ghamarian

#include <hip/hip_runtime.h>
#include <iostream>

#define BLOCK_SIZE 256

__global__ void vecAdd(const float* __restrict__ A,
                       const float* __restrict__ B,
                       float* __restrict__ C,
                       int n)
{

ghamarian

# Make sure you have these installed:
# pip install torch pandas faiss-gpu (or faiss-cpu if no GPU)

import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

ghamarian

def next_row(a):
    return [0] + [a + b for a, b in zip(a, a[1:])] + [0]


def g(n):
    lst = [[0, 1, 0]]
    for _ in range(n):
        lst.append(next_row(lst[-1]))
    return lst

ghamarian

from pynput import keyboard
import pyperclip
import time

# Initialize
with open("your_file.txt", "r") as f:
    lines = f.readlines()

# Remove newline characters
lines = [line.strip() for line in lines]

ghamarian

Torch objects used in learnpytorch.io website

name	type
torch.Tensor	<class 'torch._C._TensorMeta'>
torch.e	<class 'float'>
torch.t	<class 'builtin_function_or_method'>
torch.torch	<class 'module'>
torch.eig	<class 'function'>
torch.ne	<class 'builtin_function_or_method'>
torch.nn

ghamarian

# Torch objects used in learnpytorch.io website 
|name|type|
|----|----| 
|torch.Tensor|<class 'torch._C._TensorMeta'>|
|torch.e|<class 'float'>|
|torch.t|<class 'builtin_function_or_method'>|
|torch.torch|<class 'module'>|
|torch.eig|<class 'function'>|
|torch.ne|<class 'builtin_function_or_method'>|
|torch.nn|<class 'module'>|

ghamarian

#include <algorithm>
#include <concepts>
#include <functional>
#include <iostream>
#include <memory>
#include <vector>

template <typename T>
concept Drawable = requires(T t) {
                     { t.draw() } -> std::same_as<void>;

ghamarian

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import style

fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)

xs = []
ys = []

ghamarian

import operator
from itertools import cycle
from numbers import Number

import numpy as np


def broadcast(a, b, op):

    if isinstance(a, Number) and isinstance(b, Number):

ghamarian

#include <Eigen/Dense>
#include <Eigen/IterativeLinearSolvers>

#include <algorithm>
#include <iostream>
#include <random>
#include <vector>

std::pair<Eigen::MatrixXf, Eigen::MatrixXf> GenerateData(size_t n) {
  std::vector<float> x_data(n);