code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
primos = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]
# números entre (8 - 26) e (44 - 44)
intervalo = list(range(8, 27)) + list(range(49, 50))
is_magic = []
for n in primos:
quadrado = n ** 2
if quadrado in intervalo:
is_magic.append(quadrado)
print(len(is_magic)) # 3 | normal | {
"blob_id": "b7f443521e165f327aae9ff5d7bbb7b8462abeb5",
"index": 2890,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor n in primos:\n quadrado = n ** 2\n if quadrado in intervalo:\n is_magic.append(quadrado)\nprint(len(is_magic))\n",
"step-3": "primos = [2, 3, 5, 7, 11, 13, 17, 19, 23, ... | [
0,
1,
2,
3
] |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2018 Cesar Sinchiguano <cesarsinchiguano@hotmail.es>
#
# Distributed under terms of the BSD license.
"""
"""
import numpy as np
from open3d import *
def main():
print("Load a ply point cloud, print it, and render it")
pcd = read_... | normal | {
"blob_id": "30e8e269cf6500ab804566a85c9b96b3ef9bda36",
"index": 4143,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n print('Load a ply point cloud, print it, and render it')\n pcd = read_point_cloud('11.ply')\n \"\"\" read_point_cloud reads a point cloud from a file.\n ... | [
0,
1,
2,
3,
4
] |
target = []
with open('IntegerArray.txt', 'r') as f:
target = f.readlines()
for x in range(len(target)):
target[x] = int(target[x])
def f(A):
if len(A) == 1:
return 0
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = co... | normal | {
"blob_id": "b5611c668a40e1735c92d6d00867885023ad713f",
"index": 248,
"step-1": "<mask token>\n\n\ndef f(A):\n if len(A) == 1:\n return 0\n else:\n rightStart = len(A) // 2\n leftArray = A[0:rightStart]\n righArray = A[rightStart:]\n B, b = count_and_sort(leftArray)\n ... | [
2,
3,
4,
5
] |
# Name: CreateDatabase.py
# Description: Connects to a point in time in the geodatabase in
# PostgreSQL using database authentication.
# Import system modules
import arcpy
import os
arcpy.env.workspace="Database Connections"
if arcpy.Exists ("Prueba6.sde")==False:
arcpy.CreateDatabaseConnection_m... | normal | {
"blob_id": "6e98dfd758700c57ddbb17624472ce2c23cbee6a",
"index": 2036,
"step-1": "# Name: CreateDatabase.py\n# Description: Connects to a point in time in the geodatabase in\n# PostgreSQL using database authentication.\n\n# Import system modules\nimport arcpy\nimport os\n\n\n\n\narcpy.env.workspace=... | [
0
] |
import sys
sys.stdin = open('retire.txt', 'r')
def counseling(pay, row):
global max_sum
if row == N - 1:
if arr[row][0] == 1:
pay += arr[row][1]
max_sum = max(pay, max_sum)
return
if row == N:
max_sum = max(pay, max_sum)
return
if row > N - 1:
... | normal | {
"blob_id": "9db2377f15aaf28373959dad88c6ec7b6dacffd2",
"index": 9512,
"step-1": "<mask token>\n\n\ndef counseling(pay, row):\n global max_sum\n if row == N - 1:\n if arr[row][0] == 1:\n pay += arr[row][1]\n max_sum = max(pay, max_sum)\n return\n if row == N:\n max... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/python
import argparse
import contextlib
import os.path
import shutil
import subprocess
import sys
import tempfile
from Bio import SeqIO
BOOTSTRAP_MODES = 'a',
# Some utilities
@contextlib.contextmanager
def sequences_in_format(sequences, fmt='fasta', **kwargs):
with tempfile.NamedTemporaryFile(**kwa... | normal | {
"blob_id": "28532fe798b6a764bec7ea511ba9e66a1d096b6f",
"index": 9364,
"step-1": "#!/usr/bin/python\n\nimport argparse\nimport contextlib\nimport os.path\nimport shutil\nimport subprocess\nimport sys\nimport tempfile\n\nfrom Bio import SeqIO\n\nBOOTSTRAP_MODES = 'a',\n\n# Some utilities\n@contextlib.contextmanag... | [
0
] |
from output.models.sun_data.ctype.content_type.content_type00401m.content_type00401m_xsd.content_type00401m import (
A1,
A,
)
__all__ = [
"A1",
"A",
]
| normal | {
"blob_id": "846a42a997539a45576d3ecbe0bd290e00b55935",
"index": 3258,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n__all__ = ['A1', 'A']\n",
"step-3": "from output.models.sun_data.ctype.content_type.content_type00401m.content_type00401m_xsd.content_type00401m import A1, A\n__all__ = ['A1', 'A']\n",
... | [
0,
1,
2,
3
] |
import math
# type defining of the variable and playing with variables.
a = 5.0
print(id(a))
a = 10
print("hello.....")
print(type(a))
print(id(a))
# locating addresses...
b = [5, 6, 7]
print(id(b))
b.append(10)
print(id(b))
# Strings...
name = input("Enter Your Name:: ") # iNPUTTING AS NAME
pri... | normal | {
"blob_id": "95b75395cafc6ba9f75ecf48157421e37ced2518",
"index": 815,
"step-1": "<mask token>\n\n\ndef rows(**ro):\n print(ro)\n\n\n<mask token>\n",
"step-2": "<mask token>\nprint(id(a))\n<mask token>\nprint('hello.....')\nprint(type(a))\nprint(id(a))\n<mask token>\nprint(id(b))\nb.append(10)\nprint(id(b))\... | [
1,
3,
4,
5,
6
] |
import cv2
import glob
import numpy as np
import csv
import matplotlib.pyplot as plt
from pydarknet import Detector,Image
"""
Calculates the average precision based on the precision and recall values,
which are essentially the output of getPrecisionRecall
Returns the 101pt interpolation curve and a single av... | normal | {
"blob_id": "f8a31cdf5f55b5aed33a407d2c008ba9b969d655",
"index": 9493,
"step-1": "<mask token>\n\n\ndef getIntersection(a, b):\n intersection = [0, 0, 0, 0]\n if b[0] <= a[0] and a[0] <= b[2]:\n intersection[0] = a[0]\n elif a[0] <= b[0] and b[0] <= a[2]:\n intersection[0] = b[0]\n else... | [
3,
5,
6,
7,
8
] |
import datetime
now = datetime.datetime.now()
print(now.year, now.month, now.day, now.hour, now.minute, now.second)
| normal | {
"blob_id": "3af91de0b25f575ec9d981d7711c710a7e9695e4",
"index": 6819,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(now.year, now.month, now.day, now.hour, now.minute, now.second)\n",
"step-3": "<mask token>\nnow = datetime.datetime.now()\nprint(now.year, now.month, now.day, now.hour, now.minut... | [
0,
1,
2,
3
] |
#Creating function
def name_of_function():
'''
Docstring explains function.
'''
return "Hello" #use return instead of print since return can be stored as a variable.
