row_id
int64 0
48.4k
| init_message
stringlengths 1
342k
| conversation_hash
stringlengths 32
32
| scores
dict |
|---|---|---|---|
33,026
|
To create a UI using HTML, CSS, and JavaScript for sorting IP addresses based on responsiveness with an adjustable default timeout of 5 seconds, while queuing the IPs sequentially, follow these steps:
HTML:
Create an input field for entering IP addresses.
Add a button to add IPs to the queue.
Create a container element to display the sorted IP addresses.
Add an input field to set the timeout value (defaulted to 5 seconds).
CSS:
Style the input fields, button, and container element as desired.
JavaScript:
Create an array to store the IP addresses.
Implement a queue mechanism to process IPs sequentially.
Set an adjustable default timeout of 5 seconds for each IP address.
Use JavaScript’s Fetch API or XMLHttpRequest to make HTTP or HTTPS requests to the IP addresses.
Set a timeout using JavaScript’s setTimeout function for each request and remove IPs that exceed the timeout.
Keep track of the IPs that receive a response (regardless of the response content).
Sort the IP addresses based on responsiveness.
Update the UI by displaying the sorted IP addresses in the container element.
Utilize event listeners to add IPs to the queue when the button is clicked.
Adjustments:
Implement logic to queue IPs sequentially without overflowing the stack.
Consider limiting the number of simultaneous requests to avoid overwhelming the system.
Remember to handle any errors or exceptions that may occur during the HTTP request process.
By following this technical prompt, you should be able to create a UI in HTML, CSS, and JavaScript that sorts IP addresses based on responsiveness within the specified timeout, while queuing IPs sequentially and allowing adjustment of the default timeout value.", "<!DOCTYPE html> <html> <head> <style> /* CSS styles for the UI elements */
.input-container {
margin-bottom: 10px;
}
.ip-address {
margin-top: 10px;
}
</style> </head> <body> <div class="input-container"> <input type="text" id="ipInput" placeholder="Enter IP Address"> <button id="addIpButton">Add IP</button> </div> <div class="input-container"> <label for="timeoutInput">Timeout (in seconds):</label> <input type="number" id="timeoutInput" value="5"> </div> <div id="ipContainer"></div> <script> let ipAddresses = []; let responsiveIPs = []; function processQueue() { if (ipAddresses.length > 0) { const ipAddress = ipAddresses.shift(); makeRequest(ipAddress); } } function makeRequest(ipAddress) { const timeout = document.getElementById('timeoutInput').value * 1000; const timeoutId = setTimeout(() => {}, timeout); fetch('http://' + ipAddress) .then(response => { clearTimeout(timeoutId); responsiveIPs.push(ipAddress); }) .catch(error => { clearTimeout(timeoutId); }); } function sortIPs() { responsiveIPs.sort((a, b) => {}); } function updateUI() { const ipContainer = document.getElementById('ipContainer'); ipContainer.innerHTML = ''; responsiveIPs.forEach(ip => { const div = document.createElement('div'); div.classList.add('ip-address'); div.textContent = ip; ipContainer.appendChild(div); }); } const addIpButton = document.getElementById('addIpButton'); addIpButton.addEventListener('click', () => { const ipInput = document.getElementById('ipInput'); const ipAddress = ipInput.value; if (ipAddress) { ipAddresses.push(ipAddress); ipInput.value = ''; processQueue(); } }); </script> </body> </html>". got the problem that modern browsers simply blocking all insecure traffic through http or mixed-mode. any ideas how to bypass this for code to be able to knock-knock through http somewhere in the browser?
|
a8efa809b202ae00d41c327fabef9c2e
|
{
"intermediate": 0.38394373655319214,
"beginner": 0.3802828788757324,
"expert": 0.23577342927455902
}
|
33,027
|
using System.Collections;
using System.Collections.Generic;
using UnityEngine;
public class BowAttack : MonoBehaviour
{
public float arrowSpeed = 1;
public GameObject arrowPrefab;
Rigidbody2D rigid;
private float fireDelay = 1f; // Delay time in seconds
private float lastFireTime;
void Start()
{
rigid = GetComponent<Rigidbody2D>();
}
void Update()
{
// Check if enough time has passed since the last firing
if (Time.time - lastFireTime >= fireDelay)
{
//Fire when we click the mouse
if (Input.GetMouseButtonDown(0))
{
//Get the vector from the player to the mouse
Vector3 mousePos = Input.mousePosition;
mousePos.z = 10;
Vector3 worldPos = Camera.main.ScreenToWorldPoint(mousePos);
Vector3 p2m = worldPos - transform.position;
p2m.Normalize();
//Spawn an arrow
GameObject go = Instantiate<GameObject>(arrowPrefab);
go.transform.position = transform.position;
go.transform.right = p2m - go.transform.position;
//Shoot it in that direction
Rigidbody2D arrowRigid = go.GetComponent<Rigidbody2D>();
arrowRigid.velocity = p2m * arrowSpeed;
// Update the last firing time
lastFireTime = Time.time;
}
}
}
}
make it so the arrow is being instantiated as a child of the player
|
336b316fe3c1d872d065b112741145ac
|
{
"intermediate": 0.48563840985298157,
"beginner": 0.2788526117801666,
"expert": 0.2355089783668518
}
|
33,029
|
const char* shadowvert = "varying vec4 shadowCoordinate;\n"
"varying vec3 normal;\n"
"varying vec4 vpos, lightPos;\n"
"uniform mat4 lightingInvMatrix;\n"
"uniform mat4 modelMat;\n"
"uniform mat4 projMatrix;\n"
"uniform mat4 biasMatrix;\n"
"void main() {\n"
" shadowCoordinate = biasMatrix*projMatrix*lightingInvMatrix*modelMat* gl_Vertex;\n"
" normal = normalize(gl_NormalMatrix * gl_Normal);\n"
" vpos = gl_ModelViewMatrix * gl_Vertex;\n"
" lightPos=inverse(lightingInvMatrix)[3]; \n"
" gl_TexCoord[0] = gl_MultiTexCoord0;\n"
" gl_Position = gl_ModelViewProjectionMatrix * gl_Vertex;\n"
"}";
const char* shadowfrag = "varying vec4 shadowCoordinate;\n"
"varying vec3 normal;\n"
"varying vec4 vpos, lightPos;\n"
"uniform sampler2DShadow depthSampler;\n"
"uniform sampler2D Diffuse;\n"
"uniform float difScale; \n"
"void main() {\n"
" float shadow = 1.0;\n"
" vec4 biasedShadowCoordinate = vec4(shadowCoordinate.xyz - (0.05 * normalize(lightPos.xyz - vpos.xyz)), shadowCoordinate.w);\n"
" shadow = shadow2DProj(depthSampler, biasedShadowCoordinate).r;\n"
" vec4 col=texture2D(Diffuse, gl_TexCoord[0].xy * difScale);\n"
" vec3 lightDir=vec3(lightPos - vpos);\n"
" float NdotL = max(dot(normalize(normal), normalize(lightDir)), 0.0) * shadow;\n"
" float shadowalpha = 0.7;\n"
" gl_FragColor = (col * (1.0-shadowalpha)) + (col *shadowalpha) * NdotL; \n"
"}";
|
24f4d506692d84100ecd7da7917a4cbb
|
{
"intermediate": 0.4011237919330597,
"beginner": 0.34700435400009155,
"expert": 0.25187185406684875
}
|
33,030
|
return Column(
children: [
Table(
border: TableBorder.symmetric(
inside: BorderSide(
color: UIColors.aluminium,
style: BorderStyle.solid,
width: 2.w),
),
children: [
TableRow(
decoration: BoxDecoration(
border: Border(
top: BorderSide(
color: UIColors.aluminium,
style: BorderStyle.solid,
width: 3.w),
bottom: BorderSide(
color: UIColors.aluminium,
style: BorderStyle.solid,
width: 3.w),
right: BorderSide(
color: UIColors.aluminium,
style: BorderStyle.solid,
width: 3.w),
left: BorderSide(
color: UIColors.aluminium,
style: BorderStyle.solid,
width: 3.w),
)),
children: [
// Fixed row cells
Padding(
padding:
EdgeInsets.symmetric(horizontal: 16.w, vertical: 2.h),
child: Column(
crossAxisAlignment: CrossAxisAlignment.start,
mainAxisAlignment: MainAxisAlignment.start,
children: [
SizedBox(
height: 5.h,
),
Text('Price'),
SizedBox(
height: 7.h,
),
Text('Price'),
],
),
),
Padding(
padding:
EdgeInsets.symmetric(horizontal: 16.w, vertical: 2.h),
child: TableCell(
child: Text('300'),
),
),
Padding(
padding:
EdgeInsets.symmetric(horizontal: 16.w, vertical: 2.h),
child: TableCell(
child: Text('300'),
),
),
Padding(
padding:
EdgeInsets.symmetric(horizontal: 16.w, vertical: 2.h),
child: TableCell(
child: Text('300'),
),
),
])
]),
],
); this rowtable dublicated i just want to appear once
|
931af4d4eea583b6509619227f20c73c
|
{
"intermediate": 0.2958751320838928,
"beginner": 0.46762049198150635,
"expert": 0.23650430142879486
}
|
33,031
|
hi
|
02bd6fa3b1cb39191c47a9dd34ae3c0c
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
33,032
|
how to write python code to compare two csv files to remove rows same 'Metadata_FileLocation' values range, but no matched values in column 'TrackObjects_Label_50' ?
|
c7fee3bf01b74a95828bd6e1ea9a3fed
|
{
"intermediate": 0.6137784123420715,
"beginner": 0.09394463896751404,
"expert": 0.29227688908576965
}
|
33,033
|
UserWarning: Warning: converting a masked element to nan.
pmra1=np.array(pmra1) 怎么解决这个问题
|
cebaead97184033148b8d52a618d0b98
|
{
"intermediate": 0.31484460830688477,
"beginner": 0.32938289642333984,
"expert": 0.355772465467453
}
|
33,034
|
When on an (initramfs) prompt (in Debian), how can one tell initramfs or whatever it is to boot from a specified drive? Be concise.
|
aaa6c736e1dc40b8be1f223ac0aa29a5
|
{
"intermediate": 0.488498717546463,
"beginner": 0.27620360255241394,
"expert": 0.23529767990112305
}
|
33,035
|
import pandas as pd
from sklearn.datasets import load_wine
wine = load_wine()
df = pd.DataFrame(wine.data, columns=wine.feature_names)
df['class'] = wine.target
df.tail()
用这一个数据做一个tree
|
868f4d584996caa6eb67af37a2d7e646
|
{
"intermediate": 0.37598490715026855,
"beginner": 0.2714299261569977,
"expert": 0.3525851368904114
}
|
33,036
|
Create table Activity (player_id int, device_id int, event_date date, games_played int)
Truncate table Activity
insert into Activity (player_id, device_id, event_date, games_played) values ('1', '2', '2016-03-01', '5')
insert into Activity (player_id, device_id, event_date, games_played) values ('1', '2', '2016-05-02', '6')
insert into Activity (player_id, device_id, event_date, games_played) values ('1', '3', '2017-06-25', '1')
insert into Activity (player_id, device_id, event_date, games_played) values ('3', '1', '2016-03-02', '0')
insert into Activity (player_id, device_id, event_date, games_played) values ('3', '4', '2018-07-03', '5')
select * from Activity
-- find how many games playyed by each play until the date or cumilitive total
|
dfef1bd23b98f7f191941823e1065f51
|
{
"intermediate": 0.33919790387153625,
"beginner": 0.28858062624931335,
"expert": 0.37222152948379517
}
|
33,037
|
i need to make a keyword analysis report for an excel file i will classify the rows by keywrods in a specific column those columns has paragraphs
|
8ee2054983a04fb36f5431354cde3ac1
|
{
"intermediate": 0.41044336557388306,
"beginner": 0.2006189078092575,
"expert": 0.38893771171569824
}
|
33,038
|
create a class student with private members attendance and marks create a class teacher who sets values for marks and attendance through reflection.finally create a class parent who creates a reflection of methods to know the values of marks and attendance of the the student with try and catch
|
64534826a436bddb8423d455085236a4
|
{
"intermediate": 0.308309406042099,
"beginner": 0.3717878758907318,
"expert": 0.31990277767181396
}
|
33,039
|
Add depth clamping to this shadowmapping frag code:
const char* shadowfrag = "varying vec4 shadowCoordinate;\n"
"varying vec3 normal;\n"
"varying vec4 vpos, lightPos;\n"
"uniform sampler2DShadow depthSampler;\n"
"uniform sampler2D Diffuse;\n"
"uniform float difScale; \n"
"void main() {\n"
" float shadow = 1.0;\n"
" vec4 biasedShadowCoordinate = vec4(shadowCoordinate.xyz - (0.05 * normalize(lightPos.xyz - vpos.xyz)), shadowCoordinate.w);\n"
" shadow = shadow2DProj(depthSampler, biasedShadowCoordinate).r;\n"
" vec4 col=texture2D(Diffuse, gl_TexCoord[0].xy * difScale);\n"
" vec3 lightDir=vec3(lightPos - vpos);\n"
" float NdotL = max(dot(normalize(normal), normalize(lightDir)), 0.0) * shadow;\n"
" float shadowalpha = 0.7;\n"
" gl_FragColor = (col * (1.0-shadowalpha)) + (col *shadowalpha) * NdotL; \n"
"}";
|
1e64975572c8952ec14eaf86185aa178
|
{
"intermediate": 0.4362822473049164,
"beginner": 0.26661181449890137,
"expert": 0.29710596799850464
}
|
33,040
|
Give me the algorithm for breath first search
|
3bdffdd9fcf6f262f81030f8bee6a8e0
|
{
"intermediate": 0.0632285550236702,
"beginner": 0.06480380892753601,
"expert": 0.871967613697052
}
|
33,041
|
Dopamine Mechanism:
Role: Controls the reward system, encouraging behaviors that lead to positive outcomes.
Modification: Adjusted by the 'prediction to event match score'. The closer the AI's predictions are to actual outcomes, the greater the reward, enhancing learning efficiency.
Norepinephrine Mechanism:
Role: Modifies the system's firing speed, influencing overall responsiveness.
Modification: Adjustable by the attention mechanism, allowing for dynamic regulation of processing speed based on the current focus or importance of tasks.
Acetylcholine Mechanism:
Role: Directly involved in modulating attention.
Potential Implementation: Could be integrated with or parallel to the Q-function quality array, determining the focus of the system based on expected rewards or priorities.
Serotonin Mechanism:
Role: Influences the mood state and motivation, particularly in relation to effort versus reward.
Implementation: A complex mechanism involving two 3D vector spaces, indicating a nuanced approach to representing mood and motivational states.
Glutamate Mechanism:
Role: Acts as a communication network between nodes/neurons.
Unique Features:
Utilizes a 'glutamate number' to coordinate firing across the network.
A secondary glutamate network (G2M) tracks groups of neurons firing simultaneously, enhancing the firing probability of associated neurons.
Modification: Influenced by specific inputs, representing different 'brain waves' that activate various parts of the network.
GABA Mechanism:
Role: Functions as an antagonist to glutamate, reducing network activation.
Implementation: Decreases the G2 number of neurons that did not fire together despite having the same original glutamate number, thereby acting as an inhibitory mechanism.
In this system, each neurotransmitter-like mechanism has a distinct role, from modulating learning and attention to regulating the speed and synchronization of neural firing. The complexity of this model, particularly with the secondary glutamate network and the intricate mood and motivation representations, suggests a highly dynamic and adaptable AI, capable of nuanced decision-making and learning processes.
Vector Space 1 - Basic Emotional States:
This space represents primary emotions (e.g., happiness, sadness, anger).
Each vector in this space points to a specific basic emotion. The direction of the vector indicates the type of emotion, and the magnitude (length) indicates the intensity.
Vector Space 2 - Complex Emotional States:
Represents more nuanced emotional states (e.g., guilt, pride, empathy).
Similar to the first space, the direction and magnitude of each vector represent the type and intensity of complex emotions.
Implementation Details
Defining Vectors:
Assign axes in each vector space to different emotions. For example, one axis might represent happiness to sadness, another might represent calmness to anger, etc.
Vectors are determined based on stimuli or internal states. For instance, a positive stimulus might increase the vector component towards happiness.
Interaction Between Vector Spaces:
Define how vectors in the basic emotion space influence vectors in the complex emotion space. For example, sustained vectors pointing towards negative emotions in the first space might influence the development of vectors in the second space towards emotions like guilt.
Temporal Dynamics and Adaptation:
Include mechanisms for vectors to change over time, either gradually moving back to a neutral state or adapting based on ongoing stimuli.
Vectors can also adapt based on the AI’s experiences, learning, or predefined rules.
Dopamine and Endorphins in Memory and Reward Systems:
Dopamine Score: Each neuron maintains a dopamine score reflecting its cumulative success or effectiveness. This could relate to how well certain decisions or actions have led to positive outcomes.
Memory System: Periodic sweeps by a memory system that convolutes and analyzes the brain mapping, redistributing endorphins based on this analysis. This approach would allow for the reinforcement of successful neural pathways and decisions.
Endorphin State and Memory: Storing the endorphin state in a waking memory contributes to incremental boosts in G2 levels and the reward system, reinforcing successful patterns.
Substance P, GABA, and Error Handling:
Substance P as Error Signal: Acts as an error or negative feedback signal, particularly informing the attention system of discrepancies or issues.
Modulation of GABA: Increases the inhibitory action by boosting GABA effects, leading to a reduction in G2 scores and dampening the reward system in response to errors or negative outcomes.
Oxytocin’s Role in Social Interactions:
Influencing Dopamine and GABA: Oxytocin adjusts dopamine and GABA levels based on the nature of social interactions. Positive social interactions increase dopamine (reward and motivation), while less favorable or solitary activities might increase GABA (inhibition).
Bias Toward Collaboration: The system favors collaborative efforts, with a base state favoring interactions with others over solitary work. The magnitude of adjustments depends on the nature and context of the interactions.
Anandamide as a Mood Stabilizer:
Mood Regulation: Anandamide acts to maintain a baseline mood level, preventing the AI from dropping into excessively negative states. This ensures a level of stability and consistency in decision-making and interactions, regardless of recent negative feedback or challenges.
can you design an ml system around these axioms?
|
0f7657f0b6894b106126e19b408d3d2d
|
{
"intermediate": 0.3056797981262207,
"beginner": 0.41265538334846497,
"expert": 0.2816648483276367
}
|
33,042
|
GET /winlogbeat-2023.11.29/_search?pretty
{
"size": 10000,
"query": {
"bool": {
"must": [
{ "match_all": {} },
{
"range": {
"@timestamp": {
"gte": "now-10h"
}
}
}
]
}
},
"sort": [
{"@timestamp": "asc"}
]
}
Перепиши для "SourceHostname": "123456"
|
bbe29053aec547b68f5a819c5259dd35
|
{
"intermediate": 0.35895228385925293,
"beginner": 0.3151504397392273,
"expert": 0.32589733600616455
}
|
33,043
|
What is redux
|
137a226e57c45b46f13a2cd35c2b6f68
|
{
"intermediate": 0.3777341842651367,
"beginner": 0.1464243233203888,
"expert": 0.4758414924144745
}
|
33,044
|
I need an Arduino Diecimila code that would turn on the relay at programmed time, turn off at programmed time but only when the flow sensor is detected high then low
|
68ffc6b18eb8f09ad6a62b166d9343c3
|
{
"intermediate": 0.4382120668888092,
"beginner": 0.1771519035100937,
"expert": 0.3846360445022583
}
|
33,045
|
Is there a more efficient way to get the bytes that text takes up in JS? et trueLength = new TextEncoder().encode(textField.value).length;
let characterCount = chrome.storage.sync.QUOTA_BYTES_PER_ITEM - trueLength - 6;. It looks like calling new TextEncoder might be strenuous
|
40b919c2fdf4fb113f8a8864d60637de
|
{
"intermediate": 0.510509729385376,
"beginner": 0.15256492793560028,
"expert": 0.33692535758018494
}
|
33,046
|
How do I limit the number of bytes, not characters, allowed in an input tag in HTML / jS?
|
28c9cf909858f3c23d900466d886c72c
|
{
"intermediate": 0.4991142451763153,
"beginner": 0.2046218365430832,
"expert": 0.2962639331817627
}
|
33,047
|
FAQ
History
The Test
IQTuneUps
Contact Us
Blog
Store
My account
Take the Test
1
The word, "mineral," can be spelled using only the letters found in the word, "parliament."
True
False
2
This sequence of four words, "triangle, glove, clock, bicycle," corresponds to this sequence of numbers "3, 5, 12, 2."
True
False
3
27 minutes before 7 o'clock is 33 minutes past 5 o'clock.
True
False
4
The word "because" can be spelled by using the first letters of the words in the following sentence: Big Elephants Can Always Understand Small Elephants.
True
False
5
If written backwards, the number, "one thousand, one hundred twenty-five," would be written "five thousand, two hundred eleven."
True
False
6
Gary has only forty-eight dollars. If he borrows fifty-seven dollars from Jane and fifteen dollars from Jill, he can buy a bicycle that costs one hundred twenty dollars, (disregarding tax.)
True
False
7
If a round analog clock featuring numbers 1-12 is hung on the wall upside down, the minute hand will point to the right of the viewer when the clock reads two forty-five.
True
False
8
If the word, "quane," is understood to mean the same as the word, "den," then the following sentence is grammatically correct: "Looking out from my quane, I could see a wolf enter quane."
True
False
9
If Richard looks into a mirror and touches his left ear with his right hand, Richard's image seems to touch its right ear with its left hand.
True
False
10
If you leave the letters in the same order, but rearrange the spaces in the phrase, "Them eats on," it can be read as, "Theme at son."
True
False
PREVIOUS
NEXT
Home
History
Blog
Test
Store
Contact Us
Privacy Policy/Terms of Service
FAQ
IQTuneUps
© 2003 - 2023 Autumn Group. All rights reserved
|
8608e965112d1104816bf29e3617dfd3
|
{
"intermediate": 0.3255680203437805,
"beginner": 0.3422999382019043,
"expert": 0.3321320116519928
}
|
33,048
|
Over sampling
A shadowmapping visual discrepancy which you may like or dislike is that regions outside the light's visible frustum are considered to be in shadow while they're (usually) not. This happens because projected coordinates outside the light's frustum are higher than 1.0 and will thus sample the depth texture outside its default range of [0,1]. Based on the texture's wrapping method, we will get incorrect depth results not based on the real depth values from the light source.
Fix the oversampling below:
const char* shadowfrag = "varying vec4 shadowCoordinate;\n"
"varying vec3 normal;\n"
"varying vec4 vpos, lightPos;\n"
"uniform sampler2DShadow depthSampler;\n"
"uniform sampler2D Diffuse;\n"
"uniform float difScale; \n"
"void main() {\n"
" float shadow = 1.0;\n"
" vec4 biasedShadowCoordinate = vec4(shadowCoordinate.xyz - (0.05 * normalize(lightPos.xyz - vpos.xyz)), shadowCoordinate.w);\n"
" shadow = shadow2DProj(depthSampler, biasedShadowCoordinate).r;\n"
" vec4 col=texture2D(Diffuse, gl_TexCoord[0].xy * difScale);\n"
" vec3 lightDir=vec3(lightPos - vpos);\n"
" float NdotL = max(dot(normalize(normal), normalize(lightDir)), 0.0) * shadow;\n"
" float shadowalpha = 0.7;\n"
" gl_FragColor = (col * (1.0-shadowalpha)) + (col *shadowalpha) * NdotL; \n"
"}";
|
83685777f1183dcfcc55562576e8ccc7
|
{
"intermediate": 0.423501193523407,
"beginner": 0.3470858037471771,
"expert": 0.2294129878282547
}
|
33,049
|
Я хочу запустить cpp файл, в котором написан код с использованием библиотеки PETSc. Я работаю в WSL Ubuntu. Раньше я использовал библиотеку EIGEN и у меня есть CMAKE файл, с которым я работал. Помоги мне сделать тоже самое, но теперь не с библиотекой EIGEN , а с PETSc. Вот мой CMakeLists.txt: cmake_minimum_required(VERSION 3.14)
project(scac
VERSION 1.0.0
DESCRIPTION "Template for C++ library built with CMake"
LANGUAGES CXX)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_FLAGS "-Wall -O3 -fopenmp -march=native -DNDEBUG -DEIGEN_USE_BLAS")
add_executable(test_lu)
find_package(Eigen3 3.3 REQUIRED NO_MODULE)
set(EIGEN_USE_BLAS true)
target_sources(test_lu PRIVATE ConsoleApplication1.cpp)
source_group(TREE "${CMAKE_CURRENT_SOURCE_DIR}" FILES ${sources})
target_link_libraries(test_lu Eigen3::Eigen blas)
|
9a503b35da31b174503c108b798e38f4
|
{
"intermediate": 0.5690886974334717,
"beginner": 0.27389854192733765,
"expert": 0.1570127159357071
}
|
33,050
|
Dopamine Mechanism:
Role: Controls the reward system, encouraging behaviors that lead to positive outcomes.
