidenceCollected,
name=name,
cvssScore=cvssScore,
cvssAccessVector=cvssAccessVector,
cvssAccessComplexity=cvssAccessComplexity,
cvssAuthenticationr=cvssAuthenticationr,
cvssConfidentialImpact=cvssConfidentialImpact,
cvssIntegrityImpact=cvssIntegrityImpact,
cvssAvailabilityImpact=cvssAvailabilityImpact,
severity=severity,
cwe=cwe,
description=description,
references=references,
vulnerableSoftware=vulnerableSoftware,
vul_col=vul_col,
vuln_status='Open',
dup_hash=duplicate_hash,
vuln_duplicate=duplicate_vuln,
false_positive=false_positive
)
save_all.save()
all_dependency_data = dependencycheck_scan_results_db.objects.filter(scan_id=scan_id, false_positive='No')
total_vul = len(all_dependency_data)
total_high = len(all_dependency_data.filter(severity="High"))
total_medium = len(all_dependency_data.filter(severity="Medium"))
total_low = len(all_dependency_data.filter(severity="Low"))
total_duplicate = len(all_dependency_data.filter(vuln_duplicate='Yes'))
dependencycheck_scan_db.objects.filter(scan_id=scan_id).update(
total_vuln=total_vul,
SEVERITY_HIGH=total_high,
SEVERITY_MEDIUM=total_medium,
SEVERITY_LOW=total_low,
total_dup=total_duplicate
)
subject = 'Archery Tool Scan Status - DependencyCheck Report Uploaded'
message = 'DependencyCheck Scanner has completed the scan ' \
' %s <br> Total: %s <br>High: %s <br>' \
'Medium: %s <br>Low %s' % (name, total_vul, total_high, total_medium, total_low)
email_sch_notify(subject=subject, message=message)
return HttpResponse(status=201)
# If the current model has a better score than one we've already trained then save it
if acc > best:
best = acc
with open(Constants.pickle_dump, "wb") as f:
pickle.dump(linear, f)
x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(
training_data, result_only, test_size=Constants.training_sample_size)
pickle_in = open(Constants.pickle_dump, "rb")
linear = pickle.load(pickle_in)
print(
'The %s different coefficients (m):' %
(len(Constants.dataset_member_names) - 1), linear.coef_)
print('Y Intercept:', linear.intercept_)
predictions = linear.predict(x_test)
for i in range(len(predictions)):
rounded_prediction = int(round(predictions[i]))
#rounded_prediction = 0 if rounded_prediction < 0 else rounded_prediction
print(
"Prediction: {0}, Actual result: {1}, Data used to make prediction: {2}"
.format(
str(rounded_prediction).zfill(2),
str(y_test[i]).zfill(2), x_test[i]))
p = "studytime"
def __str__(self):
return "<Cube of " + len(self.clusters) + " clusters"
def Culstring_Players(My_data):
# creating dictionary of play style with their characteristics
play_feature_dic = {
'items_visited_total': {
'Achiever': 'low',
'Explorer': 'high',
'Careless': 'low',
'other': 'low'
},
'questions_right_ratio': {
'Achiever': 'high',
'Explorer': 'high',
'Careless': 'low',
'other': 'low'
},
'questions_visited_total': {
'Achiever': 'high',
'Explorer': 'low',
'Careless': 'low',
'other': 'low'
},
'questions_wrong_ratio': {
'Achiever': 'low',
'Explorer': 'low',
'Careless': 'high',
'other': 'high'
},
'time_read_time_total': {
'Achiever': 'high',
'Explorer': 'low',
'Careless': 'low',
'other': 'low'
},
'time_nav_time_total': {
'Achiever': 'high',
'Explorer': 'low',
'Careless': 'low',
'other': 'low'
},
'time_map/time_total': {
'Achiever': 'high',
'Explorer': 'high',
'Careless': 'low',
'other': 'low'
}
}
play_style_dic = {
'Achiever': {
'items_visited_total': 'low',
'questions_right_ratio': 'high',
'questions_visited_total': 'high',