#Simple example
def dog_check(mystring):
if 'dog' in mystring.lower():
return True
else:
return False
#This is a beginner mo... | normal | {
"blob_id": "1deb070dd91c01190b70fa678add31ecb82f34fa",
"index": 3404,
"step-1": "def name_of_function():\n \"\"\"\n Docstring explains function.\n \"\"\"\n return 'Hello'\n\n\ndef dog_check(mystring):\n if 'dog' in mystring.lower():\n return True\n else:\n return False\n\n\n<mask tok... | [
5,
6,
7,
8,
9
] |
import argparse
import sys
import subprocess
import getpass
# Process arguments
parser = argparse.ArgumentParser(description='Setup a new apache virtual host on an Ubuntu system. Only tested on versions 18.04 and 20.04')
parser.add_argument('domain_name', metavar='D', type=str, nargs='+', help='domain name to give to ... | normal | {
"blob_id": "a8e67ddbb741af6a9ff7540fef8c21468321ede0",
"index": 7996,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('domain_name', metavar='D', type=str, nargs='+', help=\n 'domain name to give to virtual host. multiple domains can be specified at once'\n )\n<mask token>\nprin... | [
0,
1,
2,
3,
4
] |
import numpy
from scipy.optimize import OptimizeResult
from logging import getLogger
logger = getLogger(__name__)
def minimize_neldermead(func, x0, args=(), callback=None,
maxiter=None, maxfev=None, disp=False,
return_all=False, initial_simplex=None,
... | normal | {
"blob_id": "35921b081e8e8c4da2b16afc20b27b636e9a6676",
"index": 4761,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef minimize_neldermead(func, x0, args=(), callback=None, maxiter=None,\n maxfev=None, disp=False, return_all=False, initial_simplex=None, xatol=\n 0.0001, fatol=0.0001, **unkno... | [
0,
1,
2,
3,
4
] |
from typing import Any, Callable, Generator, List, Optional
import pytest
from _pytest import nodes
from _pytest.config import hookimpl
from _pytest.python import Function, PyCollector # type: ignore
from hypothesis.errors import InvalidArgument # pylint: disable=ungrouped-imports
from .._hypothesis import create_t... | normal | {
"blob_id": "2060f0af351c1487f8aa45943dbaa050f4291c58",
"index": 7791,
"step-1": "<mask token>\n\n\nclass SchemathesisCase(PyCollector):\n <mask token>\n\n def _get_test_name(self, endpoint: Endpoint) ->str:\n return f'{self.name}[{endpoint.method}:{endpoint.path}]'\n\n def _gen_items(self, endpo... | [
4,
6,
7,
8,
9
] |
from data.dataframe_sequence_multi import DataFrameSequenceMulti
from metrics import Metrics
from models.models_ts_multi import lstm_model_multi
import threading
import sys
from keras import optimizers
from data.data_helper import plot_history
epochs = 100
start = 6
end = 18
res = []
sets = []
min_vals = []
min_loss ... | normal | {
"blob_id": "af903feda57e4ace0c7f909abbeb86bb9a7e4d8c",
"index": 1806,
"step-1": "<mask token>\n\n\ndef run_final_test_days():\n sqs = [5]\n cams = [1]\n permutations = [(True, True, True)]\n permutations_names = ['all data perez']\n for pidx, p in enumerate(permutations):\n for s in sqs:\n... | [
3,
5,
7,
8,
9
] |
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
tf.__version__
import glob
import imageio
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
from tensorflow.keras import layers
import time
import pathlib
from IPython import display
###---... | normal | {
"blob_id": "e007e2d32fa799e7658813f36911616f7bf58b48",
"index": 3972,
"step-1": "<mask token>\n\n\ndef preprocess_image(image):\n image = tf.image.decode_jpeg(image, channels=3)\n image = tf.image.resize(image, [280, 280])\n image /= 255.0\n return image\n\n\n<mask token>\n\n\ndef make_generator_mod... | [
8,
10,
12,
13,
14
] |
class Book:
"""Class that defines book model."""
def __init__(self, title, authors, pub_year):
self.title = title
self.authors = authors
self.pub_year = pub_year
| normal | {
"blob_id": "14345a8c4e20d84dfc87476d890f59530a8f4d96",
"index": 7237,
"step-1": "<mask token>\n",
"step-2": "class Book:\n <mask token>\n <mask token>\n",
"step-3": "class Book:\n <mask token>\n\n def __init__(self, title, authors, pub_year):\n self.title = title\n self.authors = a... | [
0,
1,
2,
3
] |
import unittest
import hospital.employee.nurse as n
class TestNurse(unittest.TestCase):
@classmethod
def setUpClass(cls):
print('Start testing nurse')
def setUp(self):
self.n1 = n.Nurse('Tess',18,"5436890982",3200,25)
self.n2 = n.Nurse('Melissa',40,"8920953924",9000,5)
def... | normal | {
"blob_id": "f24075ea70851ce95bb6b3cd87b6417f8141d546",
"index": 9112,
"step-1": "<mask token>\n\n\nclass TestNurse(unittest.TestCase):\n <mask token>\n\n def setUp(self):\n self.n1 = n.Nurse('Tess', 18, '5436890982', 3200, 25)\n self.n2 = n.Nurse('Melissa', 40, '8920953924', 9000, 5)\n <m... | [
7,
8,
9,
11,
13
] |
from django.contrib import admin
from lesson.models import ProgrammingEnvironment, Language, Lesson, LessonHint
# list_display - Show these fields for each model on the Admin site
# search_fields - Allow searching in these fields
# Register models for the Admin site
class ProgrammingEnvironmentAdmin(admin.ModelAdmin)... | normal | {
"blob_id": "2500c3562819e4e85ce3cbc30e0ddf1b8437e0a2",
"index": 6448,
"step-1": "<mask token>\n\n\nclass LanguageAdmin(admin.ModelAdmin):\n <mask token>\n list_display = 'language_name', 'description', 'environment'\n filter_horizontal = ()\n list_filter = ()\n fieldsets = ()\n\n\nclass LessonAdm... | [
8,
9,
12,
14,
15
] |
from datetime import datetime
import struct
BEACON_LENGTH = 84
EPS_LENGTH = 20
COM_LENGTH = 10
# reverse engineered
ADCS1_LENGTH = 7
ADCS2_LENGTH = 6
AIS_LENGTH = 20
class EPS(object):
def __init__(self, eps_data):
if len(eps_data) != EPS_LENGTH:
raise InputException(len(eps_data), EPS_LENGTH... | normal | {
"blob_id": "505689803c8f4490619ab1a7579fde1e2c18c538",
"index": 5532,
"step-1": "<mask token>\n\n\nclass ADCS2(object):\n\n def __init__(self, adcs2_data):\n self.gyro = tuple(struct.unpack('>hhh', adcs2_data))\n <mask token>\n\n\nclass AIS(object):\n\n def __init__(self, ais_data):\n sel... | [
8,
10,
19,
20,
21
] |
"""SamsungTV Encrypted."""
import aiohttp
from aioresponses import aioresponses
import pytest
from yarl import URL
from samsungtvws.encrypted.authenticator import SamsungTVEncryptedWSAsyncAuthenticator
@pytest.mark.asyncio
async def test_authenticator(aioresponse: aioresponses) -> None:
with open("tests/fixtures... | normal | {
"blob_id": "e1448e62020f87e315d219be97d9af84607441df",
"index": 9104,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.mark.asyncio\nasync def test_authenticator(aioresponse: aioresponses) ->None:\n with open('tests/fixtures/auth_pin_status.xml') as file:\n aioresponse.get('http://1.... | [
0,
1,
2,
3
] |
# -*- coding:UTF-8 -*-
from __future__ import print_function
import logging
import numpy as np
from optparse import OptionParser
import sys
from time import time
import matplotlib.pyplot as plt
import os
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from skl... | normal | {
"blob_id": "84a516e924252d897be7444e11acfecd66474090",
"index": 1177,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(forbidpath, 'rb') as f:\n for line in f:\n word = line.strip()\n forbidkword[word] = 0\n<mask token>\nwith open(inputpath, 'rb') as f:\n for line in f:\n ... | [
0,
1,
2,
3,
4
] |
import numpy as np
er = ['why','who','how','where','which','what','when','was','were','did','do','does','is','are','many','much']
qst = []
txt = None
ans = None
fnd = []
def chek_qst(qst):
global er
for h in er:
for i in qst:
if i == h:
qst.remove(i)
# qst ... | normal | {
"blob_id": "d30129248f5245560ee0d3ee786e118427e169d7",
"index": 4616,
"step-1": "<mask token>\n\n\ndef search_word(qst):\n global txt\n for h in qst:\n temp = []\n for n, l in enumerate(txt):\n if [n for i, j in enumerate(l) if h in j] != []:\n temp.append(n)\n ... | [
3,
6,
7,
8,
9
] |
from __future__ import unicode_literals
from django.db import models
from django.contrib.auth.models import User
