added stringdate 2024-11-18 17:59:49 2024-11-19 03:44:43 | created int64 0 2,086B | id stringlengths 40 40 | int_score int64 2 5 | metadata dict | score float64 2.31 5.5 | source stringclasses 1
value | text stringlengths 258 23.4k | num_lines int64 16 649 | avg_line_length float64 15 61 | max_line_length int64 31 179 | ast_depth int64 8 40 | length int64 101 3.8k | lang stringclasses 1
value | sast_codeql_findings stringlengths 2 265k | sast_codeql_findings_count int64 0 45 | sast_codeql_success bool 1
class | sast_codeql_error stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2024-11-18T20:48:33.314436+00:00 | 1,564,613,826,000 | a38211db9d463b32507381b30612a11a2e50e91f | 3 | {
"blob_id": "a38211db9d463b32507381b30612a11a2e50e91f",
"branch_name": "refs/heads/master",
"committer_date": 1564613826000,
"content_id": "b9b22fb7cc3d083c106d00be6430d31d3c560bb7",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "860679e655975afad5721f4c784b025e4b0f05b7",
"extension": "p... | 2.84375 | stackv2 | from loguru import logger
import numpy as np
import torch
def describe(x) -> None:
logger.info("{} [{}]\n{}".format(x.type(), x.shape, x))
def make_tensor() -> None:
describe(torch.FloatTensor(2, 3))
def make_rand_tensors() -> None:
describe(torch.rand(2, 3))
describe(torch.randn(4, 2))
def make... | 38 | 18.82 | 59 | 12 | 205 | python | [] | 0 | true | |
2024-11-18T20:48:33.376258+00:00 | 1,545,332,990,000 | 9ac7981bffc2c0b0cb09cc3786159a3f2339c6e5 | 3 | {
"blob_id": "9ac7981bffc2c0b0cb09cc3786159a3f2339c6e5",
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"committer_date": 1545332990000,
"content_id": "9272febffd66cd673a2b283a97de9de587fd68dd",
"detected_licenses": [
"MIT"
],
"directory_id": "bfc25f1ad7bfe061b57cfab82aba9d0af1453491",
"extension": "py",
"fi... | 3.328125 | stackv2 | #!/usr/bin/env python
'''
Identify the location of a missing word in a sentence
using an n-gram model. Print the top N most probable
sentences.
'''
import sys, argparse, pickle
from itertools import islice
import numpy as np
import kenlm
from util import tokenize_words, load_vocab, TopK
def max_prob_word_at(words, i... | 75 | 31.23 | 72 | 13 | 620 | python | [] | 0 | true | |
2024-11-18T20:48:33.463475+00:00 | 1,572,540,959,000 | 4be0d9e71de4dfaabd3e2a9671279d6b9215b6e7 | 3 | {
"blob_id": "4be0d9e71de4dfaabd3e2a9671279d6b9215b6e7",
"branch_name": "refs/heads/master",
"committer_date": 1572540959000,
"content_id": "ac7bfcd5e270c3d8e7859c455329fa2177511e88",
"detected_licenses": [
"MIT"
],
"directory_id": "c809d406778c3312d96f1dba1c2490333f330ac9",
"extension": "py",
"fi... | 2.796875 | stackv2 | from abc import ABC, abstractmethod
import numpy as np
from scipy.optimize import minimize
from ..model.sources import FarField1DSourcePlacement
from ..utils.math import projm, vec
from .core import ensure_covariance_size, ensure_n_resolvable_sources
def f_nll_stouc(R, array, sources, wavelength, p, sigma):
# log|... | 362 | 39.13 | 85 | 14 | 3,687 | python | [] | 0 | true | |
2024-11-18T20:48:33.635582+00:00 | 1,295,606,289,000 | a589f99723df39a061151b8aab5a2538648aa3f2 | 3 | {
"blob_id": "a589f99723df39a061151b8aab5a2538648aa3f2",
"branch_name": "refs/heads/master",
"committer_date": 1295606289000,
"content_id": "921d824b70d5c8f319d5d3b6d68a0ee3a773de94",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "9587494eebe935202a9cd99bb6ecfaaf1d882b13",
"extension": "p... | 2.546875 | stackv2 | # From MCP 2.6 Test 3
import csv
def parse_config(filename, renamer_options, miss_crit=True, unk_crit=True):
ff = open(filename, 'r')
while True:
buffer = ff.readline()
if not buffer:
break
buffer = buffer.split('=')
if buffer[0].strip() in re... | 161 | 28.52 | 105 | 19 | 1,064 | python | [] | 0 | true | |
2024-11-18T20:48:33.684150+00:00 | 1,631,171,593,000 | c143879d8ed9da53a0ea35d5b8301021098e6d76 | 3 | {
"blob_id": "c143879d8ed9da53a0ea35d5b8301021098e6d76",
"branch_name": "refs/heads/master",
"committer_date": 1631171593000,
"content_id": "eb3225a2151a33ef9f23a6a2222ef94ad6199fc4",
"detected_licenses": [
"MIT"
],
"directory_id": "fc5a948714cd9177a4f63408d1c5ff47910000f1",
"extension": "py",
"fi... | 2.71875 | stackv2 | from typing import List
from adversarial_music_generator.interfaces import TuneMutatorInterface
from adversarial_music_generator.models.note import Note
from adversarial_music_generator.models.tune import Tune
from adversarial_music_generator.seed import Seed
class NaiveRandomMutator(TuneMutatorInterface):
def ... | 41 | 35.27 | 99 | 17 | 322 | python | [] | 0 | true | |
2024-11-18T20:48:33.746757+00:00 | 1,627,913,540,000 | 024dd2530d19a6dfee4c5e32092058634655a85b | 3 | {
"blob_id": "024dd2530d19a6dfee4c5e32092058634655a85b",
"branch_name": "refs/heads/main",
"committer_date": 1627913540000,
"content_id": "577d5afd387e6afd549b74d08d0216d920b8c973",
"detected_licenses": [
"MIT"
],
"directory_id": "ca375d5e278c3ba8943d259375c5429206237133",
"extension": "py",
"file... | 2.921875 | stackv2 | """
Aggregation methods.
"""
import functools
import util
class VotingRules:
"""
Summing composition of votes.
"""
@staticmethod
def multipath_combine(v1, v2):
return max(v1, v2)
@staticmethod
def negative(v):
return -v
@staticmethod
def combine(v1, v2):
... | 143 | 32.9 | 148 | 18 | 1,116 | python | [] | 0 | true | |
2024-11-18T20:48:33.821008+00:00 | 1,589,525,088,000 | b90cc8c3bf828418a934a3954fc31205f43940f4 | 2 | {
"blob_id": "b90cc8c3bf828418a934a3954fc31205f43940f4",
"branch_name": "refs/heads/master",
"committer_date": 1589525088000,
"content_id": "2bd9b88c203a0d87a90cc4efef806cb6c55fb669",
"detected_licenses": [
"MIT"
],
"directory_id": "99f3437f3f8752d1aab883cc9e29c6ae48eb435c",
"extension": "py",
"fi... | 2.328125 | stackv2 | import os, json
DOMAIN = 'ha_cloud_music'
VERSION = '2.4.1'
DOMAIN_API = '/' + DOMAIN + '-api'
ROOT_PATH = '/' + DOMAIN + '-local/' + VERSION
def TrueOrFalse(val, trueStr, falseStr):
if val:
return trueStr
return falseStr
# 获取配置路径
def get_config_path(name):
return os.path.join(os.path.dirname(__f... | 29 | 24.48 | 90 | 14 | 205 | python | [] | 0 | true | |
2024-11-18T20:48:33.879385+00:00 | 1,570,862,489,000 | cd8951d975641c86c3a87204da90ea00f79067ee | 3 | {
"blob_id": "cd8951d975641c86c3a87204da90ea00f79067ee",
"branch_name": "refs/heads/master",
"committer_date": 1570862489000,
"content_id": "66abdcd1da3330018722e765dd16eee7a5f6dba4",
"detected_licenses": [
"MIT"
],
"directory_id": "859bb3e856fb68f741414b14282e03053c08a47c",
"extension": "py",
"fi... | 2.9375 | stackv2 | import pickle
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from data_prep import DataPrep
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import metrics
class Model():
def __init__(self):
self.rfr = RandomForestRegressor(bootstrap=True,
max_fe... | 66 | 35.59 | 110 | 15 | 534 | python | [] | 0 | true | |
2024-11-18T20:48:34.105604+00:00 | 1,629,082,577,000 | c015d8b322c558153a9fe380f0bc19a7360ff92d | 3 | {
"blob_id": "c015d8b322c558153a9fe380f0bc19a7360ff92d",
"branch_name": "refs/heads/main",
"committer_date": 1629082577000,
"content_id": "6c4925738e9c7540764bc61839c5c5a07a933064",
"detected_licenses": [
"MIT"
],
"directory_id": "4c304b381dc08dc5229a2ad6d6fa84b0c11d22dc",
"extension": "py",
"file... | 2.625 | stackv2 | import json
import requests
from datetime import datetime
import time
import re
import sys
MAXTIMEDIFF = 28
f = "%Y-%m-%dT%H:%M:%SZ"
def parsedate(inp, frmt="%Y-%m-%dT%H:%M:%SZ", rms=True):
dt = datetime.strptime(inp, frmt)
if rms:
return dt.timestamp() * 1000
else:
return dt
now = re.sub... | 44 | 32.68 | 111 | 16 | 530 | python | [] | 0 | true | |
2024-11-18T20:48:34.222050+00:00 | 1,626,430,584,000 | 68e2324f784d5d03f382e998232fa8ce2cdec3eb | 4 | {
"blob_id": "68e2324f784d5d03f382e998232fa8ce2cdec3eb",
"branch_name": "refs/heads/master",
"committer_date": 1626430584000,
"content_id": "df8c26806855e513c29be6f7b752532db086e3f4",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "88308a83f8dfb496c3dcc0149a6657e90991c091",
"extension": "py"... | 3.65625 | stackv2 | # Find the smallest positive number missing from an unsorted array
N = int(input(''))
array = list(map(int, input().split(' ')[:N]))
array.sort()
for index in range(0, len(array)):
if(array[index] > 0):
positiveInteger = array[index]
positiveIndex = index
break
if(1 < positiveInteger):
... | 21 | 27.05 | 66 | 14 | 142 | python | [] | 0 | true | |
2024-11-18T20:48:34.350903+00:00 | 1,595,381,652,000 | f10074309cc48af8b041478416743ff25e4c7b47 | 3 | {
"blob_id": "f10074309cc48af8b041478416743ff25e4c7b47",
"branch_name": "refs/heads/master",
"committer_date": 1595381652000,
"content_id": "0643bf82a281b2a708c9695e76d42350b056f0f0",
"detected_licenses": [
"BSD-2-Clause"
],
"directory_id": "5d484450406cb726cd26b61c1af898856c6057f6",
"extension": "p... | 2.90625 | stackv2 | import tensorflow
import numpy
import matplotlib.pyplot as plt
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
import pandas as pd
import numpy
from .helpers import ... | 184 | 39.4 | 96 | 15 | 2,051 | python | [] | 0 | true | |
2024-11-18T20:48:34.400061+00:00 | 1,474,924,104,000 | c17e35f4b9625a28f1c1f25d00d59ca5926381a8 | 3 | {
"blob_id": "c17e35f4b9625a28f1c1f25d00d59ca5926381a8",
"branch_name": "refs/heads/master",
"committer_date": 1474924104000,
"content_id": "df38e1d691d4f60fac978b4c8c1495cea17d6d64",
"detected_licenses": [
"MIT"
],
"directory_id": "5abba2cda976e4ec04b879c73a8b79184c1b5f3b",
"extension": "py",
"fi... | 2.6875 | stackv2 | """
Prepares features and target inputs, creates and trains an ML model and
evaluates it.
