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import numpy as np

def spirals(n_samples=400):
    N = n_samples
    theta = np.sqrt(np.random.rand(N))*2*np.pi

    r_a = 2*theta + np.pi
    data_a = np.array([np.cos(theta)*r_a, np.sin(theta)*r_a]).T
    x_a = data_a + np.random.randn(N,2)

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# Based on min-char-rnn.py
# https://gist.github.com/karpathy/d4dee566867f8291f086

import torch as th
import torch.nn as nn
import numpy as np


class Model(nn.Module):
    def __init__(self, hidden_size, vocab_size, num_layers):

masterdezign

from copy import deepcopy
from typing import Any, Dict, Generator, List, Optional, Union
from typing import NamedTuple, Tuple

from gymnasium import spaces
import numpy as np
import torch as th

from stable_baselines3.common.buffers import BaseBuffer
from stable_baselines3.common.vec_env import VecNormalize

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FROM pytorch/pytorch:2.1.0-cuda11.8-cudnn8-runtime

ARG USER="user"
ARG UID="1000"
ARG GID="100"

USER "root"

RUN apt-get update && apt-get install -y gnupg

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version: '3.1'

services:

  db:
    image: mysql
    restart: always
    environment:
      MYSQL_ROOT_PASSWORD: CHANGE_THIS_PASSWORD
      MYSQL_DATABASE: test_db

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#!/bin/bash

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

### to verify your gpu is cuda enable check

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import pandas as pd
# .read_csv()
# .shape
# .head(N) .tail(N)
# .dtypes
# .loc[3, 'sepal_length'] .iloc
# .to_csv()


# pd.set_option('max_columns', 2)

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import sys

import gym
from gym.envs.registration import register

from tensorforce.environments import Environment
from tensorforce.agents import Agent

if len(sys.argv) < 2:
    print("Usage: python {} path/to/checkpoint".format(sys.argv[0]))

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/* NB: HAT speaker needs to be connected */

#include <M5StickC.h>
#include "esp32-hal-timer.h"

volatile int interruptCounter;
int totalInterruptCounter;

// https://techtutorialsx.com/2017/10/07/esp32-arduino-timer-interrupts/
hw_timer_t * timer = NULL; //Created Hardware Timer

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#%%
import os
import numpy as np
import mne
from mne.preprocessing import compute_current_source_density
# %matplotlib qt

# First, we need to load the data
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',