code stringlengths 13 1.2M | order_type stringclasses 1
value | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
import os
import json
import pathlib
from gql import gql, Client
from gql.transport.aiohttp import AIOHTTPTransport
# Select your transport with a defined url endpoint
transport = AIOHTTPTransport(url="https://public-api.nbatopshot.com/graphql")
# Create a GraphQL client using the defined transport
client = Client(tr... | normal | {
"blob_id": "df518fd719b7eafffd8fee92c926d4d24b65ce18",
"index": 7877,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npathlib.Path(DIR).mkdir(parents=True, exist_ok=True)\nprint('--------Query Topshot GraphQL Endpoint--------')\nfor setsId in setsIdList:\n for setId in setsId:\n count += 1\n ... | [
0,
1,
2,
3,
4
] |
""" 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
] |
print ("Welcome to the Guessing Game 2.0\n")
print ("1 = Easy\t(1 - 10)")
print ("2 = Medium\t(1 - 50)")
print ("3 = Hard\t(1 - 100)")
# Player: Input user's choice
# while: Check if user enters 1 or 2 or 3
# CPU: Generate a random number
# Player: Input user's number
# Variable: Add a variable 'attempt... | normal | {
"blob_id": "7f2489aa440441568af153b231420aa2736716ca",
"index": 4052,
"step-1": "<mask token>\n",
"step-2": "print('Welcome to the Guessing Game 2.0\\n')\nprint('1 = Easy\\t(1 - 10)')\nprint('2 = Medium\\t(1 - 50)')\nprint('3 = Hard\\t(1 - 100)')\n",
"step-3": "print (\"Welcome to the Guessing Game 2.0\\n\"... | [
0,
1,
2
] |
# Generated by Django 3.0.5 on 2020-04-23 11:23
from django.conf import settings
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('review', '0002_auto_20200419_1409'),
]
operations = [
... | normal | {
"blob_id": "8471e6a3b6623236740ad5219e5038a64e0c0056",
"index": 2083,
"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 = [migrations.sw... | [
0,
1,
2,
3,
4
] |
import fs
gInfo = {
'obj': g2.go(capUrl),
'Headers-C-T': g2.response.headers['Content-Type'],
'url': g2.response.url,
'urlDetails': g2.response.url_details()
}
capHtml = capHtml = gInfo['obj'].unicode_body(ignore_errors=True, fix_special_entities=True)
b64cap = re.findall(r'base64,(.*?)\\" id=', capHtml, re.DO... | normal | {
"blob_id": "2a5f69fbb26bd1f94c10ff0da687391bf5bd3c23",
"index": 6054,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsavecaptcha.write(b64cap[0])\nsavecaptcha.close()\n<mask token>\nf.close()\n<mask token>\nfincapfile.close()\n",
"step-3": "<mask token>\ngInfo = {'obj': g2.go(capUrl), 'Headers-C-T': g... | [
0,
1,
2,
3,
4
] |
from numpy import array, sum
def comp_point_ref(self, is_set=False):
"""Compute the point ref of the Surface
Parameters
----------
self : SurfLine
A SurfLine object
is_set: bool
True to update the point_ref property
Returns
-------
point_ref : complex
the refe... | normal | {
"blob_id": "b7721e95cfb509a7c0c6ccdffa3a8ca2c6bd6033",
"index": 6713,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef comp_point_ref(self, is_set=False):\n \"\"\"Compute the point ref of the Surface\n\n Parameters\n ----------\n self : SurfLine\n A SurfLine object\n is_set: ... | [
0,
1,
2
] |
''' Converts luptitudes to maggies and stores in folder output
Written by P. Gallardo
'''
import numpy as np
import pandas as pd
import sys
assert len(sys.argv) == 2 # usage: lups2maggies.py /path/to/cat.csv
fname = sys.argv[1]
print("Converting maggies from catalog \n%s" % fname)
df = pd.read_csv(fname)
z = d... | normal | {
"blob_id": "e8971b3d183ded99a5fc03f031ef807280b8cc7f",
"index": 1744,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nassert len(sys.argv) == 2\n<mask token>\nprint(\"\"\"Converting maggies from catalog \n%s\"\"\" % fname)\n<mask token>\nnp.savetxt('./output/maggies.txt', to_exp)\n",
"step-3": "<mask t... | [
0,
1,
2,
3,
4
] |
g#https://www.acmicpc.net/problem/9461
'''
1. Divide 2 case △ and ▽
d[0] is △ sequence
d[1] is ▽ sequence
2. find a role between d[0] and d[1]
'''
import math
t = int(input())
n = []
for _ in range(t):
n.append(int(input()))
index = math.ceil(max(n)/2)
d = [[0 for _ in range(52)] for _ in range(2)]
d[0][1],d[0][2],... | normal | {
"blob_id": "524b6ebd0be4c2285fac540627bb48baca71452e",
"index": 2989,
"step-1": "<mask token>\n",
"step-2": "g\n<mask token>\nfor _ in range(t):\n n.append(int(input()))\n<mask token>\nfor i in range(3, index + 1):\n d[0][i] = d[1][i - 1] + d[1][i - 3]\n d[1][i] = d[0][i] + d[0][i - 2]\nfor k in n:\n... | [
0,
1,
2,
3,
4
] |
from django.db import models
from django.contrib.auth.models import AbstractUser
from django.db.models import Max
from django.core.validators import RegexValidator
from django.utils import timezone
class User(AbstractUser):
is_developer = models.BooleanField('developer status', default=False)
is_marketing = mo... | normal | {
"blob_id": "94e9e7c4c09c8c4de4c8f2649707a949d5f5f856",
"index": 7836,
"step-1": "<mask token>\n\n\nclass Location(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n... | [
38,
40,
46,
50,
55
] |
from . import resources
from jsonschema import validate
from jsonschema.exceptions import ValidationError
import aiohttp_client
import importlib.resources as pkg_resources
import json
import logging
log = logging.getLogger("amplitude-client")
API_URL = "https://api.amplitude.com/2/httpapi"
class AmplitudeLogger:
... | normal | {
"blob_id": "d32f009f373249b7b602ac36f29982273a2ed192",
"index": 2289,
"step-1": "<mask token>\n\n\nclass AmplitudeLogger:\n <mask token>\n\n async def log_event(self, event):\n event = {'api_key': self.api_key, 'events': [event]}\n try:\n validate(instance=event, schema=self.api_s... | [
1,
2,
3,
4,
5
] |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division
import os
from solid import *
from solid.utils import *
from shapes import *
import sys
# Assumes SolidPython is in site-packages or elsewhwere in sys.path
from solid import *
from solid.utils import *
def voxels():
# shape = cube([1,... | normal | {
"blob_id": "27ca60435c614e4d748917da45fc2fc75ee59f1c",
"index": 1682,
"step-1": "<mask token>\n\n\ndef voxels():\n shape = []\n for x in range(-5, 4, 1):\n for y in range(-5, 4, 1):\n for z in range(0, 10, 1):\n translate([x, y, z])\n new_cube = color([0, 0,... | [
2,
3,
4,
5,
6
] |
from django.apps import AppConfig
class AdminrequestsConfig(AppConfig):
name = 'adminRequests'
| normal | {
"blob_id": "e08b7a96c957895068e584a0564f02c52acd48ec",
"index": 3753,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass AdminrequestsConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass AdminrequestsConfig(AppConfig):\n name = 'adminRequests'\n",
"step-4": "from djan... | [
0,
1,
2,
3
] |
from django.db import models
from django.contrib.contenttypes.models import ContentType
from widgy.generic import ProxyGenericForeignKey, ProxyGenericRelation
from django.contrib.contenttypes.generic import GenericForeignKey, GenericRelation
class Base(models.Model):
content_type = models.ForeignKey(ContentType)... | normal | {
"blob_id": "c70df1fab0db6f71d22a23836b11d66879879656",
"index": 6336,
"step-1": "<mask token>\n\n\nclass Related(models.Model):\n <mask token>\n <mask token>\n\n\nclass AbstractModel(models.Model):\n bases = ProxyGenericRelation(Base, content_type_field='content_type',\n object_id_field='content... | [
6,
7,
8,
9,
11
] |
#!/usr/bin/python
"""
Created on Aug 1 2014
"""
import rospy
def my_callback(event):
print 'Timer called at ' + str(event.current_real)
if __name__ == '__main__':
rospy.init_node('timer')
rospy.Timer(rospy.Duration(2), my_callback)
rospy.spin() | normal | {
"blob_id": "4e61f9fefe8e6b5203ba05ac9bd626db1102df36",
"index": 122,
"step-1": "#!/usr/bin/python\n\"\"\"\nCreated on Aug 1 2014\n\n\"\"\"\n\nimport rospy\ndef my_callback(event):\n print 'Timer called at ' + str(event.current_real)\n\nif __name__ == '__main__':\n rospy.init_node('timer')\n\n rospy.Ti... | [
0
] |
""""Module for miscellaneous behavior stuff
For example, stuff like extracting lick times or choice times.