from django.core.exceptions import ValidationError
from django.utils import timezone
from timesheets.models import TimeSheet
from channels import Group
class ProjectTS(models.Model):
class Meta:
... | normal | {
"blob_id": "df39a97db25f03aca8ebd501283fd6a7c486db8c",
"index": 1243,
"step-1": "<mask token>\n\n\nclass ProjectTSEntry(models.Model):\n description = models.CharField(max_length=150, default='')\n project_time_sheet = models.ForeignKey(ProjectTS, related_name=\n 'project_time_sheet')\n project_... | [
3,
4,
5,
6,
7
] |
from __future__ import absolute_import, unicode_literals
from django.db import DataError, IntegrityError, connection
import pytest
from .models import Page
pytestmark = pytest.mark.django_db
MYSQL_REASON = 'MySQL parses check constraints but are ignored by all engines'
def test_match():
Page.objects.create(u... | normal | {
"blob_id": "96065e7e61b63f915561f117d71092e4bfb9a5da",
"index": 1149,
"step-1": "<mask token>\n\n\n@pytest.mark.skipif('connection.vendor == \"mysql\"', reason=MYSQL_REASON)\ndef test_invalid_regex():\n exception = IntegrityError if connection.vendor == 'sqlite' else DataError\n with pytest.raises(excepti... | [
1,
3,
4,
5,
7
] |
import requests
import urllib.request
from utilities.read_write_utilities import read_set,write_to_csv
import time
from bs4 import BeautifulSoup
import pickledb
import json
import glob
import csv
drugs = read_set('/Users/sandeep.dey/Downloads/2020-02-06_scrape/drugs')
print(drugs)
output_records = []
# fields = ["equ... | normal | {
"blob_id": "e7f511b97f316157a768203afe9f36ea834ebb6c",
"index": 5493,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(drugs)\n<mask token>\nfor drug in drugs:\n with open('/Users/sandeep.dey/Downloads/2020-02-06_scrape/%s' % drug\n ) as json_file:\n for record in json.load(json_fil... | [
0,
1,
2,
3,
4
] |
import ccxt
import json
import time
from baglanti import mysql_baglan
import datetime
import requests
from urllib.parse import urljoin
import sys
db = mysql_baglan("bingo")
cursor = db.cursor()
cursor.execute('SET NAMES utf8;')
cursor.execute('SET CHARACTER SET utf8;')
cursor.execute('SET character_set_co... | normal | {
"blob_id": "1d29ce58ca626155d626216fbbd70d7b241efa25",
"index": 6363,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncursor.execute('SET NAMES utf8;')\ncursor.execute('SET CHARACTER SET utf8;')\ncursor.execute('SET character_set_connection=utf8;')\n<mask token>\ncursor.execute(sql)\n<mask token>\nfor ro... | [
0,
1,
2,
3,
4
] |
from app.routes import home
from .home import bp as home
from .dashboard import bp as dashboard
| normal | {
"blob_id": "358a4948ac1f60e0966328cebf401777042c3d0e",
"index": 5239,
"step-1": "<mask token>\n",
"step-2": "from app.routes import home\nfrom .home import bp as home\nfrom .dashboard import bp as dashboard\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
# encoding=utf-8
from lib.calculate_time import tic,toc
import scipy as sp
import numpy as np
from lib.make_A import make_A
from lib.make_distance import make_distance
from lib.lambda_sum_smallest import lambda_sum_smallest
from lib.fiedler import fiedler
from lib.make_al import make_al
import math
from lib.newmatrix i... | normal | {
"blob_id": "77d545d1a4fc5f96ae19f654a32ab75707434d46",
"index": 7614,
"step-1": "# encoding=utf-8\nfrom lib.calculate_time import tic,toc\nimport scipy as sp\nimport numpy as np\nfrom lib.make_A import make_A\nfrom lib.make_distance import make_distance\nfrom lib.lambda_sum_smallest import lambda_sum_smallest\n... | [
0
] |
def prime_sieve(n):
if n==2: return [2]
elif n<2: return []
s=range(3,n+1,2)
mroot = n ** 0.5
half=(n+1)/2-1
i=0
m=3
while m <= mroot:
if s[i]:
j=(m*m-3)/2
s[j]=0
while j<half:
s[j]=0
j+=m
i=i+1
m=2*i+3
return [2]+[x for x in s if x]
ps = prime_sieve(1000000)
def get_primes_upto(n):
... | normal | {
"blob_id": "5771f49ad5254588f1683a8d45aa81ce472bb562",
"index": 30,
"step-1": "\ndef prime_sieve(n): \n\tif n==2: return [2]\n\telif n<2: return []\n\ts=range(3,n+1,2)\n\tmroot = n ** 0.5\n\thalf=(n+1)/2-1\n\ti=0\n\tm=3\n\twhile m <= mroot:\n\t\tif s[i]:\n\t\t\tj=(m*m-3)/2\n\t\t\ts[j]=0\n\t\t\twhile j<half:\n\t... | [
0
] |
import bs4
from urllib.request import urlopen as uReq
from bs4 import BeautifulSoup as soup
import pandas as pd
import time
from urllib.request import Request
import requests
import json
import re
import sys
def compare(mystring):
def usd_to_ngn():
print("Getting USD to NGN Rate")
r... | normal | {
"blob_id": "d96038a715406388b4de4611391dee18fc559d5a",
"index": 2693,
"step-1": "<mask token>\n\n\ndef compare(mystring):\n\n def usd_to_ngn():\n print('Getting USD to NGN Rate')\n req = requests.get(\n 'http://free.currconv.com/api/v7/convert?q=USD_NGN&apiKey=5029a99b396929294f63'\n... | [
1,
2,
3,
4,
5
] |
import configure
import connectify
import userlog
import dirlog
import time
def getUser(sock):
try:
userinfo = userlog.getInfo()
except:
userinfo = configure.init(sock)
userinfo = userinfo.split('^')[0]
# print userinfo
return userinfo
if __name__=="__main__":
sock = connectify.createCon()
userinfo = get... | normal | {
"blob_id": "2ca1b603b18316bc1d970b5e32389e10e4b532e2",
"index": 1071,
"step-1": "import configure\nimport connectify\nimport userlog\nimport dirlog\nimport time\n\n\ndef getUser(sock):\n\ttry:\n\t\tuserinfo = userlog.getInfo()\n\texcept:\t\n\t\tuserinfo = configure.init(sock)\n\tuserinfo = userinfo.split('^')[0... | [
0
] |
# coding=utf-8
# Copyright 2019 SK T-Brain Authors.
#
# 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 required by applicable law or... | normal | {
"blob_id": "b6e4214ace89165f6cfde9f2b97fcee8be81f2ed",
"index": 4301,
"step-1": "<mask token>\n\n\ndef get_onnx_kobert_model(cachedir='.cache'):\n \"\"\"Get KoBERT ONNX file path after downloading\"\"\"\n onnx_kobert = {'url':\n 's3://skt-lsl-nlp-model/KoBERT/models/kobert.onnx1.8.0.onnx',\n ... | [
1,
2,
3,
4,
5
] |
# The error measures used in this project
#
# Rooth Mean Squared Error
# Mean Absolute Error
#
# ! Both calculated after descaling the output of the system first
import numpy as np
def RMSE(min_y, max_y, yhat, y):
# first scale output and target back to
# original scale, to prevent scale bias
yhat = descale(yhat,... | normal | {
"blob_id": "4fd4c9cf3bdb73a003ce860bf2ee0ccab01f0009",
"index": 4646,
"step-1": "<mask token>\n\n\ndef RMSE(min_y, max_y, yhat, y):\n yhat = descale(yhat, min_y, max_y)\n y = descale(y, min_y, max_y)\n return np.mean(np.power(np.subtract(yhat, y), 2))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
elements = str(input("Type the elements of list: ")).split()
elements = list(map(float,elements))
times = int(input("How many times you wish shift to right: "))
for _ in range(times):
removed = elements.pop()
elements.insert(0,removed)
print(elements) | normal | {
"blob_id": "307bb7461a729ba979f6a862fe7c292c42f96ce6",
"index": 1164,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor _ in range(times):\n removed = elements.pop()\n elements.insert(0, removed)\nprint(elements)\n",
"step-3": "elements = str(input('Type the elements of list: ')).split()\neleme... | [
0,
1,
2,
3
] |
# encoding:utf-8
import tensorflow as tf
import p182.py as p182
# 创建文件列表，并通过文件列表创建输入文件队列。在调用输入数据处理流程前，需要
# 统一所有原始数据的格式并将它们存储到TFRcord文件中。下面给出的文件列表应该包含所
# 有提供训练数据的TFRcord文件
files = tf.train.match_filenames_once("/home/shenxj/tf-work/datasets/file_pattern-*")
filename_queue = tf.train.string_input_producer(files, shuffle=... | normal | {
"blob_id": "1685a2c49bea14e6fcaffb03634f6875f8fa1049",
"index": 3726,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndecoded_image.set_shape([height, width, channels])\n<mask token>\nwith tf.Session() as sess:\n tf.initialize_all_variables().run()\n coord = tf.train.Coordinator()\n threads = tf... | [
0,
1,
2,
3,
4
] |
# -*- coding:utf-8 -*-
#随机森林调参
#RandomizedSearchCV 随机最佳
