Naelson Douglas C. Oliveira NaelsonDouglas

## v1.csv

          
             0.0

            
               0.499

            
               0.865

            
               1.0

            
               0.869

            
               0.506

            
               0.009

            
               0.491

            
               0.86

            
               1.0

## DataSet in C#.cs
DataSet dataSet = new DataSet();
DataTable table = new DataTable("BATCHES");

table.Columns.Add("Teste", typeof(string));
table.Columns.Add("Teste2", typeof(string));

table.Rows.Add("2", "2");
table.Rows.Add("2", "2");
table.Rows.Add("2", "2");

## SAP.cs
using System;
using System.Collections.Generic;
using System.Configuration;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using SAP.Middleware.Connector;

namespace exemplo
{

## POST: api-request.json
{
    "Company": 9,
    "Customer": "1",
    "IdCode": "1",
    "ConsigneeTelephone": "1",
    "DestinationAddress": "1",
    "RequestedBy": "1",
    "CostCenter": "1",
    "GLAccount": "1",
    "SmartPartNumbers": [

## Clothing review.csv
Review Text,Rating
Absolutely wonderful - silky and sexy and comfortable,4
"Love this dress!  it's sooo pretty.  i happened to find it in a store, and i'm glad i did bc i never would have ordered it online bc it's petite.  i bought a petite and am 5'8"".  i love the length on me- hits just a little below the knee.  would definitely be a true midi on someone who is truly petite.",5
"I had such high hopes for this dress and really wanted it to work for me. i initially ordered the petite small (my usual size) but i found this to be outrageously small. so small in fact that i could not zip it up! i reordered it in petite medium, which was just ok. overall, the top half was comfortable and fit nicely, but the bottom half had a very tight under layer and several somewhat cheap (net) over layers. imo, a major design flaw was the net over layer sewn directly into the zipper - it c",3
"I love, love, love this jumpsuit. it's fun, flirty, and fabulous! every time i wear it, i get nothing but great compliments!",5
This s

## tfidf.py
from sklearn.feature_extraction.text import TfidfVectorizer
import glob
import pandas as pd

files = [x for x in glob.glob("inputs/*.txt")]

#Read data from a folder
data = [open(f).read() for f in files]

#Random data

## Uso de memória em C.c
//Créditos: http://appcrawler.com/wordpress/2013/05/13/simple-example-of-tracking-memory-using-getrusage/
#include <sys/resource.h>
#include <stdio.h>
#include <string.h>
#include <stdlib.h>

int main() {
  int i = 0;
  struct rusage r_usage;
  while (++i <= 10) {

## system.csv

          
            calculate_maxmin_seconds
            clustering_time_seconds
            elapsed_time_seconds
            create_histogram_seconds
            MAPE
            MSE
            R2
            testing_model_seconds
            train_global_model_seconds
            local_training_seconds

            
              3.58
              1.35
              40.74
              0.67
              0.0
              0.0
              0.0
              0.88
              3.43
              16.72

            
              0.42
              0.0
              0.0
              0.0
              619.7398757365526
              0.07637474266326814
              -0.114300886520726
              0.0
              0.98
              13.79

            
              0.42
              0.0
              0.0
              0.0
              736.5794694390204
              0.024514104329560017
              -1.207146025777877
              0.0
              0.98
              13.37

## containers.csv

          
            CONTAINER
            NAME
            CPU%
            MEMUSAGE/LIMIT
            MEM%
            NETI/O
            BLOCKI/O
            PIDS_JULIA
            PIDS_DOCKER
            TIMESTAMP

            
              79897c34b719
              romantic_brown
              100.09%
              180.7MiB/1.907GiB
              9.25%
              3.98MB/40.9kB
              6
              0B/0B
              0
              11:02:27

            
              8eb95bb95fc8
              dreamy_shtern
              99.67%
              187MiB/1.907GiB
              9.57%
              5.55MB/85.2kB
              7
              0B/0B
              0
              11:02:27

            
              79897c34b719
              romantic_brown
              99.95%
              197.6MiB/1.907GiB
              10.12%
              7.41MB/85.2kB
              6
              0B/0B
              0
              11:02:37

            
              8eb95bb95fc8
              dreamy_shtern
              98.93%
              201.6MiB/1.907GiB
              10.32%
              7.46MB/115kB
              7
              0B/106kB
              0
              11:02:37

            
              79897c34b719
              romantic_brown
              90.18%
              216.4MiB/1.907GiB
              11.08%
              7.41MB/88.6kB
              6
              0B/123kB
              0
              11:02:47

            
              8eb95bb95fc8
              dreamy_shtern
              100.15%
              218.3MiB/1.907GiB
              11.18%
              7.46MB/118kB
              7
              0B/123kB
              0
              11:02:47

            
              79897c34b719
              romantic_brown
              99.74%
              243.7MiB/1.907GiB
              12.48%
              7.42MB/94.2kB
              6
              0B/909kB
              0
              11:02:56

            
              8eb95bb95fc8
              dreamy_shtern
              95.91%
              243.6MiB/1.907GiB
              12.47%
              7.46MB/125kB
              7
              0B/1.21MB
              0
              11:02:56

