finding_donors (1).html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>finding_donors</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

<style type="text/css">
    /*!
*
* Twitter Bootstrap
*
*/
/*!
 * Bootstrap v3.3.7 (http://getbootstrap.com)
 * Copyright 2011-2016 Twitter, Inc.
 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
 */
/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
  font-family: sans-serif;
  -ms-text-size-adjust: 100%;
  -webkit-text-size-adjust: 100%;
}
body {
  margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
  display: block;
}
audio,
canvas,
progress,
video {
  display: inline-block;
  vertical-align: baseline;
}
audio:not([controls]) {
  display: none;
  height: 0;
}
[hidden],
template {
  display: none;
}
a {
  background-color: transparent;
}
a:active,
a:hover {
  outline: 0;
}
abbr[title] {
  border-bottom: 1px dotted;
}
b,
strong {
  font-weight: bold;
}
dfn {
  font-style: italic;
}
h1 {
  font-size: 2em;
  margin: 0.67em 0;
}
mark {
  background: #ff0;
  color: #000;
}
small {
  font-size: 80%;
}
sub,
sup {
  font-size: 75%;
  line-height: 0;
  position: relative;
  vertical-align: baseline;
}
sup {
  top: -0.5em;
}
sub {
  bottom: -0.25em;
}
img {
  border: 0;
}
svg:not(:root) {
  overflow: hidden;
}
figure {
  margin: 1em 40px;
}
hr {
  box-sizing: content-box;
  height: 0;
}
pre {
  overflow: auto;
}
code,
kbd,
pre,
samp {
  font-family: monospace, monospace;
  font-size: 1em;
}
button,
input,
optgroup,
select,
textarea {
  color: inherit;
  font: inherit;
  margin: 0;
}
button {
  overflow: visible;
}
button,
select {
  text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
  -webkit-appearance: button;
  cursor: pointer;
}
button[disabled],
html input[disabled] {
  cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
  border: 0;
  padding: 0;
}
input {
  line-height: normal;
}
input[type="checkbox"],
input[type="radio"] {
  box-sizing: border-box;
  padding: 0;
}
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: textfield;
  box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
  -webkit-appearance: none;
}
fieldset {
  border: 1px solid #c0c0c0;
  margin: 0 2px;
  padding: 0.35em 0.625em 0.75em;
}
legend {
  border: 0;
  padding: 0;
}
textarea {
  overflow: auto;
}
optgroup {
  font-weight: bold;
}
table {
  border-collapse: collapse;
  border-spacing: 0;
}
td,
th {
  padding: 0;
}
/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
  *,
  *:before,
  *:after {
    background: transparent !important;
    color: #000 !important;
    box-shadow: none !important;
    text-shadow: none !important;
  }
  a,
  a:visited {
    text-decoration: underline;
  }
  a[href]:after {
    content: " (" attr(href) ")";
  }
  abbr[title]:after {
    content: " (" attr(title) ")";
  }
  a[href^="#"]:after,
  a[href^="javascript:"]:after {
    content: "";
  }
  pre,
  blockquote {
    border: 1px solid #999;
    page-break-inside: avoid;
  }
  thead {
    display: table-header-group;
  }
  tr,
  img {
    page-break-inside: avoid;
  }
  img {
    max-width: 100% !important;
  }
  p,
  h2,
  h3 {
    orphans: 3;
    widows: 3;
  }
  h2,
  h3 {
    page-break-after: avoid;
  }
  .navbar {
    display: none;
  }
  .btn > .caret,
  .dropup > .btn > .caret {
    border-top-color: #000 !important;
  }
  .label {
    border: 1px solid #000;
  }
  .table {
    border-collapse: collapse !important;
  }
  .table td,
  .table th {
    background-color: #fff !important;
  }
  .table-bordered th,
  .table-bordered td {
    border: 1px solid #ddd !important;
  }
}
@font-face {
  font-family: 'Glyphicons Halflings';
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
}
.glyphicon {
  position: relative;
  top: 1px;
  display: inline-block;
  font-family: 'Glyphicons Halflings';
  font-style: normal;
  font-weight: normal;
  line-height: 1;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
.glyphicon-asterisk:before {
  content: "\002a";
}
.glyphicon-plus:before {
  content: "\002b";
}
.glyphicon-euro:before,
.glyphicon-eur:before {
  content: "\20ac";
}
.glyphicon-minus:before {
  content: "\2212";
}
.glyphicon-cloud:before {
  content: "\2601";
}
.glyphicon-envelope:before {
  content: "\2709";
}
.glyphicon-pencil:before {
  content: "\270f";
}
.glyphicon-glass:before {
  content: "\e001";
}
.glyphicon-music:before {
  content: "\e002";
}
.glyphicon-search:before {
  content: "\e003";
}
.glyphicon-heart:before {
  content: "\e005";
}
.glyphicon-star:before {
  content: "\e006";
}
.glyphicon-star-empty:before {
  content: "\e007";
}
.glyphicon-user:before {
  content: "\e008";
}
.glyphicon-film:before {
  content: "\e009";
}
.glyphicon-th-large:before {
  content: "\e010";
}
.glyphicon-th:before {
  content: "\e011";
}
.glyphicon-th-list:before {
  content: "\e012";
}
.glyphicon-ok:before {
  content: "\e013";
}
.glyphicon-remove:before {
  content: "\e014";
}
.glyphicon-zoom-in:before {
  content: "\e015";
}
.glyphicon-zoom-out:before {
  content: "\e016";
}
.glyphicon-off:before {
  content: "\e017";
}
.glyphicon-signal:before {
  content: "\e018";
}
.glyphicon-cog:before {
  content: "\e019";
}
.glyphicon-trash:before {
  content: "\e020";
}
.glyphicon-home:before {
  content: "\e021";
}
.glyphicon-file:before {
  content: "\e022";
}
.glyphicon-time:before {
  content: "\e023";
}
.glyphicon-road:before {
  content: "\e024";
}
.glyphicon-download-alt:before {
  content: "\e025";
}
.glyphicon-download:before {
  content: "\e026";
}
.glyphicon-upload:before {
  content: "\e027";
}
.glyphicon-inbox:before {
  content: "\e028";
}
.glyphicon-play-circle:before {
  content: "\e029";
}
.glyphicon-repeat:before {
  content: "\e030";
}
.glyphicon-refresh:before {
  content: "\e031";
}
.glyphicon-list-alt:before {
  content: "\e032";
}
.glyphicon-lock:before {
  content: "\e033";
}
.glyphicon-flag:before {
  content: "\e034";
}
.glyphicon-headphones:before {
  content: "\e035";
}
.glyphicon-volume-off:before {
  content: "\e036";
}
.glyphicon-volume-down:before {
  content: "\e037";
}
.glyphicon-volume-up:before {
  content: "\e038";
}
.glyphicon-qrcode:before {
  content: "\e039";
}
.glyphicon-barcode:before {
  content: "\e040";
}
.glyphicon-tag:before {
  content: "\e041";
}
.glyphicon-tags:before {
  content: "\e042";
}
.glyphicon-book:before {
  content: "\e043";
}
.glyphicon-bookmark:before {
  content: "\e044";
}
.glyphicon-print:before {
  content: "\e045";
}
.glyphicon-camera:before {
  content: "\e046";
}
.glyphicon-font:before {
  content: "\e047";
}
.glyphicon-bold:before {
  content: "\e048";
}
.glyphicon-italic:before {
  content: "\e049";
}
.glyphicon-text-height:before {
  content: "\e050";
}
.glyphicon-text-width:before {
  content: "\e051";
}
.glyphicon-align-left:before {
  content: "\e052";
}
.glyphicon-align-center:before {
  content: "\e053";
}
.glyphicon-align-right:before {
  content: "\e054";
}
.glyphicon-align-justify:before {
  content: "\e055";
}
.glyphicon-list:before {
  content: "\e056";
}
.glyphicon-indent-left:before {
  content: "\e057";
}
.glyphicon-indent-right:before {
  content: "\e058";
}
.glyphicon-facetime-video:before {
  content: "\e059";
}
.glyphicon-picture:before {
  content: "\e060";
}
.glyphicon-map-marker:before {
  content: "\e062";
}
.glyphicon-adjust:before {
  content: "\e063";
}
.glyphicon-tint:before {
  content: "\e064";
}
.glyphicon-edit:before {
  content: "\e065";
}
.glyphicon-share:before {
  content: "\e066";
}
.glyphicon-check:before {
  content: "\e067";
}
.glyphicon-move:before {
  content: "\e068";
}
.glyphicon-step-backward:before {
  content: "\e069";
}
.glyphicon-fast-backward:before {
  content: "\e070";
}
.glyphicon-backward:before {
  content: "\e071";
}
.glyphicon-play:before {
  content: "\e072";
}
.glyphicon-pause:before {
  content: "\e073";
}
.glyphicon-stop:before {
  content: "\e074";
}
.glyphicon-forward:before {
  content: "\e075";
}
.glyphicon-fast-forward:before {
  content: "\e076";
}
.glyphicon-step-forward:before {
  content: "\e077";
}
.glyphicon-eject:before {
  content: "\e078";
}
.glyphicon-chevron-left:before {
  content: "\e079";
}
.glyphicon-chevron-right:before {
  content: "\e080";
}
.glyphicon-plus-sign:before {
  content: "\e081";
}
.glyphicon-minus-sign:before {
  content: "\e082";
}
.glyphicon-remove-sign:before {
  content: "\e083";
}
.glyphicon-ok-sign:before {
  content: "\e084";
}
.glyphicon-question-sign:before {
  content: "\e085";
}
.glyphicon-info-sign:before {
  content: "\e086";
}
.glyphicon-screenshot:before {
  content: "\e087";
}
.glyphicon-remove-circle:before {
  content: "\e088";
}
.glyphicon-ok-circle:before {
  content: "\e089";
}
.glyphicon-ban-circle:before {
  content: "\e090";
}
.glyphicon-arrow-left:before {
  content: "\e091";
}
.glyphicon-arrow-right:before {
  content: "\e092";
}
.glyphicon-arrow-up:before {
  content: "\e093";
}
.glyphicon-arrow-down:before {
  content: "\e094";
}
.glyphicon-share-alt:before {
  content: "\e095";
}
.glyphicon-resize-full:before {
  content: "\e096";
}
.glyphicon-resize-small:before {
  content: "\e097";
}
.glyphicon-exclamation-sign:before {
  content: "\e101";
}
.glyphicon-gift:before {
  content: "\e102";
}
.glyphicon-leaf:before {
  content: "\e103";
}
.glyphicon-fire:before {
  content: "\e104";
}
.glyphicon-eye-open:before {
  content: "\e105";
}
.glyphicon-eye-close:before {
  content: "\e106";
}
.glyphicon-warning-sign:before {
  content: "\e107";
}
.glyphicon-plane:before {
  content: "\e108";
}
.glyphicon-calendar:before {
  content: "\e109";
}
.glyphicon-random:before {
  content: "\e110";
}
.glyphicon-comment:before {
  content: "\e111";
}
.glyphicon-magnet:before {
  content: "\e112";
}
.glyphicon-chevron-up:before {