#GridSearchCV 地毯式最佳
import pandas as pd
features = pd.read_csv('data/temps_extended.csv')
features = pd.get_dummies(features)
labels = features['actual']
features = features.drop('actual', axis = 1)
feature_list = list(features.columns)
import numpy as np... | normal | {
"blob_id": "de4e14a4fa8520c1aae60805084224337dd9620c",
"index": 9009,
"step-1": "<mask token>\n\n\ndef evaluate(model, test_features, test_labels):\n predictions = model.predict(test_features)\n errors = abs(predictions - test_labels)\n mape = 100 * np.mean(errors / test_labels)\n accuracy = 100 - m... | [
1,
2,
3,
4,
5
] |
from celery.task.schedules import crontab
from celery.decorators import periodic_task
from celery.utils.log import get_task_logger
from bbapp.scripts.getScores import doScoresScrape, fixScores
logger = get_task_logger(__name__)
@periodic_task(
run_every=(crontab(minute='*/10')),
name="scrape_espn_feed",
... | normal | {
"blob_id": "a9a067ee3b176d2f2ca558b69ce2bc598bb31d22",
"index": 4501,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@periodic_task(run_every=crontab(minute='*/10'), name='scrape_espn_feed',\n ignore_result=True)\ndef scrape_espn_feed():\n \"\"\"\n Saves latest image from Flickr\n \"\"\"... | [
0,
1,
2,
3,
4
] |
import random
# library to create window in the terminal
import curses
# initialized curses by returning a window object
stdscr = curses.initscr()
curses.noecho()
curses.cbreak()
stdscr.keypad(True)
curses.curs_set(0)
height, width = stdscr.getmaxyx()
# create a new window of a given size
window = cur... | normal | {
"blob_id": "153d37b58a10847aae1fa7dbec4c7576c3d97fb2",
"index": 3407,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncurses.noecho()\ncurses.cbreak()\nstdscr.keypad(True)\ncurses.curs_set(0)\n<mask token>\nwindow.keypad(1)\nwindow.timeout(100)\n<mask token>\nwindow.addch(int(food[0]), int(food[1]), curs... | [
0,
1,
2,
3,
4
] |
from typing import Callable, List, Optional
import numpy as np
import lab1.src.grad.grad_step_strategy as st
import lab1.src.grad.stop_criteria as sc
DEFAULT_EPSILON = 1e-9
DEFAULT_MAX_ITERATIONS = 1e5
def gradient_descent(f: Callable[[np.ndarray], float],
f_grad: Callable[[np.ndarray], np.nda... | normal | {
"blob_id": "919e1f8a4b021d75496f3bcff369261a09362a65",
"index": 3645,
"step-1": "<mask token>\n\n\ndef gradient_descent(f: Callable[[np.ndarray], float], f_grad: Callable[[np\n .ndarray], np.ndarray], start: np.ndarray, step_strategy: st.\n StepStrategy, stop_criteria: sc.StopCriteria, eps_strategy: float... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python 3
# -*- coding: utf-8 -*-
#
# Copyright (c) 2020 PanXu, Inc. All Rights Reserved
#
"""
测试 label index decoder
Authors: PanXu
Date: 2020/07/05 15:10:00
"""
import pytest
import torch
from easytext.tests import ASSERT
from easytext.data import LabelVocabulary
from easytext.modules import Cond... | normal | {
"blob_id": "f64138ee5a64f09deb72b47b86bd7795acddad4d",
"index": 9980,
"step-1": "<mask token>\n\n\nclass CRFData:\n \"\"\"\n 测试用的 crf 数据\n \"\"\"\n\n def __init__(self):\n bio_labels = [['O', 'I-X', 'B-X', 'I-Y', 'B-Y']]\n self.label_vocabulary = LabelVocabulary(labels=bio_labels, padd... | [
3,
5,
6,
7,
8
] |
def parse(filename):
t1, t2 = open(filename).read().strip().split("\n\n")
return tuple(map(lambda x: list(map(int, x.split("\n")[1:])), [t1, t2]))
def score(deck):
res = 0
for i in range(len(deck)):
res += deck[i] * (len(deck)-i)
return res
def solution1(deck1, deck2):
while len(deck1) > 0 and len(deck2) > 0:... | normal | {
"blob_id": "508d016161131481ace41f3d3bda005423125fe5",
"index": 5635,
"step-1": "def parse(filename):\n t1, t2 = open(filename).read().strip().split('\\n\\n')\n return tuple(map(lambda x: list(map(int, x.split('\\n')[1:])), [t1, t2]))\n\n\ndef score(deck):\n res = 0\n for i in range(len(deck)):\n ... | [
4,
5,
6,
8,
9
] |
#!/usr/bin/python3.8
# -*- coding: utf-8 -*-
__version__ = "0.2.2"
__author__ = 'Anton Vanke <f@hpu.edu.cn>'
class Gobang:
"""
五子棋
=====
一个简单的五子棋类, 可以在控制台下五子棋. 提供以下函数 :
new(): 新局
printcb(): 打印棋盘
player(): 获取当前应落子 ID （轮走方）
sortstep(): 处理总步表
loadstep(): 将 step 步表... | normal | {
"blob_id": "e0394bfed51cd0af9bca06867e9b556b226f37d1",
"index": 1720,
"step-1": "<mask token>\n\n\nclass Gobang:\n <mask token>\n <mask token>\n\n def new(self):\n \"\"\"新局\"\"\"\n self.__init__()\n\n def printcb(self):\n \"\"\"打印棋盘\"\"\"\n print('\\x1b[7;32;40m+ ', end... | [
8,
11,
14,
15,
16
] |
import os, subprocess
os.environ['FLASK_APP'] = "app/app.py"
os.environ['FLASK_DEBUG'] = "1"
# for LSTM instead: https://storage.googleapis.com/jacobdanovitch/twtc/lstm.tar.gz
# Will have to change app.py to accept only attention_weights
subprocess.call('./serve_model.sh')
subprocess.call(['flask', 'run'])
| normal | {
"blob_id": "cbad5d6f381e788a2f064aac0a5d468f40b39c93",
"index": 3696,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsubprocess.call('./serve_model.sh')\nsubprocess.call(['flask', 'run'])\n",
"step-3": "<mask token>\nos.environ['FLASK_APP'] = 'app/app.py'\nos.environ['FLASK_DEBUG'] = '1'\nsubprocess.c... | [
0,
1,
2,
3,
4
] |
from django.contrib import admin
# from .models import Product, Client
from .models import Board
admin.site.register(Board)
# admin.site.register(Product)
# # admin.site.register(Price)
# admin.site.register(Client)
# # Register your models here.
| normal | {
"blob_id": "ea323a8398ceff8496e7f8d0f365d50f3115e954",
"index": 5228,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.site.register(Board)\n",
"step-3": "from django.contrib import admin\nfrom .models import Board\nadmin.site.register(Board)\n",
"step-4": "from django.contrib import admin\n# fr... | [
0,
1,
2,
3
] |
from django.contrib import admin
from django.urls import path
from petsApp import views
urlpatterns = [
path('user/<int:id>/', views.getUser),
path('user/addImage/', views.addImage),
path('user/getImage/<int:id>/', views.getImage),
path('user/signup/', views.signUp),
path('user/login/', views.logI... | normal | {
"blob_id": "2458b8169029b3af501b650d548925770b0da74e",
"index": 6656,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('user/<int:id>/', views.getUser), path('user/addImage/',\n views.addImage), path('user/getImage/<int:id>/', views.getImage), path(\n 'user/signup/', views.signUp... | [
0,
1,
2,
3
] |
from numpy import *
from numpy.linalg import*
preco = array(eval(input("Alimentos: ")))
alimento = array([[ 2, 1 ,4 ],
[1 , 2 , 0],
[2 , 3 , 2 ]])
r = dot(inv(alimento),preco.T) #
print("estafilococo: ", round(r[0] , 1))
print("salmonela: ", round(r[1], 1))
print("coli: ", round(r[2], 1))
if r[0] ... | normal | {
"blob_id": "0f3e12f35cc29a71be5b8e6d367908e31c200c38",
"index": 3896,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('estafilococo: ', round(r[0], 1))\nprint('salmonela: ', round(r[1], 1))\nprint('coli: ', round(r[2], 1))\nif r[0] == min(r):\n print('estafilococo')\nelif r[1] == min(r):\n pr... | [
0,
1,
2,
3,
4
] |
import sys
import os
import numpy as np
import math
sys.path.append("../")
from sir.improveagent import *
import numpy as np
import numpy.linalg as la
import matplotlib.pyplot as plt
#from sklearn.neighbors import BallTree
from scipy.spatial import KDTree
from scipy.spatial import cKDTree
from scipy.spatial.distance im... | normal | {
"blob_id": "92317996f884befd646138cd3a3dc3f8345679f4",
"index": 2122,
"step-1": "<mask token>\n\n\ndef run_Simulation2(k, N=100, T=10, start=1, p=0.5, q=0.08, startcenter=\n False, startcorner=False):\n \"\"\"\n run the simulation for the pop\n \"\"\"\n recover = [0]\n infect = [start]\n su... | [
3,
4,
5,
6,
7
] |
import logging
import argparse
import getpass
import errno
import re
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
import dns.resolver
class Mail(object):
def __init__(self, recipient=None, sender=None, subject=None, body=None):
self.recipient = recipi... | normal | {
