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import Foundation

// A custom error type for cancellation
enum TaskError: Error {
    case canceled
}

// A struct to manage tasks and handle cancellation
class ConcurrentTaskManager {
    

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import re
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
from sklearn.impute import SimpleImputer
import pandas as pd
from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import train_test_split
import xgboost as xgb

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import gymnasium as gym
import numpy as np
from tqdm import tqdm
import numpy as np
import matplotlib.pyplot as plt
import pickle
import os
env_train = gym.make('InvertedPendulum-v5')
# Define the dimensions of your state space
dim1 = 5  

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# Q-learning Update rule
def q_learning_update(Q, state, action, reward, next_state, lr, discount):
    """
    Q-learning update rule
    Q(s, a) <- Q(s, a) + α * [r + γ * max_a Q(s', a) - Q(s, a)]
    """
    # Compute the Q-value update using Q-learning formula
    Q[state][action] += lr * (reward + discount * np.max(Q[next_state]) - Q[state][action])
    return Q

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verified on Tplink Vc220-g3u

Dns cache disable from your os
Dhcp ip rental set 1 or 5 or 8 min not like 60
Ağ geçidi sil
qos [ssid] = (max download) - 1000~
Reset 
;)

dns cache i windowstan disable edin

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#include <iostream>
#include <queue>
#include <thread>
#include <mutex>
#include <condition_variable>

std::queue<int> dataQueue;
std::mutex mtx;
std::condition_variable cv;
const int maxQueueSize = 10; // Maximum queue size before backpressure is applied

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#include <iostream>
#include <vector>
#include <cmath>
#include <string>

int main(){
    
    std::vector<std::pair<int, int>> vec;
    std::vector<std::pair<int, int>> vecquen;
    

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#include <iostream>
#include <vector>
#include <cmath>
#include "attention.h"
// Function to compute the attention weights
std::vector<double> computeAttentionWeights(const std::vector<double>& query, const std::vector< std::vector<double> >& keys) {
    int numKeys = keys.size();
    std::vector<double> attentionWeights(numKeys, 0.0);
    double totalWeight = 0.0;

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import Foundation

class ThreadInterruptionSimulator {
    var threads: [Thread] = []
    var interrupterThread: Thread?
    
    // Function to simulate work done by threads
    func worker(threadNum: Int) {
        // for _ in 0..<5 {
        

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/* This is the original elf.h file from the GNU C Library; I only removed
   the inclusion of feature.h and added definitions of __BEGIN_DECLS and
   __END_DECLS as documented in
   https://cmd.inp.nsk.su/old/cmd2/manuals/gnudocs/gnudocs/libtool/libtool_36.html

   On macOS, simply copy the file to /usr/local/include/. 

   Mathias Lafeldt <mathias.lafeldt@gmail.com> */

/* This file defines standard ELF types, structures, and macros.