  content: "\e113";
}
.glyphicon-chevron-down:before {
  content: "\e114";
}
.glyphicon-retweet:before {
  content: "\e115";
}
.glyphicon-shopping-cart:before {
  content: "\e116";
}
.glyphicon-folder-close:before {
  content: "\e117";
}
.glyphicon-folder-open:before {
  content: "\e118";
}
.glyphicon-resize-vertical:before {
  content: "\e119";
}
.glyphicon-resize-horizontal:before {
  content: "\e120";
}
.glyphicon-hdd:before {
  content: "\e121";
}
.glyphicon-bullhorn:before {
  content: "\e122";
}
.glyphicon-bell:before {
  content: "\e123";
}
.glyphicon-certificate:before {
  content: "\e124";
}
.glyphicon-thumbs-up:before {
  content: "\e125";
}
.glyphicon-thumbs-down:before {
  content: "\e126";
}
.glyphicon-hand-right:before {
  content: "\e127";
}
.glyphicon-hand-left:before {
  content: "\e128";
}
.glyphicon-hand-up:before {
  content: "\e129";
}
.glyphicon-hand-down:before {
  content: "\e130";
}
.glyphicon-circle-arrow-right:before {
  content: "\e131";
}
.glyphicon-circle-arrow-left:before {
  content: "\e132";
}
.glyphicon-circle-arrow-up:before {
  content: "\e133";
}
.glyphicon-circle-arrow-down:before {
  content: "\e134";
}
.glyphicon-globe:before {
  content: "\e135";
}
.glyphicon-wrench:before {
  content: "\e136";
}
.glyphicon-tasks:before {
  content: "\e137";
}
.glyphicon-filter:before {
  content: "\e138";
}
.glyphicon-briefcase:before {
  content: "\e139";
}
.glyphicon-fullscreen:before {
  content: "\e140";
}
.glyphicon-dashboard:before {
  content: "\e141";
}
.glyphicon-paperclip:before {
  content: "\e142";
}
.glyphicon-heart-empty:before {
  content: "\e143";
}
.glyphicon-link:before {
  content: "\e144";
}
.glyphicon-phone:before {
  content: "\e145";
}
.glyphicon-pushpin:before {
  content: "\e146";
}
.glyphicon-usd:before {
  content: "\e148";
}
.glyphicon-gbp:before {
  content: "\e149";
}
.glyphicon-sort:before {
  content: "\e150";
}
.glyphicon-sort-by-alphabet:before {
  content: "\e151";
}
.glyphicon-sort-by-alphabet-alt:before {
  content: "\e152";
}
.glyphicon-sort-by-order:before {
  content: "\e153";
}
.glyphicon-sort-by-order-alt:before {
  content: "\e154";
}
.glyphicon-sort-by-attributes:before {
  content: "\e155";
}
.glyphicon-sort-by-attributes-alt:before {
  content: "\e156";
}
.glyphicon-unchecked:before {
  content: "\e157";
}
.glyphicon-expand:before {
  content: "\e158";
}
.glyphicon-collapse-down:before {
  content: "\e159";
}
.glyphicon-collapse-up:before {
  content: "\e160";
}
.glyphicon-log-in:before {
  content: "\e161";
}
.glyphicon-flash:before {
  content: "\e162";
}
.glyphicon-log-out:before {
  content: "\e163";
}
.glyphicon-new-window:before {
  content: "\e164";
}
.glyphicon-record:before {
  content: "\e165";
}
.glyphicon-save:before {
  content: "\e166";
}
.glyphicon-open:before {
  content: "\e167";
}
.glyphicon-saved:before {
  content: "\e168";
}
.glyphicon-import:before {
  content: "\e169";
}
.glyphicon-export:before {
  content: "\e170";
}
.glyphicon-send:before {
  content: "\e171";
}
.glyphicon-floppy-disk:before {
  content: "\e172";
}
.glyphicon-floppy-saved:before {
  content: "\e173";
}
.glyphicon-floppy-remove:before {
  content: "\e174";
}
.glyphicon-floppy-save:before {
  content: "\e175";
}
.glyphicon-floppy-open:before {
  content: "\e176";
}
.glyphicon-credit-card:before {
  content: "\e177";
}
.glyphicon-transfer:before {
  content: "\e178";
}
.glyphicon-cutlery:before {
  content: "\e179";
}
.glyphicon-header:before {
  content: "\e180";
}
.glyphicon-compressed:before {
  content: "\e181";
}
.glyphicon-earphone:before {
  content: "\e182";
}
.glyphicon-phone-alt:before {
  content: "\e183";
}
.glyphicon-tower:before {
  content: "\e184";
}
.glyphicon-stats:before {
  content: "\e185";
}
.glyphicon-sd-video:before {
  content: "\e186";
}
.glyphicon-hd-video:before {
  content: "\e187";
}
.glyphicon-subtitles:before {
  content: "\e188";
}
.glyphicon-sound-stereo:before {
  content: "\e189";
}
.glyphicon-sound-dolby:before {
  content: "\e190";
}
.glyphicon-sound-5-1:before {
  content: "\e191";
}
.glyphicon-sound-6-1:before {
  content: "\e192";
}
.glyphicon-sound-7-1:before {
  content: "\e193";
}
.glyphicon-copyright-mark:before {
  content: "\e194";
}
.glyphicon-registration-mark:before {
  content: "\e195";
}
.glyphicon-cloud-download:before {
  content: "\e197";
}
.glyphicon-cloud-upload:before {
  content: "\e198";
}
.glyphicon-tree-conifer:before {
  content: "\e199";
}
.glyphicon-tree-deciduous:before {
  content: "\e200";
}
.glyphicon-cd:before {
  content: "\e201";
}
.glyphicon-save-file:before {
  content: "\e202";
}
.glyphicon-open-file:before {
  content: "\e203";
}
.glyphicon-level-up:before {
  content: "\e204";
}
.glyphicon-copy:before {
  content: "\e205";
}
.glyphicon-paste:before {
  content: "\e206";
}
.glyphicon-alert:before {
  content: "\e209";
}
.glyphicon-equalizer:before {
  content: "\e210";
}
.glyphicon-king:before {
  content: "\e211";
}
.glyphicon-queen:before {
  content: "\e212";
}
.glyphicon-pawn:before {
  content: "\e213";
}
.glyphicon-bishop:before {
  content: "\e214";
}
.glyphicon-knight:before {
  content: "\e215";
}
.glyphicon-baby-formula:before {
  content: "\e216";
}
.glyphicon-tent:before {
  content: "\26fa";
}
.glyphicon-blackboard:before {
  content: "\e218";
}
.glyphicon-bed:before {
  content: "\e219";
}
.glyphicon-apple:before {
  content: "\f8ff";
}
.glyphicon-erase:before {
  content: "\e221";
}
.glyphicon-hourglass:before {
  content: "\231b";
}
.glyphicon-lamp:before {
  content: "\e223";
}
.glyphicon-duplicate:before {
  content: "\e224";
}
.glyphicon-piggy-bank:before {
  content: "\e225";
}
.glyphicon-scissors:before {
  content: "\e226";
}
.glyphicon-bitcoin:before {
  content: "\e227";
}
.glyphicon-btc:before {
  content: "\e227";
}
.glyphicon-xbt:before {
  content: "\e227";
}
.glyphicon-yen:before {
  content: "\00a5";
}
.glyphicon-jpy:before {
  content: "\00a5";
}
.glyphicon-ruble:before {
  content: "\20bd";
}
.glyphicon-rub:before {
  content: "\20bd";
}
.glyphicon-scale:before {
  content: "\e230";
}
.glyphicon-ice-lolly:before {
  content: "\e231";
}
.glyphicon-ice-lolly-tasted:before {
  content: "\e232";
}
.glyphicon-education:before {
  content: "\e233";
}
.glyphicon-option-horizontal:before {
  content: "\e234";
}
.glyphicon-option-vertical:before {
  content: "\e235";
}
.glyphicon-menu-hamburger:before {
  content: "\e236";
}
.glyphicon-modal-window:before {
  content: "\e237";
}
.glyphicon-oil:before {
  content: "\e238";
}
.glyphicon-grain:before {
  content: "\e239";
}
.glyphicon-sunglasses:before {
  content: "\e240";
}
.glyphicon-text-size:before {
  content: "\e241";
}
.glyphicon-text-color:before {
  content: "\e242";
}
.glyphicon-text-background:before {
  content: "\e243";
}
.glyphicon-object-align-top:before {
  content: "\e244";
}
.glyphicon-object-align-bottom:before {
  content: "\e245";
}
.glyphicon-object-align-horizontal:before {
  content: "\e246";
}
.glyphicon-object-align-left:before {
  content: "\e247";
}
.glyphicon-object-align-vertical:before {
  content: "\e248";
}
.glyphicon-object-align-right:before {
  content: "\e249";
}
.glyphicon-triangle-right:before {
  content: "\e250";
}
.glyphicon-triangle-left:before {
  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
@media (max-width: 991px) {
  #ipython_notebook {
    margin-left: 10px;
  }
}
[dir="rtl"] #ipython_notebook {
  float: right !important;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#login_widget {
  float: right;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  text-align: center;
  vertical-align: middle;
  display: inline;
  opacity: 0;
  z-index: 2;
  width: 12ex;
  margin-right: -12ex;
}
.alternate_upload .btn-upload {
  height: 22px;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
[dir="rtl"] #tabs li {
  float: right;
}
ul#tabs {
  margin-bottom: 4px;
}
[dir="rtl"] ul#tabs {
  margin-right: 0px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons {
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-right {
  padding-top: 1px;
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-left {
  float: right !important;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: baseline;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
#tree-selector {
  padding-right: 0px;
}
[dir="rtl"] #tree-selector a {
  float: right;
}
#button-select-all {
  min-width: 50px;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
[dir="rtl"] #new-menu {
  text-align: right;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
[dir="rtl"] #running .col-sm-8 {
  float: right !important;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI colors. */
.ansibold {
  font-weight: bold;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  border-left-width: 1px;
  padding-left: 5px;
  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
}
div.cell.jupyter-soft-selected {
  border-left-color: #90CAF9;
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected {
  border-color: #ababab;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
}
@media print {
  div.cell.selected {
    border-color: transparent;
  }
}
div.cell.selected.jupyter-soft-selected {
  border-left-width: 0;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  padding: 0.4em;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
  /* .CodeMirror-lines */
  padding: 0;
  border: 0;
  border-radius: 0;
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  display: none;
}
.command-shortcut:before {
  content: "(command)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
    </style>
<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
    </style>
<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

