row_id
int64 0
48.4k
| init_message
stringlengths 1
342k
| conversation_hash
stringlengths 32
32
| scores
dict |
|---|---|---|---|
47,691
|
check this code:
fn main() {
let c_fivends = vec![(5,10),(25,30),(40,45),(60,65)];
let c_exons = vec![(32,37),(47,55),(70,80)];
let tx_5end = (43,45);
if let Ok(k) = c_fivends.binary_search_by(|&(start, _)| start.cmp(&tx_5end.0)) {
println!("we are inside")
} else {
println!("we are not inside")
};
}
current result is "we are not inside". In this case the expected behavior should be "we are inside" because 43 is between 40 and 45. Your solution needs to be the most efficient, fastest and elegant. You are free to use any algorithm, function, crate, trick or unsafe code you want.
|
f8fa3067a8934456e0f228b5878c9e8d
|
{
"intermediate": 0.22177115082740784,
"beginner": 0.44464609026908875,
"expert": 0.3335827887058258
}
|
47,692
|
Hi there, please be a senior sapui5 developer and answer my following questions with working code examples.
|
44609438c16f6709854d2ae25634e206
|
{
"intermediate": 0.42116406559944153,
"beginner": 0.2712341248989105,
"expert": 0.3076017498970032
}
|
47,693
|
this is my function:
pub fn interval_search(intervals: &[(u64, u64)], query: &(u64, u64)) -> Option<(u64, u64)> {
let mut start = 0;
let mut end = intervals.len();
while start < end {
let mid = start + (end - start) / 2;
let mid_val = intervals[mid];
if query.1 <= mid_val.0 {
end = mid;
} else if query.0 >= mid_val.1 {
start = mid + 1;
} else {
return Some(mid_val);
}
}
None
}
what can i do to make it a method for any vector with tuples inside of the form (number, number)? For example, to do something like this:
let x = [(1,6), (10,15), (25,32)];
let y = (26,30);
x.interval_search(y)
|
c184351dfd138a5469b3207251aefe94
|
{
"intermediate": 0.4487825930118561,
"beginner": 0.3419005572795868,
"expert": 0.20931686460971832
}
|
47,694
|
look at my implementation:
trait IntervalSearch<T> {
fn interval_search(&self, query: &(T, T)) -> Option<(T, T)>
where
T: PartialOrd + Copy;
}
impl<T> IntervalSearch<T> for Vec<(T, T)> {
fn interval_search(&self, query: &(T, T)) -> Option<(T, T)>
where
T: PartialOrd + Copy,
{
let mut start = 0;
let mut end = self.len();
while start < end {
let mid = start + (end - start) / 2;
let mid_val = self[mid];
if query.1 <= mid_val.0 {
end = mid;
} else if query.0 >= mid_val.1 {
start = mid + 1;
} else {
return Some(mid_val);
}
}
None
}
}
do you have any idea on how to make crazy faster? You are allowed to use any trick you want
|
c820e67f5cf65655f5ce40d60bde97c4
|
{
"intermediate": 0.4128094017505646,
"beginner": 0.14692549407482147,
"expert": 0.44026514887809753
}
|
47,695
|
I have this nest prisma request:
async getGroupsByTeacherId(_teacher_id:number){
return this.prisma.studygroups.findMany({
select: {
id: true,
name: true,
// main_schedule: true,
},
where: {
main_schedule: {
some: {
teacher_id: Number(_teacher_id),
},
},
},
});
}
I have table users with this columns:
id
username
password
firstname
lastname
patronymic
type_user
email
studyGroups
resetPasswordToken
resetPasswordExpires
superAdmin
teacher_id
student_id
nik_domic
and I have table main_schedule:
id
group_id
time_id
weekday_id
week
subject_id
teacher_id
classroom_id
period
type_subject
begin_date
end_date
I need to find entry of table "users" by id = 88, get teacher_id of that entry and then, by that teacher_id get all entries of main_schedule table, where teacher_id = teacher_id
|
d3e4459be9a3a58bdbbb3d2ac54df636
|
{
"intermediate": 0.5454025268554688,
"beginner": 0.2829682528972626,
"expert": 0.17162932455539703
}
|
47,696
|
struct wl_registry * registry = wl_display_get_registry(display);
wl_registry_add_listener(registry, ®istry_listener, NULL);
// wait for the "initial" set of globals to appear
wl_display_roundtrip(display); need to mock these function using Gtest and Gmock ..
|
b0be0b81302ca0fbe8de467d4f30bcd5
|
{
"intermediate": 0.42970162630081177,
"beginner": 0.4286656379699707,
"expert": 0.14163270592689514
}
|
47,697
|
In this clojurescript code, what is the react class I need to have the edit button on the left and the other buttons on the right?
[:div.d-flex
[:div
[:button.btn.btn-danger
{:on-click #(ui.new/close-popover! popover-state-atom)}
"Edit"]]
[:div.align-items-right
[:button.btn.btn-primary
;; on-close will be called and handle transfering the local values into re-frame
{:on-click #(ui.new/close-popover! popover-state-atom)}
"Save"]
[:button.btn.btn-link
{:on-click #(do (reset! name-atom original-name)
(reset! desc-atom original-desc)
(ui.new/close-popover! popover-state-atom))}
"Cancel"]]]
|
842ec588e2aefb4f8740782fb57d7585
|
{
"intermediate": 0.4845345616340637,
"beginner": 0.3759177029132843,
"expert": 0.1395477056503296
}
|
47,698
|
write an Auction Sniper bot using python a python script, for a specific website, prompting for bid price and the time to input the bid, say yes master if you understood
|
1445f776b3ae46390d93e99c0cb9225a
|
{
"intermediate": 0.30206623673439026,
"beginner": 0.1732521504163742,
"expert": 0.524681568145752
}
|
47,699
|
привет у меня есть скрипт для увелечения значения сборки как мне сделать так что он увеличивал только если сборка успешно собирается
public class VersionIncrementor : IPreprocessBuildWithReport
{
public int callbackOrder { get { return 0; } }
public void OnPreprocessBuild(BuildReport report)
{
IncrementVersion();
}
private static void IncrementVersion()
{
string[] versionNumbers = PlayerSettings.bundleVersion.Split('.');
if (versionNumbers.Length > 0)
{
int buildNumber = int.Parse(versionNumbers[versionNumbers.Length - 1]);
buildNumber++;
versionNumbers[versionNumbers.Length - 1] = buildNumber.ToString();
string newVersion = string.Join(".", versionNumbers);
PlayerSettings.bundleVersion = newVersion;
Debug.Log("Updated version to " + newVersion);
}
else
{
Debug.LogError("Failed to increment version number.");
}
}
}
|
e2b63240e3882790d40d74e79a952f28
|
{
"intermediate": 0.4707893133163452,
"beginner": 0.37473705410957336,
"expert": 0.15447360277175903
}
|
47,700
|
привет при сборке вышла такая ошибка
Assets\Scripts\Editor\VersionIncrementor.cs(3,19): error CS0234: The type or namespace name 'Build' does not exist in the namespace 'UnityEditor' (are you missing an assembly reference?)
|
3c20e29cfce3d1330aaf4b59726120df
|
{
"intermediate": 0.3837953805923462,
"beginner": 0.36705929040908813,
"expert": 0.2491452842950821
}
|
47,701
|
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEngine.Events;
public class Clickable : Interactable
{
public UnityEvent m_onPressDown;
public UnityEvent m_onPressUp;
public UnityEvent m_onPress;
public override void ClearInteractableEvents()
{
base.ClearInteractableEvents();
m_onPress.RemoveAllListeners();
m_onPressDown.RemoveAllListeners();
m_onPressUp.RemoveAllListeners();
}
public override void OnStartInteract(Touch touch)
{
base.OnStartInteract(touch);
m_onPressDown.Invoke();
OnPressDown();
}
public override void OnEndInteract(Touch touch, bool canceled)
{
base.OnEndInteract(touch);
m_onPressUp.Invoke();
OnPressUp();
}
public virtual void OnPressDown()
{
}
public virtual void OnPressUp()
{
}
public virtual void OnPress()
{
// 检测鼠标左键按下
if (Input.GetMouseButtonDown(0))
{
Debug.Log("左键");
}
// 检测鼠标右键按下
if (Input.GetMouseButtonDown(1))
{
Debug.Log("油煎");
}
}
protected override void VirutalUpdate()
{
base.VirutalUpdate();
if (Interacting)
{
m_onPress.Invoke();
OnPress();
}
}
}
为什么鼠标右键没有反应呢
|
a31c38c20dd89bf669b014bdb5a805a1
|
{
"intermediate": 0.373688280582428,
"beginner": 0.39526838064193726,
"expert": 0.23104339838027954
}
|
47,702
|
6
I'm trying to proxy a request to a backend with Next rewrites.
next.config.js:
async rewrites() {
return [
{
source: "/api/:path*",
destination: "http://somedomain.loc/api/:path*",
},
]
},
/etc/host:
127.0.0.1 somedomain.loc
and in the end i get this error:
Failed to proxy http://somedomain.loc/api/offers Error: connect ECONNREFUSED 127.0.0.1:80
at TCPConnectWrap.afterConnect [as oncomplete] (net.js:1159:16) {
errno: -111,
code: 'ECONNREFUSED',
syscall: 'connect',
address: '127.0.0.1',
port: 80
}
error - Error: connect ECONNREFUSED 127.0.0.1:80
at TCPConnectWrap.afterConnect [as oncomplete] (net.js:1159:16) {
errno: -111,
code: 'ECONNREFUSED',
syscall: 'connect',
address: '127.0.0.1',
port: 80
}
While if you make a request via postman or directly from the browser, everything works fine.
Please help me understand what is the problem here.
tried: Proxy request to backend expected: The request is proxied to the backend as a result: Proxy error
|
3d99bcc1370b8e97f2a9dee9798ea390
|
{
"intermediate": 0.55784672498703,
"beginner": 0.2582094073295593,
"expert": 0.18394392728805542
}
|
47,703
|
how to write gtest for STATIC void draw_image(struct graphics_priv *gr, struct graphics_gc_priv *fg, struct point *p,
struct graphics_image_priv *img) {
// draw_image_es(gr, p, img->img->w, img->img->h, img->img->pixels);
}
|
1f453a0aa73a0556d7b63123c785d668
|
{
"intermediate": 0.34079909324645996,
"beginner": 0.3954678177833557,
"expert": 0.26373305916786194
}
|
47,704
|
Write function on python which have text with \n on input. I need that function split this text by "\n" and make multiline text on output which must be in """ """ without "\n"
|
b5df040518d7d87e5ca1a2aacc0bac4f
|
{
"intermediate": 0.4060955345630646,
"beginner": 0.3170996308326721,
"expert": 0.2768048346042633
}
|
47,705
|
vue 3 composition api script setup ts. How to create ref of component with generic
|
08c3600ced2d17a7fe5102f548e37f6f
|
{
"intermediate": 0.5820887684822083,
"beginner": 0.2532646656036377,
"expert": 0.16464661061763763
}
|
47,706
|
write a c code for floyd warshall algo in dynamic programming
|
d435b335c2211ee48561b7cc1304ca15
|
{
"intermediate": 0.13805843889713287,
"beginner": 0.16276834905147552,
"expert": 0.6991732120513916
}
|
47,707
|
make an array of 3 animals
|
9db1c022d2b078eb1e682a5b6617418f
|
{
"intermediate": 0.3331804871559143,
"beginner": 0.3962669372558594,
"expert": 0.2705525755882263
}
|
47,708
|
Mi puoi ottimizzare questa condizione "(showForm && isPrimaryInDocument && doc.entity === DocumentProcessActionRm.Primary) ||
(isPrimaryInDocument &&
doc.entity === DocumentProcessActionRm.Primary &&
selectedDoc?.document?.processInfo?.documentProcessAction !== DocumentProcessActionRm.Primary)"?
|
0496afa5d72237a0ecf028f5dfba99d0
|
{
"intermediate": 0.38888710737228394,
"beginner": 0.3234640955924988,
"expert": 0.2876487672328949
}
|
47,709
|
dans le fichier my.load de pgloader comment dire uqe je veux que les tables academies champs departements syndicats syndicats_deps. LOAD DATABASE
FROM mysql://ariel@localhost/Anostic
INTO postgresql://ariel:5432/Anostic
WITH include drop, create tables, create indexes, reset sequences,
workers = 8, concurrency = 1,
multiple readers per thread, rows per range = 50000
SET PostgreSQL PARAMETERS
maintenance_work_mem to '128MB',
work_mem to '12MB',
SET MySQL PARAMETERS
net_read_timeout = '120',
net_write_timeout = '120'
CAST type bigint when (= precision 20) to bigserial drop typemod,
type date drop not null drop default using zero-dates-to-null,
type year to integer
-- INCLUDING ONLY TABLE NAMES MATCHING ~/film/, 'actor'
-- EXCLUDING TABLE NAMES MATCHING ~<ory>
-- DECODING TABLE NAMES MATCHING ~/messed/, ~/encoding/ AS utf8
-- ALTER TABLE NAMES MATCHING 'film' RENAME TO 'films'
-- ALTER TABLE NAMES MATCHING ~/_list$/ SET SCHEMA 'mv'
|
b9b58ab1346a67e7bc310e006095a221
|
{
"intermediate": 0.48315128684043884,
"beginner": 0.29879704117774963,
"expert": 0.21805167198181152
}
|
47,710
|
how can we write statement for nonzero value return in .WillOnce(); statemen
|
b7d675723d291df7faddcffc21ce5a2c
|
{
"intermediate": 0.35682186484336853,
"beginner": 0.41625985503196716,
"expert": 0.22691835463047028
}
|
47,711
|
how can we use .WillOnce(SetArgPointee<0>(42)); for an unknown nonzero value
|
a5554f73f0e6e8857201c6efa2c4f7e1
|
{
"intermediate": 0.4887596666812897,
"beginner": 0.2918165326118469,
"expert": 0.2194238007068634
}
|
47,712
|
est-ce car la bd postgresql a un mot de passe que ça me fait cette erreur lors de pgloader de my.load ?
KABOOM!
DB-CONNECTION-ERROR: Failed to connect to pgsql at "ariel" (port 5432) as user "root": Database error: Name service error in "getaddrinfo": -2 (Name or service not known)
An unhandled error condition has been signalled:
Failed to connect to pgsql at "ariel" (port 5432) as user "root": Database error: Name service error in "getaddrinfo": -2 (Name or service not known)
What I am doing here?
Failed to connect to pgsql at "ariel" (port 5432) as user "root": Database error: Name service error in "getaddrinfo": -2 (Name or service not known)
LOAD DATABASE
FROM mysql://ariel@localhost/Anostic
INTO postgresql://ariel:5432/Anostic
WITH include drop, create tables, create indexes, reset sequences,
workers = 8, concurrency = 1,
multiple readers per thread, rows per range = 50000
SET MySQL PARAMETERS
net_read_timeout = '120',
net_write_timeout = '120'
CAST type bigint when (= precision 20) to bigserial drop typemod
INCLUDING ONLY TABLE NAMES MATCHING 'academies', 'champs', 'departements', 'syndicats', 'syndicats_deps';
|
7d57d0f2641ecd320445c7a529aed722
|
{
"intermediate": 0.6152352094650269,
"beginner": 0.21226747334003448,
"expert": 0.17249737679958344
}
|
47,713
|
avec postgresERROR: invalid byte sequence for encoding "UTF8": 0xc3 0x2a
bd=# ERROR: invalid byte sequence for encoding "UTF8": 0xc3 0x2a
|
b1c31fbed1e26d50a6240c12ceb87b3d
|
{
"intermediate": 0.40680283308029175,
"beginner": 0.29852741956710815,
"expert": 0.2946697175502777
}
|
47,714
|
how can I create a 30x30 1 meter square grid of polygons in geojson
|
eec5500c11fa0304872d9391b5f7c444
|
{
"intermediate": 0.3157772719860077,
"beginner": 0.2675897777080536,
"expert": 0.4166329503059387
}
|
47,715
|
how can we define EXPECT_CALL for a function returns noz zero value via argument, if the argument is &ret
|
70ef2c0a34404ce06990757fe1189214
|
{
"intermediate": 0.3736134171485901,
"beginner": 0.37350183725357056,
"expert": 0.25288477540016174
}
|
47,716
|
My view code as "from django.shortcuts import render
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from .models import Uer,Address
from .serializers import UerSerializer,AddressSerializer
class UsersList(APIView):
""" Retrieve all users or create a new user """
def get(self, request,format=None):
users = User.objects.all()
serializer = UerSerializer(data=request.data,many=True)
return Response(serializer.data)
def post(self,request,format=None):
serializer = UerSerializer(data=request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data,status=status.HTTP_201_CREATED)
return Response(serializer.errors,status=status.HTTP_400_BAD_REQUEST)
"
|
1e5c902901820a3c200cebd642f68258
|
{
"intermediate": 0.48925742506980896,
"beginner": 0.3314541280269623,
"expert": 0.17928852140903473
}
|
47,717
|
I want to make a cricket project using pyhton that will provide me the details about cricket player when i provide them players name. use web scrapping from https://www.espncricinfo.com/
|
41a098d742303584aa1caedcf9dcd5c0
|
{
"intermediate": 0.5723773837089539,
"beginner": 0.19025731086730957,
"expert": 0.237365260720253
}
|
47,718
|
Write function on python which converts input text like "example\ntext" to multiline text without /n
|
fe77ec619a2c7b820cf7ef61da42cc82
|
{
"intermediate": 0.38876813650131226,
"beginner": 0.24483764171600342,
"expert": 0.3663943111896515
}
|
47,719
|
donne l'équivalent avec postgresql et plus mysql : connection.execute(text("""CREATE TABLE IF NOT EXISTS 'CS_'""" + nom_os + """("NUMEN" VARCHAR(64) PRIMARY KEY) AS SELECT DISTINCT "NUMEN" FROM public WHERE "NUMEN" IS NOT NULL AND (""" + rg + """)"""))
|
b76d7f0d47f1a0bb4edc0bb5192b5974
|
{
"intermediate": 0.36833634972572327,
"beginner": 0.3687041103839874,
"expert": 0.2629595100879669
}
|
47,720
|
donc connection.execute(text("""CREATE TABLE IF NOT EXISTS 'CS_'""" + nom_os + """` ("NUMEN" VARCHAR(64) PRIMARY KEY) AS SELECT DISTINCT "NUMEN" FROM public WHERE "NUMEN" IS NOT NULL AND (""" + rg + """)"""))
est équivalent à connection.execute(text(f"""
CREATE TABLE IF NOT EXISTS CS_{nom_os} AS
SELECT DISTINCT "NUMEN"
FROM public
WHERE "NUMEN" IS NOT NULL AND ({rg})
"""))
connection.execute(text(f"""
ALTER TABLE CS_{nom_os} ADD CONSTRAINT CS_{nom_os}_pkey PRIMARY KEY (“NUMEN”);
"""))
??
