Qu3tzal

class SelfAttention(torch.nn.Module):
  def __init__(self, input_dimension, output_dimension):
    super().__init__()

    self.input_dimension = input_dimension
    self.output_dimension = output_dimension

    self.Qw = torch.nn.Linear(self.input_dimension, self.output_dimension)
    self.Kw = torch.nn.Linear(self.input_dimension, self.output_dimension)
    self.Vw = torch.nn.Linear(self.input_dimension, self.output_dimension)

Qu3tzal

import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import argparse


def parse_args():
    parser = argparse.ArgumentParser(description='Quickly compare a list of stocks or indices prices.')
    parser.add_argument('--history', type=str, default='ytd', help='History period of the price data', choices=["1d", "5d", "1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "ytd", "max"])

Qu3tzal

#include <SFML/Graphics.hpp>
#include <cmath>

// Program parameters.
const float TICK_PER_SEC = 60.f;
const float MAX_JERK_LENGTH = 100.f;

// Computed constants.
const sf::Time TICK_TIME = sf::seconds(1.f / TICK_PER_SEC);

Qu3tzal

# -*- coding: utf-8 -*-

#####
# Maxime Alvarez 18085322
# Daniel Regnard 18130194
###

import numpy as np
import random
from sklearn import linear_model

Qu3tzal

rpi <-> arduino
  through driver

Qu3tzal

From 2 cameras set up parallely and of the same lzns configuration, we should try to feed the images to a deep learning system that will learn how to estimatr the distance of an object
by the difference of position of a same point in the two images.