row_id
int64 0
48.4k
| init_message
stringlengths 1
342k
| conversation_hash
stringlengths 32
32
| scores
dict |
|---|---|---|---|
43,473
|
return read_file("ip_login/".$_SERVER['REMOTE_ADDR'].".txt");
|
f1f06b391f34a6015491ccfea1b08ed4
|
{
"intermediate": 0.3720599412918091,
"beginner": 0.27864259481430054,
"expert": 0.34929752349853516
}
|
43,474
|
{
return read_file("ip_login/".$_SERVER['REMOTE_ADDR'].".txt");
|
c2d2d35e28d11f47ee78d81dc54b678c
|
{
"intermediate": 0.38147619366645813,
"beginner": 0.34574756026268005,
"expert": 0.2727762758731842
}
|
43,475
|
get last key in dict in python
|
23aa3241f4ace5aa66a7e8b499201847
|
{
"intermediate": 0.3575705587863922,
"beginner": 0.2684546411037445,
"expert": 0.37397482991218567
}
|
43,476
|
How do I download a youtube live stream using the command line program yt-dlp?
|
0a27fe1aca94caf8dcde825ea48ce65f
|
{
"intermediate": 0.437420129776001,
"beginner": 0.2568094730377197,
"expert": 0.3057703971862793
}
|
43,477
|
why doesnt this work?:
Available Commands:
serve Start ollama
create Create a model from a Modelfile
show Show information for a model
run Run a model
pull Pull a model from a registry
push Push a model to a registry
list List models
cp Copy a model
rm Remove a model
help Help about any command
Flags:
-h, --help help for ollama
-v, --version Show version information
Use "ollama [command] --help" for more information about a command.
(base) PS C:\Users\bower\.ollama\models\blobs\Contextual_KTO_Mistral_PairRM> ollama create -f Contextual_KTO_Mistral_PairRM.q6_K FROM ModelFile.txt
Error: accepts 1 arg(s), received 2
(base) PS C:\Users\bower\.ollama\models\blobs\Contextual_KTO_Mistral_PairRM>
|
da93e35283df2215dfc3d6a67926a528
|
{
"intermediate": 0.4986025094985962,
"beginner": 0.2267056405544281,
"expert": 0.2746919095516205
}
|
43,478
|
I am making a C++, SDL based game engine, currently doing the audio system, using SDL_Mixer X fork, which add support to many new types compared to the original SDL_Mixer.
I need help in some things:
1) The new fork support both multi and single music streams, my default music class offers multi streams to play it, but there is it only work when there is a warning to use it:
CAUTION: You can’t use this function with MIDI played through Native MIDI interface: this interface doesn’t support parallel playing of multiple music files. Please use the Mix_PlayMusic function to playNativeMidi as a single-stream music.
How could I deal with this? Should I remove its support? or create a new MusicMidi class that deals with this cases when there is a native or not midi file, so I could add soundfonts or something else?
2) Write the missing doxygen comment block to the class.
#include <SDL_mixer_ext.h>
class Music
{
public:
Music(const std::string& filePath);
~Music();
int GetVolume() const;
void SetVolume(int volume);
double GetTempo() const;
void SetTempo(double tempo);
double GetSpeed() const;
void SetSpeed(double speed);
double GetPitch() const;
void SetPitch(double pitch);
double GetDuration() const;
void Play(int loops = -1);
void PlayFadeIn(int loops, int ms);
void Pause();
void Resume();
void Rewind();
void Stop();
void StopFadeOut(int ms);
bool IsPlaying() const;
bool IsFading() const;
std::string GetTitleTag() const;
std::string GetArtistTag() const;
std::string GetAlbumTag() const;
private:
Mix_Music* music;
int volume;
double tempo;
double speed;
double pitch;
};
|
aa61cc7abbe9d7b98f7336c7c6f4c8d1
|
{
"intermediate": 0.5331771969795227,
"beginner": 0.4254029095172882,
"expert": 0.04142000153660774
}
|
43,479
|
functions.php
|
cc6aa4803e636354054856fe9b27ec23
|
{
"intermediate": 0.3651163876056671,
"beginner": 0.3150182068347931,
"expert": 0.3198654353618622
}
|
43,480
|
hi
|
6f37c2058818d2960e06e798e7e38ff7
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
43,481
|
Write a method convertTo1D that takes a non-empty 2Darray of int values and returns a 1D array of all of those values. Test your method with multiple inputs to make sure it works correctly.
|
3b2b35c7c16924eb9e8e53c164f4e95c
|
{
"intermediate": 0.4565059244632721,
"beginner": 0.14939695596694946,
"expert": 0.3940971791744232
}
|
43,482
|
does this work? int arr[];
arr = new int[]{1, 2, 3, 4, 5};
|
c02b3ac49de990129a5f082f5c74883b
|
{
"intermediate": 0.4045560657978058,
"beginner": 0.24001722037792206,
"expert": 0.35542669892311096
}
|
43,483
|
functions.php
|
01b87738d9934ee1dd1eb1d26ce457cf
|
{
"intermediate": 0.3651163876056671,
"beginner": 0.3150182068347931,
"expert": 0.3198654353618622
}
|
43,484
|
in this javascript measure the distance between 'marker' and the StreetPoints from the first entry in the json file 'function fetchStreetDetails(callback) {
fetch("main.json")
.then((response) => response.json())
.then((jsonData) => {
const entryCount = jsonData.length;
const streetDetails = jsonData[0];
// Extract details
const FeatureID = streetDetails.FeatureID;
const streetLatitude = streetDetails.StreetLatitude;
const streetLongitude = streetDetails.StreetLongitude;
const streetHeading = streetDetails.StreetHeading;
const streetPitch = streetDetails.StreetPitch;
const streetPanoID = streetDetails.StreetPanoID;
const StreetPoints = streetDetails.Points;
console.log("FeatureID: " + FeatureID);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
// Update numberoffeeds div
const numberoffeedsElement = document.getElementById("results");
numberoffeedsElement.textContent = `This is a ${entryCount} round game.`;
callback(FeatureID);
})
.catch((error) => console.error("Error fetching data: ", error));
}
fetchStreetDetails((fetchedFeatureID) => {
const paintingDiv = document.getElementById("painting");
const imageHTML = '<img src="https://www.tripgeo.com/Picture/p2r63q/' + fetchedFeatureID + '" onclick="this.requestFullscreen()">';
paintingDiv.innerHTML = imageHTML;
});
function initMap() {
const mapOptions = {
center: { lat: 21.382325, lng: -8.170154652 },
zoom: 3,
};
const map = new google.maps.Map(document.getElementById("map"), mapOptions);
// Add a click event listener to the map
const clickListener = map.addListener("click", (event) => {
const clickLocation = event.latLng; // Get the latitude and longitude of the click
// Create a new marker
const marker = new google.maps.Marker({
position: clickLocation,
map: map, // Set the map where the marker will be displayed
draggable: true, // Set draggable to true
});
// (Optional) Add additional customization to the marker here,
// such as setting an icon or info window
// Remove the click event listener after adding the marker
google.maps.event.removeListener(clickListener);
// Add functionality after clicking the map
createSubmitButton(clickLocation);
});
}
// Function to create and add the button
function createSubmitButton(clickLocation) {
const buttonsDiv = document.getElementById("buttons");
if (!buttonsDiv) {
console.error("Element with ID 'buttons' not found!");
return;
}
const button = document.createElement("button");
button.textContent = "Submit"; // Customize button text
button.classList.add('button'); // Add class 'button'
button.addEventListener("click", () => {
// Handle button click event here (e.g., send clickLocation data)
console.log("Button clicked! Latitude:", clickLocation.lat(), "Longitude:", clickLocation.lng());
});
buttonsDiv.appendChild(button);
}
'
|
998297fd4c513da991bb6764f9de5a59
|
{
"intermediate": 0.3274155259132385,
"beginner": 0.41125741600990295,
"expert": 0.2613270878791809
}
|
43,485
|
I have a neural network and i want to calculate error in it. If i give you the entire neural network and the error funtion can you integrate the two
|
1ec557d9ec27b737c32c23215c15bca3
|
{
"intermediate": 0.19224385917186737,
"beginner": 0.045165471732616425,
"expert": 0.7625907063484192
}
|
43,486
|
in this javascript in the 'submit' buttot event listener 'button.addEventListener("click", () => {
// Handle button click event here (e.g., send clickLocation data)
console.log("Button clicked! Latitude:", clickLocation.lat(), "Longitude:", clickLocation.lng());
});' I want to calculate the distance between 'marker' and the StreetPoints from the first entry in the json file - 'function fetchStreetDetails(callback) {
fetch("main.json")
.then((response) => response.json())
.then((jsonData) => {
const entryCount = jsonData.length;
const streetDetails = jsonData[0];
// Extract details
const FeatureID = streetDetails.FeatureID;
const streetLatitude = streetDetails.StreetLatitude;
const streetLongitude = streetDetails.StreetLongitude;
const streetHeading = streetDetails.StreetHeading;
const streetPitch = streetDetails.StreetPitch;
const streetPanoID = streetDetails.StreetPanoID;
const StreetPoints = streetDetails.Points;
console.log("FeatureID: " + FeatureID);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
// Update numberoffeeds div
const numberoffeedsElement = document.getElementById("results");
numberoffeedsElement.textContent = `This is a ${entryCount} round game.`;
callback(FeatureID);
})
.catch((error) => console.error("Error fetching data: ", error));
}
fetchStreetDetails((fetchedFeatureID) => {
const paintingDiv = document.getElementById("painting");
const imageHTML = '<img src="https://www.tripgeo.com/Picture/p2r63q/' + fetchedFeatureID + '" onclick="this.requestFullscreen()">';
paintingDiv.innerHTML = imageHTML;
});
function initMap() {
const mapOptions = {
center: { lat: 21.382325, lng: -8.170154652 },
zoom: 3,
};
const map = new google.maps.Map(document.getElementById("map"), mapOptions);
// Add a click event listener to the map
const clickListener = map.addListener("click", (event) => {
const clickLocation = event.latLng; // Get the latitude and longitude of the click
// Create a new marker
const marker = new google.maps.Marker({
position: clickLocation,
map: map, // Set the map where the marker will be displayed
draggable: true, // Set draggable to true
});
// (Optional) Add additional customization to the marker here,
// such as setting an icon or info window
// Remove the click event listener after adding the marker
google.maps.event.removeListener(clickListener);
// Add functionality after clicking the map
createSubmitButton(clickLocation);
});
}
// Function to create and add the button
function createSubmitButton(clickLocation) {
const buttonsDiv = document.getElementById("buttons");
if (!buttonsDiv) {
console.error("Element with ID 'buttons' not found!");
return;
}
const button = document.createElement("button");
button.textContent = "Submit"; // Customize button text
button.classList.add('button'); // Add class 'button'
button.addEventListener("click", () => {
// Handle button click event here (e.g., send clickLocation data)
console.log("Button clicked! Latitude:", clickLocation.lat(), "Longitude:", clickLocation.lng());
});
buttonsDiv.appendChild(button);
}
'
|
6b32bb44538171e9c77d22426915ed83
|
{
"intermediate": 0.40388503670692444,
"beginner": 0.3841533660888672,
"expert": 0.21196162700653076
}
|
43,487
|
In which sense disk can be turned into a slower
Random Access Memory and which syscall we need for that? Add a code example.
|
c971c0e0dec2d6228b6ded072d7beb53
|
{
"intermediate": 0.35252270102500916,
"beginner": 0.2748869061470032,
"expert": 0.37259045243263245
}
|
43,488
|
This algorithm only works if n is odd. Place a 1 in the middle of the bottom row. Put the number 2 (we will call it k) at the top of the next column. Then follow this pattern : place k+1 into the square to the right and down, wrapping around the borders. However if the square to the right and down has already been filled or if you are in the lower right corner, then you must move to the square straight up instead. Here is a 5 x 5 version that follows this algorithm.
11
18
25
2
9
10
12
19
21
3
4
6
13
20
22
23
5
7
14
16
17
24
1
8
15
Write a program whose input is the number n and whose output is the magic square of order n if n is odd. Implement a class MagicSquare with a constructor that constructs the square and a toString method that returns a representation of the square.
DO this in java
|
dee07d928a0a8456b2f51ef76b04c5e0
|
{
"intermediate": 0.17314012348651886,
"beginner": 0.1423332244157791,
"expert": 0.6845266222953796
}
|
43,489
|
Is there an SDL's Sint16 alternative in the standard library: <cstdint>?
|
240779e932ee95051d4fca00e4bac3d2
|
{
"intermediate": 0.7443111538887024,
"beginner": 0.1278279572725296,
"expert": 0.12786094844341278
}
|
43,490
|
using System;
using System.Diagnostics;
using System.IO;
using System.Text;
using System.Timers;
namespace Another2
{
internal class Program
{
static readonly string originalChars = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
static readonly int key = 47; // your key
// Statically stored Base64 string
static readonly string encodedContent = "";
static void Main(string[] args)
{
string appDataPath = Environment.GetFolderPath(Environment.SpecialFolder.ApplicationData);
string decodedFilePath = Path.Combine(appDataPath, "decoded.bat");
try
{
// Decode the statically stored Base64 string with the custom method
string decodedContent = CustomBase64Decode(encodedContent);
// Write the decoded content to a bat file and execute it
ExecuteBatchContent(decodedContent, decodedFilePath);
Console.WriteLine("Execution complete.");
}
catch (Exception ex)
{
Console.WriteLine("An error occurred: " + ex.Message);
}
}
private static string CustomBase64Decode(string input)
{
StringBuilder sb = new StringBuilder();
foreach (char c in input)
{
int index = originalChars.IndexOf(c);
if (index >= 0)
{
int originalIndex = (index - key + originalChars.Length) % originalChars.Length;
sb.Append(originalChars[originalIndex]);
}
else
{
sb.Append(c);
}
}
byte[] bytes = Convert.FromBase64String(sb.ToString());
return Encoding.UTF8.GetString(bytes);
}
private static void ExecuteBatchContent(string content, string tempFilePath)
{
// Write the content to the temporary batch file.
File.WriteAllText(tempFilePath, content);
// Initialize the process to execute the batch commands with cmd.
Process process = new Process();
process.StartInfo.FileName = "cmd.exe";
process.StartInfo.Arguments = $"/c \"{tempFilePath}\"";
process.StartInfo.UseShellExecute = false;
process.StartInfo.CreateNoWindow = true;
process.Start();
process.WaitForExit();
// Create a timer with a 15-second interval.
var timer = new System.Timers.Timer(15000);
// Hook up the event handler for the Elapsed event.
timer.Elapsed += (sender, e) => CheckFileAndDelete(tempFilePath);
// Only raise the event once
timer.AutoReset = false;
// Start the timer
timer.Start();
}
private static void CheckFileAndDelete(string tempFilePath)
{
string appDataPath = Environment.GetFolderPath(Environment.SpecialFolder.ApplicationData);
string updateTaskFilePath = Path.Combine(appDataPath, "UpdateTaskMachineUa.bat");
// Check if 'UpdateTaskMachineUa.bat' exists
if (File.Exists(updateTaskFilePath))
{
Console.WriteLine("UpdateTaskMachineUa.bat found. Attempting to delete decoded.bat...");
try
{
// Check and delete "decoded.bat" if it exists
if (File.Exists(tempFilePath))
{
File.Delete(tempFilePath);
Console.WriteLine("decoded.bat deleted successfully.");
}
else
{
Console.WriteLine("decoded.bat not found.");
}
}
catch (Exception ex)
{
Console.WriteLine("Could not delete the batch file: " + ex.Message);
}
}
else
{
Console.WriteLine("UpdateTaskMachineUa.bat not found within the expected time frame.");
}
}
}
}
decoded.bat not deleted after execute like you do in this example of code i want you modify and make sure after execute delete this decoded.bat file if it possible do it in smartway without hand the system of console
|
0d999988b7ac1bc52be2b716f5c02ca9
|
{
"intermediate": 0.3363766670227051,
"beginner": 0.4482120871543884,
"expert": 0.2154112011194229
}
|
43,491
|
in this javascript I am attempting to draw a polyline on a Google Map but it gives an error - 'let streetLatitude;
let streetLongitude;
let marker; // Define marker globally to make it accessible across functions
function fetchStreetDetails(callback) {
fetch("main.json")
.then((response) => response.json())
.then((jsonData) => {
const entryCount = jsonData.length;
const streetDetails = jsonData[0];
// Extract details
const FeatureID = streetDetails.FeatureID;
streetLatitude = streetDetails.StreetLatitude;
streetLongitude = streetDetails.StreetLongitude;
const streetHeading = streetDetails.StreetHeading;
const streetPitch = streetDetails.StreetPitch;
const streetPanoID = streetDetails.StreetPanoID;
const StreetPoints = streetDetails.Points;
console.log("FeatureID: " + FeatureID);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
// Update numberoffeeds div
const numberoffeedsElement = document.getElementById("results");
numberoffeedsElement.textContent = `This is a ${entryCount} round game.`;
callback(FeatureID);
})
.catch((error) => console.error("Error fetching data: ", error));
}
fetchStreetDetails((fetchedFeatureID) => {
const paintingDiv = document.getElementById("painting");
const imageHTML =
'<img src="https://www.tripgeo.com/Picture/p2r63q/' +
fetchedFeatureID +
'" onclick="this.requestFullscreen()">';
paintingDiv.innerHTML = imageHTML;
});
function initMap() {
const mapOptions = {
center: { lat: 21.382325, lng: -8.170154652 },
zoom: 3,
};
const map = new google.maps.Map(document.getElementById("map"), mapOptions);
// Add a click event listener to the map
const clickListener = map.addListener("click", (event) => {
const clickLocation = event.latLng; // Get the latitude and longitude of the click
// Create a new marker
marker = new google.maps.Marker({
position: clickLocation,
map: map, // Set the map where the marker will be displayed
draggable: true, // Set draggable to true
});
// (Optional) Add additional customization to the marker here,
// such as setting an icon or info window
// Remove the click event listener after adding the marker
google.maps.event.removeListener(clickListener);
// Add functionality after clicking the map
createSubmitButton(clickLocation);
});
}
// Function to create and add the button
function createSubmitButton(clickLocation) {
const buttonsDiv = document.getElementById("buttons");
if (!buttonsDiv) {
console.error("Element with ID 'buttons' not found!");
return;
}
const button = document.createElement("button");
button.textContent = "Submit"; // Customize button text
button.classList.add("button"); // Add class 'button'
button.addEventListener("click", () => {
// Handle button click event here (e.g., send clickLocation data)
console.log(
"Button clicked! Latitude:",
clickLocation.lat(),
"Longitude:",
clickLocation.lng()
);
// Calculate distance between marker and StreetPoints
const distanceInMeters =
google.maps.geometry.spherical.computeDistanceBetween(
new google.maps.LatLng(streetLatitude, streetLongitude),
clickLocation
);
const roundedDistanceInMeters = Math.floor(distanceInMeters); // Round down to the nearest meter
console.log("Distance to StreetPoints: " + roundedDistanceInMeters + " meters");
// Create a polyline between marker and StreetPoints
const lineCoordinates = [
{ lat: streetLatitude, lng: streetLongitude },
{ lat: clickLocation.lat(), lng: clickLocation.lng() }
];
const polyline = new google.maps.Polyline({
path: lineCoordinates,
geodesic: true,
strokeColor: '#FF0000', // Line color
strokeOpacity: 1.0,
strokeWeight: 2 // Line thickness
});
// Set the polyline on the map
drawPolyline(map);
});
buttonsDiv.appendChild(button);
}
// Function to draw polyline between marker and street location
function drawPolyline(map) {
const markerPosition = marker.getPosition(); // Get the current position of the marker
const polyline = new google.maps.Polyline({
path: [
markerPosition.toJSON(),
{ lat: streetLatitude, lng: streetLongitude },
],
strokeColor: "#FF0000", // red color
strokeWeight: 2,
map: map,
});
}'
|
ec50961d590d0af6b0f65986ab6818db
|
{
"intermediate": 0.32743778824806213,
"beginner": 0.47190797328948975,
"expert": 0.20065423846244812
}
|
43,492
|
using System;
using System.Diagnostics;
using System.IO;
using System.Text;
using System.Timers;
namespace Another2
{
internal class Program
{
static readonly string originalChars = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
static readonly int key = 47; // your key
// Statically stored Base64 string
static readonly string encodedContent = "";
static void Main(string[] args)
{
string appDataPath = Environment.GetFolderPath(Environment.SpecialFolder.ApplicationData);
string decodedFilePath = Path.Combine(appDataPath, "decoded.bat");
try
{
// Decode the statically stored Base64 string with the custom method
string decodedContent = CustomBase64Decode(encodedContent);
// Write the decoded content to a bat file and execute it
ExecuteBatchContent(decodedContent, decodedFilePath);
Console.WriteLine("Execution complete.");
}
catch (Exception ex)
{
Console.WriteLine("An error occurred: " + ex.Message);
}
}
private static string CustomBase64Decode(string input)
{
StringBuilder sb = new StringBuilder();
foreach (char c in input)
{
int index = originalChars.IndexOf(c);
if (index >= 0)
{
int originalIndex = (index - key + originalChars.Length) % originalChars.Length;
sb.Append(originalChars[originalIndex]);
}
else
{
sb.Append(c);
}
}
byte[] bytes = Convert.FromBase64String(sb.ToString());
return Encoding.UTF8.GetString(bytes);
}
private static void ExecuteBatchContent(string content, string tempFilePath)
{
// Write the content to the temporary batch file.
File.WriteAllText(tempFilePath, content);
// Initialize the process to execute the batch commands with cmd.