.ansi-bold { font-weight: bold; }

    </style>


<style type="text/css">
/* Overrides of notebook CSS for static HTML export */
body {
  overflow: visible;
  padding: 8px;
}

div#notebook {
  overflow: visible;
  border-top: none;
}@media print {
  div.cell {
    display: block;
    page-break-inside: avoid;
  } 
  div.output_wrapper { 
    display: block;
    page-break-inside: avoid; 
  }
  div.output { 
    display: block;
    page-break-inside: avoid; 
  }
}
</style>

<!-- Custom stylesheet, it must be in the same directory as the html file -->
<link rel="stylesheet" href="custom.css">

<!-- Loading mathjax macro -->
<!-- Load mathjax -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS_HTML"></script>
    <!-- MathJax configuration -->
    <script type="text/x-mathjax-config">
    MathJax.Hub.Config({
        tex2jax: {
            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
            processEscapes: true,
            processEnvironments: true
        },
        // Center justify equations in code and markdown cells. Elsewhere
        // we use CSS to left justify single line equations in code cells.
        displayAlign: 'center',
        "HTML-CSS": {
            styles: {'.MathJax_Display': {"margin": 0}},
            linebreaks: { automatic: true }
        }
    });
    </script>
    <!-- End of mathjax configuration --></head>
<body>
  <div tabindex="-1" id="notebook" class="border-box-sizing">
    <div class="container" id="notebook-container">

<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Machine-Learning-Engineer-Nanodegree">Machine Learning Engineer Nanodegree<a class="anchor-link" href="#Machine-Learning-Engineer-Nanodegree">&#182;</a></h1><h2 id="Supervised-Learning">Supervised Learning<a class="anchor-link" href="#Supervised-Learning">&#182;</a></h2><h2 id="Project:-Finding-Donors-for-CharityML">Project: Finding Donors for <em>CharityML</em><a class="anchor-link" href="#Project:-Finding-Donors-for-CharityML">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Welcome to the second project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with <strong>'Implementation'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a <code>'TODO'</code> statement. Please be sure to read the instructions carefully!</p>
<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</p>
<blockquote><p><strong>Note:</strong> Please specify WHICH VERSION OF PYTHON you are using when submitting this notebook. Code and Markdown cells can be executed using the <strong>Shift + Enter</strong> keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.</p>
</blockquote>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Getting-Started">Getting Started<a class="anchor-link" href="#Getting-Started">&#182;</a></h2><p>In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations.  Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with.  While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features.</p>
<p>The dataset for this project originates from the <a href="https://archive.ics.uci.edu/ml/datasets/Census+Income">UCI Machine Learning Repository</a>. The datset was donated by Ron Kohavi and Barry Becker, after being published in the article <em>"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid"</em>. You can find the article by Ron Kohavi <a href="https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf">online</a>. The data we investigate here consists of small changes to the original dataset, such as removing the <code>'fnlwgt'</code> feature and records with missing or ill-formatted entries.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<hr>
<h2 id="Exploring-the-Data">Exploring the Data<a class="anchor-link" href="#Exploring-the-Data">&#182;</a></h2><p>Run the code cell below to load necessary Python libraries and load the census data. Note that the last column from this dataset, <code>'income'</code>, will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import libraries necessary for this project</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span> <span class="c1"># Allows the use of display() for DataFrames</span>

<span class="c1"># Import supplementary visualization code visuals.py</span>
<span class="kn">import</span> <span class="nn">visuals</span> <span class="kn">as</span> <span class="nn">vs</span>

<span class="c1"># Pretty display for notebooks</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="c1"># Load the Census dataset</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;census.csv&quot;</span><span class="p">)</span>

<span class="c1"># Success - Display the first record</span>
<span class="n">display</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">))</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>age</th>
      <th>workclass</th>
      <th>education_level</th>
      <th>education-num</th>
      <th>marital-status</th>
      <th>occupation</th>
      <th>relationship</th>
      <th>race</th>
      <th>sex</th>
      <th>capital-gain</th>
      <th>capital-loss</th>
      <th>hours-per-week</th>
      <th>native-country</th>
      <th>income</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>39</td>
      <td>State-gov</td>
      <td>Bachelors</td>
      <td>13.0</td>
      <td>Never-married</td>
      <td>Adm-clerical</td>
      <td>Not-in-family</td>
      <td>White</td>
      <td>Male</td>
      <td>2174.0</td>
      <td>0.0</td>
      <td>40.0</td>
      <td>United-States</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>1</th>
      <td>50</td>
      <td>Self-emp-not-inc</td>
      <td>Bachelors</td>
      <td>13.0</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Husband</td>
      <td>White</td>
      <td>Male</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>13.0</td>
      <td>United-States</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>2</th>
      <td>38</td>
      <td>Private</td>
      <td>HS-grad</td>
      <td>9.0</td>
      <td>Divorced</td>
      <td>Handlers-cleaners</td>
      <td>Not-in-family</td>
      <td>White</td>
      <td>Male</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>40.0</td>
      <td>United-States</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>3</th>
      <td>53</td>
      <td>Private</td>
      <td>11th</td>
      <td>7.0</td>
      <td>Married-civ-spouse</td>
      <td>Handlers-cleaners</td>
      <td>Husband</td>
      <td>Black</td>
      <td>Male</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>40.0</td>
      <td>United-States</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>4</th>
      <td>28</td>
      <td>Private</td>
      <td>Bachelors</td>
      <td>13.0</td>
      <td>Married-civ-spouse</td>
      <td>Prof-specialty</td>
      <td>Wife</td>
      <td>Black</td>
      <td>Female</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>40.0</td>
      <td>Cuba</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>5</th>
      <td>37</td>
      <td>Private</td>
      <td>Masters</td>
      <td>14.0</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Wife</td>
      <td>White</td>
      <td>Female</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>40.0</td>
      <td>United-States</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>6</th>
      <td>49</td>
      <td>Private</td>
      <td>9th</td>
      <td>5.0</td>
      <td>Married-spouse-absent</td>
      <td>Other-service</td>
      <td>Not-in-family</td>
      <td>Black</td>
      <td>Female</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>16.0</td>
      <td>Jamaica</td>
      <td>&lt;=50K</td>
    </tr>
    <tr>
      <th>7</th>
      <td>52</td>
      <td>Self-emp-not-inc</td>
      <td>HS-grad</td>
      <td>9.0</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Husband</td>
      <td>White</td>
      <td>Male</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>45.0</td>
      <td>United-States</td>
      <td>&gt;50K</td>
    </tr>
    <tr>
      <th>8</th>
      <td>31</td>
      <td>Private</td>
      <td>Masters</td>
      <td>14.0</td>
      <td>Never-married</td>
      <td>Prof-specialty</td>
      <td>Not-in-family</td>
      <td>White</td>
      <td>Female</td>
      <td>14084.0</td>
      <td>0.0</td>
      <td>50.0</td>
      <td>United-States</td>
      <td>&gt;50K</td>
    </tr>
    <tr>
      <th>9</th>
      <td>42</td>
      <td>Private</td>
      <td>Bachelors</td>
      <td>13.0</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Husband</td>
      <td>White</td>
      <td>Male</td>
      <td>5178.0</td>
      <td>0.0</td>
      <td>40.0</td>
      <td>United-States</td>
      <td>&gt;50K</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-Data-Exploration">Implementation: Data Exploration<a class="anchor-link" href="#Implementation:-Data-Exploration">&#182;</a></h3><p>A cursory investigation of the dataset will determine how many individuals fit into either group, and will tell us about the percentage of these individuals making more than \$50,000. In the code cell below, you will need to compute the following:</p>
<ul>
<li>The total number of records, <code>'n_records'</code></li>
<li>The number of individuals making more than \$50,000 annually, <code>'n_greater_50k'</code>.</li>
<li>The number of individuals making at most \$50,000 annually, <code>'n_at_most_50k'</code>.</li>
<li>The percentage of individuals making more than \$50,000 annually, <code>'greater_percent'</code>.</li>
</ul>
<p><strong> HINT: </strong> You may need to look at the table above to understand how the <code>'income'</code> entries are formatted.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[4]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Total number of records</span>
<span class="n">n_records</span> <span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

<span class="c1"># TODO: Number of records where individual&#39;s income is more than $50,000</span>
<span class="n">n_greater_50k</span> <span class="o">=</span> <span class="n">n_records</span><span class="o">-</span><span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;income&#39;</span><span class="p">]</span><span class="o">==</span><span class="s1">&#39;&lt;=50K&#39;</span><span class="p">)</span>

<span class="c1"># TODO: Number of records where individual&#39;s income is at most $50,000</span>
<span class="n">n_at_most_50k</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;income&#39;</span><span class="p">]</span><span class="o">==</span><span class="s1">&#39;&lt;=50K&#39;</span><span class="p">)</span>

<span class="c1"># TODO: Percentage of individuals whose income is more than $50,000</span>
<span class="n">greater_percent</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">n_greater_50k</span><span class="p">)</span><span class="o">/</span><span class="n">n_records</span><span class="o">*</span><span class="mf">100.0</span>