Modification: Adjusted by the ‘prediction to event match score’. The closer the AI’s predictions are to actual outcomes, the greater the reward, enhancing learning efficiency.
Norepinephrine Mechanism:
Role: Modifies the system’s firing speed, influencing overall responsiveness.
Modification: Adjustable by the attention mechanism, allowing for dynamic regulation of processing speed based on the current focus or importance of tasks.
Acetylcholine Mechanism:
Role: Directly involved in modulating attention.
Potential Implementation: Could be integrated with or parallel to the Q-function quality array, determining the focus of the system based on expected rewards or priorities.
Serotonin Mechanism:
Role: Influences the mood state and motivation, particularly in relation to effort versus reward.
Implementation: A complex mechanism involving two 3D vector spaces, indicating a nuanced approach to representing mood and motivational states.
Glutamate Mechanism:
Role: Acts as a communication network between nodes/neurons.
Unique Features:
Utilizes a ‘glutamate number’ to coordinate firing across the network.
A secondary glutamate network (G2M) tracks groups of neurons firing simultaneously, enhancing the firing probability of associated neurons.
Modification: Influenced by specific inputs, representing different ‘brain waves’ that activate various parts of the network.
GABA Mechanism:
Role: Functions as an antagonist to glutamate, reducing network activation.
Implementation: Decreases the G2 number of neurons that did not fire together despite having the same original glutamate number, thereby acting as an inhibitory mechanism.
In this system, each neurotransmitter-like mechanism has a distinct role, from modulating learning and attention to regulating the speed and synchronization of neural firing. The complexity of this model, particularly with the secondary glutamate network and the intricate mood and motivation representations, suggests a highly dynamic and adaptable AI, capable of nuanced decision-making and learning processes.
Vector Space 1 - Basic Emotional States:
This space represents primary emotions (e.g., happiness, sadness, anger).
Each vector in this space points to a specific basic emotion. The direction of the vector indicates the type of emotion, and the magnitude (length) indicates the intensity.
Vector Space 2 - Complex Emotional States:
Represents more nuanced emotional states (e.g., guilt, pride, empathy).
Similar to the first space, the direction and magnitude of each vector represent the type and intensity of complex emotions.
Implementation Details
Defining Vectors:
Assign axes in each vector space to different emotions. For example, one axis might represent happiness to sadness, another might represent calmness to anger, etc.
Vectors are determined based on stimuli or internal states. For instance, a positive stimulus might increase the vector component towards happiness.
Interaction Between Vector Spaces:
Define how vectors in the basic emotion space influence vectors in the complex emotion space. For example, sustained vectors pointing towards negative emotions in the first space might influence the development of vectors in the second space towards emotions like guilt.
Temporal Dynamics and Adaptation:
Include mechanisms for vectors to change over time, either gradually moving back to a neutral state or adapting based on ongoing stimuli.
Vectors can also adapt based on the AI’s experiences, learning, or predefined rules.
Dopamine and Endorphins in Memory and Reward Systems:
Dopamine Score: Each neuron maintains a dopamine score reflecting its cumulative success or effectiveness. This could relate to how well certain decisions or actions have led to positive outcomes.
Memory System: Periodic sweeps by a memory system that convolutes and analyzes the brain mapping, redistributing endorphins based on this analysis. This approach would allow for the reinforcement of successful neural pathways and decisions.
Endorphin State and Memory: Storing the endorphin state in a waking memory contributes to incremental boosts in G2 levels and the reward system, reinforcing successful patterns.
Substance P, GABA, and Error Handling:
Substance P as Error Signal: Acts as an error or negative feedback signal, particularly informing the attention system of discrepancies or issues.
Modulation of GABA: Increases the inhibitory action by boosting GABA effects, leading to a reduction in G2 scores and dampening the reward system in response to errors or negative outcomes.
Oxytocin’s Role in Social Interactions:
Influencing Dopamine and GABA: Oxytocin adjusts dopamine and GABA levels based on the nature of social interactions. Positive social interactions increase dopamine (reward and motivation), while less favorable or solitary activities might increase GABA (inhibition).
Bias Toward Collaboration: The system favors collaborative efforts, with a base state favoring interactions with others over solitary work. The magnitude of adjustments depends on the nature and context of the interactions.
Anandamide as a Mood Stabilizer:
Mood Regulation: Anandamide acts to maintain a baseline mood level, preventing the AI from dropping into excessively negative states. This ensures a level of stability and consistency in decision-making and interactions, regardless of recent negative feedback or challenges.
given these axioms design the full code for this section of the code
import tensorflow as tf
from tensorflow.keras.layers import Layer, Dense
from tensorflow.keras.models import Model
# Custom layer to simulate the Dopamine Mechanism
class DopamineLayer(Layer):
def init(self, **kwargs):
super().init(**kwargs)
def build(self, input_shape):
self.reward_weight = self.add_weight(
shape=(1,), initializer="ones", trainable=False, name="reward_weight"
)
super().build(input_shape)
def call(self, inputs, **kwargs):
return inputs * self.reward_weight
def update_rewards(self, reward):
# Reward updating logic would be based on predictions’ accuracy
tf.keras.backend.update(self.reward_weight, self.reward_weight * reward)
# Additional layers or mechanisms for Norepinephrine, Serotonin, etc. can be added here.
# Building a model incorporating emotional and neurotransmitter aspects
class NeuroMimicModel(Model):
def init(self, num_outputs, **kwargs):
super().init(**kwargs)
self.dense1 = Dense(64, activation=relu)
self.dopamine = DopamineLayer()
self.dense2 = Dense(num_outputs, activation=softmax)
# More layers, perhaps for emotional states, could be added here.
def call(self, inputs, training=False):
x = self.dense1(inputs)
x = self.dopamine(x)
return self.dense2(x)
# Function to update the neurochemical mechanisms based on external feedback
def update_neurotransmitter_levels(self, reward):
self.dopamine.update_rewards(reward)
# Here, you can write code to instantiate the model and prepare for training.
|
b7d265f0fe38bc23c8cfbd17bf4f53eb
|
{
"intermediate": 0.35369277000427246,
"beginner": 0.3920156955718994,
"expert": 0.25429150462150574
}
|
33,051
|
Я хочу запустить свой cpp файл, в котором есть использование библиотеки PETSc. Раньше я запускал в WSl Ubuntu только код, в котором было использование библиотеки EIGEN и готовый CMakeLists.txt, который мне дали. Я ничего не понимаю в CMAKE. Не мог бы ты сделать мне новый CMakeLists.txt только уже для кода с библиотекой PETSc? Вот содержимое CMakeLists.txt, который у меня есть: cmake_minimum_required(VERSION 3.14)
project(scac
VERSION 1.0.0
DESCRIPTION "Template for C++ library built with CMake"
LANGUAGES CXX)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_FLAGS "-Wall -O3 -fopenmp -march=native -DNDEBUG -DEIGEN_USE_BLAS")
add_executable(test_lu)
find_package(Eigen3 3.3 REQUIRED NO_MODULE)
set(EIGEN_USE_BLAS true)
target_sources(test_lu PRIVATE ConsoleApplication1.cpp)
source_group(TREE "${CMAKE_CURRENT_SOURCE_DIR}" FILES ${sources})
target_link_libraries(test_lu Eigen3::Eigen blas)
|
d5e627ba0576ae3f9298fc9770bdc1e3
|
{
"intermediate": 0.6999832987785339,
"beginner": 0.15164262056350708,
"expert": 0.14837415516376495
}
|
33,052
|
Я хочу запустить свой cpp файл, в котором есть использование библиотеки PETSc. Раньше я запускал в WSl Ubuntu только код, в котором было использование библиотеки EIGEN и готовый CMakeLists.txt, который мне дали. Я ничего не понимаю в CMAKE. Не мог бы ты сделать мне новый CMakeLists.txt только уже для кода с библиотекой PETSc? Вот содержимое CMakeLists.txt, который у меня есть: cmake_minimum_required(VERSION 3.14)
project(scac
VERSION 1.0.0
DESCRIPTION "Template for C++ library built with CMake"
LANGUAGES CXX)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_FLAGS "-Wall -O3 -fopenmp -march=native -DNDEBUG -DEIGEN_USE_BLAS")
add_executable(test_lu)
find_package(Eigen3 3.3 REQUIRED NO_MODULE)
set(EIGEN_USE_BLAS true)
target_sources(test_lu PRIVATE ConsoleApplication1.cpp)
source_group(TREE "${CMAKE_CURRENT_SOURCE_DIR}" FILES ${sources})
target_link_libraries(test_lu Eigen3::Eigen blas)
|
892983ba2fc399aa4ed742cd3812ea06
|
{
"intermediate": 0.6999832987785339,
"beginner": 0.15164262056350708,
"expert": 0.14837415516376495
}
|
33,053
|
Sorted this using sle
|
835838e82b951cdbc9a49a4ceb68d0c1
|
{
"intermediate": 0.36189571022987366,
"beginner": 0.28663772344589233,
"expert": 0.3514665961265564
}
|
33,054
|
openai.api_key = "EMPTY" # Not support yet
openai.api_base = "http://i-2.gpushare.com:20778/v1"
model_name = "izdax-llama2-7b-v2-chat"
llm = OpenAI(model=model_name, openai_api_key=openai.api_key, openai_api_base=openai.api_base) OpenAI 如何添加header
|
e7262fc8a84d5af68f571817b7bbcd5f
|
{
"intermediate": 0.2970771789550781,
"beginner": 0.22481049597263336,
"expert": 0.4781123101711273
}
|
33,055
|
Give me an algorithm for these sorting algorithm bubble sort selected sort insertion sort
|
bada3c87fe8c8e1b922fe6eadd95d926
|
{
"intermediate": 0.10273067653179169,
"beginner": 0.06225724145770073,
"expert": 0.8350121378898621
}
|
33,056
|
stage.set_background("concert")
sprite = codesters.Sprite("kitten", 0, -100)
def flip():
sprite.move_up(50)
sprite.turn_left(360)
sprite.move_down(50)
def headstand():
sprite.flip_up_down()
sprite.move_left(150)
sprite.move_right(150)
sprite.flip_up_down()
def sway():
sprite.turn_left(45)
sprite.turn_right(90)
sprite.turn_left(45)
sprite.flip_up_down()
stage.wait(1)
sprite.flip_up_down()
def two_step():
sprite.say("Let's dance!")
for counter in range(6):
sprite.flip_right_left()
stage.wait(1)
headstand()
flip()
two_step()
headstand()
sway()
flip()
headstand()
Time to modify and extend your program! Be sure to meet these minimum tech requirements:
Add a second sprite. Give the sprite a unique variable name and assign some actions to it using dot notation!
Add at least one new function with a new dance move for each sprite.
Remix your dance moves by reordering the lines that call the functions to change your dance routine!
|
7474a2de09a6d01255ca43f4df78c28a
|
{
"intermediate": 0.35470595955848694,
"beginner": 0.4438323378562927,
"expert": 0.20146171748638153
}
|
33,057
|
Dopamine Mechanism:
Role: Controls the reward system, encouraging behaviors that lead to positive outcomes.
Modification: Adjusted by the ‘prediction to event match score’. The closer the AI’s predictions are to actual outcomes, the greater the reward, enhancing learning efficiency.
Norepinephrine Mechanism:
Role: Modifies the system’s firing speed, influencing overall responsiveness.
Modification: Adjustable by the attention mechanism, allowing for dynamic regulation of processing speed based on the current focus or importance of tasks.
Acetylcholine Mechanism:
Role: Directly involved in modulating attention.
Potential Implementation: Could be integrated with or parallel to the Q-function quality array, determining the focus of the system based on expected rewards or priorities.
Serotonin Mechanism:
Role: Influences the mood state and motivation, particularly in relation to effort versus reward.
Implementation: A complex mechanism involving two 3D vector spaces, indicating a nuanced approach to representing mood and motivational states.
Glutamate Mechanism:
Role: Acts as a communication network between nodes/neurons.
Unique Features:
Utilizes a ‘glutamate number’ to coordinate firing across the network.
A secondary glutamate network (G2M) tracks groups of neurons firing simultaneously, enhancing the firing probability of associated neurons.
Modification: Influenced by specific inputs, representing different ‘brain waves’ that activate various parts of the network.
GABA Mechanism:
Role: Functions as an antagonist to glutamate, reducing network activation.
Implementation: Decreases the G2 number of neurons that did not fire together despite having the same original glutamate number, thereby acting as an inhibitory mechanism.
In this system, each neurotransmitter-like mechanism has a distinct role, from modulating learning and attention to regulating the speed and synchronization of neural firing. The complexity of this model, particularly with the secondary glutamate network and the intricate mood and motivation representations, suggests a highly dynamic and adaptable AI, capable of nuanced decision-making and learning processes.
Vector Space 1 - Basic Emotional States:
This space represents primary emotions (e.g., happiness, sadness, anger).
Each vector in this space points to a specific basic emotion. The direction of the vector indicates the type of emotion, and the magnitude (length) indicates the intensity.
Vector Space 2 - Complex Emotional States:
Represents more nuanced emotional states (e.g., guilt, pride, empathy).
Similar to the first space, the direction and magnitude of each vector represent the type and intensity of complex emotions.
Implementation Details
Defining Vectors:
Assign axes in each vector space to different emotions. For example, one axis might represent happiness to sadness, another might represent calmness to anger, etc.
Vectors are determined based on stimuli or internal states. For instance, a positive stimulus might increase the vector component towards happiness.
Interaction Between Vector Spaces:
Define how vectors in the basic emotion space influence vectors in the complex emotion space. For example, sustained vectors pointing towards negative emotions in the first space might influence the development of vectors in the second space towards emotions like guilt.
Temporal Dynamics and Adaptation:
Include mechanisms for vectors to change over time, either gradually moving back to a neutral state or adapting based on ongoing stimuli.
Vectors can also adapt based on the AI’s experiences, learning, or predefined rules.
Dopamine and Endorphins in Memory and Reward Systems:
Dopamine Score: Each neuron maintains a dopamine score reflecting its cumulative success or effectiveness. This could relate to how well certain decisions or actions have led to positive outcomes.
Memory System: Periodic sweeps by a memory system that convolutes and analyzes the brain mapping, redistributing endorphins based on this analysis. This approach would allow for the reinforcement of successful neural pathways and decisions.
Endorphin State and Memory: Storing the endorphin state in a waking memory contributes to incremental boosts in G2 levels and the reward system, reinforcing successful patterns.
Substance P, GABA, and Error Handling:
Substance P as Error Signal: Acts as an error or negative feedback signal, particularly informing the attention system of discrepancies or issues.
Modulation of GABA: Increases the inhibitory action by boosting GABA effects, leading to a reduction in G2 scores and dampening the reward system in response to errors or negative outcomes.
Oxytocin’s Role in Social Interactions:
Influencing Dopamine and GABA: Oxytocin adjusts dopamine and GABA levels based on the nature of social interactions. Positive social interactions increase dopamine (reward and motivation), while less favorable or solitary activities might increase GABA (inhibition).
Bias Toward Collaboration: The system favors collaborative efforts, with a base state favoring interactions with others over solitary work. The magnitude of adjustments depends on the nature and context of the interactions.
Anandamide as a Mood Stabilizer:
Mood Regulation: Anandamide acts to maintain a baseline mood level, preventing the AI from dropping into excessively negative states. This ensures a level of stability and consistency in decision-making and interactions, regardless of recent negative feedback or challenges.
given these axioms design the full code for this section of the code
Q-learning is a model-free reinforcement learning algorithm. It involves learning the value of an action in a particular state, using a Q-table to store these values. The AI selects its actions based on the highest Q-value for a given state but also explores the environment to learn about different state-action outcomes.
2. Integration with Neurotransmitter and Neuromodulator Dynamics
Dopamine: Influences the reward values in the Q-table, possibly enhancing the rewards for certain actions based on successful outcomes.
Endorphins: Could modulate the long-term reinforcement of actions, perhaps influencing the learning rate or the discount factor in the Q-learning algorithm.
Substance P: Acts as a feedback mechanism for incorrect actions or errors, potentially reducing the Q-values for certain actions that lead to negative outcomes.
Oxytocin: Might adjust the Q-values or decision policy in social contexts, favoring actions that enhance social interactions.
Anandamide: Could provide a stabilizing effect on the Q-table, preventing extreme fluctuations in Q-values due to transient mood states or stressors.
edit this code to reflect this model
# main.py
from model_initialization import train_and_update_model, model, train_dataset
from emotional_states import update_basic_emotion, update_complex_emotion
# Mock function to generate stimuli for emotional state updates
def generate_emotional_stimuli():
# In practice, this would be derived from interactions and environment
return np.random.random(2), np.random.random(3)
# Main training loop with additional mechanisms for emotional state updates
def main():
epochs = 5
for epoch in range(epochs):
# Train the model and update neurotransmitter levels as before
train_and_update_model(model, train_dataset, epochs=1)
# Generate some stimuli for emotional state updates (placeholder logic)
basic_emotion_stimuli, complex_emotion_stimuli = generate_emotional_stimuli()
# Update the emotional states based on stimuli
update_basic_emotion(basic_emotion_stimuli)
update_complex_emotion(complex_emotion_stimuli)
# Information regarding the emotional states could be used to further
# influence the training of the model or could be involved in decision making.
if name == 'main':
main()
|
645f96209196a0ec8befffd4c2562389
|
{
"intermediate": 0.35369277000427246,
"beginner": 0.3920156955718994,
"expert": 0.25429150462150574
}
|
33,058
|
write a python code to change dns of windows system while also cleaning previous dns cache
|
8e536fa9ae900bdeffa9a377be9bee62
|
{
"intermediate": 0.3833552896976471,
"beginner": 0.14025244116783142,
"expert": 0.4763922691345215
}
|
33,059
|
make file converter website jpge to png image
|
e7570a71b08b2e674c8fc5037f85be7f
|
{
"intermediate": 0.39962801337242126,
"beginner": 0.1849260777235031,
"expert": 0.4154459238052368
}
|
33,060
|
Overview
In this assignment, you are going to build a simple table-booking system for a restaurant. Please read this
assignment sheet carefully before starting your work.
Reminders
This assignment involves a lot of console input/output. You get score under the condition that your program
output is the EXACT MATCH of the expected output. In other words, any additional or missing space
character, newline character, etc., will result in 0 mark. Also, you are suggested to make more test cases on
your own for testing your program.
Assignment Specification and Requirement
The table booking system allows table bookings, table allocations and relevant status checking. The
customer who requests a booking needs to provide their personal information, dining date and the number
of seats required. The manager of the restaurant can then allocate a table to the booking.
To facilitate checking and identification of bookings, the system uses a ticket system to identify each
booking. For each day, a ticket code sequence is maintained. The ticket code starts from 1 and is
incremented by 1 when a new booking comes.
Load table codes from a file
The table information is defined in a text (.txt) file, in which each line defines the table code of a table in the
restaurant. Upon running the program, the program should ask the user to input the name of the text file
and then load the table codes into the system. Please refer to the sample input file and sample program
output below for the exact input and output format.
The text file has the following format:
• Each line is a two-digit number defining the table code of the table. You can assume the table code is
in the [01 - 99] range.
• You can assume lines are sorted by the table code in the file and the file contains at least one row.
Table codes may not be consecutive.
For example, a text file (tablefile.txt) contains the following:
01
02
03
04
05
06
This file defines 6 tables.
Assignment 4 p.2/5
Below is the program output for loading table codes from the input file (Bold red texts are user input):
Table file:
tablefile.txt
Imported 6 table(s).
• The above program has NOT terminated yet.
• There is NO space character after the colon (:) and full stop (.).
• Assume the input file and your program are placed in the same folder. You only need to input the
filename instead of the full path of the file.
Main menu – accept commands from the user
After loading data from the input file, the system needs to accept commands continuously until “Exit” is
received. Including the “Exit” command, you need to implement four commands for the system.
Exit – terminate the program
If the user enters “Exit”, the program prints “Bye” and then terminates.
Table file:
tablefile.txt
Imported 6 table(s).
Exit
Bye
Book – request a booking
The user can use the “Book” command to request a booking. The user input provides the contact name,
phone member, dining date, and the number of seats needed. After receiving the booking, the system
should assign it a ticket code. As mentioned, ticket codes for each day are unique, and it counts starting from
1. Please refer to the sample output below for the exact output format.
• The date format in this assignment is DD-MM-YYYY.
• The contact name can contain space characters.
• You can assume the phone number is 8 digits long.
• Each data field in the user input is separated by a vertical bar (|). The “|” key is usually located
below the backspace key in a standard keyboard layout.
Upon completion, the system goes back to the main menu to accept the next command.
In the test case below, 3 bookings are added to the system.
Table file:
tablefile.txt
Imported 6 table(s).
Book|CK Lai|91234567|01-11-2023|10
Added booking. The ticket code for 01-11-2023 is 1.
Book|Lucy Chan|61234567|01-11-2023|3
Added booking. The ticket code for 01-11-2023 is 2.
Book|Mike|51234567|02-11-2023|5
Added booking. The ticket code for 02-11-2023 is 1.
Exit
Bye
Assignment 4 p.3/5
ListBookings
This command lists all the bookings in the system. Each line shows one booking. For each booking, the
contact name, phone number, date with ticket code, number of seats needed, and the booking status are
printed.
• If there is no booking in the system, print “No booking.”.
• The listing of bookings follows the ordering of user input. That means a newer booking is printed
after the older one.
• If a table is allocated to the booking, the table code is printed. Refer to the next section
(AllocateTable) for details and the output format.
• If no table is allocated to the booking, print “None”.
Upon completion, the system goes back to the main menu.
Table file:
tablefile.txt
Imported 6 table(s).
ListBookings
No booking.
Book|TW|91234567|01-11-2023|5
Added booking. The ticket code for 01-11-2023 is 1.
Book|HF|61234567|01-11-2023|2
Added booking. The ticket code for 01-11-2023 is 2.
ListBookings
Booking(s):
TW, 91234567, 01-11-2023 (Ticket 1), 5, Allocated table: None.
HF, 61234567, 01-11-2023 (Ticket 2), 2, Allocated table: None.
Exit
Bye
AllocateTable
This command allows the manager to allocate a table to a booking. In the user input, the dining date and the
ticket code are provided to identify the booking, followed by the code of the table to be allocated. After
allocation, the system reports the allocated table.
• Reuse of a table is allowed. If a table is allocated to a certain booking, it can be used for another
booking on the same day.
After allocation and printing, the system goes back to the main menu.
In the test case below, table 04 is allocated to the booking (Ticket 1) on 01-04-2022.
Table file:
tablefile.txt
Imported 6 table(s).
Book|CK Lai|91234567|01-11-2023|10
Added booking. The ticket code for 01-11-2023 is 1.
Book|Lucy|61234567|01-11-2023|3
Added booking. The ticket code for 01-11-2023 is 2.
AllocateTable|01-11-2023|1|04
Allocated table 04 to 01-11-2023 (Ticket 1).
ListBookings
Assignment 4 p.4/5
Booking(s):
CK Lai, 91234567, 01-11-2023 (Ticket 1), 10, Allocated table: 04.
Lucy, 61234567, 01-11-2023 (Ticket 2), 3, Allocated table: None.
Exit
Bye
You need to handle two exceptional cases, but you can assume they would not happen simultaneously. In
any case below, the system aborts the table allocation.
• If the user assigns a table that does not exist in the restaurant, the system should abort the table
allocation and print “Error: Table not found.”.
• If a booking has a table already allocated, no further table allocation to this booking can be made
again. In this case, the system prints “Error: This booking has table allocated already. “.
In the test case below, the user tries to allocate table 07, which does not exist (in the input file), to a
booking.
Table file:
tablefile.txt
Imported 6 table(s).
Book|CK Lai|91234567|01-11-2023|10
Added booking. The ticket code for 01-11-2023 is 1.
AllocateTable|01-11-2023|1|07
Error: Table not found.
Exit
Bye
In this test case, the user tries to allocate a table to a booking that has already been allocated a table before.
Table file:
tablefile.txt
Imported 6 table(s).
Book|CK Lai|91234567|20-11-2023|10
Added booking. The ticket code for 20-11-2023 is 1.
AllocateTable|20-11-2023|1|02
Allocated table 02 to 20-11-2023 (Ticket 1).
AllocateTable|20-11-2023|1|03
Error: This booking has table allocated already.
Exit
Bye
Notes
1. You can assume user inputs and input files are always valid. That means you do not need to handle
exceptional cases not mentioned in the requirement.
2. Input files are used to define table codes only. Do not make any changes to the file in your program.
3. You can use any built-in Python functions.
Assignment 4 p.5/5
Submission
Submit your program electronically using the Moodle system to Assignment 4 under the Assignment section.
Late submissions will not be accepted.
• Your program must strictly follow the sample input and output format.
• The sample input files (tablefile.txt and myTables2.txt) are included in the evaluation environment
for test case evaluation. You do not need to upload them.