'questions_wrong_ratio': 'low',
'time_read_time_total': 'high',
'time_nav_time_total': 'low',
'time_map/time_total': 'high'
},
'Explorer': {
'items_visited_total': 'high',
'questions_right_ratio': 'high',
'questions_visited_total': 'low',
'questions_wrong_ratio': 'low',
'time_read_time_total': 'low',
'time_nav_time_total': 'high',
'time_map/time_total': 'high'
},
'Careless': {
'items_visited_total': 'low',
'questions_right_ratio': 'low',
'questions_visited_total': 'low',
'questions_wrong_ratio': 'high',
'time_read_time_total': 'low',
'time_nav_time_total': 'low',
'time_map/time_total': 'low'
},
'other': {
'items_visited_total': 'low',
'questions_right_ratio': 'low',
'questions_visited_total': 'low',
'questions_wrong_ratio': 'high',
'time_read_time_total': 'low',
'time_nav_time_total': 'high',
'time_map/time_total': 'low'
}
}
#df=pd.DataFrame(data)
#Defining Lables
labels_Player_Style = ["Achiever", "Explorer", "CareLess", "Lost"]
# list_features=['items_visited_total','time_map/time_total','questions_visited_total','time_read_time_total','questions_right_ratio','time_nav_time_total'] # main order info gain
list_features = [
'items_visited_total', 'questions_right_ratio',
'questions_visited_total', 'questions_wrong_ratio',
'time_read_time_total', 'time_nav_time_total', 'time_map/time_total'
] # inf gain
# list_features=['items_visited_total','time_map/time_total','questions_visited_total','questions_wrong_ratio','time_read_time_total','time_nav_time_total','questions_right_ratio'] # inf gain
BnData = My_data.iloc[:, 1:8] # selecting categorical data from dataset
df = pd.DataFrame(BnData)
#Assigning players type
My_data['Player_Type'] = 'other' # assignin other as a defult type
counter = 0
ThresholdValue = 0.1
while (counter < len(list_features) + 1):
counter = counter + 1
print(My_data)
########### 3 Features to compare #############^
if counter == 1:
###### new code with new dictionary
#########
My_data['Player_Type'] = np.where(
(df[list_features[0]] == 'low') &
(df[list_features[1]] == 'high'),
'Achiever', #
np.where((df[list_features[0]] == 'high') &
(df[list_features[1]] == 'high'), 'Explorer',
np.where((df[list_features[0]] == 'low') &
(df[list_features[1]] == 'low'), 'Careless',
np.where((df[list_features[0]] == 'low') &
(df[list_features[1]] == 'high'),
'other', My_data['Player_Type']))))
print(My_data)
if My_data[My_data['Player_Type'] ==
"other"].shape[0] / My_data.shape[0] < ThresholdValue:
break
elif counter == 2:
My_data['Player_Type'] = np.where(
(df[list_features[0]] == 'medium') &
(df[list_features[2]] == 'high') &
(My_data['Player_Type'] == 'other'), 'Achiever',
np.where((df[list_features[0]] == 'medium') &
(df[list_features[2]] == 'low') &
(My_data['Player_Type'] == 'other'), 'Explorer',
My_data['Player_Type']))
print(My_data)
if My_data[My_data['Player_Type'] ==
"other"].shape[0] / My_data.shape[0] < ThresholdValue:
break
elif counter == 3:
My_data['Player_Type'] = np.where(
(df[list_features[2]] == 'medium') &
(df[list_features[3]] == 'high') &
(My_data['Player_Type'] == 'other'), 'Careless',
np.where((df[list_features[2]] == 'medium') &
(df[list_features[3]] == 'low') &
(My_data['Player_Type'] == 'other'), 'Explorer',
My_data['Player_Type']))
print(My_data)
if My_data[My_data['Player_Type'] ==
"other"].shape[0] / My_data.shape[0] < ThresholdValue:
break
elif counter == 4:
My_data['Player_Type'] = np.where(
(df[list_features[3]] == 'medium') &