"blob_id": "3a678f9b5274f008a510a23b2358fe2a506c3221",
"index": 4061,
"step-1": "<mask token>\n\n\nclass Mail(object):\n <mask token>\n <mask token>\n\n @property\n def message(self):\n m = MIMEMultipart('alternative')\n m['Subject'] = self.subject\n m['From'] = self.sender\n ... | [
5,
6,
7,
8,
11
] |
import json
import jieba
import util
from pypinyin import pinyin, Style
class Song:
def __init__(self, songName, artistName, lyric):
self.songName = songName
self.artistName = artistName
self.lyric = lyric
self.phrasePinyinDict = util.lyricToPinYi(self.lyric)
def getSongName(se... | normal | {
"blob_id": "fa3cec0781b9ca5c1d99a7500748104d7cdce631",
"index": 130,
"step-1": "<mask token>\n\n\nclass Song:\n\n def __init__(self, songName, artistName, lyric):\n self.songName = songName\n self.artistName = artistName\n self.lyric = lyric\n self.phrasePinyinDict = util.lyricToP... | [
6,
7,
8,
9,
10
] |
import discord
from discord.ext import commands
import datetime
from discord.utils import get
from discord import User
class Sinner(commands.Converter):
async def convert(self, ctx, argument):
argument = await commands.MemberConverter().convert(ctx, argument)
permission = argument.guild_permissions... | normal | {
"blob_id": "16cd89a43a1985276bd14d85ad8ddb990c4d82c3",
"index": 6136,
"step-1": "<mask token>\n\n\nclass Redeemed(commands.Converter):\n\n async def convert(self, ctx, argument):\n argument = await commands.MemberConverter().convert(ctx, argument)\n muted = discord.utils.get(ctx.guild.roles, na... | [
4,
5,
7,
8,
9
] |
# https://py.checkio.org/blog/design-patterns-part-1/
class ImageOpener(object):
@staticmethod
def open(filename):
raise NotImplementedError()
class PNGImageOpener(ImageOpener):
@staticmethod
def open(filename):
print('PNG: open with Paint')
class JPEGImageOpener(ImageOpener):
@... | normal | {
"blob_id": "c199b2f87b7a4ac820001dab13f24fdd287a1575",
"index": 3507,
"step-1": "<mask token>\n\n\nclass UnknownImageOpener(ImageOpener):\n\n @staticmethod\n def open(filename):\n print(\"You don't hame program for %s extension\" % filename.split(\n '.')[-1].upper())\n\n\nclass Image(obj... | [
5,
6,
12,
13,
15
] |
from tkinter import *
import mathcalc as c
root= Tk()
root.title("CALCULATOR")
ent=Entry(root,width=35)
ent.grid(row=0,column=0,columnspan=3,padx=10,pady=10)
#ent.grid(row=0,column=0)
ch=''
num=ent.get()
def clicked(num):
current=ent.get()
ent.delete(0,END)
ent.insert(0,str(current)+str(num))
def click... | normal | {
"blob_id": "bdd9ebfa9a2f14d57efd527ca88032bfb0160a5e",
"index": 7504,
"step-1": "<mask token>\n\n\ndef clicked(num):\n current = ent.get()\n ent.delete(0, END)\n ent.insert(0, str(current) + str(num))\n\n\ndef click_clear():\n ent.delete(0, END)\n\n\ndef add():\n global ch\n ch = '+'\n clic... | [
7,
8,
9,
10,
11
] |
import datetime
import time
import boto3
from botocore.config import Config
# FinSpace class with Spark bindings
class SparkFinSpace(FinSpace):
import pyspark
def __init__(
self,
spark: pyspark.sql.session.SparkSession = None,
config = Config(retries = {'max_attempts': 0, 'mode': 'sta... | normal | {
"blob_id": "4f4af4caf81397542e9cd94c50b54303e2f81881",
"index": 3926,
"step-1": "<mask token>\n\n\nclass SparkFinSpace(FinSpace):\n import pyspark\n <mask token>\n\n def upload_dataframe(self, data_frame: pyspark.sql.dataframe.DataFrame):\n resp = self.client.get_user_ingestion_info()\n u... | [
3,
5,
6,
7,
8
] |
from layers import TrueSkillFactorGraph
from math import e, sqrt
from numerics import atLeast, _Vector, _DiagonalMatrix, Matrix
from objects import SkillCalculator, SupportedOptions, argumentNotNone, \
getPartialPlayPercentage, sortByRank
class FactorGraphTrueSkillCalculator(SkillCalculator):
def __init__(self):
s... | normal | {
"blob_id": "009be282e45d191eb8f4d7d2986a2f182d64c1dd",
"index": 2935,
"step-1": "<mask token>\n\n\nclass FactorGraphTrueSkillCalculator(SkillCalculator):\n\n def __init__(self):\n super(FactorGraphTrueSkillCalculator, self).__init__(\n SupportedOptions.PARTIAL_PLAY | SupportedOptions.PARTIA... | [
6,
7,
8,
9,
10
] |
# getting a sample of data to parse for the keys of the players
import requests
import xml.etree.ElementTree as ET
currentPlayerInfoUrl="http://stats.nba.com/stats/commonallplayers?IsOnlyCurrentSeason=1&LeagueID=00&Season=2015-16"
r=requests.get(currentPlayerInfoUrl)
if r.status_code == requests.codes.ok:
with open(... | normal | {
"blob_id": "68f8b301d86659f9d76de443b0afe93fd7f7e8c2",
"index": 6588,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif r.status_code == requests.codes.ok:\n with open('currentPlayerDump.json', 'w') as f:\n for line in r.text:\n f.write(line)\n",
"step-3": "<mask token>\ncurrentPl... | [
0,
1,
2,
3,
4
] |
"""
Authentication views.
login()
Flask view to log a user in.
"""
import functools
from typing import Any, Callable, cast, Dict
from flask import Blueprint, make_response, request, session
from werkzeug.security import check_password_hash as _check_password_hash
from .accesscontrol import PERMISSIONS
from .api... | normal | {
"blob_id": "2d36ae916ad257615016ed6c0bc67e506ee313c9",
"index": 1528,
"step-1": "<mask token>\n\n\n@bp.route('/login', methods=('POST',))\ndef login() ->Any:\n \"\"\"Flask view for logging a user in.\"\"\"\n user_dict = UserSchema().load(request.json, partial=('id',\n 'qualifications') + PERMISSION... | [
4,
6,
7,
8,
9
] |
import numpy as np
import cv2
def optical_flow_from_video():
cap = cv2.VideoCapture("/home/ubuntu/data1.5TB/异常dataset/Avenue_dataset/training_videos/01.avi")
# 设置 ShiTomasi 角点检测的参数
feature_params = dict(maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)
# 设置 lucas kanade 光流场的参数
# maxLe... | normal | {
"blob_id": "ae0547aa1af2d4dd73bb60154574e64e74107a58",
"index": 4062,
"step-1": "<mask token>\n\n\ndef optical_flow_from_video():\n cap = cv2.VideoCapture(\n '/home/ubuntu/data1.5TB/异常dataset/Avenue_dataset/training_videos/01.avi'\n )\n feature_params = dict(maxCorners=100, qualityLevel=0.3,... | [
5,
6,
7,
8,
10
] |
import media
import fresh_tomatoes
toy_story = media.Movie("Toy Story",
"A story of a boy and his toys that come to life",
'<p><a href="https://en.wikipedia.org/wiki/File:Toy_Story.jpg#/media/File:Toy_Story.jpg"><img src="https://upload.wikimedia.org/wikipedia/en/1/13/To... | normal | {
"blob_id": "e2f6e6e872f95471ebbc8b25bde08247fe8f7e61",
"index": 8829,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfresh_tomatoes.open_movies_page(movies)\n",
"step-3": "<mask token>\ntoy_story = media.Movie('Toy Story',\n 'A story of a boy and his toys that come to life',\n '<p><a href=\"http... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
school = "Old boy"
def chang_name(name):
global school #声明全局变量
school = "Mage Linux"
print("Before change:", name, school)
name = 'Stack Cong'
age = 33
print("After change:", name)
print("School:", school)
name = "Stack"
chang_name(name)
print(na... | normal | {
"blob_id": "a9531fb020428e573d189c377652692e301ea4d3",
"index": 3026,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef chang_name(name):\n global school\n school = 'Mage Linux'\n print('Before change:', name, school)\n name = 'Stack Cong'\n age = 33\n print('After change:', name)... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python
import sys
import random
def has_duplicates(list) :
"""Returns True if there are duplicate in list, false otherwise"""
copy = list[:]
copy.sort()
for item in range(len(list)-1):
if copy[item] == copy[item + 1]:
return True;
return False;
def gen_birthdays(n):
"""returns a list ... | normal | {
"blob_id": "e34e1e220c6d0fe2dc3d42caaefb04b178cdd120",
"index": 3768,
"step-1": "#!/usr/bin/python\nimport sys\nimport random\n\ndef has_duplicates(list) :\n \"\"\"Returns True if there are duplicate in list, false otherwise\"\"\"\n copy = list[:]\n copy.sort()\n for item in range(len(list)-1):\n if copy... | [