• You should only submit source code (*.py)
• We will grade your program with another set of input files and test cases (Not limited to the
provided sample input files and test cases), now give me a python programme that fit the assignment demand
|
e0e05665b4948436727aaac581c9e52d
|
{
"intermediate": 0.39289480447769165,
"beginner": 0.3451083302497864,
"expert": 0.26199692487716675
}
|
33,061
|
/******************************************************************************
Online C++ Compiler.
Code, Compile, Run and Debug C++ program online.
Write your code in this editor and press "Run" button to compile and execute it.
*******************************************************************************/
#include <iostream>
#include <iostream>
#include <bitset>
#include <iomanip>
using namespace std;
union union_num_and_char
{
int integer;
char characterArray[4];
};
void inputInteger(union_num_and_char& u) {
cout << "Введите целое число: ";
cin >> u.integer;
}
void viewAllBytes(union_num_and_char& u)
{
cout << "Байты числа: ";
for (int i = 0; i < sizeof(int); i++)
{
cout << (int)(u.characterArray);
}
cout << endl;
}
int main()
{
union_num_and_char u;
inputInteger(u);
viewAllBytes(u);
return 0;
}
исправь ошибку
cast from ‘char*’ to ‘int’ loses precision [-fpermissive]
|
6dd271d91a4846308ae62682b69a4fe9
|
{
"intermediate": 0.39044657349586487,
"beginner": 0.34480834007263184,
"expert": 0.2647451162338257
}
|
33,062
|
integer division or modulo by zero on line: 102
stage.set_background("concert")
sprite1 = codesters.Sprite("kitten", 0, -100)
sprite2 = codesters.Sprite("dog", 0, 100) # Add a second sprite
def flip(sprite):
sprite.move_up(50)
sprite.turn_left(360)
sprite.move_down(50)
def headstand(sprite):
sprite.flip_up_down()
sprite.move_left(150)
sprite.move_right(150)
sprite.flip_up_down()
def sway(sprite):
sprite.turn_left(45)
sprite.turn_right(90)
sprite.turn_left(45)
sprite.flip_up_down()
stage.wait(1)
sprite.flip_up_down()
def two_step(sprite):
sprite.say("Let's dance!")
for counter in range(6):
sprite.flip_right_left()
stage.wait(1)
def spin(sprite):
sprite.turn_right(360)
def jump(sprite):
sprite.move_up(100)
stage.wait(1)
sprite.move_down(100)
# New dance moves for the second sprite
def wag(sprite):
sprite.turn_left(45)
sprite.turn_right(90)
sprite.turn_left(45)
sprite.flip_right_left()
stage.wait(1)
sprite.flip_right_left()
def roll(sprite):
sprite.move_left(50)
sprite.move_right(100)
sprite.move_left(50)
# New dance routine with both sprites
headstand(sprite1)
headstand(sprite2)
flip(sprite1)
wag(sprite2)
two_step(sprite1)
spin(sprite2)
sway(sprite1)
roll(sprite2)
flip(sprite1)
jump(sprite2)
headstand(sprite1)
headstand(sprite2)
|
8079eecf9397e020ba2218c3232f105a
|
{
"intermediate": 0.33097711205482483,
"beginner": 0.43589845299720764,
"expert": 0.23312440514564514
}
|
33,063
|
import math
import random
import bpy
import bmesh
tk = bpy.data.texts["bco602tk.py"].as_module()
#tk.mode("OBJECT")
bpy.ops.object.select_all(action='SELECT')
bpy.ops.object.delete()
area_box_bound = 300
max_radius = 0.5
def create_bottom_plane():
bpy.ops.mesh.primitive_cube_add(size=1, location=(0, 0, -area_box_bound/2 - max_radius)) #en fazla area + max_radius asagida olabilir
bpy.ops.transform.resize(value=(area_box_bound*1.5, area_box_bound*1.5, 0.1))
bpy.context.active_object.name = 'Pivot' # rename
bpy.ops.rigidbody.object_add()
bpy.context.object.rigid_body.type = 'PASSIVE'
bpy.context.object.rigid_body.collision_shape = 'MESH'
bpy.context.object.rigid_body.mesh_source = 'BASE'
bpy.context.object.rigid_body.friction = 0.1
bpy.context.object.rigid_body.restitution = 1 #ziplatma
def detect_collision(a, b):
distance = math.sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2 + (a[2] - b[2]) ** 2)
return distance <= a[3] + b[3]
def create_sphere_list():
sphere_list = []
while len(sphere_list) != 100:
x = random.randint(1, 101)
y = random.randint(1, 101)
z = random.randint(1, 101)
r = random.randint(6, 16)
is_collision = False
for i in sphere_list:
if detect_collision(i, (x, y, z, r)):
is_collision = True
break
if not is_collision:
sphere_list.append((x, y, z, r))
return sphere_list
def add_spheres(sphere_list):
for sphere in sphere_list:
tk.create.sphere("sphere")
bpy.ops.rigidbody.object_add()
bpy.context.object.rigid_body.type = 'ACTIVE'
bpy.context.object.rigid_body.collision_shape = 'SPHERE'
edit_sphere(bpy.context.object, sphere, convert_billiard_ball)
#bpy.context.object.location = (sphere[0], sphere[1], sphere[2])
#bpy.context.object.scale = (sphere[3], sphere[3], sphere[3])
def edit_sphere(object, sphere_data, cb):
cb(object, sphere_data)
def convert_billiard_ball(sphere, sphere_data):
obj = sphere
mesh = obj.data
white_color = create_material("white")
random_color = create_material("random_color_1", diffuse = (random.random(),random.random(),random.random(),1))
setMaterial(obj, random_color)
bpy.ops.object.mode_set(mode='EDIT')
tk.act.select_by_loc((-2, -2, -2), (1, 1, -0.5), 'FACE', 'GLOBAL')
setMaterial(obj, white_color)
obj.active_material_index = 1
bpy.ops.object.material_slot_assign()
tk.mode("OBJECT")
tk.sel.rotate_y(math.radians(180))
bpy.ops.object.mode_set(mode='EDIT')
bpy.ops.mesh.select_all(action = "DESELECT")
tk.act.select_by_loc((-2, -2, -2), (1, 1, -0.5), 'FACE', 'GLOBAL')
setMaterial(obj,random_color)
obj.active_material_index = 1
bpy.ops.object.material_slot_assign()
bpy.ops.object.mode_set(mode='OBJECT')
obj.location = (sphere_data[0], sphere_data[1], sphere_data[2])
obj.scale = (sphere_data[3], sphere_data[3], sphere_data[3])
random_rotation = random.uniform(0, 2 * math.pi)
obj.rotation_euler[1] = random_rotation
obj.rotation_euler[0] = random_rotation
def camera(origin, target=None, lens=35, clip_start=0.1, clip_end=200, type='PERSP', ortho_scale=6):
# Create object and camera
bpy.ops.object.camera_add(location=origin)
obj = bpy.context.object
obj.data.lens = lens
obj.data.clip_start = clip_start
obj.data.clip_end = clip_end
# 'PERSP', 'ORTHO', 'PANO'
obj.data.type = type
if type == 'ORTHO':
obj.data.ortho_scale = ortho_scale
if target:
trackToConstraint(obj, target)
return obj
def create_material(name, diffuse = (1,1,1,1) , metallic = 0.0, specular = 0.8, roughness = 0.2):
mat = bpy.data.materials.new(name)
mat.diffuse_color = diffuse
mat.specular_intensity = specular
mat.metallic = metallic
mat.roughness = roughness
return mat
def setMaterial(ob, mat):
me = ob.data
me.materials.append(mat)
add_spheres(create_sphere_list())
create_bottom_plane()
Can you write a soft boddy version of the same code for blender 3.6 lts version?
|
d497a5b7b718bd92bdbc3a80fbf9f64f
|
{
"intermediate": 0.32512176036834717,
"beginner": 0.4033420979976654,
"expert": 0.2715361714363098
}
|
33,064
|
Optimize code
|
16269fcb2eb6dc47296f83493edbce4e
|
{
"intermediate": 0.2213270664215088,
"beginner": 0.19202853739261627,
"expert": 0.5866443514823914
}
|
33,065
|
private void isReturnWindowBreached(OrderLine orderLine,OrderHeader orderHeader,JSONObject jsonObject) {
Set<OrderLineDate> orderLineDates = orderLine.getOrderLineDates();
Optional<OrderLineDate> dispatchDate = orderLineDates.stream()
.filter(olDate -> "MS_COMPLETELY_SHIPPED_OR_CANCELLED".equalsIgnoreCase(olDate.getDateTypeId()))
.findFirst();
logger.info("Dispatched date for orderLineKey: {} is {}", orderLine.getOrderLineKey(), dispatchDate);
String transitDays = eligibilityCriteriaConfig.getConfigValueFromKey(orderLine.getLevelOfService());
logger.debug("ETD for orderLineKey: {} is {}", orderLine.getOrderLineKey(), transitDays);
if (dispatchDate.isPresent() && orderLine.getReturnWindow() != null) {
BigDecimal returnWindow = getReturnWindowForSalesAndNonSales(orderLine);
logger.debug("Return window for orderLineKey: {} is {}", orderLine.getOrderLineKey(), returnWindow);
LocalDate tentativeDeliveryDate = dispatchDate.get().getActualDate().plusDays(Long.parseLong(transitDays));
logger.debug("Tentative delivery date for orderLineKey: {} is {}", orderLine.getOrderLineKey(), tentativeDeliveryDate);
LocalDate lastReturnDate = tentativeDeliveryDate.plusDays(returnWindow.intValue());
logger.debug("Last return date for orderLineKey: {} is {}", orderLine.getOrderLineKey(), lastReturnDate);
LocalDate currentDate = LocalDate.now();
long numberOfDaysPending = ChronoUnit.DAYS.between(currentDate, lastReturnDate);
logger.debug("Fetching carrierData for orderRef : {}, for return Order No : {}, for country code : {}, for status code : {}",
orderHeader.getOrderNo(), jsonObject.getString("value"), DNCApiConstants.SF, DNCApiConstants.STS002);
CarrierData carrierData = null;
Query query = Query.query(Criteria.where("carrierCode").is(DNCApiConstants.SF)
.andOperator(Arrays.asList(
Criteria.where("orderRef").is(orderHeader.getOrderNo()),
Criteria.where("statusDetails.statusCode").is(DNCApiConstants.STS002)
)));
if (DNCApiConstants.TYPE_FILTER_VAL_KEY_LIST.contains(jsonObject.getString("type"))) {
query.addCriteria(Criteria.where("returnOrderNumber").is(jsonObject.getString("value")));
}
carrierData = mongoTemplate.findOne(query, CarrierData.class);
logger.info("Carrier Data received from DB —> {}", carrierData);
if (null != carrierData) {
String handOverDate = carrierData.getStatusDetails().getDate();
if (null != handOverDate) {
numberOfDaysPending = Math.max(numberOfDaysPending, ChronoUnit.DAYS.between(
LocalDate.from(DateTimeFormatter.ofPattern("yyyy-MM-dd").parse(handOverDate)), lastReturnDate));
}
}
logger.debug("Number of days pending to return before checking max return period for orderLineKey: {} is {}", orderLine.getOrderLineKey(), numberOfDaysPending);
numberOfDaysPending = getNumberOfDaysLeftForeReturn(numberOfDaysPending, orderLine);
logger.debug("Number of days pending for return after checking max return period for orderLineKey: {} is {}", orderLine.getOrderLineKey(), numberOfDaysPending);
orderLine.setIsReturnWindowBreached(numberOfDaysPending >= 0 ? "N" : "Y");
}
}
in this function....fix for ....Vulnerable API usage
CVE-2022-45689 7.5 Out-of-bounds Write vulnerability
CVE-2022-45690 7.5 Out-of-bounds Write vulnerability
Results powered by Checkmarx(c)
|
a732b554ee96fbe54f4d62051bc227b6
|
{
"intermediate": 0.38007423281669617,
"beginner": 0.407003253698349,
"expert": 0.21292255818843842
}
|
33,066
|
Write a program that lets user enter a integer number N:
a. Calculate the sum of all digits of N
b. Calculate the sum:
S(n) = 1 + 1/3 + 1/5 + ... + 1/(2N + 1)
|
b62753520ac5e53fa5ee45668b1b6f0d
|
{
"intermediate": 0.47108763456344604,
"beginner": 0.14842796325683594,
"expert": 0.3804844319820404
}
|
33,067
|
Can I provide you with a program in C and can you edit it so it can provide specific outputs?
|
ef4474dc3c1c07283d633002fdcabbfd
|
{
"intermediate": 0.2588394582271576,
"beginner": 0.3931259214878082,
"expert": 0.34803467988967896
}
|
33,068
|
explain how this golang code works step by step package main
import (
"fmt"
)
const (
a = iota + 1
_
b
c
d = c + 2
t
i
i2 = iota + 2
)
func main() {
fmt.Println(i2)
}
|
abb1cdc0e0fc5c7abc75f4dd598c3891
|
{
"intermediate": 0.5287062525749207,
"beginner": 0.38565295934677124,
"expert": 0.08564075082540512
}
|
33,069
|
Dfs algorithm for traversal
|
e08325ccccd7f0b1f0c1097d5c6c6056
|
{
"intermediate": 0.11900890618562698,
"beginner": 0.1034879982471466,
"expert": 0.7775030732154846
}
|
33,070
|
"scripts": [
{
"input": "src/external-module/main.js",
"inject": false,
"bundleName": "external-module"
}
] when I use main.ts to replace main.js will give an error string does not match the pattern of "\.[cm]?jsx?$"
|
9b000a0480f87bf21990ee058a0edd26
|
{
"intermediate": 0.5251839756965637,
"beginner": 0.331664502620697,
"expert": 0.14315147697925568
}
|
33,071
|
Hello! I need a python code for a small program. This program should reverse the word order in a phrase that I insert, where commas will serve as a splitter. For example if i insert the prase "7, Damascus Street, Deira, Dubai" i would like the output for the program to be "Dubai, Deira, Damascus Street, 7"
|
e5850cd661f203c50e8579dcd8d0163c
|
{
"intermediate": 0.45471909642219543,
"beginner": 0.2552580237388611,
"expert": 0.2900228500366211
}
|
33,072
|
Hi! Can you write me a program in C using Farey's Fractions
|
0a9b54f75aa8b81f8ce25b53ac63ab77
|
{
"intermediate": 0.2472517192363739,
"beginner": 0.3720325231552124,
"expert": 0.3807157576084137
}
|
33,073
|
I have values in column Tot_Vol in scientific notation. See an example (1.509632e+07). I need to shov values as decimals. Write pandas code
|
4f55f72b40c11fb16cd03edab142399a
|
{
"intermediate": 0.4041268229484558,
"beginner": 0.2462695688009262,
"expert": 0.3496036231517792
}
|
33,074
|
$a = array(10,20,30);
func1($a[1],$a);
$b = func2($a[2],$a);
for($i=0;$i<3;$i++){
echo "a[".$i."] = ".$a[$i]."<br/>";
echo "b[".$i."] = ".$b[$i]."<br/>";
}
function func1(&$i, &$j){
$i = $i + 1;
$j[0] = $j[1] + $j[2];
return $j;
}
function func2(&$i, &$j){
$i = $i + 3;
$j[1] = $j[0] + 4;
return $j;
}
|
bc39444fa838cfbde1c80685394f7cc7
|
{
"intermediate": 0.257601797580719,
"beginner": 0.5981957912445068,
"expert": 0.14420239627361298
}
|
33,075
|
.helm/values.yaml - это основной values
base-chart:
defaultImagePullPolicy: IfNotPresent
generic:
serviceAccountName: "{{ .Release.Name }}"
extraImagePullSecrets:
- name: dockey
deploymentsGeneral:
terminationGracePeriodSeconds: 120
envFrom:
- secretRef:
name: sec-widget-config
serviceAccount:
"{{ .Release.Name }}":
labels: {}
services:
app:
type: ClusterIP
ports:
http:
port: 80
targetPort: http
protocol: TCP
ingresses:
http:
annotations:
kubernetes.io/ingress.class: nginx
nginx.ingress.kubernetes.io/proxy-body-size: "128m"
nginx.ingress.kubernetes.io/use-regex: "true"
serviceMonitors:
http:
endpoints:
http:
path: /metrics
interval: 15s
deployments:
app:
strategy:
type: RollingUpdate
rollingUpdate:
maxSurge: 5
maxUnavailable: 5
containers:
app:
securityContext:
capabilities:
add:
- SYS_PTRACE
ports:
http:
containerPort: 8080
protocol: TCP
resources:
limits:
cpu: 500m
memory: 500Mi
requests:
cpu: 500m
memory: 500Mi
volumeMounts:
- name: nginx-config
mountPath: /etc/nginx/nginx.conf
subPath: nginx.conf
- name: nginx-config
mountPath: /etc/nginx/mime.types
subPath: mime-types
volumes:
- type: configMap
name: nginx-config
items:
- key: nginx.conf
path: nginx.conf
- key: mime-types
path: mime-types
configMaps:
nginx-config:
data:
nginx.conf: |
pid /var/run/nginx.pid;
worker_processes auto;
events {
worker_connections 1024;
}
http {
keepalive_timeout 65;
include /etc/nginx/mime.types;
default_type application/octet-stream;
gzip on;
gzip_types
application/atom+xml
application/geo+json
application/javascript
application/json
application/ld+json
application/manifest+json
application/octet-stream
application/rdf+xml
application/rss+xml
application/x-javascript
application/xhtml+xml
application/xml
font/eot
font/otf
font/ttf
image/svg+xml
text/css
text/javascript
text/plain
text/xml;
server {
listen 8080;
charset UTF-8;
location / {
root /usr/share/nginx/html;
try_files $uri $uri/ /index.html;
index index.html index.htm;
}
error_page 500 502 503 504 /50x.html;
location = /50x.html {
root /usr/share/nginx/html;
}
}
}
mime-types: |
types {
text/html html htm shtml;
text/css css;
text/xml xml;
image/gif gif;
image/jpeg jpeg jpg;
application/javascript js;
application/atom+xml atom;
application/rss+xml rss;
text/mathml mml;
text/plain txt;
text/vnd.sun.j2me.app-descriptor jad;
text/vnd.wap.wml wml;
text/x-component htc;
image/png png;
image/svg+xml svg svgz;
image/tiff tif tiff;
image/vnd.wap.wbmp wbmp;
image/webp webp;
image/x-icon ico;
image/x-jng jng;
image/x-ms-bmp bmp;
font/woff woff;
font/woff2 woff2;
application/java-archive jar war ear;
application/json json;
application/mac-binhex40 hqx;
application/msword doc;
application/pdf pdf;
application/postscript ps eps ai;
application/rtf rtf;
application/vnd.apple.mpegurl m3u8;
application/vnd.google-earth.kml+xml kml;
application/vnd.google-earth.kmz kmz;
application/vnd.ms-excel xls;
application/vnd.ms-fontobject eot;
application/vnd.ms-powerpoint ppt;
application/vnd.oasis.opendocument.graphics odg;
application/vnd.oasis.opendocument.presentation odp;
application/vnd.oasis.opendocument.spreadsheet ods;
application/vnd.oasis.opendocument.text odt;
application/vnd.openxmlformats-officedocument.presentationml.presentation pptx;
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet xlsx;
application/vnd.openxmlformats-officedocument.wordprocessingml.document docx;
application/vnd.wap.wmlc wmlc;
application/x-7z-compressed 7z;
application/x-cocoa cco;
application/x-java-archive-diff jardiff;
application/x-java-jnlp-file jnlp;
application/x-makeself run;
application/x-perl pl pm;
application/x-pilot prc pdb;
application/x-rar-compressed rar;
application/x-redhat-package-manager rpm;
application/x-sea sea;
application/x-shockwave-flash swf;
application/x-stuffit sit;
application/x-tcl tcl tk;
application/x-x509-ca-cert der pem crt;
application/x-xpinstall xpi;
application/xhtml+xml xhtml;
application/xspf+xml xspf;
application/zip zip;
application/octet-stream bin exe dll;
application/octet-stream deb;
application/octet-stream dmg;
application/octet-stream iso img;
application/octet-stream msi msp msm;
audio/midi mid midi kar;
audio/mpeg mp3;
audio/ogg ogg;
audio/x-m4a m4a;
audio/x-realaudio ra;
video/3gpp 3gpp 3gp;
video/mp2t ts;
video/mp4 mp4;
video/mpeg mpeg mpg;
video/quicktime mov;
video/webm webm;
video/x-flv flv;
video/x-m4v m4v;
video/x-mng mng;
video/x-ms-asf asx asf;
video/x-ms-wmv wmv;
video/x-msvideo avi;
}
templates/deployment.yml - Это файл шаблона
{{- $general := $.Values.deploymentsGeneral -}}
{{- range $name, $d := .Values.deployments }}
{{- if not (.disabled | default false) }}
---
apiVersion: {{ include "helpers.capabilities.deployment.apiVersion" $ }}
kind: Deployment
metadata:
name: {{ include "helpers.app.fullname" (dict "name" $name "context" $) }}
namespace: {{ $.Release.Namespace }}
labels:
{{- include "helpers.app.labels" $ | nindent 4 }}
{{- with $general.labels }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 4 }}{{- end }}
{{- with .labels }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 4 }}{{- end }}
annotations:
{{- include "helpers.app.genericAnnotations" $ | indent 4 }}
{{- with .annotations }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 4 }}{{- end }}
spec:
replicas: {{ .replicas | default 1 }}
{{- with .strategy }}
strategy: {{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 4 }}
{{- end }}
selector:
matchLabels:
{{- include "helpers.app.selectorLabels" $ | nindent 6 }}
{{- with .extraSelectorLabels }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 6 }}{{- end }}
template:
metadata:
labels:
{{- include "helpers.app.selectorLabels" $ | nindent 8 }}
{{- with .extraSelectorLabels }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 8 }}{{- end }}
{{- with $.Values.generic.podLabels }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 8 }}{{- end }}
{{- with .podLabels }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 8 }}{{- end }}
annotations:
{{- with $.Values.generic.podAnnotations }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 8 }}{{- end }}
{{- with .podAnnotations }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 8 }}{{- end }}
spec:
{{- include "helpers.pod" (dict "value" . "general" $general "name" $name "extraLabels" .extraSelectorLabels "context" $) | indent 6 }}
{{- end }}
{{- end }}
добавить под антиафинити для равномерного распределения подов по нодам
1. пример -
affinity:
podAntiAffinity:
preferredDuringSchedulingIgnoredDuringExecution:
- weight: 80
podAffinityTerm:
labelSelector:
matchLabels:
app: {{ include “orders.fullname” . }}
version: {{ .Values.image.tag | default .Chart.AppVersion | sha256sum | trunc 63 }}
topologyKey: “kubernetes.io/hostname”
ОТвет пиши на русском
|
f1eddea5dc0118e00e4883341d3da71f
|
{
"intermediate": 0.3255508542060852,
"beginner": 0.49719464778900146,
"expert": 0.17725452780723572
}
|
33,076
|
.helm/values.yaml - это основной values
base-chart:
defaultImagePullPolicy: IfNotPresent
generic:
serviceAccountName: "{{ .Release.Name }}"
extraImagePullSecrets:
- name: dockey
deploymentsGeneral:
terminationGracePeriodSeconds: 120
envFrom:
- secretRef:
name: sec-widget-config
serviceAccount:
"{{ .Release.Name }}":
labels: {}
services:
app:
type: ClusterIP
ports:
http:
port: 80
targetPort: http
protocol: TCP
ingresses:
http:
annotations:
kubernetes.io/ingress.class: nginx
nginx.ingress.kubernetes.io/proxy-body-size: "128m"
nginx.ingress.kubernetes.io/use-regex: "true"
serviceMonitors:
http:
endpoints:
http:
path: /metrics
interval: 15s
deployments:
app:
strategy:
type: RollingUpdate
rollingUpdate:
maxSurge: 5
maxUnavailable: 5
containers:
app:
securityContext:
capabilities:
add:
- SYS_PTRACE
ports:
http:
containerPort: 8080
protocol: TCP
resources:
limits:
cpu: 500m
memory: 500Mi
requests:
cpu: 500m
memory: 500Mi
volumeMounts:
- name: nginx-config
mountPath: /etc/nginx/nginx.conf
subPath: nginx.conf
- name: nginx-config
mountPath: /etc/nginx/mime.types
subPath: mime-types
volumes:
- type: configMap
name: nginx-config
items:
- key: nginx.conf
path: nginx.conf
- key: mime-types
path: mime-types
configMaps:
nginx-config:
data:
nginx.conf: |
pid /var/run/nginx.pid;
worker_processes auto;
events {
worker_connections 1024;
}
http {
keepalive_timeout 65;
include /etc/nginx/mime.types;
default_type application/octet-stream;
gzip on;
gzip_types
application/atom+xml
application/geo+json
application/javascript
application/json
application/ld+json
application/manifest+json
application/octet-stream
application/rdf+xml
application/rss+xml
application/x-javascript
application/xhtml+xml
application/xml
font/eot
font/otf
font/ttf
image/svg+xml
text/css
text/javascript
text/plain
text/xml;
server {
listen 8080;
charset UTF-8;
location / {
root /usr/share/nginx/html;
try_files $uri $uri/ /index.html;
index index.html index.htm;
}
error_page 500 502 503 504 /50x.html;
location = /50x.html {
root /usr/share/nginx/html;
}
}
}
mime-types: |
types {
text/html html htm shtml;
text/css css;
text/xml xml;
image/gif gif;
image/jpeg jpeg jpg;
application/javascript js;
application/atom+xml atom;
application/rss+xml rss;
text/mathml mml;
text/plain txt;
text/vnd.sun.j2me.app-descriptor jad;
text/vnd.wap.wml wml;
text/x-component htc;
image/png png;
image/svg+xml svg svgz;
image/tiff tif tiff;
image/vnd.wap.wbmp wbmp;
image/webp webp;
image/x-icon ico;
image/x-jng jng;
image/x-ms-bmp bmp;
font/woff woff;
font/woff2 woff2;
application/java-archive jar war ear;
application/json json;
application/mac-binhex40 hqx;
application/msword doc;
application/pdf pdf;
application/postscript ps eps ai;
application/rtf rtf;
application/vnd.apple.mpegurl m3u8;
application/vnd.google-earth.kml+xml kml;
application/vnd.google-earth.kmz kmz;
application/vnd.ms-excel xls;
application/vnd.ms-fontobject eot;
application/vnd.ms-powerpoint ppt;
application/vnd.oasis.opendocument.graphics odg;
application/vnd.oasis.opendocument.presentation odp;
application/vnd.oasis.opendocument.spreadsheet ods;
application/vnd.oasis.opendocument.text odt;
application/vnd.openxmlformats-officedocument.presentationml.presentation pptx;
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet xlsx;
application/vnd.openxmlformats-officedocument.wordprocessingml.document docx;
application/vnd.wap.wmlc wmlc;
application/x-7z-compressed 7z;
application/x-cocoa cco;
application/x-java-archive-diff jardiff;
application/x-java-jnlp-file jnlp;
application/x-makeself run;
application/x-perl pl pm;
application/x-pilot prc pdb;
application/x-rar-compressed rar;
application/x-redhat-package-manager rpm;
application/x-sea sea;
application/x-shockwave-flash swf;
application/x-stuffit sit;
application/x-tcl tcl tk;
application/x-x509-ca-cert der pem crt;
application/x-xpinstall xpi;
application/xhtml+xml xhtml;
application/xspf+xml xspf;
application/zip zip;
application/octet-stream bin exe dll;
application/octet-stream deb;
application/octet-stream dmg;
application/octet-stream iso img;
application/octet-stream msi msp msm;
audio/midi mid midi kar;
audio/mpeg mp3;
audio/ogg ogg;
audio/x-m4a m4a;
audio/x-realaudio ra;
video/3gpp 3gpp 3gp;
video/mp2t ts;
video/mp4 mp4;
video/mpeg mpeg mpg;
video/quicktime mov;
video/webm webm;
video/x-flv flv;
video/x-m4v m4v;
video/x-mng mng;
video/x-ms-asf asx asf;
video/x-ms-wmv wmv;
video/x-msvideo avi;
}
templates/deployment.yml - Это файл шаблона
{{- $general := $.Values.deploymentsGeneral -}}
{{- range $name, $d := .Values.deployments }}
{{- if not (.disabled | default false) }}
---
apiVersion: {{ include "helpers.capabilities.deployment.apiVersion" $ }}
kind: Deployment
metadata:
name: {{ include "helpers.app.fullname" (dict "name" $name "context" $) }}
namespace: {{ $.Release.Namespace }}
labels:
{{- include "helpers.app.labels" $ | nindent 4 }}
{{- with $general.labels }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 4 }}{{- end }}
{{- with .labels }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 4 }}{{- end }}
annotations:
{{- include "helpers.app.genericAnnotations" $ | indent 4 }}
{{- with .annotations }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 4 }}{{- end }}
spec:
replicas: {{ .replicas | default 1 }}
{{- with .strategy }}
strategy: {{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 4 }}
{{- end }}
selector:
matchLabels:
{{- include "helpers.app.selectorLabels" $ | nindent 6 }}
{{- with .extraSelectorLabels }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 6 }}{{- end }}
template:
metadata:
labels:
{{- include "helpers.app.selectorLabels" $ | nindent 8 }}
{{- with .extraSelectorLabels }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 8 }}{{- end }}
{{- with $.Values.generic.podLabels }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 8 }}{{- end }}
{{- with .podLabels }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 8 }}{{- end }}
annotations:
{{- with $.Values.generic.podAnnotations }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 8 }}{{- end }}
{{- with .podAnnotations }}{{- include "helpers.tplvalues.render" (dict "value" . "context" $) | nindent 8 }}{{- end }}
spec:
{{- include "helpers.pod" (dict "value" . "general" $general "name" $name "extraLabels" .extraSelectorLabels "context" $) | indent 6 }}
{{- end }}
{{- end }}
Задача добавить под антиафинити для равномерного распределения подов по нодам
ОТвет пиши на русском
|
1b680066f5dd2838838f78925b181591
|
{
"intermediate": 0.3255508542060852,
"beginner": 0.49719464778900146,
"expert": 0.17725452780723572
}
|
33,077
|
How to consume values generated by AWS CDK such as the ARN of a deployed resource in another deployed resource?