|
1db91897d659f55e7657e9ec51046fd9
|
{
"intermediate": 0.34169888496398926,
"beginner": 0.4194145202636719,
"expert": 0.23888655006885529
}
|
47,721
|
Comment passer d'une requete qui utilisait MySQL de cette forme a une requete similaire enutilisant postgres :
with engine.begin() as connection:
connection.execute(text("""CREATE TABLE IF NOT EXISTS 'CS_'""" + nom_os + """` ("NUMEN" VARCHAR(64) PRIMARY KEY) AS SELECT DISTINCT "NUMEN" FROM public WHERE "NUMEN" IS NOT NULL AND (""" + rg + """)"""))
|
8cebf0606c21ea5a1f7c40c1e47f1ba3
|
{
"intermediate": 0.4281786382198334,
"beginner": 0.29367363452911377,
"expert": 0.27814772725105286
}
|
47,722
|
Comment passer d'une requete qui utilisait MySQL de cette forme a une requete similaire enutilisant postgresSQL, j'utilise SQLAlchemy :
with engine.begin() as connection:
connection.execute(text("""CREATE TABLE IF NOT EXISTS 'CS_'""" + nom_os + """` ("NUMEN" VARCHAR(64) PRIMARY KEY) AS SELECT DISTINCT "NUMEN" FROM public WHERE "NUMEN" IS NOT NULL AND (""" + rg + """)"""))
|
dd0b65ad526095dd611a99289c3d1fa4
|
{
"intermediate": 0.4656018316745758,
"beginner": 0.27725741267204285,
"expert": 0.25714078545570374
}
|
47,723
|
Voici une requete depuis SQLAlchemy a une base MySQL correcte, maintenant je voudrais une requete qui fasse la meme chose mais en utilisant PostgreSQL: with engine.begin() as connection:
connection.execute(text("""CREATE TABLE IF NOT EXISTS 'CS_'""" + nom_os + """` ("NUMEN" VARCHAR(64) PRIMARY KEY) AS SELECT DISTINCT "NUMEN" FROM public WHERE "NUMEN" IS NOT NULL AND (""" + rg + """)"""))
|
183e054b8de725ed1b65c811a769b030
|
{
"intermediate": 0.41606104373931885,
"beginner": 0.32172322273254395,
"expert": 0.2622157335281372
}
|
47,724
|
> git push origin main:main
remote: error: Trace: 79660510055d1926b80593f2fcfc68ce21f5f6ee1e78e776b09b5de4b0dc8783
remote: error: See https://gh.io/lfs for more information.
remote: error: File data/TinyStories_all_data/data00.json is 133.92 MB; this exceeds GitHub's file size limit of 100.00 MB 但事实上，我的仓库中并没有这个文件， 远程库中也不存在，git rm --cache data/TinyStories_all_data/data00.json 也不行 怎么解决
|
25d46776f7cba11e671af733fd1ef09f
|
{
"intermediate": 0.33062541484832764,
"beginner": 0.3519754409790039,
"expert": 0.3173990845680237
}
|
47,725
|
wl_display_connect(NULL);
|
a7ede8f59e2900cc2633b06bc0eed5ea
|
{
"intermediate": 0.3706818222999573,
"beginner": 0.27008575201034546,
"expert": 0.35923242568969727
}
|
47,726
|
A single set of typedefs shall be used in place of standard C variable definitions in all modules.
|
03fbcfc096cd5dfc7734895525625037
|
{
"intermediate": 0.25033462047576904,
"beginner": 0.4437524080276489,
"expert": 0.30591297149658203
}
|
47,727
|
4Question 2: Need For Speed
4.1Introduction
Lukarp has started his own tech company. He received a lot of funding from
Igen with which he opened many offices around the world. Each office needs
to communicate with one other, for which they’re using high speed connections
between the offices. Office number 1 is Lukarp’s HQ. Some offices are important
and hence need faster connections to the HQ for which Lukarp has use special
fiber connections. Lukarp has already planned the connections but feels some
fiber connections are redundant. You have been hired by Lukarp to remove
those fiber connections which don’t cause faster connections.
4.2Problem Statement
4.2.1The Problem
The offices and (bi-directional) connections (both normal and fiber) are given
to you. HQ is numbered as 1. The ith normal connection connects any two
offices ai and bi . Normal connections have latency li . The ith fiber connection
connects the HQ with the office ci . Fiber connections also come with a latency
pi . The total latency of a path is the sum of latencies on the connections.
You are to output the maximum number of fiber connections that can be
removed, such that the latency of the smallest latency path between the
HQ and any other node remains the same as before.
• There are n offices with m normal connections and k high-speed fiber
connections.
• The ith normal connection connects offices ai and bi (bi-directionally) with
latency li .
• The ith fiber connection connects offices 1 and ci (bi-directionally) with
latency pi .
4.2.2
Input Format
The first line of the input file will contain three space-separated integers n, m
and k, the number of offices, the number of normal connections and the number
of fiber connections.
There will be m lines after this, the ith line signifying the ith normal connection,
each containing three space-separated integers ai , bi and li the two offices that
are connected and the latency of the connection respectively.
There will be k lines after this, the ith line signifying the ith fiber connection,
each containing three space-separated integers ci and pi , the office connected to
the HQ and the latency of the fiber connection respectively.
64.2.3
Output Format
Output only one integer m - the maximum number of fiber connections that
can be removed without changing the latency of smallest latency path from
office 1 to any other office.
4.2.4
Constraints
• 2 ≤ n ≤ 105
• 1 ≤ m ≤ 2 · 105
• 1 ≤ k ≤ 105
• 1 ≤ ai , bi , ci ≤ n
• 1 ≤ li , pi ≤ 109
4.2.5
Example
Input:
4 5 2
1 2 2
1 4 9
1 3 3
2 4 4
3 4 5
3 4
4 5
Output:
1
Explanation:
In this example, there are five normal connections as shown in the figure below.
The fiber connection going from 1 to 3 can be removed because the normal con-
nection (3) is faster than the fiber connection (4). However, the fiber connection
with 4 cannot be removed.Hence the maximum number of fiber connections
that can be removed is 1.
give c++ code
|
e65cd86934291f557e6f081c33241d5d
|
{
"intermediate": 0.3162199854850769,
"beginner": 0.3101998567581177,
"expert": 0.3735801875591278
}
|
47,728
|
write code to Discuss how asynchronous functions (defined with async def) work in Python and how they differ from synchronous functions. Provide an example of an asynchronous function that makes multiple web requests in parallel and waits for all of them to complete, using asyncio.
|
904bdde5acadbf13680450703932b336
|
{
"intermediate": 0.5353137254714966,
"beginner": 0.19993337988853455,
"expert": 0.26475289463996887
}
|
47,729
|
Привет у меня не работает скрипт на увеличение версии приложения при сборки
#if UNITY_EDITOR
using UnityEngine;
using UnityEditor;
using UnityEditor.Build;
using UnityEditor.Build.Reporting;
using System;
public class VersionIncrementor : IPostprocessBuildWithReport
{
public int callbackOrder { get { return 0; } }
public void OnPostprocessBuild(BuildReport report)
{
if (report.summary.result == BuildResult.Succeeded)
{
IncrementVersion();
}
else
{
Debug.Log("Build failed, version number was not incremented.");
}
}
private static void IncrementVersion()
{
string[] versionNumbers = PlayerSettings.bundleVersion.Split('.');
if (versionNumbers.Length > 0)
{
// Преобразовываем последний элемент в номер сборки и инкрементируем его
int buildNumber = int.Parse(versionNumbers[versionNumbers.Length - 1]);
buildNumber++;
versionNumbers[versionNumbers.Length - 1] = buildNumber.ToString();
string newVersion = string.Join(".", versionNumbers);
// Обновляем номер версии в настройках проекта Unity
PlayerSettings.bundleVersion = newVersion;
Debug.Log("Updated version to " + newVersion);
}
else
{
Debug.LogError("Failed to increment version number.");
}
}
}
#endif
|
abf3dbaac87a073cebdd8de48c79553b
|
{
"intermediate": 0.45722874999046326,
"beginner": 0.367343008518219,
"expert": 0.17542816698551178
}
|
47,730
|
give an example of finding eigen value and eigen vector using python
|
dc179c113998ea8b76d0a99bfdcbc7c7
|
{
"intermediate": 0.2872425317764282,
"beginner": 0.06788566708564758,
"expert": 0.6448718309402466
}
|
47,731
|
write me a python script, i want to give an URL to a python script and this script exports me the content of <span> which has this attribute: type="a_title"
|
a5cb8201d3895876a8e786812ba57bf3
|
{
"intermediate": 0.3891353905200958,
"beginner": 0.23307712376117706,
"expert": 0.37778744101524353
}
|
47,732
|
#include <bits/stdc++.h>
using namespace std;
void dijkstra(int s, const vector<vector<pair<long long, int>>>& graph, vector<long long>& dist) {
priority_queue<pair<long long, int>, vector<pair<long long, int>>, greater<pair<long long, int>>> pq;
fill(dist.begin(), dist.end(), 1e18);
dist[s] = 0;
pq.push({0, s});
while (!pq.empty()) {
pair<long long, int> front = pq.top(); pq.pop();
long long d = front.first;
int u = front.second;
if (d > dist[u]) continue;
for (size_t i = 0; i < graph[u].size(); ++i) {
long long l = graph[u][i].first;
int v = graph[u][i].second;
if (dist[v] > d + l) {
dist[v] = d + l;
pq.push({dist[v], v});
}
}
}
}
int main() {
int n, m, k;
cin >> n >> m >> k;
vector<vector<pair<long long, int>>> graph(n + 1);
for (int i = 0; i < m; i++) {
int a, b;
long long l;
cin >> a >> b >> l;
graph[a].emplace_back(l, b);
graph[b].emplace_back(l, a);
}
vector<pair<int, long long>> fibers(k);
for (int i = 0; i < k; i++) {
cin >> fibers[i].first >> fibers[i].second;
graph[1].emplace_back(fibers[i].second, fibers[i].first);
graph[fibers[i].first].emplace_back(fibers[i].second, 1);
}
vector<long long> dist(n + 1);
dijkstra(1, graph, dist);
int removable = 0;
for (int i = 0; i < k; i++) {
int c = fibers[i].first;
long long p = fibers[i].second;
vector<long long> newDist(n + 1);
dijkstra(1, graph, newDist);
bool isEssential = false;
for (int j = 1; j <= n; ++j) {
if (newDist[j] != dist[j]) {
isEssential = true;
break;
}
}
if (!isEssential) ++removable;
}
cout << removable;
return 0;
}
avoid using emplace_back
|
91ce739e7aed4b7eb0d2c1b46541ab2e
|
{
"intermediate": 0.42958760261535645,
"beginner": 0.3955005407333374,
"expert": 0.17491184175014496
}
|
47,733
|
1_ Translate the following legal text into colloquial Farsi 2_ Place the Persian and English text side by side in the table 3_ From the beginning to the end of the text, there should be an English sentence on the left side and a Persian sentence on the right side.
4- Using legal language for Persian translation
._ Place the Farsi and English text line by line from one point to the first point next to each other in such a way that one line of English text is followed by two empty lines, followed by the Persian translation and continue this process until the end of the text.Consideration
As was explained earlier, consideration is tbe technical name given to the
contribution each party makes to te bargain- "I promise to pay il you promise to
deliver" - "You can be the owner of this car for £1500", and so on. In Dunlop
Pneumatic Tyre Co.v.Selfridge (1915), consideration is defined as:
An act or forbearance of one party or te promise thereof is te price
for which te promise of the other is bougbt and the promise thus
given for value is enforceable.
In practical terms, consideration is the point of making the bargain. It is wbat
you wanted to get out of it. If you do not receive it (if the consideration fails) tben it
usually amounts to a breach of contract. Professor Atiyah wrote
|
ef28033ee9d1f940d823859d7753901b
|
{
"intermediate": 0.2918255627155304,
"beginner": 0.40573588013648987,
"expert": 0.30243852734565735
}
|
47,734
|
for example I have a page like this ->
<div class="card">
<img class="card-img-top mx-auto img-fluid" src="/assets/img/avatar.png" alt="Card image">
<ul class="list-group list-group-flush">
<li class="list-group-item flex-fill">Name: John</li>
<li class="list-group-item flex-fill">Surname: Doe</li>
<li class="list-group-item flex-fill">Age: 35</li>
</ul>
<div class="card-body">
<p class="card-text">...</p>
</div>
</div>
</div>
</div>
<div class="row text-center mt-5">
<div class="col">
<div class="form-group">
<p id="approvalToken" class="d-none">0234789AIKLMNOPSUYZabefmnprsuvwx</p>
<p id="rejectToken" class="d-none">0234579ABDGIKPQRSUWYbgknoqstuvyz</p>
<a id="approveBtn" data-id="1" class="btn btn-primary" role="button">Approve submission</a>
<a id="rejectBtn" data-id="1" class="btn btn-danger" role="button">Reject submission</a>
<div id="responseMsg"></div>
</div>
</div>
</div>
</div>
</main>
<script type="text/javascript" src="/assets/js/jquery-3.6.0.min.js"></script>
<script type="text/javascript" src="/assets/js/admin.js"></script>
And I want to access approvalToken id via CSS to customize its font. How to do this ?
|
e1d3a55b55f34552ae4151db1f524469
|
{
"intermediate": 0.49024873971939087,
"beginner": 0.3330337107181549,
"expert": 0.17671753466129303
}
|
47,735
|
When I hover over a video on discord, the preload attribute changes from "none" to "metadata". How do I disable that in Firefox?
|
6b52bf0d2a16f7c091c3b9d22b0cee08
|
{
"intermediate": 0.4955331087112427,
"beginner": 0.22242611646652222,
"expert": 0.2820408046245575
}
|
47,736
|
le problème c'est que la syntaxe AS ne fonctionne pas en postgresql : connection.execute(text(f"""DO $$
BEGIN
IF NOT EXISTS (
SELECT * FROM pg_tables
WHERE schemaname = 'public'
AND tablename = 'CS_{nom_os}'
) THEN
CREATE TABLE "public.CS_" ("NUMEN" VARCHAR(64) PRIMARY KEY) AS SELECT DISTINCT "NUMEN" FROM public.public WHERE "NUMEN" IS NOT NULL AND ({rg})', %s);
END IF;
END $$"""))
|
5fad798758fc3db7889d5577d375b077
|
{
"intermediate": 0.1771858036518097,
"beginner": 0.7130206227302551,
"expert": 0.10979359596967697
}
|
47,737
|
le problème c'est que la syntaxe AS ne fonctionne pas en postgresql : connection.execute(text(f"""DO $$
BEGIN
IF NOT EXISTS (
SELECT * FROM pg_tables
WHERE schemaname = 'public'
AND tablename = 'CS_{nom_os}'
) THEN
CREATE TABLE "public.CS_" ("NUMEN" VARCHAR(64) PRIMARY KEY) AS SELECT DISTINCT "NUMEN" FROM public.public WHERE "NUMEN" IS NOT NULL AND ({rg})', %s);
END IF;
END $$"""))
|
7febea08f076db0ae6a1616d11037304
|
{
"intermediate": 0.1771858036518097,
"beginner": 0.7130206227302551,
"expert": 0.10979359596967697
}
|
47,738
|
le problème c'est que la syntaxe AS ne fonctionne pas en postgresql : connection.execute(text(f"""DO $$
BEGIN
IF NOT EXISTS (
SELECT * FROM pg_tables
WHERE schemaname = 'public'
AND tablename = 'CS_{nom_os}'
) THEN
CREATE TABLE "public.CS_" ("NUMEN" VARCHAR(64) PRIMARY KEY) AS SELECT DISTINCT "NUMEN" FROM public.public WHERE "NUMEN" IS NOT NULL AND ({rg})', %s);
END IF;
END $$"""))
|
cc4d196a96ee27769686c2f9081b2ced
|
{
"intermediate": 0.1771858036518097,
"beginner": 0.7130206227302551,
"expert": 0.10979359596967697
}
|
47,739
|
create me a website in html5 and java script. this website should have a news system, admin area, member area, a home area, write news, send news, upload pictures, font size, font type,
|
cc15a3b5f9f3d2bc7f85c5126e1d0da2
|
{
"intermediate": 0.4549117088317871,
"beginner": 0.225625678896904,
"expert": 0.3194626569747925
}
|
47,740
|
4Question 2: Need For Speed
4.1Introduction
Lukarp has started his own tech company. He received a lot of funding from
Igen with which he opened many offices around the world. Each office needs
to communicate with one other, for which they’re using high speed connections
between the offices. Office number 1 is Lukarp’s HQ. Some offices are important
and hence need faster connections to the HQ for which Lukarp has use special
fiber connections. Lukarp has already planned the connections but feels some
fiber connections are redundant. You have been hired by Lukarp to remove
those fiber connections which don’t cause faster connections.
