barron9

shader_type spatial;

uniform vec3 light_dir = vec3(1.2, -2.0, 0.2);
uniform float water_depth = 1.5;

uniform vec2 ripple_center = vec2(2.5, 2.5);
uniform float ripple_strength = 0.5;
uniform float decay_rate = 0.8; // Higher = loses height faster

float get_height(vec2 pos, float time) {

barron9

Input image (optional) → VAE encoder → latent z
for t = T → 1:
    Add noise to latent → z_t
    UNet predicts noise:
        Inputs:
            - z_t (noisy latent)
            - t (timestep)
            - text embedding (from text encoder)
        Output:
            - predicted noise

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()]

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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

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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")

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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,

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#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>();

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

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

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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