0
] |
# Head start.
# ask me for this solution: 6cb9ce6024b5fd41aebb86ccd40d8080
# this line is not needed, just for better output:
from pprint import pprint
# just remove the top line
def count_or_add_trigrams(trigram, trigrams_so_far):
'''
Takes a trigram, and a list of previously seen trigrams
and ... | normal | {
"blob_id": "753cc532e4d049bacff33c97de4d80bb9ab8ece8",
"index": 2655,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef count_or_add_trigrams(trigram, trigrams_so_far):\n \"\"\"\n Takes a trigram, and a list of previously seen trigrams\n and yields the same list with all discovered and cou... | [
0,
2,
3,
4,
5
] |
# coding: utf-8
# # Configuration
# In[1]:
CONNECTION_STRING = "mongodb://localhost:27017"
DATABASE_NAME = "off"
COLLECTION_NAME = "products"
# # MongDB connection
# In[2]:
from pymongo import MongoClient
from bson.code import Code
import plotly, pymongo
plotly.offline.init_notebook_mode()
from plotly.graph_obj... | normal | {
"blob_id": "2ecd234753fabbca2829dc86db2f740e371e4ea7",
"index": 6499,
"step-1": "\n# coding: utf-8\n\n# # Configuration\n\n# In[1]:\n\nCONNECTION_STRING = \"mongodb://localhost:27017\"\nDATABASE_NAME = \"off\"\nCOLLECTION_NAME = \"products\"\n\n\n# # MongDB connection\n\n# In[2]:\n\nfrom pymongo import MongoCli... | [
0
] |
import torch
import torch.nn as nn
import torch.nn.functional as F
# Const. low-rank version
class xCNNlow(torch.nn.Module):
def __init__(self, channels, filters, kernel_size, padding=0, stride=1, groups=1, rank=1, bias=True):
super(xCNNlow, self).__init__()
self.filters = filters
self.time... | normal | {
"blob_id": "f714c7006f50379cc7508a13d710d902d38d2d1f",
"index": 425,
"step-1": "<mask token>\n\n\nclass xCNNlow(torch.nn.Module):\n\n def __init__(self, channels, filters, kernel_size, padding=0, stride=1,\n groups=1, rank=1, bias=True):\n super(xCNNlow, self).__init__()\n self.filters =... | [
2,
3,
4,
5,
6
] |
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data
import numpy as np
def weight_init(layers):
for layer in layers:
if isinstance(layer, nn.BatchNorm1d):
layer.weight.data.fill_(1)
layer.bias.data.zero_()
elif isinstance(layer, nn.Lin... | normal | {
"blob_id": "2c2b075f9ea9e8d6559e44ad09d3e7767c48205e",
"index": 6772,
"step-1": "<mask token>\n\n\nclass LR(nn.Module):\n <mask token>\n <mask token>\n\n\nclass RNN(nn.Module):\n\n def __init__(self, feature_nums, hidden_dims, bi_lstm, out_dims=1):\n super(RNN, self).__init__()\n self.fea... | [
7,
9,
10,
11,
12
] |
# _*_ coding: utf-8 _*_
from service import service_logger
from service.TaskService import TaskService
class ApiException(Exception):
def __init__(self, message, code=400, data=None):
Exception.__init__(self, message)
self.code = code
self.msg = message
self.data = data... | normal | {
"blob_id": "0ac14b023c51bfd1cf99bd2d991baa30a671e066",
"index": 9994,
"step-1": "<mask token>\n\n\nclass ApiException(Exception):\n\n def __init__(self, message, code=400, data=None):\n Exception.__init__(self, message)\n self.code = code\n self.msg = message\n self.data = data\n\... | [
3,
4,
5,
6,
7
] |
import unittest
import numpy
import set_solver
class TestSets(unittest.TestCase):
def test_is_set(self):
"""Test set validator (Exercise 3a)."""
cards = numpy.array([[1, 1, 1, 2, 0], [0, 1, 2, 2, 2], [0, 1, 2, 2,
2], [0, 1, 2, 2, 2]])
self.assertTrue(set_solver.is_set(cards, [... | normal | {
"blob_id": "6065fae2a11f6b525ef10346e297505ec9d4e9d5",
"index": 8550,
"step-1": "<mask token>\n\n\nclass TestSets(unittest.TestCase):\n\n def test_is_set(self):\n \"\"\"Test set validator (Exercise 3a).\"\"\"\n cards = numpy.array([[1, 1, 1, 2, 0], [0, 1, 2, 2, 2], [0, 1, 2, 2,\n 2],... | [
2,
3,
4,
5
] |
# Generated by Django 3.1.6 on 2021-04-22 07:46
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('projects', '0004_project_is_featured'),
]
operations = [
migrations.AlterField(
model_name='project',
name='pin_id',... | normal | {
"blob_id": "24ed29dfaaf7ce508b2d80740bad1304b291c596",
"index": 8466,
"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 = [('projects', ... | [
0,
1,
2,
3,
4
] |
import random
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import fetch_mldata
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelBinarizer
from ann.act import relu, softmax_with_xentropy
from ann.loss import xentropy_with_softmax
fr... | normal | {
"blob_id": "2f6e5ed4e2d52190551dec2ac18441b8355699b5",
"index": 7096,
"step-1": "<mask token>\n\n\ndef plot(ax, ls_batch, ls_dev, its, title):\n ax.plot(range(len(ls_batch)), ls_batch, label='Batch')\n ax.plot(range(len(ls_dev)), ls_dev, label='Dev')\n ax.text(0.3, 0.93, 'Batch: {:.3f}'.format(ls_batch... | [
1,
2,
3,
4,
5
] |
import pandas as pd
import folium
ctx = '../data/'
json = ctx + 'us-states.json'
csv = ctx + 'US_Unemployment_Oct2012.csv'
data = pd.read_csv(csv)
m = folium.Map(location=[37, -102], zoom_start=5)
m.choropleth(geo_data=json, name='choropleth', data=data, columns=['State',
'Unemployment'], Key_on='feature.id', fill_... | normal | {
"blob_id": "382cb55a6b849f0240276d8f45746e995b16d714",
"index": 4455,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nm.choropleth(geo_data=json, name='choropleth', data=data, columns=['State',\n 'Unemployment'], Key_on='feature.id', fill_color='YlGn', fill_opacity=\n 0.7, line_opacity=0.2, legend_... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
# Copyright 2017 Objectif Libre
#
# 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 required ... | normal | {
"blob_id": "0ea67ac97ec8e7f287a2430c67f8f7d841d8b646",
"index": 813,
"step-1": "<mask token>\n\n\nclass TestSummary(base.BaseTestCase):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass TestSummary(base.BaseTestCase):\n\n def setUp(self):\n super(TestSummary, self).setUp()\n... | [
1,
2,
3,
4,
5
] |
import numpy as np
from sklearn import model_selection
from iterstrat.ml_stratifiers import MultilabelStratifiedKFold
"""
- binary cross-validate
- multi-class cross-validate
- multi-label cross-validate
- holdout
- regression
"""
class CrossValidate(object):
def __init__(self, df,
target_cols... | normal | {
"blob_id": "0dad1937df39c012f7991c3897f27964bed1d5a0",
"index": 1533,
"step-1": "<mask token>\n\n\nclass CrossValidate(object):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass CrossValidate(object):\n\n def __init__(self, df, target_cols, problem_type, num_folds=3, shuffle=\n ... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
# Generated by Django 1.11 on 2017-05-12 20:48
from __future__ import unicode_literals
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('home', '0010_auto_20170512_2248'),
]
o... | normal | {
"blob_id": "438efbaf35401a29ea5408fee3b49b85f237760e",
"index": 1089,
"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 = [('home', '001... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
class Migration(migrations.Migration):
dependencies = [
('examen', '0002_auto_20161122_1836'),
]
operations = [
migrations.RemoveField(
model_name='actuacionventa',
... | normal | {
"blob_id": "5acbd6002c5e3cfac942d52b788f18c6afa92da2",
"index": 7028,
"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 = [('examen', '0... | [
0,
1,
2,
3,
4
] |
import requests
from app.main.model.location import Location
from app.main.util.db_util import save_changes
key = 'a544aecdde85a1f52a56292f77ecde6e'
def save_location(ip_addr):
try:
existing_location = Location.query.filter_by(ip=ip_addr).first()
if existing_location:
location_data = e... | normal | {
"blob_id": "eb8aec947cc1eeeb56b3884286b46ec7468dcc23",
"index": 9035,
"step-1": "<mask token>\n\n\ndef save_location(ip_addr):\n try:\n existing_location = Location.query.filter_by(ip=ip_addr).first()\n if existing_location:\n location_data = existing_location.location\n else:... | [
1,
2,
3,
4
] |
class _ProtectedClass:
pass
class MyClass:
pass
class OtherClass(MyClass):
pass
def _protected_fun() -> MyClass:
return variable # noqa: F821
def my_fun() -> MyClass:
return variable # noqa: F821
def my_fun2() -> MyClass:
return variable # noqa: F821
variable: MyClass
variable_wit... | normal | {
"blob_id": "b5949b40d731178bdbab776af8877921dcdfbf15",
"index": 3215,
"step-1": "class _ProtectedClass:\n pass\n\n\nclass MyClass:\n pass\n\n\nclass OtherClass(MyClass):\n pass\n\n\ndef _protected_fun() ->MyClass:\n return variable\n\n\n<mask token>\n\n\ndef my_fun2() ->MyClass:\n return variable... | [
5,
6,
7,
8,
9
] |
# Fuck you Disyer. Stealing my fucking paypal. GET FUCKED: toontown.shtiker.CogPageGlobals
COG_QUOTAS = ((30, 25, 20, 15, 10, 5, 2, 1), (45, 40, 35, 30, 25, 20, 15, 10))
COG_UNSEEN = 1
COG_BATTLED = 2
COG_DEFEATED = 3
COG_COMPLETE1 = 4
COG_COMPLETE2 = 5 | normal | {
"blob_id": "fdb680f12dfb4b29f25cfe4f7af80469dc4294cf",
"index": 2437,
"step-1": "<mask token>\n",
"step-2": "COG_QUOTAS = (30, 25, 20, 15, 10, 5, 2, 1), (45, 40, 35, 30, 25, 20, 15, 10)\nCOG_UNSEEN = 1\nCOG_BATTLED = 2\nCOG_DEFEATED = 3\nCOG_COMPLETE1 = 4\nCOG_COMPLETE2 = 5\n",
"step-3": "# Fuck you Disyer.... | [
0,
1,
2
] |
from math import sqrt
def prime_generator(n):
pp=[2,3]
for i in range(3,n):
i+=2
count=0
for ps in pp:
if ps>(sqrt(i)+1):
break
if i%ps==0:
count+=1
break
if count==0:
pp.append(i)
return pp
... | normal | {
"blob_id": "cfa064611a4aa16638bd649c68d64872b9fac1ff",
"index": 4647,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef prime_generator(n):\n pp = [2, 3]\n for i in range(3, n):\n i += 2\n count = 0\n for ps in pp:\n if ps > sqrt(i) + 1:\n break\... | [
0,
1,
2,
3
] |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2020 sungminoh <smoh2044@gmail.com>
