code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
random.choice(imgs)
<|reserved_special_token_0|>
print(served_img)
<|reserved_special_token_0|>
if input == 'yes':
print('YOU FLUCKED IT')
elif input == 'no':
print('WHAT ARE YOU???..')
<|reserved_special_token_1|>
<|re... | flexible | {
"blob_id": "4ae611ee8c019c76bb5d7c1d733ffb4bd06e2e8d",
"index": 5508,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nrandom.choice(imgs)\n<mask token>\nprint(served_img)\n<mask token>\nif input == 'yes':\n print('YOU FLUCKED IT')\nelif input == 'no':\n print('WHAT ARE YOU???..')\n",
"step-3": "<... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class NodeLookup(object):
"""Converts integer node ID's to human readable labels."""
def __init__(self, label_lookup_path=None, uid_lookup_path=None):
if not label_lookup_path:
label_lookup_path = os.path.join(FLAGS.model_dir,
'imagenet_2012_ch... | flexible | {
"blob_id": "b4ce95d754dd0d7c1b91fa0348de0194a4397aca",
"index": 6830,
"step-1": "<mask token>\n\n\nclass NodeLookup(object):\n \"\"\"Converts integer node ID's to human readable labels.\"\"\"\n\n def __init__(self, label_lookup_path=None, uid_lookup_path=None):\n if not label_lookup_path:\n ... | [
6,
7,
11,
13,
14
] |
<|reserved_special_token_0|>
def connectMongoCollection(collection=COLLECTION):
uri = 'mongodb://localhost'
client = MongoClient(uri)
db = client[DB]
return db[collection]
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def connectMongo():
uri = 'mongodb://localhost'
client = Mo... | flexible | {
"blob_id": "7a5106456d0fdd905829c5aa1f4a69b027f3a04c",
"index": 4198,
"step-1": "<mask token>\n\n\ndef connectMongoCollection(collection=COLLECTION):\n uri = 'mongodb://localhost'\n client = MongoClient(uri)\n db = client[DB]\n return db[collection]\n",
"step-2": "<mask token>\n\n\ndef connectMong... | [
1,
2,
3,
4,
5
] |
from p5 import *
import numpy as np
from numpy.random import default_rng
from boids import Boid
from data import Data
n=30;
width = 1920
height = 1080
flock=[]
infected=[]
rng = default_rng()
frames=0
for i in range(n):
x = rng.integers(low=0, high=1920)
y = rng.integers(low=0, high=1080)
if i==0:
... | normal | {
"blob_id": "78c4e14e5afdf857082b60bf4020f0f785d93a0d",
"index": 9704,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(n):\n x = rng.integers(low=0, high=1920)\n y = rng.integers(low=0, high=1080)\n if i == 0:\n flock.append(Boid(x, y, width, height, infected=True, curado=Fa... | [
0,
3,
4,
5,
6
] |
import random as rnd
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.metrics import f1_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_fscore_support, roc_auc_score
import os
def mkdir_tree(source):
... | normal | {
"blob_id": "11ca13aca699b1e0744243645b3dbcbb0dacdb7e",
"index": 9588,
"step-1": "<mask token>\n\n\ndef mkdir_tree(source):\n if source is None:\n source = 'default'\n base_dirs = ['../data/clf_meta/%s/' % source]\n print('base_dirsssssss', base_dirs)\n for base_dir in base_dirs:\n if n... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
def nextsteps(point):
for ns in nextsteps2d(point):
yield ns
if point in portals:
yield portals[point]
def should_visit(point):
return lines[point[0]][point[1]] == '.'
<|reserved_special_token_0|>
def nextsteps_with_recursion(point):
i, j, level = poi... | flexible | {
"blob_id": "973fc3a973d952cb0f192221dfda63e255e4a8a0",
"index": 2543,
"step-1": "<mask token>\n\n\ndef nextsteps(point):\n for ns in nextsteps2d(point):\n yield ns\n if point in portals:\n yield portals[point]\n\n\ndef should_visit(point):\n return lines[point[0]][point[1]] == '.'\n\n\n<m... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from . import scramsha1, scrammer
| flexible | {
"blob_id": "8c336edddadbf4689721b474c254ded061ecf4b5",
"index": 743,
"step-1": "<mask token>\n",
"step-2": "from . import scramsha1, scrammer\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
from django import forms
from .models import Appointment, Prescription
from account.models import User
class AppointmentForm(forms.ModelForm):
class Meta:
model = Appointment
fields = '__all__'
widgets = {
'date': forms.DateInput(attrs={'type': 'date'}),
'time': for... | normal | {
"blob_id": "d3425017d4e604a8940997afd0c35a4f7eac1170",
"index": 6944,
"step-1": "<mask token>\n\n\nclass PrescriptionForm(forms.ModelForm):\n\n\n class Meta:\n model = Prescription\n exclude = ['doctor']\n widgets = {'prescription': forms.Textarea(attrs={'rows': 4})}\n\n def __init__(... | [
2,
3,
4,
5,
6
] |
a = 10
b = 20
c = a + b
d = b - a
print(c)
print(d)
| normal | {
"blob_id": "632fdb95874f0beeb6d178788f7c7e7c9e8512e5",
"index": 8239,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(c)\nprint(d)\n",
"step-3": "a = 10\nb = 20\nc = a + b\nd = b - a\nprint(c)\nprint(d)\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
import numpy as np
import pandas as pd
import nltk
from collections import defaultdict
import os.path
stop_words = ['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours',
'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers',
'herself',... | normal | {
"blob_id": "0356b408624988100c10b20facecef14f1552203",
"index": 4537,
"step-1": "<mask token>\n\n\ndef build_statements_features(df, vectorizer, train=True, tokenizer=\n tokenizer_nltk):\n filtered_statements_dic = {}\n for index, row in df.iterrows():\n filtered_statement = []\n tokenize... | [
3,
5,
7,
9,
10
] |
import os
import json
from nltk.corpus import wordnet as wn
from itertools import combinations #計算排列組合
# 需要被計算的分類
myTypes = ['animal', 'vehicle', 'food', 'fashion', 'dog', 'cat', 'car', 'motorcycle']
# 計算完網紅權重存放的位置
scorePath = "..\\data\\score"
# getUsersData.py儲存網紅貼文資料的json檔案，拿來計算分數
usersDataFile = "..\\data\\us... | normal | {
"blob_id": "879482e4df9c3d7f32d9b2a883201ae043e1189f",
"index": 871,
"step-1": "<mask token>\n\n\ndef get_similar_words(words):\n words = [w.lower() for w in words]\n if len(words) > 1:\n maxScore = 0\n firstWord = ''\n secondWord = ''\n labelCom = list(combinations(words, 2))\... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if len(sys.argv) != 3:
print('Usage: std_dev_eval.py <std_dir> <ans>')
quit()
<|reserved_special_token_0|>
subprocess.call('rm -f {}/result'.format(std_dir), shell=True)
<|reserved_special_token_0|>
with open(query, 'rb') ... | flexible | {
"blob_id": "ba216642935d19b85e379b66fb514854ebcdedd9",
"index": 666,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif len(sys.argv) != 3:\n print('Usage: std_dev_eval.py <std_dir> <ans>')\n quit()\n<mask token>\nsubprocess.call('rm -f {}/result'.format(std_dir), shell=True)\n<mask token>\nwith op... | [
0,
1,
2,
3,
4
] |
from sqlalchemy import Integer, String, Column
from sqlalchemy.orm import Query
from server import db
class Formation(db):
__tablename__ = "formation"
query: Query
id_form = Column(Integer, primary_key=True)
filiere = Column(String, nullable=False)
lieu = Column(String, nullable=False)
niveau = Column(Str... | normal | {
"blob_id": "fff70312fa7c3259cf4c3d9e7ebd8ca5b9a56887",
"index": 2714,
"step-1": "<mask token>\n\n\nclass Formation(db):\n <mask token>\n query: Query\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n @staticmethod\n def create(filiere: str, lieu: str, niveau: str):\n ... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