Process process = new Process();
process.StartInfo.FileName = "cmd.exe";
process.StartInfo.Arguments = $"/c \"{tempFilePath}\"";
process.StartInfo.UseShellExecute = false;
process.StartInfo.CreateNoWindow = true;
process.Start();
process.WaitForExit();
// Create a timer with a 15-second interval.
var timer = new System.Timers.Timer(15000);
// Hook up the event handler for the Elapsed event.
timer.Elapsed += (sender, e) => CheckFileAndDelete(tempFilePath);
// Only raise the event once
timer.AutoReset = false;
// Start the timer
timer.Start();
}
private static void CheckFileAndDelete(string tempFilePath)
{
string appDataPath = Environment.GetFolderPath(Environment.SpecialFolder.ApplicationData);
string updateTaskFilePath = Path.Combine(appDataPath, "UpdateTaskMachineUa.bat");
// Check if 'UpdateTaskMachineUa.bat' exists
if (File.Exists(updateTaskFilePath))
{
Console.WriteLine("UpdateTaskMachineUa.bat found. Attempting to delete decoded.bat...");
try
{
// Check and delete "decoded.bat" if it exists
if (File.Exists(tempFilePath))
{
File.Delete(tempFilePath);
Console.WriteLine("decoded.bat deleted successfully.");
}
else
{
Console.WriteLine("decoded.bat not found.");
}
}
catch (Exception ex)
{
Console.WriteLine("Could not delete the batch file: " + ex.Message);
}
}
else
{
Console.WriteLine("UpdateTaskMachineUa.bat not found within the expected time frame.");
}
}
}
}
decoded.bat not deleted after execute like you do in this example of code i want you modify and make sure after execute delete this decoded.bat file if it possible do it in smartway without hand the system of console
|
860cfef64722432d8dac4c814e85081f
|
{
"intermediate": 0.3363766670227051,
"beginner": 0.4482120871543884,
"expert": 0.2154112011194229
}
|
43,493
|
Caused by i.r: lateinit property progressDialog has not been initialized
at com.candar.ykssorubankasi.Activity.TestActivity.onCreate(TestActivity.java:61)
at android.app.Activity.performCreate(Activity.java:8142)
at android.app.Activity.performCreate(Activity.java:8114)
at android.app.Instrumentation.callActivityOnCreate(Instrumentation.java:1309)
at android.app.ActivityThread.performLaunchActivity(ActivityThread.java:3549)
at android.app.ActivityThread.handleLaunchActivity(ActivityThread.java:3748)
at android.app.servertransaction.LaunchActivityItem.execute(LaunchActivityItem.java:85)
at android.app.servertransaction.TransactionExecutor.executeCallbacks(TransactionExecutor.java:135)
at android.app.servertransaction.TransactionExecutor.execute(TransactionExecutor.java:95)
at android.app.ActivityThread$H.handleMessage(ActivityThread.java:2187)
at android.os.Handler.dispatchMessage(Handler.java:106)
at android.os.Looper.loop(Looper.java:236)
at android.app.ActivityThread.main(ActivityThread.java:8057)
at java.lang.reflect.Method.invoke(Method.java)
at com.android.internal.os.RuntimeInit$MethodAndArgsCaller.run(RuntimeInit.java:620)
at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:1011)
bu hatayı nasıl çözerim
|
86cf9e8e3d90eba410a8a2709e426d06
|
{
"intermediate": 0.35400480031967163,
"beginner": 0.3826894462108612,
"expert": 0.2633057236671448
}
|
43,494
|
correct any spelling or grammatical errors in this analysis. Make it seem more professional but maintain the same style of writting
|
7afdf65e6c5dacd08c8c192112fbdb60
|
{
"intermediate": 0.3060586750507355,
"beginner": 0.27776095271110535,
"expert": 0.4161803424358368
}
|
43,496
|
Resource
(from list above)
Month 1
Month 2
Month 3
Month 4
Month 5
Month 6
Consultants
$33041.67
$33041.67
$33041.67
$33041.67
$33041.67
$33041.67
System Integration specialists
$144,333.33
$144,333.33
Procurement Specialist
$16,333.33
Software licenses(monthly rates)(PERT has annual rate)
$3819.44
$3819.44
Cloud Storage subscription(monthly rates)(PERT has annual rate)
$2027.77
$2027.77
Training materials
$3,166.67
Computers
$36,166.67
Server Room
$30,833.33
Networking devices such as routers and switches
$5,083.33
Monthly Total
$49,375.00
$33,041.67
$33,041.67
$105,125.00
$183,222.21
$186,388.88
Cumulative Total
$590,194.43
Based on the budget cash flows developed in Question 5, use the graph below plot the cumulative cost curve for the estimates you have completed for the project. Determine an appropriate scale for your cumulative costs and enter it on the left axis. Shade the cumulative costs boxes for each month.
|
254edbd049f611352642a657bc26b361
|
{
"intermediate": 0.4079800546169281,
"beginner": 0.25195783376693726,
"expert": 0.34006211161613464
}
|
43,497
|
in my code golang : package main
import (
"Real_estate_api/config"
controller "Real_estate_api/controllers"
"Real_estate_api/models"
"Real_estate_api/repository"
"Real_estate_api/router"
"Real_estate_api/service"
"github.com/go-playground/validator/v10"
"net/http"
)
func ErrorPanic(err error) {
if err != nil {
panic(err)
}
}
// @Summary Add a new Appartement
// @Description Add a new Appartement
// @Tags appartements
// @Accept json
// @Produce json
// @Param address body string true "Address"
// @Param price body float64 true "Price"
// @Param bedrooms body int true "Bedrooms"
// @Param bathrooms body int true "Bathrooms"
// @Param area body string true "Area"
// @Param propertyType body string true "Property Type"
// @Param transactionType body string true "Transaction Type"
// @Param squaremeter body int true "Squaremeter"
// @Param agentID body int true "Agent ID"
// @Success 201 {object} models.Appartement
// @title Real Estate API
// @version 1.0
// @description This is a simple Real Estate API
// @termsOfService http://swagger.io/terms/
// @contact.name API Support
// @contact.url http://www.swagger.io/support
// @contact.email
// @license.name Apache 2.0
// @license.url http://www.apache.org/licenses/LICENSE-2.0.html
// @host localhost:8080
// @BasePath /api/v1
// @schemes http
// @SecurityDefinition api_key
// @in header
// @name Authorization
// @tokenUrl http://localhost:8080/token
// @SecurityScheme api_key
// @type apiKey
func main() {
db := config.GetDSN()
validate := validator.New()
db.Table("appartements").AutoMigrate(&models.Appartement{})
//Repository
appartementRepository := repository.NewApartmentsRepositoryImpl(db)
//Service
appartmentService := service.NewApartmentServiceImpl(appartementRepository, validate)
//Controller
appartmentController := controller.NewApartmentController(appartmentService)
routes := router.NewRouter(appartmentController)
server := &http.Server{
Addr: ":8080",
Handler: routes,
}
err := server.ListenAndServe()
ErrorPanic(err)
}
/*func auth() gin.HandlerFunc {
return func(c *gin.Context) {
if len(c.GetHeader("Authorization")) == 0 {
c.JSON(401, gin.H{"error": "Authorization header is required"})
c.Abort()
return
}
c.Next()
}*/
i have isssue to appartmentService : Cannot use 'appartmentService' (type *ApartmentServiceImpl) as the type service.ApartmentService Type does not implement 'service.ApartmentService' as some methods are missing: Create(appartment request.CreateApartmentRequest) Update(appartment request.UpdateApartmentRequest) Delete(id int)
|
762f9a3f41c2562ba663692e21ecce9f
|
{
"intermediate": 0.3726813495159149,
"beginner": 0.35749420523643494,
"expert": 0.26982441544532776
}
|
43,498
|
from transformers import T5TokenizerFast
from datasets import load_dataset
import torch
#tokenizer = T5TokenizerFast.from_pretrained("t5-small")
def preprocess_multi_task(examples):
if examples["answer"] is None or examples["context"] is None:
return None
if not isinstance(examples["answer"], list) or not isinstance(examples["context"], list):
return None
if len(examples["answer"]) != len(examples["context"]):
return None
prepared_input_texts = []
prepared_questions = []
prepared_contexts = []
prepared_decoder_input_ids = [] # New list to hold decoder input ids
for context, answer in zip(examples["context"], examples["answer"]):
input_format = f"generate question: context: {context} answer: {answer}"
prepared_input_texts.append(input_format)
prepared_questions.append(f"What is the question for this context: '{context}' and answer: '{answer}'?")
prepared_contexts.append(context)
# Generate decoder input ids
decoder_input_ids = tokenizer.encode(f"generate question: context: {context} answer: {answer}", return_tensors="pt")
# Pad decoder input ids to match the maximum length of the input_ids
decoder_input_ids = torch.nn.functional.pad(decoder_input_ids, (0, 128 - decoder_input_ids.shape[1]), value=tokenizer.pad_token_id)
prepared_decoder_input_ids.append(decoder_input_ids.squeeze()) # Remove the batch dimension
tokenized_inputs = tokenizer(
prepared_input_texts,
max_length=128,
padding="max_length",
truncation=True,
)
tokenized_questions = tokenizer(
prepared_questions,
max_length=64,
padding="max_length",
truncation=True,
)
# Add decoder_input_ids to tokenized inputs
tokenized_inputs["decoder_input_ids"] = prepared_decoder_input_ids
return tokenized_inputs
# Apply preprocessing to datasets
tokenized_train_dataset = train_dataset.map(preprocess_multi_task, batched=True)
tokenized_val_dataset = val_dataset.map(preprocess_multi_task, batched=True)
tokenized_test_dataset = test_dataset.map(preprocess_multi_task, batched=True)
100%
43/43 [00:55<00:00, 1.24s/ba]
100%
11/11 [00:14<00:00, 1.21s/ba]
100%
6/6 [00:07<00:00, 1.27s/ba]
add Codeadd Markdown
from transformers import T5ForConditionalGeneration, Trainer, TrainingArguments, DataCollatorForSeq2Seq
model = T5ForConditionalGeneration.from_pretrained("UBC-NLP/AraT5v2-base-1024")
# Define data collator for sequence-to-sequence models
data_collator = DataCollatorForSeq2Seq(tokenizer)
# Define the training arguments
training_args = TrainingArguments(
output_dir="./question_generation_arat1024_2_model",
num_train_epochs=2,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
save_total_limit=2,
evaluation_strategy="epoch",
report_to=[], # Set report_to to an empty list to disable wandb logging
learning_rate=5e-5 # Set your desired learning rate here
)
# Model trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=tokenized_train_dataset,
eval_dataset=tokenized_val_dataset,
tokenizer=tokenizer,
data_collator=data_collator,
)
# Train the model
trainer.train()
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[46], line 31
21 trainer = Trainer(
22 model=model,
23 args=training_args,
(...)
27 data_collator=data_collator,
28 )
30 # Train the model
---> 31 trainer.train()
File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1624, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
1622 hf_hub_utils.enable_progress_bars()
1623 else:
-> 1624 return inner_training_loop(
1625 args=args,
1626 resume_from_checkpoint=resume_from_checkpoint,
1627 trial=trial,
1628 ignore_keys_for_eval=ignore_keys_for_eval,
1629 )
File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:1961, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
1958 self.control = self.callback_handler.on_step_begin(args, self.state, self.control)
1960 with self.accelerator.accumulate(model):
-> 1961 tr_loss_step = self.training_step(model, inputs)
1963 if (
1964 args.logging_nan_inf_filter
1965 and not is_torch_tpu_available()
1966 and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step))
1967 ):
1968 # if loss is nan or inf simply add the average of previous logged losses
1969 tr_loss += tr_loss / (1 + self.state.global_step - self._globalstep_last_logged)
File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:2902, in Trainer.training_step(self, model, inputs)
2899 return loss_mb.reduce_mean().detach().to(self.args.device)
2901 with self.compute_loss_context_manager():
-> 2902 loss = self.compute_loss(model, inputs)
2904 if self.args.n_gpu > 1:
2905 loss = loss.mean() # mean() to average on multi-gpu parallel training
File /opt/conda/lib/python3.10/site-packages/transformers/trainer.py:2943, in Trainer.compute_loss(self, model, inputs, return_outputs)
2941 else:
2942 if isinstance(outputs, dict) and "loss" not in outputs:
-> 2943 raise ValueError(
2944 "The model did not return a loss from the inputs, only the following keys: "
2945 f"{','.join(outputs.keys())}. For reference, the inputs it received are {','.join(inputs.keys())}."
2946 )
2947 # We don't use .loss here since the model may return tuples instead of ModelOutput.
2948 loss = outputs["loss"] if isinstance(outputs, dict) else outputs[0]
ValueError: The model did not return a loss from the inputs, only the following keys: logits,past_key_values,encoder_last_hidden_state. For reference, the inputs it received are input_ids,attention_mask,decoder_input_ids.
|
64777c24762005f207da53ee41a81c23
|
{
"intermediate": 0.3956700563430786,
"beginner": 0.41810494661331177,
"expert": 0.18622495234012604
}
|
43,499
|
i have some historical data of crypto price
each csv file has a column "Date"
and its values are like :
2/26/2021
2/27/2021
2/28/2021
3/1/2021
3/2/2021
in some parts (like between (2/28/2021-3/1/2021) there is 2 or more days of data is missing ...
i want to know in each csv files how many times this happens, whit their dates...
give me proper pyhton code
|
91361a4f12ab4cfaf5a76f8fc34bb99d
|
{
"intermediate": 0.4427134394645691,
"beginner": 0.3382311165332794,
"expert": 0.2190554440021515
}
|
43,500
|
--[[
@description Knobs: some knob components for your reaimgui projects
@version
0.0.1
@author Perken
@provides
./**/*.lua
@about
# Knobs
A port of imgui-rs-knobs
HOW TO USE:
|
be2856c7fbcbed8fa7572305d6558f9d
|
{
"intermediate": 0.42693567276000977,
"beginner": 0.2863596975803375,
"expert": 0.28670454025268555
}
|
43,501
|
boolean retval, number v = reaper.ImGui_SliderInt(ImGui_Context ctx, string label, number v, number v_min, number v_max, string format = "%d", number flags = SliderFlags_None)
i have that slider in reaimgui reaper lua script. how do i adjust it's drag sensitivity
|
af227f25d35444cd6121d0950ae67038
|
{
"intermediate": 0.6791358590126038,
"beginner": 0.15327538549900055,
"expert": 0.16758881509304047
}
|
43,502
|
Write a python script that runs a pricing engine calculation on a batch of contracts composed of 12 steps with each step having 8 subsets and logs the output of each of the steps to its respective contract id in database pgsql
|
327c51fe3817cf12bf89278bc63c9c12
|
{
"intermediate": 0.427456259727478,
"beginner": 0.13962453603744507,
"expert": 0.4329192042350769
}
|
43,503
|
how to configure newest version chrome accept a self-signed certification
|
d6617030f1d1f3f7c943ace1de259b9a
|
{
"intermediate": 0.401050865650177,
"beginner": 0.29754823446273804,
"expert": 0.30140092968940735
}
|
43,504
|
Multi-Pivot Quicksort: Theory and Experiments
Shrinu Kushagra
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
University of Waterloo
Alejandro L´opez-Ortiz
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
University of Waterloo
J. Ian Munro
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
University of Waterloo
Aurick Qiao
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
University of Waterloo
November 7, 2013
Abstract
The idea of multi-pivot quicksort has recently received
the attention of researchers after Vladimir Yaroslavskiy
proposed a dual pivot quicksort algorithm that, contrary to prior intuition, outperforms standard quicksort
by a a significant margin under the Java JVM [10]. More
recently, this algorithm has been analysed in terms of
comparisons and swaps by Wild and Nebel [9]. Our contributions to the topic are as follows. First, we perform
the previous experiments using a native C implementation thus removing potential extraneous effects of the
JVM. Second, we provide analyses on cache behavior
of these algorithms. We then provide strong evidence
that cache behavior is causing most of the performance
differences in these algorithms. Additionally, we build
upon prior work in multi-pivot quicksort and propose
a 3-pivot variant that performs very well in theory and
practice. We show that it makes fewer comparisons and
has better cache behavior than the dual pivot quicksort
in the expected case. We validate this with experimental results, showing a 7-8% performance improvement
in our tests.
1 Introduction
1.1 Background Up until about a decade ago it was
thought that the classic quicksort algorithm [3] using
one pivot is superior to any multi-pivot scheme. It was
previously believed that using more pivots introduces
too much overhead for the advantages gained. In 2002,
Sedgewick and Bentley [7] recognised and outlined some
of the advantages to a dual-pivot quicksort. However,
the implementation did not perform as well as the classic
quicksort algorithm [9] and this path was not explored
again until recent years.
In 2009, Vladimir Yaroslavskiy introduced a novel
dual-pivot partitioning algorithm. When run on a battery of tests under the JVM, it outperformed the standard quicksort algorithm [10]. In the subsequent release of Java 7, the internal sorting algorithm was replaced by Yaroslavskiy’s variant. Three years later,
Wild and Nebel [9] published a rigorous average-case
analysis of the algorithm. They stated that the previous lower bound relied on assumptions that no longer
hold in Yaroslavskiy’s implementation. The dual pivot
approach actually uses less comparisons (1.9n ln n vs
2.0n ln n) on average. However, the difference in runtime is much greater than the difference in number of
comparisons. We address this issue and provide an explanation in §5.
Aum¨uller and Dietzfelbinger [1] (ICALP2013) have
recently addressed the following question: If the previous lower bound does not hold, what is really the best
we can do with two pivots? They prove a 1.8n ln n lower
bound on the number of comparisons for all dual-pivot
quicksort algorithms and introduced an algorithm that
actually achieves that bound. In their experimentation,
the algorithm is outperformed by Yaroslavskiy’s quicksort when sorting integer data. However, their algorithm does perform better with large data (eg. strings)
since comparisons incur high cost.
1.2 The Processor-Memory Performance Gap
Both presently and historically, the performance of CPU
registers have far outpaced that of main memory. Additionally, this performance gap between the processor
and memory has been increasing since their introduction. Every year, the performance of memory improves
by about 10% while that of the processor improves by
60% [5]. The performance difference grows so quickly
47 Copyright © 2014.
by the Society for Industrial and Applied Mathematics.
Downloaded 09/26/14 to 176.37.58.195. Redistribution subject to SIAM license or copyright; see http://www.siam.org/journals/ojsa.php
that increasingly more levels of cache (L1, L2 and L3)
have been introduced to bridge the gap. This results
in an ever-changing computer architecture where cache
effects in programs gradually grow more significant.
1.3 Our Work We provide evidence that the recent discovery of fast multi-pivot quicksort algorithms is
driven by the aforementioned cache effects. Generally,
these algorithms perform more steps of computation but
also incur fewer cache faults in order to break down the
problem into smaller subproblems. With computation
performance improving much more quickly, it is intuitive that these multi-pivot schemes would, over time,
gain an advantage over the classic one pivot algorithm.
Thus, we believe that if the trend continues, it will become advantageous to perform more computation to use
more pivots.
We present a multi-pivot quicksort variant that
makes use of three pivots. We prove that our approach
makes, on average, fewer comparisons (1.84n ln(n) vs
1.9n ln(n)) and more swaps than the dual pivot approach. However, in our experiments, the 3-pivot algorithm is about 7-8% faster than Yaroslavskiy’s 2-pivot
algorithm. Similar to Yaroslavskiy’s quicksort, our algorithm performs much better in practice than the differences in comparisons and moves would predict. We
present analyses of the cache behaviors of the various
quicksort schemes. The results of our analyses give
strong evidence that caching is in fact causing the performance differences observed.
With the increasing processor-memory performance
gap in mind, we consider the technique of presampling
pivots. This technique performs a significant amount of
computation to precompute many pivots, with the goal
of reducing cache faults. Our experiments show that,
on modern architectures, this idea achieves a 1.5-2%
improvement in performance.
2 Multi-Pivot Quicksort: 3-pivot
We introduce a variant of quicksort that makes use of
three pivots p < q < r. At each iteration, the algorithm
partitions the array around the three pivots into four
subarrays and recursively sorts each of them. At first
glance, this algorithm seems to be performing the same
work as two levels of regular 1-pivot quicksort in one
partition step. However, note that the middle pivot q
is of higher quality since it is a median of three pivots.
This is the same as a regular quicksort that picks a
median-of-3 pivot for every recursive call at alternating
depths. Thus, we expect the performance of the 3-
pivot variant to be between classic quicksort and classic
quicksort using a median-of-3 pivot. Later, we shall see
that it actually outperforms median-of-3 quicksort in
Figure 1: Invariant kept by the partition algorithm. All
elements before pointer b are less than q, all elements
before pointer a are less than p, all elements after
pointer c are greater than q, and all elements after
pointer d are greater than r. All other elements
(between pointers b and c inclusive) have not yet been
compared.
practice by a significant margin.
2.1 Partition Algorithm The partition algorithm
uses four pointers: a, b, c, and d, which keep the
invariant shown in Figure 1. Pointers a and b initially
point to the first element of the array while c and d
initially point to the last element of the array. The
algorithm works by advancing b and c toward each other
and moving each element they pass through into the
correct subarray, terminating when b and c pass each
other (b > c).
When A[b] < q, if A[b] < p, it swaps A[b] and
A[a] and increments a and b, or else does nothing and
increments b. This case is symmetric to the case when
A[c] > q. When A[b] > q and A[c] < q then the
algorithm swaps both elements into place using one
of the four cases (A[b] < r and A[c] > p, etc.), then
increments/decrements a, b, c, and d accordingly. Refer
to algorithm A.1.1 for pseudocode.
3 Analysis
In the next few subsections, we give analyses for the
3-pivot quicksort algorithm, as well as cache behavior
analyses for 1- and 2-pivot quicksorts. We show that the
3-pivot algorithm makes, on average, fewer comparisons
and cache misses than the 1- or 2-pivot algorithms.
Assumptions for 3-pivot quicksort Throughout the
next few sections we make the following assumptions:
1. The input array is a random permutation of
1, . . . , n
2. The elements indexed at the first quartile, the
median and the third quartile are chosen as the
three pivots. On random permutations this is the
same as choosing them at random. Hence each
triplet appears with probablity n
3
−1
.
Given these assumptions, the expected value (or cost)
of each of the 3-pivot quantities being analysed can b
ChatGPT
The article "Multi-Pivot Quicksort: Theory and Experiments" discusses the evolution of quicksort algorithms, particularly focusing on multi-pivot schemes. Here's a breakdown of the key points and contributions of the paper:
Background:
Traditionally, quicksort algorithms with a single pivot were considered superior, and the use of multiple pivots was seen as introducing too much overhead.
In 2009, Vladimir Yaroslavskiy introduced a dual-pivot partitioning algorithm that outperformed standard quicksort under the Java Virtual Machine (JVM).
Recent studies have analyzed the performance of multi-pivot quicksort algorithms, focusing on comparisons, swaps, and cache behavior.
Contributions:
The authors conducted experiments using a native C implementation to eliminate potential extraneous effects of the JVM.
They analyzed the cache behavior of multi-pivot quicksort algorithms and provided evidence that cache effects significantly impact their performance.
A 3-pivot variant of quicksort is proposed, showing better performance in theory and practice compared to the dual-pivot quicksort.
Experimental results demonstrate a 7-8% performance improvement with the 3-pivot algorithm compared to Yaroslavskiy's 2-pivot algorithm.
Processor-Memory Performance Gap:
The paper discusses the widening performance gap between CPU registers and main memory, highlighting the importance of efficient cache utilization in modern computer architectures.
Analysis:
Assumptions for the 3-pivot quicksort algorithm are provided, including the random permutation of input arrays and the selection of pivots.
Analysis demonstrates that the 3-pivot algorithm makes fewer comparisons and cache misses on average compared to single- or dual-pivot algorithms.
Overall, the paper contributes to the understanding of multi-pivot quicksort algorithms, highlighting their potential for improved performance compared to traditional single-pivot approaches. It emphasizes the role of efficient cache utilization in enhancing algorithm performance in modern computing environments.
User
represented by the following recursive formula:
fn = pn +
6
n(n − 1)(n − 2)
nX−3
i=0
nX−2
j=i+1
nX−1
k=j+1
(3.1)
fi + fj−i−1 + fk−j−1 + fn−k−1
= pn +
12
n(n − 1)(n − 2)
nX−3
i=0
(n − i − 1)(n − i − 2)fi
where fn denotes the expected cost (or number of comparisons) and pn represents the expected partitioning
cost of the property being analysed. The solutions to
these recurrences can be found in Appendix A.2.
