repo_name stringclasses 400
values | branch_name stringclasses 4
values | file_content stringlengths 16 72.5k | language stringclasses 1
value | num_lines int64 1 1.66k | avg_line_length float64 6 85 | max_line_length int64 9 949 | path stringlengths 5 103 | alphanum_fraction float64 0.29 0.89 | alpha_fraction float64 0.27 0.89 |
|---|---|---|---|---|---|---|---|---|---|
shuishen112/pairwise-rnn | refs/heads/master |
import data_helper
import time
import datetime
import os
import tensorflow as tf
import numpy as np
import evaluation
now = int(time.time())
timeArray = time.localtime(now)
timeStamp = time.strftime("%Y%m%d%H%M%S", timeArray)
timeDay = time.strftime("%Y%m%d", timeArray)
print (timeStamp)
def main(args):
ar... | Python | 116 | 36.043102 | 159 | /main.py | 0.553088 | 0.546842 |
shuishen112/pairwise-rnn | refs/heads/master | class Singleton(object):
__instance=None
def __init__(self):
pass
def getInstance(self):
if Singleton.__instance is None:
# Singleton.__instance=object.__new__(cls,*args,**kwd)
Singleton.__instance=self.get_test_flag()
print("build FLAGS over")
ret... | Python | 197 | 60.426395 | 121 | /config.py | 0.627396 | 0.597571 |
shuishen112/pairwise-rnn | refs/heads/master | from tensorflow import flags
import tensorflow as tf
from config import Singleton
import data_helper
import datetime,os
import models
import numpy as np
import evaluation
import sys
import logging
import time
now = int(time.time())
timeArray = time.localtime(now)
timeStamp = time.strftime("%Y%m%d%H%M%S", timeArray)... | Python | 164 | 35.829269 | 161 | /run.py | 0.628704 | 0.62142 |
shuishen112/pairwise-rnn | refs/heads/master | #coding:utf-8
import tensorflow as tf
import numpy as np
from tensorflow.contrib import rnn
import models.blocks as blocks
# model_type :apn or qacnn
class QA_CNN_extend(object):
# def __init__(self,max_input_left,max_input_right,batch_size,vocab_size,embedding_size,filter_sizes,num_filters,hidden_size,
# dro... | Python | 381 | 46.682415 | 147 | /models/QA_CNN_pairwise.py | 0.592162 | 0.5751 |
shuishen112/pairwise-rnn | refs/heads/master | from my.general import flatten, reconstruct, add_wd, exp_mask
import numpy as np
import tensorflow as tf
_BIAS_VARIABLE_NAME = "bias"
_WEIGHTS_VARIABLE_NAME = "kernel"
def linear(args, output_size, bias, bias_start=0.0, scope=None, squeeze=False, wd=0.0, input_keep_prob=1.0,
is_train=None):#, name_w='',... | Python | 160 | 36.712502 | 116 | /models/my/nn.py | 0.572754 | 0.558005 |
shuishen112/pairwise-rnn | refs/heads/master | from .QA_CNN_pairwise import QA_CNN_extend as CNN
from .QA_RNN_pairwise import QA_RNN_extend as RNN
from .QA_CNN_quantum_pairwise import QA_CNN_extend as QCNN
def setup(opt):
if opt["model_name"]=="cnn":
model=CNN(opt)
elif opt["model_name"]=="rnn":
model=RNN(opt)
elif opt['model_name']=='qcnn':
model=QCNN(opt... | Python | 14 | 25.642857 | 58 | /models/__init__.py | 0.691689 | 0.689008 |
shuishen112/pairwise-rnn | refs/heads/master | # -*- coding: utf-8 -*-
from tensorflow import flags
import tensorflow as tf
from config import Singleton
import data_helper
import datetime
import os
import models
import numpy as np
import evaluation
from data_helper import log_time_delta,getLogger
logger=getLogger()
args = Singleton().get_rnn_flag()
#args... | Python | 114 | 31.622807 | 92 | /test.py | 0.651019 | 0.64324 |
shuishen112/pairwise-rnn | refs/heads/master | #-*- coding:utf-8 -*-
import os
import numpy as np
import tensorflow as tf
import string
from collections import Counter
import pandas as pd
from tqdm import tqdm
import random
from functools import wraps
import time
import pickle
def log_time_delta(func):
@wraps(func)
def _deco(*args, **kwargs):
star... | Python | 363 | 33.487602 | 167 | /data_helper.py | 0.582389 | 0.574804 |
pablor0mero/Placester_Test_Pablo_Romero | refs/heads/master | # For this solution I'm using TextBlob, using it's integration with WordNet.