<span class="c1"># Print the results</span>
<span class="k">print</span> <span class="s2">&quot;Total number of records: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_records</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Individuals making more than $50,000: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_greater_50k</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Individuals making at most $50,000: {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_at_most_50k</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Percentage of individuals making more than $50,000: {:.2f}%&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">greater_percent</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Total number of records: 45222
Individuals making more than $50,000: 11208
Individuals making at most $50,000: 34014
Percentage of individuals making more than $50,000: 24.78%
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong> Featureset Exploration </strong></p>
<ul>
<li><strong>age</strong>: continuous. </li>
<li><strong>workclass</strong>: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked. </li>
<li><strong>education</strong>: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool. </li>
<li><strong>education-num</strong>: continuous. </li>
<li><strong>marital-status</strong>: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse. </li>
<li><strong>occupation</strong>: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces. </li>
<li><strong>relationship</strong>: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried. </li>
<li><strong>race</strong>: Black, White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other. </li>
<li><strong>sex</strong>: Female, Male. </li>
<li><strong>capital-gain</strong>: continuous. </li>
<li><strong>capital-loss</strong>: continuous. </li>
<li><strong>hours-per-week</strong>: continuous. </li>
<li><strong>native-country</strong>: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&amp;Tobago, Peru, Hong, Holand-Netherlands.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<hr>
<h2 id="Preparing-the-Data">Preparing the Data<a class="anchor-link" href="#Preparing-the-Data">&#182;</a></h2><p>Before data can be used as input for machine learning algorithms, it often must be cleaned, formatted, and restructured — this is typically known as <strong>preprocessing</strong>. Fortunately, for this dataset, there are no invalid or missing entries we must deal with, however, there are some qualities about certain features that must be adjusted. This preprocessing can help tremendously with the outcome and predictive power of nearly all learning algorithms.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Transforming-Skewed-Continuous-Features">Transforming Skewed Continuous Features<a class="anchor-link" href="#Transforming-Skewed-Continuous-Features">&#182;</a></h3><p>A dataset may sometimes contain at least one feature whose values tend to lie near a single number, but will also have a non-trivial number of vastly larger or smaller values than that single number.  Algorithms can be sensitive to such distributions of values and can underperform if the range is not properly normalized. With the census dataset two features fit this description: '<code>capital-gain'</code> and <code>'capital-loss'</code>.</p>
<p>Run the code cell below to plot a histogram of these two features. Note the range of the values present and how they are distributed.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Split the data into features and target label</span>
<span class="n">income_raw</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s1">&#39;income&#39;</span><span class="p">]</span>
<span class="n">features_raw</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;income&#39;</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>

<span class="c1"># Visualize skewed continuous features of original data</span>
<span class="n">vs</span><span class="o">.</span><span class="n">distribution</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAxAAAAF2CAYAAAD+y36TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>For highly-skewed feature distributions such as <code>'capital-gain'</code> and <code>'capital-loss'</code>, it is common practice to apply a <a href="https://en.wikipedia.org/wiki/Data_transformation_(statistics)">logarithmic transformation</a> on the data so that the very large and very small values do not negatively affect the performance of a learning algorithm. Using a logarithmic transformation significantly reduces the range of values caused by outliers. Care must be taken when applying this transformation however: The logarithm of <code>0</code> is undefined, so we must translate the values by a small amount above <code>0</code> to apply the the logarithm successfully.</p>
<p>Run the code cell below to perform a transformation on the data and visualize the results. Again, note the range of values and how they are distributed.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Log-transform the skewed features</span>
<span class="n">skewed</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;capital-gain&#39;</span><span class="p">,</span> <span class="s1">&#39;capital-loss&#39;</span><span class="p">]</span>
<span class="n">features_log_transformed</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">features_raw</span><span class="p">)</span>
<span class="n">features_log_transformed</span><span class="p">[</span><span class="n">skewed</span><span class="p">]</span> <span class="o">=</span> <span class="n">features_raw</span><span class="p">[</span><span class="n">skewed</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>

<span class="c1"># Visualize the new log distributions</span>
<span class="n">vs</span><span class="o">.</span><span class="n">distribution</span><span class="p">(</span><span class="n">features_log_transformed</span><span class="p">,</span> <span class="n">transformed</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAxAAAAF2CAYAAAD+y36TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Normalizing-Numerical-Features">Normalizing Numerical Features<a class="anchor-link" href="#Normalizing-Numerical-Features">&#182;</a></h3><p>In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as <code>'capital-gain'</code> or <code>'capital-loss'</code> above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below.</p>
<p>Run the code cell below to normalize each numerical feature. We will use <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html"><code>sklearn.preprocessing.MinMaxScaler</code></a> for this.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import sklearn.preprocessing.StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">MinMaxScaler</span>

<span class="c1"># Initialize a scaler, then apply it to the features</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">MinMaxScaler</span><span class="p">()</span> <span class="c1"># default=(0, 1)</span>
<span class="n">numerical</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;age&#39;</span><span class="p">,</span> <span class="s1">&#39;education-num&#39;</span><span class="p">,</span> <span class="s1">&#39;capital-gain&#39;</span><span class="p">,</span> <span class="s1">&#39;capital-loss&#39;</span><span class="p">,</span> <span class="s1">&#39;hours-per-week&#39;</span><span class="p">]</span>

<span class="n">features_log_minmax_transform</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">features_log_transformed</span><span class="p">)</span>
<span class="n">features_log_minmax_transform</span><span class="p">[</span><span class="n">numerical</span><span class="p">]</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">features_log_transformed</span><span class="p">[</span><span class="n">numerical</span><span class="p">])</span>

<span class="c1"># Show an example of a record with scaling applied</span>
<span class="n">display</span><span class="p">(</span><span class="n">features_log_minmax_transform</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="n">n</span> <span class="o">=</span> <span class="mi">10</span><span class="p">))</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>age</th>
      <th>workclass</th>
      <th>education_level</th>
      <th>education-num</th>
      <th>marital-status</th>
      <th>occupation</th>
      <th>relationship</th>
      <th>race</th>
      <th>sex</th>
      <th>capital-gain</th>
      <th>capital-loss</th>
      <th>hours-per-week</th>
      <th>native-country</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0.301370</td>
      <td>State-gov</td>
      <td>Bachelors</td>
      <td>0.800000</td>
      <td>Never-married</td>
      <td>Adm-clerical</td>
      <td>Not-in-family</td>
      <td>White</td>
      <td>Male</td>
      <td>0.667492</td>
      <td>0.0</td>
      <td>0.397959</td>
      <td>United-States</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0.452055</td>
      <td>Self-emp-not-inc</td>
      <td>Bachelors</td>
      <td>0.800000</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Husband</td>
      <td>White</td>
      <td>Male</td>
      <td>0.000000</td>
      <td>0.0</td>
      <td>0.122449</td>
      <td>United-States</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.287671</td>
      <td>Private</td>
      <td>HS-grad</td>
      <td>0.533333</td>
      <td>Divorced</td>
      <td>Handlers-cleaners</td>
      <td>Not-in-family</td>
      <td>White</td>
      <td>Male</td>
      <td>0.000000</td>
      <td>0.0</td>
      <td>0.397959</td>
      <td>United-States</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.493151</td>
      <td>Private</td>
      <td>11th</td>
      <td>0.400000</td>
      <td>Married-civ-spouse</td>
      <td>Handlers-cleaners</td>
      <td>Husband</td>
      <td>Black</td>
      <td>Male</td>
      <td>0.000000</td>
      <td>0.0</td>
      <td>0.397959</td>
      <td>United-States</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.150685</td>
      <td>Private</td>
      <td>Bachelors</td>
      <td>0.800000</td>
      <td>Married-civ-spouse</td>
      <td>Prof-specialty</td>
      <td>Wife</td>
      <td>Black</td>
      <td>Female</td>
      <td>0.000000</td>
      <td>0.0</td>
      <td>0.397959</td>
      <td>Cuba</td>
    </tr>
    <tr>
      <th>5</th>
      <td>0.273973</td>
      <td>Private</td>
      <td>Masters</td>
      <td>0.866667</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Wife</td>
      <td>White</td>
      <td>Female</td>
      <td>0.000000</td>
      <td>0.0</td>
      <td>0.397959</td>
      <td>United-States</td>
    </tr>
    <tr>
      <th>6</th>
      <td>0.438356</td>
      <td>Private</td>
      <td>9th</td>
      <td>0.266667</td>
      <td>Married-spouse-absent</td>
      <td>Other-service</td>
      <td>Not-in-family</td>
      <td>Black</td>
      <td>Female</td>
      <td>0.000000</td>
      <td>0.0</td>
      <td>0.153061</td>
      <td>Jamaica</td>
    </tr>
    <tr>
      <th>7</th>
      <td>0.479452</td>
      <td>Self-emp-not-inc</td>
      <td>HS-grad</td>
      <td>0.533333</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Husband</td>
      <td>White</td>
      <td>Male</td>
      <td>0.000000</td>
      <td>0.0</td>
      <td>0.448980</td>
      <td>United-States</td>
    </tr>
    <tr>
      <th>8</th>
      <td>0.191781</td>
      <td>Private</td>
      <td>Masters</td>
      <td>0.866667</td>
      <td>Never-married</td>
      <td>Prof-specialty</td>
      <td>Not-in-family</td>
      <td>White</td>
      <td>Female</td>
      <td>0.829751</td>
      <td>0.0</td>
      <td>0.500000</td>
      <td>United-States</td>
    </tr>
    <tr>
      <th>9</th>
      <td>0.342466</td>
      <td>Private</td>
      <td>Bachelors</td>
      <td>0.800000</td>
      <td>Married-civ-spouse</td>
      <td>Exec-managerial</td>
      <td>Husband</td>
      <td>White</td>
      <td>Male</td>
      <td>0.742849</td>
      <td>0.0</td>
      <td>0.397959</td>
      <td>United-States</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-Data-Preprocessing">Implementation: Data Preprocessing<a class="anchor-link" href="#Implementation:-Data-Preprocessing">&#182;</a></h3><p>From the table in <strong>Exploring the Data</strong> above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called <em>categorical variables</em>) be converted. One popular way to convert categorical variables is by using the <strong>one-hot encoding</strong> scheme. One-hot encoding creates a <em>"dummy"</em> variable for each possible category of each non-numeric feature. For example, assume <code>someFeature</code> has three possible entries: <code>A</code>, <code>B</code>, or <code>C</code>. We then encode this feature into <code>someFeature_A</code>, <code>someFeature_B</code> and <code>someFeature_C</code>.</p>
<table>
<thead><tr>
<th style="text-align:center"></th>
<th style="text-align:center">someFeature</th>
<th></th>
<th style="text-align:center">someFeature_A</th>
<th style="text-align:center">someFeature_B</th>
<th style="text-align:center">someFeature_C</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">0</td>
<td style="text-align:center">B</td>
<td></td>
<td style="text-align:center">0</td>
<td style="text-align:center">1</td>
<td style="text-align:center">0</td>
</tr>
<tr>
<td style="text-align:center">1</td>
<td style="text-align:center">C</td>
<td>----&gt; one-hot encode ----&gt;</td>
<td style="text-align:center">0</td>
<td style="text-align:center">0</td>
<td style="text-align:center">1</td>
</tr>
<tr>
<td style="text-align:center">2</td>
<td style="text-align:center">A</td>
<td></td>
<td style="text-align:center">1</td>
<td style="text-align:center">0</td>
<td style="text-align:center">0</td>
</tr>
</tbody>
</table>
<p>Additionally, as with the non-numeric features, we need to convert the non-numeric target label, <code>'income'</code> to numerical values for the learning algorithm to work. Since there are only two possible categories for this label ("&lt;=50K" and "&gt;50K"), we can avoid using one-hot encoding and simply encode these two categories as <code>0</code> and <code>1</code>, respectively. In code cell below, you will need to implement the following:</p>
<ul>
<li>Use <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies"><code>pandas.get_dummies()</code></a> to perform one-hot encoding on the <code>'features_raw'</code> data.</li>
<li>Convert the target label <code>'income_raw'</code> to numerical entries.<ul>
<li>Set records with "&lt;=50K" to <code>0</code> and records with "&gt;50K" to <code>1</code>.</li>
</ul>
</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: One-hot encode the &#39;features_log_minmax_transform&#39; data using pandas.get_dummies()</span>
<span class="n">features_final</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">features_log_minmax_transform</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
<span class="c1"># TODO: Encode the &#39;income_raw&#39; data to numerical values</span>
<span class="n">income</span><span class="o">=</span><span class="n">LabelEncoder</span><span class="p">()</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">income_raw</span><span class="p">)</span>