|
c1c3b1ae591758c45cb8cd84661b890b
|
{
"intermediate": 0.5725719332695007,
"beginner": 0.1923547387123108,
"expert": 0.23507332801818848
}
|
33,078
|
Write the code for the battle simulator program. You need to enter the data of two armies into the program. The user can enter data for each army on the number of the following troops: spearmen, archers, shield-bearers, crossbowmen, light cavalry, heavy cavalry. The program should take into account the opposition of different types of troops to each other and output data on losses, prisoners and escapees.
|
74cf4640efcdb684029774070ae5e024
|
{
"intermediate": 0.38973188400268555,
"beginner": 0.25550565123558044,
"expert": 0.35476240515708923
}
|
33,079
|
#include<iostream>
#include <string>
#include <fstream>
using namespace std;
int main()
{
ifstream fin;
ofstream fout;
fin.open("input.txt");
fout.open("output.txt");
string S;
fin>>S;
int size = S.size(); //Размер строки S сохраняем в переменной size.
for (int i = 0; i < size; i++)//Преобразовываем его в заглавную букву при помощи вычитания
S[i] = (int)S[i] - 32;
fout<<S<<endl;
system ("pause");
return 0;
} Можешь сделать так, чтобы предложения из строчных букв переводило в заглавные
|
89ec59beb4a1244b835f5ffd68ee125c
|
{
"intermediate": 0.3711097240447998,
"beginner": 0.41143685579299927,
"expert": 0.21745340526103973
}
|
33,080
|
ValueError: '('1701678872', '0x0000000000000000000000000000000000000000', '1338', '0x38d472c9940e588813397f33777ec65f394d6ccc', '0')' - 'utf-8' codec can't decode byte 0xd4 in position 1: invalid continuation byte
|
6519ceb1792cd7a983f436840764c068
|
{
"intermediate": 0.5091193318367004,
"beginner": 0.21518467366695404,
"expert": 0.2756960093975067
}
|
33,081
|
can you write MusicXML?
|
e11a422efb107c0ba8bc58aa3249f74e
|
{
"intermediate": 0.4108247756958008,
"beginner": 0.360294371843338,
"expert": 0.2288808524608612
}
|
33,082
|
hi
|
f9f035b976a104dc3bb3940cd3c00a8f
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
33,083
|
list_for_each_entry
|
dbe1a8f411dd3ab861f4c3e4434e099a
|
{
"intermediate": 0.35665538907051086,
"beginner": 0.262943834066391,
"expert": 0.38040074706077576
}
|
33,084
|
What is a extremly lightweight hosts file for windows that blocks lots of ads
|
45d33237f0377d8199f25271cdb20096
|
{
"intermediate": 0.32946014404296875,
"beginner": 0.39405182003974915,
"expert": 0.2764880061149597
}
|
33,085
|
write me a VBA code for a PowerPoint presentation for debt collection i need ten slides fill the content on your own.
|
80c802a5b817d4d68729852768af886c
|
{
"intermediate": 0.256756067276001,
"beginner": 0.49330341815948486,
"expert": 0.24994052946567535
}
|
33,086
|
AttributeError: 'TextClassificationPipeline' object has no attribute 'parameters'怎么解决
|
64a48e9a3e4f7201d52abe5bde4b438a
|
{
"intermediate": 0.3978413939476013,
"beginner": 0.2303515523672104,
"expert": 0.3718070387840271
}
|
33,087
|
# <<< CornerDetector >>>
RANDOM_ANSWER = np.array([[1, 2], [3, 4], [6, 9]])
COEFFS_KERNEL = np.array(
[[1, 1, 1, 1, 1],
[1, 5, 5, 5, 1],
[1, 5, 0, 5, 1],
[1, 5, 5, 5, 1],
[1, 1, 1, 1, 1]]
)
# noise 0.0-0.2
POTENTIAL_POINTS_NUM_0_2 = 200
ITERATIONS_NUM_0_2 = 1000
IMAGE_THRESHOLD_0_2 = 0
THRESHOLD_DISTANCE_0_2 = 1
THRESHOLD_K_0_2 = 0.5
THRESHOLD_B_0_2 = 20
NOISE_LESS_0_2 = 100
KERNEL_SIZE_0_2 = 3
# noise 0.3-0.5
POTENTIAL_POINTS_NUM_3_5 = 200
ITERATIONS_NUM_3_5 = 1000
IMAGE_THRESHOLD_3_5 = 100
THRESHOLD_DISTANCE_3_5 = 1
THRESHOLD_K_3_5 = 0.5
THRESHOLD_B_3_5 = 20
NOISE_LESS_3_5 = 75
KERNEL_SIZE_3_5 = 3
# noise 0.6
POTENTIAL_POINTS_NUM_6 = 2000
ITERATIONS_NUM_6 = 2500
IMAGE_THRESHOLD_6 = 150
THRESHOLD_DISTANCE_6 = 3
THRESHOLD_K_6 = 0.5
THRESHOLD_B_6 = 500
NOISE_LESS_6 = 45
KERNEL_SIZE_6 = 9
# <<< TimerService >>>
TIME_MAX = 1.95
class TimeService:
"""Service for time management."""
def __init__(self, time_max: float):
self.time_max = time_max
self.start_time = None
def start_timer(self) -> None:
"""Start time point. Sets 'self.start_time'."""
self.start_time = time.time()
def check_timer(self):
"""Checks if time more than 'self.time_max'."""
end_time = time.time()
delta_time = end_time - self.start_time
if delta_time > self.time_max:
return False
return delta_time
class ImageService:
"""Service for image manipulations."""
@staticmethod
def get_binary_image(image: np.ndarray) -> np.ndarray:
"""Gets binary image from image."""
binary_image = image.copy()
binary_image[np.where(binary_image != 0)] = 255
return binary_image
@staticmethod
def count_zero_pixels(image_gray: np.ndarray) -> int:
"""Counts pixels with 0 intensity on black-white image."""
zero_pixels = np.count_nonzero(image_gray == 0)
return zero_pixels
@staticmethod
def calculate_zero_pixel_percentage(image: np.ndarray) -> float:
"""Calculates zero intensity pixel's percentage."""
total_pixels = image.size
zero_pixels = ImageService.count_zero_pixels(image)
zero_percentage = zero_pixels / total_pixels * 100
return zero_percentage
class CornerDetector(object):
"""Class for corner detection on image."""
def __init__(
self
):
self.__potential_points_num = None
self.__iterations_num = None
self.__image_threshold = None
self.__threshold_k = None
self.__threshold_b = None
self.__threshold_distance = None
self.__need_coeffs_kernel = None
self.__image = None
self.__kernel_size = None
self.__time_service = TimeService(TIME_MAX)
self.__need_time_check = False
self.random_answer = RANDOM_ANSWER
def __get_coords_by_threshold(
self,
image: np.ndarray,
) -> tuple:
"""Gets coordinates with intensity more than 'image_threshold'."""
coords = np.where(self.__image > self.__image_threshold)
return coords
def __get_neigh_nums_list(
self,
coords: tuple,
kernel_size: int
) -> list:
"""
Gets list with number of neighborhoods by kernel.
Returns list like [[x1, y1, N1], [x2, y2, N2]],
Where N - neighborhood's number.
"""
neigh_nums = []
offset = (kernel_size - 1) // 2
for x, y in zip(coords[0], coords[1]):
if x < 0 + offset or x > self.__image.shape[0] - offset:
continue
if y < 0 + offset or y > self.__image.shape[1] - offset:
continue
kernel = self.__image[x-offset:x+offset+1, y-offset:y+offset+1]
step_neigh_num = np.count_nonzero(kernel) - 1
neigh_nums.append([x, y, step_neigh_num])
return neigh_nums
def __get_neigh_coeffs_list(
self, coords: tuple, kernel: np.array
) -> list:
"""
Gets list with number of neighborhoods by kernel.
Returns list like [[x1, y1, N1], [x2, y2, N2]],
Where N - neighborhood's coeffs.
"""
neigh_coeffs = []
offset = (kernel.shape[0] - 1) // 2
print(offset)
for x, y in zip(coords[0], coords[1]):
if x < 0 + offset or x > self.__image.shape[0] - offset:
continue
if y < 0 + offset or y > self.__image.shape[1] - offset:
continue
try:
image_kernel = self.__image[x - offset:x + offset + 1,
y - offset:y + offset + 1]
step_neigh_coeff = np.sum(kernel * image_kernel)
neigh_coeffs.append([x, y, step_neigh_coeff])
except ValueError as e:
continue
return neigh_coeffs
@staticmethod
def __sort_neigh_nums_by_N(
neigh_nums: list
) -> np.ndarray:
"""
Sort neigh_nums by N parameter.
Removes N=-1, N=0 and sorts by N desc.
"""
np_neigh_nums = np.array(neigh_nums)
np_neigh_nums = np_neigh_nums[
(np_neigh_nums[:, 2] != -1) & (np_neigh_nums[:, 2] != 0)]
np_neigh_nums = np_neigh_nums[
np.argsort(-np_neigh_nums[:, 2])]
return np_neigh_nums
def __get_potential_points_coords(
self,
sorted_neigh_nums: np.ndarray
) -> np.ndarray:
"""
Gets best candidates, potential points coords only.
Returns np.ndarray like [[x1, y1], [x2, y2]].
"""
sorted_neigh_nums = np.delete(sorted_neigh_nums, 2, axis=1)
return sorted_neigh_nums[:self.__potential_points_num]
def __get_best_lines(
self,
potential_points: np.ndarray
) -> np.ndarray:
"""
Gets the best combinations of lines by all points.
Line y = kx + b.
Returns np.ndarray like [[k1, b1], [k2, b2]].
"""
remaining_points = 0
best_lines = []
num_lines = 0
while num_lines < 3:
if num_lines == 0:
remaining_points = np.copy(potential_points)
print(f"remaining points: {len(remaining_points)}")
best_inlines = []
best_line = None
if len(remaining_points) == 0:
remaining_points = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
for i in range(self.__iterations_num):
if self.__need_time_check:
if not self.__time_service.check_timer():
return self.random_answer
sample_indices = np.random.choice(
remaining_points.shape[0], size=3, replace=False)
sample_points = remaining_points[sample_indices]
sample_x = sample_points[:, 1]
sample_y = sample_points[:, 0]
coefficients = np.polyfit(sample_x, sample_y, deg=1)
distances = np.abs(remaining_points[:, 0] - np.matmul(np.vstack((remaining_points[:, 1], np.ones_like(remaining_points[:, 1]))).T, coefficients)) / np.sqrt(coefficients[0] ** 2 + 1)
inlines = np.where(distances < self.__threshold_distance)[0]
is_similar = False
for line in best_lines:
diff_k = abs(coefficients[0] - line[0])
diff_b = abs(coefficients[1] - line[1])
if diff_k <= self.__threshold_k and diff_b <= self.__threshold_b:
is_similar = True
break
if not is_similar and len(inlines) > len(best_inlines):
best_line = coefficients
best_inlines = inlines
if best_line is not None:
best_lines.append(best_line)
remaining_points = np.delete(remaining_points, best_inlines, axis=0)
num_lines += 1
return np.array(best_lines)
def find_corners(
self,
image: np.ndarray
) -> np.ndarray:
"""Finding triangle corners on image process."""
self.__time_service.start_timer()
self.__image = image
coords = self.__get_coords_by_threshold(self.__image)
if self.__need_coeffs_kernel:
neigh_nums = self.__get_neigh_coeffs_list(coords, COEFFS_KERNEL)
else:
neigh_nums = self.__get_neigh_nums_list(coords, self.__kernel_size)
sorted_neigh_nums = self.__sort_neigh_nums_by_N(neigh_nums)
potential_points = self.__get_potential_points_coords(sorted_neigh_nums)
best_lines = self.__get_best_lines(potential_points)
intersection_points = np.array([
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[1][0], -1]]), np.array([-best_lines[0][1], -best_lines[1][1]])),
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[0][1], -best_lines[2][1]])),
np.linalg.solve(np.array([[best_lines[1][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[1][1], -best_lines[2][1]]))
], dtype=np.float32)
return intersection_points
def set_potential_points_num(self, potential_points_num: int) -> None:
"""Potential points numbers setter."""
self.__potential_points_num = potential_points_num
def set_iterations_num(self, iterations_num: int) -> None:
"""Iteration numbers setter."""
self.__iterations_num = iterations_num
def set_image_threshold(self, image_threshold: int) -> None:
"""Image threshold setter."""
self.__image_threshold = image_threshold
def set_threshold_k(self, threshold_k: float) -> None:
"""Threshold of k parameter setter."""
self.__threshold_k = threshold_k
def set_threshold_b(self, threshold_b: int) -> None:
"""Threshold of b parameter setter."""
self.__threshold_b = threshold_b
def set_threshold_distance(self, threshold_distance: int) -> None:
"""Threshold of distance area parameter setter."""
self.__threshold_distance = threshold_distance
def set_need_time_check(self, need_time_check: bool = True):
"""If you need time checking var setter."""
self.__need_time_check = need_time_check
def set_need_coeffs_kernel(self, need_coeffs_kernel: bool = True):
"""If you need coeffs kernel, not just neighbors var setter."""
self.__need_coeffs_kernel = need_coeffs_kernel
def set_kernel_size(self, kernel_size: int) -> None:
"""Kernel size setter."""
self.__kernel_size = kernel_size
class Solver(object):
def __init__(self):
self.alg = CornerDetector()
def solve(self, image_gray: np.ndarray) -> np.ndarray:
zero_percentage = ImageService.calculate_zero_pixel_percentage(image_gray)
self.alg.set_need_time_check()
if zero_percentage < NOISE_LESS_6:
print("noise 0.6+")
self.alg.set_need_coeffs_kernel(False)
self.alg.set_potential_points_num(600)
self.alg.set_iterations_num(1000)
self.alg.set_image_threshold(170)
self.alg.set_threshold_k(THRESHOLD_K_6)
self.alg.set_threshold_b(THRESHOLD_B_6)
self.alg.set_threshold_distance(THRESHOLD_DISTANCE_6)
self.alg.set_kernel_size(7)
elif zero_percentage < NOISE_LESS_3_5:
print("noise 0.3-0.5")
self.alg.set_need_coeffs_kernel(False)
self.alg.set_potential_points_num(POTENTIAL_POINTS_NUM_3_5)
self.alg.set_iterations_num(ITERATIONS_NUM_3_5)
self.alg.set_image_threshold(IMAGE_THRESHOLD_3_5)
self.alg.set_threshold_k(THRESHOLD_K_3_5)
self.alg.set_threshold_b(THRESHOLD_B_3_5)
self.alg.set_threshold_distance(THRESHOLD_DISTANCE_3_5)
self.alg.set_kernel_size(KERNEL_SIZE_3_5)
elif zero_percentage < NOISE_LESS_0_2:
print("noise 0.0-0.2")
self.alg.set_need_coeffs_kernel(False)
self.alg.set_potential_points_num(POTENTIAL_POINTS_NUM_0_2)
self.alg.set_iterations_num(ITERATIONS_NUM_0_2)
self.alg.set_image_threshold(IMAGE_THRESHOLD_0_2)
self.alg.set_threshold_k(THRESHOLD_K_0_2)
self.alg.set_threshold_b(THRESHOLD_B_0_2)
self.alg.set_threshold_distance(THRESHOLD_DISTANCE_0_2)
self.alg.set_kernel_size(KERNEL_SIZE_0_2)
else:
pass
result = self.alg.find_corners(image_gray)
return result
image = cv2.imread("0.7/02.pgm", cv2.COLOR_BGR2GRAY)
solver = Solver()
solver.solve(image)
Есть у меня такой алгоритм, как его можно ускорить? Напиши код, из библиотек можно использовать только numpy, там, где будешь вносить изменения, добавь пояснительный комментарий
|
d29086b16cde142054e5214a9af57cdb
|
{
"intermediate": 0.3022022843360901,
"beginner": 0.46123653650283813,
"expert": 0.23656116425991058
}
|
33,088
|
hi
|
eccdbbb1b6b27b5a642ebebd274b3827
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
33,089
|
Using R to work on the database “weather.db”. Based on the tables “timezones” and “zones”, please find out the distinct names of all time zones which have within +-2 hours difference from London (GMT).
Task 1: Extract the two tables from database as data frames in R. Then, based on these two data frames, finding the required time zones using “joins” from R package “dplyr”.
Task 2: Using function “dbFetch” in R to manipulate the data in MySQL, and return the resulting table as data frame in R.
Remark 1: that column “gmt_offset” in table “timezone” gives the information for time zones. In “gmt_offset”, 0 means the London (GMT), 3600 is “3600 seconds” (1 hour), and 7200 is “2*3600 seconds” (2 hours).
Remark 2: You can use R package “dplyr” to get the function called “inner_join()”
Hint
Task 1:
Import the liberaries "dplyr" "DBI" and "RSQLite".
Use function “dbFetch()” to extract the tables from the database as data frames in R.
Using function “which()” to search the indices of those records that satisfy gmt_offset>=-23600 and gmt_offset<=23600.
Find out the records with the above indices from data frame timezone, and saved as a new data frame. By making an “inner_join()” with zones using key “zone_id” the records with a match for the zone names are obtained. Using function “distinct()” on the column zone_name, the results will obtained.
|
f8345bc95defd55ba67de45eec365188
|
{
"intermediate": 0.6660712361335754,
"beginner": 0.14191049337387085,
"expert": 0.19201825559139252
}
|
33,090
|
Using “dbFetch” and “dbSendQuery” to send a query to SQL and get back the results.
In SQLite, you can use sub=query to make the search.