try:
data = requests.get('http://en.wikipedia.org/wiki/Python')
data.raise_for_status()
my_data = bs4.BeautifulSoup(data.text, 'lxml')
print('List of all the header tags: \n\n')
for the_data in my_data.find_all... | flexible | {
"blob_id": "27e9635adf6109f3ab13b9d8dd5809973b61ca03",
"index": 413,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n data = requests.get('http://en.wikipedia.org/wiki/Python')\n data.raise_for_status()\n my_data = bs4.BeautifulSoup(data.text, 'lxml')\n print('List of all the header tag... | [
0,
1,
2,
3
] |
import hashlib
import math
import random
from set5.ch_4 import get_num_byte_len
class Server:
def __init__(self):
self.private_key = random.randint(0, 2**100)
self.salt = random.randint(0, 2**100)
self.salt_bytes = self.salt.to_bytes(
byteorder="big",
length=get_n... | normal | {
"blob_id": "cf7aeacedec211e76f2bfcb7f6e3cb06dbfdc36e",
"index": 3907,
"step-1": "<mask token>\n\n\nclass Server:\n\n def __init__(self):\n self.private_key = random.randint(0, 2 ** 100)\n self.salt = random.randint(0, 2 ** 100)\n self.salt_bytes = self.salt.to_bytes(byteorder='big', leng... | [
17,
19,
20,
24,
26
] |
import numpy
import multiprocessing
from functools import partial
from textutil.text import read_file
from textutil.util import B
import mmap
import tqdm
class Growable(object):
def __init__(self, capacity=1024, dtype=numpy.uint32, grow=2):
self.grow = grow
self.capacity=capacity
self.dty... | normal | {
"blob_id": "8a2fe83ab1adae7de94eb168290ce4843ab39fe1",
"index": 9476,
"step-1": "<mask token>\n\n\nclass Growable(object):\n\n def __init__(self, capacity=1024, dtype=numpy.uint32, grow=2):\n self.grow = grow\n self.capacity = capacity\n self.dtype = dtype\n self.arr = numpy.empty... | [
5,
6,
7,
9,
10
] |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'MainMenu.ui'
#
# Created by: PyQt5 UI code generator 5.9.2
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWind... | normal | {
"blob_id": "f4094a81f90cafc9ae76b8cf902221cbdbc4871a",
"index": 6711,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Ui_MainWindow(object):\n <mask token>\n\n def retranslateUi(self, MainWindow):\n _translate = QtCore.QCoreApplication.translate\n MainWindow.setWindowTitle(_... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def train(env, nb_epochs, nb_epoch_cycles, render_eval, reward_scale,
render, param_noise, actor, critic, normalize_returns,
normalize_observations, critic_l2_reg, actor_lr, critic_lr,
action_noise, popart, gamma, clip_norm, nb_train_steps,
nb_rollout_steps, nb_eval_steps,... | flexible | {
"blob_id": "3f92bf194058c97a40cd5728cfc7c9d1be6b2548",
"index": 8099,
"step-1": "<mask token>\n\n\ndef train(env, nb_epochs, nb_epoch_cycles, render_eval, reward_scale,\n render, param_noise, actor, critic, normalize_returns,\n normalize_observations, critic_l2_reg, actor_lr, critic_lr,\n action_noise,... | [
2,
3,
4,
5,
6
] |
import matplotlib.pyplot as plt
def xyplot(xdata,ydata,title):
fname = "/Users/nalmog/Desktop/swa_equipped_cumulative_"+title+".png"
#plt.figure(figsize=(500,500))
plt.plot(xdata, ydata)
plt.ylabel('some numbers')
# plt.savefig("/Users/nalmog/Desktop/swa_equipped_cumulative_"+title+".png", format... | normal | {
"blob_id": "10a7c1827abb8a87f5965453aa2d8f5e8b4914e5",
"index": 6563,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef xyplot(xdata, ydata, title):\n fname = '/Users/nalmog/Desktop/swa_equipped_cumulative_' + title + '.png'\n plt.plot(xdata, ydata)\n plt.ylabel('some numbers')\n plt.ti... | [
0,
1,
2,
3
] |
import tensorflow as tf
import tensorflow_io as tfio
import h5py
class GeneratorVGGNet():
def __call__(self, filename, is_test):
with h5py.File(filename, 'r') as hf:
keys = list(hf.keys())
for key in keys:
if not is_test:
for f, g, z in zip(hf[str(key) + "/left-eye"], hf[str(key) +... | normal | {
"blob_id": "f94fcf6ed54f247093050216c0c331ce188da919",
"index": 9228,
"step-1": "<mask token>\n\n\nclass Dataset:\n\n def __init__(self, config, path, batch_size, shuffle, is_training,\n is_testing):\n self.config = config\n self.is_training = is_training\n self.is_testing = is_te... | [
2,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def myswap(a, b):
temp = a
a = b
b = temp
if a < b:
print(a, b)
else:
print(b, a)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def myswap(a, b):
temp = a
a = b
b = temp
if a < b:
pr... | flexible | {
"blob_id": "e6efd2de5f92d66f1b734a2173fc8681af3c4cc8",
"index": 8040,
"step-1": "<mask token>\n",
"step-2": "def myswap(a, b):\n temp = a\n a = b\n b = temp\n if a < b:\n print(a, b)\n else:\n print(b, a)\n\n\n<mask token>\n",
"step-3": "def myswap(a, b):\n temp = a\n a = ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class CustomUserAdmin(UserAdmin):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class CustomUserAdmin(UserAdmin):
list_display = 'username', 'email', 'is_staff', 'is_activ... | flexible | {
"blob_id": "c95eaa09241428f725d4162e0e9f6ed3ce6f8fdd",
"index": 6709,
"step-1": "<mask token>\n\n\nclass CustomUserAdmin(UserAdmin):\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass CustomUserAdmin(UserAdmin):\n list_display = 'username', 'email', 'is_staff', 'is... | [
1,
2,
3,
4,
5
] |
import os
from pathlib import Path
import Algorithmia
API_KEY = os.environ.get('ALGO_API_KEY')
DATA_DIR_BASE = os.environ.get('DATA_DIR')
ORIGINAL_DATA_DIR = DATA_DIR_BASE + 'original/'
TRANSFERD_DATA_DIR = DATA_DIR_BASE + 'transferd/'
def upload(client, fnames):
for im in fnames:
im = Path(im)
... | normal | {
"blob_id": "2536b22c2d154e87bdecb72cc967d8c56ddb73fb",
"index": 609,
"step-1": "<mask token>\n\n\ndef upload(client, fnames):\n for im in fnames:\n im = Path(im)\n client.file(ORIGINAL_DATA_DIR + str(im.name)).put(im.read_bytes())\n\n\n<mask token>\n\n\ndef style_transfer(fnames, out_folder, fi... | [
2,
3,
4,
5,
6
] |
import thread
import time
import ctypes
lib = ctypes.CDLL('/home/ubuntu/workspace/35SmartPy/CAN/brain/CANlib.so')
init = lib.init
read = lib.readGun
read.restype = ctypes.POINTER(ctypes.c_ubyte * 8)
send = lib.sendBrake
init()
| normal | {
"blob_id": "866571341a587c8b1b25437f5815429875bbe5ad",
"index": 9285,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ninit()\n",
"step-3": "<mask token>\nlib = ctypes.CDLL('/home/ubuntu/workspace/35SmartPy/CAN/brain/CANlib.so')\ninit = lib.init\nread = lib.readGun\nread.restype = ctypes.POINTER(ctypes.... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def execute(event, context):
print(event)
pass
<|reserved_special_token_1|>
<|reserved_special_token_0|>
environ['ACCESS_KEY'] = '1234567890'
environ['SECRET_KEY'] = '1234567890'
environ['ENDPOINT_URL'] = 'http://loca... | flexible | {
"blob_id": "a4eca0f5b7d5a03ca3600554ae3fe3b94c59fc68",
"index": 8622,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef execute(event, context):\n print(event)\n pass\n",
"step-3": "<mask token>\nenviron['ACCESS_KEY'] = '1234567890'\nenviron['SECRET_KEY'] = '1234567890'\nenviron['ENDPOINT_U... | [
0,
1,
2,
3,
4
] |
# Merge sort is used to sort the elements
def merge_sort(arr):
if len(arr) > 1:
# Recursion is used to continuously split the array in half.
mid = len(arr) // 2
# Using Auxiliary storage here
left = arr[:mid]
right = arr[mid:]
# Traverse the left side of the array
... | normal | {
"blob_id": "264b48c2b9ce4ec948ca5ba548e708848760f3dc",
"index": 8271,
"step-1": "<mask token>\n\n\ndef rearrange_digits(input_list):\n if len(input_list) == 0:\n return []\n merge_sort(input_list)\n first_number = ''\n second_number = ''\n for i in range(0, len(input_list)):\n if i ... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
plt.subplot(121), plt.imshow(canny, cmap='gray')
plt.title('Canny'), plt.xticks([]), plt.yticks([])
<|reserved_special_token_0|>
cv2.drawContours(imagen, contornos, -1, (255, 0, 0), 2)
cv2.imshow('contornos', imagen)
cv2.waitKey(0... | flexible | {
"blob_id": "9f42a9d0ca622d6c4e2cf20bc2e494262c16055b",
"index": 7744,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.subplot(121), plt.imshow(canny, cmap='gray')\nplt.title('Canny'), plt.xticks([]), plt.yticks([])\n<mask token>\ncv2.drawContours(imagen, contornos, -1, (255, 0, 0), 2)\ncv2.imshow('co... | [
0,
1,
2,
3,
4
] |
file = open('../_datasets/moby_dick.txt', mode='r')
print(file.read())
print(file.closed)
file.close()
print(file.closed)
| normal | {
"blob_id": "dfe0ee5bbb906e5a23adcf06d2d704700fa1567d",
"index": 1179,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(file.read())\nprint(file.closed)\nfile.close()\nprint(file.closed)\n",
"step-3": "file = open('../_datasets/moby_dick.txt', mode='r')\nprint(file.read())\nprint(file.closed)\nfile... | [
0,
1,
2
] |
<|reserved_special_token_0|>
def on_connection_resumed(connection, return_code, session_present, **kwargs):
print('Connection resumed. return_code: {} session_present: {}'.format(
return_code, session_present))
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
d... | flexible | {
"blob_id": "2ff398e38b49d95fdc8a36a08eeb5950aaea1bc9",
"index": 2279,
"step-1": "<mask token>\n\n\ndef on_connection_resumed(connection, return_code, session_present, **kwargs):\n print('Connection resumed. return_code: {} session_present: {}'.format(\n return_code, session_present))\n\n\n<mask token>... | [
1,
2,
3,
4,
5
] |
REDIRECT_MAP = {
'90':'19904201',
'91':'19903329',
'92':'19899125',
'93':'19901043',
'94':'19903192',
'95':'19899788',
'97':'19904423',
'98':'19906163',
'99':'19905540',
'100':'19907871',
'101':'19908147',
'102':'19910103',
'103':'19909980',
'104':'19911813',
... | normal | {
"blob_id": "fb92912e1a752f3766f9439f75ca28379e23823f",
"index": 3600,
"step-1": "<mask token>\n",
"step-2": "REDIRECT_MAP = {'90': '19904201', '91': '19903329', '92': '19899125', '93':\n '19901043', '94': '19903192', '95': '19899788', '97': '19904423', '98':\n '19906163', '99': '19905540', '100': '19907... | [
0,
1,
2
] |
import getpass
print('****************************')
print('***** Caixa Eletronico *****')
print('****************************')
account_typed = input("Digite sua conta: ")
password_typed = getpass.getpass("Digite sua senha: ")
| normal | {
"blob_id": "44b6ee8488869da447882457897ce87b2fdea726",
"index": 7846,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('****************************')\nprint('***** Caixa Eletronico *****')\nprint('****************************')\n<mask token>\n",
"step-3": "<mask token>\nprint('*******************... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
def process_option(food, option):
food_name = list(food.keys())[option - 1]
food_price = food[food_name]
print(food_price)
print('You have chosen: ', option, food_name, '!', ' For unit price: ',
food_price)
q = int(input('How many... | flexible | {
"blob_id": "07bd3c7cacbf8d0e39d06b21456258ad92cb2294",
"index": 676,
"step-1": "<mask token>\n",
"step-2": "def process_option(food, option):\n food_name = list(food.keys())[option - 1]\n food_price = food[food_name]\n print(food_price)\n print('You have chosen: ', option, food_name, '!', ' For u... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import sqlite3 as lite
con = lite.connect('./logs.db')
with con:
cur = con.cursor()
cur.execute("DROP TABLE IF EXISTS log")
cur.execute('''CREATE TABLE log (msg_id text, u_id text, username text, first_name text, last_name text, msg text, ch_id text, d... | normal | {
"blob_id": "1c31649ac75214a6d26bcb6d6822579be91e5074",
"index": 2748,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith con:\n cur = con.cursor()\n cur.execute('DROP TABLE IF EXISTS log')\n cur.execute(\n 'CREATE TABLE log (msg_id text, u_id text, username text, first_name text, last_n... | [
0,
1,
2,
3,
4
] |
"""
code: pmap_io_test.py
"""
import os
import time
import tables as tb
import numpy as np
from pytest import mark
from .. core.system_of_units_c import units
from .. database import load_db
from .. sierpe import blr
from . import tbl_functions as tbl
from .... | normal | {
"blob_id": "c36adc3cf5de2f0ae3ee9b9823304df393ebce63",
"index": 5679,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@mark.parametrize('filename, with_', (('test_pmaps_auto.h5', \n True), ('test_pmaps_manu.h5', False)))\ndef test_pmap_writer(config_tmpdir, filename, with_,\n s12_dat... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
stu.say()
p.sayHello()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
stu = p.Student()
stu.say()
p.sayHello()
<|reserved_special_token_1|>
import p01 as p
stu = p.Student()
stu.say()
p.sayHello()
| flexible | {
"blob_id": "8be3a3d32da208e2f45aad61813bc6f5ea513f01",
"index": 9803,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nstu.say()\np.sayHello()\n",
"step-3": "<mask token>\nstu = p.Student()\nstu.say()\np.sayHello()\n",
"step-4": "import p01 as p\nstu = p.Student()\nstu.say()\np.sayHello()\n",
"step-... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def length_of_cars(car):
return len(car)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def length(data):
return len(data)
<|reserved_special_token_0|>
def year(data):
return data['year']
<|reserved_special_token_0|>
def l... | flexible | {
"blob_id": "5ab8d9eab30d72557f1a85b5b82c0df456e3843d",
"index": 1740,
"step-1": "<mask token>\n\n\ndef length_of_cars(car):\n return len(car)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef length(data):\n return len(data)\n\n\n<mask token>\n\n\ndef year(data):\n return data['year']\n\n\n<mask... | [
1,
3,
4,
5,
6
] |
import math
import os
import sys
import pandas
import numpy as np
import seaborn as sns
import tensorflow as tf
import logging
# from utils.simulation_functions import simulation_cox_gompertz
from utils.preprocessing import formatted_data, normalize_batch, event_t_bin_prob,risk_t_bin_prob,\
batch_t_categorize, next_b... | normal | {
"blob_id": "ebebdb0e79e9d78b818dab3f93d130ccddd2914e",
"index": 1185,
"step-1": "<mask token>\n\n\ndef saveDatadic(file_path, name, dataset):\n np.save(file_path + name + '_x', dataset['x'])\n np.save(file_path + name + '_t', dataset['t'])\n np.save(file_path + name + '_e', dataset['e'])\n\n\n<mask tok... | [
7,
10,
14,
17,
18
] |
import unittest
from .context import *
class BasicTestSuite(unittest.TestCase):
"""Basic test cases."""