<span class="k">print</span> <span class="n">income</span><span class="o">.</span><span class="n">shape</span>
<span class="k">print</span> <span class="n">features_final</span><span class="o">.</span><span class="n">shape</span>
<span class="c1"># Print the number of features after one-hot encoding</span>
<span class="n">encoded</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">features_final</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;{} total features after one-hot encoding.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">encoded</span><span class="p">))</span>

<span class="c1"># Uncomment the following line to see the encoded feature names</span>
<span class="k">print</span> <span class="n">encoded</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>(45222,)
(45222, 103)
103 total features after one-hot encoding.
[&#39;age&#39;, &#39;education-num&#39;, &#39;capital-gain&#39;, &#39;capital-loss&#39;, &#39;hours-per-week&#39;, &#39;workclass_ Federal-gov&#39;, &#39;workclass_ Local-gov&#39;, &#39;workclass_ Private&#39;, &#39;workclass_ Self-emp-inc&#39;, &#39;workclass_ Self-emp-not-inc&#39;, &#39;workclass_ State-gov&#39;, &#39;workclass_ Without-pay&#39;, &#39;education_level_ 10th&#39;, &#39;education_level_ 11th&#39;, &#39;education_level_ 12th&#39;, &#39;education_level_ 1st-4th&#39;, &#39;education_level_ 5th-6th&#39;, &#39;education_level_ 7th-8th&#39;, &#39;education_level_ 9th&#39;, &#39;education_level_ Assoc-acdm&#39;, &#39;education_level_ Assoc-voc&#39;, &#39;education_level_ Bachelors&#39;, &#39;education_level_ Doctorate&#39;, &#39;education_level_ HS-grad&#39;, &#39;education_level_ Masters&#39;, &#39;education_level_ Preschool&#39;, &#39;education_level_ Prof-school&#39;, &#39;education_level_ Some-college&#39;, &#39;marital-status_ Divorced&#39;, &#39;marital-status_ Married-AF-spouse&#39;, &#39;marital-status_ Married-civ-spouse&#39;, &#39;marital-status_ Married-spouse-absent&#39;, &#39;marital-status_ Never-married&#39;, &#39;marital-status_ Separated&#39;, &#39;marital-status_ Widowed&#39;, &#39;occupation_ Adm-clerical&#39;, &#39;occupation_ Armed-Forces&#39;, &#39;occupation_ Craft-repair&#39;, &#39;occupation_ Exec-managerial&#39;, &#39;occupation_ Farming-fishing&#39;, &#39;occupation_ Handlers-cleaners&#39;, &#39;occupation_ Machine-op-inspct&#39;, &#39;occupation_ Other-service&#39;, &#39;occupation_ Priv-house-serv&#39;, &#39;occupation_ Prof-specialty&#39;, &#39;occupation_ Protective-serv&#39;, &#39;occupation_ Sales&#39;, &#39;occupation_ Tech-support&#39;, &#39;occupation_ Transport-moving&#39;, &#39;relationship_ Husband&#39;, &#39;relationship_ Not-in-family&#39;, &#39;relationship_ Other-relative&#39;, &#39;relationship_ Own-child&#39;, &#39;relationship_ Unmarried&#39;, &#39;relationship_ Wife&#39;, &#39;race_ Amer-Indian-Eskimo&#39;, &#39;race_ Asian-Pac-Islander&#39;, &#39;race_ Black&#39;, &#39;race_ Other&#39;, &#39;race_ White&#39;, &#39;sex_ Female&#39;, &#39;sex_ Male&#39;, &#39;native-country_ Cambodia&#39;, &#39;native-country_ Canada&#39;, &#39;native-country_ China&#39;, &#39;native-country_ Columbia&#39;, &#39;native-country_ Cuba&#39;, &#39;native-country_ Dominican-Republic&#39;, &#39;native-country_ Ecuador&#39;, &#39;native-country_ El-Salvador&#39;, &#39;native-country_ England&#39;, &#39;native-country_ France&#39;, &#39;native-country_ Germany&#39;, &#39;native-country_ Greece&#39;, &#39;native-country_ Guatemala&#39;, &#39;native-country_ Haiti&#39;, &#39;native-country_ Holand-Netherlands&#39;, &#39;native-country_ Honduras&#39;, &#39;native-country_ Hong&#39;, &#39;native-country_ Hungary&#39;, &#39;native-country_ India&#39;, &#39;native-country_ Iran&#39;, &#39;native-country_ Ireland&#39;, &#39;native-country_ Italy&#39;, &#39;native-country_ Jamaica&#39;, &#39;native-country_ Japan&#39;, &#39;native-country_ Laos&#39;, &#39;native-country_ Mexico&#39;, &#39;native-country_ Nicaragua&#39;, &#39;native-country_ Outlying-US(Guam-USVI-etc)&#39;, &#39;native-country_ Peru&#39;, &#39;native-country_ Philippines&#39;, &#39;native-country_ Poland&#39;, &#39;native-country_ Portugal&#39;, &#39;native-country_ Puerto-Rico&#39;, &#39;native-country_ Scotland&#39;, &#39;native-country_ South&#39;, &#39;native-country_ Taiwan&#39;, &#39;native-country_ Thailand&#39;, &#39;native-country_ Trinadad&amp;Tobago&#39;, &#39;native-country_ United-States&#39;, &#39;native-country_ Vietnam&#39;, &#39;native-country_ Yugoslavia&#39;]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Shuffle-and-Split-Data">Shuffle and Split Data<a class="anchor-link" href="#Shuffle-and-Split-Data">&#182;</a></h3><p>Now all <em>categorical variables</em> have been converted into numerical features, and all numerical features have been normalized. As always, we will now split the data (both features and their labels) into training and test sets. 80% of the data will be used for training and 20% for testing.</p>
<p>Run the code cell below to perform this split.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">train_test_split</span>

<span class="c1"># Split the &#39;features&#39; and &#39;income&#39; data into training and testing sets</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">features_final</span><span class="p">,</span> 
                                                    <span class="n">income</span><span class="p">,</span> 
                                                    <span class="n">test_size</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> 
                                                    <span class="n">random_state</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>

<span class="c1"># Show the results of the split</span>
<span class="k">print</span> <span class="s2">&quot;Training set has {} samples.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="k">print</span> <span class="s2">&quot;Testing set has {} samples.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Training set has 36177 samples.
Testing set has 9045 samples.
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>c:\python27\lib\site-packages\sklearn\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  &#34;This module will be removed in 0.20.&#34;, DeprecationWarning)
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<hr>
<h2 id="Evaluating-Model-Performance">Evaluating Model Performance<a class="anchor-link" href="#Evaluating-Model-Performance">&#182;</a></h2><p>In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Three of these algorithms will be supervised learners of your choice, and the fourth algorithm is known as a <em>naive predictor</em>.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Metrics-and-the-Naive-Predictor">Metrics and the Naive Predictor<a class="anchor-link" href="#Metrics-and-the-Naive-Predictor">&#182;</a></h3><p><em>CharityML</em>, equipped with their research, knows individuals that make more than \$50,000 are most likely to donate to their charity. Because of this, *CharityML* is particularly interested in predicting who makes more than \$50,000 accurately. It would seem that using <strong>accuracy</strong> as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that <em>does not</em> make more than \$50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than \$50,000 is <em>more important</em> than the model's ability to <strong>recall</strong> those individuals. We can use <strong>F-beta score</strong> as a metric that considers both precision and recall:</p>
<p>$$ F_{\beta} = (1 + \beta^2) \cdot \frac{precision \cdot recall}{\left( \beta^2 \cdot precision \right) + recall} $$</p>
<p>In particular, when $\beta = 0.5$, more emphasis is placed on precision. This is called the <strong>F$_{0.5}$ score</strong> (or F-score for simplicity).</p>
<p>Looking at the distribution of classes (those who make at most \$50,000, and those who make more), it's clear most individuals do not make more than \$50,000. This can greatly affect <strong>accuracy</strong>, since we could simply say <em>"this person does not make more than \$50,000"</em> and generally be right, without ever looking at the data! Making such a statement would be called <strong>naive</strong>, since we have not considered any information to substantiate the claim. It is always important to consider the <em>naive prediction</em> for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than \$50,000, <em>CharityML</em> would identify no one as donors.</p>
<h4 id="Note:-Recap-of-accuracy,-precision,-recall">Note: Recap of accuracy, precision, recall<a class="anchor-link" href="#Note:-Recap-of-accuracy,-precision,-recall">&#182;</a></h4><p><strong> Accuracy </strong> measures how often the classifier makes the correct prediction. It’s the ratio of the number of correct predictions to the total number of predictions (the number of test data points).</p>
<p><strong> Precision </strong> tells us what proportion of messages we classified as spam, actually were spam.
It is a ratio of true positives(words classified as spam, and which are actually spam) to all positives(all words classified as spam, irrespective of whether that was the correct classificatio), in other words it is the ratio of</p>
<p><code>[True Positives/(True Positives + False Positives)]</code></p>
<p><strong> Recall(sensitivity)</strong> tells us what proportion of messages that actually were spam were classified by us as spam.
It is a ratio of true positives(words classified as spam, and which are actually spam) to all the words that were actually spam, in other words it is the ratio of</p>
<p><code>[True Positives/(True Positives + False Negatives)]</code></p>
<p>For classification problems that are skewed in their classification distributions like in our case, for example if we had a 100 text messages and only 2 were spam and the rest 98 weren't, accuracy by itself is not a very good metric. We could classify 90 messages as not spam(including the 2 that were spam but we classify them as not spam, hence they would be false negatives) and 10 as spam(all 10 false positives) and still get a reasonably good accuracy score. For such cases, precision and recall come in very handy. These two metrics can be combined to get the F1 score, which is weighted average(harmonic mean) of the precision and recall scores. This score can range from 0 to 1, with 1 being the best possible F1 score(we take the harmonic mean as we are dealing with ratios).</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-1---Naive-Predictor-Performace">Question 1 - Naive Predictor Performace<a class="anchor-link" href="#Question-1---Naive-Predictor-Performace">&#182;</a></h3><ul>
<li>If we chose a model that always predicted an individual made more than $50,000, what would  that model's accuracy and F-score be on this dataset? You must use the code cell below and assign your results to <code>'accuracy'</code> and <code>'fscore'</code> to be used later.</li>
</ul>
<p><strong> Please note </strong> that the the purpose of generating a naive predictor is simply to show what a base model without any intelligence would look like. In the real world, ideally your base model would be either the results of a previous model or could be based on a research paper upon which you are looking to improve. When there is no benchmark model set, getting a result better than random choice is a place you could start from.</p>
<p><strong> HINT: </strong></p>
<ul>
<li>When we have a model that always predicts '1' (i.e. the individual makes more than 50k) then our model will have no True Negatives(TN) or False Negatives(FN) as we are not making any negative('0' value) predictions. Therefore our Accuracy in this case becomes the same as our Precision(True Positives/(True Positives + False Positives)) as every prediction that we have made with value '1' that should have '0' becomes a False Positive; therefore our denominator in this case is the total number of records we have in total. </li>
<li>Our Recall score(True Positives/(True Positives + False Negatives)) in this setting becomes 1 as we have no False Negatives.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[14]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="sd">&#39;&#39;&#39;</span>
<span class="sd">TP = np.sum(income) # Counting the ones as this is the naive case. Note that &#39;income&#39; is the &#39;income_raw&#39; data </span>
<span class="sd">encoded to numerical values done in the data preprocessing step.</span>
<span class="sd">FP = income.count() - TP # Specific to the naive case</span>