## tf-idf.py
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer


data = open('./texto_aleatorio.txt')
custom_stop_words = frozenset(["palavra1", "palavra2","palavra3"])
vectorizer = CountVectorizer(stop_words=custom_stop_words)
vectorizer.fit(data)
print(vectorizer.vocabulary_)
	DataSet dataSet = new DataSet();
	DataTable table = new DataTable("BATCHES");

	table.Columns.Add("Teste", typeof(string));
	table.Columns.Add("Teste2", typeof(string));

	table.Rows.Add("2", "2");
	table.Rows.Add("2", "2");
	table.Rows.Add("2", "2");
	using System;
	using System.Collections.Generic;
	using System.Configuration;
	using System.Linq;
	using System.Text;
	using System.Threading.Tasks;
	using SAP.Middleware.Connector;

	namespace exemplo
	{
	{
	"Company": 9,
	"Customer": "1",
	"IdCode": "1",
	"ConsigneeTelephone": "1",
	"DestinationAddress": "1",
	"RequestedBy": "1",
	"CostCenter": "1",
	"GLAccount": "1",
	"SmartPartNumbers": [
	Review Text,Rating
	Absolutely wonderful - silky and sexy and comfortable,4
	"Love this dress! it's sooo pretty. i happened to find it in a store, and i'm glad i did bc i never would have ordered it online bc it's petite. i bought a petite and am 5'8"". i love the length on me- hits just a little below the knee. would definitely be a true midi on someone who is truly petite.",5
	"I had such high hopes for this dress and really wanted it to work for me. i initially ordered the petite small (my usual size) but i found this to be outrageously small. so small in fact that i could not zip it up! i reordered it in petite medium, which was just ok. overall, the top half was comfortable and fit nicely, but the bottom half had a very tight under layer and several somewhat cheap (net) over layers. imo, a major design flaw was the net over layer sewn directly into the zipper - it c",3
	"I love, love, love this jumpsuit. it's fun, flirty, and fabulous! every time i wear it, i get nothing but great compliments!",5
	This s
	from sklearn.feature_extraction.text import TfidfVectorizer
	import glob
	import pandas as pd

	files = [x for x in glob.glob("inputs/*.txt")]

	#Read data from a folder
	data = [open(f).read() for f in files]

	#Random data
	//Créditos: http://appcrawler.com/wordpress/2013/05/13/simple-example-of-tracking-memory-using-getrusage/
	#include <sys/resource.h>
	#include <stdio.h>
	#include <string.h>
	#include <stdlib.h>

	int main() {
	int i = 0;
	struct rusage r_usage;
	while (++i <= 10) {
calculate_maxmin_seconds	clustering_time_seconds	elapsed_time_seconds	create_histogram_seconds	MAPE	MSE	R2	testing_model_seconds	train_global_model_seconds	local_training_seconds
3.58	1.35	40.74	0.67	0.0	0.0	0.0	0.88	3.43	16.72
0.42	0.0	0.0	0.0	619.7398757365526	0.07637474266326814	-0.114300886520726	0.0	0.98	13.79
0.42	0.0	0.0	0.0	736.5794694390204	0.024514104329560017	-1.207146025777877	0.0	0.98	13.37
CONTAINER	NAME	CPU%	MEMUSAGE/LIMIT	MEM%	NETI/O	BLOCKI/O	PIDS_JULIA	PIDS_DOCKER	TIMESTAMP
79897c34b719	romantic_brown	100.09%	180.7MiB/1.907GiB	9.25%	3.98MB/40.9kB	6	0B/0B	0	11:02:27
8eb95bb95fc8	dreamy_shtern	99.67%	187MiB/1.907GiB	9.57%	5.55MB/85.2kB	7	0B/0B	0	11:02:27
79897c34b719	romantic_brown	99.95%	197.6MiB/1.907GiB	10.12%	7.41MB/85.2kB	6	0B/0B	0	11:02:37
8eb95bb95fc8	dreamy_shtern	98.93%	201.6MiB/1.907GiB	10.32%	7.46MB/115kB	7	0B/106kB	0	11:02:37
79897c34b719	romantic_brown	90.18%	216.4MiB/1.907GiB	11.08%	7.41MB/88.6kB	6	0B/123kB	0	11:02:47
8eb95bb95fc8	dreamy_shtern	100.15%	218.3MiB/1.907GiB	11.18%	7.46MB/118kB	7	0B/123kB	0	11:02:47
79897c34b719	romantic_brown	99.74%	243.7MiB/1.907GiB	12.48%	7.42MB/94.2kB	6	0B/909kB	0	11:02:56
8eb95bb95fc8	dreamy_shtern	95.91%	243.6MiB/1.907GiB	12.47%	7.46MB/125kB	7	0B/1.21MB	0	11:02:56
	from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer


	data = open('./texto_aleatorio.txt')
	custom_stop_words = frozenset(["palavra1", "palavra2","palavra3"])
	vectorizer = CountVectorizer(stop_words=custom_stop_words)
	vectorizer.fit(data)
	print(vectorizer.vocabulary_)