finding_donors.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;[2]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><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="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">time</span> <span class="k">import</span> <span class="n">time</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">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="k">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">1</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>
  </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;[25]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#print(data.shape)</span>
<span class="c1">#print(data[&#39;income&#39;].unique())</span>
<span class="c1">#num_of_records = data.groupby([&#39;income&#39;]).size()</span>
<span class="n">n_at_most_50k</span><span class="p">,</span> <span class="n">n_greater_50k</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">income</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[26]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#num_of_records[&quot;&lt;=50K&quot;]</span>
</pre></div>

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

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[27]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">n_at_most_50k</span><span class="p">,</span> <span class="n">n_greater_50k</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>34014 11208
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[28]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><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_greater_50k</span> <span class="c1">#num_of_records[&#39;&gt;50K&#39;]</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="n">n_at_most_50k</span> <span class="c1">#num_of_records[&#39;&lt;=50K&#39;]</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="p">(</span><span class="n">n_greater_50k</span><span class="o">/</span><span class="n">n_records</span><span class="p">)</span><span class="o">*</span> <span class="mi">100</span>

<span class="c1"># Print the results</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Total number of records: </span><span class="si">{}</span><span class="s2">&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="nb">print</span><span class="p">(</span><span class="s2">&quot;Individuals making more than $50,000: </span><span class="si">{}</span><span class="s2">&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="nb">print</span><span class="p">(</span><span class="s2">&quot;Individuals making at most $50,000: </span><span class="si">{}</span><span class="s2">&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="nb">print</span><span class="p">(</span><span class="s2">&quot;Percentage of individuals making more than $50,000: </span><span class="si">{}</span><span class="s2">%&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: 24.78439697492371%
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[29]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">((</span><span class="n">n_at_most_50k</span><span class="o">/</span><span class="n">n_records</span><span class="p">)</span><span class="o">*</span><span class="mi">100</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>75.2156030251
</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;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><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
AAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo
dHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3XmYLFV9//H3h1VERVRABBQlxiXG
BRAxGgQXRFyIW4IRubgbNdGoP8UVxF0jCjFuUQSXuKEiIoqIgiuyiYALiwJ6ZRUUWQQEzu+Pc5rb
t+mZqbl3eqZn+v16nn6m69TpqlNVPXX6W+fUqZRSkCRJkqQu1ljoAkiSJElaPAwgJEmSJHVmACFJ
kiSpMwMISZIkSZ0ZQEiSJEnqzABCkiRJUmcGEFpQSf4pyfeSXJLkL0nOT3JYkl368uyVpCT5m4Us
66rqK/+WM+Q7uOUrSW5KckWSXyT5eJKHrupyh3zmObMs/8FJzuub3rKt93mzWc6qlGtVtnGcJFkj
yfuTXNiO6WEz5F8/yWuTnJLkyiTXJjkzyQdG+f1Psm+SRw5JX+nYL3VJ7tr29dlt31+V5MQkr0+y
wUKXb1T6zjslyV+TXJrk+0nemGTj1Vju0O/VapZ134Hy9r9G8j+yKudNaalba6ELoMmV5D+AA4CD
gPcAVwNbAY8HHgl8c+FKt2AuBZ7U3q8P3AvYA/hRkneWUl7bl/frwEOBC2ex/L2o//cHzeIzb6Ee
p1Hai+HlWpVtHCdPA14GvBL4MXDZVBmTbAp8G7gL8AHgB8D1wH2B5wAPAx40onLuA7wN+M5A+nwc
+7GQZAfgcOAS4EDgDGBtYHvgJcCdgP9csAKO3sHAR6gXFu9I3e5/B/4jyW6llB+twjKn+l7NhYcD
Nw6k/W4E64FVO29KS5oBhBbSq4DDSinP7Uv7DvC/SSa1dez6UsrxfdPHJPkQ8D5g7yQnlVK+BFBK
uZQacIxEknVLKdeVUn49qnXMZNTbOA/u0/6+v5Ry0wx5PwVsCmxXSjm7L/27ST4I7DaKAk5nIY/9
fEqyIXAo8Evg0aWUq/tmfyvJe4F/WJDCzZ/fD5x7vpbkQOD7wJeTbDWwXxbaT0opNyx0IVZVkrWB
G4pP89UiNak/0jQe7gBcNGzGTD+2kmyT5OIkX05yq5a2Vuv+8ask1yW5IMl7e/NbnjOSfKxveoMk
NyZZPrD8Hyb5Qt/0jMtu+e6R5OtJrmndAA4A1p3NThmyLwrwauBi4OV967pF954k/5rkp63rxRVJ
Tk/ywjbvWOARwMP6mvyPHVjWDkm+mORPwE/avKm6sayTZP/U7mfXJDlisKtRW+a+A2m9LlB7zaJc
/du4dpK3JjkvyfXt71tbhTy4jhcm2S+1C9GfknwtyeYD5Zlyn00nyS5Jfpza9e6K1K539+qbfx7Q
2/Yb+7d5yLK2Ax4FvH0geADqd6CUclhf/jnbB0l6P2Be37f/923zpuq+1mW/znjs+9L3SPKz1G5D
f0jyqdQWmVkvL8mDkxyd5LL2vfxNagA2necDGwH/PuxHcinl6lLK0X3ruHWSdyU5t+3/c1O7Oa3R
l2fHVrYnpXaL+kPqOeHTSW4/sB0vS/LL9l36Y5KTkjy5b/55SQ4eLNfgPknyt0m+0v4nr03y29T/
51W6WFhKuRj4f8AmwO5969k5yZHt+F+Tel59ZZI1+8vW3g77Xj04yaFJlrdtPjPJ25OstyrlHCbJ
3ZN8pu3z65Kc2r9PW56/ad+1c1s5fpPkQ6kBZS/PsUx9ftq3bzv7lzvV/82Lk7w7yQXAdcDtZ1HW
OT220uryi6eFdAKwLMlvgK+WUs7q8qEkOwNfAj4DvKSU0mvG/jTwROBdwI+oV3/fAmwJPLXl+Q7w
hL7F7Ug9kW+W5G9LKWclWR94cFtez4zLTrIOcDSwHrXLwyXAC4GndNmu6ZRSrk9yDPC0JGsNu/KW
5OGtnAdSK/01gHvTKingxW3+mq1cAH8eWMxngM9Su97MdH54LXAq8GxgY+Dt1Ku1f1dK+essNq9L
ufodAvxzW98PqF2c3gDcA/jXIWX8EbUL0MbAe6nb+AjotM+GSr1H5+vU79O/ALcB9gN+kOSBpZTf
A08G/oPa/aF3D8tUV/Qf3f4ePt16+8zZPmif/TErurAALGd6My2zsyQvaOv9fFvuXdp2PSTJ1qWU
q2axrNsAR1HPLXsBV1L/R2dqPXg0cFEp5aQO61irreO+1HPA6dTuPm+kXhR55cBHDgCOoB6XewHv
pna9WdaW90zq/tuPerV/PeD+bVmzdQTwJ+DfgD8AmwG7snoXC78F3EDtQvfxlnYP4Bjgv4FrgW2p
wfJGwN4tz3Tfq7tSzx0HU4/R3wFvasu9OVCZwZpJ+qdv6l14SrIF9QLIJdRuZ5dS/0+/lOSfSim9
/7O7tDK9HPhjW//rgCNZ8T872/PTdF4PnAi8oC3v2lmUdRTHVlp1pRRfvhbkBfwtcBpQ2usP1B+v
Ow/k26vN/xvgmdR+4fsN5PnHlmfPgfRntvQHtuknt+m7ten3U3+0nQ28sKXt0vLce5bLfn6b3r4v
zxrAz1v6ljPsj4OB5dPMf0dbziYD+2XLNv0q4PIZ1nEs8IMh6b1lvW+Kcp3XN71ly/sLYI2+9Ie1
9Of2pRVg34Hl9T6/1yzK1dvG+02xzDe09PsPrOO4gXyvaul36brPptiPJ7XvzFp9aXcH/grs35f2
Vloj0gzL+1Ar17od8s7pPug7Tm+dxbHvusxpjz31R9TFwHcH8j285fuPWS5v2/59MIvj+Uvgxx3z
PqutY4eB9NdTz00bt+kdW75DBvJ9gPqjO33Tp8ywzvOAg4ek37xPqPdoFOBJq/B9Hnr8++ZfCHxj
inmhXmx4PfVH+Bpdlzvw+T2Am4A7zpB/X1bUGf2vT/fl+Tj1h/gdBz57NHDqNMteq++796C+9GMZ
fn7alyH/39P835zSO+6zKevqHFtfvkb1MnLVgim1xeFB1KuWb6NekXoycFSSNwz5yMupJ+aXlVLe
NDBvF2rl/aXU7kZrtSuF32rzd2h/j6NWUr2RQR5JvYr8nYG0C0spv5rlsh8K/K709SMu9YrYzV2h
VlPvcluZYv6JwIati8QTBrtJdPSVWeQ9tPR1NSul/JB6Ne8WI0bNod6+/vRAem968Ar41wemT29/
79r+znqftRaqrYHPl76WoFLKucAPh5Rhrs31PlgVc7XMe1FbMD7Tn1hK+QFwPrPfl2dTr9J+JLVb
1Baz/HwXu1DL9qMh54PeTdf9hu2rdandgqB+Bx+Y5L+TPDrJrVexXJcBvwHemeT5Se65issZJvSd
d5JsmuQjSc6nnhv/Sg2Wb089ntMvLLldahewX1NbgP9KvQcoQNdyb09tKe693tg3bxdqK8IVA8fo
KOABSW7XyrFOkteldk39SyvH99sy7sXcO6yUMnj+7lLWUR5baZUYQGhBlVJuLKV8r5TyhlLKo6lN
yKcD+/T3Q212B35P7b40aGNgHeAqaiXQe13S5t+xre9y4GfATknuRL2a+9322rHl3alNz2rZ1Btg
Lx5StmFpq2ILamV9+bCZpZTjgKe3fF8BLk3y7ST3n8U6ZjPa0VTbutksljFbvW4dg+W8aGB+z+C+
uq79vRWs8j7bkPpDZ9i+umhIGbrojR5ztw5553QfrKK5WuZU2wKrsC9LKVdQ/38vAD4I/Lb1z3/q
9J/kd9SrxF1sTD1Ofx14ndDm33Eg/0z76pPUbikPof5ovDz13q6u5QFuvlfqMdTWsXcAZ7U+/f82
m+UMavcl3Il2jFLv8zic2hX0rdQLLg+mXgSCbt+BTwAvonYdfEz7/Etm8XmAk0spJ/W9zu2btzGw
J7c8Ru9p83vH6B3UVoRPU0f/244VXU5X5/9jKsO+5zOWdVTHVlod3gOhsVJKuSD1JucDqFeiTuib
/VTgo8CxSR5ZSum/AfsyareAf5xi0Rf0vf8utY/pTu1zp1FP7Bsn6Q2V+ZG+/F2XfSG1L++gTYak
zUq7v+LRwPFlmpFHSimHAoe2vuA7Uu/Z+GaSzcvMowDB1K0bwwzbrk2oLUk911GDr36DP7Bmo/dj
7M6sfD/BndvfKYdJncoq7LM/UvfTnYfMu/OqlIE6fOvbqPfZvHeGvHO+D0aky7Hv35ZBd6b+YJrN
8iilnAo8tV3F3ZZ6X8UXkjyglHLGFGX9NvCYJNuUUk6eIk/PZcC51HtQhjlvhs8PlrdQzzcfaRdN
dqZ+Bz5PDSqgnn9W2vYktwiuSim/AfZMvTngAcBLgQ8mOa+U8o3ZlKvPY6ldzX7Qprei7tdnlVJu
bgVL8sQuC0sdeGI3aterA/rS/34VyzfMZdSWhHdNMb93zt4d+GQp5a195bjNLNZzbfvMOqWU6/vS
pzrHDTu/dirriI6ttMpsgdCCmaZ7wb3b38ERmn5P/YG3BnVoy/5RWr5JvWK0wcBVqd5rMIDYjHpD
3LGluoR6r8KbqZXld1Zh2T8GtkhycxeGdrVuqh8anbQK493UK1Xv6/KZUspVpZQjqD9MNmVFhXYd
9SbNufC0rDzqzMOAzan7oed8aitPv8cPWVbXch3X/g7eaPnM9vd7HZYx1DT7bDDf1cDJwNOz8qgz
d6PerHvcsM/NsO4TqDelvi5TPAwrSW8Y11Hsg+uZu+9FT5djfya11WqlbUnyD9Sr/P37sut3CYBS
yg2tO+EbqeeM+0yVF/gY9R6sD7QuaitJHXWpd6P7N6ktVldNcT74wzTrmVYp5Y+llM9Tuz32b+uw
bX8CU2jntFOBV7Skwc92kvoQuXdTL458riX3ulj9tS/f2qz4/vUb9r1al3qOHRxoYa9VKeMUvkm9
Ef3nUxyjXivQrYeU49lDljfV+en89vfm/du6Qc5myN+uZQXm7thKq8sWCC2kM5J8l9p15FzgdtRR
JV4EfKGU8tvBD5RSLkyyI/XH1rFJdiqlXFBKOTbJZ6lXkventlzcRO2WsCvwmrJilKfvUUdBeRQr
ms2hBhYvBX7brvb01tl12YdQRyD5cpLXUbs4vahtV1fr9AUgt2bFg+QeSr0ZcconGSfZj9oC8F3q
VavNqaMAnVrq8xSg3vj84iT/Qr16fWUp5cxZlK/fbYHDknyEOvrKO6h90D/Zl+dzwBuSvB44ntqK
84why+pUrlLKz9ux2LddYf4Rdd+8EfhsKeW02WxAx302zBupfduPSB0i9DbU4PMKZm5BmMqzqFfC
T0zy36x4kNy9qaMdrU0drWxO90HzC+DxSb5JbWG5YCDoXhUzHvtSyo1J3kS9+v5paleSzaitMWdT
u7p0Xl6SJ1BHuDmMek5Zn3o8r2TlwHYlpZTLWzenw4FT2v7vPUhuO+r/8aHU4/MZ6o/MY1KfD/Ez
auvAVtSHQP5TKeWarjspyUf7yncJdXCJZ7HiHqveth+U5H3U0XgewMAP7tbt7gBqy8U51B/pe1FH
UOryILfN2rlnDWrXse2pA0MEeGIp5S8t3y+pP5zfluRG6g/wqR6wN/R7leR44JVJLqQGbs9hbrs+
vol6nv5ekg9QW4U2pP7YvkcppfdU6W9SRwI8nbrPnsLwH/9TnZ++Qf2f/98k+1CDo1dTu7vOWVnn
4NhKc6+MwZ3cvibzRa2UD6dWRtdSn0T9U+oJeJ2+fHvRRmHqS9uYeq/EWcBmLW0N6lN/f9aWd0V7