4.2Problem Statement
4.2.1The Problem
The offices and (bi-directional) connections (both normal and fiber) are given
to you. HQ is numbered as 1. The ith normal connection connects any two
offices ai and bi . Normal connections have latency li . The ith fiber connection
connects the HQ with the office ci . Fiber connections also come with a latency
pi . The total latency of a path is the sum of latencies on the connections.
You are to output the maximum number of fiber connections that can be
removed, such that the latency of the smallest latency path between the
HQ and any other node remains the same as before.
• There are n offices with m normal connections and k high-speed fiber
connections.
• The ith normal connection connects offices ai and bi (bi-directionally) with
latency li .
• The ith fiber connection connects offices 1 and ci (bi-directionally) with
latency pi .
4.2.2
Input Format
The first line of the input file will contain three space-separated integers n, m
and k, the number of offices, the number of normal connections and the number
of fiber connections.
There will be m lines after this, the ith line signifying the ith normal connection,
each containing three space-separated integers ai , bi and li the two offices that
are connected and the latency of the connection respectively.
There will be k lines after this, the ith line signifying the ith fiber connection,
each containing three space-separated integers ci and pi , the office connected to
the HQ and the latency of the fiber connection respectively.
64.2.3
Output Format
Output only one integer m - the maximum number of fiber connections that
can be removed without changing the latency of smallest latency path from
office 1 to any other office.
4.2.4
Constraints
• 2 ≤ n ≤ 105
• 1 ≤ m ≤ 2 · 105
• 1 ≤ k ≤ 105
• 1 ≤ ai , bi , ci ≤ n
• 1 ≤ li , pi ≤ 109
4.2.5
Example
Input:
4 5 2
1 2 2
1 4 9
1 3 3
2 4 4
3 4 5
3 4
4 5
Output:
1
Explanation:
In this example, there are five normal connections as shown in the figure below.
The fiber connection going from 1 to 3 can be removed because the normal con-
nection (3) is faster than the fiber connection (4). However, the fiber connection
with 4 cannot be removed. Hence the maximum number of fiber connections
that can be removed is 1.
also keep into consideration that a fiber path to node A can be a part of the shortest path to node B
time complexity is not an issue. give a valid answer that satisfies all cases
|
87ad0ca1b1055c24f5810fe4cd975aae
|
{
"intermediate": 0.34173357486724854,
"beginner": 0.24726171791553497,
"expert": 0.4110047519207001
}
|
47,741
|
I have a text area in my react app and when I insert some xss code in there it gets executed. How can I prevent it?
|
b80cfc907f15ac2486d11fa9f9caa515
|
{
"intermediate": 0.44861212372779846,
"beginner": 0.33028221130371094,
"expert": 0.221105694770813
}
|
47,742
|
Переменная args получается через parser в питоне
Она выдает вот такое значение Namespace(supplier_id='65bd5', json_cfg='[{"tz":"Europe/Moscow","start_time":"10:00","end_time":"21:00","sources":["10.8.6.82_badge.marya2"]}]', mode='select-source', index=None, source=None, max_try=15, high_priority=False, params=None)
И потом к нему можно обращаться как args.supplier_id и тд.
Как можно задать тоже самое через код
|
02e124e0fabfe8068e9a8f88239d02eb
|
{
"intermediate": 0.3584040105342865,
"beginner": 0.3344143033027649,
"expert": 0.3071817457675934
}
|
47,743
|
how to verify in python where symbolic link is pointing
|
05f17409a5d5eb34554e53d193da1869
|
{
"intermediate": 0.4130477011203766,
"beginner": 0.24358849227428436,
"expert": 0.34336382150650024
}
|
47,744
|
How to capture the client Ip address on asp.net core
|
4c0a4bf83452125d91162136eae0ba13
|
{
"intermediate": 0.3924889862537384,
"beginner": 0.19444303214550018,
"expert": 0.41306793689727783
}
|
47,745
|
I have this data
Shift output Scrap Good Units
A 85 5 80
A 96 4 92
A 89 7 82
A 84 6 78
A 91 5 86
A 90 6 84
A 79 3 76
A 79 5 74
A 90 6 84
A 87 6 81
A 90 5 85
A 88 6 82
A 98 4 94
A 93 3 90
A 85 6 79
A 90 7 83
A 91 6 85
A 101 5 96
A 98 6 92
A 93 3 90
B 95 7 88
B 78 11 67
B 83 9 74
B 98 10 88
B 96 6 90
B 84 9 75
B 86 6 80
B 98 6 92
B 78 8 70
B 88 9 79
B 89 9 80
B 116 6 110
B 73 11 62
B 90 10 80
B 97 6 91
B 85 10 75
B 109 7 102
B 87 10 77
B 97 7 90
B 71 11 60
C 92 3 89
C 104 0 104
C 88 2 86
C 98 3 95
C 81 0 81
C 79 4 75
C 71 1 70
C 94 4 90
C 82 3 79
C 90 5 85
C 77 5 72
C 65 4 61
C 81 5 76
C 95 0 95
C 87 4 83
C 65 6 59
C 70 3 67
C 90 6 84
C 100 3 97
C 84 5 79
Perform a statistical analysis and come up with a hypothesis and prove it.
|
b7832ba410b7200d18422dcfa5c716ef
|
{
"intermediate": 0.2731783092021942,
"beginner": 0.30308353900909424,
"expert": 0.4237380921840668
}
|
47,746
|
在# 将 4输入分开，构建新的相同模态结合的2输入，2分支
import math
import logging
from functools import partial
from collections import OrderedDict
from copy import deepcopy
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import to_2tuple
from lib.models.layers.patch_embed import PatchEmbed, PatchEmbed_event, xcorr_depthwise
from .utils import combine_tokens, recover_tokens
from .vit import VisionTransformer
from ..layers.attn_blocks import CEBlock
from .new_counter_guide import Counter_Guide
_logger = logging.getLogger(__name__)
class VisionTransformerCE(VisionTransformer):
""" Vision Transformer with candidate elimination (CE) module
A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`
- https://arxiv.org/abs/2010.11929
Includes distillation token & head support for `DeiT: Data-efficient Image Transformers`
- https://arxiv.org/abs/2012.12877
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,
act_layer=None, weight_init='',
ce_loc=None, ce_keep_ratio=None):
super().__init__()
if isinstance(img_size, tuple):
self.img_size = img_size
else:
self.img_size = to_2tuple(img_size)
self.patch_size = patch_size
self.in_chans = in_chans
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.num_tokens = 2 if distilled else 1
norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
act_layer = act_layer or nn.GELU
self.patch_embed = embed_layer(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
num_patches = self.patch_embed.num_patches
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
self.pos_drop = nn.Dropout(p=drop_rate)
self.pos_embed_event = PatchEmbed_event(in_chans=32, embed_dim=768, kernel_size=4, stride=4)
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
blocks = []
ce_index = 0
self.ce_loc = ce_loc
for i in range(depth):
ce_keep_ratio_i = 1.0
if ce_loc is not None and i in ce_loc:
ce_keep_ratio_i = ce_keep_ratio[ce_index]
ce_index += 1
blocks.append(
CEBlock(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer,
keep_ratio_search=ce_keep_ratio_i)
)
self.blocks = nn.Sequential(*blocks)
self.norm = norm_layer(embed_dim)
self.init_weights(weight_init)
# 添加交互模块counter_guide
self.counter_guide = Counter_Guide(768, 768)
def forward_features(self, z, x, event_z, event_x,
mask_z=None, mask_x=None,
ce_template_mask=None, ce_keep_rate=None,
return_last_attn=False
):
# 分支1 处理流程
B, H, W = x.shape[0], x.shape[2], x.shape[3]
x = self.patch_embed(x)
z = self.patch_embed(z)
z += self.pos_embed_z
x += self.pos_embed_x
if mask_z is not None and mask_x is not None:
mask_z = F.interpolate(mask_z[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0]
mask_z = mask_z.flatten(1).unsqueeze(-1)
mask_x = F.interpolate(mask_x[None].float(), scale_factor=1. / self.patch_size).to(torch.bool)[0]
mask_x = mask_x.flatten(1).unsqueeze(-1)
mask_x = combine_tokens(mask_z, mask_x, mode=self.cat_mode)
mask_x = mask_x.squeeze(-1)
if self.add_cls_token:
cls_tokens = self.cls_token.expand(B, -1, -1)
cls_tokens = cls_tokens + self.cls_pos_embed
if self.add_sep_seg:
x += self.search_segment_pos_embed
z += self.template_segment_pos_embed
x = combine_tokens(z, x, mode=self.cat_mode)
if self.add_cls_token:
x = torch.cat([cls_tokens, x], dim=1)
x = self.pos_drop(x)
lens_z = self.pos_embed_z.shape[1]
lens_x = self.pos_embed_x.shape[1]
global_index_t = torch.linspace(0, lens_z - 1, lens_z).to(x.device)
global_index_t = global_index_t.repeat(B, 1)
global_index_s = torch.linspace(0, lens_x - 1, lens_x).to(x.device)
global_index_s = global_index_s.repeat(B, 1)
removed_indexes_s = []
# # 分支2 处理流程
event_x = self.pos_embed_event(event_x)
event_z = self.pos_embed_event(event_z)
event_x += self.pos_embed_x
event_z += self.pos_embed_z
event_x = combine_tokens(event_z, event_x, mode=self.cat_mode)
if self.add_cls_token:
event_x = torch.cat([cls_tokens, event_x], dim=1)
lens_z = self.pos_embed_z.shape[1]
lens_x = self.pos_embed_x.shape[1]
global_index_t1 = torch.linspace(0, lens_z - 1, lens_z).to(event_x.device)
global_index_t1 = global_index_t1.repeat(B, 1)
global_index_s1 = torch.linspace(0, lens_x - 1, lens_x).to(event_x.device)
global_index_s1 = global_index_s1.repeat(B, 1)
removed_indexes_s1 = []
for i, blk in enumerate(self.blocks):
# 第一个分支处理
x, global_index_t, global_index_s, removed_index_s, attn = \
blk(x, global_index_t, global_index_s, mask_x, ce_template_mask, ce_keep_rate)
# 第二个分支处理
event_x, global_index_t1, global_index_s1, removed_index_s1, attn = \
blk(event_x, global_index_t1, global_index_s1, mask_x, ce_template_mask, ce_keep_rate)
if self.ce_loc is not None and i in self.ce_loc:
removed_indexes_s.append(removed_index_s)
removed_indexes_s1.append(removed_index_s1)
# 在第1层和第6层以及最后一层增加counter_guide模块
if i in [0,5,11]:
enhanced_x, enhanced_event_x = self.counter_guide(x, event_x)
# 将增强后的特征与原特征相加
x = x + enhanced_x
event_x = event_x + enhanced_event_x
# 应用LayerNorm归一化处理
x = self.norm(x)
event_x = self.norm(event_x)
x_cat = torch.cat([x,event_x], dim=1)
x = x_cat
aux_dict = {
"attn": attn,
"removed_indexes_s": removed_indexes_s, # used for visualization
}
return x, aux_dict
def forward(self, z, x, event_z, event_x,
ce_template_mask=None, ce_keep_rate=None,
tnc_keep_rate=None,
return_last_attn=False):
x, aux_dict = self.forward_features(z, x, event_z, event_x, ce_template_mask=ce_template_mask, ce_keep_rate=ce_keep_rate,)
return x, aux_dict
def _create_vision_transformer(pretrained=False, **kwargs):
model = VisionTransformerCE(**kwargs)
if pretrained:
if 'npz' in pretrained:
model.load_pretrained(pretrained, prefix='')
else:
checkpoint = torch.load(pretrained, map_location="cpu")
missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=False)
print('Load pretrained model from: ' + pretrained)
return model
def vit_base_patch16_224_ce(pretrained=False, **kwargs):
""" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
"""
model_kwargs = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer(pretrained=pretrained, **model_kwargs)
return model
def vit_large_patch16_224_ce(pretrained=False, **kwargs):
""" ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
"""
model_kwargs = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
model = _create_vision_transformer(pretrained=pretrained, **model_kwargs)
return model中，第12层的处理： if i in [0,5,11]:
enhanced_x, enhanced_event_x = self.counter_guide(x, event_x)
# 将增强后的特征与原特征相加
x = x + enhanced_x
event_x = event_x + enhanced_event_x ，其中import torch
import torch.nn as nn
import torch.nn.functional as F
class Multi_Context(nn.Module):
def __init__(self, input_channels, output_channels): # 修正了def init为def init
super(Multi_Context, self).__init__()
self.linear1 = nn.Linear(input_channels, output_channels)
self.linear2 = nn.Linear(input_channels, output_channels)
self.linear3 = nn.Linear(input_channels, output_channels)
self.linear_final = nn.Linear(output_channels * 3, output_channels)
def forward(self, x):
x1 = F.relu(self.linear1(x))
x2 = F.relu(self.linear2(x))
x3 = F.relu(self.linear3(x))
x = torch.cat([x1, x2, x3], dim=-1) # Concatenate along the feature dimension
x = self.linear_final(x)
return x
class Adaptive_Weight(nn.Module):
def __init__(self, input_channels): # 修正了def init为def init
super(Adaptive_Weight, self).__init__()
self.fc1 = nn.Linear(input_channels, input_channels // 4)
self.fc2 = nn.Linear(input_channels // 4, input_channels)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x_avg = torch.mean(x, dim=1) # Global Average Pooling along the ‘sequence length’ dimension
weight = F.relu(self.fc1(x_avg))
weight = self.fc2(weight)
weight = self.sigmoid(weight).unsqueeze(1)
out = x * weight # 此处的x是 enhanced_x 和 event_x 相乘的结果
return out
class Counter_attention(nn.Module):
def __init__(self, input_channels, output_channels): # 修正了def init为def init
super(Counter_attention, self).__init__()
self.mc = Multi_Context(input_channels, output_channels) # enhanced_x
self.ada_w = Adaptive_Weight(output_channels)
def forward(self, assistant, present):
mc_output = self.mc(assistant) # enhanced_x
weighted_present = self.ada_w(present * torch.sigmoid(mc_output)) # enhanced_x → sigmoid → * event_x
return weighted_present
class Counter_Guide(nn.Module):
def __init__(self, input_channels, output_channels): # 简化输入参数
super(Counter_Guide, self).__init__()
# 使用单一的Counter_attention模块
self.counter_atten = Counter_attention(input_channels, output_channels)
# self.dropout = nn.Dropout(0.1)
def forward(self, x, event_x):
# 对两种模态特征每种进行一次互相作为assistant和present的增强过程
out_x = self.counter_atten(x, event_x)
out_event_x = self.counter_atten(event_x, x)
# out_x = self.dropout(out_x)
# out_event_x = self.dropout(out_event_x)
return out_x, out_event_x。现在分析一下第12层操作，counter_guide和后续的操作分别如何处理的？
|
c572c747ba4d46fe4c5ae8d61ab86ff1
|
{
"intermediate": 0.29621145129203796,
"beginner": 0.4656831622123718,
"expert": 0.2381054162979126
}
|
47,747
|
File "/appli/ostic/venv/lib64/python3.6/site-packages/sqlalchemy/engine/base.py", line 1379, in execute
meth = statement._execute_on_connection
AttributeError: 'Composed' object has no attribute '_execute_on_connection'query = sql.SQL("""CREATE TABLE IF NOT EXISTS "CS_{}" AS SELECT DISTINCT "NUMEN" FROM public WHERE "NUMEN" IS NOT NULL AND {} = %s""").format(sql.Identifier(nom_os), sql.Identifier(" rg"))
connection.execute(query, (rg,))
|
74cc77b202753c05424c9b6862b78553
|
{
"intermediate": 0.4487437903881073,
"beginner": 0.3170483708381653,
"expert": 0.2342078685760498
}
|
47,748
|
@app.route("/add", methods=["POST"])
def add_annonce():
try:
json_data = request.form.get('data')
data = json.loads(json_data) if json_data else {}
# Initialize a list to hold paths of saved images
saved_image_paths = []
saved_video_path = None
# Process images
images = request.files.getlist("images")
for image in images:
filename = datetime.datetime.now().strftime("%Y%m%d%H%M%S%f") + secure_filename(image.filename)
save_path = os.path.join(app.config['UPLOAD_FOLDER'],filename)
image.save(save_path)
saved_image_paths.append(os.path.join(filename))
# Process video file, assuming there's one video with the form field name 'video'
video = request.files.get('video')
if video:
video_filename = datetime.datetime.now().strftime("%Y%m%d%H%M%S%f") + secure_filename(video.filename)
video_save_path = os.path.join(app.config['UPLOAD_FOLDER'], video_filename)
video.save(video_save_path)
saved_video_path = video_filename # Or you may customize this path as needed
# Update or add the video path to your data structure here
data['video'] = saved_video_path
data['images'] = saved_image_paths
if saved_video_path:
data['video'] = saved_video_path
# Insert data into MongoDB
result = Annonce_collection.insert_one(data)
return jsonify({"message": "Data successfully inserted", "id": str(result.inserted_id)}), 201
except Exception as e:
return jsonify({"error": str(e)}), 500
dosame thing for update
@app.route("/annonce/update/<annonce_id>", methods=["PUT"])
def update_annonce(annonce_id):
if not ObjectId.is_valid(annonce_id):
return jsonify({"message": "Invalid ID format"}), 400
data = request.json
result = Annonce_collection.update_one({"_id": ObjectId(annonce_id)}, {"$set": data})
if result.matched_count == 0:
return jsonify({"message": "Announcement not found"}), 404
return jsonify({"message": "Announcement updated successfully"}), 200
do an update
|
c902f99a1c0b87fda14189677121f824
|
{
"intermediate": 0.29673638939857483,
"beginner": 0.49473080039024353,
"expert": 0.20853282511234283
}
|
47,749
|
I have csv file containing columns: image name, width, height, class, xmin, ymin, xmax, ymax. Write a python script to generate a text file for each image in yolo annotation format. That is class name x y w h. Here, x,y,w,h are normalized values which are obtained by dividing original x,y,w,h with width and height
|
026696b219cecfe1f30d2af995ef3deb
|
{
"intermediate": 0.4110800623893738,
"beginner": 0.26471149921417236,
"expert": 0.32420846819877625
}
|
47,750
|
In python I want to draw a graph using networkx. But I also want a menu on the top with 2 drops downs and a button. I will add code for what they do later.