TrialSpeak shouldn't depend on stuff like that.
# Also get the pldf and use that to get lick times
ldf = ArduFSM.TrialSpeak.read_logfile_into_df(bdf.loc[idx, 'filename'])
# Get the lick times
... | normal | {
"blob_id": "78761eda403ad8f54187e5858a23c23d3dd79b09",
"index": 8821,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_choice_times(behavior_filename, verbose=False):\n \"\"\"Calculates the choice time for each trial in the logfile\"\"\"\n state_num2names = MCwatch.behavior.db.get_state_... | [
0,
1,
2,
3,
4
] |
from lib import gen, core
Shellcode = gen.Varname_Creator()
Hide_Window = gen.Varname_Creator()
def Start():
Start_Code = "#include <windows.h>\n"
Start_Code += "#include <tlhelp32.h>\n"
Start_Code += "#include <stdio.h>\n"
Start_Code += "#include <stdlib.h>\n"
Start_Code += "#include <string.... | normal | {
"blob_id": "e9b9f87a18a5788ac86b1e85c0f3d7858946e03a",
"index": 2999,
"step-1": "<mask token>\n\n\ndef Start():\n Start_Code = '#include <windows.h>\\n'\n Start_Code += '#include <tlhelp32.h>\\n'\n Start_Code += '#include <stdio.h>\\n'\n Start_Code += '#include <stdlib.h>\\n'\n Start_Code += '#in... | [
4,
5,
6,
7,
8
] |
from z3 import *
import re
dna = re.compile("dna_(\d+)")
opt = Optimize()
opt.from_file("../benchmarks/bench.smt2")
set_option("sat.random_seed",23)
def get_soft(soft):
return [f.arg(0) for f in soft.children()]
def free_vars(fs):
seen = set([])
vars = set([])
def fv(seen, vars, f):
if f in... | normal | {
"blob_id": "c0d8f2542f9cf9a5097011c61c90073c031d2708",
"index": 9831,
"step-1": "<mask token>\n\n\ndef get_soft(soft):\n return [f.arg(0) for f in soft.children()]\n\n\ndef free_vars(fs):\n seen = set([])\n vars = set([])\n\n def fv(seen, vars, f):\n if f in seen:\n return\n ... | [
7,
8,
9,
10,
11
] |
from numpy.testing import assert_almost_equal
from fastats.maths.norm_cdf import norm_cdf
def test_norm_cdf_basic_sanity():
assert_almost_equal(0.5, norm_cdf(0.0, 0, 1))
def test_norm_cdf_dartmouth():
"""
Examples taken from:
https://math.dartmouth.edu/archive/m20f12/public_html/matlabnormal
sto... | normal | {
"blob_id": "0229783467b8bcd0361baf6be07e3261f34220c7",
"index": 6581,
"step-1": "<mask token>\n\n\ndef test_norm_cdf_dartmouth():\n \"\"\"\n Examples taken from:\n https://math.dartmouth.edu/archive/m20f12/public_html/matlabnormal\n stored in literature directory as dartmouth_normcdf_norminv.pdf\n ... | [
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
import scrapy
import MySQLdb
import openpyxl
from scrapy.crawler import CrawlerProcess
import sys
class AllabolaSpider(scrapy.Spider):
name = 'allabola'
allowed_domains = ['https://www.allabolag.se']
start_urls = []
#'https://www.allabolag.se/7696250484/befattningar'
host =... | normal | {
"blob_id": "d60a2100127db859162890204655d313cdc2a4a5",
"index": 4614,
"step-1": "<mask token>\n\n\nclass AllabolaSpider(scrapy.Spider):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n f.write('fn,ln,zip,ct,st,co... | [
2,
4,
5,
6,
8
] |
from zExceptions import Unauthorized
if REQUEST is not None:
raise Unauthorized
portal = context.getPortalObject()
compute_node = context
reference = "TIOCONS-%s-%s" % (compute_node.getReference(), source_reference)
version = "%s" % context.getPortalObject().portal_ids.generateNewId(
id_group=('slap_tioxml_consum... | normal | {
"blob_id": "6c27f70e820202f6cc4348de3c9198e7b20ec7d9",
"index": 4470,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif REQUEST is not None:\n raise Unauthorized\n<mask token>\ndocument.submit()\nreturn document.getRelativeUrl()\n",
"step-3": "<mask token>\nif REQUEST is not None:\n raise Unauth... | [
0,
1,
2,
3,
4
] |
from django.conf.urls.defaults import *
#from wiki.feeds import *
from django.conf import settings
from django.conf.urls.defaults import *
# feeds for wikiPages and wikiNews
"""
feeds = {
'latestpages': LatestPages,
}
sitemaps = {
'wiki': Wiki,
}
"""
urlpatterns = patterns('',
# Example:
# (r'^goimcommu... | normal | {
"blob_id": "f44ff7488ae8fc64bc1785fb6cbe80c4cc011fbe",
"index": 6808,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = patterns('', ('^admin/', include('django.contrib.admin.urls')\n ), ('^polls/', include('goimcommunity.polls.urls')), ('^league/',\n include('goimcommunity.leaguesystem... | [
0,
1,
2,
3
] |
import operator
import theano.tensor as T
from collections import OrderedDict
from lasagne.layers import get_output
from stanza.research import config
from neural import SimpleLasagneModel, NeuralLearner
from vectorizers import SequenceVectorizer, BucketsVectorizer
from neural import OPTIMIZERS, get_named_layers
from ... | normal | {
"blob_id": "3496216de9f6b7d9d3db69eb4d8f8c0fdcd5123c",
"index": 1358,
"step-1": "<mask token>\n\n\nclass RSAGraphModel(SimpleLasagneModel):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass... | [
15,
21,
23,
36,
38
] |
from flask_sqlalchemy import SQLAlchemy
from flask_security import UserMixin, RoleMixin
db = SQLAlchemy()
roles_users = db.Table('roles_users',
db.Column('user_id', db.Integer(), db.ForeignKey('user.id')),
db.Column('role_id', db.Integer(), db.ForeignKey('role.id')))
class Role(db.Model, RoleMixin):
... | normal | {
"blob_id": "f561846c943013629e417d16f4dae77df43b25c4",
"index": 3806,
"step-1": "<mask token>\n\n\nclass Role(db.Model, RoleMixin):\n <mask token>\n <mask token>\n <mask token>\n\n def __repr__(self):\n return f'<Role {self.name}'\n\n\nclass User(db.Model, UserMixin):\n id = db.Column(db.I... | [
10,
11,
12,
13,
14
] |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 1 11:52:48 2022
@author: ccamargo
"""
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
import os
# 1. get filelist
path = "/Volumes/LaCie_NIOZ/data/steric/data/"
path_to_original_files = path + "original/"
flist = [file for ... | normal | {
"blob_id": "4fc4bb81d47a33e4669df46033033fddeca6544e",
"index": 8858,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor file in flist:\n print(file)\n name = file.split('.nc')[0]\n ds = xr.open_dataset(path_to_regrided_files + file, decode_times=False)\n timespan = [ds.timespan]\n print(... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
# @Time : 2020/6/12 20:19
# @Author : damon
# @Site :
# @File : work0612
# @Software: PyCharm
import math
"""
1、给定n=10，计算1! + 2! + 3! + ... + n!的值
"""
# 解法1：
n = 10
factorial = 1
sum = 0
for i in range(1, n+1):
factorial = i * factorial
sum += factorial
print(f"阶乘之和{sum}")... | normal | {
"blob_id": "af9adc0faad4fc1426a2bd75c1c77e23e37b60bf",
"index": 2431,
"step-1": "<mask token>\n\n\ndef fa(x):\n dict2 = {(1): 'one', (2): 'two', (3): 'three', (4): 'four', (5): 'five',\n (6): 'six', (7): 'seven', (8): 'eight', (9): 'nine', (0): 'zero'}\n return dict2[int(x)]\n\n\n<mask token>\n",