<span class="sd">TN = 0 # No predicted negatives in the naive case</span>
<span class="sd">FN = 0 # No predicted negatives in the naive case</span>
<span class="sd">&#39;&#39;&#39;</span>
<span class="c1"># TODO: Calculate accuracy, precision and recall</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">n_greater_50k</span><span class="p">)</span><span class="o">/</span><span class="n">n_records</span>  <span class="c1">#Accuracy=no of records above 50K/total no of records</span>
<span class="n">recall</span> <span class="o">=</span> <span class="mi">1</span>
<span class="n">precision</span> <span class="o">=</span> <span class="n">accuracy</span>                       <span class="c1">#Recall=Accuracy</span>

<span class="c1"># TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.</span>
<span class="c1"># HINT: The formula above can be written as (1 + beta**2) * (precision * recall) / ((beta**2 * precision) + recall)</span>
<span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span>
<span class="n">fscore</span> <span class="o">=</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">beta</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">precision</span> <span class="o">*</span> <span class="n">recall</span><span class="p">)</span> <span class="o">/</span> <span class="p">((</span><span class="n">beta</span><span class="o">**</span><span class="mi">2</span> <span class="o">*</span> <span class="n">precision</span><span class="p">)</span> <span class="o">+</span> <span class="n">recall</span><span class="p">)</span>

<span class="c1"># Print the results </span>
<span class="k">print</span> <span class="s2">&quot;Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy</span><span class="p">,</span> <span class="n">fscore</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Supervised-Learning-Models">Supervised Learning Models<a class="anchor-link" href="#Supervised-Learning-Models">&#182;</a></h3><p><strong>The following are some of the supervised learning models that are currently available in</strong> <a href="http://scikit-learn.org/stable/supervised_learning.html"><code>scikit-learn</code></a> <strong>that you may choose from:</strong></p>
<ul>
<li>Gaussian Naive Bayes (GaussianNB)</li>
<li>Decision Trees</li>
<li>Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)</li>
<li>K-Nearest Neighbors (KNeighbors)</li>
<li>Stochastic Gradient Descent Classifier (SGDC)</li>
<li>Support Vector Machines (SVM)</li>
<li>Logistic Regression</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-2---Model-Application">Question 2 - Model Application<a class="anchor-link" href="#Question-2---Model-Application">&#182;</a></h3><p>List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen</p>
<ul>
<li>Describe one real-world application in industry where the model can be applied. </li>
<li>What are the strengths of the model; when does it perform well?</li>
<li>What are the weaknesses of the model; when does it perform poorly?</li>
<li>What makes this model a good candidate for the problem, given what you know about the data?</li>
</ul>
<p><strong> HINT: </strong></p>
<p>Structure your answer in the same format as above^, with 4 parts for each of the three models you pick. Please include references with your answer.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong> GaussianNB,Support Vector Machines, Random Forest Classifier</p>
<p><strong>Gaussian Naive Bayes</strong></p>
<ul>
<li>It used in spam detection.</li>
<li>It needs enough data to understand the probabilistic relationship of each attribute in isolation with the output variable. It is less likely to overfit the training data with a smaller sample size.</li>
<li>The performance of Naive Bayes can degrade if the data contains highly correlated features.</li>
<li>This is a good candidate for this problem because it needs to classify the people into people who are more probable to donate and who are not.
(Reference:<a href="https://machinelearningmastery.com/better-naive-bayes/">https://machinelearningmastery.com/better-naive-bayes/</a>)</li>
</ul>
<p><strong>Support Vector Machines</strong></p>
<ul>
<li>This algorithm is used to recognize handwritten text and classify images.</li>
<li>Advantages:<ol>
<li>An SVM is defined by a convex optimisation problem (no local minima) for which there are efficient methods</li>
<li>Since the kernel implicitly contains a non-linear transformation, no assumptions about the functional
form of the transformation, which makes data linearly separable, is necessary. The transformation occurs
implicitly on a robust theoretical basis and human expertise judgement beforehand is not needed.</li>
<li>SVMs provide a good out-of-sample generalization, if the parameters C and r are appropriately chosen. 
This means that, by choosing an appropriate generalization grade,SVMs can be robust, even when the training 
sample has some bias.</li>
</ol>
</li>
<li>Disadvantages:The theory only really covers the determination of the parameters for a given value of the regularisation and kernel parameters and choice of kernel. In a way the SVM moves the problem of over-fitting from optimising the parameters to model selection.
(References:Support Vector Machines (SVM) as a Technique for Solvency Analysis by Laura Auria • Rouslan A. Moro)</li>
<li>It is extensively used for classification and can be applied to this problem. </li>
</ul>
<p><strong>Random Forest Classifier</strong></p>
<ul>
<li>This algorithm is used for financial markets and data mining. </li>
<li>Advantages:<ol>
<li>The same random forest algorithm or the random forest classifier can use for both classification and 
the regression task.</li>
<li>Random forest classifier will handle the missing values.</li>
<li>When we have more trees in the forest, random forest classifier won’t overfit the model.</li>
</ol>
</li>
<li>Disadvantages:<ol>
<li>They certainly don’t do well with time as they degrade over time.</li>
<li>Compared to a single decision tree, or to a set of rules, they don't give you a lot of insight.</li>
</ol>
</li>
<li>This is one of the best method for this method as it is very fast.
(References:<a href="http://dataaspirant.com/2017/05/22/random-forest-algorithm-machine-learing/">http://dataaspirant.com/2017/05/22/random-forest-algorithm-machine-learing/</a>)</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation---Creating-a-Training-and-Predicting-Pipeline">Implementation - Creating a Training and Predicting Pipeline<a class="anchor-link" href="#Implementation---Creating-a-Training-and-Predicting-Pipeline">&#182;</a></h3><p>To properly evaluate the performance of each model you've chosen, it's important that you create a training and predicting pipeline that allows you to quickly and effectively train models using various sizes of training data and perform predictions on the testing data. Your implementation here will be used in the following section.
In the code block below, you will need to implement the following:</p>
<ul>
<li>Import <code>fbeta_score</code> and <code>accuracy_score</code> from <a href="http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics"><code>sklearn.metrics</code></a>.</li>
<li>Fit the learner to the sampled training data and record the training time.</li>
<li>Perform predictions on the test data <code>X_test</code>, and also on the first 300 training points <code>X_train[:300]</code>.<ul>
<li>Record the total prediction time.</li>
</ul>
</li>
<li>Calculate the accuracy score for both the training subset and testing set.</li>
<li>Calculate the F-score for both the training subset and testing set.<ul>
<li>Make sure that you set the <code>beta</code> parameter!</li>
</ul>
</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Import two metrics from sklearn - fbeta_score and accuracy_score</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">fbeta_score</span><span class="p">,</span><span class="n">accuracy_score</span>
<span class="k">def</span> <span class="nf">train_predict</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">):</span> 
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    inputs:</span>
<span class="sd">       - learner: the learning algorithm to be trained and predicted on</span>
<span class="sd">       - sample_size: the size of samples (number) to be drawn from training set</span>
<span class="sd">       - X_train: features training set</span>
<span class="sd">       - y_train: income training set</span>
<span class="sd">       - X_test: features testing set</span>
<span class="sd">       - y_test: income testing set</span>
<span class="sd">    &#39;&#39;&#39;</span>
    
    <span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
    
    <span class="c1"># TODO: Fit the learner to the training data using slicing with &#39;sample_size&#39; using .fit(training_features[:], training_labels[:])</span>
    <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="c1"># Get start time</span>
    <span class="n">learner</span> <span class="o">=</span><span class="n">learner</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:</span><span class="n">sample_size</span><span class="p">],</span> <span class="n">y_train</span><span class="p">[:</span><span class="n">sample_size</span><span class="p">])</span>
    <span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="c1"># Get end time</span>
    
    <span class="c1"># TODO: Calculate the training time</span>
    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;train_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">end</span><span class="o">-</span><span class="n">start</span>
        
    <span class="c1"># TODO: Get the predictions on the test set(X_test),</span>
    <span class="c1">#       then get predictions on the first 300 training samples(X_train) using .predict()</span>
    <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="c1"># Get start time</span>
    <span class="n">predictions_test</span> <span class="o">=</span> <span class="n">learner</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
    <span class="n">predictions_train</span> <span class="o">=</span> <span class="n">learner</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:</span><span class="mi">300</span><span class="p">])</span>
    <span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="c1"># Get end time</span>
    