#
# Distributed under terms of the MIT license.
"""
You are given coins of different denominations and a total amount of money. Write a function to compute the number of combinations that make up that a... | normal | {
"blob_id": "332c530d221c9441d6ff3646f8e9226dc78067f9",
"index": 2902,
"step-1": "<mask token>\n\n\nclass Solution:\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n\n def change(self, amount: int, coins: List[int]) ->int:\n coins = sorted(coins, reverse=True)\n\n... | [
1,
2,
4,
5,
6
] |
import random
print(random.choice(['python', 'c++', 'java']))
print(random.choice((1.1, -5, 6, 4, 7)))
| normal | {
"blob_id": "44f18d7e7713073c27fec38f0b847803eceefbc9",
"index": 2687,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(random.choice(['python', 'c++', 'java']))\nprint(random.choice((1.1, -5, 6, 4, 7)))\n",
"step-3": "import random\nprint(random.choice(['python', 'c++', 'java']))\nprint(random.cho... | [
0,
1,
2
] |
import numpy as np
import pandas as pd
from pathlib import Path
import matplotlib as mpl
from matplotlib import pyplot as plt
plt.style.use('seaborn-muted')
#from IPython import get_ipython
from IPython.display import HTML, Markdown
import air_cargo_problems as acp
problems = ['Air Cargo Problem 1',
'... | normal | {
"blob_id": "cd49230be3c418853aa2986ed727204e51a6b6ae",
"index": 3794,
"step-1": "<mask token>\n\n\ndef get_results_df(fname, problem):\n \"\"\"Process csv into dataframe.\n \"\"\"\n t = '\\t'\n val_cols = ['Actions', 'Expansions', 'GoalTests', 'NewNodes',\n 'PlanLength', 'ElapsedSeconds']\n ... | [
6,
12,
14,
16,
17
] |
"""
Simple neural network using pytorch
"""
import torch
import torch.nn as nn
# Prepare the data
# X represents the amount of hours studied and how much time students spent sleeping
X = torch.tensor(([2, 9], [1, 5], [3, 6]), dtype=torch.float) # 3 X 2 tensor
# y represent grades.
y = torch.tensor(([92], [100], [89]... | normal | {
"blob_id": "2d5e7c57f58f189e8d0c7d703c1672ea3586e4ac",
"index": 6771,
"step-1": "<mask token>\n\n\nclass Neural_Network(nn.Module):\n <mask token>\n\n def __init__(self, input_size=2, output_size=1, hidden_size=3):\n super(Neural_Network, self).__init__()\n self.input_size = input_size\n ... | [
9,
10,
12,
13,
14
] |
#!/usr/bin python
import socket
import json
import threading
import sys
from db_util import DBUtil
from cryptoLib import AesCtr,Hmac
class Client(threading.Thread):
def __init__(self, (client_conn, client_addr), sema):
threading.Thread.__init__(self)
self.client_conn = client_conn
self.client_addr = client_ad... | normal | {
"blob_id": "1338d6578a94338c6e75acc025ddddd14097ee10",
"index": 2044,
"step-1": "#!/usr/bin python\n\nimport socket\nimport json\nimport threading\nimport sys\nfrom db_util import DBUtil\nfrom cryptoLib import AesCtr,Hmac\n\n\nclass Client(threading.Thread):\n\tdef __init__(self, (client_conn, client_addr), sem... | [
0
] |
#!/usr/bin/env python
# pylama:ignore=E221,E251
from setuptools import find_packages, setup
setup(
name = 'coding_exercises',
version = '1.0',
description = 'Coding Exercises in Python',
author = 'Gustavo Gama',
author_email = 'gustavo.gama@gmail.com',
url = 'https... | normal | {
"blob_id": "5f4abc7e9397034737ee214b0d0aae39ebf1548b",
"index": 8098,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='coding_exercises', version='1.0', description=\n 'Coding Exercises in Python', author='Gustavo Gama', author_email=\n 'gustavo.gama@gmail.com', url='https://gama.igenesi... | [
0,
1,
2,
3
] |
import urllib.request
import json
def kind():
data={}
with open("dataset.json", "r") as read_file:
data = json.load(read_file)
return data["kind"]
def items():
data={}
with open("dataset.json", "r") as read_file:
data = json.load(read_file)
return data["items"]
#Can add a bunc... | normal | {
"blob_id": "630480e9458491a26ea9060bd36541a0d5805a11",
"index": 647,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef kind():\n data = {}\n with open('dataset.json', 'r') as read_file:\n data = json.load(read_file)\n return data['kind']\n\n\n<mask token>\n",
"step-3": "<mask toke... | [
0,
1,
2,
3,
4
] |
"""Helpers for FormatCBFMiniPilatus..."""
from __future__ import annotations
import calendar
import time
def get_pilatus_timestamp(timestamp_string):
if "." in timestamp_string:
timestamp, milliseconds = timestamp_string.split(".")
else:
timestamp = timestamp_string
milliseconds = "... | normal | {
"blob_id": "21526dabe8456c599e4409228fa69ffd0d672c5b",
"index": 4689,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_pilatus_timestamp(timestamp_string):\n if '.' in timestamp_string:\n timestamp, milliseconds = timestamp_string.split('.')\n else:\n timestamp = timestamp_... | [
0,
1,
2,
3
] |
'''
Handprint module for handling credentials.