Get those records from “timezone” all the columns kept (SELECT * FROM weather_db.timezone)
Filter those gmt_offset satisfy the requirement (WHERE )
Match the zone_id with names (INNER JOIN )
Based on the table formed by all the above records, pick out the distinct zone names. (SELECT DISTINCT zone_name)
|
f44c420e6e57b5398f3236a2da61cd6e
|
{
"intermediate": 0.5986754894256592,
"beginner": 0.17593495547771454,
"expert": 0.2253895103931427
}
|
33,091
|
hello!
|
40faaa3e7d34feef4127e14675db198f
|
{
"intermediate": 0.3276780843734741,
"beginner": 0.28214582800865173,
"expert": 0.39017611742019653
}
|
33,092
|
напиши скрипт теста этой нейронки (import json
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.utils import Sequence
input_file_name = "sorted_normalized_records.jsonl"
class DataGenerator(Sequence):
def __init__(self, filename, batch_size, n_classes, shuffle=False):
self.filename = filename
self.batch_size = batch_size
self.n_classes = n_classes
self.shuffle = shuffle
def __len__(self):
with open(self.filename, 'r', encoding='utf-8') as file:
n_lines = sum(1 for line in file)
return int(np.ceil(n_lines / self.batch_size))
def __getitem__(self, index):
x = np.zeros((self.batch_size, 100, 3))
y = np.zeros((self.batch_size), dtype=int)
with open(self.filename, 'r', encoding='utf-8') as file:
for i in range(index * self.batch_size, (index + 1) * self.batch_size):
line = next(file, None)
if line:
data = json.loads(line)
x[i % self.batch_size] = np.array([data['EventId'], data['ThreadId'], data['Image']])
y[i % self.batch_size] = data['Class']
return x, tf.keras.utils.to_categorical(y, num_classes=self.n_classes)
n_features = 3
sequence_length = 100
n_classes = 2
model = Sequential([
Flatten(input_shape=(sequence_length, n_features)),
Dense(300, activation='relu'),
Dense(128, activation='relu'),
Dense(128, activation='relu'),
Dense(128, activation='relu'),
Dense(n_classes, activation='softmax')
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
train_generator = DataGenerator(filename=input_file_name, batch_size = 100, n_classes=n_classes)
model.fit(train_generator, epochs=200)
model.save('neural_network_model.h5'))
|
7ff0fc0cda60b39ad819be96d43d703c
|
{
"intermediate": 0.325434148311615,
"beginner": 0.4117559790611267,
"expert": 0.2628098726272583
}
|
33,093
|
code for single cell QC
|
224391c3c9eeb6cb5dd5b062228cdeab
|
{
"intermediate": 0.25729721784591675,
"beginner": 0.39211583137512207,
"expert": 0.3505868911743164
}
|
33,094
|
help me set up the paddle settings so I can train on this dataset:
train: ../train/images
val: ../valid/images
test: ../test/images
nc: 12
names: ['-', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'kwh']
each folder has images folder and labels folder with images in the images folder and txt files in the labels to get the position and what number it is on the corresponding picture
|
cb2f8562c7ad075cce1c7f85ef4d1c14
|
{
"intermediate": 0.3274141252040863,
"beginner": 0.15699604153633118,
"expert": 0.5155898332595825
}
|
33,095
|
Перепиши данный код на Python
#include <cstdio>
#include <iostream>
#include <vector>
#include <math.h>
class Drob {
private:
int chisl;
int znam;
public:
Drob() {
chisl = 0;
znam = 0;
}
Drob(int ch, int zn) {
chisl = ch;
znam = zn;
}
int get_ch() {
return chisl;
}
int get_zn() {
return znam;
}
void set_ch(int new_ch) {
chisl = new_ch;
}
void set_zn(int new_zn) {
znam = new_zn;
}
// Метод возвращает представление дроби Фарея при помощи цепной дроби
std::vector<int> chain_drob() {
std::vector<int> vect;
int celoe = chisl / znam, ostat = chisl % znam, r_i = chisl, r_j = znam;
vect.push_back(celoe);
printf("%d = %d * %d + %d\n", r_i, celoe, r_j, ostat);
do {
r_i = r_j;
r_j = ostat;
celoe = r_i / ostat;
ostat = r_i % ostat;
printf("%d = %d * %d + %d\n", r_i, celoe, r_j, ostat);
vect.push_back(celoe);
} while (ostat != 0);
return vect;
}
// Метод для нахождения подходящих дробей
void get_podhod_drob(std::vector<int> v, std::vector<int> &vP, std::vector<int> &vQ) {
int P = -1, Q = -1;
for (int i = 0; i < v.size(); i++) {
if (i == 0) {
P = v[0];
Q = 1;
}
else if (i == 1) {
P = v[0] * v[1] + 1;
Q = v[1];
}
else {
P = v[i] * vP[i - 1] + vP[i - 2];
Q = v[i] * vQ[i - 1] + vQ[i - 2];
}
vP.push_back(P);
vQ.push_back(Q);
std::cout << "P" << i << " = " << vP[i] << " " << "Q" << i << " = " << vQ[i] << "\n";
}
}
// Метод для расчета значений на основе расширенного алгоритма Евклида
void main_calculations(int* start_matr, std::vector<int> a, Drob& res) {
int iter = 0, bi, ui, vi;
int bi_2, bi_1; // первая переменная это b(i-2), вторая b(i-1)
int ui_2, ui_1; // первая переменная это u(i-2), вторая u(i-1)
int vi_2, vi_1; // первая переменная это v(i-2), вторая v(i-1)
printf("%2s %5s %3s %5s %5s\n", "i", "bi", "ai", "ui", "vi");
while (iter < a.size() + 2) {
if (iter == 0) {
bi = this->get_ch();
ui = start_matr[0];
vi = start_matr[1];
bi_2 = bi;
ui_2 = ui;
vi_2 = vi;
printf("%2d %5d %3c %5d %5d\n", iter - 2, bi, '-', ui, vi);
}
else if (iter == 1) {
bi = this->get_zn();
ui = start_matr[2];
vi = start_matr[3];
bi_1 = bi;
ui_1 = ui;
vi_1 = vi;
printf("%2d %5d %3c %5d %5d\n", iter - 2, bi, '-', ui, vi);
}
else {
bi = -bi_1 * a[iter - 2] + bi_2;
ui = ui_1 * a[iter - 2] + ui_2;
vi = vi_1 * a[iter - 2] + vi_2;
bi_2 = bi_1; // новое значение b(i-2)
bi_1 = bi; // новое значение b(i-1)
ui_2 = ui_1; // новое значение u(i-2)
ui_1 = ui; // новое значение u(i-1)
vi_2 = vi_1; // новое значение v(i-2)
vi_1 = vi; // новое значение v(i-1)
printf("%2d %5d %3d %5d %5d\n", iter - 2, bi, a[iter - 2], ui, vi);
res.set_ch(ui);
res.set_zn(vi);
}
iter += 1;
}
if (res.get_zn() < 0) {
res.set_ch(0 - res.get_ch());
res.set_zn(abs(res.get_zn()));
}
}
// Метод для вычитания дробей
Drob sub(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
-right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для сложения дробей
Drob add(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для перемножения дробей
Drob mult(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
0, right_oper.get_zn(),
right_oper.get_ch(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для деления дробей
Drob div(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
0, right_oper.get_ch(),
right_oper.get_zn(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для расчета скалярного произведения новой арифметикой
Drob scalar_mult_1(Drob* arr_drA, Drob* arr_drB, const int size) {
Drob res(0, 0);
for (int i = 0; i < size; i++) {
Drob cur_res = arr_drA[i].mult(arr_drA[i].chain_drob(), arr_drB[i]);
printf("------------------------------\n");
printf("Итерация # %d: %5d / %-5d\n", i, cur_res.get_ch(), cur_res.get_zn());
if (i == 0) {
res.set_ch(cur_res.get_ch());
res.set_zn(cur_res.get_zn());
}
else res = res.add(res.chain_drob(), cur_res);
printf("Промежуточная сумма: %5d / %-5d\n", res.get_ch(), res.get_zn());
printf("------------------------------\n");
}
return res;
}
};
// Функция для расчета скалярного произведения обычной арифметикой
double scalar_mult_2(double* vect1, double* vect2, const int size) {
double res = 0;
for (int i = 0; i < size; i++)
res += vect1[i] * vect2[i];
return res;
}
void main(int argc, char* argv[]) {
setlocale(LC_ALL, "ru");
/*Drob a(371, 243), b(-26, 17), c;
std::vector<int> v1 = a.chain_drob();
printf("Цепная дробь: [ ");
for (int i = 0; i < v1.size(); i++)
std::cout << v1[i] << " ";
printf("]\n");
c = a.sub(v1, b);
printf("%d / %d\n", c.get_ch(), c.get_zn());
c = a.add(v1, b);
printf("%d / %d\n", c.get_ch(), c.get_zn());
c = a.mult(v1, b);
printf("%d / %d\n", c.get_ch(), c.get_zn());
c = a.div(v1, b);
printf("%d / %d\n", c.get_ch(), c.get_zn());*/
const int n = 3;
Drob* arr_a = new Drob[n]{
Drob(1, 2),
Drob(2, 3),
Drob(5, 12)
}, * arr_b = new Drob[n]{
Drob(6, 4),
Drob(3, 1),
Drob(10, 13)
};
Drob r = arr_a->scalar_mult_1(arr_a, arr_b, n);
printf("Результат скалярного произведения: %5d / %-5d\n", r.get_ch(), r.get_zn());
printf("Результат скалярного произведения 1: %5.16f\n", double(r.get_ch()) / double(r.get_zn()));
delete[] arr_a, arr_b;
/*Drob c = Drob(6, 8).add(Drob(6, 8).chain_drob(), Drob(6, 3));
printf("%d / %d\n", c.get_ch(), c.get_zn());*/
double* v_a = new double [n] { 1.0 / 2, 2.0 / 3, 5.0 / 12},
* v_b = new double [n] {6.0 / 4, 3.0 / 1, 10.0 / 13};
double res = scalar_mult_2(v_a, v_b, n);
printf("Результат скалярного произведения 2: %5.16f\n", res);
delete[] v_a, v_b;
}
|
8f5ea3c2ac71878d5843638376df302f
|
{
"intermediate": 0.29605287313461304,
"beginner": 0.39910605549812317,
"expert": 0.3048410713672638
}
|
33,096
|
write me a formula to split "/" in one cell in excel
|
8105839f48f9ee5512607e282004dfee
|
{
"intermediate": 0.37617743015289307,
"beginner": 0.20717687904834747,
"expert": 0.41664567589759827
}
|
33,097
|
Что токое sumbols в c#
Посмотрел как зделать игровой автом и не понимаю что токое sumbles и в целом эту строку string result1 = symbols[ random.Next (0, symbols.Length)]; зачем тут ленф
while (true)
{
Console.WriteLine("\nНажмите Enter, чтобы начать вращение барабанов...");
Console.ReadLine();
string[] symbols = { "Топор", "Алмаз", "Сапог" };
Random random = new Random();
string result1 = symbols[ random.Next (0, symbols.Length)];
string result2 = symbols[ random.Next (0, symbols.Length)];
string result3 = symbols[ random.Next (0, symbols.Length)];
Console.WriteLine($"Результат: {result1} - {result2} - {result3}");
if (result1 == result2 && result2 == result3)
{
Console.WriteLine("Вы выиграли!");
}
else
{
Console.WriteLine("Вы проиграли. Попробуйте еще раз.");
}
|
99094768881117f4dc023d6521183ce4
|
{
"intermediate": 0.3741452991962433,
"beginner": 0.41190654039382935,
"expert": 0.21394817531108856
}
|
33,098
|
Перепиши данный код под язык Python
#include <cstdio>
#include <iostream>
#include <vector>
#include <math.h>
class Drob {
private:
int chisl;
int znam;
public:
Drob() {
chisl = 0;
znam = 0;
}
Drob(int ch, int zn) {
chisl = ch;
znam = zn;
}
int get_ch() {
return chisl;
}
int get_zn() {
return znam;
}
void set_ch(int new_ch) {
chisl = new_ch;
}
void set_zn(int new_zn) {
znam = new_zn;
}
// Метод возвращает представление дроби Фарея при помощи цепной дроби
std::vector<int> chain_drob() {
std::vector<int> vect;
int celoe = chisl / znam, ostat = chisl % znam, r_i = chisl, r_j = znam;
vect.push_back(celoe);
printf("%d = %d * %d + %d\n", r_i, celoe, r_j, ostat);
do {
r_i = r_j;
r_j = ostat;
celoe = r_i / ostat;
ostat = r_i % ostat;
printf("%d = %d * %d + %d\n", r_i, celoe, r_j, ostat);
vect.push_back(celoe);
} while (ostat != 0);
return vect;
}
// Метод для нахождения подходящих дробей
void get_podhod_drob(std::vector<int> v, std::vector<int> &vP, std::vector<int> &vQ) {
int P = -1, Q = -1;
for (int i = 0; i < v.size(); i++) {
if (i == 0) {
P = v[0];
Q = 1;
}
else if (i == 1) {
P = v[0] * v[1] + 1;
Q = v[1];
}
else {
P = v[i] * vP[i - 1] + vP[i - 2];
Q = v[i] * vQ[i - 1] + vQ[i - 2];
}
vP.push_back(P);
vQ.push_back(Q);
std::cout << "P" << i << " = " << vP[i] << " " << "Q" << i << " = " << vQ[i] << "\n";
}
}
// Метод для расчета значений на основе расширенного алгоритма Евклида
void main_calculations(int* start_matr, std::vector<int> a, Drob& res) {
int iter = 0, bi, ui, vi;
int bi_2, bi_1; // первая переменная это b(i-2), вторая b(i-1)
int ui_2, ui_1; // первая переменная это u(i-2), вторая u(i-1)
int vi_2, vi_1; // первая переменная это v(i-2), вторая v(i-1)
printf("%2s %5s %3s %5s %5s\n", "i", "bi", "ai", "ui", "vi");
while (iter < a.size() + 2) {
if (iter == 0) {
bi = this->get_ch();
ui = start_matr[0];
vi = start_matr[1];
bi_2 = bi;
ui_2 = ui;
vi_2 = vi;
printf("%2d %5d %3c %5d %5d\n", iter - 2, bi, '-', ui, vi);
}
else if (iter == 1) {
bi = this->get_zn();
ui = start_matr[2];
vi = start_matr[3];
bi_1 = bi;
ui_1 = ui;
vi_1 = vi;
printf("%2d %5d %3c %5d %5d\n", iter - 2, bi, '-', ui, vi);
}
else {
bi = -bi_1 * a[iter - 2] + bi_2;
ui = ui_1 * a[iter - 2] + ui_2;
vi = vi_1 * a[iter - 2] + vi_2;
bi_2 = bi_1; // новое значение b(i-2)
bi_1 = bi; // новое значение b(i-1)
ui_2 = ui_1; // новое значение u(i-2)
ui_1 = ui; // новое значение u(i-1)
vi_2 = vi_1; // новое значение v(i-2)
vi_1 = vi; // новое значение v(i-1)
printf("%2d %5d %3d %5d %5d\n", iter - 2, bi, a[iter - 2], ui, vi);
res.set_ch(ui);
res.set_zn(vi);
}
iter += 1;
}
if (res.get_zn() < 0) {
res.set_ch(0 - res.get_ch());
res.set_zn(abs(res.get_zn()));
}
}
// Метод для вычитания дробей
Drob sub(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
-right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для сложения дробей
Drob add(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для перемножения дробей
Drob mult(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
0, right_oper.get_zn(),
right_oper.get_ch(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для деления дробей
Drob div(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
0, right_oper.get_ch(),
right_oper.get_zn(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для расчета скалярного произведения новой арифметикой
Drob scalar_mult_1(Drob* arr_drA, Drob* arr_drB, const int size) {
Drob res(0, 0);
for (int i = 0; i < size; i++) {
Drob cur_res = arr_drA[i].mult(arr_drA[i].chain_drob(), arr_drB[i]);
printf("------------------------------\n");
printf("Итерация # %d: %5d / %-5d\n", i, cur_res.get_ch(), cur_res.get_zn());
if (i == 0) {
res.set_ch(cur_res.get_ch());
res.set_zn(cur_res.get_zn());
}
else res = res.add(res.chain_drob(), cur_res);
printf("Промежуточная сумма: %5d / %-5d\n", res.get_ch(), res.get_zn());
printf("------------------------------\n");
}
return res;
}
};
// Функция для расчета скалярного произведения обычной арифметикой
double scalar_mult_2(double* vect1, double* vect2, const int size) {
double res = 0;
for (int i = 0; i < size; i++)
res += vect1[i] * vect2[i];
return res;
}
void main(int argc, char* argv[]) {
setlocale(LC_ALL, "ru");
/*Drob a(371, 243), b(-26, 17), c;
std::vector<int> v1 = a.chain_drob();
printf("Цепная дробь: [ ");
for (int i = 0; i < v1.size(); i++)
std::cout << v1[i] << " ";
printf("]\n");
c = a.sub(v1, b);
printf("%d / %d\n", c.get_ch(), c.get_zn());
c = a.add(v1, b);
printf("%d / %d\n", c.get_ch(), c.get_zn());
c = a.mult(v1, b);
printf("%d / %d\n", c.get_ch(), c.get_zn());
c = a.div(v1, b);
printf("%d / %d\n", c.get_ch(), c.get_zn());*/
const int n = 3;
Drob* arr_a = new Drob[n]{
Drob(1, 2),
Drob(2, 3),
Drob(5, 12)
}, * arr_b = new Drob[n]{
Drob(6, 4),
Drob(3, 1),
Drob(10, 13)
};
Drob r = arr_a->scalar_mult_1(arr_a, arr_b, n);
printf("Результат скалярного произведения: %5d / %-5d\n", r.get_ch(), r.get_zn());
printf("Результат скалярного произведения 1: %5.16f\n", double(r.get_ch()) / double(r.get_zn()));
delete[] arr_a, arr_b;
/*Drob c = Drob(6, 8).add(Drob(6, 8).chain_drob(), Drob(6, 3));
printf("%d / %d\n", c.get_ch(), c.get_zn());*/
double* v_a = new double [n] { 1.0 / 2, 2.0 / 3, 5.0 / 12},
* v_b = new double [n] {6.0 / 4, 3.0 / 1, 10.0 / 13};
double res = scalar_mult_2(v_a, v_b, n);
printf("Результат скалярного произведения 2: %5.16f\n", res);
delete[] v_a, v_b;
}
|
badd07b726794578902784abf8e18556
|
{
"intermediate": 0.301773339509964,
"beginner": 0.380838006734848,
"expert": 0.3173886239528656
}
|
33,099
|
connected to google colab with a t4 gpu:
Traceback (most recent call last):
File "/content/PaddleOCR/tools/train.py", line 224, in <module>
config, device, logger, vdl_writer = program.preprocess(is_train=True)
File "/content/PaddleOCR/tools/program.py", line 678, in preprocess
.dev_id) if use_gpu else 'cpu'
File "/usr/local/lib/python3.10/dist-packages/paddle/distributed/parallel.py", line 774, in device_id
return self._device_id
AttributeError: 'ParallelEnv' object has no attribute '_device_id'. Did you mean: 'device_id'?
[28]
1 s
from paddleocr import PaddleOCR
ocr = PaddleOCR(use_angle_cls=True, lang='en')
Global:
use_gpu: true
use_xpu: false
use_mlu: false
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/db_mv3/
save_epoch_step: 1200
# evaluation is run every 2000 iterations
eval_batch_step: [0, 2000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Architecture:
model_type: det
algorithm: DB
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
Neck:
name: DBFPN
out_channels: 256
Head:
name: DBHead
k: 50
Loss:
name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
learning_rate: 0.001
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: DBPostProcess
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DetMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: /content/train/images/
label_file_list:
- /content/train/labels/train_labels.txt
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- IaaAugment:
augmenter_args:
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
- EastRandomCropData:
size: [640, 640]
max_tries: 50
keep_ratio: true
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
loader:
shuffle: True
drop_last: False
batch_size_per_card: 16
num_workers: 8
use_shared_memory: True
Eval:
dataset:
name: SimpleDataSet
data_dir: /content/valid/images/
label_file_list:
- /content/valid/labels/val_labels.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
image_shape: [736, 1280]
- NormalizeImage:
scale: 1./255.
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 8
use_shared_memory: True
|
262d6ffb9cfbf58c127c5040a7184f81
|
{
"intermediate": 0.4024750888347626,
"beginner": 0.34756752848625183,
"expert": 0.2499573677778244
}
|
33,100
|
Почему код работает, но ничего не выводит
from fractions import Fraction
import math
class Drob:
def init(self, ch=0, zn=1):
# Инициализация числителя и знаменателя
self.chisl = ch
self.znam = zn
def get_ch(self):
# Получение числителя
return self.chisl
def get_zn(self):
# Получение знаменателя
return self.znam
def set_ch(self, new_ch):
# Установка нового числителя
self.chisl = new_ch
def set_zn(self, new_zn):
# Установка нового знаменателя
self.znam = new_zn
# Метод возвращает представление дроби в виде цепной дроби
def chain_drob(self):
vect = []
celoe = self.chisl // self.znam
ostat = self.chisl % self.znam
r_i = self.chisl
r_j = self.znam
vect.append(celoe)
print(f"{r_i} = {celoe} * {r_j} + {ostat}")
while ostat != 0:
r_i, r_j = r_j, ostat
celoe = r_i // ostat
ostat = r_i % ostat
print(f"{r_i} = {celoe} * {r_j} + {ostat}")
vect.append(celoe)
return vect
# Метод для нахождения подходящих дробей
def get_podhod_drob(self, v):
vP, vQ = [], []
for i in range(len(v)):
if i == 0:
P = v[0]
Q = 1
elif i == 1:
P = v[0] * v[1] + 1
Q = v[1]
else:
P = v[i] * vP[i - 1] + vP[i - 2]
Q = v[i] * vQ[i - 1] + vQ[i - 2]
vP.append
vQ.append(Q)
print(f"P{i} = {vP[i]} Q{i} = {vQ[i]}")
return vP, vQ
# Метод для расчета значений на основе расширенного алгоритма Евклида
def main_calculations(self, start_matr, a):
iter = 0
res = Drob()
ui_2, ui_1, vi_2, vi_1 = start_matr[0], start_matr[2], start_matr[1], start_matr[3]
bi_2 = self.get_ch()
bi_1 = self.get_zn()
print(f"{'i':>2} {'bi':>5} {'ai':>3} {'ui':>5} {'vi':>5}")
for ai in [-1] + a:
if iter == 0:
bi, ui, vi = bi_2, ui_2, vi_2
elif iter == 1:
bi, ui, vi = bi_1, ui_1, vi_1
else:
bi = -bi_1 * ai + bi_2
ui = ui_1 * ai + ui_2
vi = vi_1 * ai + vi_2
bi_2, ui_2, vi_2 = bi_1, ui_1, vi_1
bi_1, ui_1, vi_1 = bi, ui, vi
print(f"{iter - 2:>2} {bi:>5} {'-' if iter < 2 else ai:>3} {ui:>5} {vi:>5}")
res.set_ch(ui)
res.set_zn(vi)
iter += 1
if res.get_zn() < 0:
res.set_ch(-res.get_ch())
res.set_zn(abs(res.get_zn()))
return res
# Операции над дробями
def sub(self, a, right_oper):
# Вычитание дробей
start_matr = [
-right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
]
return self.main_calculations(start_matr, a)
def add(self, a, right_oper):
# Сложение дробей
start_matr = [
right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
]
return self.main_calculations(start_matr, a)
def mult(self, a, right_oper):
# Умножение дробей
start_matr = [
0, right_oper.get_zn(),
right_oper.get_ch(), 0
]
return self.main_calculations(start_matr, a)
def div(self, a, right_oper):
# Деление дробей
start_matr = [
0, right_oper.get_ch(),
right_oper.get_zn(), 0
]
return self.main_calculations(start_matr, a)
def scalar_mult_1(self, arr_drA, arr_drB, size):
# Расчет скалярного произведения новой арифметикой
res = Drob()
for i in range(size):
cur_res = arr_drA[i].mult(arr_drA[i].chain_drob(), arr_drB[i])
print("------------------------------")
print(f"Iteration # {i}: {cur_res.get_ch()} / {cur_res.get_zn()}")
if i == 0:
res = cur_res
else:
res = res.add(res.chain_drob(), cur_res)
print(f"Intermediate sum: {res.get_ch()} / {res.get_zn()}")
print("------------------------------")
return res
def scalar_mult_2(vect1, vect2, size):
# Расчет скалярного произведения обычной арифметикой
res = 0
for i in range(size):
res += vect1[i] * vect2[i]
return res
# Пример использования:
arr_a = [Drob(1, 2), Drob(2, 3), Drob(5, 12)]
arr_b = [Drob(6, 4), Drob(3, 1), Drob(10, 13)]
r = arr_a[0].scalar_mult_1(arr_a, arr_b, len(arr_a))
print(f"Результат скалярного произведения (новые дроби): {r.get_ch()} / {r.get_zn()}")
print(f"Результат скалярного произведения в виде числа с плавающей точкой (новые дроби): {float(r.get_ch()) / float(r.get_zn())}")
v_a = [1/2, 2/3, 5/12]
v_b = [6/4, 3/1, 10/13]
res = scalar_mult_2(v_a, v_b, len(v_a))
print(f"Результат скалярного произведения (арифметика чисел с плавающей точкой): {res}")
|
4750563560475a42d9a486c7200d8ca8
|
{
"intermediate": 0.16783519089221954,
"beginner": 0.5614607930183411,
"expert": 0.27070409059524536
}
|
33,101
|
Hi - in Python I have a csv-file that I update on a regular basis. I want to store the last 5 copies of the file in a subfolder. The subfolder should be updated every time I make a new update of the csv-file.