"""
import os
# preprocessing
import numpy as np
import pandas as pd
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, MinMaxScaler
import jsonpickle
import jso... | 271 | 33.54 | 110 | 15 | 2,268 | python | [] | 0 | true | |
2024-11-18T20:48:34.630504+00:00 | 1,574,105,488,000 | 1ad39b36394a7cf764e697c77f57be1863b5cca9 | 3 | {
"blob_id": "1ad39b36394a7cf764e697c77f57be1863b5cca9",
"branch_name": "refs/heads/master",
"committer_date": 1574105488000,
"content_id": "20fd7c2287a6dcfcd515547bb5481f327bf60aa8",
"detected_licenses": [
"MIT"
],
"directory_id": "e91b77e309066f0ab98ff0843fe7accd753a8e81",
"extension": "py",
"fi... | 2.515625 | stackv2 | #!/usr/bin/env python3
import requests
import fileinput
import json
import glob
import os
import sys
import re
results = {
"success" : [],
"skipped" : []
}
options = {
"h" : {
'status' : False,
'description': "print this help message and exit (also --help)"
},
"i" : {
'status' : False,
'description': "in... | 333 | 22.98 | 107 | 21 | 2,246 | python | [] | 0 | true | |
2024-11-18T21:11:15.988910+00:00 | 1,474,501,254,000 | c8a1bc1615f3b3db926818708b836097189b8276 | 2 | {
"blob_id": "c8a1bc1615f3b3db926818708b836097189b8276",
"branch_name": "refs/heads/master",
"committer_date": 1474501254000,
"content_id": "22a1ea53f2b62f1fef80eef3f6a1ad7bfab0817a",
"detected_licenses": [
"MIT"
],
"directory_id": "a143c2e5d045204d4df70612d8c452ae16585fae",
"extension": "py",
"fi... | 2.390625 | stackv2 | from __future__ import division
from __future__ import print_function
from PyQt4 import QtCore, QtGui
class CollectionView(QtGui.QTableView):
########################################################
# DEFAULT VIEW PROPERTIES
########################################################
# Height of rows ... | 67 | 32.39 | 125 | 14 | 408 | python | [] | 0 | true | |
2024-11-18T21:11:16.103586+00:00 | 1,539,000,938,000 | 37d29e7fd6ba64644a53fe0d76604a55fd29bd07 | 3 | {
"blob_id": "37d29e7fd6ba64644a53fe0d76604a55fd29bd07",
"branch_name": "refs/heads/master",
"committer_date": 1539000938000,
"content_id": "faaa33d18cd5d28c951201ddfd3829ceed68b648",
"detected_licenses": [
"MIT"
],
"directory_id": "78356b0d7fe7eeb470758a3b19d524fcd3e8cadc",
"extension": "py",
"fi... | 2.75 | stackv2 | # tables.py
import django_tables2 as tables
from .models import Exercise
class ExerciseTable(tables.Table):
time = tables.Column(accessor='time', verbose_name='Time', order_by=('hours', 'minutes'))
# aver_speed = tables.Column(verbose_name='Average speed')
aver_speed = tables.Column(accessor='average_spee... | 82 | 31.34 | 105 | 15 | 653 | python | [] | 0 | true | |
2024-11-18T21:11:16.244036+00:00 | 1,516,830,877,000 | e14811b85e6ebc8e1453eaa335b39e60181161ae | 3 | {
"blob_id": "e14811b85e6ebc8e1453eaa335b39e60181161ae",
"branch_name": "refs/heads/master",
"committer_date": 1516830877000,
"content_id": "eeb605a35c3284dbdde01f1181939be928663d18",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "8356c227dacafc32bec5440a91c1c7fe8a1021bc",
"extension": "p... | 2.5625 | stackv2 | #!/usr/bin/env python
import os
import sys
import glob
import subprocess
import md5
import datetime
archivelogfn = "/home/primefocus/bass/archive/bassarchive.log"
dtskp = "bokpf@dtskp.kpno.noao.edu"
curtime = datetime.datetime.now()
logpfx = "[{}] ".format(str(curtime)[:-7])
def check_file_size(year=''):
'''List ... | 142 | 32.4 | 74 | 18 | 1,316 | python | [] | 0 | true | |
2024-11-18T21:11:20.141705+00:00 | 1,522,186,657,000 | f0d1a28c8fbee97aa14687fa572636a0aafaff0a | 3 | {
"blob_id": "f0d1a28c8fbee97aa14687fa572636a0aafaff0a",
"branch_name": "refs/heads/master",
"committer_date": 1522186657000,
"content_id": "e5bd9846792ca0380e9358b95728eb11553310f8",
"detected_licenses": [
"MIT"
],
"directory_id": "b2a4090f2098e899d24783a6e51a98c16e643769",
"extension": "py",
"fi... | 2.671875 | stackv2 | from collections import namedtuple
from fbchat import Client
from fbchat.models import ThreadType
class Bot(Client):
Command = namedtuple('Command', ['func', 'admin', 'directed'])
def __init__(self, email, password, name, admins=[], protected=[], *args, **kwargs):
super(Bot, self).__init__(email=ema... | 48 | 38.31 | 88 | 17 | 433 | python | [] | 0 | true | |
2024-11-18T21:11:20.191299+00:00 | 1,463,177,624,000 | 23cde744886a268c2d423081a10c06167610699c | 3 | {
"blob_id": "23cde744886a268c2d423081a10c06167610699c",
"branch_name": "refs/heads/master",
"committer_date": 1463177624000,
"content_id": "b86c15f6a9578f8611e22578c24f81f34240173b",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "3147c0900dc985917fdb95124e82b2402428e219",
"extension": "p... | 2.9375 | stackv2 |
import pandas as pd
import re
import sys
sent=[]
inc=[]
f = open("t2.txt", 'r')
for line in f:
row = line.strip().split(',')
sent.append(row)
f.close()
g = open("zincome.csv", 'r')
for i in g:
irow = i.strip().split(',')
inc.append(irow)
g.close()
sentf=pd.DataFrame(sent)
sentf.columns = ['sent', 'zi... | 30 | 18.47 | 49 | 10 | 182 | python | [] | 0 | true | |
2024-11-18T21:11:20.289609+00:00 | 1,515,960,000,000 | df33939c92e59f6cf7f520ede32396e1d2efa9b2 | 3 | {
"blob_id": "df33939c92e59f6cf7f520ede32396e1d2efa9b2",
"branch_name": "refs/heads/master",
"committer_date": 1515960000000,
"content_id": "7adef7d57d29d801701047520e97360549df549e",
"detected_licenses": [
"MIT"
],
"directory_id": "a484bd40a5c5c722ab6a0a7ca80e237bb78d14c9",
"extension": "py",
"fi... | 2.5625 | stackv2 | import json
import ply.lex
from .. import logger
class Lexer:
tokens = [
'IDENTIFIER',
'REGULAR_EXPRESSION',
'MATCH_OPERATOR',
'AND_OPERATOR',
'OR_OPERATOR',
]
t_IDENTIFIER = r'\w+'
t_REGULAR_EXPRESSION = r'/(?:\\.|[^/])+/'
t_MATCH_OPERATOR = '=~'
t_AND... | 42 | 20.4 | 63 | 13 | 231 | python | [] | 0 | true | |
2024-11-18T21:11:20.350313+00:00 | 1,585,774,472,000 | 6c562167bd41d5cb20f20069627e654d450a923f | 4 | {
"blob_id": "6c562167bd41d5cb20f20069627e654d450a923f",
"branch_name": "refs/heads/master",
"committer_date": 1585774472000,
"content_id": "4b03d28ffbedff5db383dbdc1a91d3431ea84fb4",
"detected_licenses": [
"MIT"
],
"directory_id": "11fbb786b162b16289672d6a6bf6277ac5ef2f40",
"extension": "py",
"fi... | 4.34375 | stackv2 | # Silvio Dunst
# Program that outputs today is a weekday or not
import datetime # imports date time function
now = datetime.datetime.now() # create a new variable for weekday
weekday = now.weekday() # create a new variable for (weekday), extract the weekday out of the now function
#if weekday >= 0 & <= 4 : # weeday... | 27 | 34.26 | 112 | 9 | 260 | python | [] | 0 | true | |
2024-11-18T21:11:24.227664+00:00 | 1,585,870,303,000 | 16c95c158787fd2fbed1b5d96b1b34290f1cabfc | 3 | {
"blob_id": "16c95c158787fd2fbed1b5d96b1b34290f1cabfc",
"branch_name": "refs/heads/master",
"committer_date": 1585870303000,
"content_id": "78c81df9cb8101cc26b496c81f85eb2fc42f4790",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "c91475644fe64b3226e55c7cb24edcf362a201a6",
"extension": "p... | 2.59375 | stackv2 | import tqdm
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torch3d.models as models
import torch3d.datasets as dsets
import torch3d.transforms as transforms
from torch3d.metrics import Accuracy
import pyvista as pv
def main(args):
# ToTensor t... | 171 | 30.77 | 82 | 18 | 1,223 | python | [] | 0 | true | |
2024-11-18T21:11:25.839581+00:00 | 1,545,408,146,000 | e38ad57b8ac35cb6506f819cc4bf617ad2f65101 | 3 | {
"blob_id": "e38ad57b8ac35cb6506f819cc4bf617ad2f65101",
"branch_name": "refs/heads/master",
"committer_date": 1545408146000,
"content_id": "efd221a585d84b0d64407d5260fab91894b40b93",
"detected_licenses": [
"MIT"
],
"directory_id": "b849b6259d36bf51f387756ed36c5c68cd72213e",
"extension": "py",
"fi... | 3.140625 | stackv2 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Inverse Captcha
https://adventofcode.com/2017/day/1
"""
# yup, could probably improve this (specially solver), but for now...
import solutions.utils as utils
def _get_input(fpath):
with open(fpath) as f:
return int(f.read())
def solver1(n):
s = ... | 39 | 17.05 | 91 | 16 | 231 | python | [] | 0 | true | |
2024-11-18T21:11:25.887697+00:00 | 1,553,816,050,000 | 5c2cc5ecd6488dd096b849b7917f0ac8995f3b0e | 2 | {
"blob_id": "5c2cc5ecd6488dd096b849b7917f0ac8995f3b0e",
"branch_name": "refs/heads/master",
"committer_date": 1553816050000,
"content_id": "66e9f3403aeb4fd7e718e8a8a743bfc014ef3986",
"detected_licenses": [
"MIT"
],
"directory_id": "4b96fbca2e1b18322428840cb624bab4e6b0a643",
"extension": "py",
"fi... | 2.46875 | stackv2 | import os
from flask import Flask, request
import tensorflow as tf
from werkzeug.utils import secure_filename
from exercices.models import Models
app = Flask(__name__)
# Flask / Tensorflow fix
# https://towardsdatascience.com/deploying-keras-deep-learning-models-with-flask-5da4181436a2
# global graph
# graph = tf.... | 34 | 23.09 | 93 | 11 | 199 | python | [] | 0 | true | |
2024-11-18T21:11:25.938186+00:00 | 1,669,388,897,000 | c62fa6790b351d91d7b2983a609495697747906d | 2 | {
"blob_id": "c62fa6790b351d91d7b2983a609495697747906d",
"branch_name": "refs/heads/master",
"committer_date": 1669388897000,
"content_id": "040cb6ef9224a22287330a82dbc38e5a448e2ffa",
"detected_licenses": [
"MIT"
],
"directory_id": "47eac81c566e4b35d024d30cc1b7548dbad02450",
"extension": "py",
"fi... | 2.453125 | stackv2 | """ madstuff: oneliners and debug-useful stuff """
from __future__ import annotations
import sys
import traceback
from ..base import o_repr
from .datadiff import _dumprepr
from .reprstuff import genreprwrap
__all__ = (
"_try",
"_try2",
"_iter_ar",
"_filter",
"_filter_n",
"_ipdbg",
"_ipdb... | 199 | 24.27 | 96 | 15 | 1,388 | python | [] | 0 | true | |
2024-11-18T21:11:26.158838+00:00 | 1,690,961,161,000 | 56b2c6fbfa182e3f8775d7f47e3e96375a8abcf0 | 3 | {
"blob_id": "56b2c6fbfa182e3f8775d7f47e3e96375a8abcf0",
"branch_name": "refs/heads/master",
"committer_date": 1690961161000,
"content_id": "6be146991d6b14b5ab362220d148d33b9b272b2e",
"detected_licenses": [
"Apache-2.0",
"MIT"
],
"directory_id": "ea401c3e792a50364fe11f7cea0f35f99e8f4bde",
"exten... | 2.90625 | stackv2 | """
Fixed and improved version based on "extracting from C++ doxygen documented file Author G.D." and py++ code.