def test_hello_world(self):
self.assertEqual(hello_world(), 'hello world')
if __name__ == '__main__':
unittest.main()
| normal | {
"blob_id": "6420d1b9da7ff205e1e138f72b194f63d1011012",
"index": 4554,
"step-1": "<mask token>\n\n\nclass BasicTestSuite(unittest.TestCase):\n <mask token>\n\n def test_hello_world(self):\n self.assertEqual(hello_world(), 'hello world')\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Basi... | [
2,
3,
4,
5
] |
dict1 = [
{'a':1},
{'a':2},
{'a':3}
]
a = dict1[1]['a']
# print(a)
correlation_dict = {'${class_id}':123}
data = {'token': '${self.token}', 'name': 'api测试','class_id': '${class_id}'}
for k in data:
for key in correlation_dict:
if data[k] in key:
data[k] = correlation_dict[key]
pr... | normal | {
"blob_id": "9c05b39a12ab29db99397e62315efddd8cdf1df4",
"index": 456,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor k in data:\n for key in correlation_dict:\n if data[k] in key:\n data[k] = correlation_dict[key]\nprint(data)\n",
"step-3": "dict1 = [{'a': 1}, {'a': 2}, {'a': 3... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
result = sess.run(fetches=s_t, feed_dict={s_t: [state]})
print(result)
result = sess.run(fetches=conv2d, feed_dict={s_t: [state]})
print(result... | flexible | {
"blob_id": "5a3b88f899cfb71ffbfac3a78d38b748bffb2e43",
"index": 6295,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith tf.Session() as sess:\n sess.run(tf.initialize_all_variables())\n result = sess.run(fetches=s_t, feed_dict={s_t: [state]})\n print(result)\n result = sess.run(fetches=con... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class BaseCache(metaclass=ABCMeta):
<|reserved_special_token_0|>
@abstractmethod
def __init__(self, kvstore, makekey, lifetime, fail_silent):
self._kvstore = kvstore
self._makekey = makekey
self._lifetime = lifetime
self._fail_silent = fail_sil... | flexible | {
"blob_id": "e810cde7f77d36c6a43f8c277b66d038b143aae6",
"index": 6746,
"step-1": "<mask token>\n\n\nclass BaseCache(metaclass=ABCMeta):\n <mask token>\n\n @abstractmethod\n def __init__(self, kvstore, makekey, lifetime, fail_silent):\n self._kvstore = kvstore\n self._makekey = makekey\n ... | [
3,
4,
5,
6,
7
] |
#! /usr/bin/env python3
import arg_parser
import colors
import logging
import sys
def parse_args(argv):
parser = arg_parser.RemoteRunArgParser()
return parser.parse(argv[1:])
def main(argv):
logging.basicConfig(
format='%(levelname)s: %(message)s',
level='INFO',
handlers=[colors... | normal | {
"blob_id": "72d1a0689d4cc4f78007c0cfa01611e95de76176",
"index": 3908,
"step-1": "<mask token>\n\n\ndef main(argv):\n logging.basicConfig(format='%(levelname)s: %(message)s', level='INFO',\n handlers=[colors.ColorizingStreamHandler(sys.stderr)])\n try:\n args = parse_args(argv)\n except Ex... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class WebcamVideoStream:
<|reserved_special_token_0|>
def start(self):
Thread(target=self.update, args=()).start()
return self
def update(self):
while True:
if self.stopped:
print('returning')
cv2.destroyAll... | flexible | {
"blob_id": "8a4fe88bfa39eeeda42198260a1b22621c33183e",
"index": 7894,
"step-1": "<mask token>\n\n\nclass WebcamVideoStream:\n <mask token>\n\n def start(self):\n Thread(target=self.update, args=()).start()\n return self\n\n def update(self):\n while True:\n if self.stopp... | [
4,
5,
6,
7,
8
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
try:
from espeak import espeak
except ImportError:
class espeak():
@classmethod
def synth(*args):
print('Cannot generate speech. Please, install python3-espeak module.')
return 1
def run(*args, **kwargs):
text = ' '.jo... | normal | {
"blob_id": "cd5929496b13dd0d5f5ca97500c5bb3572907cc5",
"index": 2769,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef run(*args, **kwargs):\n text = ' '.join(map(str, args))\n espeak.synth(text)\n",
"step-3": "try:\n from espeak import espeak\nexcept ImportError:\n\n\n class espeak:... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def str2bool(v):
return v.lower() in ('true', '1')
<|reserved_special_token_0|>
def add_argument_group(name):
arg = parser.add_argument_group(name)
arg_lists.append(arg)
return arg
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_... | flexible | {
"blob_id": "dfaea1687238d3d09fee072689cfdea392bc78f9",
"index": 8967,
"step-1": "<mask token>\n\n\ndef str2bool(v):\n return v.lower() in ('true', '1')\n\n\n<mask token>\n\n\ndef add_argument_group(name):\n arg = parser.add_argument_group(name)\n arg_lists.append(arg)\n return arg\n\n\n<mask token>\... | [
2,
3,
5,
6,
7
] |
#!/usr/bin/python
#Program for functions pay scale from user input
hrs = raw_input("Enter Hours:")
h = float(hrs)
rate = raw_input("Enter Rate:")
r = float(rate)
def computepay(h,r):
if (h>40) :
pay = (40*r)+(h-40)*1.5*r
else:
pay = (h*r)
return pay
print computepay(h,r)
| normal | {
"blob_id": "8f30de819412b03ef12009320978cb1becd85131",
"index": 2767,
"step-1": "#!/usr/bin/python\n#Program for functions pay scale from user input\n\nhrs = raw_input(\"Enter Hours:\")\n\nh = float(hrs)\n\nrate = raw_input(\"Enter Rate:\")\n\nr = float(rate)\n\n\n\ndef computepay(h,r):\n\n if (h>40) : \n\n ... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def crawl(file):
gis = GIS()
map = gis.map('United States')
map
job_df = pd.read_csv(Point_v1.CONSULTING_FILE).append(pd.read_csv(
Point_v1.DS_FILE)).append(pd.read_csv(Point_v1.SDE_FILE))
company_loc... | flexible | {
"blob_id": "902159d9ad3a1e36b69142518007b5d4bcaef0f3",
"index": 1320,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef crawl(file):\n gis = GIS()\n map = gis.map('United States')\n map\n job_df = pd.read_csv(Point_v1.CONSULTING_FILE).append(pd.read_csv(\n Point_v1.DS_FILE)).appe... | [
0,
1,
2,
3
] |
'Attempts to use <http://countergram.com/software/pytidylib>.'
try:
import tidylib
def tidy(html):
html, errors = tidylib.tidy_document(html, options={'force-output': True,
'output-xhtml': True, 'tidy-mark': False})
return html
except ImportError:
def tidy(html):
return html
| normal | {
"blob_id": "33ec822f6149a57244edf6d8d99a5b3726600c2e",
"index": 3236,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n import tidylib\n\n def tidy(html):\n html, errors = tidylib.tidy_document(html, options={'force-output':\n True, 'output-xhtml': True, 'tidy-mark': False})\... | [
0,
1,
2
] |
from __future__ import with_statement # this is to work with python2.5
from pyps import workspace, module
def invoke_function(fu, ws):
return fu._get_code(activate = module.print_code_out_regions)
if __name__=="__main__":
workspace.delete('paws_out_regions')
with workspace('paws_out_regions.c',name='paws_ou... | normal | {
"blob_id": "299432b095f16c3cb4949319705800d06f534cf9",
"index": 1017,
"step-1": "from __future__ import with_statement # this is to work with python2.5\nfrom pyps import workspace, module\n\ndef invoke_function(fu, ws):\n return fu._get_code(activate = module.print_code_out_regions)\n\nif __name__==\"__m... | [
0
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 1 11:14:13 2019
@author: dobri
"""
import numpy as np
from astropy.stats import circmean
x = np.multiply(np.pi,[(0,1/4,2/4,3/4,4/4),(1,5/4,6/4,7/4,8/4),(5/4,5/4,5/4,5/4,5/4),(0/5,2/5,4/5,6/5,8/5)])
s = np.shape(x)
phikprime = np.array(x*0, dtype... | normal | {
"blob_id": "c35ecad842477fc8501a763f7eb972f6e7fc13e1",
"index": 7525,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor j in range(0, len(x)):\n for k in range(0, len(x[j, :])):\n phikprime[j, k] = np.complex(np.cos(x[j, k]), np.sin(x[j, k]))\n phikprimebar[j] = np.sum(phikprime[j, :]) / s... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def ping_ip_addresses(ip_addresses):
result1 = []
result2 = []
for ip_address in ip_addresses:
reply = subprocess.run(['ping', '-c', '3', '-n', ip_address],
stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding='utf-8')
if reply.returncode == 0:
... | flexible | {
"blob_id": "dd7e8556405f07172ce2b1e9f486c2cd2f4bad58",
"index": 7613,
"step-1": "<mask token>\n\n\ndef ping_ip_addresses(ip_addresses):\n result1 = []\n result2 = []\n for ip_address in ip_addresses:\n reply = subprocess.run(['ping', '-c', '3', '-n', ip_address],\n stdout=subprocess.P... | [
2,
3,
4,
5,
6
] |
# -*- coding:utf-8 -*-
__author__ = 'yangxin_ryan'
"""
Solutions:
题目要求非递归的中序遍历，
中序遍历的意思其实就是先遍历左孩子、然后是根结点、最后是右孩子。我们按照这个逻辑，应该先循环到root的最左孩子，
然后依次出栈，然后将结果放入结果集合result，然后是根的val，然后右孩子。
"""
class BinaryTreeInorderTraversal(object):
def inorderTraversal(self, root: TreeNode) -> List[int]:
result = list()
... | normal | {
"blob_id": "8e629ee53f11e29aa026763508d13b06f6ced5ba",
"index": 940,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass BinaryTreeInorderTraversal(object):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass BinaryTreeInorderTraversal(object):\n\n def inorderTraversal(self, root: TreeNod... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Solution(object):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Solution(object):
def isPalindrome(self, x):
"""
:type x: int
:rtype: bool
"""
if x < 0:
return False
... | flexible | {
"blob_id": "ef1b759872de6602646ce095823ff37f043ffd9d",
"index": 5148,
"step-1": "<mask token>\n",
"step-2": "class Solution(object):\n <mask token>\n",
"step-3": "class Solution(object):\n\n def isPalindrome(self, x):\n \"\"\"\n :type x: int\n :rtype: bool\n \"\"\"\n ... | [
0,
1,
2
] |
print('Hello World!')