|
22af160e5884423bca2ccf862f92b3e9
|
{
"intermediate": 0.414171427488327,
"beginner": 0.13114292919635773,
"expert": 0.45468565821647644
}
|
47,751
|
Could u rewrite this w/o using this
|
b4debcc0cda1d98be6331f47371d9965
|
{
"intermediate": 0.34754088521003723,
"beginner": 0.36172211170196533,
"expert": 0.29073694348335266
}
|
47,752
|
Hi there, please be a senior sapui5 developer and answer my following questions with working code examples.
|
0e0ceadc9268386c23fb5085dbfa0ac2
|
{
"intermediate": 0.42116406559944153,
"beginner": 0.2712341248989105,
"expert": 0.3076017498970032
}
|
47,753
|
I have this
import tkinter as tk
from tkinter import ttk
import networkx as nx
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import json
def load_network(file_path):
# Load network from JSON file
with open(file_path, 'r') as file:
data = json.load(file)
G = nx.Graph()
# Add nodes
G.add_nodes_from(data["nodes"])
# Add edges with attributes
for path in data["paths"]:
G.add_edge(path["path"][0], path["path"][1], distance=path["distance"], trafficCondition=path["trafficCondition"], deliveryUrgency=path["deliveryUrgency"])
return G
def draw_graph(filePath):
plt.clf()
G = load_network(filePath)
pos = nx.spring_layout(G)
fig, ax = plt.subplots()
nx.draw(G, pos, with_labels=True, node_color='skyblue', node_size=1200, edge_color='k', linewidths=0.5, font_size=15, ax=ax)
canvas.draw()
def on_dropdown1_select(event):
filepathnum=dropdown1.current()+1
draw_graph("output" + str(filepathnum) +".json")
# Create main window
root = tk.Tk()
root.title("Graph Visualization")
# Create graph drawing area
figure = plt.figure(figsize=(6, 4))
ax = figure.add_subplot(111)
canvas = FigureCanvasTkAgg(figure, master=root)
canvas_widget = canvas.get_tk_widget()
canvas_widget.pack(side=tk.TOP, fill=tk.BOTH, expand=1)
# Create menu widgets
frame_menu = tk.Frame(root)
frame_menu.pack(side=tk.TOP, pady=10)
# Dropdown 1
options1 = ["Output 1", "Output 2", "Output 3", "Output 4", "Output 5"]
file_path_var = ["output1.json", "output2.json", "output3.json", "output4.json", "output5.json"]
dropdown1 = ttk.Combobox(frame_menu, values=options1, textvariable=file_path_var)
dropdown1.set(options1[0])
dropdown1.bind("<<ComboboxSelected>>", on_dropdown1_select)
dropdown1.grid(row=0, column=0, padx=5)
# Draw initial graph
draw_graph("output1.json")
# Start GUI event loop
root.mainloop()
How can I clear what was previously drawn by networkx before I draw something else
|
704783d1558995d6afa9dfc45bdba2d1
|
{
"intermediate": 0.6026609539985657,
"beginner": 0.23627744615077972,
"expert": 0.1610615849494934
}
|
47,754
|
4Question 2: Need For Speed
4.1Introduction
Lukarp has started his own tech company. He received a lot of funding from
Igen with which he opened many offices around the world. Each office needs
to communicate with one other, for which they’re using high speed connections
between the offices. Office number 1 is Lukarp’s HQ. Some offices are important
and hence need faster connections to the HQ for which Lukarp has use special
fiber connections. Lukarp has already planned the connections but feels some
fiber connections are redundant. You have been hired by Lukarp to remove
those fiber connections which don’t cause faster connections.
4.2Problem Statement
4.2.1The Problem
The offices and (bi-directional) connections (both normal and fiber) are given
to you. HQ is numbered as 1. The ith normal connection connects any two
offices ai and bi . Normal connections have latency li . The ith fiber connection
connects the HQ with the office ci . Fiber connections also come with a latency
pi . The total latency of a path is the sum of latencies on the connections.
You are to output the maximum number of fiber connections that can be
removed, such that the latency of the smallest latency path between the
HQ and any other node remains the same as before.
• There are n offices with m normal connections and k high-speed fiber
connections.
• The ith normal connection connects offices ai and bi (bi-directionally) with
latency li .
• The ith fiber connection connects offices 1 and ci (bi-directionally) with
latency pi .
4.2.2
Input Format
The first line of the input file will contain three space-separated integers n, m
and k, the number of offices, the number of normal connections and the number
of fiber connections.
There will be m lines after this, the ith line signifying the ith normal connection,
each containing three space-separated integers ai , bi and li the two offices that
are connected and the latency of the connection respectively.
There will be k lines after this, the ith line signifying the ith fiber connection,
each containing three space-separated integers ci and pi , the office connected to
the HQ and the latency of the fiber connection respectively.
64.2.3
Output Format
Output only one integer m - the maximum number of fiber connections that
can be removed without changing the latency of smallest latency path from
office 1 to any other office.
4.2.4
Constraints
• 2 ≤ n ≤ 105
• 1 ≤ m ≤ 2 · 105
• 1 ≤ k ≤ 105
• 1 ≤ ai , bi , ci ≤ n
• 1 ≤ li , pi ≤ 109
4.2.5
Example
Input:
4 5 2
1 2 2
1 4 9
1 3 3
2 4 4
3 4 5
3 4
4 5
Output:
1
Explanation:
In this example, there are five normal connections as shown in the figure below.
The fiber connection going from 1 to 3 can be removed because the normal con-
nection (3) is faster than the fiber connection (4). However, the fiber connection
with 4 cannot be removed. Hence the maximum number of fiber connections
that can be removed is 1.
also keep into consideration that a fiber path to node A can be a part of the shortest path to node B
give c++ code
you can do it using a struct and dijkstra . dont use many advanced functions of c++. try to keep it beginner level code. dont worry about time efficiency.
|
485b3f391267183b0dccf0234a9e99a0
|
{
"intermediate": 0.2716849744319916,
"beginner": 0.47465693950653076,
"expert": 0.25365808606147766
}
|
47,755
|
how to extract latitude and longitude from a zipcode in teradata
|
7dff825944df9b32478b2ad6234d8e72
|
{
"intermediate": 0.36802762746810913,
"beginner": 0.37238091230392456,
"expert": 0.2595914900302887
}
|
47,756
|
Is this the proper way to use two loras on the same diffusers pipe: pipe = StableDiffusionPipeline.from_pretrained(
base_model_path,
torch_dtype=torch.float16,
scheduler=noise_scheduler,
vae=vae,
safety_checker=None
).to(device)
#pipe.load_lora_weights("h94/IP-Adapter-FaceID", weight_name="ip-adapter-faceid-plusv2_sd15_lora.safetensors")
#pipe.fuse_lora()
pipe.load_lora_weights("OedoSoldier/detail-tweaker-lora", weight_name="add_detail.safetensors")
pipe.fuse_lora()
pipe.load_lora_weights("adhikjoshi/epi_noiseoffset", weight_name="epiNoiseoffset_v2.safetensors")
pipe.fuse_lora()
|
c6fbce14ea02c06f09a3a23fcb0aea29
|
{
"intermediate": 0.3753963112831116,
"beginner": 0.3496910333633423,
"expert": 0.27491265535354614
}
|
47,757
|
I am using diffusers to create images. Is there a way to add 'strength' of the lora when using it in this way: pipe = StableDiffusionPipeline.from_pretrained(
base_model_path,
torch_dtype=torch.float16,
scheduler=noise_scheduler,
vae=vae,
safety_checker=None
).to(device)
pipe.load_lora_weights("OedoSoldier/detail-tweaker-lora", weight_name="add_detail.safetensors")
pipe.fuse_lora()
|
bd8399d7893de659a8f140eb5c803c01
|
{
"intermediate": 0.4094417095184326,
"beginner": 0.2886768579483032,
"expert": 0.30188143253326416
}
|
47,758
|
write a Rust function to return the network's up usage, the bytes sent by the network interface(s)
|
997e3b63fcc4cc0a4654932ec46d1eb1
|
{
"intermediate": 0.3372700810432434,
"beginner": 0.28216251730918884,
"expert": 0.3805674612522125
}
|
47,759
|
I am making a c++ sdl based game engine, currently in an stage of moving all my raw pointers into smart pointers. I already finished several classes, and I already know how to do it. The thing is, when I was revisiting old code to convert, I found out my VertexCollection class (it wraps SDL_Vertex and adds more functionalities to it) has a SetTexture method which use a raw Texture pointer, Texture is my own class which wraps SDL_Texture and adds more functionalities to it and I don't know if I should just use an object and use a make_unique or receive the smart pointer and use std::move.
1)
void VertexCollection::SetTexture(std::unique_ptr<Texture> texture)
{
if (texture)
{
this->texture = std::move(texture);
}
}
2)
void VertexCollection::SetTexture(const Texture& texture)
{
this->texture = std::make_unique<Texture>(texture);
}
Which of these options is better?
|
966c3aaf6418f8002992a7a88b6177e0
|
{
"intermediate": 0.4477916359901428,
"beginner": 0.40150511264801025,
"expert": 0.15070326626300812
}
|
47,760
|
模型通过三个head得到响应输出，即Rf（RGB输出）；Re（event输出）；Rc（融合特征输出）。此时为了探索不同模态信息对跟踪任务的重要程度，设计了自适应模态响应决策机制，使得网络在学习的过程中判断利用RGB模态信息、Event模态信息还是融合模态信息，以提升特征信息利用的灵活性，最大化模型性能。那么基于"""
Basic ceutrack model.
"""
import math
import os
from typing import List
import torch
from torch import nn
from torch.nn.modules.transformer import _get_clones
from lib.models.layers.head import build_box_head
from lib.models.ceutrack.vit import vit_base_patch16_224
from lib.models.ceutrack.vit_ce import vit_large_patch16_224_ce, vit_base_patch16_224_ce
# from lib.models.ceutrack.vit_ce_BACKUPS import vit_large_patch16_224_ce, vit_base_patch16_224_ce
from lib.utils.box_ops import box_xyxy_to_cxcywh
class CEUTrack(nn.Module):
""" This is the base class for ceutrack """
def __init__(self, transformer, box_head, aux_loss=False, head_type="CORNER"):
""" Initializes the model.
Parameters:
transformer: torch module of the transformer architecture.
aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
"""
super().__init__()
self.backbone = transformer
self.box_head_x = box_head
# 新增event模态的head,以输出独立模态响应
self.box_head_event_x = box_head
self.aux_loss = aux_loss
self.head_type = head_type
if head_type == "CORNER" or head_type == "CENTER":
self.feat_sz_s = int(box_head.feat_sz)
self.feat_len_s = int(box_head.feat_sz ** 2)
if self.aux_loss:
self.box_head = _get_clones(self.box_head, 6)
def forward(self, template: torch.Tensor,
search: torch.Tensor,
event_template: torch.Tensor, # torch.Size([4, 1, 19, 10000])
event_search: torch.Tensor, # torch.Size([4, 1, 19, 10000])
ce_template_mask=None,
ce_keep_rate=None,
return_last_attn=False,
):
# before feeding into backbone, we need to concat four vectors, or two two concat;
x, event_x, aux_dict = self.backbone(z=template, x=search, event_z=event_template, event_x=event_search,
ce_template_mask=ce_template_mask,
ce_keep_rate=ce_keep_rate,
return_last_attn=return_last_attn, )
# Forward head for x
feat_last = x
if isinstance(x, list):
feat_last_x = x[-1]
out_x = self.forward_head_x(feat_last_x, None)
# Forward head for event_x
feat_last_event_x = event_x
if isinstance(event_x, list):
feat_last_event_x = event_x[-1]
out_event_x = self.forward_head_event_x(feat_last_event_x, None)
# 新增 fusion of response
fused_output = self.fuse_outputs(out_x,out_event_x)
# update aux_dict
fused_output.update(fused_output)
return fused_output
# 单独构建forward_head
def forward_head_x(self, cat_feature_x, gt_score_map=None):
"""
Forward pass for the head of x modality.
"""
# dual head
enc_opt1_x = cat_feature_x[:, -self.feat_len_s:]
enc_opt2_x = cat_feature_x[:, :self.feat_len_s]
enc_opt_x = torch.cat([enc_opt1_x, enc_opt2_x], dim=-1)
opt_x = (enc_opt_x.unsqueeze(-1)).permute((0, 3, 2, 1)).contiguous()
bs, Nq, C, HW = opt_x.size()
opt_feat_x = opt_x.view(-1, C, self.feat_sz_s, self.feat_sz_s)
# Forward pass for x modality
if self.head_type == "CORNER":
pred_box_x, score_map_x = self.box_head_x(opt_feat_x, True)
outputs_coord_x = box_xyxy_to_cxcywh(pred_box_x)
outputs_coord_new_x = outputs_coord_x.view(bs, Nq, 4)
out_x = {'pred_boxes_x': outputs_coord_new_x,
'score_map_x': score_map_x}
return out_x
elif self.head_type == "CENTER":
score_map_ctr_x, bbox_x, size_map_x, offset_map_x = self.box_head_x(opt_feat_x, gt_score_map)
outputs_coord_x = bbox_x
outputs_coord_new_x = outputs_coord_x.view(bs, Nq, 4)
out_x = {'pred_boxes_x': outputs_coord_new_x,
'score_map_x': score_map_ctr_x,
'size_map_x': size_map_x,
'offset_map_x': offset_map_x}
return out_x
else:
raise NotImplementedError
# Event模态响应forward head
def forward_head_event_x(self, cat_feature_event_x, gt_score_map=None):
"""
Forward pass for the head of event_x modality.