from textblob import TextBlob
from textblob import Word
from textblob.wordnet import VERB
import nltk
import os
import sys
import re
import json
results = { "results" : [] }
#Override NLTK data path to use the one I uploaded in the folder
d... | Python | 71 | 44.929577 | 157 | /main.py | 0.670813 | 0.666869 |
GabinCleaver/Auto_Discord_Bump | refs/heads/main | import requests
import time
token = "TOKEN"
headers = {
'User-Agent' : 'Mozilla/5.0 (Windows; U; Windows NT 5.1; en-US; rv:1.7.12) Gecko/20050915 Firefox/1.0.7',
'Authorization' : token
}
id = input(f"[?] Salon ID: ")
print("")
while True:
requests.post(
f"https://discord.com/api/... | Python | 21 | 20.809525 | 109 | /autobump.py | 0.561845 | 0.51153 |
altopalido/yelp_python | refs/heads/master | # Madis Settings
MADIS_PATH='/Users/alexiatopalidou/Desktop/erg/madis/src'
# Webserver Settings
# IMPORTANT: The port must be available.
web_port = 9090 # must be integer (this is wrong:'9090')
| Python | 6 | 31.666666 | 57 | /yelp_python/settings.py | 0.75 | 0.709184 |
altopalido/yelp_python | refs/heads/master | # ----- CONFIGURE YOUR EDITOR TO USE 4 SPACES PER TAB ----- #
import settings
import sys
def connection():
''' User this function to create your connections '''
import sys
sys.path.append(settings.MADIS_PATH)
import madis
con = madis.functions.Connection('/Users/alexiatopalidou/Desktop/erg/yelp_p... | Python | 206 | 26.485437 | 361 | /yelp_python/app.py | 0.622395 | 0.612681 |
smellycats/SX-CarRecgServer | refs/heads/master | from car_recg import app
from car_recg.recg_ser import RecgServer
from ini_conf import MyIni
if __name__ == '__main__':
rs = RecgServer()
rs.main()
my_ini = MyIni()
sys_ini = my_ini.get_sys_conf()
app.config['THREADS'] = sys_ini['threads']
app.config['MAXSIZE'] = sys_ini['threads'] * 16
app... | Python | 14 | 27.857143 | 64 | /run.py | 0.613861 | 0.59901 |
smellycats/SX-CarRecgServer | refs/heads/master | # -*- coding: utf-8 -*-
import Queue
class Config(object):
# 密码 string
SECRET_KEY = 'hellokitty'
# 服务器名称 string
HEADER_SERVER = 'SX-CarRecgServer'
# 加密次数 int
ROUNDS = 123456
# token生存周期，默认1小时 int
EXPIRES = 7200
# 数据库连接 string
SQLALCHEMY_DATABASE_URI = 'mysql://root:root@127.0.0... | Python | 53 | 17.905661 | 69 | /car_recg/config.py | 0.593812 | 0.56986 |
smellycats/SX-CarRecgServer | refs/heads/master | # -*- coding: utf-8 -*-
import os
import Queue
import random
from functools import wraps
import arrow
from flask import g, request
from flask_restful import reqparse, Resource
from passlib.hash import sha256_crypt
from itsdangerous import TimedJSONWebSignatureSerializer as Serializer
from car_recg import app, db, api... | Python | 176 | 30.78409 | 134 | /car_recg/views.py | 0.558634 | 0.540222 |
josemiche11/reversebycondition | refs/heads/master | '''
Input- zoho123
Output- ohoz123
'''
char= input("Enter the string: ")
char2= list(char)
num= "1234567890"
list1= [0]*len(char)
list2=[]
for i in range(len(char)):
if char2[i] not in num:
list2.append( char2.index( char2[i]))
char2[i]= "*"
list2.reverse()
k=0
for j in range( len(c... | Python | 26 | 17.461538 | 45 | /reversebycondition.py | 0.539526 | 0.472332 |
Lasyin/batch-resize | refs/heads/master | import os
import sys
import argparse
from PIL import Image # From Pillow (pip install Pillow)
def resize_photos(dir, new_x, new_y, scale):
if(not os.path.exists(dir)):