(df[list_features[4]] == 'high') &
(My_data['Player_Type'] == 'other'), 'Achiever',
np.where((df[list_features[3]] == 'medium') &
(df[list_features[4]] == 'low') &
(My_data['Player_Type'] == 'other'), 'Explorer',
My_data['Player_Type']))
print(My_data)
if My_data[My_data['Player_Type'] ==
"other"].shape[0] / My_data.shape[0] < ThresholdValue:
break
elif counter == 5:
My_data['Player_Type'] = np.where(
(df[list_features[4]] == 'medium') &
(df[list_features[5]] == 'high') &
(My_data['Player_Type'] == 'other'), 'Explorer',
np.where((df[list_features[4]] == 'medium') &
(df[list_features[5]] == 'low') &
(My_data['Player_Type'] == 'other'), 'Achiever',
My_data['Player_Type']))
print(My_data)
if My_data[My_data['Player_Type'] ==
"other"].shape[0] / My_data.shape[0] < ThresholdValue:
break
elif counter == 6:
My_data['Player_Type'] = np.where(
(df[list_features[5]] == 'medium') &
(df[list_features[6]] == 'high') &
(My_data['Player_Type'] == 'other'), 'Explorer',
np.where((df[list_features[5]] == 'medium') &
(df[list_features[6]] == 'low') &
(My_data['Player_Type'] == 'other'), 'Careless',
My_data['Player_Type']))
print(My_data)
if My_data[My_data['Player_Type'] ==
"other"].shape[0] / My_data.shape[0] < ThresholdValue:
break
print('Player Style Assigning is done')
return My_data
def clean_password2(self):
value = super(CustomSetPasswordForm, self).clean_password2()
if value:
if len(value) > 128:
self._errors['password1'] = self.error_class(
['Password is too long (max: 128 characters)'])
def reconstruct(self):
print("\tStep 4 of 4: Image reconstruction")
# create an array as the canvas of the final image
groundtruthImage = np.zeros((self.imageHeight, self.imageWidth))
linedImage = np.array(self.imageData.convert('RGB'))
for key in sorted(
self.offsetDictionary,
key=lambda key: builtins.len(self.offsetDictionary[key]),
reverse=True):
if self.offsetDictionary[key].__len__() < self.Nf * 2:
break
print('\t', key, self.offsetDictionary[key].__len__())
for i in range(self.offsetDictionary[key].__len__()):
# The original image (grayscale)
for j in range(
self.offsetDictionary[key][i][1],
self.offsetDictionary[key][i][1] +
self.blockDimension):
for k in range(
self.offsetDictionary[key][i][0],
self.offsetDictionary[key][i][0] +
self.blockDimension):
groundtruthImage[j][k] = 255
# creating a line edge from the original image (for the visual purpose)
for xCoordinate in range(2, self.imageHeight - 2):
for yCordinate in range(2, self.imageWidth - 2):
if groundtruthImage[xCoordinate, yCordinate] == 255 and \
(groundtruthImage[xCoordinate + 1, yCordinate] == 0 or groundtruthImage[
xCoordinate - 1, yCordinate] == 0 or
groundtruthImage[xCoordinate, yCordinate + 1] == 0 or groundtruthImage[
xCoordinate, yCordinate - 1] == 0 or
groundtruthImage[xCoordinate - 1, yCordinate + 1] == 0 or groundtruthImage[
xCoordinate + 1, yCordinate + 1] == 0 or
groundtruthImage[xCoordinate - 1, yCordinate - 1] == 0 or groundtruthImage[
xCoordinate + 1, yCordinate - 1] == 0):
# creating the edge line, respectively left-upper, right-upper, left-down, right-down
if groundtruthImage[xCoordinate - 1, yCordinate] == 0 and \
groundtruthImage[xCoordinate, yCordinate - 1] == 0 and \
groundtruthImage[xCoordinate - 1, yCordinate - 1] == 0:
linedImage[xCoordinate - 2:xCoordinate, yCordinate,
1] = 255
linedImage[xCoordinate, yCordinate - 2:yCordinate,
1] = 255
linedImage[xCoordinate - 2:xCoordinate,
