YoongiKim

import math
import torch
from torch.autograd import Variable
import matplotlib.pyplot as plt
import numpy as np

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

def num_division(value):
    count = 0

digoreis

claymcleod

import sys,os
import curses

def draw_menu(stdscr):
    k = 0
    cursor_x = 0
    cursor_y = 0

    # Clear and refresh the screen for a blank canvas
    stdscr.clear()

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

Newmu

"""
The MIT License (MIT)

Copyright (c) 2015 Alec Radford

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

joates

numeric.linspace = function linspace(a,b,n) {
    if(typeof n === "undefined") n = Math.max(Math.round(b-a)+1,1);
    if(n<2) { return n===1?[a]:[]; }
    var i,ret = Array(n);
    n--;
    for(i=n;i>=0;i--) { ret[i] = (i*b+(n-i)*a)/n; }
    return ret;
}

mblondel

# Mathieu Blondel, September 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

def linear_kernel(x1, x2):
    return np.dot(x1, x2)