Distributed under the Boost Software License, Version 1.0. (See
accompanying file LICENSE_1_0.txt or copy at
http://www.boost.org/LICENSE_1_0.txt)
Extensively modified by C.M. Bruns April 2010.
"""
import... | 467 | 30.82 | 110 | 16 | 3,646 | python | [] | 0 | true | |
2024-11-18T21:11:26.218145+00:00 | 1,518,163,674,000 | de921100a78ad749d810f8b29badb3521c27dc32 | 4 | {
"blob_id": "de921100a78ad749d810f8b29badb3521c27dc32",
"branch_name": "refs/heads/master",
"committer_date": 1518163674000,
"content_id": "9332907f50280ce7e29d12e606793f806ecfd78c",
"detected_licenses": [
"MIT"
],
"directory_id": "644984edc7bd12c395f20b204e0201aca068c541",
"extension": "py",
"fi... | 4.3125 | stackv2 | # Problem: Text Justification
# Difficulty: Hard
# Category: String
# Leetcode 68: https://leetcode.com/problems/text-justification/description/
# Description:
"""
Given an array of words and a length L,
format the text such that each line has exactly L characters and is fully (left and right) justified.
You should p... | 98 | 26.27 | 101 | 21 | 746 | python | [] | 0 | true | |
2024-11-18T21:11:26.267163+00:00 | 1,559,152,799,000 | c37d5479fa8df00c8c486385aac8719f896fde53 | 4 | {
"blob_id": "c37d5479fa8df00c8c486385aac8719f896fde53",
"branch_name": "refs/heads/master",
"committer_date": 1559152799000,
"content_id": "2938f55bc765e10c87a35f031352b863523936ff",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "e069b0ae254c4e97766d9865b9550eb2f0be32e3",
"extension": "py"... | 3.5625 | stackv2 | """
functional programming on Python example: check if string is a palendrome
dependencies:
pyfpm for pattern matching
python 3.7.1
author:
L1ttl3S1st3r
4/14/2019
"""
from pyfpm.matcher import Matcher
import typing
class IncorrectArgException(ValueError):
pass
# watch a descriptions in pyfmp docs
is_p... | 59 | 19.93 | 73 | 17 | 321 | python | [] | 0 | true | |
2024-11-18T21:11:26.487362+00:00 | 1,620,218,580,000 | 8ed25e58ff9633600cbba9eef3a40da6ff0e2642 | 3 | {
"blob_id": "8ed25e58ff9633600cbba9eef3a40da6ff0e2642",
"branch_name": "refs/heads/master",
"committer_date": 1620218580000,
"content_id": "e8b10f57d385b14753d811aba0f52c3133f8145f",
"detected_licenses": [
"MIT"
],
"directory_id": "4bd23102943b77d47f45630270387d84b034be2c",
"extension": "py",
"fi... | 2.765625 | stackv2 | import cv2
import imutils
import numpy as np
from shared.sort_cont import sort_cont
from skimage import measure
from skimage.filters import threshold_local
def segment_chars(plate_img, fixed_width):
"""
extract Value channel from the HSV format of image and apply adaptive thresholding
to reveal the charac... | 93 | 37.18 | 100 | 15 | 883 | python | [] | 0 | true | |
2024-11-18T21:11:26.766263+00:00 | 1,592,760,170,000 | 5440b057a68ad38abd07c6e67e3f7dd0e3bbbe51 | 4 | {
"blob_id": "5440b057a68ad38abd07c6e67e3f7dd0e3bbbe51",
"branch_name": "refs/heads/master",
"committer_date": 1592760170000,
"content_id": "dae678b358c897cf695785b21c56df948ca578d6",
"detected_licenses": [
"MIT"
],
"directory_id": "80d1fbb1941ad155c8f08b25e71eb1a816cfef87",
"extension": "py",
"fi... | 3.546875 | stackv2 | import random as r
from PIL import Image, ImageDraw
def squares(xMax, yMax, draw):
step = 25
for a in range(0, xMax, step):
for b in range(0, yMax, step):
d = r.randint(0, 100)
dark = (d, d, d)
light = (r.randint(120, 150), r.randint(175, 230), r.randint(240, 255))
... | 27 | 24.56 | 83 | 16 | 227 | python | [] | 0 | true | |
2024-11-18T21:11:26.886832+00:00 | 1,687,931,621,000 | c131e07ae910b96cd2058515d6310290be4b1651 | 3 | {
"blob_id": "c131e07ae910b96cd2058515d6310290be4b1651",
"branch_name": "refs/heads/master",
"committer_date": 1687931621000,
"content_id": "bd2900b582d06a156abc565c29655741fa6eae69",
"detected_licenses": [
"MIT"
],
"directory_id": "49cbc5f4735152ecd0dfff45fd719f2705c0ab30",
"extension": "py",
"fi... | 2.96875 | stackv2 | import pickle
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Load our stored results
with open('forum_posts.pkl', "rb") as input_file:
posts = pickle.load(input_file)
def add_interaction(users, fu, tu):
if fu not in users:
users[fu] = {}
if tu not in users[fu]:
us... | 46 | 27.24 | 60 | 14 | 345 | python | [] | 0 | true | |
2024-11-18T21:11:27.130594+00:00 | 1,569,928,333,000 | 142c3a9fcc685658e1b97efd04d63021fadfdbeb | 2 | {
"blob_id": "142c3a9fcc685658e1b97efd04d63021fadfdbeb",
"branch_name": "refs/heads/master",
"committer_date": 1586717151000,
"content_id": "cc5e22ec2c8678d09dfde8c799929817913dca45",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "0d45fa71db161ffe77bda9351d9b3aeccdd8d3c2",
"extension": "py"... | 2.328125 | stackv2 | # -*- encoding: utf-8 -*-
#
# etis.py
#
import xml.etree.ElementTree as ET
import datetime
import urllib
import urllib2
from HTMLParser import HTMLParser
from rjetis.common import namespacify, NS
URL_GET_JOURNEY = 'http://www.etis.skanetrafiken.se/Journey/Service.asmx/GetJourney'
URL_GET_JOURNEY_PATH = 'http://www.e... | 76 | 24.09 | 93 | 12 | 556 | python | [] | 0 | true | |
2024-11-18T21:11:27.358813+00:00 | 1,692,243,220,000 | a8398e097f6409bb4500930bb0cd75c5441504d2 | 3 | {
"blob_id": "a8398e097f6409bb4500930bb0cd75c5441504d2",
"branch_name": "refs/heads/master",
"committer_date": 1692243220000,
"content_id": "dc6dcf13e89373c3baaf49a84698481737d74c72",
"detected_licenses": [
"MIT"
],
"directory_id": "af741f8ada5dc2860faa87534536ccef3d97738e",
"extension": "py",
"fi... | 2.671875 | stackv2 | from base import Base
from sqlalchemy import Column, String, Integer, Date, Boolean, Numeric, ForeignKey, UniqueConstraint, PrimaryKeyConstraint
class SituationReport(Base):
__tablename__ = 'situation_report_data'
country = Column('country', String(128))
cases = Column('cases', Integer)
new_cases = C... | 28 | 37.14 | 122 | 11 | 250 | python | [] | 0 | true | |
2024-11-18T21:11:27.426343+00:00 | 1,522,855,675,000 | 492d96213264c8f9ca4e88ebbd8a75146e3e2a69 | 3 | {
"blob_id": "492d96213264c8f9ca4e88ebbd8a75146e3e2a69",
"branch_name": "refs/heads/master",
"committer_date": 1522855675000,
"content_id": "cb60cb0d57c14fe13d947acdf1265d03fa9f7aab",
"detected_licenses": [
"MIT"
],
"directory_id": "1c997e6b4ca5dc3e5152432dc398860660e366d6",
"extension": "py",
"fi... | 2.640625 | stackv2 | import networkx as nx
import numpy as np
import pandas as pd
import csv
import matplotlib.pyplot as plt
# defining which country plays which role per year within the Phoenix dataset:
role_dict = {}
for i in list(['USA', 'RUS', 'USSR', 'TUR', 'IRN', 'KSA', 'CHN', 'FRA', 'GER', 'GBR', 'CAN', 'IRQ', 'SYR', 'GRC']):
r... | 224 | 37.28 | 115 | 16 | 2,519 | python | [] | 0 | true | |
2024-11-18T21:11:27.496196+00:00 | 1,535,011,358,000 | bde32e8bab8f9fdb274519213a6cd2f73af34a5f | 3 | {
"blob_id": "bde32e8bab8f9fdb274519213a6cd2f73af34a5f",
"branch_name": "refs/heads/master",
"committer_date": 1535011358000,
"content_id": "249d91bbdef37b0da0c55ad76c84c2f66f187f68",
"detected_licenses": [
"MIT"
],
"directory_id": "085e766b2fbb31f32bf1fe09396433cd86e0dd12",
"extension": "py",
"fi... | 2.96875 | stackv2 | """This is a util that read the data and sample 100 rows from each and save them."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pandas as pd
def sampling():
"""
Sample each data file, extract 100 rows from each and save them into new f... | 46 | 31.7 | 84 | 9 | 408 | python | [] | 0 | true | |
2024-11-18T21:11:27.592920+00:00 | 1,691,969,733,000 | 686447c8750dc9d9b74689d4a78b182f77fc71ac | 3 | {
"blob_id": "686447c8750dc9d9b74689d4a78b182f77fc71ac",
"branch_name": "refs/heads/master",
"committer_date": 1691969733000,
"content_id": "d7940ae14d5521c7cf060245ea925d245ccb5511",
"detected_licenses": [
"MIT"
],
"directory_id": "f5e61e489e529c47aad126f3a79e4583a869f676",
"extension": "py",
"fi... | 2.890625 | stackv2 | import ast
import operator
import statistics
from collections import defaultdict
from decimal import Decimal
from api import settings
from api.ext.moneyformat import truncate
NO_RATE = Decimal("NaN")
MAX_DEPTH = 8
class ExchangePair:
def __init__(self, left, right=None):
if right is None:
pa... | 177 | 34.57 | 123 | 20 | 1,408 | python | [] | 0 | true | |
2024-11-18T21:11:27.665167+00:00 | 1,376,578,549,000 | e9e32d76b7637dcfd6ce2250949854840056a12d | 3 | {
"blob_id": "e9e32d76b7637dcfd6ce2250949854840056a12d",
"branch_name": "refs/heads/master",
"committer_date": 1376578549000,
"content_id": "26b993d47ccb6aa6d6b3b44026249f7e64ff93e4",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "60d0bae9f7a88bfab5dbb1e71ae19bb654f3ed99",
"extension": "py"... | 3 | stackv2 | # This module contains some web-related code common to all web interfaces.
import urllib, xml.sax.saxutils
# From kpython
import kodict
from kbase import *
# HTTP status codes to return to the user when his request has been processed.
KWEB_LIB_STATUS_OK = 200
KWEB_LIB_STATUS_MOVED_TEMPORARILY = 307
KWEB_LIB_STATUS_I... | 63 | 32.19 | 141 | 13 | 520 | python | [] | 0 | true | |
2024-11-18T21:11:27.876675+00:00 | 1,525,954,828,000 | 2f107ab0ce987909583a68c27dcb2d3470b745d0 | 3 | {
"blob_id": "2f107ab0ce987909583a68c27dcb2d3470b745d0",
"branch_name": "refs/heads/master",
"committer_date": 1525954828000,
"content_id": "aae4e0951463f52c303a91cb13f0d34217012639",
"detected_licenses": [
"MIT"
],
"directory_id": "f1fa9b7f018de3bdca8ba899fae781d32fbc9646",
"extension": "py",
"fi... | 3.34375 | stackv2 | import numpy as np
from np_ml import HMM
if __name__ == '__main__':
print("--------------------------------------------------------")
print("Hidden Markov Model simple example!")
print("example in Statistical Learning Method(《统计学习方法》)")
print("--------------------------------------------------------")
... | 44 | 26.18 | 85 | 10 | 352 | python | [] | 0 | true | |
2024-11-18T21:11:28.451939+00:00 | 1,596,045,418,000 | 64130709ffc49e8cf68d9b6f32eab2ca1209192f | 2 | {
"blob_id": "64130709ffc49e8cf68d9b6f32eab2ca1209192f",
"branch_name": "refs/heads/master",
"committer_date": 1596045418000,
"content_id": "002adee6feaccba3748c8ad7ec011bfcdd9de163",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "bc8b920a4efa725716b062ac410ce665cef02ade",
"extension": "py"... | 2.390625 | stackv2 | from opfu.euro_option import EuroCall, EuroPut
from opfu.synthetic import Synthetic
class Butterfly(Synthetic):
def __init__(self, K1, K3, T, price_1=0, price_2=0, price_3=0, S0=None, r=0.01, sigma=0.1, use_call=True):
if use_call:
Security = EuroCall
else:
Security = Euro... | 20 | 40.15 | 110 | 11 | 291 | python | [] | 0 | true | |
2024-11-18T21:11:28.571417+00:00 | 1,592,795,778,000 | 1d15528644c0537153454cc758e37dd1aba3a5d9 | 3 | {
"blob_id": "1d15528644c0537153454cc758e37dd1aba3a5d9",
"branch_name": "refs/heads/master",
"committer_date": 1592795778000,
"content_id": "f9f618e7b7cf86dd8e39fdc4c423e64a280afa1d",
"detected_licenses": [
"MIT"
],
"directory_id": "0f197956c3ce90c31ae34bbb2e5d706bbeed3d96",
"extension": "py",
"fi... | 3.1875 | stackv2 | """
Translator functions are all defined here.