print('2nd Test')
d = dict()
d['a'] = dict()
d['a']['b'] = 5
d['a']['c'] = 6
d['x'] = dict()
d['x']['y'] = 10
print(d)
print(d['a'])
import random
random.seed(30)
r = random.randrange(0,5)
print(r)
import numpy as np
np.random.seed
for i in range(20):
newArray = list(set(np.random.ran... | normal | {
"blob_id": "e4a60008ca7d61d825b59e6202b40c6be02841cd",
"index": 2024,
"step-1": "<mask token>\n",
"step-2": "print('Hello World!')\nprint('2nd Test')\n<mask token>\nprint(d)\nprint(d['a'])\n<mask token>\nrandom.seed(30)\n<mask token>\nprint(r)\n<mask token>\nnp.random.seed\nfor i in range(20):\n newArray =... | [
0,
1,
2,
3,
4
] |
from django.conf import settings
from django.contrib import admin
from django.urls import path, include, reverse_lazy
from django.views.generic import RedirectView, TemplateView
from mainapp.views import ShortURLRedirect
urlpatterns = [
path('', TemplateView.as_view(template_name='mainapp/index.html'), name='inde... | normal | {
"blob_id": "573674e50e05880a2822f306c125207b382d872f",
"index": 6389,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif settings.DEBUG:\n import debug_toolbar\n urlpatterns += path('__debug__/', include(debug_toolbar.urls)),\n",
"step-3": "<mask token>\nurlpatterns = [path('', TemplateView.as_vi... | [
0,
1,
2,
3,
4
] |
# system
import os
import numpy as np
import random
import copy
import time
# ROS
import rospy
import std_msgs.msg
import sensor_msgs.msg
import geometry_msgs.msg
import visualization_msgs.msg
import tf2_ros
import rosbag
import actionlib
from actionlib_msgs.msg import GoalStatus
import ros_numpy
# spartan ROS
import... | normal | {
"blob_id": "33867677611ceb757f6973eb70368c9f75f3ce92",
"index": 1341,
"step-1": "# system\nimport os\nimport numpy as np\nimport random\nimport copy\nimport time\n\n# ROS\nimport rospy\nimport std_msgs.msg\nimport sensor_msgs.msg\nimport geometry_msgs.msg\nimport visualization_msgs.msg\nimport tf2_ros\nimport r... | [
0
] |
#!/usr/bin/python3
from datetime import datetime
import time
import smbus
SENSOR_DATA_FORMAT = "Speed: {} km/h\nSteering: {}\nThrottle: {}\nTemperature: {} C"
class SensorDataFrame:
def __init__(self, data):
self.speed, self.steering, self.throttle, self.temp = data
self.timestamp = datetime.now... | normal | {
"blob_id": "cf4170760fe6210d8b06f179484258f4ae3f8796",
"index": 7284,
"step-1": "<mask token>\n\n\nclass SensorDataFrame:\n\n def __init__(self, data):\n self.speed, self.steering, self.throttle, self.temp = data\n self.timestamp = datetime.now()\n\n def __str__(self):\n return SENSOR... | [
4,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def strategy(history, memory):
if not history.size:
counts.clear()
for x in patterns:
counts.append(0)
if memory:
return memory.pop(0), memory
for i, (pattern, response) in enumera... | flexible | {
"blob_id": "8ae6630ccd2f2b5a10401cadb4574772f6ecbc4a",
"index": 4478,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef strategy(history, memory):\n if not history.size:\n counts.clear()\n for x in patterns:\n counts.append(0)\n if memory:\n return memory.pop(0... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
test_beam.add_support(0, 'roller')
test_beam.add_support(2, 'roller')
test_beam.add_support(6, 'pin')
test_beam.add_support(4, 'hinge')
test_beam.add_distributed_load(0, 4, -5)
test_beam.add_distributed_load(4, 6, '-(-3*(x-5)**2 +... | flexible | {
"blob_id": "bdbeebab70a6d69e7553807d48e3539b78b48add",
"index": 2946,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntest_beam.add_support(0, 'roller')\ntest_beam.add_support(2, 'roller')\ntest_beam.add_support(6, 'pin')\ntest_beam.add_support(4, 'hinge')\ntest_beam.add_distributed_load(0, 4, -5)\ntest_... | [
0,
1,
2,
3,
4
] |
# Find a list of patterns in a list of string in python
any([ p in s for p in patterns for s in strings ])
| normal | {
"blob_id": "c0b6c0636d1900a31cc455795838eb958d1daf65",
"index": 9421,
"step-1": "<mask token>\n",
"step-2": "any([(p in s) for p in patterns for s in strings])\n",
"step-3": "# Find a list of patterns in a list of string in python\nany([ p in s for p in patterns for s in strings ])\n",
"step-4": null,
"... | [
0,
1,
2
] |
from rlbot.agents.base_agent import BaseAgent, GameTickPacket, SimpleControllerState
#from rlbot.utils.structures.game_data_struct import GameTickPacket
from Decisions.challengeGame import ChallengeGame
from Decisions.info import MyInfo, Car
from Decisions.strat import Strategy
from Drawing.Drawing import DrawingTool
f... | normal | {
"blob_id": "1a0d4e77f09b4ce752631ae36a83ff57f96b89b1",
"index": 600,
"step-1": "<mask token>\n\n\nclass MyBot(BaseAgent):\n <mask token>\n\n def initialize_agent(self):\n self.boost_pad_tracker.initialize_boosts(self.get_field_info())\n self.info = MyInfo(self.team, self.index)\n self... | [
5,
6,
7,
8,
9
] |
'''
EXERCICIO: Faça um programa que leia quantidade de pessoas que serão convidadas para uma festa.
O programa irá perguntar o nome de todas as pessoas e colcar num lista de convidados.