"""
# dual head
enc_opt1_event_x = cat_feature_event_x[:, -self.feat_len_s:]
enc_opt2_event_x = cat_feature_event_x[:, :self.feat_len_s]
enc_opt_event_x = torch.cat([enc_opt1_event_x, enc_opt2_event_x], dim=-1)
opt_event_x = (enc_opt_event_x.unsqueeze(-1)).permute((0, 3, 2, 1)).contiguous()
bs, Nq, C, HW = opt_event_x.size()
opt_feat_event_x = opt_event_x.view(-1, C, self.feat_sz_s, self.feat_sz_s)
# Forward pass for event_x modality
if self.head_type == "CORNER":
pred_box_event_x, score_map_event_x = self.box_head_event_x(opt_feat_event_x, True)
outputs_coord_event_x = box_xyxy_to_cxcywh(pred_box_event_x)
outputs_coord_new_event_x = outputs_coord_event_x.view(bs, Nq, 4)
out_event_x = {'pred_boxes_event_x': outputs_coord_new_event_x,
'score_map_event_x': score_map_event_x}
return out_event_x
elif self.head_type == "CENTER":
score_map_ctr_event_x, bbox_event_x, size_map_event_x, offset_map_event_x = self.box_head_event_x(
opt_feat_event_x, gt_score_map)
outputs_coord_event_x = bbox_event_x
outputs_coord_new_event_x = outputs_coord_event_x.view(bs, Nq, 4)
out_event_x = {'pred_boxes_event_x': outputs_coord_new_event_x,
'score_map_event_x': score_map_ctr_event_x,
'size_map_event_x': size_map_event_x,
'offset_map_event_x': offset_map_event_x}
return out_event_x
else:
raise NotImplementedError
def fuse_outputs(self, out_x, out_event_x):
fused_pred_boxes = out_x['pred_boxes'] + out_event_x['pred_boxes']
fused_score_map = out_x['score_map'] + out_event_x['score_map']
fused_output = {
'pred_boxes': fused_pred_boxes,
'score_map': fused_score_map,
}
return fused_output
def build_ceutrack(cfg, training=True):
current_dir = os.path.dirname(os.path.abspath(__file__)) # This is your Project Root
pretrained_path = os.path.join(current_dir, 'pretrained_models')
if cfg.MODEL.PRETRAIN_FILE and ('CEUTrack' not in cfg.MODEL.PRETRAIN_FILE) and training:
pretrained = os.path.join(pretrained_path, cfg.MODEL.PRETRAIN_FILE)
else:
pretrained = ''
if cfg.MODEL.BACKBONE.TYPE == 'vit_base_patch16_224':
backbone = vit_base_patch16_224(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE)
hidden_dim = backbone.embed_dim
patch_start_index = 1
elif cfg.MODEL.BACKBONE.TYPE == 'vit_base_patch16_224_ce':
backbone = vit_base_patch16_224_ce(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE,
ce_loc=cfg.MODEL.BACKBONE.CE_LOC,
ce_keep_ratio=cfg.MODEL.BACKBONE.CE_KEEP_RATIO,
)
# hidden_dim = backbone.embed_dim
hidden_dim = backbone.embed_dim * 2
patch_start_index = 1
elif cfg.MODEL.BACKBONE.TYPE == 'vit_large_patch16_224_ce':
backbone = vit_large_patch16_224_ce(pretrained, drop_path_rate=cfg.TRAIN.DROP_PATH_RATE,
ce_loc=cfg.MODEL.BACKBONE.CE_LOC,
ce_keep_ratio=cfg.MODEL.BACKBONE.CE_KEEP_RATIO,
)
hidden_dim = backbone.embed_dim
patch_start_index = 1
else:
raise NotImplementedError
backbone.finetune_track(cfg=cfg, patch_start_index=patch_start_index)
box_head_x = build_box_head(cfg, hidden_dim)
box_head_event_x = build_box_head(cfg, hidden_dim)
model = CEUTrack(
backbone,
box_head_x,
box_head_event_x,
aux_loss=False,
head_type=cfg.MODEL.HEAD.TYPE,
)
if 'CEUTrack' in cfg.MODEL.PRETRAIN_FILE and training:
checkpoint = torch.load(cfg.MODEL.PRETRAIN_FILE, map_location="cpu")
missing_keys, unexpected_keys = model.load_state_dict(checkpoint["net"], strict=False)
print('Load pretrained model from: ' + cfg.MODEL.PRETRAIN_FILE)
return model
按照需求，写一段代码
|
4d325cbfd188dc949ae1c0242c822f94
|
{
"intermediate": 0.30486300587654114,
"beginner": 0.48184919357299805,
"expert": 0.2132878452539444
}
|
47,761
|
hi, can you make a ffmpeg 6.0 arg showing for capture of xwininfo using xwininfo: Window id to record a window on with specified id for example you can use this testing id like:xwininfo: Window id: 0x1e0901c
|
7eebf3702c42c106698825a5d448fc74
|
{
"intermediate": 0.614904522895813,
"beginner": 0.11286090314388275,
"expert": 0.27223458886146545
}
|
47,762
|
in this code the states is a tuple of containing 'states = (node_features_tensor, edge_feature_tensor, edge_index)'
but i need to access the node feature tensor, where it contains the 20 nodes and in each node the 24 feature represents its stability, from the stability information i need to calculate the loss. But i am getting error from the below code please rectify the error.
stabilities = states[:, :, 23]
stability_loss = self.compute_stability_loss(stabilities, target_stability=1.0)
def compute_stability_loss(self, stabilities, target_stability=1.0):
"""Compute stability loss based on stabilities tensor."""
stability_loss = F.binary_cross_entropy_with_logits(stabilities, torch.full_like(stabilities, fill_value=target_stability))
return stability_loss
|
e4dec18fa48c178f082c87f2901be8ff
|
{
"intermediate": 0.3498559594154358,
"beginner": 0.2287786900997162,
"expert": 0.421365350484848
}
|
47,763
|
will this code work and why? explain step by step for a complete beginner: // index.js
await async function () {
console.log("Hello World!");
};
|
611fe9bea21107003233d587b808e52c
|
{
"intermediate": 0.3919016122817993,
"beginner": 0.4442380368709564,
"expert": 0.16386036574840546
}
|
47,764
|
i have this bootstrap nav bar <nav class="navbar navbar-expand-lg navbar-light bg-light">
<nav class="navbar navbar-expand-lg navbar-light bg-light vf-navbar">
<div class="navbar-collapse w-100 dual-collapse2">
<a class="navbar-brand" routerLink="/"><img src="assets/imgs/vf-logo-red.png"></a>
<ul class="navbar-nav">
<li class="nav-item">
<h3 class="storeNameTitle">Vodafone OMS</h3>
</li>
</ul>
</div>
<div class="w-100">
<ul class="navbar-nav w-100 justify-content-end align-items-baseline">
<li class="nav-item dropdown" ngbDropdown>
<a aria-expanded="false" aria-haspopup="true" class="nav-link dropdown-toggle dropdown-toggle-caret"
data-toggle="dropdown"
href="javascript:void(0)" id="logoutDropdown"
ngbDropdownToggle role="button">
<i class="fa-solid fa-user"></i> BindinguserInfo?.username
</a>
<div aria-labelledby="logoutDropdown" class="dropdown-menu dropdown-menu-end" ngbDropdownMenu>
<a class="dropdown-item" ngbDropdownItem>Logout</a> <!--(click)="logOut()" -->
</div>
</li>
</ul>
</div>
</nav>
i want to make the brand logo and name at max left and the username at the max right
|
3e6b9a7ec3873563eea8fa9c041040e0
|
{
"intermediate": 0.32471615076065063,
"beginner": 0.3943037688732147,
"expert": 0.28098002076148987
}
|
47,765
|
will you please just take this String as an example and create a generale regexp to extract episode number ?Il.clandestino.S01E01.ITA.WEBDL.1080p.mp4 from this I just want E01 and even better 1
|
5102689ac7d70056f6b4feb7b40ee2be
|
{
"intermediate": 0.3938535451889038,
"beginner": 0.2430206537246704,
"expert": 0.36312583088874817
}
|
47,766
|
<ul class="navbar-nav">
<li class="nav-item dropdown" >
<a class="nav-link dropdown-toggle" id="navbarDropdownMenuLink" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">
Osama
</a>
<div class="dropdown-menu" aria-labelledby="navbarDropdownMenuLink">
<a class="dropdown-item">Logout</a>
</div>
</li>
</ul> i have this in angular project but dropdown menu didn't work
|
7fc4eee17f909abd3de3fc53114b05cd
|
{
"intermediate": 0.486283540725708,
"beginner": 0.3025490641593933,
"expert": 0.21116742491722107
}
|
47,767
|
will you please just take this String as an example and create a generale regexp to extract episode number ?Il.clandestino.S01E01.ITA.WEBDL.1080p.mp4 from this I just want E01 and even better 1
|
5ec23c437dcdb197e7d5fc626442bbb4
|
{
"intermediate": 0.3938535451889038,
"beginner": 0.2430206537246704,
"expert": 0.36312583088874817
}
|
47,768
|
I want a script to hide fields inside this form : "name="vat_number" name="company" "
just hide them and show a checkbox instead to show them if it is checked "company"
<div class="js-address-form">
<form method="POST" action="https://woodiz.fr/commande" data-refresh-url="//woodiz.fr/commande?ajax=1&action=addressForm">
<p>
L'adresse sélectionnée sera utilisée à la fois comme adresse personnelle (pour la facturation) et comme adresse de livraison.
</p>
<div id="delivery-address">
<div class="js-address-form">
<section class="form-fields">
<input type="hidden" name="id_address" value="">
<input type="hidden" name="id_customer" value="">
<input type="hidden" name="back" value="">
<input type="hidden" name="token" value="b9beaf896066c4cebd62b5ce225273d5">
<div class="form-group row ">
<label class="col-md-3 form-control-label required">
Prénom
</label>
<div class="col-md-6">
<input class="form-control" name="firstname" type="text" value="Testing" maxlength="255" required="">
</div>
<div class="col-md-3 form-control-comment">
</div>
</div>
<div class="form-group row ">
<label class="col-md-3 form-control-label required">
Nom
</label>
<div class="col-md-6">
<input class="form-control" name="lastname" type="text" value="Mohamed" maxlength="255" required="">
</div>
<div class="col-md-3 form-control-comment">
</div>
</div>
<div class="form-group row ">
<label class="col-md-3 form-control-label">
Société
</label>
<div class="col-md-6">
<input class="form-control" name="company" type="text" value="" maxlength="255">
</div>
<div class="col-md-3 form-control-comment">
Optionnel
</div>
</div>
<div class="form-group row ">
<label class="col-md-3 form-control-label">
Numéro de TVA
</label>
<div class="col-md-6">
<input class="form-control" name="vat_number" type="text" value="">
</div>
<div class="col-md-3 form-control-comment">
Optionnel
</div>
</div>
<div class="form-group row ">
<label class="col-md-3 form-control-label required">
Adresse
</label>
<div class="col-md-6">
<input class="form-control" name="address1" type="text" value="" maxlength="128" required="">
</div>
<div class="col-md-3 form-control-comment">
</div>
</div>
<div class="form-group row ">
<label class="col-md-3 form-control-label">
Complément d'adresse
</label>
<div class="col-md-6">
<input class="form-control" name="address2" type="text" value="" maxlength="128">
</div>
<div class="col-md-3 form-control-comment">
Optionnel
</div>
</div>
<div class="form-group row ">
<label class="col-md-3 form-control-label required">
Code postal
</label>
<div class="col-md-6">
<input class="form-control" name="postcode" type="text" value="" maxlength="12" required="">
</div>
<div class="col-md-3 form-control-comment">
</div>
</div>
<div class="form-group row ">
<label class="col-md-3 form-control-label required">
Ville
</label>
<div class="col-md-6">
<input class="form-control" name="city" type="text" value="" maxlength="64" required="">
</div>
<div class="col-md-3 form-control-comment">
</div>
</div>
<div class="form-group row ">
<label class="col-md-3 form-control-label required">
Pays
</label>
<div class="col-md-6">
<select class="form-control form-control-select js-country" name="id_country" required="">
<option value="" disabled="" selected="">-- veuillez choisir --</option>
<option value="8" selected="">France</option>
</select>
</div>
<div class="col-md-3 form-control-comment">
</div>
</div>
<div class="form-group row ">
<label class="col-md-3 form-control-label required">
Téléphone
</label>
<div class="col-md-6">
<input class="form-control" name="phone" type="tel" value="" maxlength="32" required="">
</div>
<div class="col-md-3 form-control-comment">
</div>
</div>
<input type="hidden" name="saveAddress" value="delivery">
<div class="form-group row">
<div class="col-md-9 col-md-offset-3">
<input name="use_same_address" type="checkbox" value="1" checked="">
<label>Utiliser aussi cette adresse pour la facturation</label>
</div>
</div>
</section>
<footer class="form-footer clearfix">
<input type="hidden" name="submitAddress" value="1">
<button type="submit" class="continue btn btn-primary float-xs-right" name="confirm-addresses" value="1">
Continuer
</button>
</footer>
</div>
</div></form>
</div>
|
433a143c51468b21b250969ed4ae11a0
|
{
"intermediate": 0.3460944890975952,
"beginner": 0.4107333719730377,
"expert": 0.24317209422588348
}
|
47,769
|
<ul class="navbar-nav">
<li class="nav-item dropdown" ngbDropdown>
<a aria-expanded="false" aria-haspopup="true" class="nav-link dropdown-toggle dropdown-toggle-caret" data-toggle="dropdown" href="javascript:void(0)" id="logoutDropdown" ngbDropdownToggle role="button">
<i class="fa-solid fa-user"></i> BindinguserInfo?.username
</a>
<div aria-labelledby="logoutDropdown" class="dropdown-menu dropdown-menu-end" ngbDropdownMenu>
<a (click)="logOut()"class="dropdown-item" ngbDropdownItem>Logout</a> <!--(click)="logOut()" -->
</div>
</li>
</ul> drop down didn't work
|
6b00d5f44c110396525e81278b81cfbb
|
{
"intermediate": 0.39106059074401855,
"beginner": 0.3551487922668457,
"expert": 0.25379064679145813
}
|
47,770
|
install command in cmake
|
c9f8622523b9241bb0c61570fba5612b
|
{
"intermediate": 0.4322913885116577,
"beginner": 0.189520925283432,
"expert": 0.3781876564025879
}
|
47,771
|
In workflow, we notify user via email with a confirmation of yes or no and after that i have a stage called wait for user confirmation and according to user yes or no stage will proceed further, but i want if the user hasnt give confirmation in 1 day , the workflow stage takes the response as yes automatically and proceed the workflow further.
|
09df80786dbc888a01ecaf812dcdcd70
|
{
"intermediate": 0.6342465281486511,
"beginner": 0.12357044965028763,
"expert": 0.24218297004699707
}
|
47,772
|
this is my code:
pub fn test(
c_fivends: &[(u64, u64)],
c_exons: &[(u64, u64)],
c_introns: &[(u64, u64)],
tx_exons: &[&(u64, u64)],
id: &Arc<str>,
// flags -> (skip_exon [0: true, 1: false], nt_5_end)
flags: &HashSet<(u64, u64)>,
line: String,
) {
let tx_5end = tx_exons[0];
let (skip, _) = flags.iter().next().unwrap();
let status = match c_fivends.interval_search(&tx_5end) {
// is inside
Some((s, e)) => {
// starts differ
if s != tx_5end.0 {
match skip {
1 => FivePrimeStatus::TruncatedInExon,
_ => FivePrimeStatus::Complete,
}
} else {
// starts are equal
FivePrimeStatus::Complete
}
}
// is not inside -> check c_exons overlap
None => match c_exons.interval_search(&tx_5end) {
Some((ex_s, ex_e)) => {
if ex_s != tx_5end.0 {
match skip {
1 => FivePrimeStatus::TruncatedInExon,
_ => FivePrimeStatus::Complete,
}
} else {
// starts are equal
FivePrimeStatus::Complete
}
}
None => FivePrimeStatus::Complete,
},
};
}
pub trait IntervalSearch<T> {
fn interval_search(&self, query: &(T, T)) -> Option<(T, T)>
where
T: PartialOrd + Copy;
}
impl<T> IntervalSearch<T> for Vec<(T, T)> {
fn interval_search(&self, query: &(T, T)) -> Option<(T, T)>
where
T: PartialOrd + Copy,
{
let mut start = 0;
let mut end = self.len();
while start < end {
let mid = start + (end - start) / 2;
let mid_val = self[mid];
if query.1 <= mid_val.0 {
end = mid;
} else if query.0 >= mid_val.1 {
start = mid + 1;
} else {
return Some(mid_val);
}
}
None
}
}
I got this error:
error[E0599]: no method named `interval_search` found for reference `&[(u64,
u64)]` in the current scope
--> src/bif/fivend.rs:186:34
|
186 | let status = match c_fivends.interval_search(&tx_5end) {
| ^^^^^^^^^^^^^^^ help: there is a meth
od with a similar name: `binary_search`
|
= help: items from traits can only be used if the trait is implemented a
nd in scope
note: `IntervalSearch` defines an item `interval_search`, perhaps you need t
o implement it
--> src/bif/fivend.rs:218:1
|
218 | pub trait IntervalSearch<T> {
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^
error[E0599]: no method named `interval_search` found for reference `&[(u64,
u64)]` in the current scope
--> src/bif/fivend.rs:201:31
|
201 | None => match c_exons.interval_search(&tx_5end) {
| ^^^^^^^^^^^^^^^ help: there is a method
with a similar name: `binary_search`
|
= help: items from traits can only be used if the trait is implemented a
nd in scope
note: `IntervalSearch` defines an item `interval_search`, perhaps you need t
o implement it
--> src/bif/fivend.rs:218:1
|
218 | pub trait IntervalSearch<T> {
| ^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
c50325f1bc243662ebe5407a07f8172e
|
{
"intermediate": 0.3707680404186249,
"beginner": 0.38088247179985046,
"expert": 0.24834948778152466
}
|
47,773
|
i have a stack group <Stack.Group screenOptions={{ presentation: "modal" }}>
<Stack.Screen
options={{ headerShown: false }}
name="Doc"
component={Doc}
onPress={() => navigation.navigate('Web')}
/>
</Stack.Group>
and i want access in other component with this snippet : <SafeAreaView style={{ flex: 1 }}>
<WebView source={{ uri: 'env.urlCompte' }} />
</SafeAreaView>
|
671568b99f6026ab3a42ed52e06f3367
|
{
"intermediate": 0.34635117650032043,
"beginner": 0.29405003786087036,
"expert": 0.3595988154411316
}
|
47,774
|
In servicenow, In workflow, we notify user via email with a confirmation of yes or no via create event activity in workflow and after that i have a property called wait for condition in workflow and according to user yes or no stage will proceed further, but i want if the user hasnt give confirmation in 1 day , the workflow stage takes the response as yes automatically and proceed the workflow further.
|
1bd99564bad956981f7340cfc5552e29
|
{
"intermediate": 0.6144760847091675,
"beginner": 0.16574636101722717,
"expert": 0.21977755427360535
}
|
47,775
|
Question 2: Need For Speed 4.1 Introduction
Lukarp has started his own tech company. He received a lot of funding from Igen with which he opened many offices around the world. Each office needs to communicate with one other, for which they’re using high speed connections between the offices. Office number 1 is Lukarp’s HQ. Some offices are important and hence need faster connections to the HQ for which Lukarp has use special fiber connections. Lukarp has already planned the connections but feels some fiber connections are redundant. You have been hired by Lukarp to remove those fiber connections which don’t cause faster connections.