A translator is simply a function (or callable) that takes field_name and item as the parameters, and returns the
translated field value or a tuple of (request, callback_function).
If a (request, callback_function) is returned, the callback function is expected to take re... | 46 | 37.98 | 115 | 17 | 457 | python | [] | 0 | true | |
2024-11-18T21:11:28.705603+00:00 | 1,624,271,723,000 | 080a7d0556515bea102a84710834b6b5b8bc5d2e | 2 | {
"blob_id": "080a7d0556515bea102a84710834b6b5b8bc5d2e",
"branch_name": "refs/heads/main",
"committer_date": 1624271723000,
"content_id": "862600c9d7252137cbe9887c17b09e1cee97f10d",
"detected_licenses": [
"MIT"
],
"directory_id": "c85f006472aabe62e5e365d5c93627361b3d2bcc",
"extension": "py",
"file... | 2.375 | stackv2 | import graphene
from graphene.types import ResolveInfo
from graphql_relay import from_global_id
from ..base import BaseMutationPayload
from tracker.api.schemas.roles import RoleDeletionSchema
from tracker.api.services import validate_input
from tracker.api.services.roles import (
check_user_priviliges_for_role_del... | 77 | 32.42 | 74 | 16 | 540 | python | [] | 0 | true | |
2024-11-18T21:11:28.757296+00:00 | 1,614,545,361,000 | 92962fa73d6193b30611593950bfe124748dd9f0 | 3 | {
"blob_id": "92962fa73d6193b30611593950bfe124748dd9f0",
"branch_name": "refs/heads/master",
"committer_date": 1614545361000,
"content_id": "e4c47f103a51ae5637d284d99c926f16c3f57e10",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "5b2adecd48b4de2915ec6cf39e265110f25ddfcb",
"extension": "py"... | 3.0625 | stackv2 | """Functions for working with index/columns."""
import numpy as np
import pandas as pd
from numba import njit
from collections.abc import Iterable
from vectorbt.utils import checks
def get_index(arg, axis):
"""Get index of `arg` by `axis`."""
checks.assert_type(arg, (pd.Series, pd.DataFrame))
checks.ass... | 341 | 34.26 | 114 | 20 | 2,767 | python | [] | 0 | true | |
2024-11-18T21:11:29.184890+00:00 | 1,620,058,079,000 | 996c5dbd4c2db44cd2501ee1078afdda5ee10dea | 2 | {
"blob_id": "996c5dbd4c2db44cd2501ee1078afdda5ee10dea",
"branch_name": "refs/heads/master",
"committer_date": 1620058079000,
"content_id": "7f8d156cc57f3d4911e4e8d03899ce3943400727",
"detected_licenses": [
"MIT"
],
"directory_id": "45320cb725551a5f1be492795c9b5c8a51e279c8",
"extension": "py",
"fi... | 2.328125 | stackv2 | import os
import glob
from joblib import Parallel, delayed
import multiprocessing
import numpy as np
import pandas as pd
from scipy.stats import kurtosis, skew
import traceback
import time
# from concurrent.futures import ProcessPoolExecutor
path_to_data = "/misc/vlgscratch5/RanganathGroup/lily/blood_dist/data_large/d... | 75 | 33.81 | 91 | 18 | 725 | python | [] | 0 | true | |
2024-11-18T21:11:29.395759+00:00 | 1,604,081,612,000 | 0aa7f4ee80c76c98532ccad654954eaa2bdc026c | 3 | {
"blob_id": "0aa7f4ee80c76c98532ccad654954eaa2bdc026c",
"branch_name": "refs/heads/master",
"committer_date": 1604081612000,
"content_id": "a7948378d936cc144c511e028702f8a080be6698",
"detected_licenses": [
"MIT"
],
"directory_id": "58f087e76bf4f35c840dd31a4befe8fee12075cc",
"extension": "py",
"fi... | 2.8125 | stackv2 | import sqlite3
from pathlib import Path
from time import sleep
from .web_paths import WebPathStructure
from .database_factories import SqliteDatabaseFactory
class SqliteDatabase:
def __init__(self, database_name: str, database_config: str = '', web_path: WebPathStructure = WebPathStructure()):
print(f'c... | 78 | 33.27 | 119 | 18 | 535 | python | [] | 0 | true | |
2024-11-18T21:11:29.458755+00:00 | 1,632,438,326,000 | 0da7d3dfd3b84d7e0ae03d7075517f351f8882ec | 3 | {
"blob_id": "0da7d3dfd3b84d7e0ae03d7075517f351f8882ec",
"branch_name": "refs/heads/master",
"committer_date": 1632438326000,
"content_id": "3191b810e59ad22016f821ee6339f14a6e945016",
"detected_licenses": [
"MIT"
],
"directory_id": "4274bec659b2eaf147bd667a9854d478fe837524",
"extension": "py",
"fi... | 2.859375 | stackv2 | """Functions for adding edges to a PPI Graph from parsed STRING & BIOGRID API call outputs"""
# %%
# Graphein
# Author: Arian Jamasb <arian@jamasb.io>, Ramon Vinas
# License: MIT
# Project Website: https://github.com/a-r-j/graphein
# Code Repository: https://github.com/a-r-j/graphein
import logging
import networkx as ... | 89 | 28.39 | 93 | 14 | 729 | python | [] | 0 | true | |
2024-11-18T21:11:29.604234+00:00 | 1,561,273,547,000 | d8aeaeb9d81b35e7d99e98ef3c3795ba573eb2a4 | 3 | {
"blob_id": "d8aeaeb9d81b35e7d99e98ef3c3795ba573eb2a4",
"branch_name": "refs/heads/master",
"committer_date": 1561273547000,
"content_id": "38201834de8931b406799610fe48e026ee8b04ee",
"detected_licenses": [
"MIT"
],
"directory_id": "7af32d9d5e1b09e11af3c63870f5a159a31dfc5f",
"extension": "py",
"fi... | 2.96875 | stackv2 | #!/usr/bin/env python3
"""Displaying messages on the 8x8 LED matrix on the Sense Hat."""
# 3rd party
from sense_hat import SenseHat
# custom
from colors import (
BLACK,
BLUE,
GREEN,
RED,
WHITE,)
from shapes import okay
# Error codes
# reset required
# no space on micro SD card... | 37 | 18.51 | 72 | 9 | 178 | python | [] | 0 | true | |
2024-11-18T21:11:29.798681+00:00 | 1,622,645,489,000 | 73703a93d5b4b2c3d74994d21eda62f355c22011 | 2 | {
"blob_id": "73703a93d5b4b2c3d74994d21eda62f355c22011",
"branch_name": "refs/heads/master",
"committer_date": 1622645489000,
"content_id": "1feb28924b6ef90bf1b1ffa3bccfbeab0f304d97",
"detected_licenses": [
"MIT"
],
"directory_id": "84ea3c7952c154fec8fa3c2396d27353c398c74d",
"extension": "py",
"fi... | 2.4375 | stackv2 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models
from shop.models import Product
from accounts.models import User
class Cart(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE, null=True)
created = models.DateTimeField(auto_now_add=True)
... | 42 | 27.55 | 75 | 13 | 255 | python | [] | 0 | true | |
2024-11-18T21:11:29.859005+00:00 | 1,531,171,612,000 | f1343d72d2ce60e3819c67ee9bccecd9b0ec1efe | 2 | {
"blob_id": "f1343d72d2ce60e3819c67ee9bccecd9b0ec1efe",
"branch_name": "refs/heads/master",
"committer_date": 1531171612000,
"content_id": "7c1d9c067ac5c4c15b3e8e1d7bf8c7f3292cfb8a",
"detected_licenses": [
"MIT",
"Python-2.0"
],
"directory_id": "16c861cf765069c962145b79bae9bb0dc6486f4d",
"exten... | 2.359375 | stackv2 | """Add custom filters to Jinja processor.
The original purpose of this module was to be able to put links to general index and
module index into sidebar, using manual URL composition with custom Jinja filters.
"""
import os
import posixpath
from sphinx.jinja2glue import BuiltinTemplateLoader
_template_filters = []
... | 33 | 24.73 | 84 | 13 | 181 | python | [] | 0 | true | |
2024-11-18T21:11:30.026321+00:00 | 1,529,952,921,000 | 0c248d46e6f300cd054f3aff5710c1ff1c45fe82 | 3 | {
"blob_id": "0c248d46e6f300cd054f3aff5710c1ff1c45fe82",
"branch_name": "refs/heads/master",
"committer_date": 1529952921000,
"content_id": "11efc7568158323bc2acc48689fc992d183be4e5",
"detected_licenses": [
"MIT"
],
"directory_id": "10067ec802bd38a5a9eecd70633eb95c7fb4acc7",
"extension": "py",
"fi... | 2.953125 | stackv2 | # -*- coding: utf-8 -*-
# option parser specifies the observable to plot and customizes the plot
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-c", "config",
action="store", type="string", dest="config_string",
help="specify configuration via ':'-separated string")
(option... | 36 | 38.11 | 77 | 12 | 322 | python | [] | 0 | true | |
2024-11-18T21:11:30.079658+00:00 | 1,560,104,909,000 | d5e6af75be5da54f1b02636a7cb1d2de0e44323d | 3 | {
"blob_id": "d5e6af75be5da54f1b02636a7cb1d2de0e44323d",
"branch_name": "refs/heads/master",
"committer_date": 1560104909000,
"content_id": "5c68272c6e4d2772b94f6e2622f6cc078ebcba6d",
"detected_licenses": [
"MIT"
],
"directory_id": "6e1b87b2dabd50ebd10986f801bb5c9fd4ca4ed5",
"extension": "py",
"fi... | 2.546875 | stackv2 | from pymongo import MongoClient, ASCENDING, TEXT, IndexModel
import json
import datetime
with open("../sample.json") as f:
users = json.load(f)
for user in users:
user["date_of_birth"] = datetime.datetime.strptime(
user["date_of_birth"], "%d/%m/%Y"
)
client = MongoClient()
db = client.phonebook... | 31 | 21.1 | 78 | 12 | 158 | python | [] | 0 | true | |
2024-11-18T21:11:30.139903+00:00 | 1,552,936,056,000 | bb5aae6643d62a174f28b7d492a89daffafc6120 | 3 | {
"blob_id": "bb5aae6643d62a174f28b7d492a89daffafc6120",
"branch_name": "refs/heads/master",
"committer_date": 1552936056000,
"content_id": "1c48d5f9f9521e09c32013b687664465b262cec1",
"detected_licenses": [
"MIT"
],
"directory_id": "e6ffc0832995c57335bedc8dd4faf7459e12f605",
"extension": "py",
"fi... | 2.671875 | stackv2 | from ..compound import Compound
from ..placeholder import Placeholder
from ..primitive import Primitive
from .navstate import NavState
from .horz import Horz
class VertFolder(NavState):
"""
A vertical state transitioner, that also holds recursive children.