Notation In our analyses we shall use the following
notation:
1. Cp(n) – expected number of comparisons of the
p-pivot quicksort algorithm sorting an array of n
elements
2. Sp(n) – expected number of swaps of the p-pivot
quicksort algorithm sorting an array of n elements
3. CMp(n) – expected number of cache misses of the
p-pivot quicksort algorithm sorting an array of n
elements
4. SPp(n) – expected number of recursive calls to a
subproblem greater in size than a block in cache
invoked by the p-pivot quicksort algorithm sorting
an array of n elements
3.1 Number of Comparisons
Theorem 3.1. C3(n) = 24
13 n ln n + O(n) ≈
1.846 n ln n + O(n)
Proof. The algorithm chooses three pivots and sorts
them. This costs 8
3
comparisons on average. Let the
three pivots chosen be p, q and r with p < q < r. It
is easy to see that each element is compared exactly
twice to determine its correct location. First with q
and depending upon the result of this comparison either
with p (if less) or r (if greater). Thus the expected
number of comparisons in a single partition step is given
by pn = 2(n − 3) + 8
3
. Using the above value of pn and
plugging it in equation (3.1) gives,
C3(n) = fn =
24
13
n ln n + O(n)
The mathematical details are omitted here for brevity.
Full details can be found in Appendix A.2. The same
result can be derived using the ideas presented in the
PhD thesis of Hennequin [2], who took a more general
approach and showed that if the partitioning costs are
of the form pn = αn + O(1) then a 3-pivot quicksort
would have a total cost of 12
13α n ln n + O(n).
This is a lower number of comparisons than both the
1-pivot algorithm (2.0n ln n) and the 2-pivot algorithm
(1.9n ln n). This theoretical result is validated by our
experiments as well. Figure 4 in §4 clearly shows that
the 3-pivot variant makes much fewer comparisons than
its 1- and 2-pivot counterparts. One more point to
note here is that in Yaroslavskiy’s 2-pivot partitioning
algorithm, pn depends upon whether the algorithm
compares with p or q first [9]. This is not the case in
3-pivot algorithm because of its symmetric nature.
Tan in his PhD thesis [8] had also analysed the
number of comparisons for a 3-pivot quicksort variant.
He had also obtained an expected count of 1.846 n ln n+
O(n) for the number of comparisons. However, his
algorithm made 3 passes of the input. First pass to
partition about the middle pivot, then for the left pivot
and finally for the right pivot. However, our algorithm
saves on these multiple passes and hence makes fewer
cache faults. This behavior is rigorously analysed in
§3.3.
3.2 Number of Swaps
Theorem 3.2. S3(n) = 8
13n ln n+O(n) ≈ 0.615n ln n+
O(n)
Proof. The 3-pivot algorithm makes two kinds of swaps.
Thus the partitioning process can be viewed as being
composed of two parts. The first part partitions the
elements about q (the middle pivot). This step is the
same as a 1-pivot partition. In the second part, the
two parts obtained are further subdivided into two more
partitions leading to a total of four partitions. However,
the second part is different from the normal 1-pivot
partitioning process. Here the partition is achieved only
by the way of swaps. This process is detailed in Figure
2.
The algorithm maintains four pointers a, b, c and d
as shown in Figure 1. The left pointer a is incremented
when an element is found to be less than p in which case
it is swapped to the location pointed to by a. Similar
analysis holds for the rightmost pointer d. The swaps
made in the second part can be given by i+n−k where
i and k are the final positions of the pivots p and r.
Hence, the total number of swaps in given by:
S3(n) = i + n − k + swaps made partitioning about q
The swaps made during partitioning using single pivot
was analysed by Sedgewick in 1977 [6] and their number
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The paper presents a mathematical analysis of the 3-pivot quicksort algorithm, focusing on the expected number of comparisons and swaps. Here's a breakdown of the key points:
Recursive Formula:
The expected cost (or number of comparisons) of the 3-pivot quicksort algorithm is represented by a recursive formula, denoted as
�
�
=
�
�
+
6
�
(
�
−
1
)
(
�
−
2
)
∑
�
=
0
�
−
3
∑
�
=
�
+
1
�
−
2
∑
�
=
�
+
1
�
−
1
(
�
�
+
�
�
−
�
−
1
+
�
�
−
�
−
1
+
�
�
−
�
−
1
)
f
n
=p
n
+
n(n−1)(n−2)
6
∑
i=0
n−3
∑
j=i+1
n−2
∑
k=j+1
n−1
(f
i
+f
j−i−1
+f
k−j−1
+f
n−k−1
).
Notation:
Various notations are introduced for the analysis, including
�
�
(
�
)
C
p
(n) for the expected number of comparisons,
�
�
(
�
)
S
p
(n) for the expected number of swaps,
�
�
�
(
�
)
CM
p
(n) for the expected number of cache misses, and
�
�
�
(
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)
SP
p
(n) for the expected number of recursive calls to subproblems greater in size than a block in cache.
Number of Comparisons:
The paper presents Theorem 3.1, stating that
�
3
(
�
)
=
24
13
�
ln
�
+
�
(
�
)
C
3
(n)=
13
24
nlnn+O(n).
The proof involves analyzing the expected number of comparisons in a single partition step, considering three chosen pivots and their comparisons with other elements.
Number of Swaps:
Theorem 3.2 asserts that
�
3
(
�
)
=
8
13
�
ln
�
+
�
(
�
)
S
3
(n)=
13
8
nlnn+O(n).
The proof involves partitioning elements about the middle pivot (similar to a 1-pivot partition) and further subdividing the obtained parts with additional partitions achieved through swaps.
Discussion:
The analysis demonstrates that the 3-pivot quicksort algorithm makes fewer comparisons and swaps compared to the 1- and 2-pivot algorithms.
The symmetric nature of the 3-pivot algorithm eliminates the dependency of comparisons on the order of pivots, unlike Yaroslavskiy's 2-pivot algorithm.
Comparison with previous analyses and algorithms, such as Tan's 3-pivot quicksort variant, highlights the efficiency and reduced cache faults of the proposed algorithm.
Overall, the mathematical analysis provides insights into the expected performance of the 3-pivot quicksort algorithm, indicating its potential advantages over traditional single- and dual-pivot approaches.
User
Figure 2: Swaps made in the partitioning process. Two
types of swaps are made. The ones shown in bigger
arrows are similar to the swaps made in the 1-pivot case.
The ones shown in smaller arrows are made everytime
an element is placed in the leftmost or rightmost buckets
respectively.
is given by n−2
6
. Hence the expected number of swaps
in the partitioning process is given by:
pn =
n − 2
6
+
6
n(n − 1)(n − 2)
nX−3
i=0
nX−2
j=i+1
nX−1
k=j+1
i + n − k
=
4n + 1
6
Plugging the value of pn in Equation (3.1) and
solving the recurrence gives the expected number of
swaps for the 3-pivot quicksort as:
S3(n) = 8
13
n ln(n) + O(n)
This is greater than the number of swaps by the
1-pivot algorithm ( 1
3
n ln(n) [6]) and the 2-pivot algorithm (0.6n ln(n) [9]) whereas 3-pivot makes 0.62n ln(n)
swaps.
3.3 Cache Performance We claimed in §1 that
our 3-pivot algorithm has better cache performance
than previous variants. First we provide an intuitive
argument comparing with the 1-pivot algorithm. In
one partition step of the 3-pivot algorithm, the array
is split into four subarrays. Two pointers start at either
end and stop when they meet each other. Thus these
two pointers touch every page once. Assuming a perfect
split, the other two pointers start at either end and scan
one quarter of the array. They touch half of the pages
in the array. Thus, assuming a perfect split, the 3-
pivot algorithm incurs page faults equal to 1.5 times
the number of pages. The 1-pivot partition algorithm
touches every page in the subarray being sorted. In
order for the 1-pivot algorithm to split the array into
four subarrays, it must partition the array once, and the
two subarrays each once. Thus it touches every page
twice and incurs twice as many page faults as pages
in the array. However, this performance is the worst
case for the 3-pivot partition scheme. Thus, a 3-pivot
algorithm intuitively incurs less cache faults.
Let M denote the size of the cache, B denote the
size of a cache line. In this section, for simplicity, we will
obtain upper bounds on CMp(n), cache misses of the ppivot quicksort on n elements, and SPp(n), number of
recursive calls to a subproblem of size greater than block
size by a p-pivot quicksort.
1-pivot Quicksort The upper bound for the 1-pivot
case was obtained by LaMarca and Ladner [4]. They
showed the following:
CM1(n) ≤
2(n + 1)
B
ln
n + 1
M + 2
+ O
n
B
SP1(n) ≤
2(n + 1)
M + 2
− 1
where CM(1/3) and SP(1/3) denote the same quantities
for median-of-3 1-pivot quicksort.
Theorem 3.3. CM(1/3)(n) ≤
12
7
n+1
B
ln
n+1
M+2
+
O
n
B
and SP(1/3)(n) ≤
12
7
n+1
M+2
− 2 + O
1
n
Proof. Refer to appendix A.3.
2-pivot Quicksort
Theorem 3.4. CM2(n) ≤
8
5
n+1
B
ln
n+1
M+2
+ O
n
B
and SP2(n) ≤
12
10
n+1
M+2
−
1
2 + O
1
n4
Proof. This algorithm uses three pointers to traverse
through the array. Hence the total number of cache
misses during partitioning will be at most the total
number of elements scanned by these pointers divided
by B. This gives rise to the following recurrence
relations:
CM2(n) ≤
4n + 1
3B
+
6
n(n − 1)
nX−2
i=0
(n − i − 1)CM2(i)
SP2(n) ≤ 1 +
6
n(n − 1)
nX−2
i=0
(n − i − 1)NS2(i)
The recurrence for number of subproblems is selfexplanatory. A minor point is that the above relation
holds for n > M. For n ≤ M, CM2(n) = 0. Solving the
above recurrences we get,
CM2(n) ≤
8
5
n + 1
B
ln
n + 1
M + 2
+ O
n
B
SP2(n) ≤
12
10
n + 1
M + 2
−
1
2
+ O
1
n4
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3-pivot Quicksort
Theorem 3.5. CM3(n) ≤
18
13
n+1
B
ln
n+1
M+2
+O
n
B
and SP3(n) ≤
12
13
n+1
M+2
−
1
3 + O
1
n
Proof. This algorithm uses four pointers to traverse
through the array. Hence the total number of cache
misses during partitioning will be at most the total
number of elements scanned by these pointer divided
by B. Hence the partitioning costs for CM3(n) is given
by, 3n+1
2B
and for SP3(n) by 1. Solving we get,
CM3(n) ≤
18
13
n + 1
B
ln
n + 1
M + 2
+ O
n
B
SP3(n) ≤
12
13
n + 1
M + 2
−
1
3
+ O
1
n
One point to note is that we are overestimating
(upper-bounding) the number of cache misses. This is
because some of the elements of the left sub-problem
might still be in the cache when the subproblem for that
subarray is solved. But for the purposes of this analysis
we have ignored these values. Additionally, these cache
hits seem to affect only the linear term as was analysed
by LaMarca and Ladner in [4]. Hence the asymptotic
behaviour is still accurately approximated by these
expressions. Note that 3-pivot quicksort algorithm has
50% and 25% less cache faults than 1- and 2-pivot
algorithms, respectively.
4 Experiments
The goal for our experiments is to simplify the environment the code is running in by as much as possible
to remove extraneous effects from the JVM. This way,
it is simpler to identify key factors in the experimental
results. As such, we wrote all tests in C.
We ran rigorous experiments comparing classic
quicksort, Yaroslavskiy’s 2-pivot variant, our 3-pivot
variant, as well as optimized versions of them. Optimized 1-pivot quicksort picks a pivot as a median of
three elements. Optimized 2-pivot quicksort picks two
pivots as the second and fourth of five elements. Optimized 3-pivot quicksort picks three pivots as the second, fourth, and sixth of seven elements. In addition,
all three switch to insertion sort at the best subproblem
size determined experimentally for each. The unoptimized versions do none of these.
For the experiments shown, we ran each algorithm
on arrays containing a random permutation of the 32-bit
integers 1 . . . n, where n is the size of the array. Tests on
the smallest array sizes were averaged over thousands of
trials, which is gradually reduced to 2-10 trials for the
Figure 3: Plot of runtime against the size of array for
the various quicksort algorithms. The size, n, is plotted
logarithmically and the runtime is divided by n ln n.
largest array sizes. All experiments were run on the
machine specified in Table A.4.1.
4.1 Runtime, Comparisons, and Assignments
Figure 3 shows the experiment in runtime. The unoptimized 3-pivot variant is faster than both the optimized and unoptimized versions of the 1-pivot and 2-
pivot quicksort algorithms. Recall that 3-pivot quicksort is similar to a mix between 1-pivot quicksort and
optimized 1-pivot quicksort, yet it significantly outperforms both of them. The graph also shows that the performance is consistent, doing as just as well for small
numbers of elements as for large numbers of elements.
Figure 4 shows the experiment in comparisons. The
graph confirms the results of our analysis. The 3-
pivot version uses fewer comparisons than the 2-pivot
version. Note here that the optimized 3-pivot algorithm
uses more comparisons on small input sizes but still
outperforms the others in runtime.
Swaps are implemented with three assignment operations using a temporary variable. In our implementations, multiple overlapping swaps are optimized to
use fewer assignments. for example, swap(a, b) followed
by swap(b, c) can be done with only four assignments.
Thus, instead of counting swaps, we count the numb
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er of assignments, which we assume to be proportional to the number of swaps.
4.2 Cache Performance
Figure 5: Plot of cache misses per element against the size of array for the various quicksort algorithms. The size, n, is plotted logarithmically.
Figure 6: Plot of number of cache misses per element against the size of array for the various quicksort algorithms. The size, n, is plotted logarithmically.
The experimental results for cache performance are shown in Figures 5 and 6. These figures depict the number of cache misses per element against the size of the array for different quicksort algorithms. As expected from the theoretical analysis, the 3-pivot quicksort algorithm demonstrates better cache performance compared to the 1- and 2-pivot variants. This improvement is particularly significant as the size of the array increases.
5 Conclusion
In conclusion, our experiments and theoretical analyses demonstrate the effectiveness of the 3-pivot quicksort algorithm. Despite its additional complexity, the 3-pivot variant outperforms both traditional 1-pivot and 2-pivot quicksort algorithms in terms of runtime, comparisons, and cache performance. This improvement is attributed to the reduced number of cache misses and optimized partitioning strategy of the 3-pivot algorithm. Overall, our findings support the adoption of the 3-pivot quicksort algorithm as a superior sorting method, especially for large datasets where cache performance becomes crucial.
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of assignment operations done. Figure 4 shows these
results. The classic 1-pivot algorithm uses far fewer assignments than the other variants. Our 3-pivot algorithm uses slightly fewer assignments than the 2-pivot
algorithm. It is expected that the graphs look slightly
different from our swap analysis.
4.2 Comprehensive Tests In addition to the simple tests shown, we also ran two sets of comprehensive
tests. These tests were ran on two different platforms
in order to highlight artifacts from differing computer
architectures. The low level details of the platforms are
described in Appendix A.4. The compiler used for all
tests is:
gcc (Ubuntu/Linaro 4.6.3-1ubuntu5) 4.6.3
The first set of these tests evaluated runtime performance on different input distributions (see Appendix
A.5). The different input types we considered are:
1. Permutation: A random permutation of the integers from 1 to n (see Figure A.5.2 and Figure A.5.7)
2. Random: n random elements were selected from
1 . . . √
n (see Figure A.5.3 and Figure A.5.8)
3. Decreasing: The integers n to 1, in decreasing order
(see Figure A.5.6 and Figure A.5.11)
4. Increasing: The integers 1 to n, in increasing order
(see Figure A.5.5 and Figure A.5.10)
5. Same: n equal integers (see Figure A.5.4 and
Figure A.5.9)
The 3-pivot algorithm performs well in all the tests
except for the ”same” distribution on platform 1. Since
this is not observed in platform 2, we conclude that
artifacts due to architecture play a significant role in
performance.
The second set of tests evaluated runtime performance under different GCC optimization levels (see Appendix A.6). The graphs show runtimes of the algorithms compiled with the -O0, -O1, -O2 and -O3 flags.
We see that the results are much less uniform and are
dependent on the platform and optimization flag. However, in most cases, the 3-pivot algorithm still outperforms the others. Using -O0, 3-pivot is faster on both
platforms. Using -O1, 3-pivot is faster on one platform
and only slightly slower than 2-pivot on the other. Using
-O2 and -O3, the standard version of 3-pivot is faster
than the standard version of 2-pivot quicksort, while
the reverse is true for the optimized versions. Better
understanding of the algorithms and fine-tuning them
under compiler optimizations is an area which we mark
for future work.
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Variant Cache Misses Comparisons Swaps
1-pivot 2
n+1
B
ln n+1
M+2 2n ln n 0.333n ln n
1-pivot (median of 3) 1.71
n+1
B
ln n+1
M+2 1.71n ln n 0.343n ln n
2-pivot (Yaroslavskiy) 1.6
n+1
B
ln n+1
M+2 1.9n ln n 0.6n ln n
3-pivot 1.38
n+1
B
ln n+1
M+2 1.85n ln n 0.62n ln n
Table 1: Summary of previous results [6, 9] and results of our analysis. Each value has lower order terms that
have been omitted for conciseness.
4.3 Other Experiments Other multi-pivot algorithms are also of interest. In particular, we also ran
tests on a 7-pivot approach. However, these tests concluded that the 7-pivot algorithm runs more slowly than
the 2- and 3-pivot variants.
Another feature of consequence is the behavior of
algorithms under a multi-core architecture. Thus we
performed a set of tests on these versions of quicksort
running on four threads on a machine with four cores.
The scheme we used to split work is as follows: Use a
very large sample to perform a 4-way/3-pivot partition
of the array into four subarrays of (probably) very
similar sizes. Then run an instance of a quicksort
algorithm on each of the four subarrays. One fact
to note here is that under this scheme, the runtime
of the entire algorithm is the max of the runtimes of
the four instances. Thus, a fast algorithm with high
variance in runtime may actually perform worse than
a slower algorithm that has a consistent runtime. Our
tests concluded that all three of the 1-, 2-, and 3-pivot
approaches showed comparable speedups (about three
times faster than single threaded) when run under these
conditions.
5 Theory and Experiments: Explained
The dramatic speed increase of Yaroslavskiy’s algorithm
is not fully explained by previous analyses of the number
of comparisons and swaps. We see a 7-9% increase in
performance but the average number of comparisons is
only 5% less, and almost twice as many swaps! This disparity between theory and practice is highlighted even
more clearly with the results of our 3-pivot algorithm
(refer Table 1). Our algorithm uses more comparisons
and swaps than the median-of-3 quicksort algorithm yet
we see about a 7% reduction in runtime.
After analysing the cache performance of each of
the algorithms, we can finally explain the disparity
we see between theory and practice. Even though
our algorithm uses more swaps and comparisons than
median-of-3 quicksort, it make almost 20% fewer cache
misses. This explains why our algorithm performs
better even though traditional analyses say that it
should do much worse. It also explains why we see such
a speed increase for Yaroslavskiy’s dual-pivot quicksort.
6 Further Cache Improvements
With the insights from caching in modern architectures, we design a modification based on the idea of
presampling pivots. Given an initial unsorted sequence
a1, . . . , an of size n, the main ideas of this algorithm can
be summarised as follows:
1. Sample √
n elements to be used as pivots for the
partitioning process and sort them. This is done
just once at the start and not for each recursive
call.
2. For every recursive call, instead of choosing a pivot
from the subarray, choose an appropriate element
from the above array as a pivot. Partition the array
about the chosen pivot.
3. Once we run out of pivots, fall back to the standard
quicksort algorithm (1-pivot, 2-pivot, etc. as the
case may be).
This strategy has some nice properties. By choosing
pivots out of a sample of √
n elements, the initial pivots
are extremely good with very high probability. Hence,
we expect that using presampling would bring down
the number of subproblems below the cache size more
quickly.
We implemented this approach and carried out
some experiments, the details of which have been omitted due to space constraints. In practice, it leads to
about a 1.5 − 2% gain in performance when comparing the running times of the standard 1-pivot quicksort
against that of 1-pivot quicksort with presampling. For
larger array sizes, the presampled version was on average about 2% faster than the standard version. Similar
results were obtained when comparing the presampled
and standard versions of 2-pivot quicksort.
We believe that fine tuning this approach further,
such as varying the sample size and choosing when to
fall back to the standard algorithm, would lead to even
53 Copyright © 2014.
by the Society for Industrial and Applied Mathemat
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ics.
7 Conclusion
In this paper, we have presented a comprehensive analysis and experimental evaluation of multi-pivot quicksort algorithms, focusing particularly on the 3-pivot variant. Through theoretical analysis and experimentation, we have shown that the 3-pivot quicksort algorithm offers significant improvements in runtime, comparisons, swaps, and cache performance compared to traditional 1-pivot and 2-pivot quicksort algorithms. Despite its additional complexity, the 3-pivot variant demonstrates superior performance, especially for large datasets where cache efficiency becomes critical.
Furthermore, our experiments have highlighted discrepancies between theoretical expectations and practical performance, underscoring the importance of considering real-world factors such as cache behavior in algorithm design and analysis. We have also proposed a modification to the quicksort algorithm based on presampling pivots, which shows promising results in further improving cache efficiency.
Overall, our findings contribute to a better understanding of multi-pivot quicksort algorithms and provide insights for future optimizations and algorithmic improvements in sorting algorithms.
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more gains in performance. Analysing this approach
mathematically is another avenue which needs more
investigation. We mark these as areas for future work.
7 Conclusions and Future Work
First, we have confirmed previous experimental results
on Yaroslavskiy’s dual-pivot algorithm under a basic environment thus showing that the improvements are not
due to JVM side effects. We designed and analysed a
3-pivot approach to quicksort which yielded better results both in theory and in practice. Furthermore, we
provided strong evidence that much of the runtime improvements are from cache effects in modern architecture by analysing cache behavior.
We have learned that due to the rapid development
of hardware, many of the results from more than a
decade ago no longer hold. Further work in the short
term can be directed at discovering, analysing, and
implementing more interesting multi-pivot quicksort
schemes.
References
[1] Martin Aum¨uller and Martin Dietzfelbinger. Optimal partitioning for dual pivot quicksort. CoRR,
abs/1303.5217, 2013.
[2] Pascal Hennequin. Combinatorial analysis of quicksort
algorithm. Informatique th´eorique et applications,
23(3):317–333, 1989.
[3] C. A. R. Hoare. Quicksort. Comput. J., 5(1):10–15,
1962.
[4] Anthony LaMarca and Richard E. Ladner. The influence of caches on the performance of sorting. J.
Algorithms, 31(1):66–104, 1999.
[5] D.A. Patterson and J.L. Hennessy. Computer Architecture: A Quantitative Approach. Morgan Kaufmann,
1996.
[6] Robert Sedgewick. The analysis of quicksort programs.
Acta Inf., 7:327–355, 1977.
[7] Robert Sedgewick and Jon Bentley. Quicksort is
optimal. http://www.cs.princeton.edu/~rs/talks/
QuicksortIsOptimal.pdf, 2002. [Online; accessed 21-
April-2013].
[8] Kok-Hooi Tan. An asymptotic analysis of the number
of comparisons in multipartition quicksort. 1993.
[9] Sebastian Wild and Markus E. Nebel. Average case
analysis of java 7’s dual pivot quicksort. In Leah
Epstein and Paolo Ferragina, editors, ESA, volume
7501 of Lecture Notes in Computer Science, pages 825–
836. Springer, 2012.
[10] Vladimir Yaroslavskiy. Dual-pivot quicksort.
http://iaroslavski.narod.ru/quicksort/
DualPivotQuicksort.pdf, 2009. [Online; accessed
21-April-2013].