4.2 Problem Statement
4.2.1 The Problem
The offices and (bi-directional) connections (both normal and fiber) are given to you. HQ is numbered as 1. The ith normal connection connects any two offices ai and bi. Normal connections have latency li. The ith fiber connection connects the HQ with the office ci. Fiber connections also come with a latency pi. The total latency of a path is the sum of latencies on the connections. You are to output the maximum number of fiber connections that can be removed, such that the latency of the smallest latency path between the HQ and any other node remains the same as before.
• There are n offices with m normal connections and k high-speed fiber connections.
• The ith normal connection connects offices ai and bi (bi-directionally) with latency li.
• The ith fiber connection connects offices 1 and ci (bi-directionally) with latency pi.
4.2.2 Input Format
The first line of the input file will contain three space-separated integers n, m and k, the number of offices, the number of normal connections and the number of fiber connections.
There will be m lines after this, the ith line signifying the ith normal connection, each containing three space-separated integers ai, bi and li the two offices that are connected and the latency of the connection respectively.
There will be k lines after this, the ith line signifying the ith fiber connection, each containing three space-separated integers ci and pi, the office connected to the HQ and the latency of the fiber connection respectively.
6
4.2.3 Output Format
Output only one integer m - the maximum number of fiber connections that can be removed without changing the latency of smallest latency path from office 1 to any other office.
4.2.4 Constraints
• 2≤n≤105
• 1≤m≤2·105 • 1≤k≤105
• 1≤ai,bi,ci ≤n • 1≤li,pi ≤109
4.2.5 Example
Input:
452
122
149
133
244
345
34
45
Output:
1
Explanation:
In this example, there are five normal connections as shown in the figure below. The fiber connection going from 1 to 3 can be removed because the normal con- nection (3) is faster than the fiber connection (4). However, the fiber connection with 4 cannot be removed. Hence the maximum number of fiber connections that can be removed is 1.
7
Figure 3: Normal Connections and their latencies
8
|
3e75d279a16315d4f7aa5a522d9eb4a9
|
{
"intermediate": 0.3268553614616394,
"beginner": 0.29196256399154663,
"expert": 0.3811820447444916
}
|
47,776
|
hi, can you correct this arg for linux ffmpeg 6.0: ffmpeg -f x11grab -framerate 30 $(xwininfo | gawk 'match($0, -geometry ([0-9]+x[0-9]+).([0-9]+).([0-9]+)/, a)\{ print "-video_size " a[1] " -i +" a[2] "," a[3] }') -c:v libx264rgb -preset slow -t 5 -pass 1 -an -f null /dev/null && \
ffmpeg -i /dev/null -c:v libx264rgb -preset slow -f alsa -ac 2 -i pulse -t 5 -pass 2 $(date +%Y-%m-%d_%H-%M_%S).mkv
|
9c23747db3f6b0292f5a880582660f06
|
{
"intermediate": 0.36336153745651245,
"beginner": 0.5234166979789734,
"expert": 0.11322171241044998
}
|
47,777
|
Hi, cany you correct this ffmpeg 6.0 arg: ffmpeg -f x11grab -framerate 30 $(xwininfo | gawk 'match($0, -geometry ([0-9]+x[0-9]+).([0-9]+).([0-9]+)/, a)\{ print "-video_size " a[1] " -i +" a[2] "," a[3] }') -c:v libx264rgb -preset slow -t 5 -pass 1 -an -f null /dev/null && \
ffmpeg -i /dev/null -c:v libx264rgb -preset slow -f alsa -ac 2 -i pulse -t 5 -pass 2 $(date +%Y-%m-%d_%H-%M_%S).mkv
|
1ebec0675955acad16d921aa41c3c401
|
{
"intermediate": 0.3601072430610657,
"beginner": 0.48958253860473633,
"expert": 0.15031027793884277
}
|
47,778
|
hi can you correct this ffmpeg arg: ffmpeg -f x11grab -framerate 30 $(xwininfo | gawk 'match($0, -geometry ([0-9]+x[0-9]+).([0-9]+).([0-9]+)/, a)\{ print "-video_size " a[1] " -i +" a[2] "," a[3] }') -c:v libx264rgb -preset slow -t 5 -pass 1 -an -f null /dev/null && \
ffmpeg -i /dev/null -c:v libx264rgb -preset slow -f alsa -ac 2 -i pulse -t 5 -pass 2 $(date +%Y-%m-%d_%H-%M_%S).mkv
|
8dfdf0772506d1591a777f0528720e44
|
{
"intermediate": 0.41401875019073486,
"beginner": 0.48244181275367737,
"expert": 0.10353948920965195
}
|
47,779
|
Write a python script that does the following
1. Ask for two numbers call them "start" and "max".
2. Produce and show a number sequence beginning from "start" and having "max" numbers such that each number in the sequence except the last two numbers is the average of the next two numbers that are right to it in the sequence.
|
0d8350bcbb911453040901b2db287128
|
{
"intermediate": 0.4470610022544861,
"beginner": 0.19975365698337555,
"expert": 0.35318535566329956
}
|
47,780
|
Nominal scales
Nominal scales are used for naming and categorizing data in a variable
- usually in the form of identifying groups into which people fall. Membership in such groups may occur either naturally (as in the sex or nationality groupings just discussed) or artificially (as in a study that assigns students to different experimental and control groups). On a particular nominal scale, each observation usually falls into only one category. The next observation may fall into a different category, but it, too, will fall only into one such category. Examples of variables that group people naturally include sex, nationality, native language, social and economic status, level of study in a language and a specific like or dislike (e.g., whether the subjects like studying grammar rules). Artificially occurring variables might include membership in an experimental or control group and membership in a particular class or language pro-gram. So the essence of the nominal scale is that it names categories into which people fall, naturally or artificially. One source of confusion with this type of scale is that it is variously labeled a discrete, discontinuous, categorical, or even dichotomous scale (in the special case of only two categories). In this book, I will always call this type of scale nominal.
Ordinal scales
Unlike a nominal scale, an ordinal scale is used to order, or rank, data.
For instance, if you were interested in ranking your students from best to worst on the basis of their final examination scores (with 1 representing the best; 2, the next best; and 30 being the worst), you would be dealing with an ordinal scale. In our field, numerous rankings may be of concern, including ranking students in a class, ranking teachers in a center, or even ranking grammatical structures in a language (e.g., in French tenses, subjonctif is more difficult than the passé composé, which is more difficult than the present). In an ordinal scale, then, each point on the scale is ranked as "more than" and "less than" the other points on the scale. The points are then lined up and numbered first, second, third, and so on.
Interval scales
Interval scales also represent the ordering of things. In addition, they reflect the interval, or distance, between points in the ranking. When you look at the final examination scores in one of your courses, you are dealing with an interval scale. For instance, if Jim scored 90 out of 100, Jack scored 80, and Jill scored 78, you could rank these three students first, second, and third; that would be an ordinal scale. But the scores themselves give you more information than that. They also tell you the interval, or distance, between the performances on the examination: Jim scored 10 points higher than Jack, but Jack only scored 2 points higher than Jill. Thus, an interval scale gives more information than does an ordinal scale.
An interval scale gives you the ordering and the distances between points on that ranking. Examples of interval scales include most of the variables measured with tests like language placement, language apti-tude, language proficiency, and so on. But some interval scales are measured in different ways, such as age, number of years of schooling, and years of language study.
It is important to note that an interval scale assumes that the intervals between points are equal. Hence, on a 100-point test, the distance between the scores 12 and 14 (2 points) is assumed to be equal to the distance between 98 and 100 (also 2 points). The problem is that some items on a language test may be much more difficult (particularly those that make a difference on high scores like 98 and 100) than are others, so the distances between intervals do not seem equal. Nevertheless, this assumption is one with which most researchers can live - at least until knowledge in the field of language teaching becomes exact enough to provide ratio scales.
Ratio scales
The discussion of ratio scales will be short because such scales are not generally applied to the behavioral sciences, for two reasons: (1) a ratio scale has a zero value and (2) it can be said that points on the scale are precise multiples of other points on the scale. For example, you can have zero electricity in your house. But if you have a 50-watt bulb burning and then you turn on an additional 100-watt bulb, you can say that you are now using three times as much electricity. Can you, however, say that a person knows no (zero) Spanish? I think not. Even a person who has never studied Spanish will bring certain lexical, phonological, and syntactic information from the native language to bear on the task.
Read the text above and create a table.
Columns:
Name of scale
Property
Operations
Characteristic
Example
|
c237ed428050ec87e4ab7b4281131888
|
{
"intermediate": 0.3574083745479584,
"beginner": 0.37054377794265747,
"expert": 0.27204781770706177
}
|
47,781
|
I have 6 files .h and .cpp. can i share them w/ u and u can check
|
4ea753cb7a37fdc58a471bf5738c62b1
|
{
"intermediate": 0.37581539154052734,
"beginner": 0.26994991302490234,
"expert": 0.3542346954345703
}
|
47,782
|
#include <bits/stdc++.h>
using namespace std;
int cnt = 0;
int min_distance(vector<int> &dist, vector<int> &vis, int n)
{
int min = 100000;
int min_index = -1;
for (int v = 1; v <= n; v++)
{
if (vis[v] == 0 && dist[v] <= min)
{
min = dist[v], min_index = v;
}
}
return min_index;
}
void dijkstra(vector<vector<int>> &graph, int src, int n, vector<int> &dist)
{
vector<int> vis(n + 1, 0);
for (int i = 1; i <= n; i++)
dist[i] = 100000;
dist[src] = 0;
for (int count = 0; count < n; count++)
{
int u = min_distance(dist, vis, n);
if (u == -1)
break;
vis[u] = 1;
for (int v = 1; v <= n; v++)
{
if (vis[v] == 0 && graph[u][v] != 100000 &&
dist[u] != 100000 && dist[u] + graph[u][v] < dist[v])
{
dist[v] = dist[u] + graph[u][v];
}
}
}
}
void add_edge(vector<vector<int>> &adj, int u, int v, int l)
{
if (u >= 1 && u <= adj.size() - 1 && v >= 1 && v <= adj.size() - 1)
{
adj[u][v] = l;
adj[v][u] = l;
}
}
void func(vector<vector<int>> &adj1, vector<int> &dist1, vector<int> &adj2, int n)
{
// int cnt = 0;
for (int i = 1; i <= n; i++)
{
cout << "adj2[i] is " << adj2[i]<< endl;
if (adj2[i]!=100000 && dist1[i] <= adj2[i])
{
cnt++;
}
else
{
cout << "chsnging dist of " << i << " to " << adj2[i] << "from"<< dist1[i]<< endl;
dist1[i] = adj2[i];
add_edge(adj1, 1, i, dist1[i]);
cout << "changed dist of " << i << " is " << dist1[i] << endl;
dijkstra(adj1, 1, n, dist1);
}
for (int i = 0; i < dist1.size(); i++)
{
cout << i << "dist 1 array" << dist1[i] << endl;
}
cout<<"node is "<<i<<endl;
cout<<"count is " <<cnt<<endl;
}
cout << cnt << endl
<< endl;
}
int main()
{
int n, m, k;
cin >> n >> m >> k;
vector<vector<int>> adj1(n + 1, vector<int>(n + 1, 100000));
// vector<vector<int>> adj2(n + 1, vector<int>(n + 1, 100000));
vector<int> adj2(n, 100000);
vector<int> dist1(n + 1, 100000), dist2(n + 1, 100000);
for (int i = 0; i < m; i++)
{
int u, v, l;
cin >> u >> v >> l;
add_edge(adj1, u, v, l);
}
for (int i = 0; i < k; i++)
{
int v, l;
cin >> v >> l;
if (adj2[v] != 100000)
{
cnt++;
if (adj2[v] > l)
{
adj2[v] = l;
}
}
else
adj2[v] = l;
// add_edge(adj2, 0, v, l);
}
cout << "count siisisis" << cnt << endl;
dijkstra(adj1, 1, n, dist1);
// dijkstra(adj2, 1, n, dist2);
cout << "initially the shortest paths are" << endl
<< endl;
for (int i = 0; i < dist1.size(); i++)
{
cout << i << " " << dist1[i] << endl;
}
cout << endl
<< endl;
for (int i = 0; i < dist1.size(); i++)
{
cout << i << " " << dist1[i] << endl;
}
for (int i = 0; i < dist1.size(); i++)
{
cout << i << "dist 1 array" << dist1[i] << endl;
}
for (int i = 0; i <= adj2.size(); i++)
{
cout << i << "adj 2 array" << adj2[i] << endl;
}
func(adj1, dist1, adj2, n);
for (int i = 0; i < dist1.size(); i++)
{
cout << i << " " << dist1[i] << endl;
}
return 0;
}
4 Question 2: Need For Speed 4.1 Introduction
Lukarp has started his own tech company. He received a lot of funding from Igen with which he opened many offices around the world. Each office needs to communicate with one other, for which they’re using high speed connections between the offices. Office number 1 is Lukarp’s HQ. Some offices are important and hence need faster connections to the HQ for which Lukarp has use special fiber connections. Lukarp has already planned the connections but feels some fiber connections are redundant. You have been hired by Lukarp to remove those fiber connections which don’t cause faster connections.
4.2 Problem Statement
4.2.1 The Problem
The offices and (bi-directional) connections (both normal and fiber) are given to you. HQ is numbered as 1. The ith normal connection connects any two offices ai and bi. Normal connections have latency li. The ith fiber connection connects the HQ with the office ci. Fiber connections also come with a latency pi. The total latency of a path is the sum of latencies on the connections. You are to output the maximum number of fiber connections that can be removed, such that the latency of the smallest latency path between the HQ and any other node remains the same as before.
• There are n offices with m normal connections and k high-speed fiber connections.
• The ith normal connection connects offices ai and bi (bi-directionally) with latency li.
• The ith fiber connection connects offices 1 and ci (bi-directionally) with latency pi.
4.2.2 Input Format
The first line of the input file will contain three space-separated integers n, m and k, the number of offices, the number of normal connections and the number of fiber connections.
There will be m lines after this, the ith line signifying the ith normal connection, each containing three space-separated integers ai, bi and li the two offices that are connected and the latency of the connection respectively.
There will be k lines after this, the ith line signifying the ith fiber connection, each containing three space-separated integers ci and pi, the office connected to the HQ and the latency of the fiber connection respectively.
6
4.2.3 Output Format
Output only one integer m - the maximum number of fiber connections that can be removed without changing the latency of smallest latency path from office 1 to any other office.