Após isso deve imprimir todos os nomes da lista
'''
'''
qtd = int(input("Quantas pessoas vão ser convidadas?"))
lista_pe... | normal | {
"blob_id": "426a8fb6d1adf5d4577d299083ce047c919dda67",
"index": 3525,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Programinha de controle de festinhas 1.0')\nprint('#' * 20)\n<mask token>\nwhile i <= numero_de_convidados:\n nome_do_convidado = input('Coloque o nome do convidado #' + str(i) ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open('./KF31.txt', 'w') as writeFile:
with open(txt, 'r') as readFile:
for text in readFile:
listData = text.split('\t')
surface = listData[0]
if surface == 'EOS\n':
... | flexible | {
"blob_id": "778ee9a0ea7f57535b4de88a38cd741f2d46e092",
"index": 6966,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('./KF31.txt', 'w') as writeFile:\n with open(txt, 'r') as readFile:\n for text in readFile:\n listData = text.split('\\t')\n surface = listData[0... | [
0,
1,
2,
3
] |
__version__ = '1.1.3rc0'
| normal | {
"blob_id": "2e5bbc8c6a5eac2ed71c5d8619bedde2e04ee9a6",
"index": 4932,
"step-1": "<mask token>\n",
"step-2": "__version__ = '1.1.3rc0'\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for string_address in ['192.168.1.1', '127.0.0.1']:
packed = socket.inet_aton(string_address)
print('Originale :', string_address)
print('Impacchettato:', binascii.hexlify(packed))
print('Spacchettato :', socket... | flexible | {
"blob_id": "01626772b0f47987157e9f92ba2ce66a0ec2dcb4",
"index": 4379,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor string_address in ['192.168.1.1', '127.0.0.1']:\n packed = socket.inet_aton(string_address)\n print('Originale :', string_address)\n print('Impacchettato:', binascii.hexli... | [
0,
1,
2,
3
] |
import pandas as pd
import numpy as np
import logging
import sklearn
from joblib import load
import sys
import warnings
import os
if not sys.warnoptions:
warnings.simplefilter("ignore")
class model:
def __init__(self):
#from number to labels
self.number_to_label = {1 : "Bot",2 : 'DoS attack',3... | normal | {
"blob_id": "c0f3a957613a4f4e04aeb3eb2e3fa4053bd0122c",
"index": 8438,
"step-1": "<mask token>\n\n\nclass model:\n\n def __init__(self):\n self.number_to_label = {(1): 'Bot', (2): 'DoS attack', (3):\n 'Brute Force', (5): 'DDoS attacks', (4): 0}\n try:\n self.model = load('.... | [
4,
5,
6,
8,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(2, 100):
lst.append(i * 100)
lst.append(i * 10000)
lst.append(10000)
print(297)
print(*lst)
<|reserved_special_token_1|>
w = int(input())
lst = [(i + 1) for i in range(100)]
for i in range(2, 100):
ls... | flexible | {
"blob_id": "1d004ec0f4f5c50f49834f169812737d16f22b96",
"index": 3967,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(2, 100):\n lst.append(i * 100)\n lst.append(i * 10000)\nlst.append(10000)\nprint(297)\nprint(*lst)\n",
"step-3": "w = int(input())\nlst = [(i + 1) for i in range(10... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(' Podaj 5 imion')
for i in range(1, 6):
imie = input(f' Podaj imie nr {i} ')
plik.write(f' {imie} \n')
plik.close()
<|reserved_special_token_0|>
for i in range(1, 101):
plik.write(str(i))
plik.write('\n')
pli... | flexible | {
"blob_id": "0ac99e2b33f676a99674c9a8e5d9d47c5bce084b",
"index": 5820,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(' Podaj 5 imion')\nfor i in range(1, 6):\n imie = input(f' Podaj imie nr {i} ')\n plik.write(f' {imie} \\n')\nplik.close()\n<mask token>\nfor i in range(1, 101):\n plik.wri... | [
0,
1,
2,
3
] |
from pyecharts.charts.pie import Pie
from pyecharts.charts.map import Map
import static.name_map
from pymongo import MongoClient
# html代码头尾
html1 = '<!DOCTYPE html><html lang="en"><head><meta charset="UTF-8"><title>疫情数据可视化</title><script src="/static/echarts/echarts.js"></script><script src="/static/china.js"></... | normal | {
"blob_id": "f1c65fc4acafbda59aeea4f2dfca2cf5012dd389",
"index": 8982,
"step-1": "<mask token>\n\n\ndef make_PieChart(country):\n global Data\n Data = []\n client = MongoClient()\n db = client.mydb\n if country == 'China':\n tb = db.ChinaData\n else:\n tb = db.WorldData\n re = ... | [
2,
3,
4,
5,
6
] |
# -*- coding: utf-8 -*-
import math
# 冒泡排序（Bubble Sort）
# 比较相邻的元素。如果第一个比第二个大，就交换它们两个；
# 对每一对相邻元素作同样的工作，从开始第一对到结尾的最后一对，这样在最后的元素应该会是最大的数；
# 针对所有的元素重复以上的步骤，除了最后一个；
# 重复步骤1~3，直到排序完成。
# 冒泡排序总的平均时间复杂度为：O(n^2)
def bubble_sort(input):
print("\nBubble Sort")
input_len = len(input)
print("length of input: %d" % i... | normal | {
"blob_id": "c967aa647a97b17c9a7493559b9a1577dd95263a",
"index": 7806,
"step-1": "<mask token>\n\n\ndef select_sort(input):\n print('\\nSelect Sort')\n input_len = len(input)\n for i in range(0, input_len):\n min_index = i\n for j in range(i + 1, input_len):\n if input[j] < inpu... | [
1,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Solution:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
class Solution:
def search(self, nums: List[int], target: int) ->int:
n = len(nums)
left, right = 0, n - 1
found = Fal... | flexible | {
"blob_id": "1fe6fab717a77f13ddf7059ef0a5aaef217f0fb0",
"index": 5525,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n\n\n<mask token>\n",
"step-3": "class Solution:\n\n def search(self, nums: List[int], target: int) ->int:\n n = len(nums)\n left, right = 0, n - 1\... | [
0,
1,
2,
3
] |
from datetime import datetime, timedelta
from request.insider_networking import InsiderTransactions
from db import FinanceDB
from acquisition.symbol.financial_symbols import Financial_Symbols
class FintelInsiderAcquisition():
def __init__(self, trading_date=None):
self.task_name = 'FintelInsiderAcquisiti... | normal | {
"blob_id": "08b13069020696d59028003a11b0ff06014a4c68",
"index": 3779,
"step-1": "<mask token>\n\n\nclass FintelInsiderAcquisition:\n\n def __init__(self, trading_date=None):\n self.task_name = 'FintelInsiderAcquisition'\n self.trading_date = trading_date\n self.symbols = Financial_Symbol... | [
5,
7,
9,
10,
11
] |
<|reserved_special_token_0|>
def getBboxes(bboxes):
return [bb for bb in bboxes if sum(bb) > 0.0]
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('./{0}_??.txt'.format(searchAreaName))
<|reserved_special_token_0|>
def getBboxes(bboxes):
return [bb for bb in ... | flexible | {
"blob_id": "8f9d823785d42d02a0a3d901d66b46a5cd59cdd7",
"index": 7465,
"step-1": "<mask token>\n\n\ndef getBboxes(bboxes):\n return [bb for bb in bboxes if sum(bb) > 0.0]\n\n\n<mask token>\n",
"step-2": "<mask token>\nprint('./{0}_??.txt'.format(searchAreaName))\n<mask token>\n\n\ndef getBboxes(bboxes):\n ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
try:
alp = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
idx = eval(input('请输入一个整数'))
print(alp[idx])
except NameError:
print('输入错误，请输入一个整数')
except:
print('其他错误')
else:
print('没有发生错误')
finally:
print('程序执行完毕，不知道是否发生了异常')
<|reserved_special_token... | flexible | {
"blob_id": "99a6b450792d434e18b8f9ff350c72abe5366d95",
"index": 153,
"step-1": "<mask token>\n",
"step-2": "try:\n alp = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n idx = eval(input('请输入一个整数'))\n print(alp[idx])\nexcept NameError:\n print('输入错误，请输入一个整数')\nexcept:\n print('其他错误')\nelse:\n print('没有发生错误')\... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class Command:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Command... | flexible | {