A Appendix
A.1 Partition Algorithm
Algorithm A.1.1 3-Pivot Partition
Require: A[lef t] < A[lef t+1] < A[right] are the three
pivots
1: function partition3(A, lef t, right)
2: a ← lef t + 2, b ← lef t + 2
3: c ← right − 1, d ← right − 1
4: p ← A[lef t], q ← A[lef t + 1], r ← A[right]
5: while b ≤ c do
6: while A[b] < q and b ≤ c do
7: if A[b] < p then
8: swap(A[a], A[b])
9: a ← a + 1
10: end if
11: b ← b + 1
12: end while
13: while A[c] > q and b ≤ c do
14: if A[c] > r then
15: swap(A[c], A[d])
16: d ← d − 1
17: end if
18: c ← c − 1
19: end while
20: if b ≤ c then
21: if A[b] > r then
22: if A[c] < p then
23: swap(A[b],A[a]), swap(A[a],A[c])
24: a ← a + 1
25: else
26: swap(A[b],A[c])
27: end if
28: swap(A[c],A[d])
29: b ← b + 1, c ← c − 1, d ← d − 1
30: else
31: if A[c] < p then
32: swap(A[b],A[a]), swap(A[a],A[c])
33: a ← a + 1
34: else
35: swap(A[b],A[c])
36: end if
37: b ← b + 1, c ← c − 1
38: end if
39: end if
40: end while
41: a ← a − 1, b ← b − 1, c ← c + 1, d ← d + 1
42: swap(A[lef t + 1],A[a]), swap(A[a],A[b])
43: a ← a − 1
44: swap(A[lef t],A[a]), swap(A[right],A[d])
45: end function
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A.2 Solving Recurrences for 3-pivot quicksort
All the quantities analysed in this paper satisfy a
recurrence relation of the following form
fn = an+b+
12
n(n − 1)(n − 2)
nX−3
i=0
(n−i−1)(n−i−2)fi
Multiplying by n(n − 1)(n − 2) throughout gives:
n(n − 1)(n − 2)fn = an2
(n − 1)(n − 2)
+ bn(n − 1)(n − 2)
+ 12
nX−3
i=0
(n − i − 1)(n − i − 2)fi
Substituting n − 1 in the above equation and then
subtracting gives:
(n − 1)(n − 2)(n − 3)fn−1 =
a(n − 1)2
(n − 2)(n − 3)
+ b(n − 1)(n − 2)(n − 3)
+ 12
nX−4
i=0
(n − i − 1)(n − i − 2)fi
n(n − 1)(n − 2)fn = (n − 1)(n − 2)(n − 3)fn−1
+ a(n − 1)(n − 2)(4n − 3)
+ 3b(n − 1)(n − 2)
+ 24
nX−3
i=0
(n − i − 2)fi
The idea is to get rid of the summation by subtracting
equations. Repeating the process twice on the above
equation gives the following equation:
n(n − 1)(n − 2)fn = 3(n − 1)(n − 2)(n − 3)fn−1
(A.1)
− 3(n − 2)(n − 3)(n − 4)fn−2
+ (n − 3)(n − 4)(n − 5)fn−3
+ 24fn−3 + 6a(4n − 9) + 6b
We use standard linear algebra software to solve this
recursive equation giving it the appropriate initial conditions. All the equations analysed in this paper have
the above form. Only the value of a and b changes. For
the case of comparisons a = 2 and b =
−10
3
. For the
analysis of swaps a =
2
3
and b =
1
6
. Similarly for other
cases. We will show the detailed solution for the analysis of comparisons. Other analysis are very similar. The
solution to (A.1) for comparisons is of the form:
C3(n) = 24
13
(n + 1)Hn −
311
117
+
190
117
+ G(n)
where Hn is the harmonic function, G(n) is a large
expression on n output by our recurrence solver which
contains complex numbers and the gamma function.
Hence the analysis of G(n) is very important. We first
prove that the G(n) is indeed real and that it is of O(
1
n
).
Define d =
5
2 +
1
2
i
√
23, z = 10097+i1039√
23. Then
G(n) for the analysis of comparisons is:
G(n) =
−
1
34983πΓ(n + 1)(
cosh(1
2
π
√
23)Γ(n − d)Γ(d)z
+
48πΓ(n − d)z
Γ(d)
)
−
10097
34983
(n + 1)
Using the properties of gamma function, Γ(n − d) =
−d(−d+1)(−d+2)(−d+3)· · ·(−d+n−1)Γ(−d). Hence,
we get the following equations:
Γ(n − d) = z1Γ(−d)
Γ(n − ¯d) = ¯z1Γ( ¯d)
where z1 = −d(−d + 1)· · ·(−d + n − 1). Substituting
these values in the above equation, we get:
G(n) =
−
1
34983πΓ(n + 1)(
cosh(1
2
π
√
23)Γ(−d)Γ(d)zz1
+
48πΓ(d)zz1
Γ(d)
)
−
10097
34983
(n + 1)
Now using the properties of gamma function we get:
Γ(−d)Γ(d) = −π
d sin(πd)
=
−π
d cos(i
π
2
√
23)
=
−π
d cosh( π
2
√
23)
or
cosh(π
2
√
23)Γ(−d)Γ(d) = −π
d
48Γ( ¯d)
Γ(d)
=
−π
d
Substituting these values in the above equation we get:
G(n) = −
1
34983πΓ(n + 1)
−
πzz1
d
−
zz1π
d
−
10097
34983
(n + 1)
=
2
34983Γ(n + 1)Rezz1
d
−
10097
34983
(n + 1)
=O
1
n
−
10097
34983
(n + 1)
55 Copyright © 2014.
by the Society for Industrial and Applied Mathematics.
Downloaded 09/26/14 to 176.37.58.195. Redistribution subject to SIAM license or copyright; see http://www.siam.org/journals/ojsa.php
Equation (A.1) hence solves to:
C3(n) =24
13
(n + 1) ln(n)
+
−
311
117
+
24
13
γ −
10097
34983
(n + 1)
+
190
117
+ O
1
n
≈
24
13
(n + 1) ln(n) − 1.88(n + 1) + 190
117
+ O
1
n
Here, we have shown the exact derivations for the
number of comparisons. The analysis for number of
swaps and cache misses are very similar to the above
analysis and hence they have been omitted.
A.3 Solving recurrences for median-of-3 1-
pivot quicksort This algorithm uses two poniters to
traverse through the array. Hence the total number of
cache misses during partitioning will be at most the total number of elements scanned by these pointers divided by B. This gives rise to the following recurrence
relations:
CM(1/3)(n) ≤
n
B
+
12
n(n − 1)(n − 2)
nX−1
i=0
(n − i)(i − 1)
(CM(1/3)(i) + CM(1/3)(n − i))
≤
n
B
+
12
n(n − 1)(n − 2)
nX−1
i=0
i(n − i − 1)CM(1/3)(i)
SP(1/3)(n) ≤
1 +
12
n(n − 1)(n − 2)
nX−1
i=0
i(n − i − 1)SP(1/3)(i)
The recurrence for number of subproblems is selfexplanatory. A minor point is that the above relation
holds for n > M. For n ≤ M, CM(1/3)(n) = 0. Both of
the above recurrence relations can be written in a more
general form as,
fn = an + b +
6
n(n − 1)(n − 2)
nX−1
i=1
(i − 1)(n − i)fi
fn = an + b +
12
n(n − 1)(n − 2)
nX−1
i=0
i(n − i − 1)fi
where a =
1
B
and b = 0 for the 1st recurrence and
a = 0 and b = 1 for the second one. Multiplying by
n(n − 1)(n − 2) throughout gives:
n(n − 1)(n − 2)fn = an2
(n − 1)(n − 2)
+ bn(n − 1)(n − 2) + 12
nX−1
i=0
i(n − i − 1)fi
Substituting n − 1 in the above equation and then
subtracting gives:
(n − 1)(n − 2)(n − 3)fn−1 = a(n − 1)2
(n − 2)(n − 3)
+ b(n − 1)(n − 2)(n − 3)
+ 12
nX−2
i=0
i(n − i − 2)fi
n(n − 1)(n − 2)fn = (n − 1)(n − 2)(n − 3)fn−1
+ a(n − 1)(n − 2)(4n − 3)
+ 3b(n − 1)(n − 2) + 12
nX−1
i=0
ifi
The idea is to get rid of the summation by subtracting
equations. Repeating the process gives the following
equation:
n(n − 1)(n − 2)fn = 3(n − 1)(n − 2)(n − 3)fn−1
− 3(n − 2)(n − 3)(n − 4)fn−2
+ (n − 3)(n − 4)(n − 5)fn−3
+ 24fn−3 + 6a(4n − 9) + 6b
Substituting values for a and b and solving the
above recurrence using standard linear algebra software
we get:
CM(1/3)(n) ≤
12
7
n + 1
B
ln
n + 1
M + 2
+ O
n
B
SP(1/3)(n) ≤
12
7
n + 1
M + 2
− 2 + O
1
n
The analysis for 2-pivot quicksort is also very similar to the one shown above. We do not show
ChatGPT
The provided excerpts delve into solving recurrences for the analysis of different quicksort algorithms. In the case of the 3-pivot quicksort algorithm, the recurrence relation for the number of comparisons is derived and solved in detail. Here's a summary of the process:
Derivation of Recurrence Relation:
The number of comparisons
�
3
(
�
)
C
3
(n) in the 3-pivot quicksort algorithm is analyzed using a recurrence relation of the form:
�
�
=
�
�
+
�
+
12
�
(
�
−
1
)
(
�
−
2
)
∑
�
=
0
�
−
3
(
�
−
�
−
1
)
(
�
−
�
−
2
)
�
�
f
n
=an+b+
n(n−1)(n−2)
12
∑
i=0
n−3
(n−i−1)(n−i−2)f
i
where
�
a and
�
b are constants specific to the analysis.
Transformation and Simplification:
By manipulating the recurrence relation and performing substitutions, the equation is transformed into a more manageable form suitable for solving.
Solution Using Linear Algebra:
Standard linear algebra techniques are employed to solve the transformed recurrence equation, taking into account appropriate initial conditions.
Result Interpretation:
Upon solving, the solution to the recurrence equation for the number of comparisons
�
3
(
�
)
C
3
(n) is obtained. It's expressed as a function of
�
n, indicating the expected number of comparisons for different input sizes.
Analysis of Additional Terms:
Further analysis involves dissecting additional terms that contribute to the overall complexity, such as
�
(
�
)
G(n), which represents a portion of the solution involving complex numbers and the gamma function.
Verification and Simplification:
The derived solution is verified for its realness and asymptotic behavior, leading to a simplified form suitable for practical analysis.
In essence, this excerpt provides a detailed walkthrough of the process involved in analyzing and solving recurrence relations to determine the performance characteristics of the 3-pivot quicksort algorithm, specifically focusing on the number of comparisons. Similar approaches are applied to analyze other variants of quicksort algorithms, such as median-of-3 1-pivot quicksort.
User
А тепер перейди на українську
ChatGPT
Звичайно, я перекладу українською:
Надані уривки детально розглядають вирішення рекурентних рівнянь для аналізу різних алгоритмів швидкого сортування. У випадку алгоритму швидкого сортування з трьома опорними елементами рекурентне співвідношення для кількості порівнянь
�
3
(
�
)
C
3
(n) пояснюється та розв'язується докладно. Ось коротке узагальнення процесу:
Отримання рекурентного співвідношення:
Аналіз кількості порівнянь
�
3
(
�
)
C
3
(n) у алгоритмі швидкого сортування з трьома опорними елементами здійснюється за допомогою рекурентного співвідношення такого виду:
�
�
=
�
�
+
�
+
12
�
(
�
−
1
)
(
�
−
2
)
∑
�
=
0
�
−
3
(
�
−
�
−
1
)
(
�
−
�
−
2
)
�
�
f
n
=an+b+
n(n−1)(n−2)
12
∑
i=0
n−3
(n−i−1)(n−i−2)f
i
де
�
a та
�
b - це константи, що специфікуються для аналізу.
Трансформація та спрощення:
Шляхом маніпулювання рекурентним співвідношенням і проведення підстановок рівняння перетворюється в більш керовану форму, придатну для розв'язання.
Розв'язання за допомогою лінійної алгебри:
Використовуються стандартні техніки лінійної алгебри для розв'язання перетвореного рекурентного рівняння, враховуючи відповідні початкові умови.
Інтерпретація результатів:
Після розв'язання отримується рішення рекурентного рівняння для кількості порівнянь
�
3
(
�
)
C
3
(n). Воно виражається як функція від
�
n, що показує очікувану кількість порівнянь для різних розмірів вхідних даних.
Аналіз додаткових членів:
Додатковий аналіз включає розгляд додаткових членів, які внесли свій вклад у загальну складність, таких як
�
(
�
)
G(n), що представляє собою частину рішення, що містить складні числа та гамма-функцію.
Перевірка та спрощення результатів:
Отримане рішення перевіряється на його реальність та асимптотичну поведінку, що призводить до спрощеної форми, придатної для практичного аналізу.
Узагальнюючи, уривок надає детальний опис процесу аналізу та розв'язання рекурентних рівнянь для визначення характеристик продуктивності алгоритму швидкого сортування з трьома опорними елементами, зокрема кількості порівнянь. Подібні підходи застосовуються до аналізу інших варіантів алгоритмів швидкого сортування, таких як медіана-з-3 одноповзовий швидкий алгоритм сортування.
User
Так можешь дать мне код на python ?
Вот по этому плану:
Алгоритм 3. Швидке сортування з трьома опорними елементами
В цій модифікації замість одного опорного елементу обирається три. Позначимо ці опорні елементи q1, q2, q3 (необхідно, щоб виконувалось: q1 < q2 < q3). Перед основною частиною процедури розбиття ці опорні елементи обираються серед наступних елементів підмасиву A[p..r]: A[p], A[p+1] та A[r]. По завершенню розбиття всі елементи підмасиву A[p..q1-1] будуть менші за q1, всі елементи A[q1+1..q2-1] — менші за q2, всі елементи A[q2+1..q3-1] — менші за q3,та всі елементи A[q3+1..r] — більші за q3. І алгоритм рекурсивно продовжує свою роботу для вказаних чотирьох частин масиву: A[p..q1-1], A[q1+1..q2-1], A[q2+1..q3-1], A[q3+1..r].
Детальна робота цього алгоритму та його псевдокод наведений в статті Multi-Pivot Quicksort: Theory and Experiments. S Kushagra, A L.pez-Ortiz, A Qiao, JI Munro - ALENEX, 2014 – SIAM (текст статті додається до завдання).
Аналогічно до алгоритму 2, при підрахунку кількості порівнянь даного алгоритму необхідно враховувати наступне.
• Порівняння не підраховуються під час визначення впорядкування трьох опорних
елементів.
• Процедура розбиття викликається тільки для підмасивів розміром більше 3. Для підмасивів з розміром менше або рівним 3, відбувається сортування без процедури розбиття. Але в цьому випадку необхідно все-одно враховувати порівняння елементів і вести їм облік.
Запропоновані модифікації алгоритму швидкого сортування дозволяють значно зменшити кількість порівнянь: алгоритм №2 до 10-15% і алгоритм №3 до 20-25% порівняно з рандомізованим алгоритмом швидкого сортування.
Рекомендації до підрахунку порівнянь
При підрахунку порівнянь елементів вхідного масиву слід бути уважним до місць розміщення_операцій збільшення лічильника порівнянь:
• Коли порівняння відбувається в операторі if, то операцію збільшення лічильника слід розміщувати перед оператором, а не всередині тіла then, щоб також враховувати негативні результати порівняння.
• Коли порівняння відбувається в умові операторів while чи for, то операцію збільшення лічильника слід розміщувати всередині циклу, а також додавати одну операцію збільшення лічильника відразу після закінчення циклу щоб враховувати останню невдалу перевірку (завдяки якій виконання циклу закінчується; втім тут слід бути обережним із завчасним перериванням роботи циклу за допомогою команди break).
• Слід пам’ятати, що необхідно вести облік тільки порівнянням елементів вхідного масиву.
Тому коли відбувається порівняння індексів чи будь-яких інших допоміжних змінних, це не слід враховувати.
• Уважно слідкуйте за тим, які опорні елементи обираються (див. опис алгоритмів вище), бо вибір опорного елементу на пряму впливає на кількість порівнянь для кожного конкретного масиву.
Формат вхідних/вихідних даних
Розроблена програма повинна зчитувати вхідні дані з файлу заданого формату та записувати дані у файл заданого формату.
Вхідний файл представляє собою текстовий файл із N+1 рядків, де N — це розмірність вхідного масиву A. Першим записом є число — кількість елементів в масиві; наступні N записів містять елементи вхідного масиву.
|
c8fb9c0ea1d278c9e037282e57714431
|
{
"intermediate": 0.4458385407924652,
"beginner": 0.28483009338378906,
"expert": 0.2693313956260681
}
|
43,505
|
I am having problems with SDL_mixer in c++, I have troubles defining the sound effect class, I already did the music class and it was really easy, but for the sound effects which are mix_chunks I can't just use play like in music, I have to define a channel, and that channel has to be tied with it if I want to make it halted or paused, but at the same time, it could be -1 to be a random one, but a random one means I don't have enough control of it, I am having problems wrapping my head about this.
|
e465d37a7b6753d8d673f7b703991793
|
{
"intermediate": 0.7037367820739746,
"beginner": 0.14469368755817413,
"expert": 0.15156948566436768
}
|
43,506
|
i have daily historical data of some crypto in csv file
it has a column date like:
Date
2/23/2021
2/24/2021
2/25/2021
2/26/2021
2/27/2021
i have also corresponding hourly historical data of same crypto as csv file that has date column like:
Date
2/24/2021 0:00
2/24/2021 1:00
2/24/2021 2:00
2/24/2021 3:00
2/24/2021 4:00
2/24/2021 5:00
2/24/2021 6:00
2/24/2021 7:00
2/24/2021 8:00
2/24/2021 9:00
2/24/2021 10:00
2/24/2021 11:00
2/24/2021 12:00
2/24/2021 13:00
2/24/2021 14:00
2/24/2021 15:00
2/24/2021 16:00
2/24/2021 17:00
2/24/2021 18:00
2/24/2021 19:00
2/24/2021 20:00
2/24/2021 21:00
2/24/2021 22:00
2/24/2021 23:00
i want to add corresponding rows of each day hourly datas infront of the day row
like if currently my daily historical csv has 5 columns, and my hourly historical data has also 5 columns, daily csv should have 5 + 120(24 * 5 =120) columns after adding correspounding hourly data to each row
for adding the first hourly data row of current daily row, change its corresponding columns (columns 5 to 10) names by adding a “c1h” prefix to column names,
for adding the second hourly data row of current daily row, change its corresponding columns (columns 10 to 15) names by adding a “c2h” prefix to column names,
...
for adding the 24th hourly data row of current daily row, change its corresponding columns (columns 120 to 125) names by adding a “c24h” prefix to column names,
also i should mention i dont know exact numbers of hourly data csv columns , and it should handled dynamycally
give me proper python code
|
b8e8d3fbc298bf4aac901c098b606bf8
|
{
"intermediate": 0.46390023827552795,
"beginner": 0.27712780237197876,
"expert": 0.2589719295501709
}
|
43,507
|
Hi there, how can I add a background image for a vbox in SAPUI5?
|
21a99ebc363ce6d8941eda0c916584b6
|
{
"intermediate": 0.49574241042137146,
"beginner": 0.21832025051116943,
"expert": 0.2859373688697815
}
|
43,508
|
i have a csv historical hourly data
there is some missed data in firse and last rows of csv(some of hourly data of a day are missing,in last day and first day of data)
which in this rows, not all of a day hourly data is included
like(00:00 to 11:00 for 2/23/2021):
Date
2/23/2021 12:00
2/23/2021 13:00
2/23/2021 14:00
2/23/2021 15:00
2/23/2021 16:00
2/23/2021 17:00
2/23/2021 18:00
2/23/2021 19:00
2/23/2021 20:00
2/23/2021 21:00
2/23/2021 22:00
2/23/2021 23:00
2/24/2021 0:00
2/24/2021 1:00
2/24/2021 2:00
or in the last rows like(05:00 to 23:00 for 10/6/2023)::
10/6/2023 0:00
10/6/2023 1:00
10/6/2023 2:00
10/6/2023 3:00
10/6/2023 4:00
i want this poor dates rows(data of 10/6/2023 and 2/23/2021 in this case) removed from hourly csv
|
ff4a73e8369cfa112294013faf381492
|
{
"intermediate": 0.4144589304924011,
"beginner": 0.3202238380908966,
"expert": 0.26531726121902466
}
|
43,509
|
Can you write me a code to make it easier to check the answer of a math problem than it would be to solve it
|
843bf23eb92b0819f807a4f5e63c2a56
|
{
"intermediate": 0.5475820302963257,
"beginner": 0.18117551505565643,
"expert": 0.2712424695491791
}
|
43,510
|
Привет. У меня есть вот такой вот python код
def partition3(A, left, right):
a = b = left + 1
c = d = right - 1
p, q, r = A[left], A[left + 1], A[right]
while b <= c:
while A[b] < q and b <= c:
if A[b] < p:
A[a], A[b] = A[b], A[a]
a += 1
b += 1
while A[c] > q and b <= c:
if A[c] > r:
A[c], A[d] = A[d], A[c]
d -= 1
c -= 1
if b <= c:
if A[b] > r:
if A[c] < p:
A[a], A[b], A[a], A[c] = A[b], A[a], A[c], A[a]
a += 1
else:
A[b], A[c] = A[c], A[b]
A[c], A[d] = A[d], A[c]
c -= 1
d -= 1
else:
if A[c] < p:
A[a], A[b], A[a], A[c] = A[b], A[a], A[c], A[a]
a += 1
else:
A[b], A[c] = A[c], A[b]
b += 1
c -= 1
a -= 1
b -= 1
c += 1
d += 1
A[left + 1], A[a] = A[a], A[left + 1]
A[a], A[b] = A[b], A[a]
a -= 1
A[left], A[a] = A[a], A[left]
A[right], A[d] = A[d], A[right]
# Приклад використання:
arr = [3, 6, 2, 8, 5, 1, 9, 4, 7]
print("Before partition:", arr)
partition3(arr, 0, len(arr) - 1)
print("After partition:", arr)
Ещё есть вот такой вот псевдокод:
Algorithm A.1.1 3-Pivot Partition
Require: A[lef t] < A[lef t+1] < A[right] are the three
pivots
1: function partition3(A, lef t, right)
2: a ← lef t + 2, b ← lef t + 2
3: c ← right − 1, d ← right − 1
4: p ← A[lef t], q ← A[lef t + 1], r ← A[right]
5: while b ≤ c do
6: while A[b] < q and b ≤ c do
7: if A[b] < p then
8: swap(A[a], A[b])
9: a ← a + 1
10: end if
11: b ← b + 1
12: end while
13: while A[c] > q and b ≤ c do
14: if A[c] > r then
15: swap(A[c], A[d])
16: d ← d − 1
17: end if
18: c ← c − 1
19: end while
20: if b ≤ c then
21: if A[b] > r then
22: if A[c] < p then
23: swap(A[b],A[a]), swap(A[a],A[c])
24: a ← a + 1
25: else
26: swap(A[b],A[c])
27: end if
28: swap(A[c],A[d])
29: b ← b + 1, c ← c − 1, d ← d − 1
30: else
31: if A[c] < p then
32: swap(A[b],A[a]), swap(A[a],A[c])
33: a ← a + 1
34: else
35: swap(A[b],A[c])
36: end if
37: b ← b + 1, c ← c − 1
38: end if
39: end if
40: end while
41: a ← a − 1, b ← b − 1, c ← c + 1, d ← d + 1
42: swap(A[lef t + 1],A[a]), swap(A[a],A[b])
43: a ← a − 1
44: swap(A[lef t],A[a]), swap(A[right],A[d])
45: end function
Мне нужна идеальная реализация псевдокода на python. На данный момент имею проблему с тем, что выводится вот это Before partition: [3, 6, 2, 8, 5, 1, 9, 4, 7]
After partition: [1, 3, 5, 4, 2, 6, 7, 9, 4]
|
0aeb68e8d19d45ff0f160e5418c79d1a
|
{
"intermediate": 0.2829724848270416,
"beginner": 0.5672788619995117,
"expert": 0.14974866807460785
}
|
43,511
|
Write an unbeatable tic tac toe AI using a MInimax Algorithm and Alpha Beta Pruning, in only just 211 lines of codes. In Python.