/25q60H/un9C30hLLa03QtPBQ8raadnUeziOBK6hjqxxALWl4+aRhKbZHwezYkSRm6g/Kn5JHaVj
+yH59+pfLvVq7FHUq4XXUft1f5yVR8a5cyvfle2zx061jwfKdV7f9JYt74uB/dt2XkP9QX33gc/e
qu2DC9s6P0/9QXbzyDkdy7VlX961qX2vz6f+eDm/Ta89pIzPGyjPji19x677bJrjtQv1R99f2vfh
q8C9BvJ0GoWpL/9tqMNI/pT6/3Ad9Sr9AdQfE3O+D1raw6itKtey8sg+Ux37LsvsdOxb3j2o/0/X
Ubt0fArYdLbfJWrA/Xlq8HAt9bt5JPCQjvv/btRRkXo3915Fvcl5b+B2A2XZF/hVy3d5y7cvbWSu
vn3y6Bn+b5dRR/m5pC3rXGpLY//61qD+0Dyf+r92FDVg6T9WG1MvYpzV8lxObcF5bIft7h/N6K/U
H/U/oI7stdGQ/A9s86+hDpywH/A8bvm/OtX3akvqj+8r23Z/gPq/uNJ3aIqy7tvyrTVDvs2pLUu/
pwbiF1JHNtqjL8+dqMHZH9vrM9T7MTqdn9q8h7djf03b93vQ8f+ma1lX59j68jWqV28YOUmSJEma
kfdASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLUmQGEJEmSpM4MICRJkiR1ZgAhSZIkqTMDCEmS
JEmdGUBIkiRJ6swAQkMlOTjJEXOwnH2TnDEXZZphPVsmKUm2HfW6Jl2SvZJcNaJlH5vkA33T5yV5
1YjWNbLtkCbBfNYTc7Uujc4o6/vBuqDV908b0brm5XfLYmcAsQi0E+e+87zalwF79JVhpR92Y+h3
wKbAqV0/kGTHJOfNkOe8dqLqf/1pNcs6uI4F37dtX/S276Ykf05yWpIDktx9IPvngXt0XO5sA7un
AK+dTdk7lmNYZdN5O6RxZz0xd9rFhWNnyDNYL5QkneufjuUY2QWUWZRhr77tuzHJn5KclORtSTYe
yP5fwCM6LrdX59ypY1EeDHxwNmXvUIap6qfO2zHJ1lroAmg8lVKuWOgyzEYp5UbgohEtfj/gQ33T
N41oPastydqllL+uxiL+DrgcuA3wAODlwOlJHl9KOQ6glPIX4C+rXdg+SdYppVxfSrl8Lpc7nVFs
hzRJFls9MQLPB/pbRVbn3DsySdYA0urJVXENsBUQ4HbUH/OvAZ6f5BGllF8ClFKuAua0Vbevbrh0
Lpc7nVFsx1JkC8QilGSdJG9Pcn6S65L8Jsl/tHlrJvl4knOT/CXJ2Ule3U4gvc8fnOSIJG9IcnGS
q5J8Isl6g3l676nR+Ev6rkRs2WVdHbdn/SSfbOW4OMlrW/kO7suzR5ITk1yZ5JIkX0yyWd/8la4k
9F3deFSSnyS5pl012XoVdvmVpZSL+l6X9K13gyQfbWW6Mslx/VczktwxyWeTLG/76OdJnt03f6p9
e4urM9Ns465JTkhyPfDYNu+JSU5Ocm07Pm9Lsk6Hbb2kbeM5pZQvATsCPwUOSrJmW/ZKXX+SbJHk
q0kub/v5V0l2b7PPbX9PbGU9trfd7Ri/JslyYHlLH3YF8zZJPt2+Hxdl4IpchrQupO/KXVa0Mn2x
5T1v2Ha0tBcmOSfJ9e3v84es6wXt+3d1+9/bA2nMZInVE0O2b90k729luzbJ8Uke3jd/7SQHJrmg
bf/vkryzb/5TUltZ/9LOXccl2WSWxfjTQN1wWd/yN0vyuSR/bK+vJ7ln3/yt2nnzonYuOSXJE/rm
HwvcDXhPb3+29GHnrZXqi16eVjecAVwP3KfNe3aSX7R9dlaS/+xwLErbvgtLKWeWUj4NPBT4E/Dh
vnKs1PUnyd8nOSa1RfvKJD9LslOSLYHvtmyXtrIf3NvuJB9K8l9JLgV+2NKHtcbcue3Xa9r3vL81
bGjrQlauL6aqnwa3Y40kb2zfoeuSnJ5ktyHremqSo1t5fpHkMTPs10XNAGJxOgTYE3gF9aTwXOo/
MtRj+nvgn9u81wOvA549sIxHUK8wPwp4KrAz8K4p1vcy4MfAJ6jdhDaldhnquq6ZvLeV58nAI1u5
/nEgzzrAPm3eE4A7AZ/tsOx3AHsDWwOXAZ9JklmWb6i2nK8Dm7UyPQj4HvCdJJu2bLcCTmnz/w44
APhIkke1+VPt29l4F/AG4N7AT5I8FvgM8IG2zucATwPePtttbFes3kft6vOgKbJ9ELg1sFNb38tZ
8X3crv3dhbptT+n73COA+7d5j2JqrwB+ST2G+wBvT/KUafIPenD7+/xWhgcPy5TkydR99n7gftRj
9cEkTxzI+ibgq9Tv4uepwdXdZlEeaT4stXpi0LuBf6Ge3x4EnA58s+/c+x/UOmV34J4t75kASe4M
fI66j+4D7AB8ajXLc7Mkt6b+QL6Wug8fClwIfLvNg9rK+w3gMdR9/CXgy0nu3eY/hXphZT9W7M/Z
uBW1XnghcF/g/NQLIm+nnsPuA7yS2pLw4tluY7tK/2FghyQbTZHt/6jbvR31GO1L3Se/o36foNYZ
m1K/Pz17UFs7/pH6HZ7Km4HDgQcCHwU+ORgwzGC6+qnfy4D/R91Xfw98hXqsHjiQ723AgdTjeSLw
uSS3mUV5FpdSiq9F9KKeCAuwyyw+807g233TB1Mrktv0pe0BXAes35fniL75xwIfWIV17QucMU3+
21Cvjuzel7Y+8Efg4Gk+d++2HzZv01u26W3b9I5t+rF9n3lY/2c67rvz2n65qu/1ujbvkW16vYHP
nAq8epplfg742HT7tq/8d+pLm2obnzrw2e8BbxxI+6dW1kxRplusb8i+/uc2vRdwVd/804B9plju
SmUe+A5eCqw7kL7Svmj7/+iBPB8DftA3XYCnDTlur5ohz+B2/BA4aEg5B9f1jr7ptajN+3t0/U75
8jXqF0usnhhcF7WOuB7Ys2/+msCvgbe26QOBY4ad86gXIwpwt9XYx4XaBbK/bnhmm/cc4Oz+dbfy
XdY7j06xzOOBN/RNr3Qea2krnbda2o70nb9bngJsM5Dvt8CzBtJeDvximjLdYn1983Zp69lu2HEE
/gwsm+KzK5V54Dt02pD8K+2L9tn/HcjzbeDT7f2WDK97bq4LpskzuB2/B940pJyD63ph3/zNWtrD
V/U7Nu4v74FYfB5E7YP/3akyJHkR8Dxq8+d6wNrA+QPZTiv1CkLPj6lX+bei/iDspOO6enn/kXrF
peeFwBntMyf0EkspV2dgBITUrkf7UK803IF6dQLgrrTuL1Po35YL2t+NZ/jMoP2Bj/dN9/rpb0O9
8n7pQKPGraj7kdRuP3tTr35tBqxL3c/HzmL9MzlpYHobYLskr+lLW4N6fO5MvSI0G72NK1PMPwD4
cJJdqBX2V0opJ3dY7hmllOs65PvxkOnZtEB0dR/goIG0HwBPGki7+TtVSrmhNbMP3kwoLaQlVU+U
Uj4zkG2rtowf9hJKKTcm+TH1ajvUgONo4Kwk3wKOBL5RSrkJ+Bn1x+YZbd63gUPL7PvZ/z/gm33T
F7e/2wB3B64cqBtuzYq6YX1qnfYE6tXvtal1R+f9OoMb6BtUpLUSbEFtAe+/p28tVpzjZ2umumF/
4GNJllHrhi+VUn7VYbld6g8YXjc8vuNnO0lyO+Au9H3Xmh8Auw6kTfV7Y0kygFh8pv1HT/Iv1C4Y
rwJ+RL0C8BJqU+7cFmT26zqJGgD0XEw7mTL1Cah3oj2KepJ/FnAJtQvT96mV2XT6b2rrrWO2Xfcu
K6WcMyR9Deo2DHa3grovoO6bV1KbQE+nXqV6OzOfVHo3avcf77WnyHv1kHK9GfjikLyrciNar0L+
zbCZpZSPJzmKejJ9NPCjJO8opew7w3IHy72qCrf8v5hqX3VZ1kxpgzdKFuwOqvGy1OqJWyy2/Z3y
/7WUckrra78LtbX4EOBnSR7Tgo2dge2p3bKeC7wj9Ybgn3XfOi6apm44ldp9alDvAtR/tbK9itpa
cQ3wSWau026i2/nuurLyTdO9c9SLqMdhLtyXur/PGzazlLJvks8Aj6Pen7dPkheVUgYv1Ayai7rh
FnVoklWtF2CWdUMppbTgccnWDQYQi88p1C/kTqx85aPn4cBPSin9Y+lvNSTf3ydZv5TS+0fdntok
/Osp1ns9tQl2VdYF3DzqzUon2yTnUP/ptqPd0NT6iN6vryz3pgYMryul9PKM4gr0bJ0CbALcVEoZ
+uOauo++Vkr5FNx838TfsqIvMgzft70f+pv2vR/sbzldue49RcU2K60F5eXUYzHlEIWllOXUPqgf
bS0fL6M2A1/fsgxu32xsP2T6l33Tl9LXPzj1RsjB/sJ/7VCGX1KPV3/l9nDgF7MprDQGllQ9McQ5
bV0Pp13YaOeqh1L73feWdSX1QsoX2026xwN/A5xVaj+THwM/TrIf8HNqS/FsAoipnAI8A/hDKWWq
Yb8fDnyy1MEqSNJruT6rL89UdcOtk9yulNK7UDVj3VBKuTjJ74GtSimf7L4pw7W+/S8Cjpuu5aaU
cjY1QDqwtXw8j3qOnau64aCB6V7d0F+H9gzupxnLUEr5c5ILqMfrO32zJr5uMIBYZEopZyf5ArVZ
8GXUE9XmwJbtR+pZwF5JHkc9ye5OvYnrjwOLWot68+d+1Oa5d1L7E04V+Z9H7RazJfUq+uWzWNd0
23NVkoOAdyX5A7V7zRuolV8vuv8ttd/tS5P8D7WryVu6rmOEvk1t1vxqklcDv6J2EdqF2r/3+9R9
9C+po4P8Afh3atP2T/uWcx633LfnUG802zfJ3tQ+lm/oWK79gCOSnA98gdqUfT9qP9VXz/DZjZOs
Rb035f7Af1K7Q+xaphgCMMkB1C4HZ1GH+NuFFSfWS6j9hB+bOvrRtWX2Qz9un+S1wKHUfrN7As/s
m/8d6sgvPwJupLbwXDuwjPOARyU5jnplbth39D3UHxonA99q2/FMRtNdShqZpVZPDNm+q9uP0Xe2
euNc6rlqE9qzApK8glqfnEq9gPCv1NaP5Um2p7aWHkVt4XgQtXvPXP0g/Ay1ZeGrSd5ErcO2AHYD
Ptx+VJ8FPDnJV1v59qF2Yep3HvCPST5NPW/9AfgJ9Qr9O5K8j3rDbteboPcF/jv1WUZHUlsutgY2
K6W8Y5rPpd14DrABK4Zx3YBbdvHsfWA9aivLF9t2bEILJluW86l1/OOTfA34y0B3uS6ekuREapfg
p1Fv9n8I1EA0yfHAa5L8upV1cBu71k/vAfZLcja1e9Ue1J4H28yyvEvKkm1aWeL2pF5lOZD6o/Vg
6j8HwEeoPxr/jzoKwJbUUY4GHUe94vJd6ogC3wGm+3H5X9Ro/RfUyP6us1jXTF5F7Y50eCvPadRm
7GsB2tWNZdQbgX9BPdG+YhXWM6faFaxdqfvuf6kjfHwBuBcr+j++lXp/xzeoNzdfTa1c+t1i35b6
LIfdqaMf/YzaJel1Hct1FLVRkIAnAAAgAElEQVQf6E5t3SdQ78P4bYeP/5xa6f6UGoj8FLh/KeV7
03xmDeC/W/mPplbIy1pZbqCOhvI86j75apdtGLA/NZj5KXV/vqmUcmjf/FdSr0IeSw0yPkatGBjI
sxM1KPspQ5RSDqMGeP/ZtuVlwItLKV9bhTJLC22p1RODXtOW+wlqkHB/6k3jvXu8rqTeo3ACNYB6
IPC4Uso1wBXUQTWOoF4dfy/wllKHJ11tbR07UM9LX6Tu/0OADVkROL2Cep76PrV+OL697/cmauDx
a9oV9VKflfNM6uhNpwMvAN7YsVwfo97g/SxqvfL99vlzZ/joran1wgXU/fkK4GvA/Up7BsQQN1K3
9xBq3fgVaovPK1pZfk+ty99GrTNW5QGE+1JHczoN+Dfg2aWUE/vmP6f9PZH6PVzpItws6qcDqUHE
u6n3bT6ZOnjJnD44cLFJ/Q2kSdKacu9USnnCTHkXQpJ1qVcn3lNKmYuKRpI0C+NeT0haWHZh0oJL
8iBqt6QTgNtSryzdljrGviRJksbIgnVhSvKZJGcmOSPJQb2741MdmPoU2NPS9+TgJMtSn2J5dhsW
rJe+TeqTAc9pn52TB4VpXr2C2rXkO9S+kju0G3MlTRjrB0kabyPrwpRkwyluVOzN35UVYz3/H/C9
UsqHWvq/U/uWPwQ4oJTykCR3oPaL35Z6483J1Iek/DHJCdT+ysdTbww6sJTyDSRJY8f6QZIWt1G2
QJyU5P+SPHLYFZ9SypGloXZd2bzN2o06tFkppRwP3D710fSPpT6R9vJW8RwN7NLm3a6U8uO2rE9S
b7aVJI0n6wdJWsRGeQ/E31IfHvJS4H+SfAo4uJRyQX+m1jT9LOoVIqhP6/1dX5blLW269OVD0m8h
yQuoIw6w/vrrb3Pve9971ht18mWXzSr/Nne846zXIUmjdPLJJ/+hlLLRAhZhrOqHuagbwPpB0uLX
tX4YWQDRxow/gjoe/UbU8Xd/m+QfSikn9GX9ILV5ujd82bD+qcOeNDtT+rAyfZT6sCu23XbbctJJ
J3Xaln455JBZ5T9p2bKZM0nSPGrPCFkw41Y/zEXdANYPkha/rvXDSG+iTrJBu7JzOPWK03Op4/X2
5u8DbMTKY/ovp4573LM5dXze6dI3H5IuSRpT1g+StHiNLIBoT048hfogrD1LKTuUUg4ppVzb5j+P
2m/1GaWUm/o+ejiwZxttY3vgivZgmKOAnZNsmGRDYGfgqDbvyiTbt760e7JqD6uSJM0D6wdJWtxG
eQ/EF4C92pP+hvkw9WFhP2730H25lLIfdZSMXamPvL8GeDbUpy8meQv1iYIA+7UnMkJ9AuHBwHrU
kTscYUOSxpf1gyQtYqO8B+LwGeYPXXcbKeMlU8w7CDhoSPpJwP1WoZiSpHlm/SBJi9uCPUhOkiRJ
0uJjACFJkiSpMwMISZIkSZ0ZQEiSJEnqzABCkiRJUmcGEJIkSZI6M4CQJEmS1JkBhCRJkqTODCAk
SZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLUmQGEJEmSpM4M
ICRJkiR1ZgAhSZIkqTMDCEmSJEmdGUBIkiRJ6swAQpIkSVJnBhCSJEmSOjOAkCRJktSZAYQkSZKk
zgwgJEmSJHVmACFJkiSpMwMISZIkSZ0ZQEiSJEnqzABCkiRJUmcGEJIkSZI6M4CQJEmS1JkBhCRJ
kqTODCAkSZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLUmQGE
JEmSpM4MICRJkiR1ZgAhSZIkqTMDCEmSJEmdGUBIkiRJ6swAQpIkSVJnBhCSJEmSOjOAkCRJktSZ
AYQkSZKkzgwgJEmSJHVmACFJkiSpMwMISZIkSZ0ZQEiSJEnqzABCkiRJUmcGEJIkSZI6M4CQJEmS
1JkBhCRJkqTODCAkSZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAk
SZLU2YIFEEkOSnJJkjP60vZN8vskp7bXrn3zXpvknCRnJnlsX/ouLe2cJHvP93ZIkuaW9YMkjbeF
bIE4GNhlSPr7SikPbK8jAZLcF9gd+Lv2mQ8mWTPJmsD/AI8D7gs8o+WVJC1eB2P9IElja62FWnEp
5XtJtuyYfTfgc6WU64Bzk5wDbNfmnVNK+Q1Aks+1vL+Y4+JKkuaJ9YMkjbcFCyCm8dIkewInAa8s
pfwR2Aw4vi/P8pYG8LuB9IfMSyk7yiGHdM5bli0bYUkkadFbUvWDJC1W43YT9YeArYAHAhcC723p
GZK3TJM+VJIXJDkpyUmXXnrp6pZVkjR/RlY/WDdI0uyMVQBRSrm4lHJjKeUm4H9Z0Qy9HNiiL+vm
wAXTpE+1/I+WUrYtpWy70UYbzW3hJUkjM8r6wbpBkmZnrAKIJJv2TT4Z6I3AcTiwe5J1k9wduCdw
AnAicM8kd0+yDvVGusPns8ySpNGzfpCk8bFg90Ak+SywI3CnJMuBfYAdkzyQ2sx8HvBCgFLKz5N8
gXrz2w3AS0opN7blvBQ4ClgTOKiU8vN53hRJ0hyyfpCk8baQozA9Y0jyx6fJ/zbgbUPSjwSOnMOi
SZIWkPWDJI23serCJEmSJGm8GUBIkiRJ6swAQpIkSVJnBhCSJEmSOjOAkCRJktSZAYQkSZKkzgwg
JEmSJHVmACFJkiSpMwMISZIkSZ0ZQEiSJEnqzABCkiRJUmcGEJIkSZI6M4CQJEmS1JkBhCRJkqTO
DCAkSZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLU2YwBRJKH
JVm/vd8jyf5J7jb6okmSxpn1gyRNpi4tEB8CrknyAODVwPnAJ0daKknSYmD9IEkTqEsAcUMppQC7
AQeUUg4AbjvaYkmSFgHrB0maQGt1yHNlktcCewA7JFkTWHu0xZIkLQLWD5I0gbq0QPwLcB3w3FLK
RcBmwHtGWipJ0mJg/SBJE2jGFohWKezfN/1b7OMqSRPP+kGSJtOUAUSSK4Ey1fxSyu1GUiJJ0liz
fpCkyTZlAFFKuS1Akv2Ai4BPAQGeiTfJSdLEsn6QpMnW5R6Ix5ZSPlhKubKU8udSyoeAp466YJKk
sWf9IEkTqEsAcWOSZyZZM8kaSZ4J3DjqgkmSxp71gyRNoC4BxL8C/wxc3F5Pb2mSpMlm/SBJE2ja
UZjamN5PLqXsNk/lkSQtAtYPkjS5pm2BKKXcSH3CqCRJN7N+kKTJ1eVJ1D9M8gHg88DVvcRSyikj
K5UkaTGwfpCkCdQlgPiH9ne/vrQCPHLuiyNJWkSsHyRpAnV5EvVO81EQSdLiYv0gSZNpxlGYkmyQ
ZP8kJ7XXe5NsMB+FkySNL+sHSZpMXYZxPQi4kjpU3z8DfwY+McpCSZIWBesHSZpAXe6B2KqU0v9k
0TcnOXVUBZIkLRrWD5I0gbq0QPwlycN7E0keBvxldEWSJC0S1g+SNIG6tED8G3BIX7/WPwJ7jaxE