# if not in full path format (/usrers/user/....)
# check if path is in local format (folder is in current working directory)
... | Python | 73 | 43.109589 | 179 | /batch_resize.py | 0.554969 | 0.551863 |
snehG0205/Twitter_Mining | refs/heads/master | import tweepy
import csv
import pandas as pd
from textblob import TextBlob
import matplotlib.pyplot as plt
####input your credentials here
consumer_key = 'FgCG8zcxF4oINeuAqUYzOw9xh'
consumer_secret = 'SrSu7WhrYUpMZnHw7a5ui92rUA1n2jXNoZVb3nJ5wEsXC5xlN9'
access_token = '975924102190874624-uk5zGlYRwItkj7pZO2m89NefRm5DFLg... | Python | 104 | 28.73077 | 157 | /tweepy_tester.py | 0.66796 | 0.655674 |
snehG0205/Twitter_Mining | refs/heads/master | import tweepy
import csv
import pandas as pd
# keys and tokens from the Twitter Dev Console
consumer_key = 'FgCG8zcxF4oINeuAqUYzOw9xh'
consumer_secret = 'SrSu7WhrYUpMZnHw7a5ui92rUA1n2jXNoZVb3nJ5wEsXC5xlN9'
access_token = '975924102190874624-uk5zGlYRwItkj7pZO2m89NefRm5DFLg'
access_token_secret = 'ChvmTjG8hl61xUrXkk3Ad... | Python | 52 | 32.53846 | 146 | /twitter1.py | 0.729513 | 0.716332 |
snehG0205/Twitter_Mining | refs/heads/master | import csv
csvFile = open('res.csv', 'w+') | Python | 2 | 20.5 | 31 | /tester.py | 0.642857 | 0.642857 |
snehG0205/Twitter_Mining | refs/heads/master | from test import mining
tag = "#WednesdayWisdom"
limit = "10"
sen_list = mining(tag,int(limit))
print(sen_list) | Python | 5 | 21.4 | 33 | /Twitter-Flask/untitled.py | 0.72973 | 0.711712 |
snehG0205/Twitter_Mining | refs/heads/master | from flask import Flask, render_template, request
from test import mining
app = Flask(__name__)
@app.route('/')
def index():
return render_template('hello.html')
@app.route('/', methods=['GET', 'POST'])
def submit():
if request.method == 'POST':
print (request.form) # debug line, see data printed below
tag = r... | Python | 24 | 25.416666 | 124 | /Twitter-Flask/app.py | 0.631912 | 0.627172 |
snehG0205/Twitter_Mining | refs/heads/master | #!/usr/bin/env python
print ("some output")
return "hello" | Python | 4 | 14 | 21 | /Twitter-Flask/hello.py | 0.694915 | 0.694915 |
snehG0205/Twitter_Mining | refs/heads/master | import matplotlib.pyplot as plt
# Data to plot
labels = 'Neutral', 'Positive', 'Negative'
sizes = [20, 40, 40]
colors = ['lightskyblue','yellowgreen', 'lightcoral']
explode = (0.0, 0, 0) # explode 1st slice
# Plot
plt.pie(sizes, explode=explode, labels=labels, colors=colors,
autopct='%1.1f%%', shadow=True,... | Python | 16 | 27.125 | 61 | /piPlotter.py | 0.685969 | 0.650334 |
nopple/ctf | refs/heads/master | #!/usr/bin/env python
import socket, subprocess, sys
from struct import pack, unpack
global scenes
global officers
scenes = {}
officers = {}
remote = len(sys.argv) > 1
PORT = 8888
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
if remote:
HOST = "dosfun4u_5d712652e1d06a362f7fc6d12d66755b.2014.shallweplayaga... | Python | 124 | 25.620968 | 131 | /dosfun4u/pwn.py | 0.668585 | 0.624962 |
nopple/ctf | refs/heads/master | #!/usr/bin/env python
import socket
from struct import pack, unpack
DEBUG = False
server = "shitsco_c8b1aa31679e945ee64bde1bdb19d035.2014.shallweplayaga.me"
server = "127.0.0.1"
port = 31337
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((server, port))