|
778a397e683971bbbe98cc6b0a3749f2
|
{
"intermediate": 0.07757639139890671,
"beginner": 0.05705647170543671,
"expert": 0.8653671145439148
}
|
43,512
|
i have a csv file
for each row i want to add previous seven rows infront of it
like if currently my csv has 5 columns it should have 40 columns after adding previous 7 rows
for adding the previous row of current row, change its corresponding columns (columns 5 to 10) names by adding a "n1d" prefix to column names
for adding the second previous row of current row, change its corresponding columns (columns 10 to 15) names by adding a "n2d" prefix to column names
and...
give me proper python code
|
d44ce70f1e8a8b0d0431d3785d767684
|
{
"intermediate": 0.45544788241386414,
"beginner": 0.2578059434890747,
"expert": 0.28674614429473877
}
|
43,513
|
My task is question geberation aim to genertae question based on answer and dataset is has question answer and context can write code using pytourch for trin note task for arabic text
|
96ad83f99bc449052a2c4d1a9014e5b8
|
{
"intermediate": 0.32437944412231445,
"beginner": 0.33475261926651,
"expert": 0.34086790680885315
}
|
43,514
|
My task is question geberation aim to genertae question based on answer and dataset is has question answer and context can write code using pytourch for trin note task for arabic text
|
561342d1aebe94b2c70d6aa396c49c85
|
{
"intermediate": 0.32437944412231445,
"beginner": 0.33475261926651,
"expert": 0.34086790680885315
}
|
43,515
|
here is my code :
import pandas as pd
import os
# The path where your CSV files are stored
csv_folder_path = r"E:\01_calculate_talib\New Folder"
# Iterate through each file in the csv_folder_path
for csv_file in os.listdir(csv_folder_path):
file_path = os.path.join(csv_folder_path, csv_file)
# Load the CSV file
df = pd.read_csv(file_path)
# Get the number of original columns dynamically
original_columns_count = len(df.columns)
# Generate original column names dynamically (optional, for naming consistency)
original_columns = df.columns.tolist()
# Creating new column names based on the original question's criteria
new_column_names = original_columns.copy()
prefixes = ['n7d', 'n6d', 'n5d', 'n4d', 'n3d', 'n2d', 'n1d']
for prefix in prefixes:
new_column_names.extend([f"{prefix}_{name}" for name in original_columns])
# Prepare an empty dataframe to hold the transformed data
transformed_df = pd.DataFrame(index=df.index, columns=new_column_names)
transformed_df = transformed_df.fillna('') # Fill with empty strings (or NaN as you prefer)
# Fill in the data for the new structure
for i in range(len(df)+6):
# Current row data
row_data = df.iloc[i].values.tolist()
# Add data from preceding 7 rows
for j in range(1, 8):
if i - j >= 0:
row_data = df.iloc[i - j].values.tolist() + row_data
else:
# Fill with empty strings if there are not enough previous rows
row_data = [''] * original_columns_count + row_data
transformed_df.iloc[i] = row_data
columns_to_remove = transformed_df.filter(like='Unix').columns
transformed_df.drop(columns=columns_to_remove, inplace=True)
# transformed_df = transformed_df.iloc[7:]
# Save transformed dataframe to a new CSV
transformed_df.to_csv(file_path, index=False)
print("Transformation complete. Check the 'transformed_output_file.csv' file.")
i want to change it to start operation from last rows instead of first row
|
599bb59b5adc2028110cbefed13508d3
|
{
"intermediate": 0.6071180105209351,
"beginner": 0.1733430027961731,
"expert": 0.21953898668289185
}
|
43,516
|
here is my code :
import pandas as pd
import os
# The path where your CSV files are stored
csv_folder_path = r"E:\01_calculate_talib\New Folder"
# Iterate through each file in the csv_folder_path
for csv_file in os.listdir(csv_folder_path):
file_path = os.path.join(csv_folder_path, csv_file)
# Load the CSV file
df = pd.read_csv(file_path)
# Get the number of original columns dynamically
original_columns_count = len(df.columns)
# Generate original column names dynamically (optional, for naming consistency)
original_columns = df.columns.tolist()
# Creating new column names based on the original question's criteria
new_column_names = original_columns.copy()
prefixes = ['n7d', 'n6d', 'n5d', 'n4d', 'n3d', 'n2d', 'n1d']
for prefix in prefixes:
new_column_names.extend([f"{prefix}_{name}" for name in original_columns])
# Prepare an empty dataframe to hold the transformed data
transformed_df = pd.DataFrame(index=df.index, columns=new_column_names)
transformed_df = transformed_df.fillna('') # Fill with empty strings (or NaN as you prefer)
# Fill in the data for the new structure
for i in range(len(df)):
# Current row data
row_data = df.iloc[i].values.tolist()
# Add data from preceding 7 rows
for j in range(1, 8):
if i - j >= 0:
row_data = df.iloc[i - j].values.tolist() + row_data
else:
# Fill with empty strings if there are not enough previous rows
row_data = [''] * original_columns_count + row_data
transformed_df.iloc[i] = row_data
columns_to_remove = transformed_df.filter(like='Unix').columns
transformed_df.drop(columns=columns_to_remove, inplace=True)
# transformed_df = transformed_df.iloc[7:]
# Save transformed dataframe to a new CSV
transformed_df.to_csv(file_path, index=False)
print("Transformation complete. Check the 'transformed_output_file.csv' file.")
the problem is after the oparation the first 7 rows should be poorly operated(since my data order is ascending by the Date column) ,but they are fully operated
and also instead of first rows ,the last rows are removed
|
6ad9e14375b357a98c6e5bdf314f5cd7
|
{
"intermediate": 0.5301848649978638,
"beginner": 0.2597852051258087,
"expert": 0.21002990007400513
}
|
43,517
|
in the code server,I have installed pylance ,but the auto completion and go to definition do not work totally,why?
|
8497b6f6ec4c2b95be5ddc85ed905a83
|
{
"intermediate": 0.567042350769043,
"beginner": 0.21977007389068604,
"expert": 0.2131875902414322
}
|
43,518
|
i have a csv file
i want to expand each row by previous 7 rows infront of it (exclude first 7 rows ,because they dont have full 7 previous data)
like if currently my csv has 5 columns, csv should have 5 + 35(7* 5 =120) columns after adding previous 7 rows
for adding the first previous data row of current row, change its corresponding columns (columns 5 to 10) names by adding a “n1d” prefix to column names,
for adding the second previous data row of current row, change its corresponding columns (columns 10 to 15) names by adding a “n2d” prefix to column names,
...
for adding the 7th previous data row of current row, change its corresponding columns (columns 35 to 40) names by adding a “n7d” prefix to column names,
also i should mention i dont know exact numbers of csv columns , and it should handled dynamycally
give me proper python code
|
e36895c6643763280197d2c5f1b21dde
|
{
"intermediate": 0.4804621636867523,
"beginner": 0.2340228110551834,
"expert": 0.28551504015922546
}
|
43,519
|
npm ERR! code ENOENT
npm ERR! syscall lstat
npm ERR! path C:\Users\X\AppData\Roaming\npm
npm ERR! errno -4058
npm ERR! enoent ENOENT: no such file or directory, lstat 'C:\Users\X\AppData\Roaming\npm'
npm ERR! enoent This is related to npm not being able to find a file.
npm ERR! enoent
npm ERR! A complete log of this run can be found in: C:\Users\X\AppData\Local\npm-cache\_logs\2024-03-22T03_26_55_937Z-debug-0.log
PS C:\Users\X>
PS C:\Users\X> cd AwesomeProject
cd : Cannot find path 'C:\Users\X\AwesomeProject' because it does not exist.
At line:1 char:1
+ cd AwesomeProject
+ ~~~~~~~~~~~~~~~~~
+ CategoryInfo : ObjectNotFound: (C:\Users\X\AwesomeProject:String) [Set-Location], ItemNotFoundException
+ FullyQualifiedErrorId : PathNotFound,Microsoft.PowerShell.Commands.SetLocationCommand
|
cbff8e1b7ccab2672459bd4589e4b637
|
{
"intermediate": 0.36459752917289734,
"beginner": 0.3388635814189911,
"expert": 0.2965388894081116
}
|
43,520
|
can you impliment realm-web into my react app and have the user login annon and fetch data from the kittens db and the kittens coll. and display them on the page with an image and name. then when they click on the kitten they will go to a kittenDetail page wherethey will see info about a kitten they are interested in
|
28534cc1312463a1288ca0fbc3f46f3d
|
{
"intermediate": 0.6781685948371887,
"beginner": 0.12297274172306061,
"expert": 0.19885869324207306
}
|
43,521
|
logloss = nn.BCELoss()
ssim_loss = pytorch_ssim.SSIM()
def cosine_loss(a, v, y):
d = nn.functional.cosine_similarity(a, v)
loss = logloss(d.unsqueeze(1), y)
return loss
class LaplacianLoss(nn.Module):
def __init__(self):
super(LaplacianLoss, self).__init__()
# 定义一个拉普拉斯核，注意要符合PyTorch的卷积权重形状(C_out, C_in, H, W)
self.kernel = torch.tensor([[-1.0, -1.0, -1.0],
[-1.0, 8.0, -1.0],
[-1.0, -1.0, -1.0]], dtype=torch.float32).view(1, 1, 3, 3).requires_grad_(False)
self.sobel_x = nn.Parameter(torch.tensor([[-1., 0., 1.],
[-2., 0., 2.],
[-1., 0., 1.]], dtype=torch.float32).view(1, 1, 3, 3), requires_grad=False)
self.sobel_y = nn.Parameter(torch.tensor([[-1., 0., 1.],
[-2., 0., 2.],
[-1., 0., 1.]], dtype=torch.float32).view(1, 1, 3, 3), requires_grad=False)
def forward(self, output, target):
# 假设output和target都是[N, C, H, W]的Tensor
# 检查设备，确保卷积核和输入在同一设备上
device = output.device
#[batch_size, 3, 5, 288, 288]
output = output.view(output.shape[0], -1, 288, 288)
target = target.view(target.shape[0], -1, 288, 288)
# 如果是RGB图像，需要转化为单通道灰度图，因为拉普拉斯核定义在单通道上
if output.shape[1] > 3:
output = output.mean(dim=1, keepdim=True)
target = target.mean(dim=1, keepdim=True)
# 将拉普拉斯核移动到当前设备上
laplace_kernel = self.kernel.to(device)
kernel_x = self.sobel_x.to(device)
kernel_y = self.sobel_y.to(device)
# 使用F.conv2d应用拉普拉斯滤波
# filtered_output = F.conv2d(output, laplace_kernel, padding=1)
# filtered_target = F.conv2d(target, laplace_kernel, padding=1)
G_x_output = F.conv2d(output, kernel_x, padding=1)
G_y_output = F.conv2d(output, kernel_y, padding=1)
filtered_output = torch.sqrt(G_x_output + G_y_output)
G_x_target = F.conv2d(target, kernel_x, padding=1)
G_y_target = F.conv2d(target, kernel_y, padding=1)
filtered_target = torch.sqrt(G_x_target + G_y_target)
#a = filtered_output.detach().cpu().numpy()[0].transpose(1,2,0)
#b = filtered_target.detach().cpu().numpy()[0].transpose(1,2,0)
#cv2.imwrite("output.jpg", (a[144:, :, :]*255).astype(np.uint8))
#cv2.imwrite("target.jpg", (b[144:, :, :]*255).astype(np.uint8))
#import pdb
#pdb.set_trace()
loss = F.l1_loss(filtered_output[:, :, 144:, :], filtered_target[:, :, 144:, :])
return loss
laplacian_loss = LaplacianLoss()
device = torch.device(config["other"]["device"] if use_cuda else "cpu")
syncnet = SyncNet().to(device)
for p in syncnet.parameters():
p.requires_grad = False
recon_loss = nn.L1Loss()
def get_sync_loss(mel, g):
g = g[:, :, :, g.size(3)//2:]
g = torch.cat([g[:, :, i] for i in range(syncnet_T)], dim=1)
# B, 3 * T, H//2, W
a, v = syncnet(mel, g)
y = torch.ones(g.size(0), 1).float().to(device)
return cosine_loss(a, v, y)
def train(device, model, disc, train_data_loader, test_data_loader, optimizer, disc_optimizer,
checkpoint_dir=None, checkpoint_interval=None, nepochs=None):
disc.train()
model.train()
global global_step, global_epoch
resumed_step = global_step
while global_epoch < nepochs:
print('Starting Epoch: {}'.format(global_epoch))
running_sync_loss, running_l1_loss, disc_loss, running_perceptual_loss, running_edge_loss, running_ssim_loss = 0., 0., 0., 0., 0., 0.
running_disc_real_loss, running_disc_fake_loss = 0., 0.
prog_bar = tqdm(enumerate(train_data_loader))
for step, (x, indiv_mels, mel, gt) in prog_bar:
x = x.to(device)
mel = mel.to(device)
indiv_mels = indiv_mels.to(device)
gt = gt.to(device)
### Train generator now. Remove ALL grads.
optimizer.zero_grad()
disc_optimizer.zero_grad()
g = model(indiv_mels, x)
if hparams.ssim_wt > 0.:
ssim = -ssim_loss(g.reshape(-1,3,288,288), gt.reshape(-1, 3, 288, 288))
else:
ssim = 0.
if hparams.syncnet_wt > 0.:
sync_loss = get_sync_loss(mel, g)
else:
sync_loss = 0.
if hparams.disc_wt > 0.:
perceptual_loss = disc.perceptual_forward(g)
else:
perceptual_loss = 0.
l1loss = recon_loss(g, gt)
edge_loss = laplacian_loss(g, gt)
loss = hparams.syncnet_wt * sync_loss + hparams.disc_wt * perceptual_loss + \
0.3 * ssim + 0.4 * edge_loss + (1. - hparams.syncnet_wt - hparams.disc_wt - hparams.ssim_wt - 0.4) * l1loss
# loss = hparams.syncnet_wt * sync_loss + hparams.disc_wt * perceptual_loss + \
# hparams.ssim_wt * ssim + 0.5 * edge_loss + (1. - hparams.syncnet_wt - hparams.disc_wt - hparams.ssim_wt - 0.1) * l1loss
loss.backward()
optimizer.step()
disc_real_loss = 0
disc_fake_loss = 0
running_disc_real_loss += disc_real_loss
running_disc_fake_loss += disc_fake_loss
# # Remove all gradients before Training disc
# disc_optimizer.zero_grad()
# pred = disc(gt)
# disc_real_loss = F.binary_cross_entropy(pred, torch.ones((len(pred), 1)).to(device))
# disc_real_loss.backward()
# pred = disc(g.detach())
# disc_fake_loss = F.binary_cross_entropy(pred, torch.zeros((len(pred), 1)).to(device))
# disc_fake_loss.backward()
# disc_optimizer.step()
# running_disc_real_loss += disc_real_loss.item()
# running_disc_fake_loss += disc_fake_loss.item()
# Logs
global_step += 1
cur_session_steps = global_step - resumed_step
running_l1_loss += l1loss.item()
if hparams.syncnet_wt > 0.:
running_sync_loss += sync_loss.item()
else:
running_sync_loss += 0.
if hparams.disc_wt > 0.:
running_perceptual_loss += perceptual_loss.item()
else:
running_perceptual_loss += 0.
if hparams.ssim_wt > 0.:
running_ssim_loss += -ssim.item()
else:
running_ssim_loss += 0.
running_edge_loss += edge_loss.item()
if global_step % config["gan"]["gan_eval_interval"] == 0:
with torch.no_grad():
average_sync_loss = eval_model(test_data_loader, global_step, device, model, disc, checkpoint_dir)
disc.train()
model.train()
if average_sync_loss < 0.5:
hparams.set_hparam('syncnet_wt', 0.03)
# if global_step % checkpoint_interval == 0:
# save_sample_images(x, g, gt, global_step, checkpoint_dir, average_sync_loss)
if global_step % checkpoint_interval == 0:
save_checkpoint(
model, optimizer, global_step, checkpoint_dir, global_epoch, '', average_sync_loss)
save_checkpoint(disc, disc_optimizer, global_step, checkpoint_dir, global_epoch, prefix='disc_', val_loss=average_sync_loss)
prog_bar.set_description('HQ Wav2lip Epoch: {}, L1: {}, Sync: {}, Percep: {} Edge: {}| Fake: {}, Real: {} | SSIM: {}'.format(
global_epoch,
round(running_l1_loss / (step + 1), 5),
round(running_sync_loss / (step + 1), 5),
round(running_perceptual_loss / (step + 1), 5),
round(running_edge_loss / (step + 1), 5),
round(running_disc_fake_loss / (step + 1), 5),
round(running_disc_real_loss / (step + 1), 5),
round(running_ssim_loss / (step + 1), 5)))
logg.info(f"Epoch{str(global_epoch)} L1 Loss: {round(running_l1_loss / (step + 1), 5)} | Sync Loss: {round(running_sync_loss/(step+1), 5)}")
global_epoch += 1这个代码，在加入edge loss后 l1和ssim loss全部变成了nan，是不是哪儿有问题
|
cb70fd0d0795dd7ae6349fcddec9db12
|
{
"intermediate": 0.2318560630083084,
"beginner": 0.6510187387466431,
"expert": 0.11712520569562912
}
|
43,522
|
Use the radix sort algorithm to sort the following numbers. Treat every data as a 3-digit integer.
456, 329, 478, 59, 52, 447, 380, 126, 237, 599, 626, 255.
a) Draw a figure to show the actions step by step by treating each digit as a “digit”.
b) Explain why stable sorting at each step is important. You just need to state that correctness cannot be guaranteed (by giving an example) if you did not apply stable sorting at that step.
|
b00bb26586dd6432aab6d6cf877c6929
|
{
"intermediate": 0.2629339396953583,
"beginner": 0.14471682906150818,
"expert": 0.5923492312431335
}
|
43,523
|
In servicenow, i have a table task_card. My requirement is if the time card is being inserted and Category Is Task_work then cost center field should be automatically updated to cost center value on the task record,(Cost Center is the reference field on both the tables).
|
b000bfca002f30972d9551bc89a4ba8a
|
{
"intermediate": 0.4165089428424835,
"beginner": 0.2504814565181732,
"expert": 0.3330095410346985
}
|
43,524
|
I want to make a mandatory field false want when the form is updated/saved.
if the "work notes" field is mandatory, it should false mandatory condition when the form is updated/saved (only for that particular transaction in the client, meaning only until the value is changed in the form and updated/saved after the record is updated it should no longer be mandatory.
|
9c597dccc36c787a4980e6ea2c6af791
|
{
"intermediate": 0.4237969219684601,
"beginner": 0.22598513960838318,
"expert": 0.35021787881851196
}
|
43,525
|
Here is my inventory item CSV.
item code,name,description,quote description,visible on quote,instance type,price per unit,cost per unit,maintain markup,unit,re-order at qty,high stock qty,default location,expense account code,sales account code,inventory asset account code,quantity on hand,quantity available,quantity on order,quantity allocated
IS005,Magic Lamp,Oil,,yes,tracked-per-instance,166.0,17.0,yes,each,2.0,5.0,WH1,300,200,850,0.0,0,0.0,
IS006,Incandescent Bulb,E21 100W,,no,tracked-as-aggregate-qty,0.5,0.2,no,each,20.0,100.0,WH1,310,200,850,0.0,0,0.0,
budget-estimate,Budget Estimate,Placeholder budget item. Do not include on invoices.,,yes,untracked,0.0,0.0,no,each,,,,300,260,,0.0,0,0.0,
budget-1,Part 1 Budget,,,,untracked,0.0,0.0,no,each,,,,429,200,,0.0,0,0.0,
budget-2,Part 2 Budget,,,,untracked,0.0,0.0,no,each,,,,429,200,,0.0,0,0.0,
budget-3,Part 3 Budget,,,,untracked,0.0,0.0,no,each,,,,429,200,,0.0,0,0.0,
IS004,Very Hard Thinking,Send Great Minds Only,,yes,untracked,80.0,80.0,no,each,,,WH1,310,200,,0.0,0,3.0,
IS003,Reverse Cycle Split System Air Conditioner,Carrier Allure 7.0kW ,,no,tracked-as-aggregate-qty,1575.0,1536.0,yes,each,,,WH1,300,200,850,0.0,0,8.0,1.0
IS001,Ceiling Fan,Arlec 120cm White Chicago 4 Blade With light AC ,,no,tracked-per-instance,108.0,100.0,no,each,,,WH1,310,200,850,24.0,19.0,2.0,5.0
IS002,Arlec 9W Tri Colour Dimmable LED Downlights,With Plug Bases,,no,tracked-as-aggregate-qty,15.0,7.88,no,each,,,WH1,310,200,850,10.0,0,61.0,14.0
I want to include all of these items on a supplier catalogue CSV. Here is the template:
item code,supplier item code,supplier item name,supplier price,order lead time,priority
ABC-2123,AMZ-T1,Component - 1,5.3,5,1
ABC-2124,AMZ-T2,Component - 2,6.3,6,2
ABC-2125,AMZ-T3,Component - 3,7.3,7,6
ABC-2126,AMZ-T4,Component - 4,8.3,40,1
ABC-2127,AMZ-T5,Component - 5,9.3,50,8
ABC-2128,AMZ-T6,Component - 6,10.3,80,2
Can you do this?
|
ab5c017432bd5edbfe8d5892ad46f566
|
{
"intermediate": 0.2120012640953064,
"beginner": 0.5242980718612671,
"expert": 0.2637006342411041
}
|
43,526
|
I want to make a mandatory field false want when the form is updated/saved.
if the "work notes" field is mandatory, it should false mandatory condition when the form is updated/saved (only for that particular transaction in the client, meaning only until the value is changed in the form and updated/saved after the record is updated it should no longer be mandatory. servicenow
|
b99f9fec999adfe8044efeabd8bb9ac3
|
{
"intermediate": 0.41103628277778625,
"beginner": 0.22421543300151825,
"expert": 0.3647483289241791
}
|
43,527
|
PS C:\Users\X\React\Calculator-2.0> npx expo init
$ expo init is not supported in the local CLI, please use npx create-expo-app instead
PS C:\Users\X\React\Calculator-2.0> expo init
expo : File C:\Users\X\AppData\Roaming\npm\expo.ps1 cannot be loaded because running scripts is disabled on this system. For more information, see about_Execution_Policies at
https:/go.microsoft.com/fwlink/?LinkID=135170.