kqTFwvpBkiZQl1GYTgUekOR2bfrPIy+VJGnsWT9I0mTqMgrT25PcvpTy51LKn5NsmOSt81E4SdL4
sn6QpMnU5R6Ix5VS/tSbKKX8Edh1dEWSJC0S1g+SNIG6BBBrJlm3N5FkPWDdafJLkiaD9YMkTaAu
N1F/GjgmySeAAjwHOGSkpZIkLQbWD5I0gbrcRP3uJKcBjwYCvKWUctTISyZJGmvWD5I0mbq0QAD8
ErihlPLtJLdOcttSypWjLJgkaVGwfpCkCdNlFKbnA4cCH2lJmwGHjbJQkqTxZ/0gSZOpy03ULwEe
BvwZoJRyNrDxKAslSVoUrB8kaQJ1CSCuK6Vc35tIshb1ZjlJ0mSzfpCkCdQlgDguyeuA9ZI8Bvgi
8LXRFkuStAhYP0jSBOoSQOwNXAqcDrwQOBJ4wygLJUlaFKwfJGkCdRnG9Sbgf9sLgCQPA344wnJJ
ksac9YMkTaYpA4gkawL/TB1V45ullDOSPAF4HbAe8KD5KaIkaZxYP0jSZJuuBeLjwBbACcCBSc4H
HgrsXUpxmD5JmlzWD5I0waYLILYF7l9KuSnJrYA/AH9TSrlofoomSRpT1g+SNMGmu4n6+ta/lVLK
tcBZVg6SJKwfJGmiTdcCce8kp7X3AbZq0wFKKeX+Iy+dJGkcWT9I0gSbLoC4z7yVQpK0mFg/SNIE
mzKAKKWcP58FkSQtDtYPkjTZujxITpIkSZIAAwhJkiRJszBlAJHkmPb3XaNaeZKDklyS5Iy+tDsk
OTrJ2e3vhi09SQ5Mck6S05Js3feZZS3/2UmWjaq8kqTR1w/WDZI03qZrgdg0ySOAJyV5UJKt+19z
tP6DgV0G0vYGjiml3BM4pk0DPA64Z3u9APgQ1EoF2Ad4CLAdsE+vYpEkjcSo64eDsW6QpLE13ShM
b6KeoDcH9h+YV4BHru7KSynfS7LlQPJuwI7t/SHAscBrWvonSykFOD7J7ZNs2vIeXUq5HCDJ0dSK
57OrWz5J0lAjrR+sGyRpvE03CtOhwKFJ3lhKecs8lmmTUsqFrQwXJtm4pW8G/K4v3/KWNlW6JGkE
Fqh+sG6QpDExXQsEAKWUtyR5ErBDSzq2lHLEaIs1VIaklWnSb7mA5AXUJm7uete7zl3JJGkCjUn9
YN0gSfNsxlGYkrwDeBnwi/Z6WUsblYtb8zPt7yUtfTmwRV++zYELpkm/hVLKR0sp25ZStt1oo43m
vOCSNEnmuX6wbpCkMdFlGNfHA48ppRxUSjmI2of08SMs0+FAb7SMZcBX+9L3bCNubA9c0ZqzjwJ2
TrJhu0Fu55YmSRqt+awfrBskaUzM2IWpuT1weXu/wVytPMlnqTe63SnJcuqIGe8EvpDkucBvgae3
7EcCuwLnANcAzwYopVye5C3AiS3ffr2b5iRJIzfn9YN1gySNty4BxDuAnyb5LrVP6Q7Aa+di5aWU
Z0wx61FD8hbgJVMs5yDgoLkokySps5HUD9YNkjTeutxE/dkkxwIPplYQrymlXDTqgkmSxpv1gyRN
pk5dmFp/0sNHXBZJ0iJj/SBJk6fLTdSSJEmSBBhASJIkSZqFaQOIJGskOWO+CiNJWhysHyRpck0b
QJRSbgJ+lsRHc0qSbmb9IEmTq8tN1JsCP09yAnB1L7GU8qSRlUqStBhYP0jSBOoSQLx55KWQJC1G
1g+SNIG6PAfiuCR3A+5ZSvl2klsDa46+aJKkcWb9IEmTacZRmJI8HzgU+EhL2gw4bJSFkiSNP+sH
SZpMXYZxfQnwMODPAKWUs4GNR1koSdKiYP0gSROoSwBxXSnl+t5EkrWAMroiSZIWCesHSZpAXQKI
45K8DlgvyWOALwJfG22xJEmLgPWDJE2gLgHE3sClwOnAC4EjgTeMslCSpEXB+kGSJlCXUZhuSnII
8BNq0/SZpRSbqCVpwlk/SNJkmjGASPJ44MPAr4EAd0/ywlLKN0ZdOEnS+LJ+kKTJ1OVBcu8Fdiql
nAOQZCvg64AVhCRNNusHSZpAXe6BuKRXOTS/AS4ZUXkkSYuH9YMkTaApWyCSPKW9/XmSI4EvUPu4
Ph04cR7KJkkaQ9YPkjTZpuvC9MS+9xcDj2jvLwU2HFmJJEnjzvpBkibYlAFEKeXZ81kQSdLiYP0g
SZOtyyhMdwf+HdiyP38p5UmjK5YkadxZP0jSZOoyCtNhwMepTxe9abTFkSQtItYPkjSBugQQ15ZS
Dhx5SSRJi431gyRNoC4BxAFJ9gG+BVzXSyylnDKyUkmSFgPrB0maQF0CiL8HngU8khVN1KVNS5Im
l/WDJE2gLgHEk4F7lFKuH3VhJEmLivWDJE2gLk+i/hlw+1EXRJK06Fg/SNIE6tICsQnwqyQnsnIf
V4fpk6TJZv0gSROoSwCxz8hLIUlajKwfJGkCzRhAlFKOm4+CSJIWF+sHSZpMXZ5EfSV1VA2AdYC1
gatLKbcbZcEkSePN+kGSJlOXFojb9k8n+Sdgu5GVSJK0KFg/SNJk6jIK00pKKYfhGN+SpAHWD5I0
Gbp0YXpK3+QawLasaLKWJE0o6wdJmkxdRmF6Yt/7G4DzgN1GUhpJ0mJi/SBJE6jLPRDPno+CSJIW
F+sHSZpMUwYQSd40zedKKeUtIyiPJGnMWT9I0mSbrgXi6iFp6wPPBe4IWEFI0mSyfpCkCTZlAFFK
eW/vfZLbAi8Dng18DnjvVJ+TJC1t1g+SNNmmvQciyR2AVwDPBA4Bti6l/HE+CiZJGl/WD5I0uaa7
B+I9wFOAjwJ/X0q5at5KJUkaW9YPkjTZpmuBeCVwHfAG4PVJeumh3iR3uxGXTZI0nqwfNBFyyCGd
85Zly0ZYEmm8THcPxKyfUi1JWvqsHyRpslkJSJIkSerMAEKSJElSZwYQkiRJkjqbdhhXjbfZ3NwF
3uAlSZKk1WcLhCRJkqTODCAkSZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJ
kjozgJAkSZLUmQGEJEmSpM4MICRJkiR1ZgAhSZIkqTMDCEmSJEmdGUBIkiRJ6swAQpIkSVJnBhCS
JEmSOjOAkCRJktTZ2AYQSc5LcnqSU5Oc1NLukOToJGe3vxu29CQ5MMk5SU5LsvXCll6SNArWDZK0
8MY2gGh2KqU8sJSybZveGzimlHJP4Jg2DfA44J7t9QLgQ/NeUknSfLFukKQFNO4BxKDdgEPa+0OA
f+pL/2Spjgdun2TThSigJGneWTdI0jwa5wCiAN9KcnKSF7S0TUopFwK0vxu39M2A3/V9dnlLW0mS
FyQ5KclJl1566QiLLkkaEesGSVpgay10AabxsFLKBUk2Bo5O8qtp8mZIWrlFQikfBT4KsO22295i
viRp7Fk3SNICG9sWiFLKBe3vJcBXgO2Ai3vNz+3vJS37cmCLvo9vDlwwf6WVJM0H6wZJWnhjGUAk
WT/JbXvvgZ2BM4DDgWUt2zLgq+394cCebcSN7YEres3ZkqSlwbpBksbDuHZh2gT4ShKoZfy/Uso3
k5wIfCHJc4HfAk9v+Y8EdgXOAa4Bnj3/RZYkjZh1gySNgbEMIEopvwEeMCT9MuBRQ9IL8JJ5KJok
aYFYN0jSeBjLLkySJEmSxpMBhCRJkqTOxrILkyRJ0lzLIYfMnEnSjGyBkCRJktSZLRCSJEmrabat
G2XZspkzSWPKFghJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLUmQGEJEmSpM4MICRJ
kiR1ZgAhSZIkqTMDCEmSJEmdGUBIkiRJ6swAQpIkSVJnBhCSJEmSOjOAkCRJktSZAYQkSZKkzgwg
JEmSJHVmACFJkiSpMwMISZIkSZ0ZQEiSJEnqzABCkiRJUmcGEJIkSZI6M4CQJEmS1JkBhCRJkqTO
DCAkSZIkdWYAIUmSJKkzAwhJkiRJnRlASJIkSerMAEKSJElSZwYQkiRJkjozgJAkSZLU2VoLXQBJ
UpVDDplV/rJs2YhKIknS1GyBkCRJktSZAYQkSZKkzgwgJEmSJHXmPRBjZLb9nyVJkqT5ZguEJEmS
pM4MICRJkiR1ZgAhSZIkqTPvgZAkSYuS9w5KC8MWCEmSJEmdGUBIkiRJ6swAQpIkSVJnBhCSJEmS
OjOAkCRJktSZozBJkiSNudmMOFWWLRthSSRbICRJkiTNgi0QkiRJ88xnWGgxswVCkiRJUmcGEJIk
SZI6M4CQJEmS1JkBhCRJkqTODCAkSZIkdeYoTJoTsx1NwjGqJUmSFidbICRJkiR1ZgAhSZIkqTO7
MEmSJC0hdivWqBlAaCifkClJkqRh7MIkSZIkqTMDCEmSJEmdLZkuTEl2AQ4A1gQ+Vkp55wIXSZI0
BqwfFhe70Erjb0kEEEnWBP4HeAywHDgxyeGllF8sbMkkSQtpUusHb6KVNEpLIoAAtgPOKaX8BiDJ
54DdgCVdQUiSZjS29cNsfuQv5h/4tigsPZPy3dXUlkoAsRnwu77p5cBDFqgsmmOjvpLmiVBa0qwf
5pgBgWZjMbeGLeayj1pKKQtdhtWW5OnAY0spz2vTzwK2K6X8+0C+FwAvaJP3As5chdXdCfjDahR3
sZiU7YTJ2Va3c+lZ1W29Wyllo7kuzDjqUj/MUd0Ak/Xdm4n7YgX3xQruixXGdV90qh+WSgvEcmCL
vunNgQsGM5VSPgp8dHVWlOSkUsq2q7OMxWBSthMmZ1vdzqVnkrZ1NcxYP8xF3QAej37uixXcFyu4
L1ZY7PtiqQzjeiJwzyR3T7IOsDtw+AKXSZK08KwfJGmOLYkWiFLKDUleChxFHabvoFLKzxe4WJKk
BWb9IElzb0kEEACllCOBI+dhVavdzL1ITMp2wuRsq9u59EzStq4y64cF4b5YwX2xgvtihUW9L5bE
TdSSJEmS5sdSuQdCkiRJ0jwwgJiFJLskOTPJOUn2XujydJFkiyTfTfLLJD9P8rKWfockRyc5u/3d
sKUnyYFtG09LsnXfspa1/GcnWdaXvk2S09tnDkyS+d/Sm8uyZpKfJjmiTd89yU9amT/fbqIkybpt
+pw2f8u+Zby2pZ+Z5LF96WNx/JPcPsmhSX7VjutDl+LxTPKf7Tt7RpLPJrnVUjmeSQ5KckmSM/rS
Rn4Mp1qHVt+4nB9GadTf28Ui81CvLhbtvHxCkp+1ffHmlj5n5+rFJiP8HTJWSim+OryoN9/9GrgH
sA7wM+C+C12uDuXeFNi6vb8tcBZwX+DdwN4tfW/gXe39rsA3gADbAz9p6XcAftP+btjeb9jmnQA8
tH3mG8DjFnB7XwH8H3BEm/4CsHt7/2Hg39r7FwMfbu93Bz7f3t+3Hdt1gbu3Y77mOB1/4BDgee39
OsDtl9rxpD7861xgvb7juNdSOZ7ADsDWwBl9aSM/hlOtw9dqH8+xOT+MeDtH+r1dLC/moV5dLK+2
Tbdp79cGftK2cU7O1Qu9fau4T0byO2Sht+sW27nQBVgsr1YZH9U3/VrgtQtdrlXYjq8Cj6E+KGnT
lrYpcGZ7/xHgGX35z2zznwF8pC/9Iy1tU+BXfekr5ZvnbdscOAZ4JHBEO7H9AVhr8BhSR2R5aHu/
VsuXwePayzcuxx+4HfWHdQbSl9TxZMXTg+/Qjs8RwGOX0vEEtmTlH2IjP4ZTrcPXah/LBf8+zeO2
juR7u9DbtZr7ZE7r1YXentXYD7cGTqE+6X1OztULvU2rsA9G9jtkobdt8GUXpu56P2h6lre0RaM1
jz2IeoVgk1LKhQDt78Yt21TbOV368iHpC+H9wKuBm9r0HYE/lVJuaNP9Zbt5e9r8K1r+2W7/fLsH
cCnwidZE+rEk67PEjmcp5ffAfwG/BS6kHp+TWXrHs998HMOp1qHVM47fp/kyV9/bRWlE9eqi0rrs
nApcAhxNvWI+V+fqxWaUv0PGigFEd8P6gS+aIayS3Ab4EvDyUsqfp8s6JK2sQvq8SvIE4JJSysn9
yUOylhnmjfV2Uq9SbA18qJTyIOBqalP5VBbldra+w7tRm2/vAqwPPG5I1sV+PLtYytu2VLjPb2nJ
fz9HWK8uKqWUG0spD6Refd8OuM+wbO3vkt0X8/A7ZKwYQHS3HNiib3pz4IIFKsusJFmbepL7TCnl
yy354iSbtvmbUq8cwNTbOV365kPS59vDgCclOQ/4HLX58P3A7ZP0nnfSX7abt6fN3wC4nNlv/3xb
DiwvpfykTR9KDSiW2vF8NHBuKeXSUspfgS8D/8DSO5795uMYTrUOrZ5x/D7Nl7n63i4qI65XF6VS
yp+AY6n3QMzVuXoxGfXvkLFiANHdicA9293061BveDl8gcs0oyQBPg78spSyf9+sw4Fl7f0yah/O
XvqebdSI7YErWlPsUcDOSTZsV4d3pvbjuxC4Msn2bV179i1r3pRSXltK2byUsiX12HynlPJM4LvA
01q2we3sbf/TWv7S0ndvoyPcHbgn9YbUsTj+pZSLgN8luVdLehTwC5bY8aR2Xdo+ya1bOXrbuaSO
54D5OIZTrUOrZxy/T/NlTr63813o1THqenVeNmKOJNkoye3b+/WoF39+ydydqxeNefgdMl4W+iaM
xfSijqRwFrV/3+sXujwdy/xwatPXacCp7bUrtZ/dMcDZ7e8dWv4A/9O28XRg275lPQc4p72e3Ze+
LXBG+8wHGLjBdwG2eUdWjH5wD+o/3jnAF4F1W/qt2vQ5bf49+j7/+rYtZ9I3AtG4HH/ggcBJ7Zge
Rh29Y8kdT+DNwK9aWT5FHZFiSRxP4LPUezv+Sr3a9Nz5OIZTrcPXnBzTsTg/jHgbR/q9XSwv5qFe
XSwv4P7AT9u+OAN4U0ufs3P1Ynwxot8h4/TySdSSJEmSOrMLkyRJkqTODCAkSZIkdWYAIUmSJKkz
AwhJkiRJnRlASJIkSerMAEJaDUmOTfLYgbSXJ/ngNJ+5avQlkyQtJOsHLWUGENLq+Sz1gTH9dm/p
kqTJZf2gJcsAQlo9hwJPSLIuQJItgbsApyY5JskpSU5PstvgB5PsmOSIvukPJNmrvd8myXFJTk5y
VJJN52NjJElzxvpBS5YBhLQaSimXUZ8guUtL2h34PPAX4MmllK2BnYD3JkmXZSZZG/hv4GmllG2A
g4C3zXXZJUmjY/2gpWythS6AtAT0mqm/2v4+Bwjw9iQ7ADcBmwGbABd1WN69gPsBR7c6ZU3gwrkv
tiRpxKwftCQZQEir7zBg/yRbA+uVUk5pTc0bAduUUv6a5DzgVgOfu4GVWwF78wP8vJTy0NEWW5I0
YtYPWpLswiStplLKVcCx1Kbk3s1xGwCXtMphJ+BuQz56PnDfJOsm2QB4VEs/E9goyUOhNlkn+btR
boMk6f+3c8coCMRAGEb/AY/owcQ7iGBh4zUERRAES29hExtBsJpiRZT3ykBgtxo+EjI984F/5QQC
prFOss3rxY1Vkl1V7SUm4XQAAABkSURBVJMck1zeN4wxblW1SXJKck1yeK7fq2qeZPkcHLMkiyTn
j/8FAFMzH/g7Ncb49jcAAAA/whUmAACgTUAAAABtAgIAAGgTEAAAQJuAAAAA2gQEAADQJiAAAIA2
AQEAALQ9AGaz6XodUMKrAAAAAElFTkSuQmCC
"
>
</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;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><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="kc">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;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Import sklearn.preprocessing.StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">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">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_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>
  </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_log_minmax_transform'</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;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><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="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">income_raw</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="s1">&#39;&gt;50K&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="mi">1</span><span class="p">)</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="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> 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="c1">#print(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>103 total features after one-hot encoding.