"blob_id": "91cef72962332e7efcc86f1b19da4382bd72a466",
"index": 9278,
"step-1": "<mask token>\n\n\nclass Command:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Command:\n <mask token>\n <mask token>\n ... | [
1,
3,
4,
5,
6
] |
# Copyright 2021 QuantumBlack Visual Analytics Limited
#
# 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
#
# THE SOFTWARE IS PROVIDED "AS IS",... | normal | {
"blob_id": "0069a61127c5968d7014bdf7f8c4441f02e67df0",
"index": 6541,
"step-1": "<mask token>\n\n\nclass WaitForException(Exception):\n \"\"\"WaitForException: if func doesn't return expected result within the specified time\"\"\"\n\n\ndef _wait_for(func: Callable, expected_result: Any=True, timeout: int=10,... | [
4,
5,
6,
7,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
DATABASE_NAME = 'user_db'
<|reserved_special_token_1|>
DATABASE_NAME = "user_db" | flexible | {
"blob_id": "8c8bbbc682889c8d79c893f27def76ad70e8bf8d",
"index": 233,
"step-1": "<mask token>\n",
"step-2": "DATABASE_NAME = 'user_db'\n",
"step-3": "DATABASE_NAME = \"user_db\"",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
from datetime import date
atual = date.today().year
totmaior = 0
totmenor = 0
for pessoas in range(1, 8):
nasc = int(input(f'Qual sua data de nascimento? {pessoas}º: '))
idade = atual - nasc
if idade >= 21:
totmaior += 1
else:
totmenor += 1
print(f'Ao todo tivemos {totmaior} pessoas maio... | normal | {
"blob_id": "f6d7ce2d020d11086640a34aac656098ab0b0f33",
"index": 9495,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor pessoas in range(1, 8):\n nasc = int(input(f'Qual sua data de nascimento? {pessoas}º: '))\n idade = atual - nasc\n if idade >= 21:\n totmaior += 1\n else:\n ... | [
0,
1,
2,
3
] |
from fastapi import FastAPI
from pydantic import BaseModel
from typing import List, Optional
from joblib import load
app = FastAPI()
clf = load("model.joblib")
class PredictionRequest(BaseModel):
feature_vector: List[float]
score: Optional[bool] = False
@app.post("/prediction")
def predict(req: PredictionReq... | normal | {
"blob_id": "d6fa3039c0987bf556c5bd78b66eb43543fd00fe",
"index": 6343,
"step-1": "<mask token>\n\n\nclass PredictionRequest(BaseModel):\n feature_vector: List[float]\n score: Optional[bool] = False\n\n\n@app.post('/prediction')\ndef predict(req: PredictionRequest):\n prediction = clf.predict([req.featur... | [
3,
4,
5,
6,
7
] |
<<<<<<< HEAD
{'_data': [['Common', [['Skin', u'Ospecifika hud-reakti oner'], ['General', u'Tr\xf6tthet']]],
['Uncommon',
[['GI',
u'Buksm\xe4rta, diarr\xe9, f\xf6r-stoppnin g, illam\xe5ende (dessa symptom g\xe5r vanligt-vis \xf6ver vid fortsatt behandling).']]],
['Rare',
... | normal | {
"blob_id": "efe13de4ed5a3f42a9f2ece68fd329d8e3147ca2",
"index": 4869,
"step-1": "<<<<<<< HEAD\n{'_data': [['Common', [['Skin', u'Ospecifika hud-reakti oner'], ['General', u'Tr\\xf6tthet']]],\n ['Uncommon',\n [['GI',\n u'Buksm\\xe4rta, diarr\\xe9, f\\xf6r-stoppnin g, illam\\xe5e... | [
0
] |
from turtle import Turtle
class Paddle(Turtle):
def __init__(self, x_position, y_position):
super().__init__()
self.shape('square')
self.shapesize(stretch_wid=5, stretch_len=1)
self.penup()
self.color("white")
self.goto(x=x_position, y=y_position)
self.speed... | normal | {
"blob_id": "f49b80d0b8b42bafc787a36d0a8be98ab7fa53e7",
"index": 3558,
"step-1": "<mask token>\n\n\nclass Paddle(Turtle):\n <mask token>\n\n def up(self):\n y_pos = self.ycor()\n x_pos = self.xcor()\n self.goto(y=y_pos + 20, x=x_pos)\n\n def down(self):\n y_pos = self.ycor()\... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def velocity_field_visualization(xmin, xmax, ymin, ymax):
with open('data_sample/argo_MixtureModel_%d_%d_%d_%d' % (xmin, xmax,
ymin, ymax), 'rb') as mix_np:
mix_model = pickle.load(mix_np)
with open('data... | flexible | {
"blob_id": "1284de6474e460f0d95f5c76d066b948bce59228",
"index": 5575,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef velocity_field_visualization(xmin, xmax, ymin, ymax):\n with open('data_sample/argo_MixtureModel_%d_%d_%d_%d' % (xmin, xmax,\n ymin, ymax), 'rb') as mix_np:\n mix... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.11.4 on 2017-10-02 14:41
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
import mptt.fields
class Migration(migrations.Migration):
dependencies = [
('barriers', '0011_auto_20170904_1658'),
... | normal | {
"blob_id": "645f8f1ebd3bfa0ba32d5be8058b07e2a30ba9b5",
"index": 1314,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('barriers', ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
app_name = 'directory'
urlpatterns = [re_path('^directory/uploader/?$', UploaderAPIView.as_view(),
name='teacher_uploader'), re_path('^directory/teachers/?$',
TeacherListAPIView.as_view(), name='teacher_list'), path(
'... | flexible | {
"blob_id": "666e839b4d66dc4eede4e7325bfd4f4b801fd47d",
"index": 5330,
"step-1": "<mask token>\n",
"step-2": "<mask token>\napp_name = 'directory'\nurlpatterns = [re_path('^directory/uploader/?$', UploaderAPIView.as_view(),\n name='teacher_uploader'), re_path('^directory/teachers/?$',\n TeacherListAPIVie... | [
0,
1,
2,
3
] |
""" Class implementing ReportGenerator """
from urllib.parse import urlparse
import requests
from src.classes.reporter.flag import Flag
from src.classes.reporter.line_finder import LineFinder
class ReportGenerator(object):
"""
Class designed to generate reports after CSP audition
The ReportGenerator cla... | normal | {
"blob_id": "2003060f7793de678b4a259ad9424cd5927a57f7",
"index": 3167,
"step-1": "<mask token>\n\n\nclass ReportGenerator(object):\n <mask token>\n <mask token>\n\n def run(self, html, url):\n print('[#] Running the report generator')\n self.html = html\n self.getting_flags_location... | [
8,
13,
14,
15,
17
] |
def resolve_data(raw_data, derivatives_prefix):
derivatives = {}
if isinstance(raw_data, dict):
for k, v in raw_data.items():
if isinstance(v, dict):
derivatives.update(resolve_data(v, derivatives_prefix + k +
'_'))
elif isinstance(v, list):
... | normal | {
"blob_id": "31b109d992a1b64816f483e870b00c703643f514",
"index": 6577,
"step-1": "<mask token>\n",
"step-2": "def resolve_data(raw_data, derivatives_prefix):\n derivatives = {}\n if isinstance(raw_data, dict):\n for k, v in raw_data.items():\n if isinstance(v, dict):\n de... | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def foo_6(x, y):
return y, x
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def foo_6(x, y):
return y, x
<|reserved_special_token_0|>
foo_6(a, b)
print(a, b)
<|reserved_... | flexible | {
"blob_id": "ad5a9e353d065eee477381aa6b1f233f975ea0ed",
"index": 3374,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef foo_6(x, y):\n return y, x\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef foo_6(x, y):\n return y, x\n\n\n<mask token>\nfoo_6(a, b)\nprint(a, b)\n",
"step-4": "<... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python
#encoding=utf-8
import os, sys
rules = {
'E': ['A'],
'A': ['A+M', 'M'],
'M': ['M*P', 'P'],
'P': ['(E)', 'N'],
'N': [str(i) for i in range(10)],
}
#st为要扫描的字符串
#target为终止状态，即最后的可接受状态
def back(st, target):
reduced_sets = set()
#cur为当前规约后的字符串，hist为记录的规约规则
def _back(cur, ... | normal | {