... | [
1,
3,
4,
5,
6
] |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.shortcuts import render
from django.db.models import Q
from django.contrib.auth import get_user_model
from rest_framework.response import Response
from rest_framework.status import HTTP_200_OK, HTTP_400_BAD_REQUEST
from rest_framework.views imp... | normal | {
"blob_id": "18f355041a9982de56ad2eb51b665dd39a156f0a",
"index": 9638,
"step-1": "<mask token>\n\n\nclass UserUpdateAPIView(UpdateAPIView):\n <mask token>\n <mask token>\n\n def post(self, request, format=None):\n data = request\n queryset = User.objects.get()\n\n\nclass UserTokenVerifyAPI... | [
11,
12,
14,
16,
18
] |
import pygame
from evolution import Darwin
from Sensor import Robot, obstacleArray
# Game Settings
pygame.init()
background_colour = (0, 0, 0)
(width, height) = (1000, 600)
target_location = (800, 300)
screen = pygame.display.set_mode((width, height))
pygame.display.set_caption("Omar's Simulation")
screen.fill(backgr... | normal | {
"blob_id": "cbcbc0d01c32693ebbdbcf285efdc8e521c447ee",
"index": 3998,
"step-1": "<mask token>\n",
"step-2": "<mask token>\npygame.init()\n<mask token>\npygame.display.set_caption(\"Omar's Simulation\")\nscreen.fill(background_colour)\n<mask token>\nfor i in range(population_size):\n robots.append(Robot(175... | [
0,
1,
2,
3,
4
] |
"""
ConstantsCommands.py
"""
TEST_HEAD = "\n >>>>>> " \
"\n >>>>>> Test in progress: {0}" \
"\n >>>>>>"
TEST_TAIL = ">>>>>> Test execution done, tearDown\n\r"
| normal | {
"blob_id": "45f0a7a78184195a593061d863ff2114abe01a46",
"index": 6321,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nTEST_HEAD = \"\"\"\n >>>>>> \n >>>>>> Test in progress: {0}\n >>>>>>\"\"\"\nTEST_TAIL = '>>>>>> Test execution done, tearDown\\n\\r'\n",
"step-3": "\"\"\"\nConstantsCommands.py\n\"\"\"\... | [
0,
1,
2
] |
import torch
from torch import nn
from torch.nn import functional as F
import torchvision
import math
from torchvision.models.resnet import Bottleneck
from dataset import load_image, load_text, ALPHABET, MAX_LEN
class ResNetFeatures(nn.Module):
def __init__(self, pretrained=True):
super().__init__()
... | normal | {
"blob_id": "79522db1316e4a25ab5a598ee035cf9b9a9a9411",
"index": 3511,
"step-1": "<mask token>\n\n\nclass PositionalEncoding(nn.Module):\n\n def __init__(self, d_model, max_len, dropout=0.1):\n super(PositionalEncoding, self).__init__()\n self.dropout = nn.Dropout(p=dropout)\n pe = torch.... | [
12,
18,
21,
23,
24
] |
n, x = map(int, input().split())
m = [int(input()) for _ in range(n)]
m.sort()
x -= sum(m)
print(n + x // m[0])
| normal | {
"blob_id": "0ff398775fd13fb5fbd23bf2359bb31dff6bd38c",
"index": 9821,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nm.sort()\nx -= sum(m)\nprint(n + x // m[0])\n",
"step-3": "n, x = map(int, input().split())\nm = [int(input()) for _ in range(n)]\nm.sort()\nx -= sum(m)\nprint(n + x // m[0])\n",
"ste... | [
0,
1,
2
] |
"""Support for binary sensor using I2C abelectronicsiopi chip."""
from custom_components.abelectronicsiopi.IOPi import IOPi
import voluptuous as vol
from homeassistant.components.binary_sensor import PLATFORM_SCHEMA, BinarySensorEntity
from homeassistant.const import DEVICE_DEFAULT_NAME
import homeassistant.help... | normal | {
"blob_id": "73d056d4ab0d268841156b21dfc2c54b5fb2f5f1",
"index": 5218,
"step-1": "<mask token>\n\n\nclass abelectronicsiopiBinarySensor(BinarySensorEntity):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def __init__(self, pinname, pin, pull_mode, invert_logic, bus):\n \"\"... | [
5,
7,
8,
10,
11
] |
# -*- coding: utf-8 -*-
# @Time : 2019/9/17 17:48
# @Author : ZhangYang
# @Email : ian.zhang.88@outlook.com
from functools import wraps
def create_new_sequence_node(zk_client, base_path, prefix, is_ephemeral=False):
if not zk_client.exists(base_path):
zk_client.ensure_path(base_path)
new_node =... | normal | {
"blob_id": "f9a0c3b643c2ee6bb6778477bf8fc21564812081",
"index": 3373,
"step-1": "<mask token>\n\n\nclass SetGetMixin:\n\n def get(path_variable):\n\n def decorator(func):\n\n @wraps(func)\n def wrapper(self, *args, **kwargs):\n if not self.zk_client.exists(getattr(... | [
2,
3,
4,
5,
6
] |
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense,Activation,Dropout
from keras.optimizers import SGD,Adam,RMSprop
from keras.utils import np_utils
x_train=n... | normal | {
"blob_id": "5b919bde9f4fe1da867695ece58f151abb9b70fb",
"index": 1492,
"step-1": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import KFold\nimport keras\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense,Activation,Dro... | [
0
] |
import utilities
import sys
if __name__ == "__main__":
print('I am main!')
else:
print(__name__)
for i in range(0,6):
print(i)
mylist = [12, 13, 14, 13, 12]
print(mylist)
#Enter iterations to run [0-5]
#value = -1
value = 3
while (value not in range(0,6)):
try:
value =... | normal | {
"blob_id": "f218f47acfb078877645de26c64e57f92dbcd953",
"index": 8003,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n print('I am main!')\nelse:\n print(__name__)\nfor i in range(0, 6):\n print(i)\n<mask token>\nprint(mylist)\n<mask token>\nwhile value not in range(0... | [
0,
1,
2,
3,
4
] |
import pandas as pd
#1. 读入数据
#从本地读入“wheat.csv”文件，指定index_col参数为00，即将第一列作为每行的索引。用head()函数查看前几行数据。
data = pd.read_csv("wheat.csv",index_col=0)
print(data.head(6))
#2. 缺失值处理
#该数据集中包含部分缺失值，在模型训练时会遇到特征值为空的问题，故对缺失值进行处理，
## 用DataFrame的fillna方法进行缺失值填充，填充值为用mean方法得到的该列平均值。
data = data.fillna(data.mean())
print(data)
#3. 划分数据... | normal | {
"blob_id": "7da2be1b530faa8ce9a8570247887e8e0d74c310",
"index": 711,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(data.head(6))\n<mask token>\nprint(data)\n<mask token>\nprint(X_train.shape)\nprint(y_train.shape)\nprint(X_test.shape)\nprint(y_test.shape)\n<mask token>\nmodel.fit(X_train, y_train... | [
0,
1,
2,
3,
4
] |
from Distributions import UniformDistribution
from EventGenerator import Generator
from Processor import Processor
class Modeller:
def __init__(self, generator, operators, computers):
self._generator = generator
self._operators = operators
self._computers = computers
def event_mode... | normal | {
"blob_id": "11ed7550c25ca9944ce7073d9655cb9af7bdeae9",
"index": 1324,
"step-1": "<mask token>\n\n\nclass Modeller:\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Modeller:\n <mask token>\n\n def event_mode(self):\n refusals = 0\n processed = 0\n generated... | [
1,
2,
3,
4,
5
] |
# -*- encoding: utf-8 -*-
class BaseException(object):
""" Common base class for all exceptions """
def with_traceback(self, tb): # real signature unknown; restored from __doc__
"""
Exception.with_traceback(tb) --
set self.__traceback__ to tb and return self.