</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;[11]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><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="k">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="nb">print</span><span class="p">(</span><span class="s2">&quot;Training set has </span><span class="si">{}</span><span class="s2"> 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="nb">print</span><span class="p">(</span><span class="s2">&quot;Testing set has </span><span class="si">{}</span><span class="s2"> 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>/Users/jjlogu/anaconda3/lib/python3.6/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;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">TP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">income</span><span class="p">)</span> <span class="c1"># Counting the ones as this is the naive case. Note that &#39;income&#39; is the &#39;income_raw&#39; data </span>
                    <span class="c1"># encoded to numerical values done in the data preprocessing step.</span>
<span class="n">FP</span> <span class="o">=</span> <span class="n">income</span><span class="o">.</span><span class="n">count</span><span class="p">()</span> <span class="o">-</span> <span class="n">TP</span> <span class="c1"># Specific to the naive case</span>

<span class="n">TN</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># No predicted negatives in the naive case</span>
<span class="n">FN</span> <span class="o">=</span> <span class="mi">0</span> <span class="c1"># No predicted negatives in the naive case</span>

<span class="c1"># TODO: Calculate accuracy, precision and recall</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="p">(</span><span class="n">TP</span><span class="o">+</span><span class="n">TN</span><span class="p">)</span><span class="o">/</span><span class="n">income</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
<span class="n">recall</span> <span class="o">=</span> <span class="n">TP</span><span class="o">/</span><span class="p">(</span><span class="n">TP</span><span class="o">+</span><span class="n">FN</span><span class="p">)</span>
<span class="n">precision</span> <span class="o">=</span> <span class="n">TP</span><span class="o">/</span><span class="p">(</span><span class="n">TP</span><span class="o">+</span><span class="n">FP</span><span class="p">)</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="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="p">)</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="nb">print</span><span class="p">(</span><span class="s2">&quot;Naive Predictor: [Accuracy score: </span><span class="si">{:.4f}</span><span class="s2">, F-score: </span><span class="si">{:.4f}</span><span class="s2">]&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></p>
<p>I decided to try three ensemble methods, because ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. As we have big dataset I think ensemble methods will give better performance. (<a href="https://en.wikipedia.org/wiki/Ensemble_learning">https://en.wikipedia.org/wiki/Ensemble_learning</a>)</p>
<p><strong>1. Adaboost</strong></p>
<p><strong>Application</strong> Used in face detection <a href="https://www.uni-obuda.hu/journal/Markoski_Ivankovic_Ratgeber_Pecev_Glusac_57.pdf">https://www.uni-obuda.hu/journal/Markoski_Ivankovic_Ratgeber_Pecev_Glusac_57.pdf</a></p>
<p><strong>Strength</strong> it is fast and less prone to overfitting. it can be used with many other learning algorithm to improve performance  <a href="https://en.wikipedia.org/wiki/AdaBoost#Implementations">https://en.wikipedia.org/wiki/AdaBoost#Implementations</a></p>
<p><strong>Weakness</strong> it is sensitive to noisy data and outliers</p>
<p><strong>Reason</strong><br>
    As we have class imbalanced in our dataset, I think this will perform  better.
    <a href="https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/">https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/</a>
    <a href="http://www.nickgillian.com/wiki/pmwiki.php/GRT/AdaBoost">http://www.nickgillian.com/wiki/pmwiki.php/GRT/AdaBoost</a>
    <a href="http://user.ceng.metu.edu.tr/~tcan/ceng734_f1112/Schedule/adaboost.pdf">http://user.ceng.metu.edu.tr/~tcan/ceng734_f1112/Schedule/adaboost.pdf</a>
    <a href="https://www.quora.com/in/What-is-AdaBoost">https://www.quora.com/in/What-is-AdaBoost</a></p>
<p><strong>2. Random Forest</strong></p>
<p><strong>Application</strong> used in Banking, medicine, stock market and finally e-commerce. In e-commerce, to recommend a product to customer random forest is used.  <a href="https://medium.com/@Synced/how-random-forest-algorithm-works-in-machine-learning-3c0fe15b6674">https://medium.com/@Synced/how-random-forest-algorithm-works-in-machine-learning-3c0fe15b6674</a></p>
<p><strong>Strength</strong> Avoid overfitting problems. 
    Can be used for feature engineering.
        A naive variable importance measure to use in tree-based ensemble methods is to merely count the number of times each variable is selected by all individual trees in the ensemble.
    <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-25">https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-25</a>
    <a href="https://chrisalbon.com/machine_learning/trees_and_forests/feature_selection_using_random_forest/">https://chrisalbon.com/machine_learning/trees_and_forests/feature_selection_using_random_forest/</a>
    <a href="https://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables/">https://www.analyticsvidhya.com/blog/2016/12/introduction-to-feature-selection-methods-with-an-example-or-how-to-select-the-right-variables/</a></p>
<p><strong>Weakness</strong> Time consuming <a href="https://www.quora.com/When-is-a-random-forest-a-poor-choice-relative-to-other-algorithms">https://www.quora.com/When-is-a-random-forest-a-poor-choice-relative-to-other-algorithms</a></p>
<p><strong>Reason</strong> 
    As we have mixture of numerical and categorical features, I think this will do good job at this case.
    <a href="https://datascience.stackexchange.com/questions/6838/when-to-use-random-forest-over-svm-and-vice-versa">https://datascience.stackexchange.com/questions/6838/when-to-use-random-forest-over-svm-and-vice-versa</a></p>
<p><strong>3. Gradient Boosting </strong></p>
<p><strong>Application</strong> used in search engine ranking system <a href="https://en.wikipedia.org/wiki/Gradient_boosting#Usage">https://en.wikipedia.org/wiki/Gradient_boosting#Usage</a></p>
<p><strong>Strength</strong> Can handle different data type, Fast Training.</p>
<p><strong>Weakness</strong> Time consuming, too many hyperparameters to tune.</p>
<p><strong>Reason</strong>
    Dataset have more number of features and class imblance, I think this will perform better.
    <a href="https://medium.com/mlreview/gradient-boosting-from-scratch-1e317ae4587d">https://medium.com/mlreview/gradient-boosting-from-scratch-1e317ae4587d</a></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="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;[13]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><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="k">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="n">beta</span><span class="o">=</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="n">beta</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
       
    <span class="c1"># Success</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> trained on </span><span class="si">{}</span><span class="s2"> 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;[14]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><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.ensemble</span> <span class="k">import</span> <span class="n">AdaBoostClassifier</span><span class="p">,</span> <span class="n">RandomForestClassifier</span><span class="p">,</span> <span class="n">GradientBoostingClassifier</span>

<span class="c1"># TODO: Initialize the three models</span>
<span class="n">clf_A</span> <span class="o">=</span> <span class="n">AdaBoostClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">23</span><span class="p">)</span>
<span class="n">clf_B</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">23</span><span class="p">)</span>
<span class="n">clf_C</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">23</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 (ensure to set the count of the values to be `int` and not `float`)</span>
<span class="c1"># HINT: samples_1 is 1% of samples_100 (ensure to set the count of the values to be `int` and not `float`)</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="n">samples_100</span><span class="o">/</span><span class="mi">10</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="n">samples_100</span><span class="o">/</span><span class="mi">100</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>AdaBoostClassifier trained on 361 samples.
AdaBoostClassifier trained on 3617 samples.
AdaBoostClassifier trained on 36177 samples.
RandomForestClassifier trained on 361 samples.
RandomForestClassifier trained on 3617 samples.
RandomForestClassifier trained on 36177 samples.
GradientBoostingClassifier trained on 361 samples.
GradientBoostingClassifier trained on 3617 samples.