VertFolder instances are toggled between by pre... | 77 | 32.14 | 79 | 15 | 565 | python | [] | 0 | true | |
2024-11-18T21:11:30.262668+00:00 | 1,550,493,763,000 | 82ea188a1c12bf31545bccc9a55bc7fee17b97b8 | 4 | {
"blob_id": "82ea188a1c12bf31545bccc9a55bc7fee17b97b8",
"branch_name": "refs/heads/master",
"committer_date": 1550493763000,
"content_id": "85b9c090ecea91988fcbe38780451132e8c2bb06",
"detected_licenses": [
"MIT"
],
"directory_id": "d2312c375caa59d0b1c8b3cf1744cb33131464cc",
"extension": "py",
"fi... | 3.515625 | stackv2 | """
Module containing functions for genetic algorithm """
import numpy as np
from random import choice, random, randint
from math import isinf
class GaIndividual:
def __init__(self, genes):
self.fitness = None
self.genes = genes
def mutate(individual, prob, values):
"""
Return a mutated co... | 143 | 26.89 | 114 | 19 | 883 | python | [] | 0 | true | |
2024-11-18T21:11:30.906395+00:00 | 1,581,007,773,000 | edb0b08943a9231f108085178a71ead6892d830f | 2 | {
"blob_id": "edb0b08943a9231f108085178a71ead6892d830f",
"branch_name": "refs/heads/master",
"committer_date": 1581007773000,
"content_id": "c3a52bddcf8a51138ca355201285687efa43fdd7",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "694140c52d7c52c7509bab8c2e5b6b597fe01900",
"extension": "py"... | 2.40625 | stackv2 | #!/usr/bin/env python
# obj_factory.py
#
# Copyright 2020 Jose Jouberto Fonseca Lopes.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 28 | 29.18 | 74 | 10 | 215 | python | [] | 0 | true | |
2024-11-18T21:11:31.244519+00:00 | 1,628,131,280,000 | 1546fa24aa80409a1cb5958a2d7098626f0ea2a4 | 3 | {
"blob_id": "1546fa24aa80409a1cb5958a2d7098626f0ea2a4",
"branch_name": "refs/heads/master",
"committer_date": 1628131280000,
"content_id": "2318cc2a65203fa98e5fcfa07409428c84a86d63",
"detected_licenses": [
"MIT"
],
"directory_id": "0ae1d2729528a03fc8f42c7dceba84e39be5499b",
"extension": "py",
"fi... | 3.375 | stackv2 | """Contains the implementation for the Topics environment.
In this environment users have a hidden preference for each topic and each item has a
hidden topic assigned to it.
"""
import collections
import numpy as np
from . import environment
class Topics(environment.DictEnvironment):
"""
An environment wher... | 243 | 46.56 | 100 | 19 | 2,539 | python | [] | 0 | true | |
2024-11-18T21:11:31.448906+00:00 | 1,594,320,936,000 | a4c791f43082d29fa1d2cc25acb76977befc7560 | 4 | {
"blob_id": "a4c791f43082d29fa1d2cc25acb76977befc7560",
"branch_name": "refs/heads/master",
"committer_date": 1594320936000,
"content_id": "c9d4fced83f0f589477d21b756615a0e169894b7",
"detected_licenses": [
"MIT"
],
"directory_id": "b400f73fc6fad2b394fc907c35795c5b92cf08eb",
"extension": "py",
"fi... | 3.6875 | stackv2 | import constants
from copy import deepcopy
PLAYERS = deepcopy(constants.PLAYERS)
TEAMS = deepcopy(constants.TEAMS)
allocated_teams = []
allocated_players = []
players_string = []
user_input = 0
def clean_data(collection):
'''This is function that will take a collection and
it will clean up the heights key w... | 119 | 27.49 | 96 | 18 | 749 | python | [] | 0 | true | |
2024-11-18T21:11:31.517174+00:00 | 1,302,094,201,000 | e18125df5e5a78fbc6aaf9bbc5c4c66fbe362429 | 4 | {
"blob_id": "e18125df5e5a78fbc6aaf9bbc5c4c66fbe362429",
"branch_name": "refs/heads/master",
"committer_date": 1302094201000,
"content_id": "7b423ba197409d2eda332d751502080f4ff54bc8",
"detected_licenses": [
"MIT"
],
"directory_id": "ad65f7978673755397da1d3935ae405bf5e6802b",
"extension": "py",
"fi... | 3.640625 | stackv2 | #!/usr/bin/env python
__author__ = 'Richard Frankel'
__email__ = 'richard@frankel.tv'
__credits__ = ['Richard Frankel']
__copyright__ = "Copyright 2011, Richard Frankel"
__license__ = 'MIT'
__version__ = (0, 1, 1)
__date__ = '6 April 2011'
import copy
from random import random, randrange
class Candidate(object):
... | 184 | 31.05 | 80 | 15 | 1,371 | python | [] | 0 | true | |
2024-11-18T21:11:31.573047+00:00 | 1,491,407,359,000 | 9336ea692a43e4ca0bad4da56c7ed3719ee39fc0 | 3 | {
"blob_id": "9336ea692a43e4ca0bad4da56c7ed3719ee39fc0",
"branch_name": "refs/heads/master",
"committer_date": 1491407359000,
"content_id": "3d331d0c108f46827a0d9ba60ea9061f28d5ad04",
"detected_licenses": [
"MIT"
],
"directory_id": "9eddc8421a586ae45d896e6384c05d4caaefb3ca",
"extension": "py",
"fi... | 2.53125 | stackv2 | #!/usr/bin/env python
import pickle
import numpy as np
import gym
from sklearn.model_selection import train_test_split
from sklearn.externals import joblib
from sklearn.linear_model import Ridge
from sklearn.ensemble import RandomForestRegressor
def main():
data = pickle.load(open('data-ant.pkl', 'rb'))
obse... | 35 | 25.86 | 76 | 13 | 288 | python | [] | 0 | true | |
2024-11-18T21:11:31.847843+00:00 | 1,549,969,583,000 | cbc07ab914063e0e1c05056d67bd4efb92ccab8f | 3 | {
"blob_id": "cbc07ab914063e0e1c05056d67bd4efb92ccab8f",
"branch_name": "refs/heads/master",
"committer_date": 1549969583000,
"content_id": "d741d94604f80c16efb7189687ac89dbbc1c5b0b",
"detected_licenses": [
"MIT"
],
"directory_id": "9bc83158dff307b3e68f4049863a29c93f5c2773",
"extension": "py",
"fi... | 2.703125 | stackv2 | # -*- coding: utf-8 -*-
"""
@author: jamin, zhiruiwang
"""
from __future__ import print_function
import pandas
import numpy
from tableausdk import *
try:
from tableausdk.Extract import *
print("You are using the Tableau SDK, please save the output as .tde format")
except:
pass
try:
from tableausdk.HyperExtract i... | 215 | 38.11 | 146 | 20 | 1,588 | python | [] | 0 | true | |
2024-11-18T21:11:31.951893+00:00 | 1,590,379,620,000 | b5f850b7cce1c05523effe1dccae29b5f1b8b533 | 3 | {
"blob_id": "b5f850b7cce1c05523effe1dccae29b5f1b8b533",
"branch_name": "refs/heads/master",
"committer_date": 1590379620000,
"content_id": "438ace7d7b101320fbec1a447b0badeb3edfb506",
"detected_licenses": [
"MIT"
],
"directory_id": "566754f63c0d665af01bdad8814873468f8be888",
"extension": "py",
"fi... | 2.78125 | stackv2 | #!/usr/bin/python3
# -*- coding: utf-8 -*-
import re
str = '''Train Epoch: 1 Train Iteration: 260 Time 1.609s / 20iters, (0.080) Data load 0.036s / 20iters, (0.001794)
Learning rate = [0.1, 0.1] Loss = {ce_loss: 2.3057, loss: 2.3117}
'''
print("#+++++++++++#")
RE_CLS_IC_TRAIN = re.compile(r'Train Epoch: (?P<epoch>\... | 47 | 39.13 | 114 | 12 | 612 | python | [] | 0 | true | |
2024-11-18T21:11:32.024310+00:00 | 1,681,179,316,000 | e58128f9a7af38426f4c8f407d03a60bd0da3f15 | 3 | {
"blob_id": "e58128f9a7af38426f4c8f407d03a60bd0da3f15",
"branch_name": "refs/heads/main",
"committer_date": 1681179316000,
"content_id": "2c4d7e118240001765b5b105110ce24dc8c99c20",
"detected_licenses": [
"MIT"
],
"directory_id": "0cca742d0d5b45b6605e42a8e3c8840d4aca595f",
"extension": "py",
"file... | 2.734375 | stackv2 | # -*- coding: utf-8 -*-
"""
Created on 2020.05.19
@author: Jiahua Rao, Weiming Li, Hui Yang, Jiancong Xie
Code based on:
Shang et al "Edge Attention-based Multi-Relational Graph Convolutional Networks" -> https://github.com/Luckick/EAGCN
Coley et al "Convolutional Embedding of Attributed Molecular Graphs for Physical... | 198 | 36.36 | 150 | 18 | 1,782 | python | [] | 0 | true | |
2024-11-18T21:11:32.255308+00:00 | 1,632,075,152,000 | ff2634daad723529f6c5930986fb3a4c44752537 | 3 | {
"blob_id": "ff2634daad723529f6c5930986fb3a4c44752537",
"branch_name": "refs/heads/master",
"committer_date": 1632075239000,
"content_id": "b2c8b09afd03ac12fdad40963f74d1836feab553",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "41db9c4110ef022a7bb5b8da8e3bf2c6bc70c464",
"extension": "py"... | 2.6875 | stackv2 | # Implements a DiscordBot class that provides a interface for interacting
# with discord's bot API
import os
import discord
import asyncio
import logging
import threading
from twitch_monitor_discord_bot.command_processor import CommandProcessor, twitch_monitor_bot_command_list
logger = logging.getLogger(__name__)
lo... | 128 | 30.45 | 107 | 18 | 774 | python | [] | 0 | true | |
2024-11-18T21:11:32.392504+00:00 | 1,475,408,267,000 | 0aa1e75833f42a72d18b7ca4662da9ad1e12cc54 | 3 | {
"blob_id": "0aa1e75833f42a72d18b7ca4662da9ad1e12cc54",
"branch_name": "refs/heads/master",
"committer_date": 1475408526000,
"content_id": "1ecea7dbdb4c63510bf392a15748fcd869ef8d4d",
"detected_licenses": [
"MIT"
],
"directory_id": "99b31155d21793bd0ec3e9bc2073b33925d3a065",
"extension": "py",
"fi... | 2.546875 | stackv2 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from src.tools import *