Authors
-------
Michael Hucka <mhucka@caltech.edu> -- Caltech Library
Copyright
---------
Copyright (c) 2018-2022 by the California Institute of Technology. This code
is open-source software released under a 3-clause BSD license. Please see the
file "LICENSE" for mor... | normal | {
"blob_id": "7e29220752b4a52be34cdf0c734695d1052d0414",
"index": 9309,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfrom .base import Credentials\nfrom .amazon_auth import AmazonCredentials\nfrom .google_auth import GoogleCredentials\nfrom .microsoft_auth import MicrosoftCredentials\n",
"step-3": "''... | [
0,
1,
2
] |
'''
3、 编写一个函数，输入n为偶数时，调用函数求1/2+1/4+...+1/n,当输入n为奇数时，调用函数1/1+1/3+...+1/n
'''
def f(n):
if n%2==0:
sum=0
for x in range(2,n+1,2):
sum+=1/x
print(sum)
if n%2!=0:
sum=0
for x in range(1,n+1,2):
sum+=1/x
print(sum)
| normal | {
"blob_id": "69cf28d32e6543271a0855d61a76808b03c06891",
"index": 4805,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef f(n):\n if n % 2 == 0:\n sum = 0\n for x in range(2, n + 1, 2):\n sum += 1 / x\n print(sum)\n if n % 2 != 0:\n sum = 0\n for x ... | [
0,
1,
2
] |
# coding: utf-8
# 02. 「パトカー」＋「タクシー」＝「パタトクカシーー」
# 「パトカー」＋「タクシー」の文字を先頭から交互に連結して文字列「パタトクカシーー」を得よ．
s1 = "パトカー"
s2 = "タクシー"
ans = ""
for c1, c2 in zip(s1, s2):
ans += c1 + c2
print(ans)
#パタトクカシーー
| normal | {
"blob_id": "4d7e30714ae209e1d09d895dadf7a19928fe253f",
"index": 6623,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor c1, c2 in zip(s1, s2):\n ans += c1 + c2\nprint(ans)\n",
"step-3": "s1 = 'パトカー'\ns2 = 'タクシー'\nans = ''\nfor c1, c2 in zip(s1, s2):\n ans += c1 + c2\nprint(ans)\n",
"step-4": ... | [
0,
1,
2,
3
] |
from __future__ import division, print_function, absolute_import
import numbers
import warnings
from abc import ABCMeta, abstractmethod
import numpy as np
from .base import check_frame
from skutil.base import overrides
from sklearn.externals import six
from sklearn.base import _pprint
from sklearn.utils.fixes import si... | normal | {
"blob_id": "c59707ba07c1659d94684c54cdd7bb2658cba935",
"index": 6,
"step-1": "<mask token>\n\n\nclass H2OShuffleSplit(H2OBaseShuffleSplit):\n <mask token>\n\n def _iter_indices(self, frame, y=None):\n \"\"\"Iterate the indices.\n\n Parameters\n ----------\n\n frame : H2OFrame\n... | [
21,
29,
40,
43,
47
] |
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import numpy.random as nr
import math
import os
from datetime import datetime
from sklearn.linear_model import LinearRegression, SGDRegressor
import sys
import time
import imp
from sklearn.ensemble import ExtraTreesRegressor
fr... | normal | {
"blob_id": "ee49ce63951721458cb98b370285d04231bb2c20",
"index": 7438,
"step-1": "<mask token>\n\n\nclass predict(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def trainExtraTreeRegressor(self):\n self.__tree_reg.fit(self.train_... | [
4,
8,
14,
17,
18
] |
import torch
from torch import nn
from torch.nn import functional as F
from models.blocks import UnetConv3, MultiAttentionBlock, UnetGridGatingSignal3, UnetUp3_CT, UnetDsv3
class AttentionGatedUnet3D(nn.Module):
"""
Attention Gated Unet for 3D semantic segmentation.
Args:
config: Mus... | normal | {
"blob_id": "55a392d63838cbef027f9cf525999c41416e3575",
"index": 3875,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass AttentionGatedUnet3D(nn.Module):\n <mask token>\n\n def __init__(self, config):\n super(AttentionGatedUnet3D, self).__init__()\n assert hasattr(config, 'num_... | [
0,
3,
4,
5,
6
] |
#!/usr/bin/env python3
from pexpect import pxssh
import time
s = pxssh.pxssh()
ip = "" #replace ip address
username= "" #replace username
password= "" #replace password
s.login (ip, username, password)
print ("SSH session login successful")
s.sendline ('application stop')
s.prompt() # match the prompt
print("S... | normal | {
"blob_id": "dd9574ea08beb9bc5f1413afd63c751fd42cba67",
"index": 6406,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ns.login(ip, username, password)\nprint('SSH session login successful')\ns.sendline('application stop')\ns.prompt()\nprint('Stopping the app')\nprint(\"\"\"\nStarting the app\"\"\")\ns.sen... | [
0,
1,
2,
3,
4
] |
import time
class Block:
def __init__(self, index, transactions, previous_hash, nonce=0):
self.index = index
self.transaction = transactions
self.timestamp = time.time()
self.previous_hash = previous_hash
self.nonce = nonce
self.hash = None
| normal | {
"blob_id": "43a23958b8c8779e3292f0f523a37b6d712fdbac",
"index": 4448,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Block:\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Block:\n\n def __init__(self, index, transactions, previous_hash, nonce=0):\n self.index = index\n ... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
import sys
import yaml
def add_sub_path(yaml_path):
file = open(yaml_path, "r", encoding="utf-8")
file_data = file.read()
file.close()
data = yaml.safe_load(file_data)
for p, p_info in data.get("paths", {}).items():
for method, m_info in p_info.items():
... | normal | {
"blob_id": "bbd50c40bc0897fe7a93f277bcfdcba3ba6d6f2a",
"index": 1531,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef add_sub_path(yaml_path):\n file = open(yaml_path, 'r', encoding='utf-8')\n file_data = file.read()\n file.close()\n data = yaml.safe_load(file_data)\n for p, p_info... | [
0,
1,
2,
3,
4
] |
""" Crie um programa onde o usuario possa digitar sete valores numericos e cadastre-os em uma lisa unicaque mantenha
separados os valores pares e impares. No final, mostre os valores ares e impares em ordem crescente """
n = [[],[]]
for c in range(0,7):
num = int(input(f'Digite o {c+1} valor: '))
res = num % ... | normal | {
"blob_id": "72bbbe78db746febc9a36a676e0fa2d97bf5e81e",
"index": 8849,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor c in range(0, 7):\n num = int(input(f'Digite o {c + 1} valor: '))\n res = num % 2\n if res == 0:\n n[0].append(num)\n else:\n n[1].append(num)\nn[0].sort()\n... | [
0,
1,
2,
3
] |
import datetime
import discord
def getTeams(reign, uprising, hunters, fuel, mayhem, gladiators, charge, outlaws, spark,
spitfire, excelsior, eternal, fusion, dynasty, shock, dragons, defiant, valiant, titans,
justice) :
teamList = discord.Embed(
title="Overwatch League Teams",
description="2021 Sea... | normal | {
"blob_id": "9a02e09cbfe2c9b6ebb9d20ba6cea639871f0838",
"index": 7647,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef getTeams(reign, uprising, hunters, fuel, mayhem, gladiators, charge,\n outlaws, spark, spitfire, excelsior, eternal, fusion, dynasty, shock,\n dragons, defiant, valiant, tit... | [
0,
1,
2,
3
] |
from mesa.visualization.modules import CanvasGrid
from mesa.visualization.ModularVisualization import ModularServer
from mesa.visualization.modules import ChartModule
from mesa.batchrunner import BatchRunner
from agentPortrayal import agent_portrayal
import metrics
from matplotlib import pyplot as plt
from Architecture... | normal | {
"blob_id": "57b51ea36e9e2a095cf7e9646db2cc400cc72b83",
"index": 1082,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n dir_path = os.path.dirname(os.path.realpath(__file__))\n if runBatch:\n fixed_params = {'width': 60, 'height': 60, 'splitSize': 1,\n '... | [
0,
1,
2,
3,
4
] |
import tensorflow as tf
import numpy as np
import time
import os
from sklearn.metrics import roc_curve
import matplotlib.pyplot as plt
from src.model import get_args
from src.funcs import linear
from src.youtubeface import load_ytf_data
from src.lfw import load_lfw_data
from src.facescrub import load_fs_data
from src... | normal | {
"blob_id": "459dd9302f7100ad02119cc94b735b19287f21e5",
"index": 5956,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n total_iteration = 300000\n m = 512\n q = 32\n lam = 0.01\n beta = 1.0\n margin = 0.5\n s = ... | [
0,
1,
2,
3
] |
# 심사문제 22
# 표준 입력으로 정수 두 개가 입력됩니다(첫 번째 입력 값의 범위는 1~20, 두 번째 입력 값의 범위는 10~30이며 첫 번째 입력 값은 두 번째 입력 값보다 항상 작습니다).
# 첫 번째 정수부터 두 번째 정수까지를 지수로 하는 2의 거듭제곱 리스트를 출력하는 프로그램을 만드세요
# (input에서 안내 문자열은 출력하지 않아야 합니다). 단, 리스트의 두 번째 요소와 뒤에서 두 번째 요소는 삭제한 뒤 출력하세요. 출력 결과는 리스트 형태라야 합니다.
start, stop = list(map(int, input().split()))
1 10
... | normal | {
"blob_id": "1f8040776a55d6fe52b64c714d4003469460e454",
"index": 7186,
"step-1": "# 심사문제 22\n# 표준 입력으로 정수 두 개가 입력됩니다(첫 번째 입력 값의 범위는 1~20, 두 번째 입력 값의 범위는 10~30이며 첫 번째 입력 값은 두 번째 입력 값보다 항상 작습니다).\n# 첫 번째 정수부터 두 번째 정수까지를 지수로 하는 2의 거듭제곱 리스트를 출력하는 프로그램을 만드세요\n# (input에서 안내 문자열은 출력하지 않아야 합니다). 단, 리스트의 두 번째 요소와 뒤에서 두 번... | [
0
] |
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