GradientBoostingClassifier 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>
<ul>
<li>Based on graph "F-score on Testing set", Gradient boost and Ada boost classifiers almost perform almost equal. </li>
<li>Ada boost takes more time to predict. Though Gradien boost takes time to train, relatively takes less time to predict.</li>
<li>Based on f-score and prediction time <strong>Gradient Boost Classifier</strong> is best model to predict individuals who make more than $50,000.</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-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>
<p>Boosting: 
    Basic idea is combining multiple weak learners to build a strong learner, which improves accuracy considerably. Weak learner is a naive model which merely better than randomly guessing. On each iteration hardest example gets more weight which makes the learner to focus on it.</p>
<p>Gradient Boosting:
    It builds simple decision tree(weak learner) and measures misclassified items(errors). Based on those misclassified items it builds another tree which minimize error. Multiple week decision trees repeat the same until it reaches sudden threshold(acceptable error). At the end combining all weak decision trees gives more accurate strong decision tree.</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="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;[16]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><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.model_selection</span> <span class="k">import</span> <span class="n">GridSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">make_scorer</span>

<span class="c1"># TODO: Initialize the classifier</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">GradientBoostingClassifier</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">23</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</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;n_estimators&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">50</span><span class="p">,</span><span class="mi">150</span><span class="p">],</span> <span class="s1">&#39;max_depth&#39;</span><span class="p">:</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="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="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="nb">print</span><span class="p">(</span><span class="s2">&quot;Unoptimized model</span><span class="se">\n</span><span class="s2">------&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy score on testing data: </span><span class="si">{:.4f}</span><span class="s2">&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="nb">print</span><span class="p">(</span><span class="s2">&quot;F-score on testing data: </span><span class="si">{:.4f}</span><span class="s2">&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="nb">print</span><span class="p">(</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="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Final accuracy score on the testing data: </span><span class="si">{:.4f}</span><span class="s2">&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="nb">print</span><span class="p">(</span><span class="s2">&quot;Final F-score on the testing data: </span><span class="si">{:.4f}</span><span class="s2">&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>      Iter       Train Loss   Remaining Time 
         1           1.0602            2.11s
         2           1.0128            2.04s
         3           0.9733            1.99s
         4           0.9416            1.94s
         5           0.9148            1.90s
         6           0.8911            1.90s
         7           0.8720            1.85s
         8           0.8550            1.80s
         9           0.8398            1.74s
        10           0.8259            1.74s
        20           0.7415            1.25s
        30           0.6986            0.79s
        40           0.6727            0.38s
        50           0.6555            0.00s
      Iter       Train Loss   Remaining Time 
         1           1.0612            2.54s
         2           1.0143            2.49s
         3           0.9754            2.35s
         4           0.9429            2.21s
         5           0.9161            2.15s
         6           0.8933            2.10s
         7           0.8738            2.05s
         8           0.8585            1.88s
         9           0.8428            1.84s
        10           0.8292            1.80s
        20           0.7450            1.23s
        30           0.7018            0.78s
        40           0.6741            0.39s
        50           0.6572            0.00s
      Iter       Train Loss   Remaining Time 
         1           1.0611            2.22s
         2           1.0142            2.16s
         3           0.9745            2.11s
         4           0.9422            2.08s
         5           0.9148            2.08s
         6           0.8919            2.08s
         7           0.8730            1.93s
         8           0.8582            1.81s
         9           0.8423            1.77s
        10           0.8290            1.72s
        20           0.7448            1.30s
        30           0.7014            0.80s
        40           0.6752            0.39s
        50           0.6581            0.00s
      Iter       Train Loss   Remaining Time 
         1           1.0602            7.01s
         2           1.0128            6.96s
         3           0.9733            6.66s
         4           0.9416            6.60s
         5           0.9148            6.60s
         6           0.8911            6.48s
         7           0.8720            6.44s
         8           0.8550            6.35s
         9           0.8398            6.13s
        10           0.8259            6.12s
        20           0.7415            5.61s
        30           0.6986            4.84s
        40           0.6727            4.22s
        50           0.6555            3.74s
        60           0.6445            3.29s
        70           0.6368            2.85s
        80           0.6307            2.42s
        90           0.6255            2.14s
       100           0.6208            1.80s
      Iter       Train Loss   Remaining Time 
         1           1.0612            6.63s
         2           1.0143            6.38s
         3           0.9754            6.27s
         4           0.9429            6.28s
         5           0.9161            6.32s
         6           0.8933            6.36s
         7           0.8738            6.27s
         8           0.8585            5.91s
         9           0.8428            5.87s
        10           0.8292            5.83s
        20           0.7450            5.06s
        30           0.7018            4.55s
        40           0.6741            4.03s
        50           0.6572            3.61s
        60           0.6464            3.21s
        70           0.6379            2.78s
        80           0.6316            2.36s
        90           0.6255            1.98s
       100           0.6209            1.62s
      Iter       Train Loss   Remaining Time 
         1           1.0611           11.95s
         2           1.0142            9.92s
         3           0.9745            9.34s
         4           0.9422            9.18s
         5           0.9148            8.96s
         6           0.8919            8.86s
         7           0.8730            8.25s
         8           0.8582            7.64s
         9           0.8423            7.46s
        10           0.8290            7.29s
        20           0.7448            5.82s
        30           0.7014            5.06s
        40           0.6752            4.80s
        50           0.6581            4.45s
        60           0.6477            3.96s
        70           0.6391            3.47s
        80           0.6330            2.92s
        90           0.6272            2.43s
       100           0.6227            1.96s
      Iter       Train Loss   Remaining Time 
         1           1.0503            3.84s
         2           0.9962            3.77s
         3           0.9534            3.76s
         4           0.9176            3.73s
         5           0.8884            3.64s
         6           0.8624            3.57s
         7           0.8406            3.48s
         8           0.8217            3.39s
         9           0.8047            3.32s
        10           0.7900            3.23s
        20           0.7043            2.51s
        30           0.6624            1.81s
        40           0.6382            0.90s
        50           0.6246            0.00s
      Iter       Train Loss   Remaining Time 
         1           1.0507            4.02s
         2           0.9968            3.87s
         3           0.9535            3.84s
         4           0.9180            4.50s
         5           0.8877            5.10s
         6           0.8623            5.47s
         7           0.8400            5.28s
         8           0.8216            4.94s
         9           0.8052            4.67s
        10           0.7910            4.46s
        20           0.7048            3.01s
        30           0.6643            2.00s
        40           0.6406            1.00s
        50           0.6263            0.00s
      Iter       Train Loss   Remaining Time 
         1           1.0503            5.79s
         2           0.9963            5.85s
         3           0.9536            5.52s
         4           0.9175            5.17s
         5           0.8877            4.86s
         6           0.8617            4.58s
         7           0.8404            4.45s
         8           0.8210            4.30s
         9           0.8043            4.12s
        10           0.7897            4.00s
        20           0.7042            2.69s
        30           0.6637            1.82s
        40           0.6409            0.93s
        50           0.6273            0.00s
      Iter       Train Loss   Remaining Time 
         1           1.0503           16.61s
         2           0.9962           17.98s
         3           0.9534           18.18s
         4           0.9176           17.36s
         5           0.8884           16.08s
         6           0.8624           15.17s
         7           0.8406           14.66s
         8           0.8217           14.20s
         9           0.8047           14.22s
        10           0.7900           14.93s
        20           0.7043           13.23s
        30           0.6624           12.03s
        40           0.6382           10.27s
        50           0.6246            9.74s
        60           0.6142            8.39s
        70           0.6068            7.12s