"""
p... | normal | {
"blob_id": "3d01910ae1c163067f4a23b3cca109a7d9e193d5",
"index": 5251,
"step-1": "class BaseException(object):\n <mask token>\n\n def with_traceback(self, tb):\n \"\"\"\n Exception.with_traceback(tb) --\n set self.__traceback__ to tb and return self.\n \"\"\"\n pass\n... | [
9,
10,
11,
12,
15
] |
from django.db import transaction
from django.contrib.auth.models import Group
from drf_yasg import openapi
from drf_yasg.utils import swagger_auto_schema
from rest_framework import status, mixins
from rest_framework.decorators import action
from rest_framework.response import Response
from rest_framework.viewsets imp... | normal | {
"blob_id": "43b5936ca9368dcae8d41b44fd9dc927fe18c9bc",
"index": 8794,
"step-1": "<mask token>\n\n\nclass CustomUsuarioViewSet(AccessViewSetMixin, mixins.CreateModelMixin,\n mixins.RetrieveModelMixin, mixins.ListModelMixin, GenericViewSet):\n <mask token>\n <mask token>\n <mask token>\n <mask toke... | [
13,
14,
19,
21,
23
] |
def squirrel_play(temp, is_summer):
if is_summer == True:
if 60 <= temp <= 100:
return True
else:
return False
if is_summer == False:
if 60 <= temp <= 90:
return True
else:
return False
| normal | {
"blob_id": "48755cf48c6696259d0c319d382021f33751ac01",
"index": 9497,
"step-1": "<mask token>\n",
"step-2": "def squirrel_play(temp, is_summer):\n if is_summer == True:\n if 60 <= temp <= 100:\n return True\n else:\n return False\n if is_summer == False:\n if 6... | [
0,
1
] |
from __future__ import annotations
import math
from abc import abstractmethod
from pytown_core.patterns.behavioral import Command
from pytown_core.serializers import IJSONSerializable
from .buildings import BuildingProcess, BuildingTransaction
from .buildings.factory import BuildingFactory
from .check import (
A... | normal | {
"blob_id": "22b9868063d6c5fc3f8b08a6e725fff40f4a1a03",
"index": 3886,
"step-1": "<mask token>\n\n\nclass BuildCommand(ServerCommand):\n\n def __init__(self, tile: tuple, building_name: str):\n ServerCommand.__init__(self)\n self._tile = tile\n self._building_name = building_name\n <ma... | [
72,
74,
84,
86,
98
] |
from setuptools import setup
import sys
if not sys.version_info >= (3, 6, 0):
msg = 'Unsupported version %s' % sys.version
raise Exception(msg)
def get_version(filename):
import ast
version = None
with open(filename) as f:
for line in f:
if line.startswith('__version__'):
... | normal | {
"blob_id": "d3b55863c6e3a1b6cbdcec37db81ee42b769938d",
"index": 9039,
"step-1": "<mask token>\n\n\ndef get_version(filename):\n import ast\n version = None\n with open(filename) as f:\n for line in f:\n if line.startswith('__version__'):\n version = ast.parse(line).body... | [
1,
2,
3,
4,
5
] |
# 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
] |
def convertEnEntier(nombre):
result = "";
if (nombre == 4):
result = "IV"
if (nombre == 3):
result = "III"
if (nombre == 2):
result = "II"
if (nombre == 1):
result = "I"
return result
print (convertEnEntier(1))
print (convertEnEntier(2))
pri... | normal | {
"blob_id": "ef7fad5019e79950e8fad56404e9ba5d302cfe1c",
"index": 7596,
"step-1": "<mask token>\n",
"step-2": "def convertEnEntier(nombre):\n result = ''\n if nombre == 4:\n result = 'IV'\n if nombre == 3:\n result = 'III'\n if nombre == 2:\n result = 'II'\n if nombre == 1:\n... | [
0,
1,
2,
3
] |
import hashlib
import hmac
import time
def hmac_sha1_token():
timestamp = str(time.time())
hmac_pass = hmac.new(b'some very secret string', timestamp.encode(
'utf-8'), hashlib.sha1).hexdigest()
token = '%s:%s' % (timestamp, hmac_pass)
return token
| normal | {
"blob_id": "65ef3b2ed5eef3d9d9e682ca18cf84457e929df2",
"index": 2222,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef hmac_sha1_token():\n timestamp = str(time.time())\n hmac_pass = hmac.new(b'some very secret string', timestamp.encode(\n 'utf-8'), hashlib.sha1).hexdigest()\n toke... | [
0,
1,
2
] |
# coding: utf-8
'''
Precision, Recall, F1で評価する
Leave-one-outの結果
K-foldの結果
'''
import sys
import os.path
import snlocest.util as util
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
from sklearn.metrics import precision_recall_fscore_support, classification_report
def precision_re... | normal | {
"blob_id": "79c7a2f2e5f0301c15efe1b26a7839a12098f793",
"index": 6618,
"step-1": "<mask token>\n\n\ndef precision_recall_fscore(nodes, y_true, y_pred):\n df = pd.DataFrame({'true': y_true, 'pred': y_pred}, index=nodes)\n n_predicted_nodes = len(df[df['pred'] != 0])\n n_corrects = len(df[df['pred'] == df... | [
2,
3,
4,
5,
6
] |
from unv.app.base import Application
def multiply():
print('multiply', 2 * 2)
def setup(app: Application):
app.register_run_task(multiply)
| normal | {
"blob_id": "760a62a94347171eb9e40015c0c43d72df8f4fc8",
"index": 1463,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef setup(app: Application):\n app.register_run_task(multiply)\n",
"step-3": "<mask token>\n\n\ndef multiply():\n print('multiply', 2 * 2)\n\n\ndef setup(app: Application):\n ... | [
0,
1,
2,
3
] |
"""
This module is used to extract features from the lines extracted from documents
using BERT encodings. This package leverages the bert-as-a-server package to create the
embeddings.
Example:
feature_extractor = FeatureExtractor(document) # document is of class Document
encoded_doc = feature_extracto... | normal | {
"blob_id": "882d265f14c04b2f2f626504d18e2cd07dcc8637",
"index": 3042,
"step-1": "<mask token>\n\n\nclass FeatureExtractor:\n <mask token>\n <mask token>\n\n def encode(self):\n \"\"\" encodes the text in the Document object, and then adds it to the encoding attribute \"\"\"\n text_lines =... | [
3,
4,
5,
6,
7
] |
# Generated by Django 3.0.1 on 2020-01-11 09:50
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('blog', '0005_auto_20200111_1513'),
]
operations = [
migrations.AlterField(
model_name='post',
name='photo',
... | normal | {
"blob_id": "8e8c72362dfb1587150aadaa6b8a0aeb77c3641a",
"index": 1516,
"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 = [('blog', '000... | [
0,
1,
2,
3,
4
] |
# -*- encoding: utf-8 -*-
from django.contrib import admin
from finish.wall.models import (Autor, Category, Announcement, Banner, Caricatura,
Video, TypePost, Post, Phrase, TypeGalery,
ImageGalery, Sponsor)
class AutorAdmin(admin.ModelAdmin):
pass
... | normal | {
"blob_id": "3c9302b5cb92e5103ed16ec56e1b349f0662950c",
"index": 154,
"step-1": "<mask token>\n\n\nclass CaricaturaAdmin(admin.ModelAdmin):\n pass\n\n\nclass VideoAdmin(admin.ModelAdmin):\n pass\n\n\nclass TypePostAdmin(admin.ModelAdmin):\n pass\n\n\nclass PostAdmin(admin.ModelAdmin):\n\n\n class Med... | [
8,
9,
10,
11,
15
] |
#Homework 09
#Raymond Guevara
#018504731
#Algorithm Workbench
#Question 1
print("Question 1")
height = int(input("Please enter your height: "))
print()
#Question 2
print("Question 2")
color = input("please enter your favorite color: ")
print()
#Question 3
print("Question 3")
a = -8/3 #Solved for variable "a" usin... | normal | {
"blob_id": "82c426836fee0560e917848084af4ca124e74dff",
"index": 7043,
"step-1": "<mask token>\n",
"step-2": "print('Question 1')\n<mask token>\nprint()\nprint('Question 2')\n<mask token>\nprint()\nprint('Question 3')\n<mask token>\nprint('%.2f' % b)\n<mask token>\nprint('%.2f' % a)\n<mask token>\nprint('%.2f'... | [
0,
1,
2,
3
] |
import string
import random
import os
from threading import Thread
class Process(Thread):
def __init__(self):
Thread.__init__(self)
def run(self):
while True:
prenom = id_generator(random.randint(4, 8))
nom = id_generator(random.randint(4, 8))
password = id_... | normal | {
"blob_id": "b9c058bdb04df93beb379d05939b00f4db423cd3",
"index": 452,
"step-1": "import string\nimport random\nimport os\nfrom threading import Thread\n\nclass Process(Thread):\n def __init__(self):\n Thread.__init__(self)\n\n def run(self):\n while True:\n prenom = id_generator(ra... | [
0
] |
FILE = "Luke"
NAME = "Luke Walker"
NATIONALITY = "American"
CLASS = "Manipulator"
WEAPON = ""
BIRTH = ""
BIRTH_LOCATION = ""
LETTER = "W"
RECRUITMENT_ORDER = 10
SUMMARY = ""
ABILITIES = ""
BACKSTORY = ""
HIGHLIGHTS = ""
SUMMONS = ("Tonberry", "Grimnir", "Griever", "Starlet")
| normal | {
"blob_id": "fa3ab879541c04e278317b11dd79e6e1b4319536",
"index": 7586,
"step-1": "<mask token>\n",
"step-2": "FILE = 'Luke'\nNAME = 'Luke Walker'\nNATIONALITY = 'American'\nCLASS = 'Manipulator'\nWEAPON = ''\nBIRTH = ''\nBIRTH_LOCATION = ''\nLETTER = 'W'\nRECRUITMENT_ORDER = 10\nSUMMARY = ''\nABILITIES = ''\nB... | [
0,
1,
2
] |
from django.db import models
from django.contrib.auth.models import User
from django.db.models.signals import post_save
from django.dispatch import receiver
from django.core.mail import EmailMultiAlternatives