# Inspect data for any missing datetime and non numeric type.
# # Checking non numeric data
# # data = grimmData['5-Grimm']
# # for i in range(0,200):
# # value = data.iloc[i]
# # if isinstance(value,object):
# # print(i)
if __nam... | 51 | 34.33 | 85 | 17 | 416 | python | [] | 0 | true | |
2024-11-18T21:11:32.458146+00:00 | 1,693,477,536,000 | cc7403e1efc14fb2e019d85deb300f9a89767386 | 3 | {
"blob_id": "cc7403e1efc14fb2e019d85deb300f9a89767386",
"branch_name": "refs/heads/master",
"committer_date": 1693477536000,
"content_id": "d23b5a46723cb743178657473b0442f807fc0548",
"detected_licenses": [
"MIT"
],
"directory_id": "c9500ad778b8521aaa85cb7fe3239989efaa4799",
"extension": "py",
"fi... | 2.640625 | stackv2 | from .base import ViperBase
class Tag(ViperBase):
pathName = "project/{project_name}/tag/"
def __init__(self, config, project_name, **data):
super().__init__(config)
self.project_name = project_name
self.id = data.get("id")
self.tag = data.get("tag")
def __str__(self):
... | 26 | 29.23 | 87 | 14 | 192 | python | [] | 0 | true | |
2024-11-18T21:11:32.517614+00:00 | 1,536,837,014,000 | bedddd88aaf2cfd78f5c94fcf81d71204e6635eb | 3 | {
"blob_id": "bedddd88aaf2cfd78f5c94fcf81d71204e6635eb",
"branch_name": "refs/heads/master",
"committer_date": 1536837014000,
"content_id": "ea3e0364c746b440cdeaa4607adbff26743add5a",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "2d91fa8375583ce83a44cb23dca31184f2375667",
"extension": "py"... | 3.3125 | stackv2 | """
Class for diffing xmls
"""
from __future__ import print_function
from copy import deepcopy
import os
from lxml import etree
from pyocnos.diff import get_element_path
from pyocnos.diff.block import Block
class XmlDiff(object):
"""
Class given two xml produces a diff
"""
def __init__(self, runnin... | 108 | 30.66 | 87 | 18 | 742 | python | [] | 0 | true | |
2024-11-18T21:11:32.562057+00:00 | 1,620,413,327,000 | 83032cc81d258ffd4f60b666760dca4b39284918 | 3 | {
"blob_id": "83032cc81d258ffd4f60b666760dca4b39284918",
"branch_name": "refs/heads/master",
"committer_date": 1620413327000,
"content_id": "d414cfc09776fa2aa4b323f598f03fe7ab1baadb",
"detected_licenses": [
"MIT"
],
"directory_id": "fce19b8d911b9ac4bd01f0c521282d739439aa5f",
"extension": "py",
"fi... | 3.078125 | stackv2 | from __future__ import division
try:
from collections.abc import Iterable
except:
from collections import Iterable
import warnings
from itertools import product
import numpy as np
# TODO: Incorporate @pablodecm's cover API.
__all__ = ["Cover", "CubicalCover"]
class Cover:
"""Helper class that defines ... | 285 | 33.39 | 142 | 20 | 2,562 | python | [] | 0 | true | |
2024-11-18T21:11:32.654974+00:00 | 1,460,248,300,000 | e0e05d376c82aafcffb8cfe66c9bacdc8d6ab7bc | 2 | {
"blob_id": "e0e05d376c82aafcffb8cfe66c9bacdc8d6ab7bc",
"branch_name": "refs/heads/master",
"committer_date": 1460248300000,
"content_id": "cc246d1c05a09fd52930bc826e199e0d97b07c28",
"detected_licenses": [
"MIT"
],
"directory_id": "af430e775b7989d912a4e5747d81e7418193bda0",
"extension": "py",
"fi... | 2.5 | stackv2 | import os
import threading
import time
class FileMonitor(threading.Thread):
def __init__(self, logfile, irc_client):
threading.Thread.__init__(self)
self._running = False
self._irc_client = irc_client
self._filehandle = None
self._logfile = logfile
def abort(self):
... | 65 | 29.6 | 105 | 17 | 426 | python | [] | 0 | true | |
2024-11-18T21:11:33.022930+00:00 | 1,555,011,841,000 | c50e226b6eb906f7dab0470b3eb14e5d99c5a31a | 3 | {
"blob_id": "c50e226b6eb906f7dab0470b3eb14e5d99c5a31a",
"branch_name": "refs/heads/master",
"committer_date": 1555011841000,
"content_id": "fed86b5845b935d3215bfcec703a5d9c1fc38f19",
"detected_licenses": [
"MIT"
],
"directory_id": "dc6f441e595aab2453ca4f0940b0f3386b37c01a",
"extension": "py",
"fi... | 2.765625 | stackv2 | file = open("test.txt", "r")
content=file.read()
measurements=content.split('\n')
class Log:
def __init__(self, alt,temp,acceleration,orientation,pressure):
#self.time = time
self.alt=float(alt)
self.temp = float(temp)
self.orientation=[float(i) for i in orientation]
self.a... | 27 | 22.48 | 99 | 15 | 186 | python | [] | 0 | true | |
2024-11-18T21:11:33.463162+00:00 | 1,637,250,094,000 | 57e9c92eab42f7d9890dda5a0609e7cf251e692d | 2 | {
"blob_id": "57e9c92eab42f7d9890dda5a0609e7cf251e692d",
"branch_name": "refs/heads/main",
"committer_date": 1637250094000,
"content_id": "da72ed455444b12c5cada60b39deec9c65ec584e",
"detected_licenses": [
"MIT"
],
"directory_id": "862d353b20881d1c334884394ffbb8e839aff7d3",
"extension": "py",
"file... | 2.359375 | stackv2 | import sys
import time
import requests
from API.AesDecrypt import decrypt, example
from API import UrlConstants
headers = {
"Host": "api.laomaoxs.com",
"Keep-Alive": "300",
"Connection": "Keep-Alive",
"Cache-Control": "no-cache",
"Accept-Encoding": "gzip",
"User-Agent": "Mozilla/5.0 (Windows NT... | 43 | 36.44 | 130 | 21 | 446 | python | [] | 0 | true | |
2024-11-18T21:11:34.033966+00:00 | 1,598,949,683,000 | d5a6f76edb7618bd539a65feb8fefb4d7df7980d | 2 | {
"blob_id": "d5a6f76edb7618bd539a65feb8fefb4d7df7980d",
"branch_name": "refs/heads/master",
"committer_date": 1598949683000,
"content_id": "0cf5d98c32b0dba2c93faffb13bc4e531c22a9ba",
"detected_licenses": [
"MIT"
],
"directory_id": "42593b78ca4414fdbb736ea4fd9ee7280d8c99be",
"extension": "py",
"fi... | 2.4375 | stackv2 | import torch
from torch.utils.data import Sampler, BatchSampler, Dataset, DataLoader
import numpy as np
import h5py
from alcokit.hdf.api import FeatureProxy
from alcokit.sources import ScoredSourceMixin, NPSource
class FlatSampler(Sampler):
"""
returns INT indices
"""
def __init__(self, score, shuffle... | 184 | 29.33 | 107 | 18 | 1,343 | python | [] | 0 | true | |
2024-11-18T21:11:34.083765+00:00 | 1,683,103,715,000 | 68a8297e7a989c1b8333bfb41f289dbb3c034a26 | 2 | {
"blob_id": "68a8297e7a989c1b8333bfb41f289dbb3c034a26",
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"content_id": "b439a0b9c43e55ce042d7184312e4083ff881bff",
"detected_licenses": [
"MIT"
],
"directory_id": "c0cfa8589bcdececd9eca58606e752df6110aaac",
"extension": "py",
"file... | 2.421875 | stackv2 | from logging import getLogger
from typing import List, Set
from gnosis.eth import EthereumClient
from ..models import EthereumEvent
from .transaction_scan_service import TransactionScanService
logger = getLogger(__name__)
class Erc20EventsServiceProvider:
def __new__(cls):
if not hasattr(cls, "instance... | 98 | 33.28 | 96 | 15 | 761 | python | [] | 0 | true | |
2024-11-18T21:11:34.198666+00:00 | 1,362,065,636,000 | 912a25c69fd1f444a86db912a924140e9872f5bd | 2 | {
"blob_id": "912a25c69fd1f444a86db912a924140e9872f5bd",
"branch_name": "refs/heads/master",
"committer_date": 1362065636000,
"content_id": "b9f07c2e48487bf272a52df6669a6ca7cee147e2",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "80b9b95464a68a4e86b035fc40047326a682444d",
"extension": "p... | 2.40625 | stackv2 | # -*- coding: utf-8 -*-
#
# Copyright (C) 2009-2011 Christopher Lenz, Dirkjan Ochtman, Matt Goodall
# Copyright (C) 2011 Alexander Shorin
# All rights reserved.
#
# This software is licensed as described in the file COPYING, which
# you should have received as part of this distribution.
#
# Reworked and adapted version... | 465 | 32.62 | 80 | 24 | 3,345 | python | [] | 0 | true | |
2024-11-18T21:11:34.313799+00:00 | 1,574,215,389,000 | 359eff8d4a4e25f98c48efe5e1cbfa2ce6af1ef4 | 3 | {
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"detected_licenses": [
"MIT"
],
"directory_id": "fac51719e067ee2a70934e3bffdc98802d6dbb35",
"extension": "py",
"fi... | 2.578125 | stackv2 | import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '../tools'))
import files
import fasta
import phylogeny
def main(argv):
lines = files.read_lines(argv[0])
tree = lines[0]
strings = fasta.read_from(lines[1:])
output = []
for r in phylogeny.reverse_substitutions(tr... | 29 | 25.1 | 123 | 14 | 204 | python | [] | 0 | true | |
2024-11-18T21:11:34.764419+00:00 | 1,412,720,483,000 | 00c665f7c947f899499afa7ad216bbf9b1a7ba5e | 3 | {
"blob_id": "00c665f7c947f899499afa7ad216bbf9b1a7ba5e",
"branch_name": "refs/heads/master",
"committer_date": 1412720483000,
"content_id": "ca9bc5a69282c42b9001a8cb302db91e6c7015e0",
"detected_licenses": [
"MIT"
],
"directory_id": "30ae29822f483c6a9206ddd8ceb7a9368db1907b",
"extension": "py",
"fi... | 3.15625 | stackv2 | """
Author: Huba Nagy
"""
import pygame
from common_util import *
class WorldBase(object):
"""
Base class for different types of grid based world,
this class is absolutely not intended for use outside this
package so don't use it, it will throw errors at you.
See the tiles and the voxels modules.
"""
def __i... | 175 | 20.22 | 79 | 15 | 899 | python | [] | 0 | true | |
2024-11-18T21:11:34.824925+00:00 | 1,629,131,007,000 | d98607a7d763a7f5e86406900155b16ad33ce306 | 3 | {
"blob_id": "d98607a7d763a7f5e86406900155b16ad33ce306",
"branch_name": "refs/heads/main",
"committer_date": 1629131007000,
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"detected_licenses": [
"MIT"
],
"directory_id": "618d02a9264a3004fe8bdf05a33344cf5eb43e7d",
"extension": "py",
"file... | 2.8125 | stackv2 | from dataclasses import dataclass
import re
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import GloVe
from torchtext.vocab import Vocab
import torch
from typing import List, Union
from synthetic_ra.data.constants import PAD_TOKEN
from synthetic_ra.data.constants import START_TOKEN
from synthetic... | 61 | 30.11 | 79 | 16 | 419 | python | [] | 0 | true | |
2024-11-18T21:11:34.915618+00:00 | 1,610,053,339,000 | 8fc2486928893d18b32ecd60ad07ef546171cdb2 | 3 | {
"blob_id": "8fc2486928893d18b32ecd60ad07ef546171cdb2",
"branch_name": "refs/heads/master",
"committer_date": 1610053339000,
"content_id": "6a755edf3114063845d3e790cf12d771644ad59b",
"detected_licenses": [
"MIT"
],
"directory_id": "ba65dbb459664e0ae48f5e4db7a0112c46a6b961",
"extension": "py",
"fi... | 2.5625 | stackv2 | from ..core import Core
from ..utils.exchangeversion import ExchangeVersion
class ResolveNames(Core):
'''Resolve names based on the provided UserConfiguration object.
This class is used as an alternative to Autodiscover since ResolveNames endpoint
is a common endpoint across all versions of Microsoft... | 87 | 38.05 | 118 | 25 | 696 | python | [] | 0 | true | |
2024-11-18T21:11:35.041224+00:00 | 1,649,299,522,000 | a328fbe0c82d400dddc4b90b54d344ca18b34ff7 | 3 | {
"blob_id": "a328fbe0c82d400dddc4b90b54d344ca18b34ff7",
"branch_name": "refs/heads/master",
"committer_date": 1649517510000,
"content_id": "8217242458be240d878855e74a1730d757795b32",
"detected_licenses": [
"MIT"
],
"directory_id": "2871296d9e86493599451001e71485893d322795",
"extension": "py",
"fi... | 2.8125 | stackv2 | from typing import Any, Dict
import torch
from torch import nn
import torchvision
class VisualBackbone(nn.Module):
r"""
Base class for all visual backbones. All child classes can simply inherit
from :class:`~torch.nn.Module`, however this is kept here for uniform
type annotations.