        80           0.6007            5.89s
        90           0.5955            4.83s
       100           0.5911            3.87s
      Iter       Train Loss   Remaining Time 
         1           1.0507           12.74s
         2           0.9968           12.69s
         3           0.9535           14.74s
         4           0.9180           14.13s
         5           0.8877           13.67s
         6           0.8623           13.41s
         7           0.8400           13.02s
         8           0.8216           12.77s
         9           0.8052           12.60s
        10           0.7910           12.55s
        20           0.7048           13.19s
        30           0.6643           12.02s
        40           0.6406           10.84s
        50           0.6263            9.39s
        60           0.6126            7.94s
        70           0.6038            6.84s
        80           0.5948            5.87s
        90           0.5890            4.83s
       100           0.5840            3.85s
      Iter       Train Loss   Remaining Time 
         1           1.0503           12.39s
         2           0.9963           12.23s
         3           0.9536           12.28s
         4           0.9175           12.07s
         5           0.8877           12.13s
         6           0.8617           12.29s
         7           0.8404           12.47s
         8           0.8210           12.29s
         9           0.8043           12.16s
        10           0.7897           12.00s
        20           0.7042           10.95s
        30           0.6637            9.74s
        40           0.6409            8.54s
        50           0.6273            7.41s
        60           0.6168            6.49s
        70           0.6084            5.57s
        80           0.6012            4.72s
        90           0.5956            4.08s
       100           0.5911            3.33s
      Iter       Train Loss   Remaining Time 
         1           1.0452            7.01s
         2           0.9873            6.78s
         3           0.9413            6.63s
         4           0.9031            6.45s
         5           0.8706            6.25s
         6           0.8433            6.09s
         7           0.8190            6.63s
         8           0.7975            6.56s
         9           0.7796            6.32s
        10           0.7634            6.31s
        20           0.6729            4.53s
        30           0.6347            2.86s
        40           0.6117            1.35s
        50           0.5968            0.00s
      Iter       Train Loss   Remaining Time 
         1           1.0458            7.27s
         2           0.9879            6.82s
         3           0.9420            6.49s
         4           0.9040            7.40s
         5           0.8704            7.08s
         6           0.8426            6.92s
         7           0.8189            7.10s
         8           0.7985            6.96s
         9           0.7809            6.78s
        10           0.7653            6.57s
        20           0.6750            4.65s
        30           0.6379            3.05s
        40           0.6133            1.43s
        50           0.5973            0.00s
      Iter       Train Loss   Remaining Time 
         1           1.0451            6.30s
         2           0.9871            6.30s
         3           0.9408            6.12s
         4           0.9028            5.98s
         5           0.8705            5.95s
         6           0.8422            5.80s
         7           0.8183            5.63s
         8           0.7984            5.50s
         9           0.7802            5.38s
        10           0.7643            5.31s
        20           0.6746            3.97s
        30           0.6358            2.56s
        40           0.6133            1.25s
        50           0.5989            0.00s
      Iter       Train Loss   Remaining Time 
         1           1.0452           18.89s
         2           0.9873           19.49s
         3           0.9413           19.25s
         4           0.9031           19.20s
         5           0.8706           18.93s
         6           0.8433           18.96s
         7           0.8190           18.75s
         8           0.7975           18.69s
         9           0.7796           18.49s
        10           0.7634           18.47s
        20           0.6729           16.51s
        30           0.6347           15.68s
        40           0.6117           14.27s
        50           0.5968           12.44s
        60           0.5848           10.89s
        70           0.5765            9.28s
        80           0.5698            8.01s
        90           0.5629            6.64s
       100           0.5577            5.44s
      Iter       Train Loss   Remaining Time 
         1           1.0458           19.03s
         2           0.9879           22.18s
         3           0.9420           21.95s
         4           0.9040           21.51s
         5           0.8704           20.85s
         6           0.8426           20.56s
         7           0.8189           20.25s
         8           0.7985           20.27s
         9           0.7809           20.05s
        10           0.7653           19.94s
        20           0.6750           17.71s
        30           0.6379           16.36s
        40           0.6133           14.67s
        50           0.5973           12.75s
        60           0.5855           11.11s
        70           0.5764            9.59s
        80           0.5685            8.18s
        90           0.5631            6.77s
       100           0.5586            5.53s
      Iter       Train Loss   Remaining Time 
         1           1.0451           30.64s
         2           0.9871           31.60s
         3           0.9408           32.89s
         4           0.9028           31.12s
         5           0.8705           28.62s
         6           0.8422           27.02s
         7           0.8183           26.41s
         8           0.7984           25.91s
         9           0.7802           25.34s
        10           0.7643           26.01s
        20           0.6746           24.59s
        30           0.6358           20.38s
        40           0.6133           17.22s
        50           0.5989           14.55s
        60           0.5880           12.39s
        70           0.5782           10.50s
        80           0.5716            8.96s
        90           0.5648            7.42s
       100           0.5592            5.97s
      Iter       Train Loss   Remaining Time 
         1           1.0455           36.76s
         2           0.9876           35.31s
         3           0.9415           35.09s
         4           0.9040           34.83s
         5           0.8710           34.54s
         6           0.8434           34.33s
         7           0.8196           34.01s
         8           0.7988           33.73s
         9           0.7808           33.32s
        10           0.7652           32.99s
        20           0.6754           29.60s
        30           0.6381           26.12s
        40           0.6158           23.17s
        50           0.6020           20.98s
        60           0.5903           18.19s
        70           0.5829           15.54s
        80           0.5752           13.11s
        90           0.5690           11.00s
       100           0.5647            9.02s
      Iter       Train Loss   Remaining Time 
         1           1.0505           17.45s
         2           0.9965           20.83s
         3           0.9538           18.11s
         4           0.9180           16.79s
         5           0.8885           17.69s
         6           0.8625           17.80s
         7           0.8403           17.01s
         8           0.8214           16.37s
         9           0.8053           17.83s
        10           0.7907           17.09s
        20           0.7051           13.32s
        30           0.6638           10.94s
        40           0.6403            8.72s
        50           0.6244            6.89s
        60           0.6146            5.29s
        70           0.6064            3.83s
        80           0.5985            2.44s
        90           0.5936            1.19s
       100           0.5888            0.00s
Unoptimized model
------
Accuracy score on testing data: 0.8630
F-score on testing data: 0.7395

Optimized Model
------
Final accuracy score on the testing data: 0.8690
Final F-score on the testing data: 0.7495
</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">0.8630</td>
<td style="text-align:center">0.8690</td>
</tr>
<tr>
<td style="text-align:center">F-score</td>
<td style="text-align:center">0.7395</td>
<td style="text-align:center">0.7495</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>
<p>Yes, Optimized model is shows slight improvement compared to unoptimized model.</p>
<p>Optimized model is much better than Base naive predictor.</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="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>
<ul>
<li>capital-gain - this is very important one as it indicates profit of the individual</li>
<li>capital-loss - this is also important one as indicates of loss of the individual</li>
<li>age - if person is relatively young and capital gains is more, I guess he/she more likely to donate.</li>
<li>relationship - this one help to determine commitment of individual</li>
<li>marital-status - I guess unmarried people more likely to donate, again it denotes commitment </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---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;[17]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Import a supervised learning model that has &#39;feature_importances_&#39;</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">GradientBoostingClassifier</span><span class="p">()</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,iVBORw0KGgoAAAANSUhEUgAAAoAAAAFgCAYAAAArYcg8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
AAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo
dHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xu8VXP+x/HXR6ULKSpEcUJCNJUT
JZLLKLeMmZDbaAaNS+Myw28wMzFhfmYw0uDnNiZGhBqmMca9XCq6kCaVqQgJJaSL0uXz++P73afV
bu9z9qlz9qnW+/l4nMfZa63v/q7PXmvttT/7+/2utc3dEREREZH02KqmAxARERGR4lICKCIiIpIy
SgBFREREUkYJoIiIiEjKKAEUERERSRklgCIiIiIpowRwE2dmfc3MzexrM9s+a1ntuOy6GgpvgyVe
V0li3hwzG1KTMeQoc4+ZfWtmW2fNPy0+9/Ecz3nCzBaYmVUyng3al2bWPT736ArKNTaz68ysY2XX
UU6dJ5rZf8xseYyhcVXVnWNdnufv4USZuWZ2fxWt78jK7I+47lzxjU6UecPMnq2K+CoR17AYx+w8
y2+Ky1dVw7prx2OuW4HlL8jadovN7O04v9o/r+K2WJ6YrhfjuKqS9VxhZr0qqr8YcmzT5N+h1bTO
3mZ2SXXULVWndk0HIAVrBPwKqNSJaDNzMvBNTQeR5VWgH3AQ8HpifjdgGXBYjuccBrzmlb/JZhdg
