paulhendricks

"""Genetic Algorithmn Implementation
see:
http://www.obitko.com/tutorials/genetic-algorithms/ga-basic-description.php
"""
import random

class GeneticAlgorithm(object):
    def __init__(self, genetics):
        self.genetics = genetics
        pass

paulhendricks

-- MarI/O by SethBling
-- Feel free to use this code, but please do not redistribute it.
-- Intended for use with the BizHawk emulator and Super Mario World or Super Mario Bros. ROM.
-- For SMW, make sure you have a save state named "DP1.state" at the beginning of a level,
-- and put a copy in both the Lua folder and the root directory of BizHawk.

if gameinfo.getromname() == "Super Mario World (USA)" then
	Filename = "DP1.state"
	ButtonNames = {
		"A",

paulhendricks

--
-- A simple genetic algorithm for function optimization, in lua
-- Copyright (c) 2009 Jason Brownlee
--
-- It uses a binary string representation, tournament selection, 
-- one-point crossover, and point mutations. The test problem is 
-- called one max (a string of all ones)
--

-- configuration

paulhendricks

## ------------------------------------------------------------------------
# Set the repo for use throughout
cran <- "https://cran.rstudio.org"

# Install
if(!require(miniCRAN)){ 
  install.packages("miniCRAN", repos = cran)
}

## ------------------------------------------------------------------------

paulhendricks

install.packages('caret')
install.packages('ccd')
install.packages('d3Network')
install.packages('data.table')
install.packages('dplyr')
install.packages('DMwR')
install.packages('e1071')
install.packages('ergm')
install.packages('ff')
install.packages('foreach')

paulhendricks

require(Mcomp)
nseries <- length(M3)

theta <- as.matrix(M3Forecast$THETA)
fpro <- as.matrix(M3Forecast$ForecastPro)
fcx <- as.matrix(M3Forecast$ForcX)
bjauto <- as.matrix(M3Forecast$`B-J auto`)
ab1 <- as.matrix(M3Forecast$AutoBox1)
ab2 <- as.matrix(M3Forecast$AutoBox2)
ab3 <- as.matrix(M3Forecast$AutoBox3)

paulhendricks

from random import randint

def create_population(create, pop_size=1000):
    """Create population list"""
    return [create() for i in xrange(pop_size)]

def crossover(combine, population, pop_size):
    """Increase population size by combining"""
    original_size = len(population)
    while len(population) < pop_size:

paulhendricks

paulhendricks

1. Feature Learning

• Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
• The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
• Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
• Scaling Learning Algorithms towards AI Benjio & LeCun
• A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson

2. Deep Learning

• Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
• [Understanding

paulhendricks

import numpy as np
import pymc
import pdb

def unconditionalProbability(Ptrans):
   """Compute the unconditional probability for the states of a
   Markov chain."""

   m = Ptrans.shape[0]