"""
def __... | 120 | 34.55 | 83 | 14 | 952 | python | [] | 0 | true | |
2024-11-18T21:11:35.101588+00:00 | 1,426,602,748,000 | 13b18a9425cd18488510b3fa264c79054239e577 | 3 | {
"blob_id": "13b18a9425cd18488510b3fa264c79054239e577",
"branch_name": "refs/heads/master",
"committer_date": 1426602748000,
"content_id": "24f41a75908db460cdeafb7a355e07b6e5344c00",
"detected_licenses": [
"MIT"
],
"directory_id": "3fa94ba1ed08f6e0ca0be8e57d333b26c62a9213",
"extension": "py",
"fi... | 3.375 | stackv2 | from __future__ import division
import nltk, re, pprint
from nltk.corpus import names
import random, string
# Fitur ML pada NLTK
# NaiveBayesClassifier
# EXPERIMEN 1
# Klasifikasi nama cewek atau cowok berdasarkan huruf akhirnya (+ tambah fitur baru sekalian)
def gender_features(word):
# Frequency of a..z
wo... | 74 | 27.69 | 148 | 11 | 564 | python | [] | 0 | true | |
2024-11-18T21:11:35.153620+00:00 | 1,593,786,928,000 | 30f5e3483bcc557df400ae9abbc27c56e2ddfc47 | 3 | {
"blob_id": "30f5e3483bcc557df400ae9abbc27c56e2ddfc47",
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"committer_date": 1593786928000,
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"detected_licenses": [
"MIT"
],
"directory_id": "b9c1e8a4f2b94b1036120339a616e773ce0e3526",
"extension": "py",
"fi... | 2.84375 | stackv2 | import speech_recognition as sr
import sys
r = sr.Recognizer()
filename = sys.argv[1]
# with sr.Microphone() as source:
# print("Say Something ---")
# audio = r.listen(source)
# print("Done")
with sr.AudioFile(filename) as source:
audio = r.listen(source)
# text = r.recognize_google(audio, language ... | 18 | 24.44 | 79 | 8 | 119 | python | [] | 0 | true | |
2024-11-18T21:11:35.253362+00:00 | 1,508,975,961,000 | 589852c73ed612b2e841485910c625fb70ed1034 | 3 | {
"blob_id": "589852c73ed612b2e841485910c625fb70ed1034",
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"content_id": "09f9432f0ae05d1ac68ab2d169ad0eae91dfc03a",
"detected_licenses": [
"MIT"
],
"directory_id": "e20cfd4f3a88e7a2eb0d7fb54e2aeda8fe66bc7d",
"extension": "py",
"fi... | 3.203125 | stackv2 | ''''
This is an helper class to the Chilean economic indicator http://mindicador.cl/
using the awesome requests library you can get uf, ivp, dolar, etc.
a simple example:
>> m = Mindicador()
>> m = m.get_uf()
>> uf
{'fecha': '2017-07-23T04:00:00.000Z', 'nombre': 'Unidad de fomento (UF)', 'codigo': 'uf',
'unidad_medid... | 84 | 26.21 | 96 | 14 | 641 | python | [] | 0 | true | |
2024-11-18T21:11:35.562461+00:00 | 1,682,430,535,000 | 880297d5ea3ce04da55591fac2ce3cee0023f115 | 3 | {
"blob_id": "880297d5ea3ce04da55591fac2ce3cee0023f115",
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"committer_date": 1682430535000,
"content_id": "ef4517833faed67999f5a1ad79dbf5408f0f2ad5",
"detected_licenses": [
"MIT"
],
"directory_id": "aabe934161efb731552e06c06d798f0e8c5553aa",
"extension": "py",
"fi... | 2.546875 | stackv2 | import vcr
from behave import given, when, then
from hamcrest import (assert_that, has_length, greater_than, is_not,
empty)
@given('I have a performance ID for event with ID "{event_id}"')
@vcr.use_cassette('fixtures/cassettes/availability-performances.yaml', record_mode='new_episodes')
def give... | 58 | 35.47 | 98 | 16 | 462 | python | [] | 0 | true | |
2024-11-18T21:11:36.224429+00:00 | 1,564,729,167,000 | 06b23218f7e52e5ead1610dd276f226ad88d67a4 | 3 | {
"blob_id": "06b23218f7e52e5ead1610dd276f226ad88d67a4",
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"committer_date": 1564729167000,
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"detected_licenses": [
"MIT"
],
"directory_id": "6e9546382c4c94cdf15a4df6c5d87a446763a125",
"extension": "py",
"fi... | 3.390625 | stackv2 | #!/bin/python3
import math
import os
import random
import re
import sys
"""
If N is odd, print Weird
If N is even and in the inclusive range of 2 to 5, print Not Weird
If N is even and in the inclusive range of 6 to 20, print Weird
If N is even and greater than , print Not Weird
"""
if __name__ == '_... | 22 | 21.86 | 70 | 11 | 166 | python | [] | 0 | true | |
2024-11-18T21:11:36.567015+00:00 | 1,515,206,626,000 | 0cc03dab10a92e4e23fa9b5609d0b3d872fce2a0 | 3 | {
"blob_id": "0cc03dab10a92e4e23fa9b5609d0b3d872fce2a0",
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"detected_licenses": [
"MIT"
],
"directory_id": "68621c990ca84dd3cf0226a12160358ad1321540",
"extension": "py",
"fi... | 3.484375 | stackv2 | from .Board import Board
from .LocalBoard import LocalBoard
from .Move import Move
class GlobalBoard(Board):
""" Represents the meta-board composed of a 3x3 grid of smaller tic-tac-toe boards
the optional 'board' parameter is used internally clone GlobalBoard objects
"""
def __init__(self, board=None... | 175 | 37.53 | 141 | 20 | 1,635 | python | [] | 0 | true | |
2024-11-18T21:11:36.746518+00:00 | 1,628,883,524,000 | 6ba30df9404784bebc2504a1d20466fba38d09d1 | 3 | {
"blob_id": "6ba30df9404784bebc2504a1d20466fba38d09d1",
"branch_name": "refs/heads/master",
"committer_date": 1628883524000,
"content_id": "f88fde5f372bfc7d2efa4bec3474deaff41b018b",
"detected_licenses": [
"BSD-2-Clause"
],
"directory_id": "f38a7681c0fb24f68a586909ddcd8b7af43f3565",
"extension": "p... | 3 | stackv2 | """Helpers for working with JWTs. Encapsulates the ``jwt`` lib."""
import copy
import datetime
import jwt
from lms.validation.authentication._exceptions import ExpiredJWTError, InvalidJWTError
__all__ = ["decode_jwt", "encode_jwt"]
def decode_jwt(jwt_str, secret):
"""
Return the payload decoded from ``jwt_... | 67 | 26.07 | 86 | 15 | 449 | python | [] | 0 | true | |
2024-11-18T21:11:36.934574+00:00 | 1,537,369,684,000 | 598cd11bdf31e4d96a3421d52d64d72f336efeef | 3 | {
"blob_id": "598cd11bdf31e4d96a3421d52d64d72f336efeef",
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"committer_date": 1537369684000,
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"detected_licenses": [
"MIT"
],
"directory_id": "9d716e3955d3c49ab67c88655fd0f010292c9ded",
"extension": "py",
"fi... | 2.75 | stackv2 | #!/usr/bin/env python3
import traceback
import better_exceptions
import numpy as np
import soundfile
import random
import time
import sys
from SpnAudioSlice import SpnAudioSlice
import SpoonModes
MODES=SpoonModes.SpoonSliceModes()
LOOP_MODES=SpoonModes.SpoonLoopModes()
from spoon_logging import D, L, W, E
class S... | 221 | 36.1 | 158 | 18 | 1,745 | python | [] | 0 | true | |
2024-11-18T21:11:37.563266+00:00 | 1,623,948,822,000 | 9cd3c0143965f8b590bca9773f0329dcf2d773e6 | 2 | {
"blob_id": "9cd3c0143965f8b590bca9773f0329dcf2d773e6",
"branch_name": "refs/heads/main",
"committer_date": 1623948822000,
"content_id": "266134b9db04dda8f0c4e77d4e0e01107caf1c08",
"detected_licenses": [
"MIT"
],
"directory_id": "68fb934e899d24e13df4022e0955dce17b5fa13e",
"extension": "py",
"file... | 2.359375 | stackv2 | #!/usr/bin/env python
#--------Include modules---------------
import rospy
import tf
from numpy import *
import numpy as np
# Node----------------------------------------------
def node():
rospy.init_node('vrpn_inital_poses', anonymous=False)
rate = rospy.Rate(100)
listener = tf.TransformListener()
listener.w... | 47 | 20.34 | 84 | 12 | 282 | python | [] | 0 | true | |
2024-11-18T21:11:37.733332+00:00 | 1,521,466,845,000 | da5c90083e360dc00de4cc3920cc5dfce9acd7bb | 3 | {
"blob_id": "da5c90083e360dc00de4cc3920cc5dfce9acd7bb",
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"committer_date": 1521466845000,
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"detected_licenses": [
"MIT"
],
"directory_id": "a4ae4271616fbac571f47e4afe08e70ebcf7ae18",
"extension": "py",
"fi... | 2.8125 | stackv2 | import math
bigR = 360 ## radius of entire plot
c = 10 ## number of emotion categories
t = 4 ## number of intensity levels per category (minus 1)
p = 0.75 ## proportion of (circle / next biggest circle)
g = 0.01 ## gap between category sectors [(gap size) / (circle size * c)]
s = 25 ## space between same intensity lev... | 181 | 28.9 | 112 | 15 | 1,828 | python | [] | 0 | true | |
2024-11-18T21:11:37.793960+00:00 | 1,584,127,637,000 | cf1c45b41d1a0394f0226d5b90dbd67896a94275 | 3 | {
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"content_id": "8e87f35a9e26b474993e31bd1687016908c01c8a",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "b645d15b85b02197c4f5096fb687e026c8e62ae9",
"extension": "py"... | 2.703125 | stackv2 | #!/usr/bin/python
#-*- coding: utf-8 -*-
"""
This script will take all the images from inputfile.txt and classify according the neural net.
It will create an output file that have the path from the image, the correct label, and the predict label.
"""
import logging
logger = logging.getLogger(__name__)
logging.basicConf... | 169 | 34.66 | 115 | 19 | 1,482 | python | [] | 0 | true | |
2024-11-18T21:11:38.033141+00:00 | 1,472,644,243,000 | ac8f3062745579ca894a04654dbbb911454d5f4e | 3 | {
"blob_id": "ac8f3062745579ca894a04654dbbb911454d5f4e",
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"content_id": "7df234e61f79ceae9c76df87421bae6a1ae7fb73",
"detected_licenses": [
"MIT"
],
"directory_id": "62eacefdda6d278335c43c86447e5e6c50d19512",
"extension": "py",
"fi... | 3.328125 | stackv2 | import pandas as pd
from sklearn.feature_selection import SelectKBest, f_classif
def select_kbest_clf(data_frame, target, k=4):
"""
Selecting K-Best features for classification
:param data_frame: A pandas dataFrame with the training data
:param target: target variable name in DataFrame
:param k: de... | 32 | 36.72 | 78 | 11 | 308 | python | [] | 0 | true | |
2024-11-18T21:11:38.390978+00:00 | 1,407,147,272,000 | 5506bf262c357c87a21e8706a5535023af1e4913 | 3 | {
"blob_id": "5506bf262c357c87a21e8706a5535023af1e4913",
"branch_name": "refs/heads/master",
"committer_date": 1407147272000,
"content_id": "f22da3368276f7496712327c094e4e634cc42c8c",
"detected_licenses": [
"BSD-3-Clause",
"BSD-2-Clause"
],
"directory_id": "ba084662496080c9366f5214f45a6d26d563d020... | 2.578125 | stackv2 | # -*- coding: utf-8 -*-
from __future__ import (
division, print_function, absolute_import, unicode_literals
)
import tempfile
import logging
import os
from operator import itemgetter
import click
from lxml import etree
from __init__ import __version__
log = logging.getLogger(__name__)
class Project(object):... | 143 | 34.03 | 96 | 17 | 1,088 | python | [] | 0 | true | |
2024-11-18T21:11:38.581420+00:00 | 1,635,287,554,000 | 8958fc2fccdfdd3bf5a556f1f346fe82fc34d26d | 4 | {
"blob_id": "8958fc2fccdfdd3bf5a556f1f346fe82fc34d26d",
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"committer_date": 1635287554000,
"content_id": "1aed47b79474e3cba7413e5a4b23adf7117fe484",
"detected_licenses": [
"MIT"
],
"directory_id": "21fec19cb8f74885cf8b59e7b07d1cd659735f6c",
"extension": "py",
"fi... | 3.5 | stackv2 | "Message and Entry"
'''
The Message and Entry widgets allow for display and input of simple text. Both are
essentially functional subsets of the Text widget; Text can do everything
Message and Entry can, but not vice versa.
