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Cloudera's QuickStart VM Setup

• If needed, see Cloudera's "Setting up the QuickStart VM"
• Check CentOS distribution

$ cat /etc/redhat-release

• Determine the IP address of the VM (make sure the network is configured on VirtualBox first; see "Network" below)

$ ifconfig

mamigot

#include <stdio.h>
#include <iostream>
#include <fstream>
#include "daq.h"
using namespace std;

// Easily toggle the P, I and D parts of the controller
#define CONTROLLER_P true
#define CONTROLLER_I true
#define CONTROLLER_D true

mamigot

#include <iostream>
#include <mutex>
#include <map>
using namespace std;


// Map file paths to their respective files' mutexes
map<string, shared_ptr<mutex>> fileMutexes = {};
mutex fileMutexesAccess;

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Barrier

Barrier.h

/*
  Barrier.h
 */

#include <mutex>
#include <condition_variable>

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# wget $URL_OF_THIS_RAW_SCRIPT -O - | sudo bash

#############################################################################################
# Basic initial configuration of Ubuntu 12.04 on VirtualBox
# (execute after installing the relevant ISO on the VM through VirtualBox: http://releases.ubuntu.com/12.04/)
# 
#
# In order to access the VM from your host (e.g. OS X), forward the relevant ports on VirtualBox:
# Network -> Adapter 1 (NAT) -> Port Forwarding (http://stackoverflow.com/a/10532299/2708484)
#

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#!/usr/bin/env bash

#
# Script for installing Ansible and the edX configuration repostory
# onto a host to enable running ansible to complete configuration.
# This script can be used by Docker, Packer or any other system
# for building images that requires having ansible available.
#
# Can be run as follows:
#

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version: '2'  
services:  
  nginx-proxy:
    image: jwilder/nginx-proxy
    container_name: nginx-proxy
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - /var/run/docker.sock:/tmp/docker.sock:ro

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root@openedx:/edx/app/edxapp/edx-platform$ git tag | grep "open-release" | tail -1
open-release/ginkgo.1rc1

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import numpy as np

def linear_regression(X, y):
  # Add a column of ones to X to represent the bias term
  X = np.concatenate([np.ones((X.shape[0], 1)), X], axis=1)

  # Compute the transpose of X
  X_t = np.transpose(X)

  # Compute the dot product of X_t and X

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import tensorflow as tf

# Define the input data
X = np.array([
  [10], # grade on first assessment
  [20], # grade on second assessment
  [30]  # grade on third assessment
])

# Define the target variable