4.2.4 Constraints
• 2≤n≤105
• 1≤m≤2·105 • 1≤k≤105
• 1≤ai,bi,ci ≤n • 1≤li,pi ≤109
4.2.5 Example
Input:
452
122
149
133
244
345
34
45
Output:
1
Explanation:
In this example, there are five normal connections as shown in the figure below. The fiber connection going from 1 to 3 can be removed because the normal con- nection (3) is faster than the fiber connection (4). However, the fiber connection with 4 cannot be removed. Hence the maximum number of fiber connections that can be removed is 1.
7
all test cases are working when i check manually but online judje is throwing wrong answer for hidden test case what could be wrong
|
c96462095b4b61525e6c30bc00169d07
|
{
"intermediate": 0.3498968780040741,
"beginner": 0.42499327659606934,
"expert": 0.22510990500450134
}
|
47,783
|
#include <bits/stdc++.h>
#include <iostream>
#include <vector>
#include <queue>
#include <limits>
#include <unordered_map>
using namespace std;
// int cnt = 0;
int min_distance(vector<pair<int, int>> &dist, vector<int> &vis, int n)
{
int mini = 100000;
int min_index = -1;
for (int v = 1; v <= n; v++)
{
if (vis[v] == 0 && dist[v].first <= mini)
{
mini = dist[v].first, min_index = v;
}
}
return min_index;
}
void dijkstra(vector<vector<int>> &graph, int src, int n, vector<pair<int, int>> &dist)
{
vector<int> vis(n + 1, 0);
for (int i = 1; i <= n; i++)
dist[i].first = 100000;
dist[src].first = 0;
for (int count = 0; count < n; count++)
{
int u = min_distance(dist, vis, n);
if (u == -1)
break;
vis[u] = 1;
for (int v = 1; v <= n; v++)
{
if (vis[v] == 0 && graph[u][v] != 100000 &&
dist[u].first != 100000 && dist[u].first + graph[u][v] < dist[v].first)
{
dist[v].first = dist[u].first + graph[u][v];
}
}
}
}
void add_edge(vector<vector<int>> &adj, int u, int v, int l)
{
if (u >= 1 && u < adj.size() && v >= 1 && v < adj.size())
{
adj[u][v] = l;
adj[v][u] = l;
}
}
void func(vector<vector<int>> &adj1, vector<pair<int, int>> &dist1, vector<int> &adj2, int n, int cnt)
{
// int cnt = 0;
// for (int i = 0; i < dist1.size(); i++)
// {
// cout << i << " " << dist1[i] << endl;
// }
// cout<<n<<"is n"<<endl;
for (int i = 1; i <= n; i++)
{
cout << adj2[i] << "is my adj of " << i << endl;
cout << dist1[i].first << "is my dist1 of " << i << endl;
if (adj2[i] != 100000)
{
if (dist1[i].first <= adj2[i])
{
cnt++;
cout << cnt << "in func and i is " << i << endl;
}
else
{
dist1[i].first = adj2[i];
if (dist1[i].second == 1)
{
cnt++;
cout << "aaaa";
}
dist1[i].second = 1;
add_edge(adj1, 1, i, dist1[i].first);
cout << "changed dist of " << i << " is " << dist1[i].first << endl;
dijkstra(adj1, 1, n, dist1);
}
}
}
cout << cnt;
//<< endl
// << endl;
}
int main()
{
int cnt = 0;
int n, m, k;
cin >> n >> m >> k;
vector<vector<int>> adj1(n + 1, vector<int>(n + 1, 100000));
// vector<vector<int>> adj2(n + 1, vector<int>(n + 1, 100000));
vector<int> adj2(n, 100000);
vector<pair<int, int>> dist1(n + 1, {100000, 0});
// vector<int> dist1(n + 1, 100000);
//, dist2(n + 1, 100000);
dist1[1].first = 0;
for (int i = 0; i < m; i++)
{
int u, v, l;
cin >> u >> v >> l;
if (adj1[u][v] != 100000)
{
if (adj1[u][v] > l)
{
add_edge(adj1, u, v, l);
}
}
else
{
add_edge(adj1, u, v, l);
}
}
for (int i = 0; i < k; i++)
{
int v, l;
cin >> v >> l;
if (adj2[v] != 100000)
{
cnt++;
if (adj2[v] > l)
{
adj2[v] = l;
}
}
else if (v == 1)
{
cnt++;
}
else
adj2[v] = l;
// add_edge(adj2, 0, v, l);
}
dijkstra(adj1, 1, n, dist1);
// dijkstra(adj2, 1, n, dist2);
cout << "initially the shortest paths are" << endl
<< endl;
for (int i = 0; i < dist1.size(); i++)
{
cout << i << " " << dist1[i].first << endl;
}
cout << endl
<< endl;
func(adj1, dist1, adj2, n, cnt);
for (int i = 0; i < dist1.size(); i++)
{
cout << i << " " << dist1[i].first << endl;
}
return 0;
}
modify this code to answer these test cases
// Test cases
// 1 will be connected to all the nodes via normal connection.
/*
3 2 2
1 2 3
2 3 4
3 10
3 8
// ans=2
4 5 2
1 2 2
1 4 9
1 3 3
2 4 4
3 4 5
3 4
4 5
// ans =1
3 3 3
1 2 5
1 3 100
2 3 1
2 4
3 2
3 1
//ans 2
4 4 2
1 2 5
2 3 4
3 4 1
1 4 10
2 1
4 8
// ans= 1
*/
/*
basic cases:
5 6 3
1 4 5
4 5 2
2 4 7
3 1 4
1 5 10
3 2 6
5 3
2 1
4 3
// ans = 0
4 4 1
2 3 2
2 4 8
4 3 1
1 4 5
3 7
// ans = 1
3 3 3
1 2 1
2 3 1
1 3 1
2 1
2 2
3 1
3
// ans 3
4 5 2
1 2 2
1 4 9
1 3 3
2 4 4
3 4 5
3 4
4 5
// ans = 1
4 4 2
2 3 2
2 4 8
4 3 3
1 4 5
3 7
2 9
// ans = 1
4 4 2
1 2 5
2 3 4
3 4 1
1 4 10
2 1
4 8
// ans = 1
3 3 3
1 2 5
1 3 100
2 3 1
2 4
3 2
3 1
// ans = 2
\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
// Good testcases
5 6 3
1 4 5
4 5 2
2 4 7
3 1 4
1 5 10
3 2 6
2 1
4 3
5 5
// ans = 1
6 16 19
4 1 6
2 2 19
3 1 14
3 6 14
6 5 17
6 4 2
4 6 7
2 1 7
3 5 4
6 6 13
5 4 7
2 3 6
1 2 19
4 6 14
4 2 16
2 6 13
5 9
1 3
4 12
2 12
4 18
3 17
1 1
4 8
3 15
6 15
3 17
1 18
6 11
1 10
3 16
3 11
5 20
5 3
4 7
// ans = 18
3 9 8
1 1 14
1 3 1
2 2 12
3 1 15
3 1 19
3 3 15
1 3 7
3 1 8
1 2 6
2 18
3 1
2 13
3 2
3 5
1 20
2 8
3 5
// ans = 8
6 5 4
4 4 20
1 4 15
5 3 14
5 5 12
2 6 19
6 3
4 10
1 18
4 4
// ans = 2
8 5 6
1 8 20
3 7 11
5 6 14
2 6 17
7 4 11
8 6
7 19
5 8
6 7
2 13
3 19
// ans = 0
10 10 10
9 8 2
8 8 9
2 8 17
1 1 1
6 6 10
10 8 11
3 9 18
7 1 5
3 2 17
5 5 10
3 16
1 20
8 13
3 7
5 12
1 10
10 14
10 3
4 1
3 3
// ans = 5
10 9 5
9 7 4
1 5 18
6 6 7
7 2 9
2 6 3
8 10 9
10 4 6
6 5 14
5 9 11
2 18
7 1
2 12
5 7
2 4
// ans = 2
*/
|
b9a1cac318a53ba782545fc8a0c0633c
|
{
"intermediate": 0.34474819898605347,
"beginner": 0.49480685591697693,
"expert": 0.1604449599981308
}
|
47,784
|
The offices and (bi-directional) connections (both normal and fiber) are given
to you. HQ is numbered as 1. The ith normal connection connects any two
offices ai and bi . Normal connections have latency li . The ith fiber connection
connects the HQ with the office ci . Fiber connections also come with a latency
pi . The total latency of a path is the sum of latencies on the connections.
You are to output the maximum number of fiber connections that can be
removed, such that the latency of the smallest latency path between the
HQ and any other node remains the same as before.
• There are n offices with m normal connections and k high-speed fiber
connections.
• The ith normal connection connects offices ai and bi (bi-directionally) with
latency li .
• The ith fiber connection connects offices 1 and ci (bi-directionally) with
latency pi .
give c++code
it should satisfy these test cases
3 2 2
1 2 3
2 3 4
3 10
3 8
// ans=2
4 5 2
1 2 2
1 4 9
1 3 3
2 4 4
3 4 5
3 4
4 5
// ans =1
3 3 3
1 2 5
1 3 100
2 3 1
2 4
3 2
3 1
//ans 2
4 4 2
1 2 5
2 3 4
3 4 1
1 4 10
2 1
4 8
// ans= 1
*/
/*
basic cases:
5 6 3
1 4 5
4 5 2
2 4 7
3 1 4
1 5 10
3 2 6
5 3
2 1
4 3
// ans = 0
4 4 1
2 3 2
2 4 8
4 3 1
1 4 5
3 7
// ans = 1
3 3 3
1 2 1
2 3 1
1 3 1
2 1
2 2
3 1
3
// ans 3
4 5 2
1 2 2
1 4 9
1 3 3
2 4 4
3 4 5
3 4
4 5
// ans = 1
4 4 2
2 3 2
2 4 8
4 3 3
1 4 5
3 7
2 9
// ans = 1
4 4 2
1 2 5
2 3 4
3 4 1
1 4 10
2 1
4 8
// ans = 1
3 3 3
1 2 5
1 3 100
2 3 1
2 4
3 2
3 1
// ans = 2
\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
// Good testcases
5 6 3
1 4 5
4 5 2
2 4 7
3 1 4
1 5 10
3 2 6
2 1
4 3
5 5
// ans = 1
6 16 19
4 1 6
2 2 19
3 1 14
3 6 14
6 5 17
6 4 2
4 6 7
2 1 7
3 5 4
6 6 13
5 4 7
2 3 6
1 2 19
4 6 14
4 2 16
2 6 13
5 9
1 3
4 12
2 12
4 18
3 17
1 1
4 8
3 15
6 15
3 17
1 18
6 11
1 10
3 16
3 11
5 20
5 3
4 7
// ans = 18
3 9 8
1 1 14
1 3 1
2 2 12
3 1 15
3 1 19
3 3 15
1 3 7
3 1 8
1 2 6
2 18
3 1
2 13
3 2
3 5
1 20
2 8
3 5
// ans = 8
6 5 4
4 4 20
1 4 15
5 3 14
5 5 12
2 6 19
6 3
4 10
1 18
4 4
// ans = 2
8 5 6
1 8 20
3 7 11
5 6 14
2 6 17
7 4 11
8 6
7 19
5 8
6 7
2 13
3 19
// ans = 0
10 10 10
9 8 2
8 8 9
2 8 17
1 1 1
6 6 10
10 8 11
3 9 18
7 1 5
3 2 17
5 5 10
3 16
1 20
8 13
3 7
5 12
1 10
10 14
10 3
4 1
3 3
// ans = 5
10 9 5
9 7 4
1 5 18
6 6 7
7 2 9
2 6 3
8 10 9
10 4 6
6 5 14
5 9 11
2 18
7 1
2 12
5 7
2 4
// ans = 2
*/
|
35c9c3a3d4c4fb13c58738bbda5f9503
|
{
"intermediate": 0.3481638431549072,
"beginner": 0.3229636549949646,
"expert": 0.3288725018501282
}
|
47,785
|
Question 2: Need For Speed 4.1 Introduction
Lukarp has started his own tech company. He received a lot of funding from Igen with which he opened many offices around the world. Each office needs to communicate with one other, for which they’re using high speed connections between the offices. Office number 1 is Lukarp’s HQ. Some offices are important and hence need faster connections to the HQ for which Lukarp has use special fiber connections. Lukarp has already planned the connections but feels some fiber connections are redundant. You have been hired by Lukarp to remove those fiber connections which don’t cause faster connections.
4.2 Problem Statement
4.2.1 The Problem
The offices and (bi-directional) connections (both normal and fiber) are given to you. HQ is numbered as 1. The ith normal connection connects any two offices ai and bi. Normal connections have latency li. The ith fiber connection connects the HQ with the office ci. Fiber connections also come with a latency pi. The total latency of a path is the sum of latencies on the connections. You are to output the maximum number of fiber connections that can be removed, such that the latency of the smallest latency path between the HQ and any other node remains the same as before.
• There are n offices with m normal connections and k high-speed fiber connections.
• The ith normal connection connects offices ai and bi (bi-directionally) with latency li.
• The ith fiber connection connects offices 1 and ci (bi-directionally) with latency pi.
4.2.2 Input Format
The first line of the input file will contain three space-separated integers n, m and k, the number of offices, the number of normal connections and the number of fiber connections.
There will be m lines after this, the ith line signifying the ith normal connection, each containing three space-separated integers ai, bi and li the two offices that are connected and the latency of the connection respectively.
There will be k lines after this, the ith line signifying the ith fiber connection, each containing three space-separated integers ci and pi, the office connected to the HQ and the latency of the fiber connection respectively.
6
4.2.3 Output Format
Output only one integer m - the maximum number of fiber connections that can be removed without changing the latency of smallest latency path from office 1 to any other office.
4.2.4 Constraints
• 2≤n≤105
• 1≤m≤2·105 • 1≤k≤105
• 1≤ai,bi,ci ≤n • 1≤li,pi ≤109
4.2.5 Example
Input:
452
122
149
133
244
345
34
45
Output:
1
Explanation:
In this example, there are five normal connections as shown in the figure below. The fiber connection going from 1 to 3 can be removed because the normal con- nection (3) is faster than the fiber connection (4). However, the fiber connection with 4 cannot be removed. Hence the maximum number of fiber connections that can be removed is 1.
7
/ Test cases
// 1 will be connected to all the nodes via normal connection.
/*
3 2 2
1 2 3
2 3 4
3 10
3 8
// ans=2
4 5 2
1 2 2
1 4 9
1 3 3
2 4 4
3 4 5
3 4
4 5
// ans =1
3 3 3
1 2 5
1 3 100
2 3 1
2 4
3 2
3 1
//ans 2
4 4 2
1 2 5
2 3 4
3 4 1
1 4 10
2 1
4 8
// ans= 1
*/
/*
basic cases:
5 6 3
1 4 5
4 5 2
2 4 7
3 1 4
1 5 10
3 2 6
5 3
2 1
4 3
// ans = 0
4 4 1
2 3 2
2 4 8
4 3 1
1 4 5
3 7
// ans = 1
3 3 3
1 2 1
2 3 1
1 3 1
2 1
2 2
3 1
3
// ans 3
4 5 2
1 2 2
1 4 9
1 3 3
2 4 4
3 4 5
3 4
4 5
// ans = 1
4 4 2
2 3 2
2 4 8
4 3 3
1 4 5
3 7
2 9
// ans = 1
4 4 2
1 2 5
2 3 4
3 4 1
1 4 10
2 1
4 8
// ans = 1
3 3 3
1 2 5
1 3 100
2 3 1
2 4
3 2
3 1
// ans = 2
\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
// Good testcases
5 6 3
1 4 5
4 5 2
2 4 7
3 1 4
1 5 10
3 2 6
2 1
4 3
5 5
// ans = 1
6 16 19
4 1 6
2 2 19
3 1 14
3 6 14
6 5 17
6 4 2
4 6 7
2 1 7
3 5 4
6 6 13
5 4 7
2 3 6
1 2 19
4 6 14
4 2 16
2 6 13
5 9
1 3
4 12
2 12
4 18
3 17
1 1
4 8
3 15
6 15
3 17
1 18
6 11
1 10
3 16
3 11
5 20
5 3
4 7
// ans = 18
3 9 8
1 1 14
1 3 1
2 2 12
3 1 15
3 1 19
3 3 15
1 3 7
3 1 8
1 2 6
2 18
3 1
2 13
3 2
3 5
1 20
2 8
3 5
// ans = 8
6 5 4
4 4 20
1 4 15
5 3 14
5 5 12
2 6 19
6 3
4 10
1 18
4 4
// ans = 2
8 5 6
1 8 20
3 7 11
5 6 14
2 6 17
7 4 11
8 6
7 19
5 8
6 7
2 13
3 19
// ans = 0
10 10 10
9 8 2
8 8 9
2 8 17
1 1 1
6 6 10
10 8 11
3 9 18
7 1 5
3 2 17
5 5 10
3 16
1 20
8 13
3 7
5 12
1 10
10 14
10 3
4 1
3 3
// ans = 5
10 9 5
9 7 4
1 5 18
6 6 7
7 2 9
2 6 3
8 10 9
10 4 6
6 5 14
5 9 11
2 18
7 1
2 12
5 7
2 4
// ans = 2
*/
PROVIDE ME C++ IMPLEMENTATION , DONT TALK TOO MUCH OR DONT EXPLAIN ANYTHING
|
8e33d5df27f2d5a95e611e3c5a805b68
|
{
"intermediate": 0.30314314365386963,
"beginner": 0.3608108460903168,
"expert": 0.336046040058136
}
|
47,786
|
The offices and (bi-directional) connections (both normal and fiber) are given
to you. HQ is numbered as 1. The ith normal connection connects any two
offices ai and bi . Normal connections have latency li . The ith fiber connection
connects the HQ with the office ci . Fiber connections also come with a latency
pi . The total latency of a path is the sum of latencies on the connections.