s.settimeout(30)
def recv_until(s, pattern):
... | Python | 71 | 25.830986 | 127 | /shitsco/pwn.py | 0.669816 | 0.618898 |
phu-bui/Nhan_dien_bien_bao_giao_thong | refs/heads/master | import tkinter as tk
from tkinter import filedialog
from tkinter import *
from PIL import Image, ImageTk
import numpy
from keras.models import load_model
model = load_model('BienBao.h5')
class_name = {
1:'Speed limit (20km/h)',
2:'Speed limit (30km/h)',
3:'Speed limit (50km/h)',
4:'Speed limit (60km/h)'... | Python | 103 | 30.747572 | 107 | /main.py | 0.631386 | 0.579688 |
Jerin-Alisha/Python-Code-Assessment | refs/heads/master | def returnSum(dict):
sum=0
for i in dict:
sum=sum+dict[i]
return sum
dict={'Rick':85,'Amit':42,'George':53,'Tanya':60,'Linda':35}
print 'sum:', returnSum(dict)
| Python | 7 | 24.714285 | 60 | /Dictionary with function.py | 0.582888 | 0.524064 |
Jerin-Alisha/Python-Code-Assessment | refs/heads/master | n=int(input("enter the numbers u want to print:"))
for i in range(1,n+1):
if(i%3==0):
print ('Fizz')
continue
elif(i%5==0):
print ('Buzz')
continue
print i
| Python | 9 | 21.222221 | 50 | /FizzBuzz.py | 0.46789 | 0.440367 |
Jerin-Alisha/Python-Code-Assessment | refs/heads/master | def switch(on_strike):
players = {1,2}
return list(players.difference(set([on_strike])))[0]
def get_player(previous_score, previous_player, previous_bowl_number):
if previous_score%2 == 0 and (previous_bowl_number%6 !=0 or previous_bowl_number ==0):
player = previous_player
elif p... | Python | 36 | 28.611111 | 90 | /Cricket Match Player Score.py | 0.646098 | 0.61343 |
Jerin-Alisha/Python-Code-Assessment | refs/heads/master | arr=[1,2,3,5,8,4,7,9,1,4,12,5,6,5,2,1,0,8,1]
a = [None] * len(arr);
visited = 0;
for i in range(0, len(arr)):
count = 1;
for j in range(i+1, len(arr)):
if(arr[i] == arr[j]):
count = count + 1;
a[j] = visited;
if(a[i] != visited):
a[i]... | Python | 14 | 31.285715 | 69 | /repeat.py | 0.408511 | 0.353191 |
TheDinner22/lightning-sim | refs/heads/main | # represent the "board" in code
# dependencies
import random
class Board:
def __init__(self, width=10):
self.width = width
self.height = width * 2
self.WALL_CHANCE = .25
self.FLOOR_CHANCE = .15
# create the grid
self.create_random_grid()
def create_random_gri... | Python | 191 | 35.329842 | 97 | /lib/board.py | 0.51333 | 0.506269 |
TheDinner22/lightning-sim | refs/heads/main | # use pygame to show the board on a window
# dependencies
import pygame, random
class Window:
def __init__(self, board):
# init py game
pygame.init()
# width height
self.WIDTH = 600
self.HEIGHT = 600
# diffenet display modes
self.display_one = False
... | Python | 159 | 28.754717 | 87 | /lib/window.py | 0.501057 | 0.487104 |
TheDinner22/lightning-sim | refs/heads/main | # this could and will be better i just needed to make it here as a
# proof of concept but it will be online and better later
import os, sys
BASE_PATH = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # adds project dir to places it looks for the modules
sys.path.append(BASE_PATH)
from lib.board import B... | Python | 15 | 25.4 | 125 | /main.py | 0.736709 | 0.736709 |
JoeChan/openbgp | refs/heads/master | # Copyright 2015 Cisco Systems, Inc.
