Daniel Marcelino dmarcelinobr

## governismo_partidos.csv

          
            date
            legislator_party
            media_mes
            cma
            tma
            bancada
            soma

            
              2020-08-28
              AVANTE
              0.748039215686275
              0.857352941176471
              0.761019607843137
              6
              1916

            
              2020-08-28
              CIDADANIA
              0.717086834733894
              0.775210084033614
              0.697221988795518
              8
              2574

            
              2020-08-28
              DEM
              0.839740514790993
              0.919870257395496
              0.862903342104668
              28
              8380

            
              2020-08-28
              MDB
              0.879152406248459
              0.939576203124229
              0.884653145577298
              35
              10992

            
              2020-08-28
              NOVO
              0.705882352941177
              0.852941176470588
              0.782941176470588
              8
              3086

            
              2020-08-28
              PATRI
              0.958823529411765
              0.979411764705882
              0.956078431372549
              6
              1898

            
              2020-08-28
              PCdoB
              0.3203125
              0.16015625
              0.34015625
              8
              872

            
              2020-08-28
              PDT
              0.519899948827285
              0.445820393581163
              0.464454263280975
              28
              4494

            
              2020-08-28
              PL
              0.959809897304683
              0.96767208153832
              0.940172709158139
              41
              12792

## model_tuning.R
n_random = 100

params_grid <-  expand.grid(
num_leaves = c(100, 200, 500, 1000, 2000, 5000),
learning_rate = c(0.01, 0.03, 0.1),
min_data_in_leaf = c(5, 10,20, 50),
feature_fraction = c(0.6, 0.8, 1),
bagging_fraction = c(0.4, 0.6, 0.8, 1),
lambda_l1 = c(0.0, 0.0, 0.01, 0.1, 0.3),
lambda_l2 = c(0.0, 0.0, 0.01, 0.1, 0.3)

## stopwords2.txt
a
acerca
adeus
agora
ainda
alem
algmas
algo
algumas
alguns

## stopwords_pt.txt
words
à
a
agora
ainda
alguém
algum
alguma
algumas
alguns

## stopwordsbr.txt
word
de
a
o
que
e
do
da
em
um

## building-john-the-ripper-osx.md

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                dmarcelinobr
                / building-john-the-ripper-osx.md
            
            
              Created
              April 15, 2019 00:22
            
          
http://blog.gmane.org/gmane.comp.security.openwall.john.user/month=20140601

error: No MPI compiler found john the ripper


phusion/passenger#1630 (comment)

brew install openssl


## 2015-Tutorial-R-Selenium.R
#Instalar e carregar pacotes de interesse
install.packages('RSelenium')
require('RSelenium')
require('XML')

#Atualiza seu aplicativo Java do Selenium
RSelenium::checkForServer()
#Abrir o servidor local
RSelenium::startServer()
remDr <- remoteDriver()

## animint.js
// Define functions to render linked interactive plots using d3.
// Another script should define e.g.
// <script>
//   var plot = new animint("#plot","path/to/plot.json");
// </script>
// Constructor for animint Object.
var animint = function (to_select, json_file) {

  function wait_until_then(timeout, condFun, readyFun) {
    function checkFun() {

## statistical_tricks.R
# boxplot: box-and-whisker plot, the data is broken up into quartiles: the box contains the middle 50% of the Ct values for each gene, the black bar is the median, the whiskers represent the top and bottom 25% of the observations. Circles represent outliers.

