Madawa Soysa madawas

## add_users.sh
#! /bin/bash

add_users() {

auth=$(echo "admin:admin"|base64)

for i in {1..1000}
  do
  username="testuser${i}"

## commands
mvn versions:update-properties -Dincludes=org.wso2.carbon.identity.* -DgenerateBackupPoms=false

mvn versions:update-properties -DgenerateBackupPoms=false -Dincludes=org.wso2.carbon.identity.*,org.wso2.carbon.extension.identity.*,org.wso2.charon,org.apache.rampart.wso2,org.apache.ws.security.wso2,org.wso2.carbon.consent.* -U

//Run in release profile with signing disabled
mvn clean install -P wso2-release -Dgpg.skip


mvn versions:set -DnewVersion=1.0.8-SNAPSHOT

## gh-dl-release
#!/usr/bin/env bash
#
# gh-dl-release! It works!
#
# This script downloads an asset from latest or specific Github release of a
# private repo. Feel free to extract more of the variables into command line
# parameters.
#
# PREREQUISITES
#

## tumblr-recent-posts.js
/*
  This widget shows Recent Posts on your Tumblr blog.
  Its dependency is jQuery.

  Usage:

    1) Add html:
      <div id="recent-posts"></div>

    2) Add code into the <head>:

## client.js
var config = {
	"transport" : "http",
	"das_host"	: "127.0.0.1:9763",
	"das_endpoint" : "/endpoints/",
	"event_receiver" : "event_receiver",
	"ml_host" : "127.0.0.1:9765",
	"ml_username" : "admin",
	"ml_password" : "admin"
};

## bgcloud.py
import json
import zipfile
import numpy as np
import pandas as pd
import ast
import time
import datetime

import httplib2
import pprint

## rfloop.r
library(caret)
library(randomForest)

trainFiles <- dir("testtt/rows", pattern = "*")
testFiles <- dir("testtt/tests", pattern = "*")
#models <- vector("list", 320)
result <- data.frame("TRIP_ID" = character(), "TRAVEL_TIME" = numeric(), stringsAsFactors = FALSE)
for(i in 1:320) {
  print(i)
  trdata <- read.csv(paste("testtt/rows", trainFiles[[i]], sep="/"))

## getrecommendations.java
/**
* Returns the list of recommendations for a given user
*
* @param userId    user id.
* @param model     best model.
* @param ratings   rating data.
* @param products  product list.
* @return          The list of recommended products.
*/
private static List<Rating> getRecommendations(final int userId, MatrixFactorizationModel model, JavaRDD<Tuple2<Integer, Rating>> ratings, Map<Integer, String> products) {

## computermse.java
/**
* Calculating the Root Mean Squared Error
*
* @param model best model generated.
* @param data  rating data.
* @return      Root Mean Squared Error
*/
public static Double computeRMSE(MatrixFactorizationModel model, JavaRDD<Rating> data) {
JavaRDD<Tuple2<Object, Object>> userProducts = data.map(
        new Function<Rating, Tuple2<Object, Object>>() {

## modeltraining.java
//training model
int[] ranks = {8, 12};
float[] lambdas = {0.1f, 10.0f};
int[] numIters = {10, 20};

double bestValidationRmse = Double.MAX_VALUE;
int bestRank = 0;
float bestLambda = -1.0f;
int bestNumIter = -1;
MatrixFactorizationModel bestModel = null;
	#! /bin/bash

	add_users() {

	auth=$(echo "admin:admin"\|base64)

	for i in {1..1000}
	do
	username="testuser${i}"
	mvn versions:update-properties -Dincludes=org.wso2.carbon.identity.* -DgenerateBackupPoms=false

	mvn versions:update-properties -DgenerateBackupPoms=false -Dincludes=org.wso2.carbon.identity.,org.wso2.carbon.extension.identity.,org.wso2.charon,org.apache.rampart.wso2,org.apache.ws.security.wso2,org.wso2.carbon.consent.* -U

	//Run in release profile with signing disabled
	mvn clean install -P wso2-release -Dgpg.skip


	mvn versions:set -DnewVersion=1.0.8-SNAPSHOT
	#!/usr/bin/env bash
	#
	# gh-dl-release! It works!
	#
	# This script downloads an asset from latest or specific Github release of a
	# private repo. Feel free to extract more of the variables into command line
	# parameters.
	#
	# PREREQUISITES
	#
	/*
	This widget shows Recent Posts on your Tumblr blog.
	Its dependency is jQuery.

	Usage:

	1) Add html:
	<div id="recent-posts"></div>

	2) Add code into the <head>:
	var config = {
	"transport" : "http",
	"das_host" : "127.0.0.1:9763",
	"das_endpoint" : "/endpoints/",
	"event_receiver" : "event_receiver",
	"ml_host" : "127.0.0.1:9765",
	"ml_username" : "admin",
	"ml_password" : "admin"
	};
	import json
	import zipfile
	import numpy as np
	import pandas as pd
	import ast
	import time
	import datetime

	import httplib2
	import pprint
	library(caret)
	library(randomForest)

	trainFiles <- dir("testtt/rows", pattern = "*")
	testFiles <- dir("testtt/tests", pattern = "*")
	#models <- vector("list", 320)
	result <- data.frame("TRIP_ID" = character(), "TRAVEL_TIME" = numeric(), stringsAsFactors = FALSE)
	for(i in 1:320) {
	print(i)
	trdata <- read.csv(paste("testtt/rows", trainFiles[[i]], sep="/"))
	/**
	* Returns the list of recommendations for a given user
	*
	* @param userId user id.
	* @param model best model.
	* @param ratings rating data.
	* @param products product list.
	* @return The list of recommended products.
	*/
	private static List<Rating> getRecommendations(final int userId, MatrixFactorizationModel model, JavaRDD<Tuple2<Integer, Rating>> ratings, Map<Integer, String> products) {
	/**
	* Calculating the Root Mean Squared Error
	*
	* @param model best model generated.
	* @param data rating data.
	* @return Root Mean Squared Error
	*/
	public static Double computeRMSE(MatrixFactorizationModel model, JavaRDD<Rating> data) {
	JavaRDD<Tuple2<Object, Object>> userProducts = data.map(
	new Function<Rating, Tuple2<Object, Object>>() {
	//training model
	int[] ranks = {8, 12};
	float[] lambdas = {0.1f, 10.0f};
	int[] numIters = {10, 20};

	double bestValidationRmse = Double.MAX_VALUE;
	int bestRank = 0;
	float bestLambda = -1.0f;
	int bestNumIter = -1;
	MatrixFactorizationModel bestModel = null;