"blob_id": "93953f025fed2bcabf29433591689c0a7adf9569",
"index": 8757,
"step-1": "#!/usr/bin/python\n#encoding=utf-8\n\nimport os, sys\n\nrules = {\n 'E': ['A'],\n 'A': ['A+M', 'M'],\n 'M': ['M*P', 'P'],\n 'P': ['(E)', 'N'],\n 'N': [str(i) for i in range(10)],\n}\n\n#st为要扫描的字符串\n#target为终止状态，即最后的可... | [
0
] |
from tkinter import*
me=Tk()
me.geometry("354x460")
me.title("CALCULATOR")
melabel = Label(me,text="CALCULATE HERE",bg='PINK',font=("ARIAL",25))
melabel.pack(side=TOP)
me.config(background='BROWN')
displayStr=StringVar()
op=""
def but(a):
global op
op=op+str(a)
displayStr.set(op)
def eq():
... | normal | {
"blob_id": "106cca8af164fa4ae946f77b40c76e03accf171c",
"index": 9645,
"step-1": "<mask token>\n\n\ndef but(a):\n global op\n op = op + str(a)\n displayStr.set(op)\n\n\ndef eq():\n global op\n result = str(eval(op))\n displayStr.set(result)\n op = ''\n\n\ndef clrbut():\n displayStr.set(''... | [
3,
4,
5,
6,
7
] |
class Solution:
# @param num, a list of integer
# @return an integer
def longestConsecutive(self, num):
sted = {}
n = len(num)
for item in num:
if item in sted:
continue
sted[item] = item
if item-1 in sted:
sted[item... | normal | {
"blob_id": "d7c4bee7245dab1cbb90ee68b8e99994ce7dd219",
"index": 3295,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n",
"step-3": "class Solution:\n\n def longestConsecutive(self, num):\n sted = {}\n n = len(num)\n for item in num:\n if item in s... | [
0,
1,
2,
3
] |
# Bengisu Ayan - 2236974
# Ceren Gürsoy - 2237485
import numpy as np
import cv2
B1 = "THE3-Images/B1.jpg"
B2 = "THE3-Images/B2.jpg"
B3 = "THE3-Images/B3.jpg"
B4 = "THE3-Images/B4.jpg"
B5 = "THE3-Images/B5.jpg"
def segmentation_function(image, name, blue_mask=False, white_mask=False, yellow_mask=False):
# Smo... | normal | {
"blob_id": "1614157c57b3d1b30087c42cb840d617dc91eecb",
"index": 493,
"step-1": "<mask token>\n\n\ndef segmentation_function(image, name, blue_mask=False, white_mask=False,\n yellow_mask=False):\n image = cv2.GaussianBlur(image, (11, 11), 0)\n hsv_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n low_... | [
1,
2,
3,
4,
5
] |
from google.cloud import pubsub_v1
import os
from flask import Flask, request, jsonify
from google.cloud import pubsub_v1
import os
from gcloud import storage
import json
import datetime
import time
app = Flask(__name__)
os.environ[
"GOOGLE_APPLICATION_CREDENTIALS"] = "/home/vishvesh/Documents/Dal/serverless/api-... | normal | {
"blob_id": "a76a0631c97ba539019790e35136f6fd7573e461",
"index": 5469,
"step-1": "<mask token>\n\n\n@app.route('/publish', methods=['GET', 'POST'])\ndef publish():\n topic_user = request.args.get('touser')\n sub_user = request.args.get('fromuser')\n subscription_id = sub_user\n msg = request.args.get... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class settings:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_s... | flexible | {
"blob_id": "5e06dfb7aac64b5b98b4c0d88a86f038baf44feb",
"index": 5412,
"step-1": "<mask token>\n\n\nclass settings:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass settings:\n order = pf.FIT_... | [
1,
3,
4,
5,
6
] |
import urllib2
import urllib
import json
import gzip
from StringIO import StringIO
service_url = 'https://babelfy.io/v1/disambiguate'
lang = 'EN'
key = ''
filehandle = open('triples/triples2.tsv') # the triples and the sentences where the triples were extracted
filehandle_write = open('triples/disambiguated_triples... | normal | {
"blob_id": "cd9f94d55eb13f5fc9959546e89a0af8ab2ea0db",
"index": 6147,
"step-1": "import urllib2\nimport urllib\nimport json\nimport gzip\n\nfrom StringIO import StringIO\n\nservice_url = 'https://babelfy.io/v1/disambiguate'\nlang = 'EN'\nkey = ''\n\nfilehandle = open('triples/triples2.tsv') # the triples and t... | [
0
] |
<|reserved_special_token_0|>
class DeepFont(tf.keras.Model):
def __init__(self):
super(DeepFont, self).__init__()
self.batch_size = 128
self.model = tf.keras.Sequential()
self.model.add(tf.keras.layers.Reshape((96, 96, 1)))
self.model.add(tf.keras.layers.Conv2D(trainable=F... | flexible | {
"blob_id": "919239391c6f74d0d8627d3b851beb374eb11d25",
"index": 4785,
"step-1": "<mask token>\n\n\nclass DeepFont(tf.keras.Model):\n\n def __init__(self):\n super(DeepFont, self).__init__()\n self.batch_size = 128\n self.model = tf.keras.Sequential()\n self.model.add(tf.keras.laye... | [
10,
11,
12,
13,
14
] |
<|reserved_special_token_0|>
def upsample1(d, p):
assert 1 <= p <= 10
return d + p
def upsample2(d, p):
assert 2 <= p <= 3
return d * p
def downsample(d, p):
assert 2 <= p <= 10
return math.ceil(d / p)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_... | flexible | {
"blob_id": "cb6f68c8b8a6cead1d9fcd25fa2a4e60f7a8fb28",
"index": 9746,
"step-1": "<mask token>\n\n\ndef upsample1(d, p):\n assert 1 <= p <= 10\n return d + p\n\n\ndef upsample2(d, p):\n assert 2 <= p <= 3\n return d * p\n\n\ndef downsample(d, p):\n assert 2 <= p <= 10\n return math.ceil(d / p)\... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
def find_saddle_points(A):
B = []
for i in range(A.shape[0]):
min_r = np.min(A[i])
ind_r = 0
max_c = 0
ind_c = 0
for j in range(A.shape[1]):
if A[i][j] == min_r:
min_r = A[i][j]
ind_r = j
... | flexible | {
"blob_id": "808fe8f106eaff00cf0080edb1d8189455c4054b",
"index": 6706,
"step-1": "<mask token>\n\n\ndef find_saddle_points(A):\n B = []\n for i in range(A.shape[0]):\n min_r = np.min(A[i])\n ind_r = 0\n max_c = 0\n ind_c = 0\n for j in range(A.shape[1]):\n if A... | [
5,
8,
9,
11,
12
] |
<|reserved_special_token_0|>
class MongoStorage(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def __init__(self, connection):
self._connection = connection
self._collection = connection.objects
... | flexible | {
"blob_id": "816c11717c4f26b9013f7a83e1dfb2c0578cbcf8",
"index": 1269,
"step-1": "<mask token>\n\n\nclass MongoStorage(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, connection):\n self._connection = connection\n self._collection = connect... | [
7,
8,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def vowel_links(txt):
import re
lst = txt.split(' ')
for i in range(len(lst) - 1):
if re.search('[aeiou]', lst[i][-1]) and re.search('[aeiou]', lst[i +
1][0]):
return True
return F... | flexible | {
"blob_id": "eefd94e7c04896cd6265bbacd624bf7e670be445",
"index": 4347,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef vowel_links(txt):\n import re\n lst = txt.split(' ')\n for i in range(len(lst) - 1):\n if re.search('[aeiou]', lst[i][-1]) and re.search('[aeiou]', lst[i +\n ... | [
0,
1,
2
] |
# models.py
from sentiment_data import *
from utils import *
import nltk
from nltk.corpus import stopwords
import numpy as np
from scipy.sparse import csr_matrix
class FeatureExtractor(object):
"""
Feature extraction base type. Takes a sentence and returns an indexed list of features.
"""
def get_inde... | normal | {
"blob_id": "5d8d47d77fba9027d7c5ec4e672fc0c597b76eae",
"index": 4091,
"step-1": "<mask token>\n\n\nclass UnigramFeatureExtractor(FeatureExtractor):\n <mask token>\n <mask token>\n <mask token>\n\n\nclass BigramFeatureExtractor(FeatureExtractor):\n \"\"\"\n Bigram feature extractor analogous to th... | [
22,
29,
30,
31,
34
] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.