Can you produce some Python code to do that?
|
62488f033d823b2c1781f95068d10145
|
{
"intermediate": 0.474977970123291,
"beginner": 0.1673983633518219,
"expert": 0.35762372612953186
}
|
33,102
|
Почему код ничего не выводит
from fractions import Fraction
import math
class Drob:
def __init__(self, ch=0, zn=1):
# Инициализация числителя и знаменателя
self.chisl = ch
self.znam = zn
def get_ch(self):
# Получение числителя
return self.chisl
def get_zn(self):
# Получение знаменателя
return self.znam
def set_ch(self, new_ch):
# Установка нового числителя
self.chisl = new_ch
def set_zn(self, new_zn):
# Установка нового знаменателя
self.znam = new_zn
# Метод возвращает представление дроби в виде цепной дроби
def chain_drob(self):
vect = []
celoe = self.chisl // self.znam
ostat = self.chisl % self.znam
r_i = self.chisl
r_j = self.znam
vect.append(celoe)
print(f"{r_i} = {celoe} * {r_j} + {ostat}")
while ostat != 0:
r_i, r_j = r_j, ostat
celoe = r_i // ostat
ostat = r_i % ostat
print(f"{r_i} = {celoe} * {r_j} + {ostat}")
vect.append(celoe)
return vect
# Метод для нахождения подходящих дробей
def get_podhod_drob(self, v):
vP, vQ = [], []
for i in range(len(v)):
if i == 0:
P = v[0]
Q = 1
elif i == 1:
P = v[0] * v[1] + 1
Q = v[1]
else:
P = v[i] * vP[i - 1] + vP[i - 2]
Q = v[i] * vQ[i - 1] + vQ[i - 2]
vP.append(P)
vQ.append(Q)
print(f"P{i} = {vP[i]} Q{i} = {vQ[i]}")
return vP, vQ
# Метод для расчета значений на основе расширенного алгоритма Евклида
def main_calculations(self, start_matr, a):
iter = 0
res = Drob()
ui_2, ui_1, vi_2, vi_1 = start_matr[0], start_matr[2], start_matr[1], start_matr[3]
bi_2 = self.get_ch()
bi_1 = self.get_zn()
print(f"{'i':>2} {'bi':>5} {'ai':>3} {'ui':>5} {'vi':>5}")
for ai in [-1] + a:
if iter == 0:
bi, ui, vi = bi_2, ui_2, vi_2
elif iter == 1:
bi, ui, vi = bi_1, ui_1, vi_1
else:
bi = -bi_1 * ai + bi_2
ui = ui_1 * ai + ui_2
vi = vi_1 * ai + vi_2
bi_2, ui_2, vi_2 = bi_1, ui_1, vi_1
bi_1, ui_1, vi_1 = bi, ui, vi
print(f"{iter - 2:>2} {bi:>5} {'-' if iter < 2 else ai:>3} {ui:>5} {vi:>5}")
res.set_ch(ui)
res.set_zn(vi)
iter += 1
if res.get_zn() < 0:
res.set_ch(-res.get_ch())
res.set_zn(abs(res.get_zn()))
return res
# Операции над дробями
def sub(self, a, right_oper):
# Вычитание дробей
start_matr = [
-right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
]
return self.main_calculations(start_matr, a)
def add(self, a, right_oper):
# Сложение дробей
start_matr = [
right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
]
return self.main_calculations(start_matr, a)
def mult(self, a, right_oper):
# Умножение дробей
start_matr = [
0, right_oper.get_zn(),
right_oper.get_ch(), 0
]
return self.main_calculations(start_matr, a)
def div(self, a, right_oper):
# Деление дробей
start_matr = [
0, right_oper.get_ch(),
right_oper.get_zn(), 0
]
return self.main_calculations(start_matr, a)
def scalar_mult_1(self, arr_drA, arr_drB, size):
# Расчет скалярного произведения новой арифметикой
res = Drob()
for i in range(size):
cur_res = arr_drA[i].mult(arr_drA[i].chain_drob(), arr_drB[i])
print("------------------------------")
print(f"Iteration # {i}: {cur_res.get_ch()} / {cur_res.get_zn()}")
if i == 0:
res = cur_res
else:
res = res.add(res.chain_drob(), cur_res)
print(f"Intermediate sum: {res.get_ch()} / {res.get_zn()}")
print("------------------------------")
return res
def scalar_mult_2(vect1, vect2, size):
# Расчет скалярного произведения обычной арифметикой
res = 0
for i in range(size):
res += vect1[i] * vect2[i]
return res
# Пример использования:
arr_a = [Drob(1, 2), Drob(2, 3), Drob(5, 12)]
arr_b = [Drob(6, 4), Drob(3, 1), Drob(10, 13)]
r = arr_a[0].scalar_mult_1(arr_a, arr_b, len(arr_a))
print(f"Результат скалярного произведения (новые дроби): {r.get_ch()} / {r.get_zn()}")
print(f"Результат скалярного произведения в виде числа с плавающей точкой (новые дроби): {float(r.get_ch()) / float(r.get_zn())}")
v_a = [1/2, 2/3, 5/12]
v_b = [6/4, 3/1, 10/13]
res = scalar_mult_2(v_a, v_b, len(v_a))
print(f"Результат скалярного произведения (арифметика чисел с плавающей точкой): {res}")
|
72c67a04ee33d8c67fab5e8e9de604c1
|
{
"intermediate": 0.19716422259807587,
"beginner": 0.5896065831184387,
"expert": 0.21322914958000183
}
|
33,103
|
Three dimensional chess board
X axis: abcdefgh
Y axis: 12345678
Z axis !?$&√£€¥
Generate move list (stop repeat when it's stuck for slatemate)
|
8b51c309ad768ee88f25df8949b2c256
|
{
"intermediate": 0.3764510154724121,
"beginner": 0.2396545112133026,
"expert": 0.3838944137096405
}
|
33,104
|
TRANSLATE THE CODE TO PYTHON #include <cstdio>
#include <iostream>
#include <vector>
#include <math.h>
class Drob {
private:
int chisl;
int znam;
public:
Drob() {
chisl = 0;
znam = 0;
}
Drob(int ch, int zn) {
chisl = ch;
znam = zn;
}
int get_ch() {
return chisl;
}
int get_zn() {
return znam;
}
void set_ch(int new_ch) {
chisl = new_ch;
}
void set_zn(int new_zn) {
znam = new_zn;
}
// Метод возвращает представление дроби Фарея при помощи цепной дроби
std::vector<int> chain_drob() {
std::vector<int> vect;
int celoe = chisl / znam, ostat = chisl % znam, r_i = chisl, r_j = znam;
vect.push_back(celoe);
printf("%d = %d * %d + %d\n", r_i, celoe, r_j, ostat);
do {
r_i = r_j;
r_j = ostat;
celoe = r_i / ostat;
ostat = r_i % ostat;
printf("%d = %d * %d + %d\n", r_i, celoe, r_j, ostat);
vect.push_back(celoe);
} while (ostat != 0);
return vect;
}
// Метод для нахождения подходящих дробей
void get_podhod_drob(std::vector<int> v, std::vector<int> &vP, std::vector<int> &vQ) {
int P = -1, Q = -1;
for (int i = 0; i < v.size(); i++) {
if (i == 0) {
P = v[0];
Q = 1;
}
else if (i == 1) {
P = v[0] * v[1] + 1;
Q = v[1];
}
else {
P = v[i] * vP[i - 1] + vP[i - 2];
Q = v[i] * vQ[i - 1] + vQ[i - 2];
}
vP.push_back(P);
vQ.push_back(Q);
std::cout << "P" << i << " = " << vP[i] << " " << "Q" << i << " = " << vQ[i] << "\n";
}
}
// Метод для расчета значений на основе расширенного алгоритма Евклида
void main_calculations(int* start_matr, std::vector<int> a, Drob& res) {
int iter = 0, bi, ui, vi;
int bi_2, bi_1; // первая переменная это b(i-2), вторая b(i-1)
int ui_2, ui_1; // первая переменная это u(i-2), вторая u(i-1)
int vi_2, vi_1; // первая переменная это v(i-2), вторая v(i-1)
printf("%2s %5s %3s %5s %5s\n", "i", "bi", "ai", "ui", "vi");
while (iter < a.size() + 2) {
if (iter == 0) {
bi = this->get_ch();
ui = start_matr[0];
vi = start_matr[1];
bi_2 = bi;
ui_2 = ui;
vi_2 = vi;
printf("%2d %5d %3c %5d %5d\n", iter - 2, bi, '-', ui, vi);
}
else if (iter == 1) {
bi = this->get_zn();
ui = start_matr[2];
vi = start_matr[3];
bi_1 = bi;
ui_1 = ui;
vi_1 = vi;
printf("%2d %5d %3c %5d %5d\n", iter - 2, bi, '-', ui, vi);
}
else {
bi = -bi_1 * a[iter - 2] + bi_2;
ui = ui_1 * a[iter - 2] + ui_2;
vi = vi_1 * a[iter - 2] + vi_2;
bi_2 = bi_1; // новое значение b(i-2)
bi_1 = bi; // новое значение b(i-1)
ui_2 = ui_1; // новое значение u(i-2)
ui_1 = ui; // новое значение u(i-1)
vi_2 = vi_1; // новое значение v(i-2)
vi_1 = vi; // новое значение v(i-1)
printf("%2d %5d %3d %5d %5d\n", iter - 2, bi, a[iter - 2], ui, vi);
res.set_ch(ui);
res.set_zn(vi);
}
iter += 1;
}
if (res.get_zn() < 0) {
res.set_ch(0 - res.get_ch());
res.set_zn(abs(res.get_zn()));
}
}
// Метод для вычитания дробей
Drob sub(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
-right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для сложения дробей
Drob add(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для перемножения дробей
Drob mult(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
0, right_oper.get_zn(),
right_oper.get_ch(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для деления дробей
Drob div(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
0, right_oper.get_ch(),
right_oper.get_zn(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для расчета скалярного произведения новой арифметикой
Drob scalar_mult_1(Drob* arr_drA, Drob* arr_drB, const int size) {
Drob res(0, 0);
for (int i = 0; i < size; i++) {
Drob cur_res = arr_drA[i].mult(arr_drA[i].chain_drob(), arr_drB[i]);
printf("------------------------------\n");
printf("Итерация # %d: %5d / %-5d\n", i, cur_res.get_ch(), cur_res.get_zn());
if (i == 0) {
res.set_ch(cur_res.get_ch());
res.set_zn(cur_res.get_zn());
}
else res = res.add(res.chain_drob(), cur_res);
printf("Промежуточная сумма: %5d / %-5d\n", res.get_ch(), res.get_zn());
printf("------------------------------\n");
}
return res;
}
};
// Функция для расчета скалярного произведения обычной арифметикой
double scalar_mult_2(double* vect1, double* vect2, const int size) {
double res = 0;
for (int i = 0; i < size; i++)
res += vect1[i] * vect2[i];
return res;
}
void main(int argc, char* argv[]) {
setlocale(LC_ALL, "ru");
/*Drob a(371, 243), b(-26, 17), c;
std::vector<int> v1 = a.chain_drob();
printf("Цепная дробь: [ ");
for (int i = 0; i < v1.size(); i++)
std::cout << v1[i] << " ";
printf("]\n");
c = a.sub(v1, b);
printf("%d / %d\n", c.get_ch(), c.get_zn());
c = a.add(v1, b);
printf("%d / %d\n", c.get_ch(), c.get_zn());
c = a.mult(v1, b);
printf("%d / %d\n", c.get_ch(), c.get_zn());
c = a.div(v1, b);
printf("%d / %d\n", c.get_ch(), c.get_zn());*/
const int n = 3;
Drob* arr_a = new Drob[n]{
Drob(1, 2),
Drob(2, 3),
Drob(5, 12)
}, * arr_b = new Drob[n]{
Drob(6, 4),
Drob(3, 1),
Drob(10, 13)
};
Drob r = arr_a->scalar_mult_1(arr_a, arr_b, n);
printf("Результат скалярного произведения: %5d / %-5d\n", r.get_ch(), r.get_zn());
printf("Результат скалярного произведения 1: %5.16f\n", double(r.get_ch()) / double(r.get_zn()));
delete[] arr_a, arr_b;
/*Drob c = Drob(6, 8).add(Drob(6, 8).chain_drob(), Drob(6, 3));
printf("%d / %d\n", c.get_ch(), c.get_zn());*/
double* v_a = new double [n] { 1.0 / 2, 2.0 / 3, 5.0 / 12},
* v_b = new double [n] {6.0 / 4, 3.0 / 1, 10.0 / 13};
double res = scalar_mult_2(v_a, v_b, n);
printf("Результат скалярного произведения 2: %5.16f\n", res);
delete[] v_a, v_b;
}
|
57f60ac2e93fe3d9588b79eed2a3d639
|
{
"intermediate": 0.25990602374076843,
"beginner": 0.4232718348503113,
"expert": 0.3168221712112427
}
|
33,105
|
in unreal engine, how can i give a custom movecomponent to a ADefaultPawn?
|
5b2d466108e4a2365fd58828190f0c42
|
{
"intermediate": 0.27302271127700806,
"beginner": 0.16831839084625244,
"expert": 0.5586588978767395
}
|
33,106
|
Here are my pc specs:
CPU: Ryzen 9 7950x 16 core 32 thread
GPU: Sapphire 11323-02-20G Pulse AMD Radeon RX 7900 XT Gaming Graphics Card with 20GB GDDR6, AMD RDNA 3 (vsync and freesync enabled in drivers)
Memory: DDR5 5600 (PC5 44800) Timing 28-34-34-89 CAS Latency 28 Voltage 1.35V
Drives: Samsung 990 Pro 2tb + WD_Black SN850X 4000GB
LAN: realtek Gaming 2.5GbE Family Controller
Wireless: RZ616 Bluetooth+WiFi 6E 160MHz (just use for bluetooth sound and xbox x and dualsense controllers)
USB DAC: Fiio New K3 Series DAC 32 bit, 384000 Hz
Monitor: 42" LG C2 10 bit TV 120 hz with freesync and HDR
Mouse: SteelSeries Aerox 9 Wireless @ 3100 DPI no accelleratioin or smoothing.
Software:
Windows 11 23H2
Process Lasso: I disable the first 2 cores, 4 threads of my process affinity for games.
MSI Afterburner: mV 1044, power limit +0, core clock 2745, memory clock 2600
Reshade: Immerse Sharpen maxxed out at 1. And Immerse Pro Clarity about 50%
AMD5 105 cTDP PPT: 142W - TDC: 110A - EDC 170A with a curve of -20
Can you optimize my PC Lasso config?
[ProcessAllowances]
EfficiencyMode=cinebench.exe,0
[ProcessDefaults]
MatchWildcardsToPathnames=false
MatchOnCommandLine=false
[AdvancedRules]
DivideCPUPercentThresholdsBySystemCPUCount=false
ProcessorGroupExtended=
ProhibitInternetExplorerExceptWhenAny=
WatchdogRules2=
[GamingMode]
GamingModeEnabled=false
GamingChangePowerPlan=false
TargetPowerPlan=Hone Ultimate Power Plan V4
GamingModeEngageForSteam=true
AutomaticGamingModeProcessPaths=gears5.exe,grim dawn.exe,mcc-win64-shipping.exe,last epoch.exe,starwarsjedifallenorder.exe,trine2_32bit.exe,hogwartslegacy.exe,diabloimmortal.exe,wowclassic.exe,heroesofthestorm_x64.exe,wow.exe,baldur's gate - dark alliance.exe,darkalliance.exe,darkalliance-win64-shipping.exe,archon-win64-shipping.exe,bf4.exe,skyrimse.exe,left4dead2.exe,hl2.exe,realms of ancient war.exe,rerev2.exe,vikings.exe,game.dll,f.e.a.r. 3.exe,diablo iii.exe,diablo iii64.exe,diablo iv.exe,heroes of the storm.exe,crysis2remastered.exe,crysisremastered.exe,nfs13.exe,pvz.main_win64_retail.exe,starwarsbattlefront.exe,starwarsbattlefrontii.exe,rpcs3.exe,xenia_canary.exe,sottr.exe,ascension.exe,hermesproxy.exe,swgame-win64-shipping.exe,wolcen.exe,baldur's gate - dark alliance ii.exe,watchdogs2.exe,yuzu.exe,borderlands3.exe,oblivity.exe,tinytina.exe,undecember.exe,undecember-win64-shipping.exe,bf2042.exe,cinebench.exe,cod22-cod.exe,cod.exe
[PowerManagement]
StartWithPowerPlan=
Display=
PC=
[PowerSaver]
PowerSaver_IdleTime=7200
EnergySaverEnabled=true
EnergySaverForceActivePowerProfile=false
EnergySaverUpPowerProfileName=
EnergySaverEvents=true
DisableEnergySaverDuringGamingMode=false
PowerSaver_Exclusions=
|
b42ba7c031e0e98a5bb74d265a917bde
|
{
"intermediate": 0.34749144315719604,
"beginner": 0.3840424418449402,
"expert": 0.2684660851955414
}
|
33,107
|
How to expand "export LDFLAGS="-fuse-ld=lld"" with --build-id=none lld option?
|
3a6885240520b3275d759b9f4e044d8a
|
{
"intermediate": 0.521323025226593,
"beginner": 0.1797381192445755,
"expert": 0.2989388704299927
}
|
33,108
|
Here are my pc's specs:
CPU: Ryzen 9 7950x 16 core 32 thread
GPU: Sapphire 11323-02-20G Pulse AMD Radeon RX 7900 XT Gaming Graphics Card with 20GB GDDR6, AMD RDNA 3 (vsync and freesync enabled in drivers)
Memory: DDR5 5600 (PC5 44800) Timing 28-34-34-89 CAS Latency 28 Voltage 1.35V
Drives: Samsung 990 Pro 2tb + WD_Black SN850X 4000GB
LAN: realtek Gaming 2.5GbE Family Controller
Wireless: RZ616 Bluetooth+WiFi 6E 160MHz (just use for bluetooth sound and xbox x and dualsense controllers)
USB DAC: Fiio New K3 Series DAC 32 bit, 384000 Hz
Monitor: 42" LG C2 10 bit TV 120 hz with freesync and HDR
Mouse: SteelSeries Aerox 9 Wireless @ 3100 DPI no accelleratioin or smoothing.
Software:
Windows 11 23H2
Process Lasso: I disable the first 2 cores, 4 threads of my process affinity for games
MSI Afterburner: mV 1044, power limit +0, core clock 2745, memory clock 2600
Reshade: Immerse Sharpen maxxed out at 1. And Immerse Pro Clarity about 50%
AMD5 105 cTDP PPT: 142W - TDC: 110A - EDC 170A with a curve of -20
can you optimize this?
[Administration]
Version=5860600
ConfigPasswordMD5=
[OutOfControlProcessRestraint]
OocOn=true
ExcludeChildrenOfForeground=true
DisableProBalanceWhenSysIdle=false
ProBalanceDropOneRandomCore=false
DoNotAdjustAffinityIfCustomized=true
OocDisableCoreParkingWhileIn=false
UseEfficiencyMode=false
DisableProBalanceIfSysIdleThisManyMS=30000
TotalProcessorUsageBeforeRestraint=8
PerProcessUsageBeforeRestraint=4
TimeOverQuotaBeforeRestraint=900
PerProcessUsageForRestore=1
PlayOnRestraint=C:\Windows\media\Windows Pop-up Blocked.wav
PlayOnRestore=C:\Windows\media\Windows Feed Discovered.wav
MinimumTimeOfRestraint=4200
MaximumTimeOfRestraint=0
TameOnlyNormal=true
LowerToIdleInsteadOfBelowNormal=false
ExcludeServices=true
PlaySoundOnRestraint=false
PlaySoundOnRestore=false
RestrainByAffinity=false
RestraintAffinity=
ExcludeForegroundProcesses2=true
DoNotLowerPriorityClass=false
LowerIOPriorityDuringRestraint=false
MatchExclusionsByPathnameToo=false
ChangeTrayIconOnRestraint=true
OocExclusions=
OocHardCodedExclusionOverrides=
[GUI]
ClearLogAtExit=false
ShowCPUCoreUtilGraphs=true
ShowGraphLegend=true
ShowGraphCPU=true
ShowGraphResponsiveness=true
ShowGraphMemoryLoad=true
ShowGraphProBalanceEvents=true
ShowGraphSelectedProcessesCPUHistory=true
ProBalanceCountersOnGraph=true
ShowGraphLicenseName=true
ShowPowerProfile=true
GraphShowTooltips=true
ShowCPUUtilityAsPrimary=true
[Advanced]
AutomaticUpdate=false
[Performance]
DefaultsLevel=327942
GracefulWaitTimeInMs=5000
UpdateSpeedGUI=1000
UpdateSpeedCore=1000
[Sampling]
SamplingEnabled=false
SamplingIntervalSeconds=900
SamplingOutputPath=
SamplingIncludePattern=*
SamplingExcludePattern=
[Performance]
ManageOnlyCurrentUser=false
ExitOnCloseWindow=false
SoundsOff=true
IsConfigWritable=true
ForcedMode=true
IgnoreProblematicProcesses=false
IgnoreSuspendedProcesses=false
SetTimerResolutionAtStartup=5000
[ForegroundBoosting]
BoostForegroundProcess=false
ForegroundBoostPriorityClass=0x8000
BoostOnlyNormal=true
ForegroundBoostExclusions=
[SystemTrayIcon]
UseStaticIcon=false
ShowResponsivnessInTrayInsteadOfProcessorUsage=false
[Logging]
LogDisable=false
IncludeCommandLines=false
LogSmartTrim=true
LogCPULimiter=true
LogEfficiencyMode=true
LogGroupExtender=true
LogCPUSets=true
LogProBalanceParkingChanges=true
LogProcessExecutions=false
LogProcessTerminations=false
LogProcessesDisallowed=true
LogDefaultPriorityAdjustments=true
LogDefaultAffinityAdjustments=true
LogProBalanceBegin=true
LogProBalanceEnd=true
LogInstanceLimitTerminations=true
LogPowerProfileChanges=true
[MemoryManagement]
SmartTrimIsEnabled=true
SmartTrimWorkingSetTrims=false
SmartTrimClearStandbyList=false
SmartTrimClearFileCache=false
ClearStandbyFreeRAMThresholdMB=1024
ClearStandbyOnlyInPerfMode=true
SmartTrimExclusions=
SmartTrimAutoMinimumRAMLoad=65
MinimumProcessWSSInMb=196
SmartTrimIntervalMins=15
[SysTrayBalloons]
EnableSystemTrayNotification=false
BalloonTipDuration=10000
ShowBalloonsForOocPriorityRestoration=false
[ProcessAllowances]
ApplyInstanceCountLimitsToAllUsers=false
AllowedProcesses=
DisallowedProcesses=
InstanceLimitedProcesses=
InstanceManagedCPUAffinities=
ProcessThrottles=
OneTimeProcessThrottles=
CPULimitRules=
|
6a3f10ae1afac75914d40902d1656c31
|
{
"intermediate": 0.3993414044380188,
"beginner": 0.3213340640068054,
"expert": 0.279324471950531
}
|
33,109
|
. Use SciPy to define three normal distributions with parameters:
• µ1 = 0, σ1 = 1
• µ2 = 3, σ2 = 0.6
• µ = 0.5, σ3 = 1.2
(a) After defining the three distributions, plot the probability density functions of each distribution in a single graph
to visually compare them.
(b) Draw a sample from each distribution, each of size 15. Store the three samples in a numpy array of size (3,15).
(c) Perform statistical t-tests to compare the means of all three possible pairs of samples. What do the p-values of
each test suggest?
|
b73a4eed5efce995f1eba6cf48a789c2
|
{
"intermediate": 0.4411200284957886,
"beginner": 0.2746601998806,
"expert": 0.28421977162361145
}
|
33,110
|
public function index()
{
$condominiun = Auth()->user()->department->building->condominium;
$departments = $condominiun->departments;
$buildings = $condominiun->buildings->pluck('id');
$bills = Bill::whereHas('Buildings', function ($query) use ($buildings) {
$query->whereIn('building_id', $buildings);
})->get();
return response()->json([
'data' => [
'bills' => BillResource::collection($bills),
'departments' => BillDepartmentResource::collection($departments)
]
]);
}
laravel optimiza
|
1fc49b7b76c97015d5559d4e084c8012
|
{
"intermediate": 0.340629518032074,
"beginner": 0.3141002357006073,
"expert": 0.34527021646499634
}
|
33,111
|
# <<< CornerDetector >>>
RANDOM_ANSWER = np.array([[1, 2], [3, 4], [6, 9]])
COEFFS_KERNEL = np.array(
[[1, 1, 1, 1, 1],
[1, 5, 5, 5, 1],
[1, 5, 0, 5, 1],
[1, 5, 5, 5, 1],
[1, 1, 1, 1, 1]]
)
# noise 0.0-0.2
POTENTIAL_POINTS_NUM_0_2 = 200
ITERATIONS_NUM_0_2 = 1000
IMAGE_THRESHOLD_0_2 = 0
THRESHOLD_DISTANCE_0_2 = 1
THRESHOLD_K_0_2 = 0.5
THRESHOLD_B_0_2 = 20
NOISE_LESS_0_2 = 100
KERNEL_SIZE_0_2 = 3
# noise 0.3-0.5
POTENTIAL_POINTS_NUM_3_5 = 200
ITERATIONS_NUM_3_5 = 1000
IMAGE_THRESHOLD_3_5 = 100
THRESHOLD_DISTANCE_3_5 = 1
THRESHOLD_K_3_5 = 0.5
THRESHOLD_B_3_5 = 20
NOISE_LESS_3_5 = 75
KERNEL_SIZE_3_5 = 3
# noise 0.6
POTENTIAL_POINTS_NUM_6 = 2000
ITERATIONS_NUM_6 = 2500
IMAGE_THRESHOLD_6 = 150
THRESHOLD_DISTANCE_6 = 3
THRESHOLD_K_6 = 0.5
THRESHOLD_B_6 = 500
NOISE_LESS_6 = 45
KERNEL_SIZE_6 = 9
# <<< TimerService >>>
TIME_MAX = 1.95
class TimeService:
“”“Service for time management.”“”
def init(self, time_max: float):
self.time_max = time_max
self.start_time = None
def start_timer(self) -> None:
“”“Start time point. Sets ‘self.start_time’.”“”
self.start_time = time.time()
def check_timer(self):
“”“Checks if time more than ‘self.time_max’.”“”
end_time = time.time()
delta_time = end_time - self.start_time
if delta_time > self.time_max:
return False
return delta_time
class ImageService:
“”“Service for image manipulations.”“”
@staticmethod
def get_binary_image(image: np.ndarray) -> np.ndarray:
“”“Gets binary image from image.”“”
binary_image = image.copy()
binary_image[np.where(binary_image != 0)] = 255
return binary_image
@staticmethod
def count_zero_pixels(image_gray: np.ndarray) -> int:
“”“Counts pixels with 0 intensity on black-white image.”“”
zero_pixels = np.count_nonzero(image_gray == 0)
return zero_pixels
@staticmethod
def calculate_zero_pixel_percentage(image: np.ndarray) -> float:
“”“Calculates zero intensity pixel’s percentage.”“”
total_pixels = image.size
zero_pixels = ImageService.count_zero_pixels(image)
zero_percentage = zero_pixels / total_pixels * 100
return zero_percentage
class CornerDetector(object):
“”“Class for corner detection on image.”“”
def init(
self
):
self.__potential_points_num = None
self.__iterations_num = None
self.__image_threshold = None
self.__threshold_k = None
self.__threshold_b = None
self.__threshold_distance = None
self.__need_coeffs_kernel = None
self.__image = None
self.__kernel_size = None
self.__time_service = TimeService(TIME_MAX)
self.__need_time_check = False
self.random_answer = RANDOM_ANSWER
def __get_coords_by_threshold(
self,
image: np.ndarray,
) -> tuple:
“”“Gets coordinates with intensity more than ‘image_threshold’.”“”
coords = np.where(self.__image > self.__image_threshold)
return coords
def __get_neigh_nums_list(
self,
coords: tuple,
kernel_size: int
) -> list:
“”“
Gets list with number of neighborhoods by kernel.
