barron9

import sympy as sp
from sympy.physics.mechanics import KanesMethod, Particle, ReferenceFrame, Point, dynamicsymbols
t = dynamicsymbols._t
x, y = dynamicsymbols('x y')
u1, u2, u3 = dynamicsymbols('u1 u2 u3')   # generalized speeds: xdot, ydot
# Angular velocity
theta_ = dynamicsymbols('theta')  # rotation about z
thetad = dynamicsymbols('theta', 1)
kd = [u1 - x.diff(t), u2 - y.diff(t), u3 - theta_.diff()]

barron9

import numpy as np
from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt

# --- parameters ---
m = 5.0
I_th = 0.12
k_y = 300.0
k_th = 12.0
rho = 1.225

barron9

import numpy as np
np.random.seed(42)
# Expanded sentence: "The cat sat on the mat and the dog chased the cat."
words = ["The", "cat", "sat", "on", "the", "mat", "and", "dog", "chased", "the", "cat"]

# Randomly initialized Q, K, V matrices with 8-dimensional vectors
Q = {word: np.random.rand(81) for word in words}
K = {word: np.random.rand(81) for word in words}
V = {word: np.random.rand(81) for word in words}

barron9

import matplotlib.pyplot as plt
import numpy as np
from arch import arch_model
import numpy as np
import pandas as pd

from arch import arch_model
import yfinance as yf
import seaborn as sns
# data = yf.download("PEKGY.IS",end="2025-10-10",start="2020-01-01")

barron9

import numpy as np

# Your real price data (daily, for example)
# Example: prices = np.array([100.5, 101.2, 102.3, ..., 120.4])
prices = np.array([
  2.04,
  2.24,
  2.46,
  2.37,
  2.6,

barron9

#include <torch/extension.h>
#include <cuda_runtime.h>
#include <c10/cuda/CUDAStream.h>

// Declare the kernel launcher (in .cu)
void matmul_kernel_launcher(const float* A, const float* B, float* C, int N, cudaStream_t stream);

// C++ wrapper
void launch_matmul_kernel(torch::Tensor A, torch::Tensor B, torch::Tensor C, int N) {
    const float* A_ptr = A.data_ptr<float>();

barron9

#include <benchmark/benchmark.h>
#include <map>
#include <queue>
#include <deque>
#include <string>
#include <functional>
#include <iostream>
#include <iomanip>

// ----------- MatchingEngine and Order Definitions -----------

barron9

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

barron9

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  

barron9

# 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