huberflores

suppressWarnings(library(tidyverse))
suppressWarnings(library(ggpubr))
suppressWarnings(library(rstatix))

mydata <- read.csv("mydata-fixedhold.csv", sep=",", header = T)

myvars <- c("id", "cigarette", "bottle", "cup")
newdata <- mydata[myvars]

huberflores

# From socket module we import the required structures and constants.
from socket import AF_INET, SOCK_DGRAM, socket

if __name__ == '__main__':

    print 'Application started'

    # Creating a UDP/IP socket
    s = socket(AF_INET, SOCK_DGRAM)

huberflores

# author Huber Flores

#Open Android Studio and connect your device via USB
#Computer and phone need to be connected to the same Network

#Open terminal

$ huberflo@hp8x-78:~$ adb tcpip 5555
restarting in TCP mode port: 5555

huberflores

suppressMessages(library(fitdistrplus))
suppressMessages(library(logspline))

fileInput <- "/Users/hflores/Desktop/datasets/code-benchmarking/output70-t2large.csv"

codebenchmark = read.csv(fileInput, sep=";")

time <- codebenchmark$response

descdist(time, discrete = FALSE)

huberflores

#
#author Huber Flores
#


library(lpSolveAPI)

library(ggplot2)
library(reshape)
library(gridExtra)

huberflores

digraph Bluetooth {
graph [ dpi = 300 ];
p0-> p9;
p0-> p10;
p0-> p11;
p0-> p12;
p0-> p13;
p0-> p14;
p0-> p15;
p0-> p16;

huberflores

#
# author Huber Flores
#

# The input of the engine is a dataset. For each tuple of the dataset a decision is expected
# Each attribute in the tuple is normalized in a 0-1 interval

library(iterators)
library(foreach)

huberflores

# calling the functions
# this is the file where we will call the functions in fun.R and times.R
times <- dget("times.R")
times(-4:4, 2)

source("fun.R")
mult(-4:4, 2)

huberflores

#
# author Huber Flores
#

# Example of fuzzy logic in R

# The main idea is to have fuzzy sets for each of the dimensions
# each attribute in the dimension can be defined as a fuzzy set with their respective linguistic variables, terms and membership functions

library(sets)

huberflores

# author Huber Flores
# Normalizing data to a target range (a, b)

#original dataset

mydata <- c(26,33,45,77,15,100,66,21,89,55)

A = min(mydata)

B = max(mydata)