Returns list like [[x1, y1, N1], [x2, y2, N2]],
Where N - neighborhood’s number.
“””
neigh_nums = []
offset = (kernel_size - 1) // 2
for x, y in zip(coords[0], coords[1]):
if x < 0 + offset or x > self.__image.shape[0] - offset:
continue
if y < 0 + offset or y > self.__image.shape[1] - offset:
continue
kernel = self.__image[x-offset:x+offset+1, y-offset:y+offset+1]
step_neigh_num = np.count_nonzero(kernel) - 1
neigh_nums.append([x, y, step_neigh_num])
return neigh_nums
def __get_neigh_coeffs_list(
self, coords: tuple, kernel: np.array
) -> list:
“”“
Gets list with number of neighborhoods by kernel.
Returns list like [[x1, y1, N1], [x2, y2, N2]],
Where N - neighborhood’s coeffs.
“””
neigh_coeffs = []
offset = (kernel.shape[0] - 1) // 2
print(offset)
for x, y in zip(coords[0], coords[1]):
if x < 0 + offset or x > self.__image.shape[0] - offset:
continue
if y < 0 + offset or y > self.__image.shape[1] - offset:
continue
try:
image_kernel = self.__image[x - offset:x + offset + 1,
y - offset:y + offset + 1]
step_neigh_coeff = np.sum(kernel * image_kernel)
neigh_coeffs.append([x, y, step_neigh_coeff])
except ValueError as e:
continue
return neigh_coeffs
@staticmethod
def __sort_neigh_nums_by_N(
neigh_nums: list
) -> np.ndarray:
“”“
Sort neigh_nums by N parameter.
Removes N=-1, N=0 and sorts by N desc.
“””
np_neigh_nums = np.array(neigh_nums)
np_neigh_nums = np_neigh_nums[
(np_neigh_nums[:, 2] != -1) & (np_neigh_nums[:, 2] != 0)]
np_neigh_nums = np_neigh_nums[
np.argsort(-np_neigh_nums[:, 2])]
return np_neigh_nums
def __get_potential_points_coords(
self,
sorted_neigh_nums: np.ndarray
) -> np.ndarray:
“”“
Gets best candidates, potential points coords only.
Returns np.ndarray like [[x1, y1], [x2, y2]].
“””
sorted_neigh_nums = np.delete(sorted_neigh_nums, 2, axis=1)
return sorted_neigh_nums[:self.__potential_points_num]
def __get_best_lines(
self,
potential_points: np.ndarray
) -> np.ndarray:
“”“
Gets the best combinations of lines by all points.
Line y = kx + b.
Returns np.ndarray like [[k1, b1], [k2, b2]].
“””
remaining_points = 0
best_lines = []
num_lines = 0
while num_lines < 3:
if num_lines == 0:
remaining_points = np.copy(potential_points)
print(f"remaining points: {len(remaining_points)}“)
best_inlines = []
best_line = None
if len(remaining_points) == 0:
remaining_points = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
for i in range(self.__iterations_num):
if self.__need_time_check:
if not self.__time_service.check_timer():
return self.random_answer
sample_indices = np.random.choice(
remaining_points.shape[0], size=3, replace=False)
sample_points = remaining_points[sample_indices]
sample_x = sample_points[:, 1]
sample_y = sample_points[:, 0]
coefficients = np.polyfit(sample_x, sample_y, deg=1)
distances = np.abs(remaining_points[:, 0] - np.matmul(np.vstack((remaining_points[:, 1], np.ones_like(remaining_points[:, 1]))).T, coefficients)) / np.sqrt(coefficients[0] ** 2 + 1)
inlines = np.where(distances < self.__threshold_distance)[0]
is_similar = False
for line in best_lines:
diff_k = abs(coefficients[0] - line[0])
diff_b = abs(coefficients[1] - line[1])
if diff_k <= self.__threshold_k and diff_b <= self.__threshold_b:
is_similar = True
break
if not is_similar and len(inlines) > len(best_inlines):
best_line = coefficients
best_inlines = inlines
if best_line is not None:
best_lines.append(best_line)
remaining_points = np.delete(remaining_points, best_inlines, axis=0)
num_lines += 1
return np.array(best_lines)
def find_corners(
self,
image: np.ndarray
) -> np.ndarray:
“”“Finding triangle corners on image process.””“
self.__time_service.start_timer()
self.__image = image
coords = self.__get_coords_by_threshold(self.__image)
if self.__need_coeffs_kernel:
neigh_nums = self.__get_neigh_coeffs_list(coords, COEFFS_KERNEL)
else:
neigh_nums = self.__get_neigh_nums_list(coords, self.__kernel_size)
sorted_neigh_nums = self.__sort_neigh_nums_by_N(neigh_nums)
potential_points = self.__get_potential_points_coords(sorted_neigh_nums)
best_lines = self.__get_best_lines(potential_points)
intersection_points = np.array([
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[1][0], -1]]), np.array([-best_lines[0][1], -best_lines[1][1]])),
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[0][1], -best_lines[2][1]])),
np.linalg.solve(np.array([[best_lines[1][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[1][1], -best_lines[2][1]]))
], dtype=np.float32)
return intersection_points
def set_potential_points_num(self, potential_points_num: int) -> None:
“”“Potential points numbers setter.””“
self.__potential_points_num = potential_points_num
def set_iterations_num(self, iterations_num: int) -> None:
“”“Iteration numbers setter.””“
self.__iterations_num = iterations_num
def set_image_threshold(self, image_threshold: int) -> None:
“”“Image threshold setter.””“
self.__image_threshold = image_threshold
def set_threshold_k(self, threshold_k: float) -> None:
“”“Threshold of k parameter setter.””“
self.__threshold_k = threshold_k
def set_threshold_b(self, threshold_b: int) -> None:
“”“Threshold of b parameter setter.””“
self.__threshold_b = threshold_b
def set_threshold_distance(self, threshold_distance: int) -> None:
“”“Threshold of distance area parameter setter.””“
self.__threshold_distance = threshold_distance
def set_need_time_check(self, need_time_check: bool = True):
“”“If you need time checking var setter.””“
self.__need_time_check = need_time_check
def set_need_coeffs_kernel(self, need_coeffs_kernel: bool = True):
“”“If you need coeffs kernel, not just neighbors var setter.””“
self.__need_coeffs_kernel = need_coeffs_kernel
def set_kernel_size(self, kernel_size: int) -> None:
“”“Kernel size setter.””"
self.__kernel_size = kernel_size
class Solver(object):
def init(self):
self.alg = CornerDetector()
def solve(self, image_gray: np.ndarray) -> np.ndarray:
zero_percentage = ImageService.calculate_zero_pixel_percentage(image_gray)
self.alg.set_need_time_check()
if zero_percentage < NOISE_LESS_6:
print(“noise 0.6+”)
self.alg.set_need_coeffs_kernel(False)
self.alg.set_potential_points_num(600)
self.alg.set_iterations_num(1000)
self.alg.set_image_threshold(170)
self.alg.set_threshold_k(THRESHOLD_K_6)
self.alg.set_threshold_b(THRESHOLD_B_6)
self.alg.set_threshold_distance(THRESHOLD_DISTANCE_6)
self.alg.set_kernel_size(7)
elif zero_percentage < NOISE_LESS_3_5:
print(“noise 0.3-0.5”)
self.alg.set_need_coeffs_kernel(False)
self.alg.set_potential_points_num(POTENTIAL_POINTS_NUM_3_5)
self.alg.set_iterations_num(ITERATIONS_NUM_3_5)
self.alg.set_image_threshold(IMAGE_THRESHOLD_3_5)
self.alg.set_threshold_k(THRESHOLD_K_3_5)
self.alg.set_threshold_b(THRESHOLD_B_3_5)
self.alg.set_threshold_distance(THRESHOLD_DISTANCE_3_5)
self.alg.set_kernel_size(KERNEL_SIZE_3_5)
elif zero_percentage < NOISE_LESS_0_2:
print(“noise 0.0-0.2”)
self.alg.set_need_coeffs_kernel(False)
self.alg.set_potential_points_num(POTENTIAL_POINTS_NUM_0_2)
self.alg.set_iterations_num(ITERATIONS_NUM_0_2)
self.alg.set_image_threshold(IMAGE_THRESHOLD_0_2)
self.alg.set_threshold_k(THRESHOLD_K_0_2)
self.alg.set_threshold_b(THRESHOLD_B_0_2)
self.alg.set_threshold_distance(THRESHOLD_DISTANCE_0_2)
self.alg.set_kernel_size(KERNEL_SIZE_0_2)
else:
pass
result = self.alg.find_corners(image_gray)
return result
image = cv2.imread(“0.7/02.pgm”, cv2.COLOR_BGR2GRAY)
solver = Solver()
solver.solve(image)
Есть у меня такой алгоритм, как его можно ускорить? Напиши код, из библиотек можно использовать только numpy, там, где будешь вносить изменения, добавь пояснительный комментарий.
Примени все возможные варианты по ускорению работы алгоритма
|
af435740d0ced21080c05cdbfabc7594
|
{
"intermediate": 0.332959920167923,
"beginner": 0.4342775344848633,
"expert": 0.23276256024837494
}
|
33,112
|
Which shell variables using lld linker from llvm project? There is shell variable that control build-id generation?
|
c749641b77294dbc65d000c09f87c142
|
{
"intermediate": 0.5048762559890747,
"beginner": 0.22413398325443268,
"expert": 0.2709897458553314
}
|
33,113
|
как подключить jdk к vs code
|
38c1235b148de92560274eee5a2092ee
|
{
"intermediate": 0.2786296606063843,
"beginner": 0.44805848598480225,
"expert": 0.2733118534088135
}
|
33,114
|
# <<< CornerDetector >>>
RANDOM_ANSWER = np.array([[1, 2], [3, 4], [6, 9]])
COEFFS_KERNEL = np.array(
[[1, 1, 1, 1, 1],
[1, 5, 5, 5, 1],
[1, 5, 0, 5, 1],
[1, 5, 5, 5, 1],
[1, 1, 1, 1, 1]]
)
# noise 0.0-0.2
POTENTIAL_POINTS_NUM_0_2 = 200
ITERATIONS_NUM_0_2 = 1000
IMAGE_THRESHOLD_0_2 = 0
THRESHOLD_DISTANCE_0_2 = 1
THRESHOLD_K_0_2 = 0.5
THRESHOLD_B_0_2 = 20
NOISE_LESS_0_2 = 100
KERNEL_SIZE_0_2 = 3
# noise 0.3-0.5
POTENTIAL_POINTS_NUM_3_5 = 200
ITERATIONS_NUM_3_5 = 1000
IMAGE_THRESHOLD_3_5 = 100
THRESHOLD_DISTANCE_3_5 = 1
THRESHOLD_K_3_5 = 0.5
THRESHOLD_B_3_5 = 20
NOISE_LESS_3_5 = 75
KERNEL_SIZE_3_5 = 3
# noise 0.6
POTENTIAL_POINTS_NUM_6 = 2000
ITERATIONS_NUM_6 = 2500
IMAGE_THRESHOLD_6 = 150
THRESHOLD_DISTANCE_6 = 3
THRESHOLD_K_6 = 0.5
THRESHOLD_B_6 = 500
NOISE_LESS_6 = 45
KERNEL_SIZE_6 = 9
# <<< TimerService >>>
TIME_MAX = 1.95
class TimeService:
"""Service for time management."""
def __init__(self, time_max: float):
self.time_max = time_max
self.start_time = None
def start_timer(self) -> None:
"""Start time point. Sets 'self.start_time'."""
self.start_time = time.time()
def check_timer(self):
"""Checks if time more than 'self.time_max'."""
end_time = time.time()
delta_time = end_time - self.start_time
if delta_time > self.time_max:
return False
return delta_time
class ImageService:
"""Service for image manipulations."""
@staticmethod
def get_binary_image(image: np.ndarray) -> np.ndarray:
"""Gets binary image from image."""
binary_image = image.copy()
binary_image[np.where(binary_image != 0)] = 255
return binary_image
@staticmethod
def count_zero_pixels(image_gray: np.ndarray) -> int:
"""Counts pixels with 0 intensity on black-white image."""
zero_pixels = np.count_nonzero(image_gray == 0)
return zero_pixels
@staticmethod
def calculate_zero_pixel_percentage(image: np.ndarray) -> float:
"""Calculates zero intensity pixel's percentage."""
total_pixels = image.size
zero_pixels = ImageService.count_zero_pixels(image)
zero_percentage = zero_pixels / total_pixels * 100
return zero_percentage
class CornerDetector(object):
"""Class for corner detection on image."""
def __init__(
self
):
self.__potential_points_num = None
self.__iterations_num = None
self.__image_threshold = None
self.__threshold_k = None
self.__threshold_b = None
self.__threshold_distance = None
self.__need_coeffs_kernel = None
self.__image = None
self.__kernel_size = None
self.__time_service = TimeService(TIME_MAX)
self.__need_time_check = False
self.random_answer = RANDOM_ANSWER
def __get_coords_by_threshold(
self,
image: np.ndarray,
) -> tuple:
"""Gets coordinates with intensity more than 'image_threshold'."""
coords = np.where(self.__image > self.__image_threshold)
return coords
def __get_neigh_nums_list(
self,
coords: tuple,
kernel_size: int
) -> list:
"""
Gets list with number of neighborhoods by kernel.
Returns list like [[x1, y1, N1], [x2, y2, N2]],
Where N - neighborhood's number.
"""
neigh_nums = []
offset = (kernel_size - 1) // 2
for x, y in zip(coords[0], coords[1]):
if x < 0 + offset or x > self.__image.shape[0] - offset:
continue
if y < 0 + offset or y > self.__image.shape[1] - offset:
continue
kernel = self.__image[x-offset:x+offset+1, y-offset:y+offset+1]
step_neigh_num = np.count_nonzero(kernel) - 1
neigh_nums.append([x, y, step_neigh_num])
return neigh_nums
def __get_neigh_coeffs_list(
self, coords: tuple, kernel: np.array
) -> list:
"""
Gets list with number of neighborhoods by kernel.
Returns list like [[x1, y1, N1], [x2, y2, N2]],
Where N - neighborhood's coeffs.
"""
neigh_coeffs = []
offset = (kernel.shape[0] - 1) // 2
print(offset)
for x, y in zip(coords[0], coords[1]):
if x < 0 + offset or x > self.__image.shape[0] - offset:
continue
if y < 0 + offset or y > self.__image.shape[1] - offset:
continue
try:
image_kernel = self.__image[
x - offset:x + offset + 1, y - offset:y + offset + 1]
step_neigh_coeff = np.sum(kernel * image_kernel)
neigh_coeffs.append([x, y, step_neigh_coeff])
except ValueError as e:
continue
return neigh_coeffs
@staticmethod
def __sort_neigh_nums_by_N(
neigh_nums: list
) -> np.ndarray:
"""
Sort neigh_nums by N parameter.
Removes N=-1, N=0 and sorts by N desc.
"""
np_neigh_nums = np.array(neigh_nums)
np_neigh_nums = np_neigh_nums[
(np_neigh_nums[:, 2] != -1) & (np_neigh_nums[:, 2] != 0)]
np_neigh_nums = np_neigh_nums[
np.argsort(-np_neigh_nums[:, 2])]
return np_neigh_nums
def __get_potential_points_coords(
self,
sorted_neigh_nums: np.ndarray
) -> np.ndarray:
"""
Gets best candidates, potential points coords only.
Returns np.ndarray like [[x1, y1], [x2, y2]].
"""
sorted_neigh_nums = np.delete(sorted_neigh_nums, 2, axis=1)
return sorted_neigh_nums[:self.__potential_points_num]
def __get_best_lines(
self,
potential_points: np.ndarray
) -> np.ndarray:
"""
Gets the best combinations of lines by all points.
Line y = kx + b.
Returns np.ndarray like [[k1, b1], [k2, b2]].
"""
remaining_points = 0
best_lines = []
num_lines = 0
remaining_points = np.copy(potential_points)
while num_lines < 3:
print(f"remaining points: {len(remaining_points)}")
best_inlines = []
best_line = None
if len(remaining_points) == 0:
remaining_points = np.array([[1, 1], [2, 2], [3, 3], [4, 4]])
for i in range(self.__iterations_num):
if self.__need_time_check:
if not self.__time_service.check_timer():
return self.random_answer
sample_indices = np.random.choice(
remaining_points.shape[0], size=3, replace=False)
sample_points = remaining_points[sample_indices]
sample_x = sample_points[:, 1]
sample_y = sample_points[:, 0]
coefficients = np.polyfit(sample_x, sample_y, deg=1)
distances = np.abs(remaining_points[:, 0] - np.matmul(np.vstack((remaining_points[:, 1], np.ones_like(remaining_points[:, 1]))).T, coefficients)) / np.sqrt(coefficients[0] ** 2 + 1)
inlines = np.where(distances < self.__threshold_distance)[0]
is_similar = False
for line in best_lines:
diff_k = abs(coefficients[0] - line[0])
diff_b = abs(coefficients[1] - line[1])
if diff_k <= self.__threshold_k and diff_b <= self.__threshold_b:
is_similar = True
break
if not is_similar and len(inlines) > len(best_inlines):
best_line = coefficients
best_inlines = inlines
if best_line is not None:
best_lines.append(best_line)
remaining_points = np.delete(remaining_points, best_inlines, axis=0)
num_lines += 1
return np.array(best_lines)
def find_corners(
self,
image: np.ndarray
) -> np.ndarray:
"""Finding triangle corners on image process."""
self.__time_service.start_timer()
self.__image = image
coords = self.__get_coords_by_threshold(self.__image)
if self.__need_coeffs_kernel:
neigh_nums = self.__get_neigh_coeffs_list(coords, COEFFS_KERNEL)
else:
neigh_nums = self.__get_neigh_nums_list(coords, self.__kernel_size)
sorted_neigh_nums = self.__sort_neigh_nums_by_N(neigh_nums)
potential_points = self.__get_potential_points_coords(sorted_neigh_nums)
best_lines = self.__get_best_lines(potential_points)
intersection_points = np.array([
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[1][0], -1]]), np.array([-best_lines[0][1], -best_lines[1][1]])),
np.linalg.solve(np.array([[best_lines[0][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[0][1], -best_lines[2][1]])),
np.linalg.solve(np.array([[best_lines[1][0], -1], [best_lines[2][0], -1]]), np.array([-best_lines[1][1], -best_lines[2][1]]))
], dtype=np.float32)
return intersection_points
def set_potential_points_num(self, potential_points_num: int) -> None:
"""Potential points numbers setter."""
self.__potential_points_num = potential_points_num
def set_iterations_num(self, iterations_num: int) -> None:
"""Iteration numbers setter."""
self.__iterations_num = iterations_num
def set_image_threshold(self, image_threshold: int) -> None:
"""Image threshold setter."""
self.__image_threshold = image_threshold
def set_threshold_k(self, threshold_k: float) -> None:
"""Threshold of k parameter setter."""
self.__threshold_k = threshold_k
def set_threshold_b(self, threshold_b: int) -> None:
"""Threshold of b parameter setter."""
self.__threshold_b = threshold_b
def set_threshold_distance(self, threshold_distance: int) -> None:
"""Threshold of distance area parameter setter."""
self.__threshold_distance = threshold_distance
def set_need_time_check(self, need_time_check: bool = True):
"""If you need time checking var setter."""
self.__need_time_check = need_time_check
def set_need_coeffs_kernel(self, need_coeffs_kernel: bool = True):
"""If you need coeffs kernel, not just neighbors var setter."""
self.__need_coeffs_kernel = need_coeffs_kernel
def set_kernel_size(self, kernel_size: int) -> None:
"""Kernel size setter."""