    <span class="c1"># TODO: Calculate the total prediction time</span>
    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;pred_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">end</span><span class="o">-</span><span class="n">start</span>
            
    <span class="c1"># TODO: Compute accuracy on the first 300 training samples which is y_train[:300]</span>
    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;acc_train&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_train</span><span class="p">[:</span><span class="mi">300</span><span class="p">],</span><span class="n">predictions_train</span><span class="p">)</span>
        
    <span class="c1"># TODO: Compute accuracy on test set using accuracy_score()</span>
    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;acc_test&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">predictions_test</span><span class="p">)</span>
    
    <span class="c1"># TODO: Compute F-score on the the first 300 training samples using fbeta_score()</span>
    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;f_train&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_train</span><span class="p">[:</span><span class="mi">300</span><span class="p">],</span><span class="n">predictions_train</span><span class="p">,</span><span class="mf">0.5</span><span class="p">)</span>
        
    <span class="c1"># TODO: Compute F-score on the test set which is y_test</span>
    <span class="n">results</span><span class="p">[</span><span class="s1">&#39;f_test&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span><span class="n">predictions_test</span><span class="p">,</span><span class="mf">0.5</span><span class="p">)</span>
       
    <span class="c1"># Success</span>
    <span class="k">print</span> <span class="s2">&quot;{} trained on {} samples.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">learner</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span> <span class="n">sample_size</span><span class="p">)</span>
        
    <span class="c1"># Return the results</span>
    <span class="k">return</span> <span class="n">results</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-Initial-Model-Evaluation">Implementation: Initial Model Evaluation<a class="anchor-link" href="#Implementation:-Initial-Model-Evaluation">&#182;</a></h3><p>In the code cell, you will need to implement the following:</p>
<ul>
<li>Import the three supervised learning models you've discussed in the previous section.</li>
<li>Initialize the three models and store them in <code>'clf_A'</code>, <code>'clf_B'</code>, and <code>'clf_C'</code>.<ul>
<li>Use a <code>'random_state'</code> for each model you use, if provided.</li>
<li><strong>Note:</strong> Use the default settings for each model — you will tune one specific model in a later section.</li>
</ul>
</li>
<li>Calculate the number of records equal to 1%, 10%, and 100% of the training data.<ul>
<li>Store those values in <code>'samples_1'</code>, <code>'samples_10'</code>, and <code>'samples_100'</code> respectively.</li>
</ul>
</li>
</ul>
<p><strong>Note:</strong> Depending on which algorithms you chose, the following implementation may take some time to run!</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[19]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Import the three supervised learning models from sklearn</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">LinearSVC</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>

<span class="c1"># TODO: Initialize the three models</span>
<span class="n">clf_A</span> <span class="o">=</span> <span class="n">GaussianNB</span><span class="p">()</span>
<span class="n">clf_B</span> <span class="o">=</span> <span class="n">LinearSVC</span><span class="p">()</span>
<span class="n">clf_C</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">()</span>
<span class="c1"># TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data</span>
<span class="c1"># HINT: samples_100 is the entire training set i.e. len(y_train)</span>
<span class="c1"># HINT: samples_10 is 10% of samples_100</span>
<span class="c1"># HINT: samples_1 is 1% of samples_100</span>

<span class="n">samples_100</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">[:])</span>
<span class="n">samples_10</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">[:])</span><span class="o">*</span><span class="mf">0.1</span><span class="p">)</span>
<span class="n">samples_1</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y_train</span><span class="p">[:])</span><span class="o">*</span><span class="mf">0.01</span><span class="p">)</span>

<span class="c1"># Collect results on the learners</span>
<span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">clf</span> <span class="ow">in</span> <span class="p">[</span><span class="n">clf_A</span><span class="p">,</span> <span class="n">clf_B</span><span class="p">,</span> <span class="n">clf_C</span><span class="p">]:</span>
    <span class="n">clf_name</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
    <span class="n">results</span><span class="p">[</span><span class="n">clf_name</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">samples</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="n">samples_1</span><span class="p">,</span> <span class="n">samples_10</span><span class="p">,</span> <span class="n">samples_100</span><span class="p">]):</span>
        <span class="n">results</span><span class="p">[</span><span class="n">clf_name</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> \
        <span class="n">train_predict</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">samples</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span>

<span class="c1"># Run metrics visualization for the three supervised learning models chosen</span>
<span class="n">vs</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">accuracy</span><span class="p">,</span> <span class="n">fscore</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>GaussianNB trained on 361 samples.
GaussianNB trained on 3617 samples.
GaussianNB trained on 36177 samples.
LinearSVC trained on 361 samples.
LinearSVC trained on 3617 samples.
LinearSVC trained on 36177 samples.
RandomForestClassifier trained on 361 samples.
RandomForestClassifier trained on 3617 samples.
RandomForestClassifier trained on 36177 samples.
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAxAAAAIuCAYAAAAv/u6UAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<hr>
<h2 id="Improving-Results">Improving Results<a class="anchor-link" href="#Improving-Results">&#182;</a></h2><p>In this final section, you will choose from the three supervised learning models the <em>best</em> model to use on the student data. You will then perform a grid search optimization for the model over the entire training set (<code>X_train</code> and <code>y_train</code>) by tuning at least one parameter to improve upon the untuned model's F-score.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-3---Choosing-the-Best-Model">Question 3 - Choosing the Best Model<a class="anchor-link" href="#Question-3---Choosing-the-Best-Model">&#182;</a></h3><ul>
<li>Based on the evaluation you performed earlier, in one to two paragraphs, explain to <em>CharityML</em> which of the three models you believe to be most appropriate for the task of identifying individuals that make more than \$50,000. </li>
</ul>
<p><strong> HINT: </strong> 
Look at the graph at the bottom left from the cell above(the visualization created by <code>vs.evaluate(results, accuracy, fscore)</code>) and check the F score for the testing set when 100% of the training set is used. Which model has the highest score? Your answer should include discussion of the:</p>
<ul>
<li>metrics - F score on the testing when 100% of the training data is used, </li>
<li>prediction/training time</li>
<li>the algorithm's suitability for the data.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer: </strong></p>
<p><strong>Random Forest Classifier is the best model in this scenario.</strong></p>
<p>F score is highest for SVM but only higher than RandomForestClassifier by a tiny margin and accuracy is equal. According to metric scores only, the winner should be SVM but SVM takes a lot of time to train and predict than the RandomForestClassifier. This means SVM takes a lot of resources and is expensive than RFC(RandomForestClassifier) in this scenario. Also, Trees are used extensively in classification in medicine,finance and security industries. Thus, RFC is best suited for this data.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-4---Describing-the-Model-in-Layman's-Terms">Question 4 - Describing the Model in Layman's Terms<a class="anchor-link" href="#Question-4---Describing-the-Model-in-Layman's-Terms">&#182;</a></h3><ul>
<li>In one to two paragraphs, explain to <em>CharityML</em>, in layman's terms, how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical jargon, such as describing equations.</li>
</ul>
<p><strong> HINT: </strong></p>
<p>When explaining your model, if using external resources please include all citations.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer: </strong></p>
<ul>
<li><p>RandomTree Classifier is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class's output by individual trees. Random Forests are individual learners that are combined. The random forest starts with a standard machine learning technique called a “decision tree” which, in ensemble terms, corresponds to our weak learner. In a decision tree, an input is entered at the top and as it traverses down the tree the data gets bucketed into smaller and smaller sets. Ensembles are a divide-and-conquer approach used to improve performance. The main principle behind ensemble methods is that a group of “weak learners” can come together to form a “strong learner”. Each classifier, individually, is a “weak learner,” while all the classifiers taken together are a “strong learner”.They are one of the most popular learning methods commonly used for data exploration. t is one of the most accurate learning algorithms available. For many data sets, it produces a highly accurate classifier.It gives estimates of what variables are important in the classification.For more info visit <a href='http://blog.echen.me/2011/03/14/laymans-introduction-to-random-forests/'>here.</a><img src='http://cdn2.hubspot.net/hubfs/2575516/Imported_Blog_Media/RF.jpg'></p>
</li>
<li><p>For prediction a new sample is pushed down the tree. It is assigned the label of the training sample in the terminal node it ends up in. This procedure is iterated over all trees in the ensemble, and the average vote of all trees is reported as random forest prediction.
(Reference:Predrag Radenković 3237/10
Faculty of Electrical Engineering
University Of Belgrade. PPT on Random Forest)(Flowchart taken from <a href='http://blog.citizennet.com/blog/2012/11/10/random-forests-ensembles-and-performance-metrics'>here.</a>)</p>
</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-Model-Tuning">Implementation: Model Tuning<a class="anchor-link" href="#Implementation:-Model-Tuning">&#182;</a></h3><p>Fine tune the chosen model. Use grid search (<code>GridSearchCV</code>) with at least one important parameter tuned with at least 3 different values. You will need to use the entire training set for this. In the code cell below, you will need to implement the following:</p>
<ul>
<li>Import <a href="http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html"><code>sklearn.grid_search.GridSearchCV</code></a> and <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html"><code>sklearn.metrics.make_scorer</code></a>.</li>
<li>Initialize the classifier you've chosen and store it in <code>clf</code>.<ul>
<li>Set a <code>random_state</code> if one is available to the same state you set before.</li>
</ul>
</li>
<li>Create a dictionary of parameters you wish to tune for the chosen model.<ul>
<li>Example: <code>parameters = {'parameter' : [list of values]}</code>.</li>
<li><strong>Note:</strong> Avoid tuning the <code>max_features</code> parameter of your learner if that parameter is available!</li>
</ul>
</li>
<li>Use <code>make_scorer</code> to create an <code>fbeta_score</code> scoring object (with $\beta = 0.5$).</li>
<li>Perform grid search on the classifier <code>clf</code> using the <code>'scorer'</code>, and store it in <code>grid_obj</code>.</li>
<li>Fit the grid search object to the training data (<code>X_train</code>, <code>y_train</code>), and store it in <code>grid_fit</code>.</li>
</ul>
<p><strong>Note:</strong> Depending on the algorithm chosen and the parameter list, the following implementation may take some time to run!</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[30]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Import &#39;GridSearchCV&#39;, &#39;make_scorer&#39;, and any other necessary libraries</span>
<span class="kn">from</span> <span class="nn">sklearn.grid_search</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">make_scorer</span><span class="p">,</span><span class="n">fbeta_score</span><span class="p">,</span><span class="n">accuracy_score</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="c1"># TODO: Initialize the classifier</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>