yCordinate - 2:yCordinate, 1] = 255
elif groundtruthImage[xCoordinate + 1, yCordinate] == 0 and \
groundtruthImage[xCoordinate, yCordinate - 1] == 0 and \
groundtruthImage[xCoordinate + 1, yCordinate - 1] == 0:
linedImage[xCoordinate + 1:xCoordinate + 3, yCordinate,
1] = 255
linedImage[xCoordinate, yCordinate - 2:yCordinate,
1] = 255
linedImage[xCoordinate + 1:xCoordinate + 3,
yCordinate - 2:yCordinate, 1] = 255
elif groundtruthImage[xCoordinate - 1, yCordinate] == 0 and \
groundtruthImage[xCoordinate, yCordinate + 1] == 0 and \
groundtruthImage[xCoordinate - 1, yCordinate + 1] == 0:
linedImage[xCoordinate - 2:xCoordinate, yCordinate,
1] = 255
linedImage[xCoordinate, yCordinate + 1:yCordinate + 3,
1] = 255
linedImage[xCoordinate - 2:xCoordinate,
yCordinate + 1:yCordinate + 3, 1] = 255
elif groundtruthImage[xCoordinate + 1, yCordinate] == 0 and \
groundtruthImage[xCoordinate, yCordinate + 1] == 0 and \
groundtruthImage[xCoordinate + 1, yCordinate + 1] == 0:
linedImage[xCoordinate + 1:xCoordinate + 3, yCordinate,
1] = 255
linedImage[xCoordinate, yCordinate + 1:yCordinate + 3,
1] = 255
linedImage[xCoordinate + 1:xCoordinate + 3,
yCordinate + 1:yCordinate + 3, 1] = 255
# creating the straigh line, respectively upper, down, left, right line
elif groundtruthImage[xCoordinate, yCordinate + 1] == 0:
linedImage[xCoordinate, yCordinate + 1:yCordinate + 3,
1] = 255
elif groundtruthImage[xCoordinate, yCordinate - 1] == 0:
linedImage[xCoordinate, yCordinate - 2:yCordinate,
1] = 255
elif groundtruthImage[xCoordinate - 1, yCordinate] == 0:
linedImage[xCoordinate - 2:xCoordinate, yCordinate,
1] = 255
elif groundtruthImage[xCoordinate + 1, yCordinate] == 0:
linedImage[xCoordinate + 1:xCoordinate + 3, yCordinate,
1] = 255
timeStamp = time.strftime("%Y%m%d_%H%M%S")
scipy.misc.imsave(
self.imageOutputDirectory + timeStamp + "_" + self.imagePath,
groundtruthImage)
scipy.misc.imsave(
self.imageOutputDirectory + timeStamp + "_lined_" + self.imagePath,
linedImage)
return self.imageOutputDirectory + timeStamp + "_lined_" + self.imagePath
def vector_length(value):
return b.len(value)
def _refresh(self):
if self.connected:
start = time.time()
retry = 5
data = None
while retry > 0:
try:
data = self._read(512)
if builtins.len(data) == 0:
self.logger.error(
'Reading data from device returned 0 bytes!')
else:
end_pos = len(data)
while end_pos > 0:
end_pos = data.rfind(self._v1_end)
start_pos = data.rfind(self._v1_start, 0, end_pos)
if start_pos != -1 and end_pos == -1:
data = data[:start_pos]
elif start_pos != -1 and end_pos != -1:
chunk = data[start_pos:end_pos +
len(self._v1_end) + 3]
self.logger.debug(
'Found chunk at {} - {} ({} bytes):{}'.
format(
start_pos, end_pos,
end_pos - start_pos,
''.join(' {:02x}'.format(x)
for x in chunk)))
chunk_crc_str = '{:02X}{:02X}'.format(
chunk[-2], chunk[-1])
chunk_crc_calc = self._crc16(chunk[:-2])
chunk_crc_calc_str = '{:02X}{:02X}'.format(
(chunk_crc_calc >> 8) & 0xff,
chunk_crc_calc & 0xff)
if chunk_crc_str != chunk_crc_calc_str:
self.logger.warn(
'CRC checksum mismatch: Expected {}, but was {}'
.format(chunk_crc_str,
chunk_crc_calc_str))
data = data[:start_pos]
else:
end_pos = 0
retry = 0
except Exception as e:
self.logger.error(
'Reading data from {0} failed: {1} - reconnecting!'.
format(self._target, e))
self.disconnect()
time.sleep(1)
self.connect()
retry = retry - 1
if retry == 0:
self.logger.warn(
'Trying to read data in next cycle due to connection errors!'
