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from matplotlib import pyplot as plt
from mpl_toolkits import mplot3d

def scatter(X, Y, c=None, ax=None):
  ax = plt.axes() if ax is None else ax
  ax.scatter(X.numpy(), Y.numpy(), c=None if c is None else c.numpy())
  return ax

def contour(X, Y, Z, levels=None, ax=None):
  ax = plt.axes() if ax is None else ax

Kaixhin

import os
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from torchvision.utils import save_image


class Encoder(nn.Module):

Kaixhin

"""
Introduction to Monte Carlo Tree Search
http://jeffbradberry.com/posts/2015/09/intro-to-monte-carlo-tree-search/
"""

from copy import deepcopy
import datetime
from math import log, sqrt
from random import choice

Kaixhin

"""
The Annotated Transformer
http://nlp.seas.harvard.edu/2018/04/03/attention.html
Note: Only includes basic example, not real world example or multi-GPU training
"""


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

Kaixhin

import torch


def cast(cuda):
  if cuda:
    return lambda x: x.cuda()
  else:
    return lambda x: x

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# Collection of LSTM cells (including forget gates)
# https://en.wikipedia.org/w/index.php?title=Long_short-term_memory&oldid=784163987

import torch
from torch import nn
from torch.nn import Parameter
from torch.nn import functional as F
from torch.nn.modules.utils import _pair
from torch.autograd import Variable

Kaixhin

--[[
-- Using Perlin Noise to Generate 2D Terrain and Water
-- http://gpfault.net/posts/perlin-noise.txt.html
--]]

local image = require 'image'

-- Fade function
local fade = function(t)
  -- Provides continuous higher order derivatives for smoothness (this specifically is in the class of sigmoid functions)

Kaixhin

--[[
--   Random walks down Wall Street, Stochastic Processes in Python
--   http://www.turingfinance.com/random-walks-down-wall-street-stochastic-processes-in-python/
--]]

local gnuplot = require 'gnuplot'

local model_parameters = {
  all_s0 = 1000, -- Starting asset value
  all_time = 800, -- Amount of time to simulate for

Kaixhin

Probability notation

Note: Great refresher/glossary on probability/statistics and related topics here

Notation	Definition
X	Random variable
P(X)	Probability distribution over random variable X
X ~ P(X)	Random variable X follows (~) the probability distribution P(X) *

Kaixhin

--[[
--   Gaussian Processes for Dummies
--   https://katbailey.github.io/post/gaussian-processes-for-dummies/
--   Note 1: The Cholesky decomposition requires positive-definite matrices, hence the addition of a small value to the diagonal (prevents zeros along the diagonal)
--   Note 2: This can also be thought of as adding a little noise to the observations
--]]

local gnuplot = require 'gnuplot'

-- Test data