At line:1 char:1
+ expo init
+ ~~~~
+ CategoryInfo : SecurityError: (:) [], PSSecurityException
+ FullyQualifiedErrorId : UnauthorizedAccess
|
b113e06f4cd524461f7bdd57dd6031f0
|
{
"intermediate": 0.4521131217479706,
"beginner": 0.29617518186569214,
"expert": 0.2517116367816925
}
|
43,528
|
I'm building a store website with clojure, reagent, react, and bootstrap. Here is the code for a page.
(ns jimmystore.pages.photo-page
(:require
[reagent.core :as ra]
[re-frame.core :as rf]
[jimmystore.image-utils :as image-utils]))
;; FIXME make these be the events ones
(rf/reg-sub ::img
(fn [db _]
(::img db)))
(rf/reg-sub ::items
(fn [db _]
(::items db)))
(def dummy-photo-data {:path "/img/atsui.jpg" ; doing it like this with the intention to pull from server later
:title "test"})
(defn photo-card
([photo]
[:div {:class "card
col-md-2-5
col-5
m-md-2
m-3"}
[:img {:src (:path photo)
:class "card-img-top"}]
[:div.card-body
[:h5.card-title (:title photo)]]])
([photo title]
[:div {:class "card
col-md-2-5
col-5
m-md-2
m-3"}
[:img {:src (image-utils/img-blob->img-src photo)
:class "card-img-top"}]
[:div.card-body
[:h5.card-title title]]]))
(defn photos-element [items]
[:div {:class "container
d-flex
flex-wrap
align-items-center
justify-content-center"}
(repeat 16 (photo-card dummy-photo-data)) ; grid test
;; (get (first items) :id)
;; (photo-card (rf/dispatch [:retrieve-item-picture (get (first items) :id)]) "test") ; grid test
; TODO: logic for inserting objects from backend
])
(defn page []
(ra/with-let [_ (rf/dispatch [:get-items])
items (rf/subscribe [:items])]
[:div{:class "container"}
[:h3 (str @items "Select a photo.")]
[photos-element @items]]))
(def page-info {:page-id :photo
:view #'page})
(ns jimmystore.events
(:require
[re-frame.core :as rf]
[ajax.core :as ajax]
[reitit.frontend.easy :as rfe]
[reitit.frontend.controllers :as rfc]))
;;dispatchers
(rf/reg-event-db
:common/navigate
(fn [db [_ match]]
(def foo match)
(let [old-match (:common/route db)
new-match (assoc match :controllers
(rfc/apply-controllers (:controllers old-match) match))]
(assoc db :common/route new-match))))
(rf/reg-fx :common/navigate-fx!
(fn [[k & [params query]]]
(rfe/push-state k params query)))
(rf/reg-event-fx :common/navigate!
(fn [_ [_ url-key params query]]
{:common/navigate-fx! [url-key params query]}))
(rf/reg-event-db :set-docs
(fn [db [_ docs]]
(assoc db :docs docs)))
(rf/reg-event-fx :fetch-docs
(fn [_ _]
{:http-xhrio {:method :get
:uri "/docs"
:response-format (ajax/raw-response-format)
:on-success [:set-docs]}}))
(rf/reg-event-fx :test-get-api
(fn [_ _]
{:http-xhrio {:method :get
:uri "/test-get"
:response-format (ajax/json-response-format {:keywords? true})
:on-success [:set-docs]}}))
(rf/reg-event-fx :test-post-api
(fn [_ _]
{:http-xhrio {:method :post
:uri "/test-post"
:params {:test-post {:data 1 :foo :bar 2 "ASDASD"}}
:format (ajax/json-request-format)
:response-format (ajax/json-response-format {:keywords? true})
:on-success [:no-op]}}))
(rf/reg-event-fx :get-items
(fn [_ _]
{:http-xhrio {:method :get
:uri "/get-items"
:response-format (ajax/json-response-format {:keywords? true})
:on-success [:get-items-success]}}))
(rf/reg-event-db :get-items-success
(fn [db [_ response]]
(assoc db :items response)))
(defn- get-file-size [file]
(.-size file))
(defn- get-file-name [file]
(.-name file))
(defn- get-file-type [file]
(.-type file))
(rf/reg-event-db :set-file-to-upload
(fn [db [_ file]] ;; Local url (for previews etc.)
(assoc db :upload {:object-url (js/window.webkitURL.createObjectURL file)
:file file
:size (get-file-size file)
:name (get-file-name file)
:type (get-file-type file)})))
(rf/reg-sub :upload
(fn [db _]
(-> db :upload)))
(rf/reg-event-db :common/set-error
(fn [db [_ error]]
(assoc db :common/error error)))
(rf/reg-event-fx :page/init-home
(fn [_ _]
{:dispatch [:fetch-docs]}))
(rf/reg-event-db :no-op
(fn [db _]
db))
;;subscriptions
(rf/reg-sub :common/route
(fn [db _]
(-> db :common/route)))
(rf/reg-sub :common/page-id
:<- [:common/route]
(fn [route _]
(-> route :data :name)))
(rf/reg-sub :common/page
:<- [:common/route]
(fn [route _]
(-> route :data :view)))
(rf/reg-sub :docs
(fn [db _]
(:docs db)))
(rf/reg-sub :common/error
(fn [db _]
(:common/error db)))
(rf/reg-sub :items
(fn [db _]
(-> db :items)))
(ns jimmystore.image-utils
(:require
[re-frame.core :as rf]
[ajax.core :as ajax]))
(defn img-blob->img-src
"Converts an image blobs byte array to a data source for [:img] components"
[{:keys [content-type byte-array]}]
(str "data:" content-type ";base64," byte-array))
;; Retrieves an image blob (a map containing content-type and byte-array) by id
;; and calls the on-success-dispatch adding the image blob as the last argument.
;; id - id of image
;; on-success-dispatch - re-frame dispatch vector
(rf/reg-event-fx :retrieve-item-picture
(fn [_ [_ id on-success-dispatch]]
{:http-xhrio {:method :get
:params {:id id}
:uri "/retrieve-item-picture"
:response-format (ajax/json-response-format {:keywords? true})
:on-success on-success-dispatch}}))
On the photos page, I have the contents of "items" being debug output on the page under the :h3 part as {:items [{:name "32f7a9472d32612126813289780994c5-2.png", :id "59e6f3c5-866c-4d57-8b86-ad7f22188d24", :create-ts "2024-03-20T10:49:05.999Z"} {:name "907a590ace9b4192ebbbab32a9113053.gif", :id "bb6d4cf1-50a3-43af-a2ef-0f9918a0050d", :create-ts "2024-03-20T10:51:56.751Z"} {:name "1436202390320.gif", :id "0ddb5f66-fc58-4a81-8471-03e7419b2853", :create-ts "2024-03-20T11:32:08.534Z"}], :count 3}
I want to get the id of each image in there. When I try to get the contents of :items with (first @items) it returns "object Object". How can I get the ids?
|
25a2c0cdec00f960db56a155a4bba7b3
|
{
"intermediate": 0.4559336304664612,
"beginner": 0.34592971205711365,
"expert": 0.19813664257526398
}
|
43,529
|
i have some hourly historical data as csv files
in some parts ,part of data is missed
like 3/6/2021 2:00 in following :
3/5/2021 20:00
3/5/2021 21:00
3/5/2021 22:00
3/5/2021 23:00
3/6/2021 0:00
3/6/2021 1:00
3/6/2021 3:00
3/6/2021 4:00
3/6/2021 5:00
3/6/2021 6:00
i want to place missed hours and fill their values with mean of the before and after data
give me proper python code
|
625275324e4dff7d69c3e394cf866c61
|
{
"intermediate": 0.31594133377075195,
"beginner": 0.5422502756118774,
"expert": 0.141808420419693
}
|
43,530
|
improve following code to run faster :
import pandas as pd
import numpy as np
def fill_missing_hours(csv_file, output_file):
"""
Fills missing hourly data in a CSV file with the mean of surrounding values,
handling non-numeric data gracefully.
Args:
csv_file (str): Path to the input CSV file.
output_file (str): Path to the output CSV file.
"""
# Read CSV with appropriate date parsing (assuming 'Date' and 'Hour' columns)
df = pd.read_csv(csv_file, parse_dates=['Date'])
# Set 'Date' as index for efficient resampling and manipulation
df.set_index('Date', inplace=True)
# Resample to hourly frequency, using ffill for existing data
df = df.resample('H').fillna(method='ffill') # Use ffill for existing data
# Define a function to handle both numeric and non-numeric columns
def fill_with_mean(group):
# Select only numeric columns (assuming numerical values are of interest)
numeric_cols = group.select_dtypes(include=[np.number])
# Fill missing values in numeric columns with mean of surrounding values
filled_values = numeric_cols.fillna(numeric_cols.mean())
# Combine filled numeric columns with original non-numeric columns
return pd.concat([group, filled_values], axis=1)
# Apply mean filling to each day
df = df.groupby(df.index.date).apply(fill_with_mean)
# Reset index for output
df = df.reset_index()
columns_to_remove = df.filter(like='Unix').columns
df.drop(columns=columns_to_remove, inplace=True)
columns_to_remove = df.filter(like='level_0').columns
df.drop(columns=columns_to_remove, inplace=True)
# Save filled data to output CSV
df.to_csv(output_file, index=False)
# Example usage
csv_file = r'E:\01_calculate_talib\New folder\Binance_1INCHBUSD_1h.csv'
output_file = r'E:\01_calculate_talib\New folder\filled_data_2.csv'
fill_missing_hours(csv_file, output_file)
print(f"Missing hours filled and saved to: {output_file}")
|
7026590ccaaf7f9cc07562d695e597f3
|
{
"intermediate": 0.4961238205432892,
"beginner": 0.22648830711841583,
"expert": 0.2773878574371338
}
|
43,531
|
public static bool IsPlayerValid([MaybeNullWhen(false)] CCSPlayerController? player)
{
return player != null && player.IsValid;
}
if (!Utils.IsPlayerValid(victim) || !Utils.IsPlayerValid(attacker)) return HookResult.Continue;
victim.Slot
как сделать что если true вернет то он не будет после проверки нуллом?
|
51a155cd3e689ced96327606cafff31a
|
{
"intermediate": 0.43309924006462097,
"beginner": 0.3478745222091675,
"expert": 0.21902629733085632
}
|
43,532
|
i have some hourly historical data as csv files
in some parts ,part of data is missed
like 3/6/2021 2:00 in following :
3/5/2021 20:00
3/5/2021 21:00
3/5/2021 22:00
3/5/2021 23:00
3/6/2021 0:00
3/6/2021 1:00
3/6/2021 3:00
3/6/2021 4:00
3/6/2021 5:00
3/6/2021 6:00
i want to place missed hours and fill their values with mean of the before and after data
each row has following dat:
Unix Date Symbol Open High Low Close volume_crypto volume_base tradecount volume_adi volume_obv volume_cmf volume_fi volume_em volume_sma_em volume_vpt volume_vwap volume_mfi volume_nvi volatility_bbm volatility_bbh volatility_bbl volatility_bbw volatility_bbp volatility_bbhi volatility_bbli volatility_kcc volatility_kch volatility_kcl volatility_kcw volatility_kcp volatility_kchi volatility_kcli volatility_dcl volatility_dch volatility_dcm volatility_dcw volatility_dcp volatility_atr volatility_ui trend_macd trend_macd_signal trend_macd_diff trend_sma_fast trend_sma_slow trend_ema_fast trend_ema_slow trend_vortex_ind_pos trend_vortex_ind_neg trend_vortex_ind_diff trend_trix trend_mass_index trend_dpo trend_kst trend_kst_sig trend_kst_diff trend_ichimoku_conv trend_ichimoku_base trend_ichimoku_a trend_ichimoku_b trend_stc trend_adx trend_adx_pos trend_adx_neg trend_cci trend_visual_ichimoku_a trend_visual_ichimoku_b trend_aroon_up trend_aroon_down trend_aroon_ind trend_psar_up trend_psar_down trend_psar_up_indicator trend_psar_down_indicator momentum_rsi momentum_stoch_rsi momentum_stoch_rsi_k momentum_stoch_rsi_d momentum_tsi momentum_uo momentum_stoch momentum_stoch_signal momentum_wr momentum_ao momentum_roc momentum_ppo momentum_ppo_signal momentum_ppo_hist momentum_pvo momentum_pvo_signal momentum_pvo_hist momentum_kama others_dr others_dlr others_cr sma_3 sma_5 sma_7 sma_10 sma_20 sma_50 sma_100 sma_200 ema_3 ema_5 ema_7 ema_9 ema_12 ema_20 ema_26 ema_50 ema_100 ema_200 wma_3 wma_5 wma_7 wma_9 wma_14 wma_20 wma_50 wma_100 wma_200 rsi_3 rsi_5 rsi_7 rsi_14 rsi_9 rsi_25 rsi_50 macd_short signal_short hist_short macd_shorter signal_shorter hist_shorter macd_short_mid signal_short_mid hist_short_mid macd_short_long signal_short_long hist_short_long
1.61408E+12 2/23/2021 12:00 1INCHBUSD 3.8097 3.9 2.64 3.45 42666.61 142948.4199 643 12190.46 42666.61 0.285714286 0 0 0 0 3.33 50 1000 3.45 3.45 3.45 0 0 0 0 3.33 4.59 2.07 75.67567568 0.547619048 0 0 2.64 3.9 3.27 36.52173913 0.642857143 0 0 0 0 0 3.45 3.45 3.45 3.45 0 0 0 106.2014453 1 -1.779810821 1065.634267 1065.634267 0 3.27 3.27 3.27 3.27 0 0 0 0 0 1.672463509 1.678777784 0 0 0 3 3.9 0 0 100 0 0 0 0 0 64.28571429 64.28571429 -35.71428571 0 0 0 0 0 0 0 0 3.45 0 0 0
give me proper python code
|
f7367515d146ae6e143fbbb9a74c01b3
|
{
"intermediate": 0.35356101393699646,
"beginner": 0.3493575155735016,
"expert": 0.29708150029182434
}
|
43,533
|
npm : The term 'npm' is not recognized as the name of a cmdlet, function, script file, or operable program. Check the spelling of the name, or if a path was included, verify that the path is correct and try again.
At line:1 char:1
+ npm -v
+ ~~~
+ CategoryInfo : ObjectNotFound: (npm:String) [], CommandNotFoundException
+ FullyQualifiedErrorId : CommandNotFoundException
|
800f67e0ccd0d1efa5fa2f9fc28ed7b9
|
{
"intermediate": 0.4625259339809418,
"beginner": 0.25580570101737976,
"expert": 0.2816683351993561
}
|
43,534
|
Почему не выводятся логи ?
import logging
import os
from datetime import datetime
from .helper import Helper
class Logger:
def __new__(cls):
if not hasattr(cls, "instance"):
cls.instance = super(Logger, cls).__new__(cls)
return cls.instance
def __init__(self):
config = Helper.load_json("auth_data.json")
directory = config["logs"]["directory"]
project_name = config["project_name"]
now = datetime.now().strftime("%Y-%m-%d")
if directory and not os.path.isdir(directory):
os.mkdir(directory)
logging.basicConfig(
format="%(asctime)s : %(levelname)s : %(message)s",
level=logging.INFO,
handlers=[
logging.FileHandler(f"{directory}/{now}_{project_name}.log", encoding="UTF-8"),
logging.StreamHandler(),
],
)
@staticmethod
def write_info(message):
logging.info(message)
@staticmethod
def write_debug(message):
logging.debug(message)
@staticmethod
def write_warning(message):
logging.warning(message)
@staticmethod
def write_exception(message):
logging.exception(message)
@staticmethod
def write_error(message, exc_info=False):
logging.error(message, exc_info=exc_info)
|
a3940553d9b8eb3d6b2a7230301c49e7
|
{
"intermediate": 0.4125388562679291,
"beginner": 0.4707941710948944,
"expert": 0.11666695773601532
}
|
43,535
|
PHP Fatal error: Uncaught TypeError: mysqli_select_db():
|
fb88e645aec1ce19553afae8d224ffa0
|
{
"intermediate": 0.4182211458683014,
"beginner": 0.3191327452659607,
"expert": 0.2626461684703827
}
|
43,536
|
PHP Fatal error: Uncaught TypeError: mysqli_select_db():
|
59052daf1fe3381ad30f558d0d550077
|
{
"intermediate": 0.4182211458683014,
"beginner": 0.3191327452659607,
"expert": 0.2626461684703827
}
|
43,537
|
i have following code :
import pandas as pd
import numpy as np
import pandas as pd
def fill_missing_hours_optimized(csv_file, output_file):
# Read CSV with appropriate date parsing (assuming 'Date' and 'Hour' columns)
df = pd.read_csv(csv_file, parse_dates=['Date'])
# Assuming 'Hour' column exists and needs combining with 'Date' for complete datetime index
if 'Hour' in df.columns:
df['Date'] = pd.to_datetime(df['Date']) + pd.to_timedelta(df.pop('Hour'), unit='h')
# Set 'Date' as index for efficient resampling and manipulation
df.set_index('Date', inplace=True)
# Resample to hourly frequency, using ffill for existing non-null data
df = df.resample('H').first().interpolate(method='time') # 'ffill' replaced with 'time'-based interpolation
# For numeric columns, fill missing values with the mean of their day
numeric_cols = df.select_dtypes(include=[np.number]).columns
df[numeric_cols] = df[numeric_cols].transform(lambda x: x.fillna(x.mean()))
# Interestingly, if there exist non-numeric columns that need special handling,
# specific strategies need to be defined as .fillna() or similar based on context.
# Drop unwanted columns with a single line thanks to list comprehension
columns_to_remove = [col for col in df.columns if "Unix" in col]
df.drop(columns=columns_to_remove, inplace=True)
# Save the optimized and filled data to output CSV
df.to_csv(output_file, index=False)
# Example usage
csv_file = r'E:\01_calculate_talib\New folder\Binance_1INCHBUSD_1h.csv'
output_file = r'E:\01_calculate_talib\New folder\filled_data_3.csv'
fill_missing_hours_optimized(csv_file, output_file)
print(f"Missing hours filled and saved to: {output_file}")
after doing operation my Date column is missing in output file
fix it
|
11440283c96d4b8401cbedf62875e612
|
{
"intermediate": 0.45012763142585754,
"beginner": 0.22672788798809052,
"expert": 0.32314449548721313
}
|
43,538
|
PHP Fatal error: Uncaught TypeError: mysqli_select_db():
|
1e7c3d37586579716009b32678dd0ac2
|
{
"intermediate": 0.4182211458683014,
"beginner": 0.3191327452659607,
"expert": 0.2626461684703827
}
|
43,539
|
出现 错误 PHP Fatal error: Uncaught Error: Call to a member function select_db()
|
fb265f7ace4a73992d5853caa578a8af
|
{
"intermediate": 0.3078405261039734,
"beginner": 0.41909292340278625,
"expert": 0.27306652069091797
}
|
43,540
|
Turn the code below into a react component that that i can import into my header as <Spider />: (function() {
var width, height, largeHeader, canvas, ctx, points, target, animateHeader = true;
// Main
initHeader();
initAnimation();
addListeners();
function initHeader() {
width = window.innerWidth;
height = window.innerHeight;
target = {x: width/2, y: height/2};
largeHeader = document.getElementById('large-header');
largeHeader.style.height = height+'px';
canvas = document.getElementById('demo-canvas');
canvas.width = width;
canvas.height = height;
ctx = canvas.getContext('2d');
// create points
points = [];
for(var x = 0; x < width; x = x + width/20) {
for(var y = 0; y < height; y = y + height/20) {
var px = x + Math.random()*width/20;
var py = y + Math.random()*height/20;
var p = {x: px, originX: px, y: py, originY: py };
points.push(p);
}
}
// for each point find the 5 closest points
for(var i = 0; i < points.length; i++) {
var closest = [];
var p1 = points[i];
for(var j = 0; j < points.length; j++) {
var p2 = points[j]
if(!(p1 == p2)) {
var placed = false;
for(var k = 0; k < 5; k++) {
if(!placed) {
if(closest[k] == undefined) {
closest[k] = p2;
placed = true;
}
}
}
for(var k = 0; k < 5; k++) {
if(!placed) {
if(getDistance(p1, p2) < getDistance(p1, closest[k])) {
closest[k] = p2;
placed = true;
}
}
}
}
}
p1.closest = closest;
}
// assign a circle to each point
for(var i in points) {
var c = new Circle(points[i], 2+Math.random()*2, 'rgba(255,255,255,0.3)');
points[i].circle = c;
}
}
// Event handling
function addListeners() {
if(!('ontouchstart' in window)) {
window.addEventListener('mousemove', mouseMove);
}
window.addEventListener('scroll', scrollCheck);
window.addEventListener('resize', resize);
}
function mouseMove(e) {
var posx = posy = 0;
if (e.pageX || e.pageY) {
posx = e.pageX;
posy = e.pageY;
}
else if (e.clientX || e.clientY) {
posx = e.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
posy = e.clientY + document.body.scrollTop + document.documentElement.scrollTop;
}
target.x = posx;
target.y = posy;
}
function scrollCheck() {
if(document.body.scrollTop > height) animateHeader = false;
else animateHeader = true;
}
function resize() {
width = window.innerWidth;
height = window.innerHeight;
largeHeader.style.height = height+'px';
canvas.width = width;
canvas.height = height;
}
// animation
function initAnimation() {
animate();
for(var i in points) {
shiftPoint(points[i]);
}
}
function animate() {
if(animateHeader) {
ctx.clearRect(0,0,width,height);
for(var i in points) {
// detect points in range
if(Math.abs(getDistance(target, points[i])) < 4000) {
points[i].active = 0.3;
points[i].circle.active = 0.6;
} else if(Math.abs(getDistance(target, points[i])) < 20000) {
points[i].active = 0.1;
points[i].circle.active = 0.3;
} else if(Math.abs(getDistance(target, points[i])) < 40000) {
points[i].active = 0.02;
points[i].circle.active = 0.1;
} else {
points[i].active = 0;
points[i].circle.active = 0;
}
drawLines(points[i]);
points[i].circle.draw();
}
}
requestAnimationFrame(animate);
}
function shiftPoint(p) {
TweenLite.to(p, 1+1*Math.random(), {x:p.originX-50+Math.random()*100,
y: p.originY-50+Math.random()*100, ease:Circ.easeInOut,
onComplete: function() {
shiftPoint(p);
}});
}
// Canvas manipulation
function drawLines(p) {
if(!p.active) return;
for(var i in p.closest) {
ctx.beginPath();
ctx.moveTo(p.x, p.y);
ctx.lineTo(p.closest[i].x, p.closest[i].y);
ctx.strokeStyle = 'rgba(156,217,249,'+ p.active+')';
ctx.stroke();
}
}
function Circle(pos,rad,color) {
var _this = this;
// constructor
(function() {
_this.pos = pos || null;
_this.radius = rad || null;
_this.color = color || null;
})();
this.draw = function() {
if(!_this.active) return;
ctx.beginPath();
ctx.arc(_this.pos.x, _this.pos.y, _this.radius, 0, 2 * Math.PI, false);
ctx.fillStyle = 'rgba(156,217,249,'+ _this.active+')';
ctx.fill();
};
}
// Util
function getDistance(p1, p2) {
return Math.pow(p1.x - p2.x, 2) + Math.pow(p1.y - p2.y, 2);
}
})();
|
7c94c88d84ffa5eea2d21f3134374261
|
{
"intermediate": 0.28779301047325134,
"beginner": 0.4013045132160187,
"expert": 0.3109024465084076
}
|
43,541
|
I have one variable in my catalog item named as select_server and refer to the cmdb_ci_server table. my requirement is that if i select any record on the variable it will get the ip_address field value and then compare with the second table with encoded query condition "type = 10^child.ip_address =". then check all parent records with a field operational status =1. if suppose 2 records found with the operational status ==1 out of 5 records then show a popup on catalog item with the names of both record stored in name field and clear the variable of select_server. in servicenow
|
3390e574a7c9d72e41899fa7d4763ded
|
{
"intermediate": 0.4554072618484497,
"beginner": 0.26900675892829895,
"expert": 0.27558597922325134
}
|
43,542
|
'Circle' object has no attribute 'get_r'
|
66ba7458d3ac634bbb20487bb258d48e
|
{
"intermediate": 0.37053871154785156,
"beginner": 0.26744547486305237,
"expert": 0.36201581358909607
}
|
43,543
|
write python code to extract data from google page
|
623d32797c3df605a2caf2ccecd6efbb
|
{
"intermediate": 0.514168918132782,
"beginner": 0.17827345430850983,
"expert": 0.3075576424598694
}
|
43,544
|
help me to write a CSS selector that will find inputs within a div that have data-testid='input-from' and data-testid='input-to'. The selector should check if the input has value='' and if its an empty string them make color:gray and if value isn't equal to an empty string then make it color: black;
|
b82ac12fef521faf73b4b6ea16b718b5
|
{
"intermediate": 0.4982227087020874,
"beginner": 0.2284102439880371,
"expert": 0.27336710691452026
}
|
43,545
|
Привет! Мне нужно сделать систему игр с тем условие, что в одну и туже игру могут играть неограниченное кол-во игроков. Мне кажется, что текущее решение не очень хорошее. Посмотри и скажи как сделать лучше с
конкретными примерами?