7oYEWaDGwLVxHW9tbGVmVhsYCowFLga+AxZvbL0VGALckzVvQeLxicCiKlrXkcCvgesq8ZxngOuz
5iWP6XOB1RsX1gZZAuxhZl3dfUxmZkyqziTstwbVsN7ahGNuFeG9VKhehP3aCDgd+D9gB+D3VR1g
BVYQ3pcfVfJ5VwBPAyOz5t8J/L0K4toQmW2a9G41ras3UAoMrqb6pQooAdx8PA/83MwGuftn1bEC
M6vr7iuqo+5CuPvbNbXucrwS/3dj/QTwXuAyM9vb3f8LYGZtgJ0SzyuYu7+xkbEW265AQ+Bxd6/M
h3tOZlYLMHcvryXqk/K2UyHHUDUf5wsqiK+6PnAr8jnwDnA2MCYx/0hgF0Iif0YNxJXP2+6e+TL0
nJntDVxGngQwtrbXcffvqjKI+CWuyt6X7v4x8HFV1VdJyW262Snw/CCVoC7gzccN8f+vKypoZgeZ
2YtmtsTMlprZS2Z2UFaZIbHLqouZjTWzb4E/xmVzzOxhMzvbzN6LXaCvmVlrM9smdosuNLPPzezW
2BKUqbeemd1mZlPj+j8zs3+a2T4FxF3WBWxmJeV0W4xOPKe2mV1tZjPMbIWZzYsx1cuqew8z+5eZ
LbPQPXs7ULeimOIJ8wNCwpepawegLfAo8GFyWeLxOgmRmZ1vZu/ErtIvzOwvsZ5kmfW6gM3s9Pja
llvoau1lZqOT2yChgZndEetfEPdh48z2jK8D4L7Etuwbl/eIx8GiuN/eM7MB+bZLjHNOnPxLcr9Y
cHms4zsz+zTGtV2O13ujmV1lZh8QWhAPyLfOQlhWF7CZnRfX09XMRpjZImICZGad4/vky3hczDaz
P8dlNxDfa4lttdEfPJboAjaz3c1sjZmdn6PctXGfN07MO83MxsdYv7LQtbtrJVb/EHCqmSWP+x8D
LwLzcsRQ10KX5YdxP35goTs3+X6vY2b/a2bvx3gXWDhXHBzfg9/GotcntuOG9GJMBJpljqF4Xrnf
Qvfmf4GVwFFxWcN4DsjEPdvM/sds3SEZFs6TY2PcH+eKy/J0AZvZgWY2Mh4735rZdDO7IhMb4Uvg
uYnXfHdclt3FPMvMhuZY7+HxeT2z1vm0heFA35rZq2bWZQO2ZU5mtpOZ3RffryvMbJqZ/SSrTPNY
ZmY8Dj8ys4fMbOdEmWHAacCeidc/Iy7LdEfvnFVvvq73AWb2WzP7kHB+aF2JWHc1s6GJMvPiPltn
KFWaqQVw8/EpcAehxekWd/8wVyEza0dofZoG9AWc0G38ipl1dvd3EsUbAcOAW4BrWHuyhpDI7Eno
dt4aGASMAN4HZgF9YpnfALOBu+Lz6hJahW6IMe8AXAS8YWb7VKL18lNC10tSW0Kr2/TEvIcJ3X5/
IHRF7kvogisBfhS3ydbAC0B9QlflfOBnwA8LjOVV4IdmVsvdVxO6eJcRulFfI2yHTNLRjdAFWbad
zewm4JeE7pArCS1nNwD7m9khsc71mNn3CS0zI+PzmxL2Qz3gvzmecjuh2+kMoA0hoV8NnEPYnj8k
dD/9L2u7pmab2R5xejgwkLUn2j3K2Sb3A1OBJ+Jr+RdruzpvBK4mdHf9E9iPsE++Z2aHu/uaRD19
CcfUFcBSciQiWSyZgAAU2CLwKPAIoSuxlpk1Av4NjCMkQUsIx0znWP5uwn7qy9rjsJAu/fXiA1bn
Gg7g7h+a2auEVrn7shafCfzT3b+OlV4G/CmWu5bQnT8QGGVm7d19WQGxPUY4Ro4H/m5m2xCOiZ+R
O/F+lPDeup7QCtYN+C2wG/DTWGYA4T11NeF4aEQYLrEDofv0cML56B5C9z1UvjsVoBXhuEyeo44F
OsWYFgKz4nv9xVj+esK5oivhGG3E2qR+51juQ8L2X004TzavKBAL4+ZejHVfCnxCeL+1iUWOI5xv
Xie81yC0wObyMHClmTV09+TwibPic16I6+wMjCLsh3OB5UB/4GUzO8jd/1NR3ITjPnlsrsm8F2NS
NC7O/w1hHx1P+HJX290zx2dTwnCBXwFfAC0I57RXzaytu6+Mz28C7AOcEp+X3G+V8TPgPULr73Jg
fiViHRbj+AVhH+0MfJ9w/hQAd9ffJvzH2iRuL8JJ9Wvggbisdlx2XaL88FimcWLedsCXwN8T84bE
556UY51zYvlGiXmXxPL3Z5V9CxhVTvy1CGOLFgOX53hdJVnrHZKnnmaERGEsUC/OOyzW8eOssmfG
+e3j9PlxunOizFaE8S/rxJBn3T+N5Urj9K3Ai/FxP2BOouyHwNOJ6RLCh8uArDq7xjp/kJiXvS/H
Ej5ULTGvYyw3OjGve5z3YNY67iCcNC0RiwPnZZXrHedvV8ljc6/4vL6JeTvEdQ7JKntWLNsr6/XO
A+oXuD7P87dXoszc5DEKnBfL3JxVV+c4f79y1ncDsRewwPjm5omve6LMG8CzielzgTWs+z7IxNYr
TjcmJMd3Za1vb8LYugsqiGsYMCs+fhx4Kj7+MSFpbwDcBKxKPKc0xnBVrm0CtInTLwKPlLPuerH8
bwrchhfE8rsTzm9NgJ/HbTQsUe4zwjmladbzz49lD86afz0hCWmceA8vB3ZOlGlEOHcuzxH/VYl5
4wnnonrlvI7PyDpXxvk3ZdW/Z6z/nMS8ujGOPyXmjSF8qaydmFeH8OV7WL44srZp9t+LiTI3xmOs
JOu5fyO8R7fKU3dtwpdFB47NdczliWXnrPnZ2yWz3T8Ets4qW2GsgBG+MPQr9P2bxj91AW9G3P1L
wonrxxbGmuXSjZCAfJ143jeEFp7Ds8quIrQY5TLO3ZOD6WfE/89llZsBtEzOMLNTzexNM/s6rmMp
sC1rvyFXSvxW/2ScPMndM10FPQlv8hEWuoJrx2+4z8flme7YLsDHnhib5eGb73pX8OaRHAeY+f9a
fPw6sLuZ7WZmuxFaR5Ldv98nnJCGZsX4JuHDN+fVkRbGu5QCIzye3WLcb7G2Kzfbv7Km/0P4MNmp
gtc3mdCFNszC1Xs7VlC+PJ3jOh/Omj+McCxkH4PPuntlWgceILT6JP8KGVP1ZNb0e4Ttf5+ZnWlm
LSoRQ3mezhHfpHLKP0FIRM5KzDub0Lry7zh9GCFJyz6G3o9/BV1hGz0EHGdmTQgJ4AjP3XqYqTN7
Pz6ctXwC8AMzG2hmh5hZnUrEUp45hGPyC+A24K+E5CHpNXf/ImteT0Lr+KQc54R6hNZJCOeEVz3R
IxHPd/+mHBa65DsBDyXOQxvM3WcTWrPOTszuRUhGH4rr3C7G+1iczrwmB16m8P1/POselxcllvUk
nMvmZm235witonvFdZuZXWJhOMoSwj7K9EZs0Pm9Av/y9cd1VhhrPGdOAq4xs/5m1rYaYtvsKQHc
/NxGaJ0bmGf5DoTuvmyfAdljH+Z7nu5H4Kus6e/KmV/WpG5mJxJOVNMJXZEHE042C9jwpvf7gP2B
E9w9eRXbjoTu6cyJKPM3Py5vEv83J3cXTL5umXXEk/QnQDcz2xbowNoEcDqh+6kba5ObZAKYSaZm
ZcW4ktAy24TcmhK+4c/PsSxf3F9mTWcudCh3u7v7LKAH4XzwN+CzmMBnJ2uFyIxrXOcY9NBNuzCx
nFzlCvCpu0/M+ivkgo7seL4CjiBsy7uBj+OH2g8qGU+2hTniy3tldOLL2VkQxtQRxk8N89CdBmuP
oddZ/xhqTf5jKJdnCe/hKwiv/6E85TL7KXvIxmdZy68jtMj0JrRSfRHHZm3sOKtMsrIPsI27n5v8
UhvlOnZ2JCQi2dsp857c2HNC5vlVeTHFQ8ARtnY859nAVHefHKebEVq0bmT913Uehe//KVnHZXIY
yY7AMTnq/1tcnlnHFYRhKP8i3LXhINae96qjazXfPi4k1pMJx/uvgakWxgdfbVa523NtyTQGcDPj
7kvM7H8JLYE35yjyJWGsQ7adWT9B8BzlNlYfQtN/38yM+KGW/cFfEDO7hpBIHufu07IWLyS0nuS6
FQusHU/2KWH8YLaKWsaSXiO05h1K6GJ6A0L/oJm9TkgAjdDamWzxWRj/H8P6yXNyebYvCCe1XK1x
O7Fh46jycvdRhPFkdQnd0wOBf5lZSY5WlvJkjrGdSdxiIn5Db8L6r7c6jsFc1ltPbE39YYytE+GD
YriZHeDu07PLV6O/AaeZWSdCYtKEtR9msHabnQHMzPH8gm+d5O6rzOxR4H8ISczoPEUz+3Enwpef
jMy5ZWGsbwUhMbnRzJoTWq9uJXwxO6fQuHKY4hVfsZrr2FlIaN09K8cyCC2mEM4Jud7/FZ0TMvui
MhffVCQzNvMMM3uA0ML1m8TyzL64ldCSnq0q3kMLCV9Sr8yzPNMD1Ad4xt3LLooxs30rsZ5Mq+nW
WfPzJbH59nGFscbW3QuAC8xsP+AnhKvIPyO0KKeeEsDN012Ega035Fj2CnB8clCxmTUkDOYeXYTY
GhC6+pLOJowFrBQz+yHhNV7o7i/kKPIsYTByI3d/qZyqxgE/iRfBvBHr3go4tRLhvEI4+V0IvJXV
bfY64Zu4EbrOVyaWvUBIGHfL8xpycvfVZjYR+JGZXZfpBjazAwkD3DckAcy0lNUvZ70rCAPLtwX+
EddVmQTwjbiePkByn5xGON9U+vY41S22To6zcNXz8YRWp+nE7WVm9SvZTV1ZzxFaes8mJIDvufv4
xPJXCePX9nD3R6tgffcTxoP+Kzm8IEtmP/UhJB4ZZyZiWoe7fwrcY2YnEVrsIfQQOOUcc1XsWUIC
9VVsuc9nHHCRme2c6QaOFwYdW17l7v61mY0nDMO5qZzW5xUU+Jrd/Ssz+xdh/y8jnCuHZi1/E2gH
XFnOPtsYzxIvyIpDjfJpQPhimvSTHOXyvf7MxYv7E89h8UvnUdUQa5nYeHClmV3E2mMz9ZQAbobc
fYWZDSRcEZvteuAE4CUz+wPh5Psrwhs3X7dxVXqWMCboNsJ4qAMJF5Bkd9+UK16Z+jfC2J134lVw
Gd+4+zR3Hx1bM4ab2Z8Ig7PXED7cjgN+Fbs5HiRc4ff32KI4n/DNcJ3bklQg84F3Iut+IEJoHcy0
xq4zZsrdZ8f9cEcct/kK4VtwS0KL4v2x9S2Xa+Prf9LM7iV0C19H+Aa7Js9zyvM54dtzHzObQmit
/IBwpV43wk2MP47ruZrQgjq1Mitw9y/jvrjazJbGOvclJPKvs/44xRoRk5SfAk8RxpttS7jS8BvC
+EwIV9IDXGFmzxMukihvPN8GSbTKnRXjuD5r+ZcWbkNyq5ntQkgYFxNaoY4A/u3uwyuxvqlAuV3d
7j7JzJ4Efm/hdi7jCS3tVwN/9bX3vfw3YXu9TXiPlxLuLXhbrGeNmb0HnGRmLxOukJ/r1XQvU0LL
zjmE1uxbCcdvXcIYtl5Ajzjs5WbCBSMvxHPpqvjaFlNxV+YvCF9uxsTz3LxY/77u/otYZhqhW/c4
wvlmvruX96XtIcI41auBl939k6zllxHG+z1j4VZZnxG6hkuBle7+2wpirsgfCd34r5vZIMK4voaE
9+7B7v6jWO5Zwv1o/4dwAWAPch9L0whJ8rnAFGCZh3tgjiGcY26Lid8awkU+lRmOVmGsZrYT4Qvs
I4QW4dXxOfWJV1YLugp4U/8jcRVw1vzahAN/nStH47KDCVfnLSF8yL8EHJRVZgjhRJxrnXOAh7Pm
dY/rOrq8eghv5BsIJ8VlhISnA1lX+FLBVcCJ9eX6G521vksJV8gtZ+0tWP7Iulcx70FIRpYRxiPe
TrjFwDoxVLAv5pN1JWucXyduZwcOz/PcswmtY0vjfplOuEq3RaJMrn15BuEEtoLQpXoy4cP2yQL2
Ta5t/APCyXllXNaXMMD8H4QT8wpC99gTxCs9y9ke610FHOcbcHmM+7tY351kXWUcn3tDJd4LFZYn
/1XAJVnl9iVcBPRBPG7mE5LT0qz32N3xeFlD4irZctY9pIIy61wFnJh/YIxzTXasiTInEd5Pi+Nx
PJPQmlfRfsp5RWZWmZuyXx8hcbqJ0FLzXdxW17HulahXExLAL2NMMwjdl8ky3QkXGq0gx5XFWevM
XCXaooJ4c15lG5c1IJyD/hvXuTDGOIB1r6g/iHCl/Yp47F9F/qtRs6+G7kQ4nyyKr3sa8IvE8gMI
yc6y+Py7E9t5eY6Ytya0tDtZdzXIqvOJeDxmYn4SOKaCbVXoNm1CuFVV5p57n8fj7aJEmW0JY7IX
EL4sPUW4Gn2dbUT4cv0E4UuBAzMSy75H+NK8hHDO/3k52z3n1eMVxQpsE+OcFteziPDeO6W8bZC2
v8ztIURkMxCvVp0F3Oju2T85JiIiUhAlgCKbKDOrT7j574uE1oE9CIP3dwLaehhzJSIiUmkaAyiy
6VpNuOryDkKXx1JC18kpSv5ERGRjqAVQREREJGV0I2gRERGRlNmsu4CbNm3qJSUlNR2GiIiIyCZh
0qRJX7h7s4rKbdYJYElJCRMnTqzpMEREREQ2CWb2YcWl1AUsIiIikjpKAEVERERSRgmgiIiISMps
1mMARaRyVq5cydy5c1m+fHlNhyJSoXr16tGiRQvq1KlT06GIbHGUAIqkyNy5c2nYsCElJSWYWU2H
I5KXu7Nw4ULmzp1Lq1atajockS2OuoBFUmT58uU0adJEyZ9s8syMJk2aqLVapJooARRJGSV/srnQ
sSpSfZQAioiIiKRM0cYAmllP4HagFnC/u9+Uo8ypwHWAA++4+xnFik8kjezBqm1h8XMq/m3xWrVq
ccABB5RNP/XUU1T2F32+/vprHnnkES666KLKhlghd6dZs2bMnDmT7bffnk8//ZRddtmF1157jUMP
PRSAZs2aMWPGDJo0aZKzjpEjRzJt2jSuuuqqvOsZPXo0t9xyC08//fR6ywYNGkS/fv1o0KBB1bwo
EZEsRWkBNLNawJ3AscB+wOlmtl9WmdbA1UBXd28LXFaM2ESkuOrXr8/kyZPL/jbk5xy//vpr7rrr
rko/b/Xq1RWWMTMOPvhgxo0bB8DYsWPp0KEDY8eOBeC9996jadOmeZM/gF69epWb/FVk0KBBLFu2
bIOfLyJSkWJ1AR8EzHL39939O2AYcFJWmfOBO939KwB3n1+k2ESkhq1evZorr7ySTp060a5dO+65
5x4AlixZwlFHHUXHjh054IAD+Mc//gHAVVddxezZs2nfvj1XXnklo0eP5oQTTiirr3///gwZMgQI
Pxk5cOBADj30UJ544glmz55Nz549OfDAAznssMOYMWPGevF07dq1LOEbO3Ysv/jFL9ZJCA855BAA
FixYwI9+9CM6depEp06dGDNmDABDhgyhf//+AMyePZvOnTvTqVMnBgwYwLbbblu2niVLltC7d2/2
2WcfzjzzTNydwYMHM2/ePI444giOOOKIqtzMIiJlitUFvCvwcWJ6LnBwVpm9AcxsDKGb+Dp3fza7
IjPrB/QD2G233aolWBGpPt9++y3t27cHoFWrVjz55JP85S9/oVGjRkyYMIEVK1bQtWtXjjnmGFq2
bMmTTz7JdtttxxdffEHnzp3p1asXN910E1OnTmXy5MlA6E4tT7169Xj99dcBOOqoo7j77rtp3bo1
b775JhdddBEvv/zyOuUPOeQQBg4cCMD48eP53e9+x6BBg4CQAHbt2hWASy+9lMsvv5xDDz2Ujz76
iB49ejB9+vR16rr00ku59NJLOf3007n77rvXWfb222/z7rvvsssuu9C1a1fGjBnDJZdcwp/+9CdG
jRpF06ZNN2ALi4hUrFgJYK6BRtmDhWoDrYHuQAvgNTPb392/XudJ7vcC9wKUlpZWPOBIRDYpmS7g
pOeff54pU6YwfPhwABYtWsTMmTNp0aIF11xzDa+++ipbbbUVn3zyCZ9//nml13naaacBocVt7Nix
nHLKKWXLVqxYsV75gw46iLfffpulS5eycuVKtt12W/bYYw9mzZrF2LFj+eUvfwnAiy++yLRp08qe
980337B48eJ16ho3bhxPPfUUAGeccQZXXHHFOutp0aIFAO3bt2fOnDll4wxFNkX24IPVVrefc061
1S3rK1YCOBdomZhuAczLUeYNd18JfGBm7xESwgnFCVFEaoq78+c//5kePXqsM3/IkCEsWLCASZMm
UadOHUpKSnLeF6527dqsWbOmbDq7zDbbbAPAmjVraNy48XoJaLYGDRqw11578cADD9CxY0cAOnfu
zDPPPMP8+fNp06ZNWX3jxo2jfv36lX/RQN26dcse16pVi1WrVm1QPSIilVWsMYATgNZm1srMtgb6
ACOzyjwFHAFgZk0JXcLvFyk+EalBPXr04P/+7/9YuXIlAP/9739ZunQpixYtYscdd6ROnTqMGjWK
Dz/8EICGDRuu09K2++67M23aNFasWMGiRYt46aWXcq5nu+22o1WrVjzxxBNASDzfeeednGW7du3K
oEGD6NKlCwBdunTh9ttvp3PnzmX3pzvmmGO44447yp6TK7Hs3LkzI0aMAGDYsGEFbY/s1yciUtWK
0gLo7qvMrD/wHGF83wPu/q6ZDQQmuvvIuOwYM5sGrAaudPeFxYhPJK0KuW1LMZx33nnMmTOHjh07
lt2G5amnnuLMM8/kxBNPpLS0lPbt27PPPvsA0KRJE7p27cr+++/Psccey80338ypp55Ku3btaN26
NR06dMi7rqFDh3LhhRdyww03sHLlSvr06cP3vve99cp17dqV22+/vSwB7NixI3PnzuW8884rKzN4
8GAuvvhi2rVrx6pVq+jWrdt64/wGDRrEWWedxa233srxxx9Po0aNKtwe/fr149hjj6V58+aMGjWq
oG0oIlIZ5r5pfABsiNLSUp84cWJNhyGy2Zg+fTr77rtvTYeRKsuWLaN+/fqYGcOGDePRRx8tu5pZ
KqZjdtOiMYCbPjOb5O6lFZUr2o2gRUTSaNKkSfTv3x93p3HjxjzwwAM1HZKIiBJAEZHqdNhhh+Ud
ZygiUlP0W8AiIiIiKaMEUERERCRllACKiIiIpIwSQBEREZGU0UUgIilW1bd0KOQ2Dp999hmXXXYZ
EyZMoG7dupSUlDBo0CD23nvvKo0lqXv37txyyy2Ulua/M8KgQYPo168fDRo0AOC4447jkUceoXHj
xhu17pKSEho2bEitWrUAuOuuuzjkkEMqXc/vf/97rrnmmo2KJZ8OHTrw17/+lfbt27Nq1SoaNWrE
Pffcw1lnnQXAgQceyH333Vf2qyjZJk6cyEMPPcTgwYPzrmPOnDmccMIJTJ06db1lQ4YM4ZhjjmGX
XXapmhckIhVSC6CIFI27c/LJJ9O9e3dmz57NtGnT+P3vf79Bv+9b1QYNGsSyZcvKpp955pmNTv4y
Ro0axeTJk5k8efIGJX8QEsDKKvSn5Q455BDGjh0LwDvvvEObNm3KppcuXcr777+f82bZGaWlpeUm
fxUZMmQI8+Zl/zqoiFQnJYAiUjSjRo2iTp06XHDBBWXz2rdvz2GHHcbo0aM54YQTyub379+fIUOG
AKEV7ZprrqFLly6Ulpby1ltv0aNHD/bcc8+yX94o7/lJF154IaWlpbRt25Zrr70WCL/oMW/ePI44
4giOOOKIsnV+8cUX/OpXv+Kuu+4qe/51113HrbfeCsDNN99Mp06daNeuXVldhcr33B/84AcceOCB
tG3blnvvvReAq666im+//Zb27dtz5plnMmfOHPbff/+y59xyyy1cd911QGjtvOaaazj88MO5/fbb
WbBgAT/60Y/o1KkTnTp1YsyYMevF0rVr17KEb+zYsVxwwQVlP2s3fvx4OnbsSK1atVi6dCk//elP
6dSpEx06dCi7oXVy2y9YsIDvf//7dOzYkZ/97GfsvvvufPHFFwCsXr2a888/n7Zt23LMMcfw7bff
Mnz4cCZOnMiZZ55J+/bt+fbbbyu1HUVkwygBFJGimTp1KgceeOAGPbdly5aMGzeOww47jL59+zJ8
+HDeeOMNBgwYUKl6brzxRiZOnMiUKVN45ZVXmDJlCpdccgm77LILo0aNWu+n1/r06cNjjz1WNv34
449zyimn8PzzzzNz5kzGjx/P5MmTmTRpEq+++mrOdR5xxBG0b9+egw8+GKDc5z7wwANMmjSJiRMn
MnjwYBYuXMhNN91E/fr1mTx5MkOHDq3wNX799de88sor/PKXv+TSSy/l8ssvZ8KECYwYMWKdn7LL
SLYAjh07lm7dulG3bl0WL17M2LFj6dq1a9m2O/LII5kwYQKjRo3iyiuvZOnSpevU9bvf/Y4jjzyS
t956i5NPPpmPPvqobNnMmTO5+OKLeffdd2ncuDEjRoygd+/elJaWMnToUCZPnkz9+vUrfH0isvE0
BlBENgu9evUC4IADDmDJkiU0bNiQhg0bUq9ePb7++uuC63n88ce59957WbVqFZ9++inTpk2jXbt2
ect36NCB+fPnM2/ePBYsWMDR3mPBAAAgAElEQVT222/PbrvtxuDBg3n++efLfnd4yZIlzJw5k27d
uq1Xx6hRo2jatGnZ9PPPP5/3uYMHD+bJJ58E4OOPP2bmzJk0adKk4NcHcNppp5U9fvHFF5k2bVrZ
9DfffMPixYtp2LBh2bySkhK+++47PvvsM2bMmEGbNm3o1KkTb775JmPHjuXnP/95WdwjR47klltu
AWD58uXrJHgAr7/+eln8PXv2ZPvtty9b1qpVK9q3bw+EcYVz5syp1OsSkaqjBFBEiqZt27YMHz48
57LatWuzZs2asunly5evs7xu3boAbLXVVmWPM9OrVq2q8PkAH3zwAbfccgsTJkxg++23p2/fvjnL
ZevduzfDhw/ns88+o0+fPkAYz3j11Vfzs5/9rMLnZ8v33NGjR/Piiy8ybtw4GjRoQPfu3XPGV9Fr