from django.template import loader
from django.conf import settings
from django.contrib.sites.shortcuts import ... | normal | {
"blob_id": "de77edaccdaada785f41828135ad2da4ae2b403e",
"index": 725,
"step-1": "<mask token>\n\n\nclass Post(models.Model):\n blog = models.ForeignKey(Blog, on_delete=models.DO_NOTHING)\n user = models.ForeignKey(User, on_delete=models.CASCADE)\n header = models.CharField(max_length=50)\n text = mod... | [
6,
8,
9,
10,
11
] |
#!/usr/bin/env python3
'''Testing File'''
import tensorflow.keras as K
def test_model(
network, data, labels, verbose=True
):
'''A Function that tests
a neural network'''
return network.evaluate(
x=data,
y=labels,
verbose=verbose
)
| normal | {
"blob_id": "39643454cbef9e6fa7979d0f660f54e07d155bc7",
"index": 7690,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_model(network, data, labels, verbose=True):\n \"\"\"A Function that tests\n a neural network\"\"\"\n return network.evaluate(x=data, y=labels, verbose=verbose)\n",
... | [
0,
1,
2,
3
] |
import time
from numpy import empty
from src.utils import normalize_input_sentence, evaluate, add_begin_and_trailing_tag, check_for_terminal_argument
from classes.BaseTagger import BaseTagger
from src.CONSTANT import POS_TAG_KEYNAME, WORD_KEYNAME, TRUETAG_KEYNAME, DEFAULT_TRAINING_FILENAME
import sys
import os
# TOD... | normal | {
"blob_id": "8cc0314d48f81ceead863245443548297e8188f8",
"index": 9610,
"step-1": "<mask token>\n\n\nclass ForwardBackward(BaseTagger):\n <mask token>\n <mask token>\n\n def probabilities(self):\n \"\"\"\n Return the probabilities of a hidden state sequence given observed output sequence\n ... | [
3,
4,
5,
7,
8
] |
import json
data = '{"var1": "harry", "var2":56}'
parsed = json.loads(data)
print(parsed['var1'])
# data2 = {"channel_name": "Chill_Out",
# "Cars": ["BMW", "Audi a8", "ferrari"],
# "fridge": ("loki", "Aalu", "pasta"),
# "isbad": False
# }
# jscomp = json.dumps(data2)
# print(jscomp)
| normal | {
"blob_id": "f0f9541eba29b4488c429c889f3b346d53d0239d",
"index": 7193,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(parsed['var1'])\n",
"step-3": "<mask token>\ndata = '{\"var1\": \"harry\", \"var2\":56}'\nparsed = json.loads(data)\nprint(parsed['var1'])\n",
"step-4": "import json\ndata = '{\... | [
0,
1,
2,
3,
4
] |
import numpy as np
import cPickle
from features import create_features, PROJECT
from parse import load_data
from dict_vectorizer import DictVectorizer
videos, users, reviews = load_data()
orig_X = np.array([(x['date'], x['text'], x['user']) for x in reviews])
feats = create_features(orig_X, None)
v = DictVectorizer(s... | normal | {
"blob_id": "e26fa69ea1f0bee82b4108ac5a541a6175645728",
"index": 5955,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncPickle.dump(v, open(PROJECT + 'db/dictvectorizer.pickle', 'wb'))\n",
"step-3": "<mask token>\nvideos, users, reviews = load_data()\norig_X = np.array([(x['date'], x['text'], x['user'])... | [
0,
1,
2,
3,
4
] |
import os, shutil, cv2
from PIL import Image
INP_DIR = '/dataset/test_set_A_full'
# Lọc thư mục data test ra thành 3 thư mục: None, Square (1:1), và phần còn lại (đã được crop ngay chính giữa)
# Trả về path dẫn đến 3 thư mục nói trên
def pre_proc(INP_DIR):
INP_DIR = INP_DIR + '/'
NONE_DIR = os.path.dirname(I... | normal | {
"blob_id": "4ad4cf46be735c6ac26b5b0953d4c2458f37496a",
"index": 9372,
"step-1": "<mask token>\n\n\ndef pre_proc(INP_DIR):\n INP_DIR = INP_DIR + '/'\n NONE_DIR = os.path.dirname(INP_DIR) + '_none'\n SQUARE_DIR = os.path.dirname(INP_DIR) + '_square'\n CROP_DIR = os.path.dirname(INP_DIR) + '_cropped'\n... | [
1,
2,
3,
4,
5
] |
from __future__ import division, print_function
import numpy as np
from copy import deepcopy
class IntegratedRegressor():
regs = []
def __init__(self, reg, predict_log=True):
self.reg = reg
self.predict_log = predict_log
def fit(self, X, y):
self.regs = []
for target in ... | normal | {
"blob_id": "72d41f939a586fbd8459927983d9d62a96b650e2",
"index": 1844,
"step-1": "<mask token>\n\n\nclass IntegratedRegressor:\n <mask token>\n <mask token>\n\n def fit(self, X, y):\n self.regs = []\n for target in y.columns:\n tmp = deepcopy(self.reg)\n if self.predi... | [
6,
7,
8,
10,
11
] |
import cv2 as cv
import numpy as np
from servo import *
from func import *
#import threading
#import dlib
# import socket
# import struct
# import pickle
def constrain(val, minv, maxv):
return min(maxv, max(minv, val))
KP = 0.22
KI = 0
KD = 0.17
last = 0
integral = 0
# constants
SIZE = (400, 300)
RECT = np.flo... | normal | {
"blob_id": "3ccbafbdc84447438c194288b1409e332bb2b479",
"index": 3630,
"step-1": "<mask token>\n\n\ndef constrain(val, minv, maxv):\n return min(maxv, max(minv, val))\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef constrain(val, minv, maxv):\n return min(maxv, max(minv, val))\n\n\n<mask token>\nc... | [
1,
2,
3,
4,
5
] |
user_input = input() #abv>1>1>2>2asdasd
exploded_str = user_input
for n in range(len(user_input)):
explosion_strength = 0
if user_input[n] == ">":
explosion_strength += int(user_input[n+1])
if user_input[n+explosion_strength] != ">":
exploded_str = user_input[:n] + user_input[n+ex... | normal | {
"blob_id": "7930bb813bd546747c7c65b661900939f5ba93f1",
"index": 273,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor n in range(len(user_input)):\n explosion_strength = 0\n if user_input[n] == '>':\n explosion_strength += int(user_input[n + 1])\n if user_input[n + explosion_streng... | [
0,
1,
2,
3
] |
import sys
sys.path.append("./")
from torchtext.datasets import Multi30k
from torchtext.data import Field
from torchtext import data
import pickle
import models.transformer as h
import torch
from datasets import load_dataset
from torch.utils.data import DataLoader
from metrics.metrics import bleu
import numpy as np
fro... | normal | {
"blob_id": "57bc34c6a23c98fd031ea6634441d4d135c06590",
"index": 8694,
"step-1": "<mask token>\n\n\nclass Batch:\n <mask token>\n <mask token>\n <mask token>\n\n\nclass MyIterator(data.Iterator):\n\n def create_batches(self):\n if self.train:\n\n def pool(d, random_shuffler):\n ... | [
7,
14,
17,
18,
21
] |
#!/usr/bin/env python3
import re
import datetime
import math
import pathlib
import os
import io
import argparse
import subprocess
import xml.sax.saxutils
from typing import (Optional, List, Iterable)
import sys
_DEFAULT_TRACK_TYPE = 'Dashcam track'
class Arguments(object):
def __init__(self):
parser = ... | normal | {
"blob_id": "fbb1254c7166fa2aa9cd8a0b9c6525dbe5b652a0",
"index": 2625,
"step-1": "<mask token>\n\n\nclass GpsDataBlockIndex(object):\n\n def __init__(self, position: int, size: int):\n if position <= 0:\n raise ValueError(f\"An invalid position: `{position}'.\")\n if size <= 0:\n ... | [
47,
55,
64,
81,
82
] |
#!/usr/bin/python
import os
from nao.tactics import Tactic
from nao.inspector import Inspector
def test_file():
print("\n[*] === file ===")
name_libmagic_so = 'libmagic.so.1'
inspector = Inspector("./sample/file", debug=True)
# find_addr = 0x1742D # ret block of is_tar
find_addr = 0x173F8 # return ... | normal | {
"blob_id": "a25fb9b59d86de5a3180e4257c4e398f22cdbb05",
"index": 6947,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef test_file():\n print('\\n[*] === file ===')\n name_libmagic_so = 'libmagic.so.1'\n inspector = Inspector('./sample/file', debug=True)\n find_addr = 95224\n cond = i... | [
0,
1,
2,
3,
4
] |
from django import forms
from myapp.models import Student
from myapp.models import Employee
class EmpForm(forms.ModelForm):
class Meta:
model = Student
fields = "__all__"
class StudentForm(forms.Form):
firstname = forms.CharField(label="Enter first name:", max_length=50)
lastname = forms... | normal | {
"blob_id": "0b141ecca501c21df50e76d0841dd5651274f0da",
"index": 8509,
"step-1": "<mask token>\n\n\nclass StudentForm(forms.Form):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass EmployeeForm(forms.ModelForm):\n\n\n class Meta:\n model = Employee\n fields = '__... | [
2,
3,
4,
5,
6
] |
from django.conf.urls import patterns, include, url
# Uncomment the next two lines to enable the admin:
# from django.contrib import admin
# admin.autodiscover()
urlpatterns = patterns('accounts.views',
url(r'^$', 'home', name='home'),
url(r'^login/$', 'login', name='login'),
url(r'^logout/$', 'logout', n... | normal | {
"blob_id": "798ddd4a6e4febb4664bf1c973877628d1a45c71",
"index": 368,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = patterns('accounts.views', url('^$', 'home', name='home'),\n url('^login/$', 'login', name='login'), url('^logout/$', 'logout', name\n ='logout'), url('^register/$', 'r... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2017 Maarten Los