public void OnGamesCommand(CCSPlayerController? player, CommandInfo commandInfo)
{
if (player.Team != CsTeam.Terrorist)
{
player.PrintToChat("This command is only for inmates");
return;
}
var players = Utilities.GetPlayers();
var inmates = players.Where(player => player.Team == CsTeam.Terrorist).ToList();
if (inmates.Count < 2)
{
player.PrintToChat("Inmates count must be more or equal 2");
return;
}
var menu = new ChatMenu("Выберите игру");
foreach (var game in _lrGames)
{
menu.AddMenuOption(game.Name, (controller, option) => { GameSelected(player, game, players); });
}
MenuManager.OpenChatMenu(player, menu);
}
private void GameSelected(CCSPlayerController inmate, LRGame lrGame, List<CCSPlayerController> players)
{
var menu = new ChatMenu("Выберите кт");
Server.PrintToConsole("SelectMenu");
var guardians = players.Where(player => player.Team == CsTeam.CounterTerrorist).ToList();
foreach (var guardian in guardians)
{
menu.AddMenuOption(guardian.PlayerName, (controller, option) =>
{
var newGame = (LRGame)Activator.CreateInstance(lrGame.GetType(), this);
newGame.Guardian = guardian;
newGame.Inmate = inmate;
RegisterAllAttributes(newGame);
newGame.Execute();
});
}
MenuManager.OpenChatMenu(inmate, menu);
}
Остальные классы:
public class LRGame : JailBreakObject
{
public virtual string Name { get; }
public bool IsEnd { get; private set; }
public CCSPlayerController Guardian { get; set; }
public CCSPlayerController Inmate { get; set; }
private Listeners.OnClientDisconnect _onClientDisconnect;
public LRGame(JailBreak jailBreak) : base(jailBreak)
{
jailBreak.RegisterEventHandler<EventPlayerDeath>(((@event, info) =>
{
var player = @event.Userid;
if (player == Guardian || player == Inmate)
{
End();
}
return HookResult.Continue;
}));
}
[GameEventHandler]
public HookResult EventRoundEnd(EventRoundEnd @event, GameEventInfo info)
{
End();
return HookResult.Continue;
}
[GameEventHandler]
public HookResult EventRoundStart(EventRoundStart @event, GameEventInfo info)
{
End();
return HookResult.Continue;
}
public void Execute()
{
_onClientDisconnect = OnClientDisconnect;
JailBreak.RegisterListener(_onClientDisconnect);
Start();
}
private void OnClientDisconnect(int slot)
{
if (slot == Inmate.Slot || slot == Guardian.Slot)
{
End();
}
}
protected virtual void Start()
{
}
protected void End()
{
JailBreak.RemoveListener("OnClientDisconnect", _onClientDisconnect);
IsEnd = true;
}
}
public class RouletteGame : LRGame
{
public override string Name => "Рулетка";
public CBasePlayerWeapon[] _weapons = new CBasePlayerWeapon[2];
public RouletteGame(JailBreak jailBreak) : base(jailBreak)
{
}
[GameEventHandler]
public HookResult EventBulletImpact(EventBulletImpact @event, GameEventInfo info)
{
if (IsEnd)
return HookResult.Continue;
var player = @event.Userid;
if (player == Inmate)
{
Server.PrintToChatAll("MORE22");
Guardian.SetAmmo(1, 0);
}
else if (player == Guardian)
{
Server.PrintToChatAll("MORE123");
Inmate.SetAmmo(1, 0);
}
return HookResult.Continue;
}
protected override void Start()
{
Inmate.RemoveWeapons(false);
Guardian.RemoveWeapons(false);
var magicNumber = Random.Shared.Next(0, 2);
_weapons[0] = new CCSWeaponBaseGun(Inmate.GiveNamedItem(CsItem.DesertEagle));
_weapons[0].SetAmmo(magicNumber == 0 ? 1 : 0, 0);
_weapons[1] = new CCSWeaponBaseGun(Guardian.GiveNamedItem(CsItem.DesertEagle));
_weapons[1].SetAmmo(magicNumber == 0 ? 0 : 1, 0);
}
}
|
528a1216f2d688018fd7ee186387d307
|
{
"intermediate": 0.3717658519744873,
"beginner": 0.45748990774154663,
"expert": 0.17074421048164368
}
|
43,546
|
Привет! Мне нужно сделать систему игр с тем условие, что в одну и туже игру могут играть неограниченное кол-во игроков. Мне кажется, что текущее решение не очень хорошее. Сделай рефакторинг
public void OnGamesCommand(CCSPlayerController? player, CommandInfo commandInfo)
{
if (player.Team != CsTeam.Terrorist)
{
player.PrintToChat(“This command is only for inmates”);
return;
}
var players = Utilities.GetPlayers();
var inmates = players.Where(player => player.Team == CsTeam.Terrorist).ToList();
if (inmates.Count < 2)
{
player.PrintToChat(“Inmates count must be more or equal 2”);
return;
}
var menu = new ChatMenu(“Выберите игру”);
foreach (var game in _lrGames)
{
menu.AddMenuOption(game.Name, (controller, option) => { GameSelected(player, game, players); });
}
MenuManager.OpenChatMenu(player, menu);
}
private void GameSelected(CCSPlayerController inmate, LRGame lrGame, List<CCSPlayerController> players)
{
var menu = new ChatMenu(“Выберите кт”);
Server.PrintToConsole(“SelectMenu”);
var guardians = players.Where(player => player.Team == CsTeam.CounterTerrorist).ToList();
foreach (var guardian in guardians)
{
menu.AddMenuOption(guardian.PlayerName, (controller, option) =>
{
var newGame = (LRGame)Activator.CreateInstance(lrGame.GetType(), this);
newGame.Guardian = guardian;
newGame.Inmate = inmate;
RegisterAllAttributes(newGame);
newGame.Execute();
});
}
MenuManager.OpenChatMenu(inmate, menu);
}
Остальные классы:
public class LRGame : JailBreakObject
{
public virtual string Name { get; }
public bool IsEnd { get; private set; }
public CCSPlayerController Guardian { get; set; }
public CCSPlayerController Inmate { get; set; }
private Listeners.OnClientDisconnect _onClientDisconnect;
public LRGame(JailBreak jailBreak) : base(jailBreak)
{
jailBreak.RegisterEventHandler<EventPlayerDeath>(((@event, info) =>
{
var player = @event.Userid;
if (player == Guardian || player == Inmate)
{
End();
}
return HookResult.Continue;
}));
}
[GameEventHandler]
public HookResult EventRoundEnd(EventRoundEnd @event, GameEventInfo info)
{
End();
return HookResult.Continue;
}
[GameEventHandler]
public HookResult EventRoundStart(EventRoundStart @event, GameEventInfo info)
{
End();
return HookResult.Continue;
}
public void Execute()
{
_onClientDisconnect = OnClientDisconnect;
JailBreak.RegisterListener(_onClientDisconnect);
Start();
}
private void OnClientDisconnect(int slot)
{
if (slot == Inmate.Slot || slot == Guardian.Slot)
{
End();
}
}
protected virtual void Start()
{
}
protected void End()
{
JailBreak.RemoveListener(“OnClientDisconnect”, _onClientDisconnect);
IsEnd = true;
}
}
public class RouletteGame : LRGame
{
public override string Name => “Рулетка”;
public CBasePlayerWeapon[] _weapons = new CBasePlayerWeapon[2];
public RouletteGame(JailBreak jailBreak) : base(jailBreak)
{
}
[GameEventHandler]
public HookResult EventBulletImpact(EventBulletImpact @event, GameEventInfo info)
{
if (IsEnd)
return HookResult.Continue;
var player = @event.Userid;
if (player == Inmate)
{
Server.PrintToChatAll(“MORE22”);
Guardian.SetAmmo(1, 0);
}
else if (player == Guardian)
{
Server.PrintToChatAll(“MORE123”);
Inmate.SetAmmo(1, 0);
}
return HookResult.Continue;
}
protected override void Start()
{
Inmate.RemoveWeapons(false);
Guardian.RemoveWeapons(false);
var magicNumber = Random.Shared.Next(0, 2);
_weapons[0] = new CCSWeaponBaseGun(Inmate.GiveNamedItem(CsItem.DesertEagle));
_weapons[0].SetAmmo(magicNumber == 0 ? 1 : 0, 0);
_weapons[1] = new CCSWeaponBaseGun(Guardian.GiveNamedItem(CsItem.DesertEagle));
_weapons[1].SetAmmo(magicNumber == 0 ? 0 : 1, 0);
}
}
|
bc19a5670aac010304d0f4993d30092e
|
{
"intermediate": 0.30935385823249817,
"beginner": 0.4456407427787781,
"expert": 0.24500544369220734
}
|
43,547
|
please to generate an python gui program with main menu and sub menu using tkinter
|
d55dd9f22487b70b0e1b427ecbaad43d
|
{
"intermediate": 0.4082731604576111,
"beginner": 0.25657469034194946,
"expert": 0.33515211939811707
}
|
43,548
|
adapt the following js to react lifecycle and use it to set the background of my Header.js. JS code: (function () {
var requestAnimationFrame = window.requestAnimationFrame || window.mozRequestAnimationFrame || window.webkitRequestAnimationFrame || window.msRequestAnimationFrame || function (callback) {
window.setTimeout(callback, 1000 / 60);
};
window.requestAnimationFrame = requestAnimationFrame;
})();
// Terrain stuff.
var background = document.getElementById(“bgCanvas”),
bgCtx = background.getContext(“2d”),
width = window.innerWidth,
height = document.body.offsetHeight;
(height < 400) ? height = 400 : height;
background.width = width;
background.height = height;
function Terrain(options) {
options = options || {};
this.terrain = document.createElement(“canvas”);
this.terCtx = this.terrain.getContext(“2d”);
this.scrollDelay = options.scrollDelay || 90;
this.lastScroll = new Date().getTime();
this.terrain.width = width;
this.terrain.height = height;
this.fillStyle = options.fillStyle || “#191D4C”;
this.mHeight = options.mHeight || height;
// generate
this.points = [];
var displacement = options.displacement || 140,
power = Math.pow(2, Math.ceil(Math.log(width) / (Math.log(2))));
// set the start height and end height for the terrain
this.points[0] = this.mHeight;//(this.mHeight - (Math.random() * this.mHeight / 2)) - displacement;
this.points[power] = this.points[0];
// create the rest of the points
for (var i = 1; i < power; i *= 2) {
for (var j = (power / i) / 2; j < power; j += power / i) {
this.points[j] = ((this.points[j - (power / i) / 2] + this.points[j + (power / i) / 2]) / 2) + Math.floor(Math.random() * -displacement + displacement);
}
displacement *= 0.6;
}
document.body.appendChild(this.terrain);
}
Terrain.prototype.update = function () {
// draw the terrain
this.terCtx.clearRect(0, 0, width, height);
this.terCtx.fillStyle = this.fillStyle;
if (new Date().getTime() > this.lastScroll + this.scrollDelay) {
this.lastScroll = new Date().getTime();
this.points.push(this.points.shift());
}
this.terCtx.beginPath();
for (var i = 0; i <= width; i++) {
if (i === 0) {
this.terCtx.moveTo(0, this.points[0]);
} else if (this.points[i] !== undefined) {
this.terCtx.lineTo(i, this.points[i]);
}
}
this.terCtx.lineTo(width, this.terrain.height);
this.terCtx.lineTo(0, this.terrain.height);
this.terCtx.lineTo(0, this.points[0]);
this.terCtx.fill();
}
// Second canvas used for the stars
bgCtx.fillStyle = ‘#05004c’;
bgCtx.fillRect(0, 0, width, height);
// stars
function Star(options) {
this.size = Math.random() * 2;
this.speed = Math.random() * .05;
this.x = options.x;
this.y = options.y;
}
Star.prototype.reset = function () {
this.size = Math.random() * 2;
this.speed = Math.random() * .05;
this.x = width;
this.y = Math.random() * height;
}
Star.prototype.update = function () {
this.x -= this.speed;
if (this.x < 0) {
this.reset();
} else {
bgCtx.fillRect(this.x, this.y, this.size, this.size);
}
}
function ShootingStar() {
this.reset();
}
ShootingStar.prototype.reset = function () {
this.x = Math.random() * width;
this.y = 0;
this.len = (Math.random() * 80) + 10;
this.speed = (Math.random() * 10) + 6;
this.size = (Math.random() * 1) + 0.1;
// this is used so the shooting stars arent constant
this.waitTime = new Date().getTime() + (Math.random() * 3000) + 500;
this.active = false;
}
ShootingStar.prototype.update = function () {
if (this.active) {
this.x -= this.speed;
this.y += this.speed;
if (this.x < 0 || this.y >= height) {
this.reset();
} else {
bgCtx.lineWidth = this.size;
bgCtx.beginPath();
bgCtx.moveTo(this.x, this.y);
bgCtx.lineTo(this.x + this.len, this.y - this.len);
bgCtx.stroke();
}
} else {
if (this.waitTime < new Date().getTime()) {
this.active = true;
}
}
}
var entities = [];
// init the stars
for (var i = 0; i < height; i++) {
entities.push(new Star({
x: Math.random() * width,
y: Math.random() * height
}));
}
// Add 2 shooting stars that just cycle.
entities.push(new ShootingStar());
entities.push(new ShootingStar());
entities.push(new Terrain({mHeight : (height/2)-120}));
entities.push(new Terrain({displacement : 120, scrollDelay : 50, fillStyle : “rgb(17,20,40)”, mHeight : (height/2)-60}));
entities.push(new Terrain({displacement : 100, scrollDelay : 20, fillStyle : “rgb(10,10,5)”, mHeight : height/2}));
//animate background
function animate() {
bgCtx.fillStyle = ‘#110E19’;
bgCtx.fillRect(0, 0, width, height);
bgCtx.fillStyle = ‘#ffffff’;
bgCtx.strokeStyle = ‘#ffffff’;
var entLen = entities.length;
while (entLen–) {
entities[entLen].update();
}
requestAnimationFrame(animate);
}
animate(); Header.js: (function () {
var requestAnimationFrame = window.requestAnimationFrame || window.mozRequestAnimationFrame || window.webkitRequestAnimationFrame || window.msRequestAnimationFrame || function (callback) {
window.setTimeout(callback, 1000 / 60);
};
window.requestAnimationFrame = requestAnimationFrame;
})();
// Terrain stuff.
var background = document.getElementById(“bgCanvas”),
bgCtx = background.getContext(“2d”),
width = window.innerWidth,
height = document.body.offsetHeight;
(height < 400) ? height = 400 : height;
background.width = width;
background.height = height;
function Terrain(options) {
options = options || {};
this.terrain = document.createElement(“canvas”);
this.terCtx = this.terrain.getContext(“2d”);
this.scrollDelay = options.scrollDelay || 90;
this.lastScroll = new Date().getTime();
this.terrain.width = width;
this.terrain.height = height;
this.fillStyle = options.fillStyle || “#191D4C”;
this.mHeight = options.mHeight || height;
// generate
this.points = [];
var displacement = options.displacement || 140,
power = Math.pow(2, Math.ceil(Math.log(width) / (Math.log(2))));
// set the start height and end height for the terrain
this.points[0] = this.mHeight;//(this.mHeight - (Math.random() * this.mHeight / 2)) - displacement;
this.points[power] = this.points[0];
// create the rest of the points
for (var i = 1; i < power; i *= 2) {
for (var j = (power / i) / 2; j < power; j += power / i) {
this.points[j] = ((this.points[j - (power / i) / 2] + this.points[j + (power / i) / 2]) / 2) + Math.floor(Math.random() * -displacement + displacement);
}
displacement *= 0.6;
}
document.body.appendChild(this.terrain);
}
Terrain.prototype.update = function () {
// draw the terrain
this.terCtx.clearRect(0, 0, width, height);
this.terCtx.fillStyle = this.fillStyle;
if (new Date().getTime() > this.lastScroll + this.scrollDelay) {
this.lastScroll = new Date().getTime();
this.points.push(this.points.shift());
}
this.terCtx.beginPath();
for (var i = 0; i <= width; i++) {
if (i === 0) {
this.terCtx.moveTo(0, this.points[0]);
} else if (this.points[i] !== undefined) {
this.terCtx.lineTo(i, this.points[i]);
}
}
this.terCtx.lineTo(width, this.terrain.height);
this.terCtx.lineTo(0, this.terrain.height);
this.terCtx.lineTo(0, this.points[0]);
this.terCtx.fill();
}
// Second canvas used for the stars
bgCtx.fillStyle = ‘#05004c’;
bgCtx.fillRect(0, 0, width, height);
// stars
function Star(options) {
this.size = Math.random() * 2;
this.speed = Math.random() * .05;
this.x = options.x;
this.y = options.y;
}
Star.prototype.reset = function () {
this.size = Math.random() * 2;
this.speed = Math.random() * .05;
this.x = width;
this.y = Math.random() * height;
}
Star.prototype.update = function () {
this.x -= this.speed;
if (this.x < 0) {
this.reset();
} else {
bgCtx.fillRect(this.x, this.y, this.size, this.size);
}
}
function ShootingStar() {
this.reset();
}
ShootingStar.prototype.reset = function () {
this.x = Math.random() * width;
this.y = 0;
this.len = (Math.random() * 80) + 10;
this.speed = (Math.random() * 10) + 6;
this.size = (Math.random() * 1) + 0.1;
// this is used so the shooting stars arent constant
this.waitTime = new Date().getTime() + (Math.random() * 3000) + 500;
this.active = false;
}
ShootingStar.prototype.update = function () {
if (this.active) {
this.x -= this.speed;
this.y += this.speed;
if (this.x < 0 || this.y >= height) {
this.reset();
} else {
bgCtx.lineWidth = this.size;
bgCtx.beginPath();
bgCtx.moveTo(this.x, this.y);
bgCtx.lineTo(this.x + this.len, this.y - this.len);
bgCtx.stroke();
}
} else {
if (this.waitTime < new Date().getTime()) {
this.active = true;
}
}
}
var entities = [];
// init the stars
for (var i = 0; i < height; i++) {
entities.push(new Star({
x: Math.random() * width,
y: Math.random() * height
}));
}
// Add 2 shooting stars that just cycle.
entities.push(new ShootingStar());
entities.push(new ShootingStar());
entities.push(new Terrain({mHeight : (height/2)-120}));
entities.push(new Terrain({displacement : 120, scrollDelay : 50, fillStyle : “rgb(17,20,40)”, mHeight : (height/2)-60}));
entities.push(new Terrain({displacement : 100, scrollDelay : 20, fillStyle : “rgb(10,10,5)”, mHeight : height/2}));
//animate background
function animate() {
bgCtx.fillStyle = ‘#110E19’;
bgCtx.fillRect(0, 0, width, height);
bgCtx.fillStyle = ‘#ffffff’;
bgCtx.strokeStyle = ‘#ffffff’;
var entLen = entities.length;
while (entLen–) {
entities[entLen].update();
}
requestAnimationFrame(animate);
}
animate();
|
a350f5495e27f547ae068034336593a6
|
{
"intermediate": 0.44710108637809753,
"beginner": 0.4082261323928833,
"expert": 0.14467275142669678
}
|
43,549
|
Write a roblox studio script for a smart AI.
|
132db3354c217897dd0ea3b5d759a62f
|
{
"intermediate": 0.27686867117881775,
"beginner": 0.21609698235988617,
"expert": 0.507034420967102
}
|
43,550
|
can site js set hook on momentary disconnection of the internet network to log this. Can I forbid this in firefox browser?
|
382c4adefd72a7a19399ecce9dcc1cca
|
{
"intermediate": 0.4937737286090851,
"beginner": 0.2738027274608612,
"expert": 0.2324235588312149
}
|
43,551
|
How do i use the regular javascript script externalFile.js to set the background of my header(id=bgCanvas) using the useEffect hook to load the js: externalFile.js: (function () {
var requestAnimationFrame = window.requestAnimationFrame || window.mozRequestAnimationFrame || window.webkitRequestAnimationFrame || window.msRequestAnimationFrame || function (callback) {
window.setTimeout(callback, 1000 / 60);
};
window.requestAnimationFrame = requestAnimationFrame;
})();
// Terrain stuff.