'''
"Message"
'''
The Message widget is simply a place to display text.
Message splits up long ... | 20 | 30.7 | 82 | 8 | 143 | python | [] | 0 | true | |
2024-11-18T21:11:38.841556+00:00 | 1,642,189,002,000 | 95cad73ca34bc933ee28fb31e6b52608ca76a0fe | 2 | {
"blob_id": "95cad73ca34bc933ee28fb31e6b52608ca76a0fe",
"branch_name": "refs/heads/master",
"committer_date": 1642189002000,
"content_id": "514ac85e76976f7f86b249d3b50983e6d719d648",
"detected_licenses": [
"MIT"
],
"directory_id": "505d1db79df0408d030f9c005447fbc17f4e04c9",
"extension": "py",
"fi... | 2.421875 | stackv2 | from ctypes import c_uint32, addressof
from bearlibterminal import terminal as blt
import pickle
# TODO: REFACTOR THESE NAMES?? WTF, PUT THEM UNDER A CLASS OR SOMETHING
openables_names = ["gate",
"gate (open)",
"gate (closed)",
"gate (locked)",
"door",
"... | 248 | 41.29 | 119 | 15 | 3,351 | python | [] | 0 | true | |
2024-11-18T21:11:38.895098+00:00 | 1,575,642,118,000 | 98b2b776bd123494a0128934559e5dda97888035 | 2 | {
"blob_id": "98b2b776bd123494a0128934559e5dda97888035",
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"committer_date": 1575642118000,
"content_id": "75ad61143498b0eb8e1b0ac7457b441f35899739",
"detected_licenses": [
"MIT"
],
"directory_id": "2fa640b59bd854c67292cb9687ba9c71704a2307",
"extension": "py",
"fi... | 2.359375 | stackv2 |
from django import forms
from django.contrib.auth.models import User
from django.contrib.auth.forms import UserCreationForm
from .models import Telefone, Endereco
##### Formulário para cadastro de usuário #####
class PessoaUserForm(UserCreationForm):
def __init__(self, *args, **kwargs):
super(PessoaUserF... | 84 | 46.86 | 117 | 12 | 968 | python | [] | 0 | true | |
2024-11-18T21:11:39.041401+00:00 | 1,624,247,135,000 | bcf5db1f0b6c39ccb53d02c370e5258ba1d3b80c | 3 | {
"blob_id": "bcf5db1f0b6c39ccb53d02c370e5258ba1d3b80c",
"branch_name": "refs/heads/master",
"committer_date": 1624247135000,
"content_id": "d181e18fe397f888eb206b19027b1e1f0d979f2c",
"detected_licenses": [
"MIT"
],
"directory_id": "7a0f78930ed6c72ac57a41d548ed2bc37ee351d4",
"extension": "py",
"fi... | 2.609375 | stackv2 | import logging
from flask import request, abort, Blueprint
from nb_processor_service import NBService
from invalid_nb_error import InvalidNotebookError
from no_content_nb_error import NoContentNotebookError
nb_controller_blue_print = Blueprint("nb_processor_controller", __name__)
@nb_controller_blue_print.route('/... | 39 | 31.41 | 89 | 11 | 270 | python | [{"finding_id": "codeql_py/log-injection_7edbd42f04da266d_f6adfc5c", "tool_name": "codeql", "rule_id": "py/log-injection", "finding_type": "path-problem", "severity": "medium", "confidence": "medium", "message": "This log entry depends on a [user-provided value](1).", "remediation": "", "location": {"file_path": "unkno... | 1 | true | |
2024-11-18T21:11:39.384528+00:00 | 1,617,231,158,000 | c023df1aefe07d91824c594785695a3fdcc39955 | 4 | {
"blob_id": "c023df1aefe07d91824c594785695a3fdcc39955",
"branch_name": "refs/heads/main",
"committer_date": 1617231158000,
"content_id": "d6acee02ba283ee3039e444529cdfa2ef9f8ab88",
"detected_licenses": [
"MIT"
],
"directory_id": "5c17fba3f5d0845ca8bd972ac62f484c17a63406",
"extension": "py",
"file... | 4.46875 | stackv2 | # Exercício 037
print('-' * 15)
print('''Digite um número inteiro qualquer e
logo em seguida digite: 1 para transformar em binário,
2 para transformar em octal e 3 para trans-
formar em hexadecimal.''')
print('-' * 15)
número = int(input('Digite um número inteiro: '))
print('Escolha uma das bases pra conversão:')
prin... | 28 | 34.89 | 68 | 12 | 282 | python | [] | 0 | true | |
2024-11-18T21:11:39.447882+00:00 | 1,599,758,226,000 | 3e72848271dabd51d6b6868986468e17369a5a33 | 3 | {
"blob_id": "3e72848271dabd51d6b6868986468e17369a5a33",
"branch_name": "refs/heads/master",
"committer_date": 1599758226000,
"content_id": "c9116bc631ba3d5adc7ce9d9ea3281dfc5d27324",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "533922c205e8298c5ebcc0ba31dc277de46a1017",
"extension": "p... | 2.890625 | stackv2 | from kivymd.app import MDApp
from kivy.lang import Builder
from kivymd.uix.list import OneLineListItem
list_helper = """
Screen:
ScrollView:
MDList:
id: container
"""
class DemoApp(MDApp):
def build(self):
screen = Builder.load_string(list_helper)
return screen
def ... | 26 | 18.12 | 60 | 17 | 119 | python | [] | 0 | true | |
2024-11-18T21:11:39.553696+00:00 | 1,592,994,217,000 | 1b0afd34e6b1c41b6d08d562077c2917b054e99e | 3 | {
"blob_id": "1b0afd34e6b1c41b6d08d562077c2917b054e99e",
"branch_name": "refs/heads/master",
"committer_date": 1592994217000,
"content_id": "0a3bfe54f7b2dab8aecd3da025ea436b7f1db45a",
"detected_licenses": [
"MIT"
],
"directory_id": "e0950322df54d3276fa1b6e4dc60237f27c4c9f4",
"extension": "py",
"fi... | 3.1875 | stackv2 | """
The Furniture problem from EngSci391 for the PuLP Modeller
Author: Dr Stuart Mitchell 2007
"""
from pulp import *
Chairs = ["A","B"]
costs = {"A":100,
"B":150}
Resources = ["Lathe","Polisher"]
capacity = {"Lathe" : 40,
"Polisher" : 48}
activity = [ #Chairs
#A B
... | 31 | 32.68 | 77 | 14 | 304 | python | [] | 0 | true | |
2024-11-18T21:11:39.717192+00:00 | 1,524,175,833,000 | 57a79ecdb0e6b4fc4da4340b89db2aea00f3883f | 3 | {
"blob_id": "57a79ecdb0e6b4fc4da4340b89db2aea00f3883f",
"branch_name": "refs/heads/master",
"committer_date": 1524175833000,
"content_id": "ace0d27036c24db7798c53ed57cf244626d9cb2e",
"detected_licenses": [
"MIT"
],
"directory_id": "a3edfb7d82206fef524516fd9c6056a005d41c59",
"extension": "py",
"fi... | 3.359375 | stackv2 | #!/usr/bin/python
import sys
import subprocess
#function for taking and storing the raspberry camera module photo
def take_photo(filename) :
process = subprocess.call(['raspistill', '-o' , filename])
if process == 0:
print("Photo taken and saved as " + filename + ".")
else:
print("An error occured while tryin... | 41 | 28.88 | 83 | 14 | 315 | python | [] | 0 | true | |
2024-11-18T21:11:40.043718+00:00 | 1,602,489,728,000 | 41ce94e5cef150edd67933965454b6f6b73ad96e | 4 | {
"blob_id": "41ce94e5cef150edd67933965454b6f6b73ad96e",
"branch_name": "refs/heads/master",
"committer_date": 1602489728000,
"content_id": "2a26461a0ace9c0d21b5017fce916c6b2af66e43",
"detected_licenses": [
"MIT"
],
"directory_id": "83d16752f129150f5e862598d6155782b33055b9",
"extension": "py",
"fi... | 3.734375 | stackv2 | class User:
'''
Class that generates new instances of user
'''
user_list = []
def __init__(self, first_name, last_name, email, username, password):
'''
method that initiates instances of User class
'''
self.first_name = first_name
self.last_name = last_name
... | 50 | 27.36 | 114 | 13 | 281 | python | [] | 0 | true | |
2024-11-18T21:11:40.187204+00:00 | 1,625,016,454,000 | 2bf0a8a5fda70e550ee82344226cb6f75dfd87de | 3 | {
"blob_id": "2bf0a8a5fda70e550ee82344226cb6f75dfd87de",
"branch_name": "refs/heads/main",
"committer_date": 1625016454000,
"content_id": "980adff6fdb4d89a801c4e0c8d293ceb0223b5a8",
"detected_licenses": [
"MIT"
],
"directory_id": "3e8adfc05b03f619761595068f4fd75bdd470859",
"extension": "py",
"file... | 2.8125 | stackv2 | import requests
import pandas as pd
from bs4 import BeautifulSoup
from goose3 import Goose
# Header para o scraper
header = {'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'}
# Links dos jornais
link_correio_do_povo = "https://www... | 107 | 33.35 | 148 | 18 | 1,012 | python | [] | 0 | true | |
2024-11-18T21:11:40.424000+00:00 | 1,692,372,777,000 | d29cfbd41d89e5fcfef8abd9b61bccade27ce5df | 3 | {
"blob_id": "d29cfbd41d89e5fcfef8abd9b61bccade27ce5df",
"branch_name": "refs/heads/master",
"committer_date": 1692372777000,
"content_id": "994bfea790705ea2b1795de51db9a51799039307",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "198d9c17c1564dcec45af536af2c54ab2423e398",
"extension": "py"... | 2.640625 | stackv2 | # PyZX - Python library for quantum circuit rewriting
# and optimization using the ZX-calculus
# Copyright (C) 2018 - Aleks Kissinger and John van de Wetering
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a ... | 374 | 37.38 | 120 | 23 | 3,614 | python | [] | 0 | true | |
2024-11-18T21:11:40.471624+00:00 | 1,623,557,659,000 | 3e7e6e483c7123571810f63c26df6bc308960c2e | 3 | {
"blob_id": "3e7e6e483c7123571810f63c26df6bc308960c2e",
"branch_name": "refs/heads/master",
"committer_date": 1623557659000,
"content_id": "7e3804be1fccdb8d5e0ea14c1d193035d24dd63a",
"detected_licenses": [
"MIT"
],
"directory_id": "0df09d6f241c045845f33465842d037da6b34a94",
"extension": "py",
"fi... | 3.4375 | stackv2 | '''
Author: SeoK106
This is the code to practice the concept of data science through python and scikit-learn.
Especially It focused on classification, evaluation, and clustering.
'''
import numpy as np
from sklearn import tree,svm,linear_model,neighbors
from sklearn.metrics import confusion_matrix,accuracy_score,preci... | 203 | 31.56 | 178 | 14 | 1,683 | python | [] | 0 | true |
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