)
if data is not None:
retry = 0
values = self._parse(self._prepare(data))
for obis in values:
self.logger.debug('Entry {}'.format(values[obis]))
if obis in self._items:
for prop in self._items[obis]:
for item in self._items[obis][prop]:
item(values[obis][prop], 'Sml')
else:
values = {}
cycletime = time.time() - start
self.logger.debug("cycle takes {0} seconds".format(cycletime))
return values
def __init__(self, elements, start=1):
self.elements = list(elements)
self.start = start
self.length = len(elements)
def fire_bullets(ai_settings, screen, ship, bullets):
if len(bullets) < ai_settings.bullets_allowed:
new_bullet = Bullet(ai_settings, screen, ship)
bullets.add(new_bullet)
radius = 0.7
stepSize = 5 / 180 * math.pi
# assumption that start and end are in degrees
for i in startpos:
assert 0 <= i <= 358.0 * math.pi / 180
for j in endpos:
assert 0 <= j <= 358.0 * math.pi / 180
updates = [startpos]
deltas = tuple(map(lambda m, n: m - n, endpos, startpos))
deltas = [i * stepSize / norm(deltas) for i in deltas]
print(deltas)
while True:
acc = True
for i in range(len(endpos)):
acc = acc and abs(updates[-1][i] - endpos[i]) < 0.05
if acc:
break
updates.append(tuple(map(lambda m, n: m + n, updates[-1], deltas)))
# TODO: valid configuration implementation without tree argument
# assert valid_configuration(startpos[0], startpos[1], startpos[2], startpos[3], startpos[4], startpos[5])
# assert valid_configuration(endpos[0], endpos[1], endpos[2], endpos[3], endpos[4], endpos[5])
finalUpdates = updates
for i in updates:
if not temp_valid_configuration(i[0], i[1], i[2], i[3], i[4], i[5]):
for k in updates[updates.index(i) + 1:]:
if temp_valid_configuration(k[0], k[1], k[2], k[3], k[4],
k[5]):
def printSubTable(self):
for row in self.subTable:
separator = ' '
print(len(row), " - ", " ".join(format(x, "8.5f") for x in row))
elif "mysql" in file_content['dependencies']:
back_list.append(y)
elif "pg" in file_content['dependencies']:
back_list.append(y)
else:
other_list.append(y)
# print('OTHERS')
except:
other_list.append(y)
continue
print(' ')
return json_designer(front_list, back_list, other_list, user_name)
df1 = pd.read_json('Json files/(2019-01-01)---(2019-06-30).json')
column_names = df1['owner_name'][0:len(df1['owner_name'])]
count = 0
part = 0
print('Processing starts')
initial_start = 0
for each_name in column_names:
start_time = time.time()
count += 1
try:
if len(collect_repo(each_name)) < 1000:
json_string = sort_repo(each_name, collect_repo(each_name))
json_file = json.loads(json_string)
if (len(json_file['front_list']) > 0) and (len(json_file['back_list']) > 0):
def get_accuracy_rate(self):
return sum(self.accuracies.values()) / len(self.accuracies) * 100
print(front_repo['repo_name'] + ' and ' + back_repo['repo_name'] +
' compared')
if similar(front_repo['repo_name'], back_repo['repo_name']) > 0.5:
matched_repos.append(front_repo['repo_name'] + '///' +
back_repo['repo_name'])
return matched_repos
with open(
'/Json files/' +
'Separated repos((2019-01-01)---(2019-06-30)(1)).json',
'r') as filehandle:
file_content = json.load(filehandle)
for user in file_content:
if len(user['front_list']) != 0 and len(user['back_list']) != 0:
preferred_users.append(user)
print('All preferred users collected.')