# See LICENSE.rst for details.
class Defaults(object):
INBUS_VERSION = 2
LOCALHOST = "127.0.0.1"
PORT = 7222
INBUS_ADDRESS = (LOCALHOST, PORT)
BUFFER_SIZE = 65536
| normal | {
"blob_id": "bc087482e901ce1831cef56aa9c7aef0c8f2d15a",
"index": 1793,
"step-1": "<mask token>\n",
"step-2": "class Defaults(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "class Defaults(object):\n INBUS_VERSION = 2\n LOCALHOST = '127.0... | [
0,
1,
2,
3
] |
import numpy as np
from datetime import date, timedelta, datetime
from pytz import timezone
import store
import psycopg2
import requests
import os
import filters
FIRST = 4
def prepareDate():
pc_tz = timezone('US/Pacific')
n = datetime.now(pc_tz)
nd = n.date()
store.updateStore(today=nd)
def getData():
toda... | normal | {
"blob_id": "5b4651f37cdcbb13f8ddd03327ef65af0f9cf61d",
"index": 1944,
"step-1": "<mask token>\n\n\ndef getDates():\n dates = store.mapStore('dates')\n data = store.mapStore('data')\n exceptions = store.mapStore('exceptions')\n if len(exceptions) > 0:\n return False\n try:\n d0 = dat... | [
3,
4,
6,
7,
8
] |
lc_headers = {
"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 11_0) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0 Safari/605.1.15",
"authority": "leetcode.com",
}
lc_all = "https://leetcode.com/api/problems/all/"
lc_submissions = "https://leetcode.com/api/submissions/?off... | normal | {
"blob_id": "f715628da2f1b950b8fbf8aa5b033e5299d3e224",
"index": 7857,
"step-1": "<mask token>\n",
"step-2": "lc_headers = {'User-Agent':\n 'Mozilla/5.0 (Macintosh; Intel Mac OS X 11_0) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/14.0 Safari/605.1.15'\n , 'authority': 'leetcode.com'}\nlc_all = 'http... | [
0,
1,
2
] |
# content of conftest.py
# adapted from http://pytest.org/latest/example/special.html
import pytest
import requests
def tear_down():
''' conftest.py tear_down - the last to go.... '''
print("\nTEARDOWN after all tests")
@pytest.fixture(scope="session", autouse=True)
def set_up(request):
''' conftest... | normal | {
"blob_id": "816b1a932208a4525230dd886adf8c67dec3af3e",
"index": 349,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.fixture(scope='session', autouse=True)\ndef set_up(request):\n \"\"\" conftest.py set_up - the first to start.... \"\"\"\n print('\\nSETUP before all tests')\n request... | [
0,
1,
2,
3,
4
] |
class Solution(object):
def countSmaller(self, nums):
"""
:type nums: List[int]
:rtype: List[int]
naive -- o(n^2)
"""
## StefanPochmann solution #2
def countSmaller(self, nums):
def sort(enum):
half = len(enum) / 2
if half:
left = sort(enum... | normal | {
"blob_id": "42021b762737a2eb21866ba029ece4ac120152cd",
"index": 5902,
"step-1": "class Solution(object):\n\n def countSmaller(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: List[int]\n naive -- o(n^2)\n \"\"\"\n\n\n<mask token>\n\n\ndef mergesort(x):\n if len(x)... | [
6,
7,
8,
9,
10
] |
from django.conf.urls import url, include
from django.contrib import admin
from rest_framework_swagger.views import get_swagger_view
schema_view = get_swagger_view(title='Pastebin API')
urlpatterns = [
url(r'^admin/', admin.site.urls),
url(r'^doc_u/', schema_view),
url(r'^', include('o.urls', )),
url(... | normal | {
"blob_id": "891588327046e26acb9a691fa8bb9a99420712d6",
"index": 913,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nschema_view = get_swagger_view(title='Pastebin API')\nurlpatterns = [url('^admin/', admin.site.urls), url('^doc_u/', schema_view),\n url('^', include('o.urls')), url('^api/', include('r... | [
0,
1,
2,
3
] |
# O(logn) T O(1) S
def binarySearch(array, target):
if len(array) == 0:
return -1
else:
return binarySearchR(array, target, 0, len(array) - 1)
def binarySearchR(array, target, leftPointer, rightPointer):
if leftPointer > rightPointer:
return -1
else:
midPointer = (leftP... | normal | {
"blob_id": "57d6b9e7f48d32e5d10bfd6a340ea56281f5d82d",
"index": 1890,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef binarySearchR(array, target, leftPointer, rightPointer):\n if leftPointer > rightPointer:\n return -1\n else:\n midPointer = (leftPointer + rightPointer) // 2\... | [
0,
1,
2,
3
] |
# vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4
# (c) Simen Sommerfeldt, @sisomm, simen.sommerfeldt@gmail.com Licensed as CC-BY-SA
import os
import argparse,time
import pygame
import paho.mqtt.client as paho
parser = argparse.ArgumentParser()
parser.add_argument("-s","--server", default="127.0.0.1", help="The I... | normal | {
"blob_id": "9852d2a15047b110c7f374fd75e531c60c954724",
"index": 3920,
"step-1": "<mask token>\n\n\ndef task_goodbye():\n pygame.mixer.music.load('../sounds/despicable.wav')\n pygame.mixer.music.play()\n\n\ndef task_hello():\n pygame.mixer.music.load('../sounds/mday.wav')\n pygame.mixer.music.play()\... | [
4,
5,
6,
8,
9
] |
from __future__ import annotations
import typing
import requests
import heapq
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer
from bs4 import BeautifulSoup
from wikiAPI import get_JSON, get_intro, compare_titles
from typing import List, Type, Callable
... | normal | {
"blob_id": "1fad591fde707c73bd52aa8518828c8b8be9cd32",
"index": 2283,
"step-1": "<mask token>\n\n\nclass Article:\n <mask token>\n title: str\n target: str\n g: float\n f: float\n parent: typing.Union[Article, Type(None)]\n heuristic: Callable[[str, str], float]\n\n def __init__(self, ti... | [
12,
15,
17,
18,
23
] |
import discord
class Leveling:
__slots__ = ('sid', 'channelID', 'message', 'noxpchannelIDs',
'noxproleID', 'remove', 'bot', 'roles')
sid: int
channelID: int
message: str
noxpchannelIDs: list[int]
noxproleID: int
remove: bool
roles: list[list]
def __init__(self, bot, sid, r... | normal | {
"blob_id": "346df9706dc222f43a77928964cd54e7d999a585",
"index": 8052,
"step-1": "<mask token>\n\n\nclass Leveling:\n <mask token>\n sid: int\n channelID: int\n message: str\n noxpchannelIDs: list[int]\n noxproleID: int\n remove: bool\n roles: list[list]\n <mask token>\n\n @property... | [