# standard error (of the sample mean): THINK ACCURACY - THE SD OF THE sample mean (x_bar), it describes the accuracy of the sample mean as an estimate of the population mean. an estimate of how close is my sample mean to the population mean (should decrease by increasing sample size): standard error of the mean (i.e., of using the sample mean as a method of estimating the population mean) is the standard deviation of those sample means over all possible samples (of a given size) drawn from the population.
    The sample mean has 95% prob of being within 2 se of the pop mean
# standard deviation (of the sample): THINK VARIABILITY - DESCRIBES THE SPREAD OF VALUES IN THE SAMPLE: how much do the individuals within the sample differ from the sample mean (unaf

## interpolate.R
# Generate data in the form of a sine wave
set.seed(1)
n <- 1e3
dat <- data.frame(
  x = 1:n,
  y = sin(seq(0, 5*pi, length.out = n)) + rnorm(n=n, mean = 0, sd=0.1)
)

approxData <- data.frame(
  with(dat,
date	legislator_party	media_mes	cma	tma	bancada	soma
2020-08-28	AVANTE	0.748039215686275	0.857352941176471	0.761019607843137	6	1916
2020-08-28	CIDADANIA	0.717086834733894	0.775210084033614	0.697221988795518	8	2574
2020-08-28	DEM	0.839740514790993	0.919870257395496	0.862903342104668	28	8380
2020-08-28	MDB	0.879152406248459	0.939576203124229	0.884653145577298	35	10992
2020-08-28	NOVO	0.705882352941177	0.852941176470588	0.782941176470588	8	3086
2020-08-28	PATRI	0.958823529411765	0.979411764705882	0.956078431372549	6	1898
2020-08-28	PCdoB	0.3203125	0.16015625	0.34015625	8	872
2020-08-28	PDT	0.519899948827285	0.445820393581163	0.464454263280975	28	4494
2020-08-28	PL	0.959809897304683	0.96767208153832	0.940172709158139	41	12792
	n_random = 100

	params_grid <- expand.grid(
	num_leaves = c(100, 200, 500, 1000, 2000, 5000),
	learning_rate = c(0.01, 0.03, 0.1),
	min_data_in_leaf = c(5, 10,20, 50),
	feature_fraction = c(0.6, 0.8, 1),
	bagging_fraction = c(0.4, 0.6, 0.8, 1),
	lambda_l1 = c(0.0, 0.0, 0.01, 0.1, 0.3),
	lambda_l2 = c(0.0, 0.0, 0.01, 0.1, 0.3)
	#Instalar e carregar pacotes de interesse
	install.packages('RSelenium')
	require('RSelenium')
	require('XML')

	#Atualiza seu aplicativo Java do Selenium
	RSelenium::checkForServer()
	#Abrir o servidor local
	RSelenium::startServer()
	remDr <- remoteDriver()
	// Define functions to render linked interactive plots using d3.
	// Another script should define e.g.
	// <script>
	// var plot = new animint("#plot","path/to/plot.json");
	// </script>
	// Constructor for animint Object.
	var animint = function (to_select, json_file) {

	function wait_until_then(timeout, condFun, readyFun) {
	function checkFun() {
	# boxplot: box-and-whisker plot, the data is broken up into quartiles: the box contains the middle 50% of the Ct values for each gene, the black bar is the median, the whiskers represent the top and bottom 25% of the observations. Circles represent outliers.

	# standard error (of the sample mean): THINK ACCURACY - THE SD OF THE sample mean (x_bar), it describes the accuracy of the sample mean as an estimate of the population mean. an estimate of how close is my sample mean to the population mean (should decrease by increasing sample size): standard error of the mean (i.e., of using the sample mean as a method of estimating the population mean) is the standard deviation of those sample means over all possible samples (of a given size) drawn from the population.
	The sample mean has 95% prob of being within 2 se of the pop mean
	# standard deviation (of the sample): THINK VARIABILITY - DESCRIBES THE SPREAD OF VALUES IN THE SAMPLE: how much do the individuals within the sample differ from the sample mean (unaf
	# Generate data in the form of a sine wave
	set.seed(1)
	n <- 1e3
	dat <- data.frame(
	x = 1:n,
	y = sin(seq(0, 5*pi, length.out = n)) + rnorm(n=n, mean = 0, sd=0.1)
	)

	approxData <- data.frame(
	with(dat,