ZachWolpe

visualize <- function(data, c1='white', c2='#779ecb') {
  data %>% 
    ggplot(aes(x=x, y=y, fill = factor(z))) +
    geom_raster()  +
    scale_fill_manual(breaks = levels(factor(data$z)),
                      values = c(c1, c2)) +
    theme(legend.position = 'none',
          axis.text = element_blank(),
          axis.title = element_blank(),
          axis.ticks = element_blank(),

ZachWolpe

library(ggplot2)
library(tidyverse)



Rule30 <- function(half_len, iterations) {
  
  # Functional Rules 
  half_len <- 2*half_len
  a = c('111', '110', '101', '100', '011', '010', '001', '000')

ZachWolpe

library(ggplot2)
library(tidyverse)



Rule30 <- function(half_len, iterations) {
  
  # Functional Rules 
  half_len <- 2*half_len
  a = c('111', '110', '101', '100', '011', '010', '001', '000')

ZachWolpe

library(ggplot2)
library(tidyverse)





Rule30 <- function(half_len, iterations) {
  

ZachWolpe

colours = [['R','G','G','R','R'],
          ['R','R','G','R','R'],
          ['R','R','G','G','R'],
          ['R','R','R','R','R']]
measurements = ['G','G','G','G','G']
motions = [[0,0],[0,1],[1,0],[1,0],[0,1]]

localization = localization(colours=colours, measurements=measurements, motions=motions, sensor_right=0.7, p_move=0.8)
posterior = localization.compute_posterior()
localization.show(posterior)

ZachWolpe

class localization():
    """Performing Bayesian Updating to Produce a Distribution of Likely Positions in the Environment"""

    def __init__(self, colours, measurements, motions, sensor_right, p_move):
        self.world = colours
        self.measurements = measurements
        self.motions = motions
        self.sensor_right = sensor_right
        self.p_move = p_move

ZachWolpe

# generate random Gene sequence
def generateParent(length):
    genes = list("")
    for i in range(0,length):
        geneIndex = np.random.randint(0, len(geneset) -1);
        genes.append(geneset[geneIndex])
    return(''.join(genes))

ZachWolpe

import numpy as np
import datetime
import time

# Encode Binary 
# genotypical_phenotype_latent_representation = bin(int.from_bytes(target.encode(), 'big'))

geneset = " abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!."

def stochastic_decryption_oscillator():

ZachWolpe

#__________________________________ Required libraries ______________________________________
library(rtweet)
library(dplyr)
library(tidytext)
library(tidyr)
library(NLP)
library(syuzhet)
library(tm)

ZachWolpe

#__________________________________ Required libraries ______________________________________
library(rtweet)
library(dplyr)
library(tidytext)
library(tidyr)
library(NLP)
library(syuzhet)
library(tm)
library(SnowballC)
library(stringi)