2,
3,
4,
5
] |
#coding=utf8
"""
Created on Thu Feb 20 00:53:28 2020
@author: Neal LONG
"""
import json
import requests
fake_header = { "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36",
"Accept":"text/html,application/xhtml+xml,... | normal | {
"blob_id": "166a1dfbd3baf766230080361d98648ec0a64455",
"index": 1038,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(parsed_json2)\n",
"step-3": "<mask token>\nfake_header = {'user-agent':\n 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113... | [
0,
1,
2,
3,
4
] |
n = int(input())
s = ""
for i in range(n):
l = list(map(lambda x:x*x,map(int, input().split())))
l.sort()
if l[0] + l[1] == l[2]:
s += "YES\n"
else:
s += "NO\n"
print(s,end="") | normal | {
"blob_id": "f8b473451a15e42319b60f44a527d715c0032614",
"index": 3411,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(n):\n l = list(map(lambda x: x * x, map(int, input().split())))\n l.sort()\n if l[0] + l[1] == l[2]:\n s += 'YES\\n'\n else:\n s += 'NO\\n'\nprint... | [
0,
1,
2,
3
] |
#n-repeated element
class Solution:
def repeatedNTimes(self, A):
freq = {}
for i in A:
if i in freq.keys():
freq[i] += 1
else:
freq[i] = 1
key = list(freq.keys())
val = list(freq.values())
m = max(val)
return key... | normal | {
"blob_id": "d50618f7784e69b46cb665ec1a9c56f7a2867785",
"index": 5033,
"step-1": "class Solution:\n <mask token>\n\n\n<mask token>\n",
"step-2": "class Solution:\n\n def repeatedNTimes(self, A):\n freq = {}\n for i in A:\n if i in freq.keys():\n freq[i] += 1\n ... | [
1,
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
"""
@author: longshuicui
@date : 2021/2/4
@function:
32. Longest Valid Parentheses (Hard)
https://leetcode.com/problems/longest-valid-parentheses/
题目描述
在给的字符串里面找到 最大长度的 有效 括号字符串
输入输出示例
Input: s = ")()())"
Output: 4
Explanation: The longest valid parentheses substring is "()()"... | normal | {
"blob_id": "0f03ff63662b82f813a18cc8ece3d377716ce678",
"index": 2318,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef longestValidParentheses(s):\n stack = []\n maxLength = 0\n stack.append(-1)\n for i in range(len(s)):\n if s[i] == '(':\n stack.append(i)\n el... | [
0,
1,
2,
3,
4
] |
from django.test import TestCase
from core.factories import CompanyFactory, EmployeeFactory
from core.pair_matcher import MaximumWeightGraphMatcher
class PairMatcherTestCase(TestCase):
def setUp(self):
self.company = CompanyFactory.create()
def test_simple(self):
employees = EmployeeFactory.... | normal | {
"blob_id": "0c68bd65cac3c8b9fd080900a00991b2d19260ee",
"index": 534,
"step-1": "<mask token>\n\n\nclass PairMatcherTestCase(TestCase):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass PairMatcherTestCase(TestCase):\n <mask token>\n\n def test_simple(self):\n employees = ... | [
1,
2,
3,
4
] |
from collections import defaultdict, deque
import numpy as np
import gym
from chula_rl.policy.base_policy import BasePolicy
from chula_rl.exception import *
from .base_explorer import BaseExplorer
class OneStepExplorerWithTrace(BaseExplorer):
"""one-step explorer but with n-step trace"""
def __init__(self... | normal | {
"blob_id": "958d7ec966179d63c6ba0a651e99fff70f0db31a",
"index": 5410,
"step-1": "<mask token>\n\n\nclass OneStepExplorerWithTrace(BaseExplorer):\n <mask token>\n <mask token>\n\n def step(self, policy: BasePolicy):\n if self.n_interaction > self.n_max_interaction:\n raise InteractionE... | [
2,
3,
4,
5,
6
] |
name = 'valentina '
print(name * 1000)
| normal | {
"blob_id": "aff1a9263e183610f403a4d6a7f27b45eacb7ff2",
"index": 0,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(name * 1000)\n",
"step-3": "name = 'valentina '\nprint(name * 1000)\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
# this is for the 12/30/2015 experiments
# varied over 1, 10, 25, 50, 100 repeat particles per particle
# 10000 particles total per filter
# bias is at 0.8 in both the "real" world (realWorld.cpp)
files = ['data0Tue_Dec_30_20_37_34_2014.txt',
'data0Tue_Dec_30_20_37_49_2014.txt',
'data0Tue_Dec_30_20_38_04_2014.txt',
'd... | normal | {
"blob_id": "b63221af86748241fdce34052819569a06d37afe",
"index": 6965,
"step-1": "<mask token>\n",
"step-2": "files = ['data0Tue_Dec_30_20_37_34_2014.txt',\n 'data0Tue_Dec_30_20_37_49_2014.txt',\n 'data0Tue_Dec_30_20_38_04_2014.txt',\n 'data0Tue_Dec_30_20_38_19_2014.txt',\n 'data0Tue_Dec_30_20_38_3... | [
0,
1,
2
] |
club_info = {'club_url':
'https://www.futbin.com///18/leagues/Major%20League%20Soccer?page=1&club=101112'
, 'club_logo':
'https://cdn.futbin.com/content/fifa18/img/clubs/101112.png',
'club_name': 'Vancouver Whitecaps FC'}
players = {}
players['Waston'] = {'player_url':
'https://www.futbin.com//18/pl... | normal | {
"blob_id": "35c4e26acbe99ca7f37b63b67f38d5c40fbf0ea4",
"index": 2503,
"step-1": "<mask token>\n",
"step-2": "club_info = {'club_url':\n 'https://www.futbin.com///18/leagues/Major%20League%20Soccer?page=1&club=101112'\n , 'club_logo':\n 'https://cdn.futbin.com/content/fifa18/img/clubs/101112.png',\n ... | [
0,
1
] |
from utils import *
name = 'topological'
def topological(above):
"Topologically sort a DAG by removing a layer of sources until empty."
result = []
while above:
sources = set(above) - set(flatten(above.values()))
result.extend(sources)
for node in sources:
del above[nod... | normal | {
"blob_id": "a8ea91797942616779ae0acc884db1e521c7ad28",
"index": 3927,
"step-1": "<mask token>\n\n\ndef topological(above):\n \"\"\"Topologically sort a DAG by removing a layer of sources until empty.\"\"\"\n result = []\n while above:\n sources = set(above) - set(flatten(above.values()))\n ... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import enhancedyaml
import vector
def roots_of_n_poly_eq(n, x, var_upper_bounds=tuple()):
'''find the all possible non-negative interger roots of a `n`-term polynomial equals `x`.'''