count = 0
for each_user in preferred_users:
if len(front_back(each_user['front_list'], each_user['back_list'])) == 0:
print('No matches for user: '******'user_name'])
else:
count += 1
found_repos.append(
front_back(each_user['front_list'], each_user['back_list']))
# print(similar('happy-ionic-frontend', 'happy-express-backend'))
print('############################################################')
print(found_repos)
import numpy as np
import numpy.linalg as alg
import scipy as spy
import builtins
import matplotlib.pylab as pl
import time
# sizeList = [2,3,4,5,6,7,8,9,10,50,100,200,300,500,707,900,1000,1100]
sizeList = [100] #[15,17,20,25]
timeList = [10]
useCVX = True
np.random.seed(0)
timingVals = np.zeros([builtins.len(sizeList), builtins.len(timeList)])
for sizeTemp in range(builtins.len(sizeList)):
for timeTemp in range(builtins.len(timeList)):
size = sizeList[sizeTemp]
timesteps = timeList[timeTemp]
print("Solving for size", size, ", timesteps ", timesteps)
samplesPerStep = 10 #int(np.log2(size))
timeShift = timesteps / 3 #Number of steps till new covariance matrix appears
eps = 1e-3
# Optimization parameters
alpha = 0.1 # Lasso parameter
beta = 0.6 # Weight between basis nodes
FroError = []
#!/usr/bin/python3
# generate config and genesis files for test
import json
import os
import string
import subprocess
import conf
import conf_type
import sys
from builtins import len, int, dict, open, set
nodeCount = len(conf.nodes)
# pkgen 명령 위치(path)를 적어줄 것
cliCmd = "./aergocli"
def low_bool(b):
if b:
return "true"
else:
return "false"
def generate_genesis(conf, template):
genesis_filename = "genesis_%s.json" % conf.testname
bps = list()
for n in conf.nodes:
if n.role == conf_type.Role.producer:
def count_uniq(iterable):
'''Return the unique element count of ``iterable``.'''
return builtins.len(builtins.set(iterable))
else:
minefield.minefield[individual.get_position_y()][individual.get_position_x()] = '0'
except IndexError:
distance_dict[str(population_copy.index(individual))] = find_distance(minefield.get_exit_x(),
minefield.get_exit_y(),
individual.get_position_x(),
individual.get_position_y())
population.remove(individual)
rows -= 1
row -= 1
row += 1
column += 1
print(len(population))
print(len(distance_dict))
if reached_end:
break
if len(distance_dict) >= individuals_per_generation:
break
print()
print()
print()
print()
minefield.print()
minefield.remove_zeros()
def len(channel):
return builtins.len(channel.buffer)
def len(s):
'''Replacement for the built-in :func:`len() <python:len>` function.'''
return builtins.len(s)
_VSCODE_columnNames = list(_VSCODE_df)
# Compute the index column. It may have been renamed
_VSCODE_indexColumn = _VSCODE_df.index.name if _VSCODE_df.index.name else "index"
_VSCODE_columnTypes = _VSCODE_builtins.list(_VSCODE_df.dtypes)
del _VSCODE_df
# Make sure the index column exists
if _VSCODE_indexColumn not in _VSCODE_columnNames:
_VSCODE_columnNames.insert(0, _VSCODE_indexColumn)
_VSCODE_columnTypes.insert(0, "int64")
# Then loop and generate our output json
_VSCODE_columns = []
for _VSCODE_n in _VSCODE_builtins.range(
0, _VSCODE_builtins.len(_VSCODE_columnNames)):
_VSCODE_column_type = _VSCODE_columnTypes[_VSCODE_n]
_VSCODE_column_name = str(_VSCODE_columnNames[_VSCODE_n])
_VSCODE_colobj = {}
_VSCODE_colobj["key"] = _VSCODE_column_name
_VSCODE_colobj["name"] = _VSCODE_column_name
_VSCODE_colobj["type"] = str(_VSCODE_column_type)
_VSCODE_columns.append(_VSCODE_colobj)
del _VSCODE_column_name
del _VSCODE_column_type
del _VSCODE_columnNames
del _VSCODE_columnTypes
# Save this in our target
_VSCODE_targetVariable["columns"] = _VSCODE_columns
def process_images(path, rgb_line_ending, depth_line_ending, manual):
""" Finds images at the specified [path] and runs the edge detection code to
generate predictions on RGB, D, and RGD versions of the image.