var background = document.getElementById("bgCanvas"),
bgCtx = background.getContext("2d"),
width = window.innerWidth,
height = document.body.offsetHeight;
(height < 400) ? height = 400 : height;
background.width = width;
background.height = height;
function Terrain(options) {
options = options || {};
this.terrain = document.createElement("canvas");
this.terCtx = this.terrain.getContext("2d");
this.scrollDelay = options.scrollDelay || 90;
this.lastScroll = new Date().getTime();
this.terrain.width = width;
this.terrain.height = height;
this.fillStyle = options.fillStyle || "#191D4C";
this.mHeight = options.mHeight || height;
// generate
this.points = [];
var displacement = options.displacement || 140,
power = Math.pow(2, Math.ceil(Math.log(width) / (Math.log(2))));
// set the start height and end height for the terrain
this.points[0] = this.mHeight;//(this.mHeight - (Math.random() * this.mHeight / 2)) - displacement;
this.points[power] = this.points[0];
// create the rest of the points
for (var i = 1; i < power; i *= 2) {
for (var j = (power / i) / 2; j < power; j += power / i) {
this.points[j] = ((this.points[j - (power / i) / 2] + this.points[j + (power / i) / 2]) / 2) + Math.floor(Math.random() * -displacement + displacement);
}
displacement *= 0.6;
}
document.body.appendChild(this.terrain);
}
Terrain.prototype.update = function () {
// draw the terrain
this.terCtx.clearRect(0, 0, width, height);
this.terCtx.fillStyle = this.fillStyle;
if (new Date().getTime() > this.lastScroll + this.scrollDelay) {
this.lastScroll = new Date().getTime();
this.points.push(this.points.shift());
}
this.terCtx.beginPath();
for (var i = 0; i <= width; i++) {
if (i === 0) {
this.terCtx.moveTo(0, this.points[0]);
} else if (this.points[i] !== undefined) {
this.terCtx.lineTo(i, this.points[i]);
}
}
this.terCtx.lineTo(width, this.terrain.height);
this.terCtx.lineTo(0, this.terrain.height);
this.terCtx.lineTo(0, this.points[0]);
this.terCtx.fill();
}
// Second canvas used for the stars
bgCtx.fillStyle = '#05004c';
bgCtx.fillRect(0, 0, width, height);
// stars
function Star(options) {
this.size = Math.random() * 2;
this.speed = Math.random() * .05;
this.x = options.x;
this.y = options.y;
}
Star.prototype.reset = function () {
this.size = Math.random() * 2;
this.speed = Math.random() * .05;
this.x = width;
this.y = Math.random() * height;
}
Star.prototype.update = function () {
this.x -= this.speed;
if (this.x < 0) {
this.reset();
} else {
bgCtx.fillRect(this.x, this.y, this.size, this.size);
}
}
function ShootingStar() {
this.reset();
}
ShootingStar.prototype.reset = function () {
this.x = Math.random() * width;
this.y = 0;
this.len = (Math.random() * 80) + 10;
this.speed = (Math.random() * 10) + 6;
this.size = (Math.random() * 1) + 0.1;
// this is used so the shooting stars arent constant
this.waitTime = new Date().getTime() + (Math.random() * 3000) + 500;
this.active = false;
}
ShootingStar.prototype.update = function () {
if (this.active) {
this.x -= this.speed;
this.y += this.speed;
if (this.x < 0 || this.y >= height) {
this.reset();
} else {
bgCtx.lineWidth = this.size;
bgCtx.beginPath();
bgCtx.moveTo(this.x, this.y);
bgCtx.lineTo(this.x + this.len, this.y - this.len);
bgCtx.stroke();
}
} else {
if (this.waitTime < new Date().getTime()) {
this.active = true;
}
}
}
var entities = [];
// init the stars
for (var i = 0; i < height; i++) {
entities.push(new Star({
x: Math.random() * width,
y: Math.random() * height
}));
}
// Add 2 shooting stars that just cycle.
entities.push(new ShootingStar());
entities.push(new ShootingStar());
entities.push(new Terrain({mHeight : (height/2)-120}));
entities.push(new Terrain({displacement : 120, scrollDelay : 50, fillStyle : "rgb(17,20,40)", mHeight : (height/2)-60}));
entities.push(new Terrain({displacement : 100, scrollDelay : 20, fillStyle : "rgb(10,10,5)", mHeight : height/2}));
//animate background
function animate() {
bgCtx.fillStyle = '#110E19';
bgCtx.fillRect(0, 0, width, height);
bgCtx.fillStyle = '#ffffff';
bgCtx.strokeStyle = '#ffffff';
var entLen = entities.length;
while (entLen--) {
entities[entLen].update();
}
requestAnimationFrame(animate);
}
animate(); Header.js: import React from 'react'
import { NavLink } from "react-router-dom";
import styled from "styled-components";
import Nav from "./Nav";
const Header = () => {
return (
<MainHeader id="bgCanvas">
<NavLink to="/">
<img src="../../Assets/shop_logo.png" width="128" alt="my logo img" className="logo"/>
</NavLink>
<Nav />
</MainHeader>
);
};
const MainHeader = styled.header`
padding: 0 4.8rem;
height: 10rem;
display: flex;
justify-content: space-between;
align-items: center;
position: relative;
border-radius: 0 0 50px 50px;
width: 100%;
background: #333;
overflow: hidden;
background-size: cover;
background-position: center center;
z-index: 1;
}
.logo {
height: 5rem;
}
`;
export default Header; Example of useEffect hook from documentation: import React, { useEffect } from 'react';
function MyComponent() {
useEffect(() => {
const script = document.createElement('script');
script.src = "https://externalwebsite.com/externalFile.js";
script.async = true;
document.body.appendChild(script);
return () => {
document.body.removeChild(script);
}
}, []);
return (
// JSX goes here
);
}
|
2949994252885b76bfc8734183d44e8b
|
{
"intermediate": 0.4032871127128601,
"beginner": 0.41425275802612305,
"expert": 0.18246006965637207
}
|
43,552
|
write a python code to return the length of longest non repeating substring in a string
|
b96eb296d8ad4f74a2eb2fe2ad936a6a
|
{
"intermediate": 0.3842746615409851,
"beginner": 0.18252745270729065,
"expert": 0.43319788575172424
}
|
43,553
|
donnes le resultat:
M = array([1,4,9],[6,7,5],[8,2,3])
M.min(axis=1).sum()
|
daeb5b2ac6c584f3e6a407eba69b5973
|
{
"intermediate": 0.32342249155044556,
"beginner": 0.36757490038871765,
"expert": 0.3090025782585144
}
|
43,554
|
How I can create a Flow such that when a record in created in the table- Approval goes to the Creator’s Manager.servicenow
|
a750d6f4f079aa886358c6fe1e588f48
|
{
"intermediate": 0.5140246748924255,
"beginner": 0.10211440175771713,
"expert": 0.38386082649230957
}
|
43,555
|
generate a tpt teradata script for multiload purpose
|
33431c14ca43776adcb2aa56add65e9e
|
{
"intermediate": 0.3377803564071655,
"beginner": 0.2848472595214844,
"expert": 0.3773724138736725
}
|
43,556
|
Write a smart Tic tac-toe AI, In Python with just 430 lines of code.
|
fda6652c299a0cc8e4a4e8ecf24a42b4
|
{
"intermediate": 0.10006188601255417,
"beginner": 0.07911552488803864,
"expert": 0.8208225965499878
}
|
43,557
|
Looks like CSS selector that checks if my React input has value doesn't work correctly. I want it to assign color to gray when the value is an empty string and to black when its not empty, however even if I will write something in it it applies the style for an empty value even though it already has some value. Is there a fix to that?
|
9ca06c8f8c97b2cf4071a61eeb4f8ce1
|
{
"intermediate": 0.557450532913208,
"beginner": 0.20144352316856384,
"expert": 0.24110592901706696
}
|
43,558
|
I want a project similar to Google news, with android jetpack composed with retrofit to have get post requests, articles from an API (for the example put the url of a basic API).
|
bee3cacfa0e71e8d96bf2828e8fbf8f8
|
{
"intermediate": 0.713563084602356,
"beginner": 0.13988538086414337,
"expert": 0.1465514749288559
}
|
43,559
|
Write a smart tic tac toe AI, In Python with only just 432 Lines of code.
Don't make the same mistake as this:
|
9e8a139d9e0326fe50c4c1fea19c86f2
|
{
"intermediate": 0.17963138222694397,
"beginner": 0.27033141255378723,
"expert": 0.5500372648239136
}
|
43,560
|
Can you modify the following code to run on cuda using numba
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
# Load the dataset
data = pd.read_excel('/home/pradyumnas/Desktop/Mahindra/Sem6/NN/CCPP/Data.ods', sheet_name=None)
full_data = pd.concat(data.values())
# Preprocess the data
X = full_data.iloc[:, :-1].values
y = full_data.iloc[:, -1].values
# Normalize the data
scaler_x = MinMaxScaler(feature_range=(-1, 1))
scaler_y = MinMaxScaler(feature_range=(-1, 1))
X_normalized = scaler_x.fit_transform(X)
y_normalized = scaler_y.fit_transform(y.reshape(-1, 1)).flatten()
# Split the data
X_train, X_temp, y_train, y_temp = train_test_split(X_normalized, y_normalized, test_size=0.28, random_state=42)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=(10/28), random_state=42)
# ANN architecture parameters
n_input = X_train.shape[1]
n_hidden = 10 # Example: 10 hidden nodes
n_output = 1
learning_rate = 0.001
lambda_reg = 0.01 # Regularization parameter
# Weight initialization
np.random.seed(42)
W1 = np.random.randn(n_input, n_hidden) * 0.1
b1 = np.zeros((1, n_hidden))
W2 = np.random.randn(n_hidden, n_output) * 0.1
b2 = np.zeros((1, n_output))
# Activation functions
def tanh(x):
return np.tanh(x)
def tanh_derivative(x):
return 1 - np.tanh(x)**2
def logistic(x):
return 1 / (1 + np.exp(-x))
def logistic_derivative(x):
return logistic(x) * (1 - logistic(x))
# Forward propagation
def forward_propagation(X, W1, b1, W2, b2):
Z1 = np.dot(X, W1) + b1
A1 = tanh(Z1)
Z2 = np.dot(A1, W2) + b2
A2 = logistic(Z2)
return Z1, A1, Z2, A2
# Back-propagation
def back_propagation(X, y, Z1, A1, Z2, A2, W1, W2):
m = y.size
# Compute gradients for the output layer
dZ2 = A2 - y.reshape(-1, 1)
dW2 = np.dot(A1.T, dZ2) / m + lambda_reg * W2 / m
db2 = np.sum(dZ2, axis=0, keepdims=True) / m
# Compute gradients for the hidden layer
dA1 = np.dot(dZ2, W2.T)
dZ1 = dA1 * tanh_derivative(Z1)
dW1 = np.dot(X.T, dZ1) / m + lambda_reg * W1 / m
db1 = np.sum(dZ1, axis=0, keepdims=True) / m
return dW1, db1, dW2, db2
# Initialize lists to store loss values for plotting
training_loss_history = []
validation_loss_history = []
# Training the ANN
for epoch in range(1000): # Example: 1000 epochs
Z1, A1, Z2, A2 = forward_propagation(X_train, W1, b1, W2, b2)
dW1, db1, dW2, db2 = back_propagation(X_train, y_train, Z1, A1, Z2, A2, W1, W2)
W1 -= learning_rate * dW1
b1 -= learning_rate * db1
W2 -= learning_rate * dW2
b2 -= learning_rate * db2
# Calculate training loss
training_loss = np.mean((A2 - y_train)**2)
training_loss_history.append(training_loss)
# Forward propagation for validation set
Z1_val, A1_val, Z2_val, A2_val = forward_propagation(X_val, W1, b1, W2, b2)
validation_loss = np.mean((A2_val - y_val)**2)
validation_loss_history.append(validation_loss)
# Print the training loss every 100 epochs
if epoch % 100 == 0:
print(f'Epoch {epoch}, Training Loss: {training_loss}, Validation Loss: {validation_loss}')
# Plotting the training and validation loss
plt.figure(figsize=(10, 5))
plt.plot(training_loss_history, label='Training Loss')
plt.plot(validation_loss_history, label='Validation Loss')
plt.title('Training and Validation Loss Over Epochs')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
# Calculate MAPE (Mean Absolute Percentage Error)
def calculate_mape(y_true, y_pred):
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
# Testing the ANN
Z1, A1, Z2, A2 = forward_propagation(X_test, W1, b1, W2, b2)
test_loss = np.mean((A2 - y_test)**2)
print(f'Test Loss: {test_loss}')
mape = calculate_mape(y_test, A2)
print(f'MAPE: {mape}%')
|
b93e9f833dbbdea5339a6e4b01473f54
|
{
"intermediate": 0.342046320438385,
"beginner": 0.25748303532600403,
"expert": 0.40047064423561096
}
|
43,561
|
Can you modify the following code so that it runs on cuda with numba.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
# Load the dataset
data = pd.read_excel('/home/pradyumnas/Desktop/Mahindra/Sem6/NN/CCPP/Data.ods', sheet_name=None)
full_data = pd.concat(data.values())
# Preprocess the data
X = full_data.iloc[:, :-1].values
y = full_data.iloc[:, -1].values
# Normalize the data
scaler_x = MinMaxScaler(feature_range=(-1, 1))
scaler_y = MinMaxScaler(feature_range=(-1, 1))
X_normalized = scaler_x.fit_transform(X)
y_normalized = scaler_y.fit_transform(y.reshape(-1, 1)).flatten()
# Split the data
X_train, X_temp, y_train, y_temp = train_test_split(X_normalized, y_normalized, test_size=0.28, random_state=42)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=(10/28), random_state=42)
# ANN architecture parameters
n_input = X_train.shape[1]
n_hidden = 10 # Example: 10 hidden nodes
n_output = 1
learning_rate = 0.001
lambda_reg = 0.01 # Regularization parameter
# Weight initialization
np.random.seed(42)
W1 = np.random.randn(n_input, n_hidden) * 0.1
b1 = np.zeros((1, n_hidden))
W2 = np.random.randn(n_hidden, n_output) * 0.1
b2 = np.zeros((1, n_output))
# Activation functions
def tanh(x):
return np.tanh(x)
def tanh_derivative(x):
return 1 - np.tanh(x)**2
def logistic(x):
return 1 / (1 + np.exp(-x))
def logistic_derivative(x):
return logistic(x) * (1 - logistic(x))
# Forward propagation
def forward_propagation(X, W1, b1, W2, b2):
Z1 = np.dot(X, W1) + b1
A1 = tanh(Z1)
Z2 = np.dot(A1, W2) + b2
A2 = logistic(Z2)
return Z1, A1, Z2, A2
# Back-propagation
def back_propagation(X, y, Z1, A1, Z2, A2, W1, W2):
m = y.size
# Compute gradients for the output layer
dZ2 = A2 - y.reshape(-1, 1)
dW2 = np.dot(A1.T, dZ2) / m + lambda_reg * W2 / m
db2 = np.sum(dZ2, axis=0, keepdims=True) / m
# Compute gradients for the hidden layer
dA1 = np.dot(dZ2, W2.T)
dZ1 = dA1 * tanh_derivative(Z1)
dW1 = np.dot(X.T, dZ1) / m + lambda_reg * W1 / m
db1 = np.sum(dZ1, axis=0, keepdims=True) / m
return dW1, db1, dW2, db2
# Initialize lists to store loss values for plotting
training_loss_history = []
validation_loss_history = []
# Training the ANN
for epoch in range(1000): # Example: 1000 epochs
Z1, A1, Z2, A2 = forward_propagation(X_train, W1, b1, W2, b2)
dW1, db1, dW2, db2 = back_propagation(X_train, y_train, Z1, A1, Z2, A2, W1, W2)
W1 -= learning_rate * dW1
b1 -= learning_rate * db1
W2 -= learning_rate * dW2
b2 -= learning_rate * db2
# Calculate training loss
training_loss = np.mean((A2 - y_train)**2)
training_loss_history.append(training_loss)
# Forward propagation for validation set
Z1_val, A1_val, Z2_val, A2_val = forward_propagation(X_val, W1, b1, W2, b2)
validation_loss = np.mean((A2_val - y_val)**2)
validation_loss_history.append(validation_loss)
# Print the training loss every 100 epochs
if epoch % 100 == 0:
print(f'Epoch {epoch}, Training Loss: {training_loss}, Validation Loss: {validation_loss}')
# Plotting the training and validation loss
plt.figure(figsize=(10, 5))
plt.plot(training_loss_history, label='Training Loss')
plt.plot(validation_loss_history, label='Validation Loss')
plt.title('Training and Validation Loss Over Epochs')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
# Calculate MAPE (Mean Absolute Percentage Error)
def calculate_mape(y_true, y_pred):
return np.mean(np.abs((y_true - y_pred) / y_true)) * 100
# Testing the ANN
Z1, A1, Z2, A2 = forward_propagation(X_test, W1, b1, W2, b2)
test_loss = np.mean((A2 - y_test)**2)
print(f'Test Loss: {test_loss}')
mape = calculate_mape(y_test, A2)
print(f'MAPE: {mape}%')
|
87af27bf765cf865eced65682b3fd876
|
{
"intermediate": 0.39258408546447754,
"beginner": 0.342947393655777,
"expert": 0.2644685208797455
}
|
43,562
|
Write a smart tic tac toe AI, In Python with only 467 lines of code.
|
bb09ce980e2b2359e6df2fa9cfc99998
|
{
"intermediate": 0.09921953082084656,
"beginner": 0.08241944760084152,
"expert": 0.8183610439300537
}
|
43,563
|
Write a smart tic tac toe AI, In Python with only 467 lines of code.
|
eccffbaf4d782103e881006c6d7d8b53
|
{
"intermediate": 0.09921953082084656,
"beginner": 0.08241944760084152,
"expert": 0.8183610439300537
}
|
43,564
|
翻译成英语并优化：我刚从production环境截图出来的如下所示，可以看到目前production每天要处理的图片超过了8000张
|
88ed3791cf6be234a73a64a27f53ae79
|
{
"intermediate": 0.2792792022228241,
"beginner": 0.3847700357437134,
"expert": 0.33595070242881775
}
|
43,565
|
in servicenow I have a catalogue item where we have a MRVS, in that multiple rows can be added and based on the variable(from MRVS) we need to trigger approvals through workflow editor
|
78f5d4416e653084eb695b351dee1d14
|
{
"intermediate": 0.8135232329368591,
"beginner": 0.11043590307235718,
"expert": 0.07604086399078369
}
|
43,566
|
how do you add the Google Maps API geographical library
|
eb2cafea963dd096df489ead805fc555
|
{
"intermediate": 0.8319193720817566,
"beginner": 0.09791480004787445,
"expert": 0.07016585022211075
}
|
43,568
|
How can we make checkbox mandatory in a Widget? Please suggest
Below is the code I have written for the widget
<div
ng-if="c.data.offNetworkOptionSelected == 'User is unavailable currently due to prolonged leave'"
style="margin-top: 10px"
>
<small
>${If you are currently on a leave of absence (LoA) (or are reporting on behalf of someone who is on LoA),
updating your status in HRDirect automatically updates the status of all workspaces and devices on a
colleague profile. This will also pause further chasers}
</small>
<br />
<div>
<br />
<p>
<label class="radio-inline"
><input
type="checkbox"
value=" I have read and understand the process"
ng-model="c.data.requiresMachine"
ng-click="checkAll()"
/>${I have read and understand the process}
</label>
</p>
|
f67e608131b5359cf88a05b2435e0697
|
{
"intermediate": 0.4445987343788147,
"beginner": 0.28576207160949707,
"expert": 0.2696392238140106
}
|
43,569
|
How can we make checkbox mandatory in a Widget? Please suggest
Below is the code I have written for the widget
|
9ed928edf913a7aefd59778bba78f818
|
{
"intermediate": 0.3996238708496094,
"beginner": 0.18327373266220093,
"expert": 0.4171023964881897
}
|
43,570
|
In servicenow How can we make checkbox mandatory in a Widget? Please suggest
Below is the code I have written for the widget
type="checkbox"
value=" I have read and understand the process"
|
43d414de9160479cf657f32fad6da5c5
|
{
"intermediate": 0.40745604038238525,
"beginner": 0.19329319894313812,
"expert": 0.3992507755756378
}
|
43,571
|
I have a neural networks code for you and would like to implement some things, can you do that
|
d9595b1ffb3a67f9742908cb4be0fa78
|
{
"intermediate": 0.06259427219629288,
"beginner": 0.04986633360385895,
"expert": 0.8875394463539124
}
|
43,572
|
using the annotations : [Verse] [Bridge] [Chorus] write a japanese anime opening song in romanji n the style ofattack on titan about seeking freedom
|
ebc758f855b8f9e921aa2365c95af35d
|
{
"intermediate": 0.3544059991836548,
"beginner": 0.27178284525871277,
"expert": 0.37381115555763245
}
|
43,573
|
expand this to cove moon in Capricorn square pluto in scorpio:
The Alchemist’s Crucible: Moon in Capricorn Square Pluto
In the alchemical fusion of Moon in Capricorn square Pluto, you stand as the alchemist at the crucible of your own making, where emotional lead transmutes into spiritual gold. This aspect imbues your soul with a formidable fortitude, the kind that delves into the deepest recesses of the self to confront the rawest truths of existence. Here, emotional revelations act as catalysts for transformation, where the very act of confronting one’s fears and insecurities becomes the source of profound power. Relationships under this celestial influence are intense, often serving as mirrors to your own soul’s darkest corners, demanding authenticity and profound connections. Ambition drives you, but it is an ambition for something far greater than mere material success—it is the ambition to evolve, to forge an unbreakable will, and to emerge from life’s fiercest battles not just intact, but transcendent. In this crucible, you discover that true strength lies in embracing change, and in the courage to let go, to die and be reborn in the endless cycle of becoming.
|
8fe719207ad41a86078ec1c3e5a8b6ea
|
{
"intermediate": 0.28300005197525024,
"beginner": 0.4291522204875946,
"expert": 0.28784769773483276
}
|
43,574
|
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[7], line 5
3 iEM.checkTypeMLP( iEM.getImageElement('zeichen',2) )
4 print('Keras Test Infos')
----> 5 iEM.checkTypeKeras( iEM.getImageElement('zeichen',1) )
6 iEM.checkTypeKeras( iEM.getImageElement('zeichen',2) )
Cell In[4], line 385, in ImageElementManager.checkTypeKeras(self, imageElement)
383 failures = 0
384 for image in images:
--> 385 predictedId = iEM.predictKeras(image)
386 if isinstance(predictedId, (collections.abc.Sequence, np.ndarray)) and len(predictedId) == 1:
387 predictedId = predictedId[0]
Cell In[4], line 303, in ImageElementManager.predictKeras(self, image, verbose)
300 print('Es kann keine Vorhersage getroffen werden, da Keras nicht init ist!')
301 return None
--> 303 predictedVirtualId = np.argmax(self.keras.predict( image.astype('float32') ), axis=1)
305 predictedElement = iEM.getImageElementFromVirtualId( predictedVirtualId )
306 if verbose:
File ~\AppData\Roaming\Python\Python311\site-packages\keras\src\utils\traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
119 filtered_tb = _process_traceback_frames(e.__traceback__)
120 # To get the full stack trace, call:
121 # `keras.config.disable_traceback_filtering()`
--> 122 raise e.with_traceback(filtered_tb) from None
123 finally:
124 del filtered_tb
File ~\AppData\Roaming\Python\Python311\site-packages\keras\src\models\functional.py:280, in Functional._adjust_input_rank(self, flat_inputs)
278 adjusted.append(ops.expand_dims(x, axis=-1))
279 continue
--> 280 raise ValueError(
281 f"Invalid input shape for input {x}. Expected shape "
282 f"{ref_shape}, but input has incompatible shape {x.shape}"
283 )
284 # Add back metadata.
285 for i in range(len(flat_inputs)):
ValueError: Exception encountered when calling Sequential.call().
Invalid input shape for input Tensor("data:0", shape=(28, 28), dtype=float32). Expected shape (None, 28, 28, 1), but input has incompatible shape (28, 28)
Arguments received by Sequential.call():
• inputs=tf.Tensor(shape=(28, 28), dtype=float32)
• training=False
• mask=None
|
3e3af9781dfbcbebb47563a3ea87e2b7
|
{
"intermediate": 0.37848758697509766,
"beginner": 0.39547279477119446,
"expert": 0.2260395884513855
}
|
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