self.__kernel_size = kernel_size
class Solver(object):
def __init__(self):
self.alg = CornerDetector()
def solve(self, image_gray: np.ndarray) -> np.ndarray:
zero_percentage = ImageService.calculate_zero_pixel_percentage(image_gray)
self.alg.set_need_time_check()
if zero_percentage < NOISE_LESS_6:
print("noise 0.6+")
self.alg.set_need_coeffs_kernel(False)
self.alg.set_potential_points_num(600)
self.alg.set_iterations_num(1000)
self.alg.set_image_threshold(170)
self.alg.set_threshold_k(THRESHOLD_K_6)
self.alg.set_threshold_b(THRESHOLD_B_6)
self.alg.set_threshold_distance(THRESHOLD_DISTANCE_6)
self.alg.set_kernel_size(7)
elif zero_percentage < NOISE_LESS_3_5:
print("noise 0.3-0.5")
self.alg.set_need_coeffs_kernel(False)
self.alg.set_potential_points_num(POTENTIAL_POINTS_NUM_3_5)
self.alg.set_iterations_num(ITERATIONS_NUM_3_5)
self.alg.set_image_threshold(IMAGE_THRESHOLD_3_5)
self.alg.set_threshold_k(THRESHOLD_K_3_5)
self.alg.set_threshold_b(THRESHOLD_B_3_5)
self.alg.set_threshold_distance(THRESHOLD_DISTANCE_3_5)
self.alg.set_kernel_size(KERNEL_SIZE_3_5)
elif zero_percentage < NOISE_LESS_0_2:
print("noise 0.0-0.2")
self.alg.set_need_coeffs_kernel(False)
self.alg.set_potential_points_num(POTENTIAL_POINTS_NUM_0_2)
self.alg.set_iterations_num(ITERATIONS_NUM_0_2)
self.alg.set_image_threshold(IMAGE_THRESHOLD_0_2)
self.alg.set_threshold_k(THRESHOLD_K_0_2)
self.alg.set_threshold_b(THRESHOLD_B_0_2)
self.alg.set_threshold_distance(THRESHOLD_DISTANCE_0_2)
self.alg.set_kernel_size(KERNEL_SIZE_0_2)
else:
pass
result = self.alg.find_corners(image_gray)
return result
image = cv2.imread("0.7/02.pgm", cv2.COLOR_BGR2GRAY)
solver = Solver()
solver.solve(image)
перепиши только метод __get_best_lines(), чтобы он реализовывал RANSAC. Детектируем три прямые, образующие треугольник, все условия оставь такими же
|
a10e38a5d5b52279cb6c2139267f8f6d
|
{
"intermediate": 0.3022022843360901,
"beginner": 0.46123653650283813,
"expert": 0.23656116425991058
}
|
33,115
|
Перепиши данный код на Python
#include <cstdio>
#include <iostream>
#include <vector>
#include <math.h>
class Drob {
private:
int chisl;
int znam;
public:
Drob() {
chisl = 0;
znam = 0;
}
Drob(int ch, int zn) {
chisl = ch;
znam = zn;
}
int get_ch() {
return chisl;
}
int get_zn() {
return znam;
}
void set_ch(int new_ch) {
chisl = new_ch;
}
void set_zn(int new_zn) {
znam = new_zn;
}
// Метод возвращает представление дроби Фарея при помощи цепной дроби
std::vector<int> chain_drob() {
std::vector<int> vect;
int celoe = chisl / znam, ostat = chisl % znam, r_i = chisl, r_j = znam;
vect.push_back(celoe);
printf("%d = %d * %d + %d\n", r_i, celoe, r_j, ostat);
do {
r_i = r_j;
r_j = ostat;
celoe = r_i / ostat;
ostat = r_i % ostat;
printf("%d = %d * %d + %d\n", r_i, celoe, r_j, ostat);
vect.push_back(celoe);
} while (ostat != 0);
return vect;
}
// Метод для нахождения подходящих дробей
void get_podhod_drob(std::vector<int> v, std::vector<int> &vP, std::vector<int> &vQ) {
int P = -1, Q = -1;
for (int i = 0; i < v.size(); i++) {
if (i == 0) {
P = v[0];
Q = 1;
}
else if (i == 1) {
P = v[0] * v[1] + 1;
Q = v[1];
}
else {
P = v[i] * vP[i - 1] + vP[i - 2];
Q = v[i] * vQ[i - 1] + vQ[i - 2];
}
vP.push_back(P);
vQ.push_back(Q);
std::cout << "P" << i << " = " << vP[i] << " " << "Q" << i << " = " << vQ[i] << "\n";
}
}
// Метод для расчета значений на основе расширенного алгоритма Евклида
void main_calculations(int* start_matr, std::vector<int> a, Drob& res) {
int iter = 0, bi, ui, vi;
int bi_2, bi_1; // первая переменная это b(i-2), вторая b(i-1)
int ui_2, ui_1; // первая переменная это u(i-2), вторая u(i-1)
int vi_2, vi_1; // первая переменная это v(i-2), вторая v(i-1)
printf("%2s %5s %3s %5s %5s\n", "i", "bi", "ai", "ui", "vi");
while (iter < a.size() + 2) {
if (iter == 0) {
bi = this->get_ch();
ui = start_matr[0];
vi = start_matr[1];
bi_2 = bi;
ui_2 = ui;
vi_2 = vi;
printf("%2d %5d %3c %5d %5d\n", iter - 2, bi, '-', ui, vi);
}
else if (iter == 1) {
bi = this->get_zn();
ui = start_matr[2];
vi = start_matr[3];
bi_1 = bi;
ui_1 = ui;
vi_1 = vi;
printf("%2d %5d %3c %5d %5d\n", iter - 2, bi, '-', ui, vi);
}
else {
bi = -bi_1 * a[iter - 2] + bi_2;
ui = ui_1 * a[iter - 2] + ui_2;
vi = vi_1 * a[iter - 2] + vi_2;
bi_2 = bi_1; // новое значение b(i-2)
bi_1 = bi; // новое значение b(i-1)
ui_2 = ui_1; // новое значение u(i-2)
ui_1 = ui; // новое значение u(i-1)
vi_2 = vi_1; // новое значение v(i-2)
vi_1 = vi; // новое значение v(i-1)
printf("%2d %5d %3d %5d %5d\n", iter - 2, bi, a[iter - 2], ui, vi);
res.set_ch(ui);
res.set_zn(vi);
}
iter += 1;
}
if (res.get_zn() < 0) {
res.set_ch(0 - res.get_ch());
res.set_zn(abs(res.get_zn()));
}
}
// Метод для вычитания дробей
Drob sub(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
-right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для сложения дробей
Drob add(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для перемножения дробей
Drob mult(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
0, right_oper.get_zn(),
right_oper.get_ch(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для деления дробей
Drob div(std::vector<int> a, Drob right_oper) {
int* start_matr = new int [4] {
0, right_oper.get_ch(),
right_oper.get_zn(), 0
};
Drob res;
this->main_calculations(start_matr, a, res);
delete[] start_matr;
return res;
}
// Метод для расчета скалярного произведения новой арифметикой
Drob scalar_mult_1(Drob* arr_drA, Drob* arr_drB, const int size) {
Drob res(0, 0);
for (int i = 0; i < size; i++) {
Drob cur_res = arr_drA[i].mult(arr_drA[i].chain_drob(), arr_drB[i]);
printf("------------------------------\n");
printf("Итерация # %d: %5d / %-5d\n", i, cur_res.get_ch(), cur_res.get_zn());
if (i == 0) {
res.set_ch(cur_res.get_ch());
res.set_zn(cur_res.get_zn());
}
else res = res.add(res.chain_drob(), cur_res);
printf("Промежуточная сумма: %5d / %-5d\n", res.get_ch(), res.get_zn());
printf("------------------------------\n");
}
return res;
}
};
// Функция для расчета скалярного произведения обычной арифметикой
double scalar_mult_2(double* vect1, double* vect2, const int size) {
double res = 0;
for (int i = 0; i < size; i++)
res += vect1[i] * vect2[i];
return res;
}
int main() {
setlocale(LC_ALL, "ru");
const int n = 3;
Drob* arr_a = new Drob[n]{
Drob(1, 2),
Drob(2, 3),
Drob(5, 12)
}, * arr_b = new Drob[n]{
Drob(6, 4),
Drob(3, 1),
Drob(10, 13)
};
Drob r = arr_a->scalar_mult_1(arr_a, arr_b, n);
printf("Результат скалярного произведения: %5d / %-5d\n", r.get_ch(), r.get_zn());
printf("Результат скалярного произведения 1: %5.16f\n", double(r.get_ch()) / double(r.get_zn()));
delete[] arr_a, arr_b;
double* v_a = new double [n] { 1.0 / 2, 2.0 / 3, 5.0 / 12},
* v_b = new double [n] {6.0 / 4, 3.0 / 1, 10.0 / 13};
double res = scalar_mult_2(v_a, v_b, n);
printf("Результат скалярного произведения 2: %5.16f\n", res);
delete[] v_a, v_b;
}
|
cde4cb761010b75a3b89a4825654d3da
|
{
"intermediate": 0.3131023645401001,
"beginner": 0.41016659140586853,
"expert": 0.27673104405403137
}
|
33,116
|
Код выводит данные ошибки исправь их
class Drob:
def __init__(self, ch=0, zn=1):
# Инициализация числителя и знаменателя
self.chisl = ch
self.znam = zn
def get_ch(self):
# Получение числителя
return self.chisl
def get_zn(self):
# Получение знаменателя
return self.znam
def set_ch(self, new_ch):
# Установка нового числителя
self.chisl = new_ch
def set_zn(self, new_zn):
# Установка нового знаменателя
self.znam = new_zn
# Метод возвращает представление дроби в виде цепной дроби
def chain_drob(self):
vect = []
celoe = self.chisl // self.znam
ostat = self.chisl % self.znam
r_i = self.chisl
r_j = self.znam
vect.append(celoe)
print(f"{r_i} = {celoe} * {r_j} + {ostat}")
while ostat != 0:
r_i, r_j = r_j, ostat
celoe = r_i // ostat
ostat = r_i % ostat
print(f"{r_i} = {celoe} * {r_j} + {ostat}")
vect.append(celoe)
return vect
# Метод для нахождения подходящих дробей
def get_podhod_drob(self, v):
vP, vQ = [], []
for i in range(len(v)):
if i == 0:
P = v[0]
Q = 1
elif i == 1:
P = v[0] * v[1] + 1
Q = v[1]
else:
P = v[i] * vP[i - 1] + vP[i - 2]
Q = v[i] * vQ[i - 1] + vQ[i - 2]
vP.append(P)
vQ.append(Q)
print(f"P{i} = {vP[i]} Q{i} = {vQ[i]}")
return vP, vQ
# Метод для расчета значений на основе расширенного алгоритма Евклида
def main_calculations(self, start_matr, a):
iter = 0
res = Drob()
ui_2, ui_1, vi_2, vi_1 = start_matr[0], start_matr[2], start_matr[1], start_matr[3]
bi_2 = self.get_ch()
bi_1 = self.get_zn()
print(f"{'i':>2} {'bi':>5} {'ai':>3} {'ui':>5} {'vi':>5}")
for ai in [-1] + a:
if iter == 0:
bi, ui, vi = bi_2, ui_2, vi_2
elif iter == 1:
bi, ui, vi = bi_1, ui_1, vi_1
else:
bi = -bi_1 * ai + bi_2
ui = ui_1 * ai + ui_2
vi = vi_1 * ai + vi_2
bi_2, ui_2, vi_2 = bi_1, ui_1, vi_1
bi_1, ui_1, vi_1 = bi, ui, vi
print(f"{iter - 2:>2} {bi:>5} {'-' if iter < 2 else ai:>3} {ui:>5} {vi:>5}")
res.set_ch(ui)
res.set_zn(vi)
iter += 1
if res.get_zn() < 0:
res.set_ch(-res.get_ch())
res.set_zn(abs(res.get_zn()))
return res
# Операции над дробями
def sub(self, a, right_oper):
# Вычитание дробей
start_matr = [
-right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
]
return self.main_calculations(start_matr, a)
def add(self, a, right_oper):
# Сложение дробей
start_matr = [
right_oper.get_ch(), right_oper.get_zn(),
right_oper.get_zn(), 0
]
return self.main_calculations(start_matr, a)
def mult(self, a, right_oper):
# Умножение дробей
start_matr = [
0, right_oper.get_zn(),
right_oper.get_ch(), 0
]
return self.main_calculations(start_matr, a)
def div(self, a, right_oper):
# Деление дробей
start_matr = [
0, right_oper.get_ch(),
right_oper.get_zn(), 0
]
return self.main_calculations(start_matr, a)
def scalar_mult_1(self, arr_drA, arr_drB, size):
# Расчет скалярного произведения новой арифметикой
res = Drob()
for i in range(size):
cur_res = arr_drA[i].mult(arr_drA[i].chain_drob(), arr_drB[i])
print("------------------------------")
print(f"Iteration # {i}: {cur_res.get_ch()} / {cur_res.get_zn()}")
if i == 0:
res = cur_res
else:
res = res.add(res.chain_drob(), cur_res)
print(f"Intermediate sum: {res.get_ch()} / {res.get_zn()}")
print("------------------------------")
return res
def scalar_mult_2(vect1, vect2, size):
# Расчет скалярного произведения обычной арифметикой
res = 0
for i in range(size):
res += vect1[i] * vect2[i]
return res
# Пример использования:
arr_a = [Drob(1, 2), Drob(2, 3), Drob(5, 12)]
arr_b = [Drob(6, 4), Drob(3, 1), Drob(10, 13)]
r = arr_a[0].scalar_mult_1(arr_a, arr_b, len(arr_a))
print(f"Результат скалярного произведения (новые дроби): {r.get_ch()} / {r.get_zn()}")
print(f"Результат скалярного произведения в виде числа с плавающей точкой (новые дроби): {float(r.get_ch()) / float(r.get_zn())}")
v_a = [1/2, 2/3, 5/12]
v_b = [6/4, 3/1, 10/13]
res = scalar_mult_2(v_a, v_b, len(v_a))
print(f"Результат скалярного произведения (арифметика чисел с плавающей точкой): {res}")
Traceback (most recent call last):
File "C:\Users\User\PycharmProjects\lab2\main.py", line 147, in <module>
r = arr_a[0].scalar_mult_1(arr_a, arr_b, len(arr_a))
File "C:\Users\User\PycharmProjects\lab2\main.py", line 131, in scalar_mult_1
res = res.add(res.chain_drob(), cur_res)
File "C:\Users\User\PycharmProjects\lab2\main.py", line 27, in chain_drob
celoe = self.chisl // self.znam
ZeroDivisionError: integer division or modulo by zero
|
c2c580635a61ce814c4aa1c8fec0eb63
|
{
"intermediate": 0.21612538397312164,
"beginner": 0.6143105030059814,
"expert": 0.1695641130208969
}
|
33,117
|
настрой стили текста для главной и корзины на css <div class="breadcrumbs">
<a href="index.php">Главная</a>
<span href="corzina.php">Корзина</span>
</div>
|
369b2be825560fc6356cd1ba6bfbd4f6
|
{
"intermediate": 0.38261711597442627,
"beginner": 0.3512612283229828,
"expert": 0.26612165570259094
}
|
33,118
|
What does this do?
Windows Registry Editor Version 5.00
[HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Image File Execution Options\3DMark.exe\PerfOptions]
"CpuPriorityClass"=dword:00000003
"IoPriority"=dword:00000003
"PagePriority"=dword:00000005
|
901aecd120e41a330cc1ed5b6bc2b848
|
{
"intermediate": 0.26151925325393677,
"beginner": 0.48644012212753296,
"expert": 0.2520406246185303
}
|
33,119
|
import 'package:cached_network_image/cached_network_image.dart';
import 'package:flutter/material.dart';
import 'package:flutter_riverpod/flutter_riverpod.dart';
import 'package:flutter_screenutil/flutter_screenutil.dart';
import 'package:product_selector/common/widgets/appbar/appbar.dart';
import 'package:product_selector/common/widgets/custom_scaffold.dart';
import 'package:product_selector/screens/handsets/common/widget/search/model/search_argument.dart';
import 'package:product_selector/screens/handsets/handset_details/view/widgets/product_section.dart';
import 'package:product_selector/utilities/colors.dart';
import 'package:product_selector/utilities/utils.dart';
import 'package:video_player/video_player.dart';
import '../model/handset_details_model.dart' as aPrefix;
import '../view_model/handset_details_viewmodel.dart';
class HandsetDetails extends ConsumerStatefulWidget {
static const routeName = '/handsetDetails';
const HandsetDetails({Key? key}) : super(key: key);
@override
ConsumerState<ConsumerStatefulWidget> createState() => _HandsetDetailsState();
}
class _HandsetDetailsState extends ConsumerState<HandsetDetails> {
late VideoPlayerController _controller;
late Future<void> _initializeVideoPlayerFuture;
bool isVideoInitialize = false;
String url = '';
String type = '';
bool primary = true;
HandsetDetailsViewState? handsetDetailsViewState;
@override
void initState() {
_controller = VideoPlayerController.network('');
super.initState();
WidgetsBinding.instance.addPostFrameCallback((_) {
final SearchArguments content =
ModalRoute.of(context)!.settings.arguments as SearchArguments;
ref
.read(handsetDetailsViewModelProvider.notifier)
.getHandsetDetailsServices(content.name!);
});
}
@override
void dispose() {
// Ensure disposing of the VideoPlayerController to free up resources.
if (isVideoInitialize) {
_controller.dispose();
}
super.dispose();
}
@override
Widget build(BuildContext context) {
final handsetDetails = ref.watch(handsetDetailsViewModelProvider);
final SearchArguments content =
ModalRoute.of(context)!.settings.arguments as SearchArguments;
return CustomScaffold(
instance: this,
isLoading: handsetDetails.isLoading,
padding: EdgeInsetsDirectional.only(start: 52.w, end: 54.w, top: 51.h),
appBarOptions: CustomAppBarOptions(instance: this),
body: handsetDetails.isHasDataProduct!
? [
Container(
color: UIColors.darkBlack,
height: 930.h,
width: 974.w,
child: RefreshIndicator(
onRefresh: () async {
ref
.read(handsetDetailsViewModelProvider.notifier)
.getHandsetDetailsServices(content.name!);
},
child: ListView(children: <Widget>[
Container(
color: UIColors.darkBlack,
height: 930.h,
width: 974.w,
child: handsetDetails
.handsetDetailsResponse.primaryAsset!.url !=
null
? videoTest(handsetDetails
.handsetDetailsResponse.primaryAsset!)
: const SizedBox(),
),
]),
),
),
SizedBox(
height: 20.h,
),
listOfImages(handsetDetails.handsetDetailsResponse.assets ?? []),
ProductSection(
handsetDetailsModel: handsetDetails.handsetDetailsResponse,
isHasAvailability:
handsetDetails.handsetDetailsResponse.isAvailable,
),
]
: [
Container(
color: Colors.transparent,
height: 930.h,
width: 974.w,
child: RefreshIndicator(
onRefresh: () async {
ref
.read(handsetDetailsViewModelProvider.notifier)
.getHandsetDetailsServices(content.name!);
},
child: ListView(children: <Widget>[
Container(
height: 930.h,
width: 974.w,
)
]),
),
),
],
);
}
Widget videoTest(aPrefix.Assets primaryAsset) {
if (primaryAsset.type!.toLowerCase() == "video" && primary) {
setState(() {
isVideoInitialize = true;
type = 'video';
url = primaryAsset.url!;
primary = true;
// mutes the video
_controller.setVolume(0);
// Plays the video once the widget is build and loaded.
_controller.play();
});
_controller = VideoPlayerController.network(getImgURL(primaryAsset.url));
_initializeVideoPlayerFuture = _controller.initialize();
}
if (primaryAsset.type!.toLowerCase() == 'image' && primary) {
setState(() {
type = primaryAsset.type!.toLowerCase();
url = primaryAsset.url!;
primary = true;
});
}
return type == 'video'
? FutureBuilder(
future: _initializeVideoPlayerFuture,
builder: (context, snapshot) {
if (snapshot.connectionState == ConnectionState.done) {
return AspectRatio(
aspectRatio: _controller.value.aspectRatio,
child: VideoPlayer(_controller),
);
} else {
return const Center(
child: CircularProgressIndicator(),
);
}
},
)
: imageCard(url);
}
Widget listOfImages(List<aPrefix.Assets> images) {
return SizedBox(
height: 80.h,
child: ListView.builder(
// itemExtent: screenSize(context).width / 11,
shrinkWrap: true,
scrollDirection: Axis.horizontal,
itemCount: images.length,
itemBuilder: ((context, index) {
return GestureDetector(
onTap: () {
setState(() {
type = images[index].type!.toLowerCase();
url = images[index].url!;
primary = false;
if (type == 'video') {
// Plays the video once the widget is build and loaded.
_controller = VideoPlayerController.network(getImgURL(url));
_initializeVideoPlayerFuture = _controller.initialize();
_controller.setVolume(0);
_controller.play();
}
});
},
child: images[index].type!.toLowerCase() == 'image'
? imageCard(images[index].url)
: Container(
width: 70.w,
height: 80.h,
decoration: const BoxDecoration(
image: DecorationImage(
image: AssetImage('assets/images/video.png'),
fit: BoxFit.fill,
),
),
));
}),
),
);
}
Widget imageCard(image) {
return Container(
width: 70.w,
height: 80.h,
child:
CachedNetworkImage(imageUrl: getImgURL(image), fit: BoxFit.fill));
}
}
i want to swipe photos in videoTest widget left and right depend on assets returned in listOfImages widget
|
6d4b956e472e12289c08152628d3427a
|
{
"intermediate": 0.37131258845329285,
"beginner": 0.31693124771118164,
"expert": 0.3117561340332031
}
|
33,120
|
Give detailed python code for setting up AutoGen (especially the setup of the AutoGen agents for this specific use case) to implement the game described here: LLM self-play on 20 Questions
https://evanthebouncy.medium.com/llm-self-play-on-20-questions-dee7a8c63377
|
74e2ca50ddd0973090535f18c993ae47
|
{
"intermediate": 0.365132600069046,
"beginner": 0.343457967042923,
"expert": 0.291409432888031
}
|
33,121
|
. The data file eu2.csv (available from https://github.com/UofGAnalyticsData/DPIP) contains the percapita gross domestic product (GDP) in PPS of seven EU countries. PPS (purchasing power standard) is an artificial
currency unit used by Eurostat. One PPS can buy the same amount of goods and services in each country.
Create a line plot of the PPS in the 6 countries. This should look similar to the plot below.
|
1c0520993fa2ea37fa307edb4f288080
|
{
"intermediate": 0.371023952960968,
"beginner": 0.2939794063568115,
"expert": 0.33499661087989807
}
|
33,122
|
how to override toString java
|
d2f69d9698531041a90e67f31992c43d
|
{
"intermediate": 0.4166712462902069,
"beginner": 0.2901047170162201,
"expert": 0.2932240962982178
}
|
33,123
|
i have to run few commands to update my pm2 server with new code, things like git pull then pm2 stop etc, how can i make a file and write these steps in that and it runs one after another
|
b335cd378aee2d6717647b3b7ed5d94a
|
{
"intermediate": 0.5884961485862732,
"beginner": 0.15260028839111328,
"expert": 0.2589035630226135
}
|
33,124
|
使用visualpython online完成以下任务
1.用这个colab笔记本内的内容替换wine数据 笔记本外链：https://
colab.research.google.com/drive/1jm4M6t1z5OAo7XHKRhDTTY79MtYD8njy
2.使用Gridsearch创建最佳决策树
3.直接发我文本代码就可以
数据如下：
mport pandas as pd
from sklearn.datasets import load_wine
wine = load_wine()
df = pd.DataFrame(wine.data, columns=wine.feature_names)
df['class'] = wine.target
df.tail(
|
cfcb2cff4f6bbb912b97946610718e05
|
{
"intermediate": 0.3828299641609192,
"beginner": 0.34645333886146545,
"expert": 0.2707166373729706
}
|
33,125
|
how to use R to change zero values in data to NA
|
417b1d046e026b51af071c90080b45eb
|
{
"intermediate": 0.2859179973602295,
"beginner": 0.12372401356697083,
"expert": 0.5903579592704773
}
|
33,126
|
Can you trim this list to only include the actual games and emulators and benchmarks ? I don’t care about anything else. so no launchers or software. Just the actual game itself. Keep all the 3dmark ones though.
3DMark.exe
3DMarkAMDFSR.exe
3DMarkCPUProfile.exe
3DMarkDXRFeatureTest.exe
3DMarkICFDemo.exe
3DMarkICFWorkload.exe
3DMarkIntelXeSS.exe
3DMarkNvidiaDLSS.exe
3DMarkNvidiaDLSS2.exe
3DMarkNvidiaDLSS3.exe
3DMarkPCIExpress.exe
3DMarkPortRoyal.exe
3DMarkSamplerFeedbackFeatureTest.exe
3DMarkSolarBay.exe
3DMarkSpeedWay.exe
3DMarkTimeSpy.exe
adapter_info.exe
antimicro.exe
AppleWin.exe
Archon-Win64-Shipping.exe
Ascension Launcher.exe
Ascension.exe
audiodg.exe
Baldur’s Gate - Dark Alliance II.exe
Baldur’s Gate - Dark Alliance.exe
Benchmark.exe
BF2042.exe
bf4.exe
bf4_x86.exe
BigPEmu.exe
Borderlands3.exe
CalMAN App.exe
camelot.exe
Cemu.exe
Cinebench.exe
citra-qt.exe
citra-room.exe
citra.exe
cod.exe
cod22-cod.exe
cod22.exe
Crysis2Remastered.exe
Crysis3Remastered.exe
CrysisRemastered.exe
cs2.exe
cwsdpmi.exe
cxbx.exe
cxbxr-ldr.exe
daphne.exe
DarkAlliance-Win64-Shipping.exe
demul.exe
Diablo III.exe
Diablo III64.exe
Diablo IV.exe
DiabloImmortal.exe
Dolphin.exe
DolphinTool.exe
DolphinWX.exe
dosbox.exe
DSPTool.exe
duckstation-nogui-x64-ReleaseLTCG.exe
duckstation-qt-x64-ReleaseLTCG.exe
DXR_info.exe
EAC.exe
EdenLauncher.exe
EMULATOR.exe
emulator_multicpu.exe
EvilWest.exe
F.E.A.R. 3.exe
Fusion.exe
Gears5.exe
Grim Dawn.exe
gsplus.exe
HermesProxy.exe
Heroes of the Storm.exe
HeroesOfTheStorm_x64.exe
HighMoon-Win64-Shipping.exe
hl2.exe
HogwartsLegacy.exe
HUNTED.EXE
hypjsch.exe
hypseus.exe
input_grabber.exe
Last Epoch.exe
left4dead2.exe
love.exe
lovec.exe
mame.exe
MCC-Win64-Shipping.exe
mednafen.exe
Mesen.exe
mgba-sdl.exe
mGBA.exe
NFS13.exe
NO$GBA.EXE
Oblivity.exe
OpenBOR.exe
Oricutron.exe
pcsx2-qtx64-avx2.exe
pcsx2-qtx64.exe
pcsx2.exe
PhoenixEmuProject.exe
PinMAME32.exe
PPSSPPWindows.exe
PPSSPPWindows64.exe
Project64.exe
PVZ.Main_Win64_Retail.exe
raine.exe
Realms Of Ancient War.exe
redream.exe
rerev2.exe
retroarch.exe
rpcs3.exe
Ryujinx.exe
scummvm.exe
Sega Model 3 UI.exe
SimCoupe.exe
SkyrimSE.exe
SkyrimTogether.exe
SkyrimTogetherServer.exe
snes9x-x64.exe
solarus-launcher.exe
solarus-run.exe
SOTTR.exe
Starfield.exe
starwarsbattlefront.exe
starwarsbattlefrontii.exe
starwarsjedifallenorder.exe
Storage.exe
StorageReader.exe
Supermodel.exe
SwGame-Win64-Shipping.exe
TinyTina.exe
trine2_32bit.exe
Tsugaru_CUI.exe
Tsugaru_GUI.exe
UNDECEMBER-Win64-Shipping.exe
UNDECEMBER.exe
Updater.exe
vikings.exe
Vita3K.exe
vpinballx.exe
VPinMameTest.exe
WatchDogs2.exe
winuae64.exe
Wolcen.exe
Wow.exe
WowClassic.exe
xemu.exe
xenia.exe
xenia_canary.exe
yuz-room.exe
yuzu-cmd.exe
yuzu-room.exe
yuzu.exe
zsnes.exe
|
e61a47178c5900482a811ad33e595641
|
{
"intermediate": 0.2724241614341736,
"beginner": 0.47435981035232544,
"expert": 0.2532159686088562
}
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.