Requires: Files of the same object MUST have the same name + an additional
line ending. The line endings for the rgb and depth frames MUST BE DISTINCT.
Ex. path="home/images", rgb_line_ending=".png", depth_line_ending="depth.png"
Each object MUST HAVE BOTH A RGB AND DEPTH IMAGE.
"""
# TODO: add a toggle to store manual/user input also
formats = ["rgb", "d", "rgd"]
# store image names of incorrect preds
incorrect_preds = [[], [], []]
manual_incorrect_preds = [[], [], []]
# store accuracy percentages
# [rgb, d, rgd] holds sum of pred evals. divide by num preds later
percentages = [0, 0, 0]
manual_percentages = [0, 0, 0]
# stores list of unique image names with line ending removed
image_names = []
# checks for image files in immediate directory
for entry in os.scandir(path):
if entry.is_file():
image_name = ""
if entry.name.endswith(rgb_line_ending):
# remove line ending
image_name = entry.name[0:-len(rgb_line_ending)]
image_names.append(image_name)
elif entry.name.endswith(depth_line_ending):
# remove line ending
image_name = entry.name[0:-len(depth_line_ending)]
image_names.append(image_name)
# get unique vals
image_names = set(image_names)
# print(image_names)
# loop over image names
for image_name in image_names:
# Generate all the actual images
# rgb image stored in bgr order because OpenCV is just like that
bgr = cv2.imread(path + image_name + rgb_line_ending)
depth = process_depth(path + image_name, depth_line_ending)
# If you haven't already, do any cropping/scaling necessary (BIGBIRD)
# resizing to fit on screen when displaying output image
whratio = bgr.shape[1] / bgr.shape[0]
# need to scale down labels also - new width/old width
scale = int(whratio * 400) / bgr.shape[1]
# resize takes parameter order (img, width, height)
bgr = cv2.resize(bgr, (int(whratio * 400), 400))
depth = cv2.resize(depth, (int(whratio * 400), 400))
b, g, r = cv2.split(bgr)
dgr = cv2.merge((depth, g, r))
depth3channel = cv2.merge((depth, depth, depth))
images = [bgr, depth3channel, dgr] # iterate over for comparison
width, height = bgr.shape[1], bgr.shape[0]
for i in range(3):
pred_coords = grab_points(0, 0, width, height, images[i])
# TODO: search image_name.txt file for labels and eval pred
pred_grasp_valid = eval_pred(images[i], pred_coords,
path + image_name + "_grasps.txt",
scale)
cv2.imshow("preds", images[i])
cv2.waitKey(0)
if pred_grasp_valid:
print(image_name + " " + str(i) + " was a valid grasp")
percentages[i] += 1
else:
print(image_name + " " + str(i) + " was NOT a valid grasp")
incorrect_preds[i].append(image_name)
if manual:
# user input to confirm whether grasp was correct
userinput = input("Is the grasp valid? (y/n) \n")
while userinput != "y" and userinput != "n":
print("Invalid input. Please enter y/n")
userinput = input("Is the grasp valid? (y/n) \n")
if userinput == "y":
manual_percentages[i] += 1
else:
manual_incorrect_preds[i].append(image_name)
print("Checked predictions on ", len(image_names), " images.")
print("Automated rgb, d, rgd percentages: ",
np.array(percentages) / len(image_names))
# print incorrectly classified image names, and the channel format
print("Automatically-identified incorrect preds ", incorrect_preds)
if manual:
print("Manual rgb, d, rgd percentages: ",
np.array(manual_percentages) / len(image_names))
print("Manually-identified incorrect preds ", manual_incorrect_preds)