# All rights reserved.
#
# 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
#
# Unless requi... | Python | 267 | 24.932585 | 80 | /openbgp/common/constants.py | 0.69103 | 0.617362 |
Glitchfix/TransposeMatrixIndorse | refs/heads/master | from flask import Flask, render_template, request, jsonify
from flask_cors import CORS
import json
import numpy as np
app = Flask(__name__)
CORS(app)
@app.route('/transpose', methods=["POST"])
def homepage():
data = request.json
result = None
error = ""
try:
mat = data["matrix"]
mat =... | Python | 29 | 19.724138 | 58 | /server.py | 0.579035 | 0.579035 |
shlsheth263/malware-detection-using-ANN | refs/heads/master | from tkinter import *
from tkinter import ttk
from tkinter import filedialog
import test_python3
class Root(Tk):
def __init__(self):
super(Root, self).__init__()
self.title("Malware Detection")
self.minsize(500, 300)
self.labelFrame = ttk.LabelFrame(self, text = " Open File")
... | Python | 35 | 24.685715 | 99 | /python/gui.py | 0.620267 | 0.600223 |
shlsheth263/malware-detection-using-ANN | refs/heads/master | #!/usr/bin/env python
import sys
import time
import pandas as pd
import pepy
import binascii
import numpy as np
from hashlib import md5
import sklearn
from tkinter import *
from tkinter import ttk
from tkinter import filedialog
from tensorflow.keras.models import load_model
def test(p):
exe = {}
print("Signature... | Python | 386 | 35.992229 | 107 | /python/test_python3_cli.py~ | 0.655182 | 0.439846 |
Sssssbo/SDCNet | refs/heads/master | import numpy as np
import os
import torch
import torch.nn.functional as F
from PIL import Image
from torch.autograd import Variable
from torchvision import transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import pandas as pd
from tqdm import tqdm
from misc import check_mkdir, AvgMete... | Python | 226 | 45.637169 | 227 | /infer_SDCNet.py | 0.54592 | 0.509962 |
Sssssbo/SDCNet | refs/heads/master | from .resnext101 import ResNeXt101
| Python | 1 | 34 | 34 | /resnext/__init__.py | 0.857143 | 0.685714 |
Sssssbo/SDCNet | refs/heads/master | import numpy as np
import os
import pylab as pl
#import pydensecrf.densecrf as dcrf
class AvgMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
... | Python | 227 | 25.356829 | 110 | /misc.py | 0.56343 | 0.531673 |
Sssssbo/SDCNet | refs/heads/master | from .make_model import ResNet50, ResNet50_BIN, ResNet50_LowIN | Python | 1 | 62 | 62 | /resnet/__init__.py | 0.822581 | 0.725806 |
Sssssbo/SDCNet | refs/heads/master | from .resnet import ResNet, BasicBlock, Bottleneck
import torch
from torch import nn
from .config import resnet50_path
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://downlo... | Python | 104 | 32.740383 | 78 | /resnet/make_model.py | 0.586492 | 0.54175 |
Sssssbo/SDCNet | refs/heads/master | import os
import os.path
import torch.utils.data as data
from PIL import Image
class ImageFolder_joint(data.Dataset):
# image and gt should be in the same folder and have same filename except extended name (jpg and png respectively)
def __init__(self, label_list, joint_transform=None, transform=None, target_... | Python | 125 | 39.872002 | 118 | /datasets.py | 0.623214 | 0.622627 |
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