countdown = lambda: xrange(x if not var_upper_bounds else var_upper_bounds[0], -... | normal | {
"blob_id": "c6b80a7dfce501bfe91f818ac7ab45238a0a126b",
"index": 3367,
"step-1": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport enhancedyaml\nimport vector\n\ndef roots_of_n_poly_eq(n, x, var_upper_bounds=tuple()):\n '''find the all possible non-negative interger roots of a `n`-term polynomial equa... | [
0
] |
from graphics import *
from random import random
def printIntro():
print("This program evaluates pi via Monte Carlo techniques")
def simDarts(n):
win = GraphWin("", 400, 400)
win.setCoords(-1.2, -1.2, 1.2, 1.2)
hits = 0
for i in range(n):
pt = getDarts()
if hitTarget(pt):
... | normal | {
"blob_id": "0bf970a84911d29a8343575ef15f2765875b8b89",
"index": 9552,
"step-1": "<mask token>\n\n\ndef printIntro():\n print('This program evaluates pi via Monte Carlo techniques')\n\n\n<mask token>\n\n\ndef getDarts():\n x = 2 * random() - 1\n y = 2 * random() - 1\n pt = Point(x, y)\n return pt\... | [
5,
6,
7,
8,
9
] |
# MODULES
import sys
sys.path.append('~/Documents/Project_3/REPO')
from scipy import *
from scipy import linalg
import cPickle as pickle
import ConfigParser
import TobySpectralMethods as tsm
config = ConfigParser.RawConfigParser()
fp = open('config.cfg')
config.readfp(fp)
N = config.getint('General', 'N')
M = config.... | normal | {
"blob_id": "1221394dfb97cbbfb00b412f60d4df521acc1262",
"index": 8029,
"step-1": "\n# MODULES\nimport sys\nsys.path.append('~/Documents/Project_3/REPO')\nfrom scipy import *\nfrom scipy import linalg\nimport cPickle as pickle\nimport ConfigParser\nimport TobySpectralMethods as tsm\n\nconfig = ConfigParser.RawCon... | [
0
] |
import psycopg2
from .connection import get_connection
def get_clientes():
query = 'SELECT nombre, t_documento ,documento, telefono, direccion, correo, ciudad_circulacion, fecha_nacimiento, comercial, primas FROM clientes'
cursor = get_connection(query)
return cursor
def get_clientes_by_id(_id... | normal | {
"blob_id": "035a87ccf21d45b2c147da4315c2143bea1ff21d",
"index": 8173,
"step-1": "<mask token>\n\n\ndef add_cliente(parametros):\n query = (\n 'INSERT INTO clientes VALUES(%s,%s,%s,%s,%s,%s,%s,%s,NULL,NULL,%s,NULL,%s)'\n )\n get_connection(query, parametros)\n print('Datos almacenados')\n ... | [
1,
5,
7,
9,
10
] |
import requests, os
def lambda_handler(event, context):
print(requests)
apiKey = os.environ['newrelic_api_key']
headers = {'content-type': 'application/json', 'Accept-Charset':
'UTF-8', 'X-api-key': apiKey}
r = requests.get('https://api.newrelic.com/v2/applications.json',
headers=heade... | normal | {
"blob_id": "e89600f109335ffdb00c13f617d61496c547ba61",
"index": 5612,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef lambda_handler(event, context):\n print(requests)\n apiKey = os.environ['newrelic_api_key']\n headers = {'content-type': 'application/json', 'Accept-Charset':\n 'U... | [
0,
1,
2
] |
array = [1, 7, 3, 8, 9, 2, 4]
index = 0
while (index < len(array)):
count = 0
while(count <= len(array)-2):
if(count == len(array)-1):
break
if (array[count] > array[count+1]):
sift = array[count]
array[count] = array[count+1]
array[count+1] = sift... | normal | {
"blob_id": "fc8976141a19afd099f92cbbdb578e9c620cb745",
"index": 5075,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile index < len(array):\n count = 0\n while count <= len(array) - 2:\n if count == len(array) - 1:\n break\n if array[count] > array[count + 1]:\n ... | [
0,
1,
2,
3
] |
from django.contrib import admin
from calc.models import CalcResult
class MyAdmin(admin.ModelAdmin):
def has_add_permission(self, request, obj=None):
return False
def has_delete_permission(self, request, obj=None):
return False
class CalcResultAdmin(MyAdmin):
list_display = ('result', ... | normal | {
"blob_id": "e2573a5dc507e9aeb811fbc254129aeb6e54cc0b",
"index": 2483,
"step-1": "<mask token>\n\n\nclass MyAdmin(admin.ModelAdmin):\n <mask token>\n <mask token>\n\n\nclass CalcResultAdmin(MyAdmin):\n list_display = 'result', 'message', 'time'\n search_fields = 'result', 'message', 'time'\n\n\n<mask... | [
3,
4,
5,
6,
8
] |
import pandas as pd
import numpy as np
#from ctaFunction import std_normalized
def barStdNormal(bars, timeperiod=5):
'''Std Normal '''
close = bars['close']
result = close.rolling(timeperiod).apply(std_normalized)
return result | normal | {
"blob_id": "6fa0e1dabd178507c32c62146b404bb42f8445d4",
"index": 9860,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef barStdNormal(bars, timeperiod=5):\n \"\"\"Std Normal \"\"\"\n close = bars['close']\n result = close.rolling(timeperiod).apply(std_normalized)\n return result\n",
"s... | [
0,
1,
2,
3
] |
import tensorflow as tf
import tensorflow_probability as tfp
import pytest
import numpy as np
from estimators import NormalizingFlowNetwork
tfd = tfp.distributions
tf.random.set_seed(22)
np.random.seed(22)
@pytest.mark.slow
def test_x_noise_reg():
x_train = np.linspace(-3, 3, 300, dtype=np.float32).reshape((300,... | normal | {
"blob_id": "303a8609cb21c60a416160264c3d3da805674920",
"index": 777,
"step-1": "<mask token>\n\n\n@pytest.mark.slow\ndef test_x_noise_reg():\n x_train = np.linspace(-3, 3, 300, dtype=np.float32).reshape((300, 1))\n noise = tfd.MultivariateNormalDiag(loc=5 * tf.math.sin(2 * x_train),\n scale_diag=ab... | [
2,
3,
4,
5,
6
] |
from collections import namedtuple
from math import tau, sin, cos, atan2
grid = 21
c = grid / 2
points = grid**3
Velocity = namedtuple('Velocity', ('x', 'y', 'z'))
velocity = []
for k in range(grid):
for j in range(grid):
for i in range(grid):
x = (i / grid + 0.25) * tau
y = (j /... | normal | {
"blob_id": "d70986b016e58877c39bfbb76c5bd622c44cbca9",
"index": 9273,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor k in range(grid):\n for j in range(grid):\n for i in range(grid):\n x = (i / grid + 0.25) * tau\n y = (j / grid + 0.25) * tau\n z = (k / gri... | [
0,
1,
2,
3,
4
] |
movies = ["Abraham Lincoln", "Blue Steel", "Behind Office Doors", "Bowery at Midnight", "Captain Kidd", "Debbie Does Dallas", "The Emperor Jones", "Rain"]
movies_tuple = [("Abraham Lincoln", 1993), ("Blue Steel", 1938), ("Behind Office Doors", 1999), ("Bowery at Midnight", 2000), ("Captain Kidd",2010), ("Debbie Does D... | normal | {
"blob_id": "8435a69ee9793435c7483df9bb15f01ef8051479",
"index": 3340,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(selected_movies)\n<mask token>\nprint(selected_movies2)\n",
"step-3": "movies = ['Abraham Lincoln', 'Blue Steel', 'Behind Office Doors',\n 'Bowery at Midnight', 'Captain Kidd',... | [
0,
1,
2,
3
] |
# 1-[2-3-4-5]-1
# 순열로 돌리고, 백트래킹으로 걷어내기
def DFS(idx, cost, cur_loc):
global min_cost
if min_cost < cost: return
if idx == N and arr[cur_loc][0]:
if min_cost > cost + arr[cur_loc][0]:
min_cost = cost + arr[cur_loc][0]
return
for i in range(1, N):
if way[i] or not arr[c... | normal | {
"blob_id": "4ff7e83c6e85a041578a8b3471cbbb7e0c2543e6",
"index": 2663,
"step-1": "<mask token>\n",
"step-2": "def DFS(idx, cost, cur_loc):\n global min_cost\n if min_cost < cost:\n return\n if idx == N and arr[cur_loc][0]:\n if min_cost > cost + arr[cur_loc][0]:\n min_cost = c... | [
0,
1,
2,
3,
4
] |
from django.contrib import admin
# Register your models here.
from django.contrib import admin
from practice_app.models import Person
class PersonAdmin(admin.ModelAdmin):
pass
admin.site.register(Person) | normal | {
"blob_id": "d90aeaaa682b371afb4771ecfbf1077fc12520b4",
"index": 3873,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass PersonAdmin(admin.ModelAdmin):\n pass\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass PersonAdmin(admin.ModelAdmin):\n pass\n\n\nadmin.site.register(Person)\n",... | [
0,
1,
2,
3,
4
] |
# -*- coding: utf-8 -*-
###########################
# CSCI 573 Data Mining - Eclat and Linear Kernel SVM
# Author: Chu-An Tsai
# 12/14/2019
###########################
import fim
import numpy as np
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.svm import SVC
from sklearn.m... | normal | {
"blob_id": "07b05093b630fc0167532884ec69a00420ed70b4",
"index": 4021,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor line in lines:\n strpline = line.rstrip()\n arr = strpline.split(',')\n newline = []\n for i in range(len(arr)):\n if arr[i] == 'y':\n newline.append(i)\... | [
0,
1,
2,
3,
4
] |
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