Vivian Zhang casunlight

## gist:06e34e9ac0a362f95d94d93b726d7a2b
dsfadsfs

## R test
code: R

## index.html
<!doctype HTML>
<meta charset = 'utf-8'>
<html>
  <head>
    <link rel='stylesheet' href="http://netdna.bootstrapcdn.com/bootstrap/3.0.3/css/bootstrap.min.css">

    <script src='http://ramnathv.github.io/rCharts/libraries/widgets/polycharts/js/polychart2.standalone.js' type='text/javascript'></script>

     <script src="http://ajax.googleapis.com/ajax/libs/angularjs/1.2.6/angular.min.js"></script>
     <script src="http://cdnjs.cloudflare.com/ajax/libs/lodash.js/2.4.1/lodash.min.js"></script>

## index.html
<!doctype HTML>
<meta charset = 'utf-8'>
<html>
  <head>
    <link rel='stylesheet' href='http://nvd3.org/assets/css/nv.d3.css'>

    <script src='http://ajax.googleapis.com/ajax/libs/jquery/1.8.2/jquery.min.js' type='text/javascript'></script>
    <script src='http://d3js.org/d3.v3.min.js' type='text/javascript'></script>
    <script src='http://timelyportfolio.github.io/rCharts_nvd3_tests/libraries/widgets/nvd3/js/nv.d3.min-new.js' type='text/javascript'></script>
    <script src='http://nvd3.org/assets/lib/fisheye.js' type='text/javascript'></script>

## index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>

body {
  font: 10px sans-serif;
}

.axis path,
.axis line {

## index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>

.bar {
  fill: steelblue;
}

.bar:hover {
  fill: brown;

## index.html
<!DOCTYPE html>
<meta charset="utf-8">
<style>

.bar {
  fill: steelblue;
}

.bar:hover {
  fill: brown;

## install_nodejs
~ sudo apt-get install nodejs
~ sudo apt-get install npm

~ node -v
v0.6.12

~ npm -v
1.1.4

## server.R
# this solution is contributed by Jay Grossman who attended that intro session! Good job, Jay!
# I asked everyone to creat a app which could upload csv data and build linear regression model

shinyServer(function(input, output) {
  #output$contents <- renderTable({

    # input$file1 will be NULL initially. After the user selects and uploads a
    # file, it will be a data frame with 'name', 'size', 'type', and 'datapath'
    # columns. The 'datapath' column will contain the local filenames where the
    # data can be found.

## ImgSVD.r
library(rARPACK);
library(jpeg);

factorize = function(m, k){
  r = svds(m[, , 1], k);
  g = svds(m[, , 2], k);
  b = svds(m[, , 3], k);
  return(list(r = r, g = g, b = b));
}
	<!doctype HTML>
	<meta charset = 'utf-8'>
	<html>
	<head>
	<link rel='stylesheet' href="http://netdna.bootstrapcdn.com/bootstrap/3.0.3/css/bootstrap.min.css">

	<script src='http://ramnathv.github.io/rCharts/libraries/widgets/polycharts/js/polychart2.standalone.js' type='text/javascript'></script>

	<script src="http://ajax.googleapis.com/ajax/libs/angularjs/1.2.6/angular.min.js"></script>
	<script src="http://cdnjs.cloudflare.com/ajax/libs/lodash.js/2.4.1/lodash.min.js"></script>
	<!DOCTYPE html>
	<meta charset="utf-8">
	<style>

	body {
	font: 10px sans-serif;
	}

	.axis path,
	.axis line {
	~ sudo apt-get install nodejs
	~ sudo apt-get install npm

	~ node -v
	v0.6.12

	~ npm -v
	1.1.4
	# this solution is contributed by Jay Grossman who attended that intro session! Good job, Jay!
	# I asked everyone to creat a app which could upload csv data and build linear regression model

	shinyServer(function(input, output) {
	#output$contents <- renderTable({

	# input$file1 will be NULL initially. After the user selects and uploads a
	# file, it will be a data frame with 'name', 'size', 'type', and 'datapath'
	# columns. The 'datapath' column will contain the local filenames where the
	# data can be found.
	library(rARPACK);
	library(jpeg);

	factorize = function(m, k){
	r = svds(m[, , 1], k);
	g = svds(m[, , 2], k);
	b = svds(m[, , 3], k);
	return(list(r = r, g = g, b = b));
	}