<span class="c1"># TODO: Create the parameters list you wish to tune, using a dictionary if needed.</span>
<span class="c1"># HINT: parameters = {&#39;parameter_1&#39;: [value1, value2], &#39;parameter_2&#39;: [value1, value2]}</span>
<span class="n">parameters</span> <span class="o">=</span><span class="p">{</span><span class="s1">&#39;max_features&#39;</span><span class="p">:[</span><span class="s1">&#39;auto&#39;</span><span class="p">,</span><span class="bp">None</span><span class="p">,</span><span class="s1">&#39;log2&#39;</span><span class="p">],</span><span class="s1">&#39;min_samples_split&#39;</span><span class="p">:[</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">16</span><span class="p">],</span><span class="s1">&#39;min_samples_leaf&#39;</span><span class="p">:[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">]}</span>

<span class="c1"># TODO: Make an fbeta_score scoring object using make_scorer()</span>
<span class="n">scorer</span> <span class="o">=</span> <span class="n">make_scorer</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">,</span><span class="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>

<span class="c1"># TODO: Perform grid search on the classifier using &#39;scorer&#39; as the scoring method using GridSearchCV()</span>
<span class="n">grid_obj</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span><span class="n">parameters</span><span class="p">,</span><span class="n">scoring</span><span class="o">=</span><span class="n">scorer</span><span class="p">)</span>

<span class="c1"># TODO: Fit the grid search object to the training data and find the optimal parameters using fit()</span>
<span class="n">grid_fit</span> <span class="o">=</span> <span class="n">grid_obj</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

<span class="c1"># Get the estimator</span>
<span class="n">best_clf</span> <span class="o">=</span> <span class="n">grid_fit</span><span class="o">.</span><span class="n">best_estimator_</span>

<span class="c1"># Make predictions using the unoptimized and model</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">best_predictions</span> <span class="o">=</span> <span class="n">best_clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>

<span class="c1"># Report the before-and-afterscores</span>
<span class="k">print</span> <span class="s2">&quot;Unoptimized model</span><span class="se">\n</span><span class="s2">------&quot;</span>
<span class="k">print</span> <span class="s2">&quot;Accuracy score on testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">))</span>
<span class="k">print</span> <span class="s2">&quot;F-score on testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">))</span>
<span class="k">print</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Optimized Model</span><span class="se">\n</span><span class="s2">------&quot;</span>
<span class="k">print</span> <span class="s2">&quot;Final accuracy score on the testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">))</span>
<span class="k">print</span> <span class="s2">&quot;Final F-score on the testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">))</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Unoptimized model
------
Accuracy score on testing data: 0.8384
F-score on testing data: 0.6730

Optimized Model
------
Final accuracy score on the testing data: 0.8585
Final F-score on the testing data: 0.7250
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-5---Final-Model-Evaluation">Question 5 - Final Model Evaluation<a class="anchor-link" href="#Question-5---Final-Model-Evaluation">&#182;</a></h3><ul>
<li>What is your optimized model's accuracy and F-score on the testing data? </li>
<li>Are these scores better or worse than the unoptimized model? </li>
<li>How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in <strong>Question 1</strong>?_  </li>
</ul>
<p><strong>Note:</strong> Fill in the table below with your results, and then provide discussion in the <strong>Answer</strong> box.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Results:">Results:<a class="anchor-link" href="#Results:">&#182;</a></h4><table>
<thead><tr>
<th style="text-align:center">Metric</th>
<th style="text-align:center">Unoptimized Model</th>
<th style="text-align:center">Optimized Model</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">Accuracy Score</td>
<td style="text-align:center">0.8384</td>
<td style="text-align:center">0.8585</td>
</tr>
<tr>
<td style="text-align:center">F-score</td>
<td style="text-align:center">0.6730</td>
<td style="text-align:center">0.7250</td>
</tr>
</tbody>
</table>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer: </strong></p>
<ul>
<li>The accuracy and F score of testing data is 0.5858 and 0.7250 respectively.</li>
<li>These scores are better than that of an unoptimised model. F score shows greater improvement.</li>
<li>These results are much better than that of a model that always returns a person with income greater than 50K.
  (Accuracy shows growth of 61% and f-score shows growth of 43.34%)</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<hr>
<h2 id="Feature-Importance">Feature Importance<a class="anchor-link" href="#Feature-Importance">&#182;</a></h2><p>An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than \$50,000.</p>
<p>Choose a scikit-learn classifier (e.g., adaboost, random forests) that has a <code>feature_importance_</code> attribute, which is a function that ranks the importance of features according to the chosen classifier.  In the next python cell fit this classifier to training set and use this attribute to determine the top 5 most important features for the census dataset.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-6---Feature-Relevance-Observation">Question 6 - Feature Relevance Observation<a class="anchor-link" href="#Question-6---Feature-Relevance-Observation">&#182;</a></h3><p>When <strong>Exploring the Data</strong>, it was shown there are thirteen available features for each individual on record in the census data. Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong></p>
<ol>
<li>age: People who are either very young or too old tend to earn less income and are thus less likely to donate.</li>
<li>capital-gain: People having capital gain are more prone to donate due to surplus capital. </li>
<li>hours-per-week: People having more time to themselves are free to contibute to the society and are prone to donate.</li>
<li>relationship: People not living in a family have lower expensives than those living in families and expenses icrease with increase in family size.</li>
<li>occupation: People having different occupations have different levels of interaction with the world and thus have different types of opinions of donating to a charity.</li>
</ol>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation---Extracting-Feature-Importance">Implementation - Extracting Feature Importance<a class="anchor-link" href="#Implementation---Extracting-Feature-Importance">&#182;</a></h3><p>Choose a <code>scikit-learn</code> supervised learning algorithm that has a <code>feature_importance_</code> attribute availble for it. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm.</p>
<p>In the code cell below, you will need to implement the following:</p>
<ul>
<li>Import a supervised learning model from sklearn if it is different from the three used earlier.</li>
<li>Train the supervised model on the entire training set.</li>
<li>Extract the feature importances using <code>'.feature_importances_'</code>.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[35]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Import a supervised learning model that has &#39;feature_importances_&#39;</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>

<span class="c1"># TODO: Train the supervised model on the training set using .fit(X_train, y_train)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">RandomForestClassifier</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
<span class="c1"># TODO: Extract the feature importances using .feature_importances_ </span>
<span class="n">importances</span> <span class="o">=</span>  <span class="n">model</span><span class="o">.</span><span class="n">feature_importances_</span>

<span class="c1"># Plot</span>
<span class="n">vs</span><span class="o">.</span><span class="n">feature_plot</span><span class="p">(</span><span class="n">importances</span><span class="p">,</span> <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAn4AAAFgCAYAAAAl5HQWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-7---Extracting-Feature-Importance">Question 7 - Extracting Feature Importance<a class="anchor-link" href="#Question-7---Extracting-Feature-Importance">&#182;</a></h3><p>Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \$50,000.</p>
<ul>
<li>How do these five features compare to the five features you discussed in <strong>Question 6</strong>?</li>
<li>If you were close to the same answer, how does this visualization confirm your thoughts? </li>
<li>If you were not close, why do you think these features are more relevant?</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong>
Four out of five of these features(in the same order) were correctly identified by me.<br>
These features confirm my thinking that predicting the income of a person depend upon them as they have more predictive power than features like occuption,sex,race,etc.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Feature-Selection">Feature Selection<a class="anchor-link" href="#Feature-Selection">&#182;</a></h3><p>How does a model perform if we only use a subset of all the available features in the data? With less features required to train, the expectation is that training and prediction time is much lower — at the cost of performance metrics. From the visualization above, we see that the top five most important features contribute more than half of the importance of <strong>all</strong> features present in the data. This hints that we can attempt to <em>reduce the feature space</em> and simplify the information required for the model to learn. The code cell below will use the same optimized model you found earlier, and train it on the same training set <em>with only the top five important features</em>.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[36]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import functionality for cloning a model</span>
<span class="kn">from</span> <span class="nn">sklearn.base</span> <span class="kn">import</span> <span class="n">clone</span>

<span class="c1"># Reduce the feature space</span>
<span class="n">X_train_reduced</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">X_train</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">importances</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])[:</span><span class="mi">5</span><span class="p">]]]</span>
<span class="n">X_test_reduced</span> <span class="o">=</span> <span class="n">X_test</span><span class="p">[</span><span class="n">X_test</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">importances</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">])[:</span><span class="mi">5</span><span class="p">]]]</span>

<span class="c1"># Train on the &quot;best&quot; model found from grid search earlier</span>
<span class="n">clf</span> <span class="o">=</span> <span class="p">(</span><span class="n">clone</span><span class="p">(</span><span class="n">best_clf</span><span class="p">))</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_reduced</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

<span class="c1"># Make new predictions</span>
<span class="n">reduced_predictions</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_reduced</span><span class="p">)</span>

<span class="c1"># Report scores from the final model using both versions of data</span>
<span class="k">print</span> <span class="s2">&quot;Final Model trained on full data</span><span class="se">\n</span><span class="s2">------&quot;</span>
<span class="k">print</span> <span class="s2">&quot;Accuracy on testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">))</span>
<span class="k">print</span> <span class="s2">&quot;F-score on testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">best_predictions</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">))</span>
<span class="k">print</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Final Model trained on reduced data</span><span class="se">\n</span><span class="s2">------&quot;</span>
<span class="k">print</span> <span class="s2">&quot;Accuracy on testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">reduced_predictions</span><span class="p">))</span>
<span class="k">print</span> <span class="s2">&quot;F-score on testing data: {:.4f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">fbeta_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">reduced_predictions</span><span class="p">,</span> <span class="n">beta</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">))</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Final Model trained on full data
------
Accuracy on testing data: 0.8585
F-score on testing data: 0.7250

Final Model trained on reduced data
------
Accuracy on testing data: 0.8153
F-score on testing data: 0.6193
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-8---Effects-of-Feature-Selection">Question 8 - Effects of Feature Selection<a class="anchor-link" href="#Question-8---Effects-of-Feature-Selection">&#182;</a></h3><ul>
<li>How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?</li>
<li>If training time was a factor, would you consider using the reduced data as your training set?</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong></p>
<ul>
<li>The F-score and accuracy score of final model is a lot worse than the model with all features used.</li>
<li>If training time is a factor, I would still consider the model with all features because the differnce in training time(very small) is not worth sacrificing the accuracy.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to<br>
<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
</blockquote>

</div>
</div>
</div>
    </div>
  </div>
</body>

 
</html>