You are to output the maximum number of fiber connections that can be
removed, such that the latency of the smallest latency path between the
HQ and any other node remains the same as before.
• There are n offices with m normal connections and k high-speed fiber
connections.
• The ith normal connection connects offices ai and bi (bi-directionally) with
latency li .
• The ith fiber connection connects offices 1 and ci (bi-directionally) with
latency pi .
give c++code
it should satisfy these test cases
20 5 8
2 7 2
7 16 11
7 19 17
1 12 16
16 3 15
16 18
16 19
14 7
5 8
14 12
20 20
3 3
15 1
output:
3
3 9 8
1 1 14
1 3 1
2 2 12
3 1 15
3 1 19
3 3 15
1 3 7
3 1 8
1 2 6
2 18
3 1
2 13
3 2
3 5
1 20
2 8
3 5
output:
8
6 16 19
4 1 6
2 2 19
3 1 14
3 6 14
6 5 17
6 4 2
4 6 7
2 1 7
3 5 4
6 6 13
5 4 7
2 3 6
1 2 19
4 6 14
4 2 16
2 6 13
5 9
1 3
4 12
2 12
4 18
3 17
1 1
4 8
3 15
6 15
3 17
1 18
6 11
1 10
3 16
3 11
5 20
5 3
4 7
output:
18
|
d68b168e9dc2361802fa84ae23f3f4df
|
{
"intermediate": 0.27379539608955383,
"beginner": 0.363313764333725,
"expert": 0.3628908097743988
}
|
47,787
|
Suppose I get a quote from a geico entering in the necessary information as well as my email and phone number. I decide I don't want to go with Geico right now, and want to check other companies. Am I stuck with this initial Geico quote or can this be changed later?
|
d07308680ddc4ae8ef52c77b4ef3c0a4
|
{
"intermediate": 0.3997032642364502,
"beginner": 0.3050665259361267,
"expert": 0.2952302098274231
}
|
47,788
|
analyze the following code and correct any potential data column naming errors or http errors in dataset construction: # Import necessary libraries
import pandas as pd
import numpy as np
from datetime import datetime
from alpha_vantage.timeseries import TimeSeries
from textblob import TextBlob
from nltk.sentiment import SentimentIntensityAnalyzer
# Set the API key for Alpha Vantage
api_key = 'OSKT2UIVOS229Q0V' # Replace with your actual API key
# Define the cryptocurrency and the time range
crypto = 'BTC' # You can change this to other cryptocurrencies
start_date = '2018-01-01'
end_date = '2023-04-30'
# Fetch historical price data from Alpha Vantage
def fetch_price_data(api_key, crypto, start_date, end_date):
"""
Fetches and preprocesses price data from Alpha Vantage.
"""
ts = TimeSeries(key=api_key, output_format='pandas')
data, meta_data = ts.get_daily(symbol=crypto, outputsize='full')
data = data.iloc[::-1] # Reverse the order of the data
data = data.reset_index()
data['date'] = pd.to_datetime(data['date'])
data = data[(data['date'] >= start_date) & (data['date'] <= end_date)]
return data
# Fetch trading volume data from CoinMetrics
def fetch_volume_data(crypto, start_date, end_date):
"""
Fetches and preprocesses volume data from CoinMetrics.
"""
volume_data = pd.read_csv(f"https://coinmetrics.io/data/{crypto.lower()}.csv")
volume_data = volume_data[['date', 'VolumeFromOpt']]
volume_data.columns = ['Date', 'Volume']
volume_data['Date'] = pd.to_datetime(volume_data['Date'])
volume_data = volume_data[(volume_data['Date'] >= start_date) & (volume_data['Date'] <= end_date)]
return volume_data
# Scrape news headlines from CryptoPanic
def fetch_news_data(auth_token, crypto, start_date, end_date):
"""
Fetches and preprocesses news sentiment data from CryptoPanic.
"""
news_data = pd.read_csv(f"https://cryptopanic.com/api/posts/?auth_token={auth_token}&kind=news¤cies={crypto}&public=true")
news_data = news_data[['id', 'title', 'published_at']]
news_data['published_at'] = pd.to_datetime(news_data['published_at'])
news_data = news_data[(news_data['published_at'] >= start_date) & (news_data['published_at'] <= end_date)]
sid = SentimentIntensityAnalyzer()
news_data['Sentiment'] = news_data['title'].apply(lambda x: sid.polarity_scores(x)['compound'])
return news_data[['published_at', 'Sentiment']]
# Set parameters
api_key = 'OSKT2UIVOS229Q0V' # Replace with your actual Alpha Vantage API key
auth_token = '20c5d89ad8d241da6b50d0190c58cdd24f73000c' # Replace with your CryptoPanic API token
crypto = 'BTC'
start_date = '2018-01-01'
end_date = '2023-04-30'
# Fetch data
price_data = fetch_price_data(api_key, crypto, start_date, end_date)
volume_data = fetch_volume_data(crypto, start_date, end_date)
news_data = fetch_news_data(auth_token, crypto, start_date, end_date)
# Merge data
merged_data = pd.merge(price_data, volume_data, on='Date', how='inner')
final_data = pd.merge(merged_data, news_data, left_on='Date', right_on='published_at', how='left')
final_data = final_data.sort_values(by='Date')
final_data = final_data.fillna(0) # Fill missing sentiment scores with 0
# Merge price and volume data
merged_data = pd.merge(data, volume_data, on='Date', how='inner')
merged_data = merged_data[(merged_data['Date'] >= start_date) & (merged_data['Date'] <= end_date)]
# Scrape news headlines from CryptoPanic
news_data = pd.read_csv('https://cryptopanic.com/api/posts/?auth_token=20c5d89ad8d241da6b50d0190c58cdd24f73000c&kind=news¤cies=BTC&public=true')
news_data = news_data[['id', 'title', 'published_at']]
news_data['published_at'] = pd.to_datetime(news_data['published_at'])
news_data = news_data[(news_data['published_at'] >= start_date) & (news_data['published_at'] <= end_date)]
# Compute sentiment scores for news headlines
sid = SentimentIntensityAnalyzer()
news_data['Sentiment'] = news_data['title'].apply(lambda x: sid.polarity_scores(x)['compound'])
# Merge news sentiment data with price and volume data
final_data = pd.merge(merged_data, news_data[['published_at', 'Sentiment']], left_on='Date', right_on='published_at', how='left')
final_data = final_data.sort_values(by='Date')
final_data = final_data.fillna(0) # Fill missing sentiment scores with 0
# Save the final dataset
final_data.to_csv('crypto_dataset.csv', index=False)
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, LSTM, GRU, TimeDistributed, Concatenate, Bidirectional, Attention, Conv1D, Dropout, LayerNormalization, MultiHeadAttention, ConvLSTM2D, Reshape, BatchNormalization
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from tqdm import tqdm
from google.colab import drive
import optuna
from tensorflow.keras import regularizers
from sklearn.metrics import mean_absolute_error, r2_score
# Mount Google Drive
drive.mount('/content/drive')
# Set GPU as the default device
with tf.device('/device:GPU:0'):
# Load and preprocess the cryptocurrency price data
data = pd.read_csv('/content/drive/MyDrive/crypto_prices.csv')
prices = data['Price'].values.reshape(-1, 1)
volume = data['Volume'].values.reshape(-1, 1)
sentiment = data['Sentiment'].values.reshape(-1, 1)
# Calculate additional features
data['EMA'] = data['Price'].ewm(span=10, adjust=False).mean()
data['Upper_BB'] = data['Price'].rolling(window=20).mean() + 2 * data['Price'].rolling(window=20).std()
data['Lower_BB'] = data['Price'].rolling(window=20).mean() - 2 * data['Price'].rolling(window=20).std()
# Normalize the input features
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_prices = scaler.fit_transform(prices)
scaled_volume = scaler.fit_transform(volume)
scaled_sentiment = scaler.fit_transform(sentiment)
scaled_ema = scaler.fit_transform(data['EMA'].values.reshape(-1, 1))
scaled_upper_bb = scaler.fit_transform(data['Upper_BB'].values.reshape(-1, 1))
scaled_lower_bb = scaler.fit_transform(data['Lower_BB'].values.reshape(-1, 1))
# Data augmentation
np.random.seed(42)
jitter_factor = 0.05
data['Price_Jittered'] = data['Price'] * (1 + np.random.normal(0, jitter_factor, size=len(data)))
scaled_price_jittered = scaler.fit_transform(data['Price_Jittered'].values.reshape(-1, 1))
# Create input sequences and corresponding labels
sequence_length = 60
num_features = 7
X = []
y = []
for i in range(sequence_length, len(scaled_prices)):
X.append(np.hstack((scaled_prices[i-sequence_length:i], scaled_volume[i-sequence_length:i], scaled_sentiment[i-sequence_length:i], scaled_ema[i-sequence_length:i], scaled_upper_bb[i-sequence_length:i], scaled_lower_bb[i-sequence_length:i], scaled_price_jittered[i-sequence_length:i])))
y.append(scaled_prices[i])
X = np.array(X)
y = np.array(y)
# Reshape the input data for HMSRNN
X = X.reshape((X.shape[0], sequence_length, num_features))
# Define the HMSRNN architecture with improvements
input_layer = Input(shape=(sequence_length, num_features))
# Residual connections
residual = Dense(64, activation='relu')(input_layer)
residual = Dense(num_features)(residual)
input_layer = tf.keras.layers.Add()([input_layer, residual])
# Layer normalization
input_layer = LayerNormalization()(input_layer)
# Multi-head attention mechanism
attention_output = MultiHeadAttention(num_heads=4, key_dim=64)(input_layer, input_layer)
input_layer = tf.keras.layers.Add()([input_layer, attention_output])
input_layer = LayerNormalization()(input_layer)
# Convolutional LSTM layers
conv_lstm = ConvLSTM2D(filters=64, kernel_size=(3, 3), padding='same', return_sequences=True)(Reshape((sequence_length, num_features, 1))(input_layer))
conv_lstm = TimeDistributed(Dense(64))(conv_lstm)
conv_lstm = Reshape((sequence_length, 64))(conv_lstm)
# High-frequency component
high_freq = Bidirectional(LSTM(256, return_sequences=True))(input_layer)
high_freq = Attention()(high_freq)
high_freq = Dense(128)(high_freq)
high_freq = Dropout(0.3)(high_freq)
# Gated recurrent unit (GRU) layers
mid_freq = Bidirectional(GRU(128, return_sequences=True))(input_layer)
mid_freq = TimeDistributed(Dense(64))(mid_freq)
mid_freq = Attention()(mid_freq)
mid_freq = Dense(32)(mid_freq)
mid_freq = Dropout(0.2)(mid_freq)
# Dilated convolutions
dilated_conv = Conv1D(128, kernel_size=3, dilation_rate=2, activation='relu', padding='same')(input_layer)
dilated_conv = Conv1D(64, kernel_size=3, dilation_rate=4, activation='relu', padding='same')(dilated_conv)
dilated_conv = Attention()(dilated_conv)
dilated_conv = Dense(32)(dilated_conv)
dilated_conv = Dropout(0.2)(dilated_conv)
# Low-frequency component
low_freq = Conv1D(128, kernel_size=5, activation='relu')(input_layer)
low_freq = Bidirectional(LSTM(128, return_sequences=True))(low_freq)
low_freq = TimeDistributed(Dense(32))(low_freq)
low_freq = Attention()(low_freq)
low_freq = Dense(16)(low_freq)
low_freq = Dropout(0.2)(low_freq)
# Concatenate the frequency components and additional features
concat = Concatenate()([high_freq, mid_freq, low_freq, conv_lstm, dilated_conv])
# Batch normalization
concat = BatchNormalization()(concat)
# Dense layers with dropout regularization and L1/L2 regularization
dense = Dense(128, kernel_regularizer=regularizers.l2(0.01))(concat)
dense = Dropout(0.4)(dense)
dense = Dense(64, kernel_regularizer=regularizers.l2(0.01))(dense)
dense = Dropout(0.4)(dense)
# Output layer with linear activation
output_layer = Dense(1, activation='linear')(dense)
# Create the HMSRNN model
model = Model(inputs=input_layer, outputs=output_layer)
# Compile the model with a custom loss function and learning rate scheduler
def custom_loss(y_true, y_pred):
mse = tf.reduce_mean(tf.square(y_true - y_pred))
mae = tf.reduce_mean(tf.abs(y_true - y_pred))
return mse + mae
lr_schedule = tf.keras.optimizers.schedules.CosineDecay(
initial_learning_rate=0.001,
decay_steps=10000,
alpha=0.001)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)
model.compile(optimizer=optimizer, loss=custom_loss)
# Custom callback for progress monitoring
class ProgressCallback(tf.keras.callbacks.Callback):
def on_epoch_end(self, epoch, logs=None):
print(f"Epoch {epoch+1} - Loss: {logs['loss']:.4f} - Val Loss: {logs['val_loss']:.4f}")
# Hyperparameter tuning
def objective(trial):
# Suggest values for the hyperparameters
optimizer = trial.suggest_categorical('optimizer', ['adam', 'rmsprop'])
dropout_rate = trial.suggest_uniform('dropout_rate', 0.1, 0.5)
num_units = trial.suggest_categorical('num_units', [64, 128, 256])
# Create a new model instance
model = create_model(optimizer, dropout_rate, num_units)
# Compile the model
model.compile(optimizer=optimizer, loss=custom_loss)
# Train the model and return the validation loss
history = model.fit(X_train, y_train, epochs=100, batch_size=32, validation_split=0.2, verbose=0)
return history.history['val_loss'][-1]
# Create a study object and optimize the objective function
study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=50)
# Print the best trial
print("Best trial:")
trial = study.best_trial
print(" Value: ", trial.value)
print(" Params: ")
for key, value in trial.params.items():
print(" {}: {}".format(key, value))
# Train the model with early stopping, model checkpointing, and progress monitoring
checkpoint_path = '/content/drive/MyDrive/crypto_model_checkpoints/best_model.h5'
early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=15, restore_best_weights=True)
model_checkpoint = tf.keras.callbacks.ModelCheckpoint(checkpoint_path, save_best_only=True)
progress_callback = ProgressCallback()
try:
history = model.fit(X, y, epochs=100, batch_size=32, validation_split=0.2, callbacks=[early_stopping, model_checkpoint, progress_callback])
except Exception as e:
print(f"Error occurred during training: {str(e)}")
raise
# Visualize training progress
try:
plt.figure(figsize=(12, 6))
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.title('Training Progress')
plt.show()
except Exception as e:
print(f"Error occurred during training progress visualization: {str(e)}")
# Load the best model from Google Drive
try:
model = tf.keras.models.load_model(checkpoint_path, custom_objects={'custom_loss': custom_loss})
except Exception as e:
print(f"Error occurred while loading the model: {str(e)}")
# Evaluate the model
y_pred = model.predict(X_test)
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f"MAE: {mae}, R2 Score: {r2}")
# Calculate additional evaluation metrics
def mean_absolute_percentage_error(y_true, y_pred):
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
mape = mean_absolute_percentage_error(y_test, y_pred)
print(f"MAPE: {mape}")
|
0595f907e86d673c4c57847448f43055
|
{
"intermediate": 0.4378766119480133,
"beginner": 0.2880159616470337,
"expert": 0.2741074562072754
}
|
47,789
|
write a program in c using sdl2
|
ba1da0a6bda2885fcfd08c3f0e4ecc5f
|
{
"intermediate": 0.3731922209262848,
"beginner": 0.2783224582672119,
"expert": 0.3484853506088257
}
|
47,790
|
├── src/ # Source files
│ ├── main.c # Main game loop and initial setup
│ ├── game.c # Game state management
│ ├── game.h
│ ├── init.c # SDL initialization and resource loading
│ ├── init.h
│ ├── cleanup.c # Resource cleanup functions
│ ├── cleanup.h
│ ├── input.c # Input handling
│ ├── input.h
│ ├── graphics.c # Graphics/rendering related functions
│ ├── graphics.h
│ ├── level.c # Level generation and management
│ ├── level.h
│ ├── entity.c # Entity management (players, monsters, NPCs)
│ └── entity.h
|
80e5d237adb117047b59c1b8863a128d
|
{
"intermediate": 0.3449593782424927,
"beginner": 0.34233421087265015,
"expert": 0.3127064108848572
}
|
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