3Wabbcoer1mzhnHjxlXYstalSxeGDx9O8+bNMTM6d+7MmDFjGD9+PJ07dy6Le8SIEbRp02ad5ybH
cJb3+/LJ/VarVi1194rUIHUBi0jRHHnkkaxYsYL77ruvbN6ECRN45ZVX2H333Zk2bRorVqxg0aJF
vPTSS5Wqu5Dnf/PNN2yzzTY0atSIzz//nH//+99lyxo2bMjixYtz1t2nTx+GDRvG8OHD6d27NwA9
evTggQceYMmSJQB88sknzJ8/v6BY8z130aJFbL/99jRo0IAZM2bwxhtvlD2nTp06rFy5EoCddtqJ
+fPns3DhQlasWMHTTz+dd13HHHMMd9xxR9l0Zmxftq5du3LbbbfRpUsXICSEDz30EDvvvHPZxTA9
evTgz3/+c1mS9/bbb69Xz6GHHsrjjz8OhBbDr776qsLtUd62F5HqoRZAkRQr5LYtVcnMePLJJ7ns
ssu46aabqFevXtltYFq2bMmpp55Ku3btaN26dVn3aKEKef73vvc9OnToQNu2bdljjz3KxrYB9OvX
j2OPPZbmzZuvNw6wbdu2LF68mF133ZXmzZsDIbGaPn16WcK07bbb8vDDD7PjjjtWGGu+5/bs2ZO7
776bdu3a0aZNm7KWt0x87dq1o2PHjgwdOpQBAwZw8MEH06pVK/bZZ5+86xo8eDAXX3wx7dq1Y9Wq
VXTr1q3swpmkrl27cvnll5fF1Lx5c1avXr3OVcu//e1vueyyy2jXrh3uTklJyXrJ57XXXsvpp5/O
Y489xuGHH07z5s1p2LBhWbKbS9++fbnggguoX79+Qa2VIrLxrLzm+k1daWmpT5w4sabDENlsTJ8+
nX333bemw5At2IoVK6hVqxa1a9dm3LhxXHjhhXlbHQuhY3bTUtX3Dk0q9hfSLZWZTXL3/Dc9jdQC
KCIiVeajjz7i1FNPZc2aNWy99dbrdPeLyKZDCaCIiFSZ1q1b5xwbKCKbFl0EIpIym/OwD0kXHasi
1UcJoEiK1KtXj4ULF+qDVTZ57s7ChQupV69eTYciskVSF7BIirRo0YK5c+eyYMGCmg5FpEL16tWj
RYsWNR2GyBZJCaBIitSpU4dWrVrVdBgiIlLD1AUsIiIikjJKAEVERERSRgmgiIiISMooARQRERFJ
GSWAIiIiIimjBFBEREQkZZQAioiIiKSMEkARERGRlFECKCIiIpIySgBFREREUkYJoIiIiEjKKAEU
ERERSRklgCIiIiIpowRQREREJGWUAIqIiIikTNESQDPraWbvmdksM7sqx/K+ZrbAzCbHv/OKFZuI
iIhImtQuxkrMrBZwJ/B9YC4wwcxGuvu0rKKPuXv/YsQkIiIiklbFagE8CJjl7u+7+3fAMOCkIq1b
RERERBKKlQDuCnycmJ4b52X7kZlNMbPhZtYyV0Vm1s/MJprZxAULFlRHrCIiIiJbtKJ0AQOWY55n
Tf8TeNTdV5jZBcCDwJHrPcn9XuBegNLS0uw6RERkE2IPPlhtdfs551Rb3SJbumK1AM4Fki16LYB5
yQLuvtDdV8TJ+4ADixSbiIiISKoUKwGcALQ2s1ZmtjXQBxiZLGBmzROTvYDpRYpNREREJFWK0gXs
7qvMrD/wHFALeMDd3zWzgcBEdx8JXGJmvYBVwJdA32LEJiIiIpI2xRoDiLs/AzyTNW9A4vHVwNXF
ikdEREQkrfRLICIiIiIpowRQREREJGWUAIqIiIikjBJAERERkZRRAigiIiKSMkoARURERFJGCaCI
iIhIyigBFBEREUkZJYAiIiIiKaMEUERERCRllACKiIiIpIwSQBEREZGUUQIoIiIikjJKAEVERERS
RgmgiIiISMooARQRERFJGSWAIiIiIimjBFBEREQkZZQAioiIiKSMEkARERGRlFECKCIiIpIySgBF
REREUkYJoIiIiEjKKAEUERERSRklgCIiIiIpowRQREREJGWUAIqIiIikjBJAERERkZRRAigiIiKS
MkoARURERFJGCaCIiIhIyigBFBEREUkZJYAiIiIiKaMEUERERCRllACKiIiIpIwSQBEREZGUUQIo
IiIikjJFSwDNrKeZvWdms8zsqnLK9TYzN7PSYsUmIiIikiZFSQDNrBZwJ3AssB9wupntl6NcQ+AS
4M1ixCUiIiKSRsVqATwImOXu77v7d8Aw4KQc5a4H/ggsL1JcIiIiIqlTrARwV+DjxPTcOK+MmXUA
Wrr70+VVZGb9zGyimU1csGBB1UcqIiIisoUrVgJoOeZ52UKzrYDbgF9WVJG73+vupe5e2qxZsyoM
UURERCQdipUAzgVaJqZbAPMS0w2B/YHRZjYH6AyM1IUgIiIiIlWvWAngBKC1mbUys62BPsDIzEJ3
X+TuTd29xN1LgDeAXu4+sUjxiYiIiKRGURJAd18F9AeeA6YDj7v7u2Y20Mx6FSMGEREREQlqF2tF
7v4M8EzWvAF5ynYvRkwiIiIiaaRfAhERERFJGSWAIiIiIilTtC5gEZGK2IMPVlvdfs451Va3iMjm
Ri2AIiIiIimjBFBEREQkZZQAioiIiKSMEkARERGRlFECKCIiIpIySgBFREREUkYJoIiIiEjKKAEU
ERERSRklgCIiIiIpowRQREREJGWUAIqIiIikTMEJoJmdkmd+76oLR0RERESqW2VaAP+SZ/69VRGI
iIiIiBRH7YoKmNke8eFWZtYKsMTiPYDl1RGYiIiIiFSPChNAYBbghMRvdtayz4DrqjgmEREREalG
FSaA7r4VgJm94u6HV39IIiIiIlKdCh4DqORPREREZMtQSBcwAHH8341Ae2Db5DJ3362K4xIRERGR
alJwAgg8QhgD+EtgWfWEIyIiIiLVrTIJYFugq7uvqa5gRERERKT6VeY+gK8CHaorEBEREREpjnJb
AM1sYGJyDvCcmf2dcPuXMu4+oOpDExEREZHqUFEXcMus6X8CdXLMFxEREZHNRLkJoLv/pFiBiIiI
iEhxVOY2MHvkWbQC+FQXh4iIiIhsHipzFXDmJ+Eg/CycJ5atMbORwEXu/nlVBSciIiIiVa8yVwGf
DwwF9gbqAW2Ah4GLgAMIyeSdVR2giIiIiFStyrQA/g7Yy92Xx+lZZnYh8F93v8fM+gIzqzpAERER
EalalWkB3AooyZq3G1ArPl5C5RJKEREREakBlUnYBgEvm9lfgY+BFsBP4nyA44FxVRueiIiIiFS1
ghNAd/+jmU0BTgE6Ap8C57r7s3H5U8BT1RKliIiIiFSZSnXZxmTv2WqKRURERESKoKKfgvu1u98Y
Hw/MV04/BSciIiKy+ajoIpAWiccty/mrkJn1NLP3zGyWmV2VY/kFZvYfM5tsZq+b2X6FvQQRERER
qYyKfgruwsTjDf5ZODOrRbhH4PeBucAEMxvp7tMSxR5x97tj+V7An4CeG7pOEREREcmtMreBwcz2
NbPfmtkdcbqNmbUr4KkHAbPc/X13/w4YBpyULODu3yQmt2HdXxoRERERkSpScAJoZqcArwK7Aj+O
sxsSWuoqsivh1jEZc+O87HVcbGazgT8Cl+SJo5+ZTTSziQsWLCg0fBERERGJKtMCOBD4vrtfAKyO
894BvlfAcy3HvPVa+Nz9TnffE/gV8JtcFbn7ve5e6u6lzZo1KyxyERERESlTmQRwR0LCB2uTN6ew
rtq5rHuxSAtgXjnlhwE/qERsIiIiIlKgyiSAk4Czs+b1AcYX8NwJQGsza2VmW8fnjUwWMLPWicnj
0e8Ki4iIiFSLytwI+hLgeTM7F9jGzJ4D9gaOqeiJ7r7KzPoDzxF+O/gBd3833ltworuPBPqb2dHA
SuAr4JxKvhYRERERKUCFCaCZnQq86u4zzGwf4ATgacJFHU+7+5JCVuTuzwDPZM0bkHh8aWUCFxER
EZENU0gL4A3AnvHq3FeBV4DH3f3Dao1MRERERKpFhWMA3X1vYBfg18C3wC+B2Wb2oZn9zczOq+YY
RURERKQKFXQRiLt/7u5PuPvP3b090JS1v+xxT3UGKCIiIiJVq6CLQMzMgPZAt/h3COE2Lo8Dr1Vb
dCIiIiJS5Qq5CORpoCPwHvA6cC/Q190XV3NsIiIiIlINCukCbgOsAD4AZhN+01fJn4iIiMhmqsIW
QHdvbWY7sbb79zIzawqMIXT/vu7uk6s3TBERERGpKgWNAXT3z4En4h9m1hjoR/i93maEmzuLiIiI
yGZgQy8CORRoDEwEHqi26ERERESkyhVyEci/CFf9bg28SbgR9B3AOHdfXr3hiYiIiEhVK6QF8DXg
RmCCu6+s5nhEREREpJoVchHITcUIRERERESKo6AxgCKbInvwwWqt3885p1rrFxERqSkF/RSciIiI
iGw5lACKiIiIpIwSQBEREZGUUQIoIiIikjJKAEVERERSRgmgiIiISMooARQRERFJGSWAIiIiIimj
BFBEREQkZZQAioiIiKSMEkARERGRlFECKCIiIpIySgBFREREUkYJoIiIiEjKKAEUERERSRklgCIi
IiIpowRQREREJGWUAIqIiIikjBJAERERkZRRAigiIiKSMkoARURERFJGCaCIiIhIyigBFBEREUkZ
JYAiIiIiKVO0BNDMeprZe2Y2y8yuyrH8F2Y2zcymmNlLZrZ7sWITERERSZOiJIBmVgu4EzgW2A84
3cz2yyr2NlDq7u2A4cAfixGbiIiISNoUqwXwIGCWu7/v7t8Bw4CTkgXcfZS7L4uTbwAtihSbiIiI
SKoUKwHcFfg4MT03zsvnXODfuRaYWT8zm2hmExcsWFCFIYqIiIikQ7ESQMsxz3MWNDsLKAVuzrXc
3e9191J3L23WrFkVhigiIiKSDrWLtJ65QMvEdAtgXnYhMzsa+DVwuLuvKFJsIiIiIqlSrBbACUBr
M2tlZlsDfYCRyQJm1gG4B+jl7vOLFJeIiIhI6hQlAXT3VUB/4DlgOvC4u79rZgPNrFcsdjOwLfCE
mU02s5F5qhMRERGRjVCsLmDc/Rngmax5AxKPjy5WLCIiIiJppl8CEREREUkZJYAiIiIiKaMEUERE
RCRllACKiIiIpIwSQBEREZGUUQIoIiIikjJKAEVERERSRgmgiIiISMooARQRERFJGSWAIiIiIimj
BFBEREQkZZQAioiIiKSMEkARERGRlFECKCIiIpIySgBFREREUkYJoIiIiEjKKAEUERERSRklgCIi
IiIpowRQREREJGWUAIqIiIikjBJAERERkZRRAigiIiKSMkoARURERFJGCaCIiIhIytSu6QCKyR58
sBpr71uNdW86/Byv6RBERERkI6kFUERERCRllACKiIiIpEyquoBFKsMetJoOoSjUrS8ikj5qARQR
ERFJGSWAIiIiIimjLmARSYW0dOmDuvVFpGJqARQRERFJGbUAiojIZiktrbpq0ZXqoBZAERERkZRR
AigiIiKSMkoARURERFKmaAmgmfU0s/fMbJaZXZVjeTcze8vMVplZ72LFJSIiIpI2RUkAzawWcCdw
LLAfcLqZ7ZdV7COgL/BIMWISERERSatiXQV8EDDL3d8HMLNhwEnAtEwBd58Tl60pUkwiIiIiqVSs
LuBdgY8T03PjvEozs35mNtHMJi5YsKBKghMRERFJk2K1AOa6WdMG3djI3e8F7gUoLS3VzZFERES2
AGm5ryNsGvd2LFYL4FygZWK6BTCvSOsWERERkYRiJYATgNZm1srMtgb6ACOLtG4RERERSShKAuju
q4D+wHPAdOBxd3/XzAaaWS8AM+tkZnOBU4B7zOzdYsQmIiIikjZF+y1gd38GeCZr3oDE4wmErmER
ERERqUb6JRARERGRlFECKCIiIpIySgBFREREUkYJoIiIiEjKKAEUERERSRklgCIiIiIpowRQRERE
JGWUAIqIiIikjBJAERERkZRRAigiIiKSMkoARURERFJGCaCIiIhIyigBFBEREUkZJYAiIiIiKaME
UERERCRllACKiIiIpIwSQBEREZGUUQIoIiIikjJKAEVERERSRgmgiIiISMooARQRERFJGSWAIiIi
IimjBFBEREQkZZQAioiIiKSMEkARERGRlFECKCIiIpIySgBFREREUkYJoIiIiEjKKAEUERERSRkl
gCIiIiIpowRQREREJGWUAIqIiIikjBJAERERkZRRAigiIiKSMkoARURERFJGCaCIiIhIyigBFBER
EUmZoiWAZtbTzN4zs1lmdlWO5XXN7LG4/E0zKylWbCIiIiJpUpQE0MxqAXcCxwL7Aaeb2X5Zxc4F
vnL3vYDbgD8UIzYRERGRtClWC+BBwCx3f9/dvwOGASdllTkJeDA+Hg4cZWZWpPhEREREUqN2kdaz
K/BxYnoucHC+Mu6+yswWAU2AL5KFzKwf0C9OLjGz96ol4k1LU7K2Q02xvsrJq4j26ZZH+3TLov25
5UnLPt29kELFSgBzvVLfgDK4+73AvVUR1ObCzCa6e2lNxyFVR/t0y6N9umXR/tzyaJ+uq1hdwHOB
lonpFsC8fGXMrDbQCPiyKNGJiIiIpEixEsAJQGsza2VmWwN9gJFZZUYC58THvYGX3X29FkARERER
2ThF6QKOY/r6A88BtYAH3P1dMxsITHT3kcBfgL+Z2SxCy1+fYsS2mUhVl3dKaJ9uebRPtyzan1se
7dMEUyObiIiISLrol0BEREREUkYJoIiIiEjKKAGsAWa2i5kNj4/bm9lxBTynu5k9XUXrLzWzwVVR
l6xV1fvVzEabmW5ZICJbHDPra2Z3VHGdP0j+ypiZDTSzo6tyHVsSJYA1wN3nuXvvONkeqDBRqOL1
T3T3S4q5zjSo6f1aE4rxZSb7pL6x5TaUmc0xs9ey5k02s6lVUPczZta4EuUr9eFpZr1y/Qa7yBbm
B4SfmwXA3Qe4+4s1GM8mTQngBjCzH5vZFDN7x8z+ZmYnmtmbZva2mb1oZjvFctfF5S+b2UwzOz/O
LzGzqfGWOAOB0+IHyWlmdpCZjY11jTWzNgXEc5yZzTCz181scObDNV9dyQ/gGOMDsbXpfTNLbWK4
qe3XrNhON7P/xPr/EOfVMrMhcd5/zOzyOP8SM5sWX8uwqt1K68RUu0hJ7zon9SootzEamlnmfqX7
VvbJFn4XPTltZraVux/n7l9XVZDZ3H2ku99UXfVvrszsKTObZGbvWviVKczsXDP7bzwn3pdJtM2s
mZmNMLMJ8a9rzUa/aTOzs8xsfDwH3hPPVz+J2/YVoGui7BAz652YXpJ4/D/x/PaOmd0U550f98E7
cZ80MLNDgF7AzXGdeybrNbOj4vn3P/Ezr26cP8fMfmdmb8Vl++R5PTnLxc+DKxLlpsbPgpL4uXx/
nDfUzI42szHxc+OgKt3gG8Ld9VeJP6At8B7QNE7vAGzP2iuqzwNujY+vA94B6hN+guZjYBegBJga
y/QF7kjUvx1QOz4+GhgRH3cHns4RT71Yb6s4/WimXCF1xRjHAnVjjAuBOjW9ndO+X+Oy0UBprPsj
oBnh1k0vE5KdA4EXEuUbx//zgLrJeYkyJcAM4H5gKjA0xjMGmAkcFMsdFI+Lt+P/NonX9QTwzxhH
Saxn6xjjAmAycFo5dZT3mm8CpgFTgFuAQwi3hfog1rsncD7h3qLvACOABnnKjQZKY71NgTmJfT0+
lpsCtC7wGJkDXANcEacHAr9K7PMS4DXgrfh3SOL1jgIeia+tBJgO3BW3ze6x7syxd1YivnuAWnH+
T4D/Aq8A95E4vrLi7BnX/w7wUvJ4JNxgfw6wVZzfgHD81smqYxvgX7GOqcBpiW3whxjfeGCvOH93
4KW4PV8CdovzhwC9E/Uuif+bA6/G1zgVOCzOPwYYF+N/Ati2mt/3O8T/9WMcu8bXuANQJ+7PO2KZ
R4BD4+PdgOk1fd7aVP+AfQnniDpx+i7CfX4z57GtCeeczLbNd5wcSzh3NMjaX00SZW8Afp6nniGE
+wpnPif3jvMfAi5LHNOZ518E3J/nNeUsR/g8uCJRbirhPV4CrAIOIDS2TQIeIPzq2UnAUzW9n4r1
U3BbkiOB4e7+BYC7f2lmBwCPmVlzwoH9QaL8P9z9W+BbMxtF+FCcXE79jYAHzaw14afw6lQQzz7A
++6eWeejrP2t5ELr+pe7rwBWmNl8YCfCL7Okyaa2X5M6AaPdfQGAmQ0FugHXA3uY2Z8JH9bPx/JT
gKFm9hTwVI769gJOIRwnE4AzgEMJ356vISSXM4BuHu7heTTwe+BH8fldgHZxG5UAuPt3ZjaAkHD1
j3FuV04d6zGzHYCTgX3c3c2ssbt/bWYjCQljpqv5a3e/Lz6+ATjX3f+co1y+VV0A3O7uQ2Nrba18
BXMYTvhQuQU4ETgTODsumw98392Xx/38KCGBh3B87O/uH8Rt1gb4ibtflIw1tiqeBnR195Vmdhdw
ppm9APyOkPQvIiSUb+fYhs0IyWG3uK4dksvdfZGZvQMcHus4EXjO3VdmVdUTmOfux8d6GyWWfePu
B5nZj4FBwAmE5PIhd3/QzH4KDCYcR/mcEdd7Y2wVbWBmTYHfAEe7+1Iz+xXwC0KiXV0uMbOT4+OW
hH35irt/CWBmTwB7x+VHA/sljqvtzKyhuy+uxvg2V0cRjtUJcXvVJ3xJS57HHmPtts3naOCv7r4M
wnk5zt8/vvcbA9sS7jFcnjbAB+7+3zj9IHAx4fgF+Hv8Pwn4YTn1FFou4wN3/w+Amb1L+ELmZvYf
QoJYo9QFXHnG+r9R/GfCN5kDgJ8Rvm1kZJet6MaL1wOj3H1/wsm5XnYBM3suNnHfT+7fUC64rmhF
4vFqivcb0ZuSTW2/Zse2Hnf/CvgeoaXrYkKrHsDxwJ2EE/AkCz+tmPSBu//H3dcAZSclIHlSagQ8
YWF8222EVrOMFxIn4vKUV0cu3wDLgfvN7IfAsjzl9jez1+JJ9MwC6s02DrgmJhi7x0S+UF8CX5lZ
H0IrXjLGOsB9Ma4nWLc7enziSxrAh+7+Ro76kx+ck+P0HsDBxA9Pd/8OeCxPfJ2BVzPryrOfHiMk
mRBuuJ+rrv8AR5vZH8zsMHdflFj2aOJ/l/i4C6GFDOBvhC8U5ZkA/MTMrgMOiElUZ8I2GxNf+zkU
+KP2G8LMuhMSjC7u/j1CQv1eOU/ZKpZtH/92VfKXlwEPJrZVG0JLWb7z5CpiPmIhY9w6UU+u5wwB
+sdz8+/I/9mWjKc8mc/Ass+/POfj9colY4/q5SgPsCYxvYZN4HNWCWDlvQScamZNoKzVohHwSVx+
Tlb5k8ysXizfnXDiS1oMNExMJ+vqmysAd+8R31TnEVpq9si0xLD2xF5QXVJmU9uvSW8Ch5tZ09ha
cjrwSmwx2crdRwC/BTqa2VZAS3cfBfwPa78hJxVyUiovYV2aK/4cKpX0uvsqQkvZCELr0bN56h1C
YSf/5Im5rIy7P0Jo7fwWeM7Mjizw9WQ8RkiwH82afznwOSEpL2Xthxisv83ybcP1Pjjd/bpM6OsV
DuOqJse/geT/wEwaCRwbj/EDgZfNrGWingtiS8mBhETwf2PrbobneUyO+Tk/2N39VUIr9ieEX4D6
cYz9hcRr38/dz63gtWyMRsBX7r4sjufqTOgSP9zMto9fnJIt1s8D/TMTZta+GmPb3L0E9DazHaHs
fPo20N3MmphZHUIvRMYcwvEGoXs000PyPPBTM2uQqAfCufXTWM+ZiXqyz7sZM4ASM9srTp9NGEqR
Vznn42xzgI4xvo5AqwrKbzKUAFaSu78L3Ej4AH4H+BPhm80TFq4Q/CLrKeMJ3XNvANe7+7ys5aMI
3QqTzew04I+EE+4YCuiaiq0XFwHPmtnrhA+gzLf1StWVZpvafs2K7VPg6ljnO8Bb7v4Pwnil0bG1
ZEgsUwt4OLZCvQ3c5ht2ccGGfHnYqKTXzLYFGrn7M8BlhItKctVb6Ml/Dms/VJIDzPcgDJsYTEiG
2hX4+jKeJOzP7G6nRsCnsWX1bDbsPbfeB6eZ7U74ErDeh6e7r04kTAMIrZuHm1mrzPOzV+DuSwjH
7+2ELvPV7v5xop67zWwXYJm7P0zo7u6YqOK0xP9x8fFY1v5855nA6/HxHHJ8sMfXND925f8l1v8G
0DXzIW1hYH9FXYQb41mgtplNIXxZeYNwvP6esL1fJIzZzJxPLwFKLVxcNY0wlEBycPdphO785+P2
fYEw7vM6wjHzImGcZ8Z9hON2PKG1e2ms51nCe3RiPM9lLrb4LWEfvUBI7jKGAVdauNhjz0Q8ywlj
aJ+I58Y1wN1V9HJHADvE+C4kjNPdPPgmMGB0S/0ja3BoNa5n2/jfCINtL6/p174l/xVrv1ZT7CXE
ixbi9BDioGnWvYilC+FENobw4Tgnzu/Luhe3JJ+zA6ElNHMRSL46upP7gqbmhMRkCqHl6Zw4vyvh
g/htwsUdFxLGY44mdNMPyVNun1jXWMJA8cz6ryZ0fU8mJAE7FLjt5hAv1Mjz+lvH9b0B/C9rB7Kv
83qz90F23XHbZS5QmQR0jvOTF4HcTv6LQI6N2+Ad4kVCOfZbb0Ir3eF56ugR1z857tPSRJzXEj58
J7D2IpASwkVB2ReB7H0OwjQAAANWSURBVBS3x/isbXIOYbD824QLLTIXsR0Z650S/3rVwHskcz6t
TbiQ4eSaft/qT3/V8affAq5GcXzLEne/pZrXcznhhLo14YR6vsdBs1L1irVfRTY1ZjaHkAxmt4hv
MczsFsLYwHqELshLXR+UsgVSAigiIgVJQwIokhZKAEUk1eKFPC/lWHSUuy8sdjwiIsWgBFBEREQk
ZXQVsIiIiEjKKAEUERERSRklgCIiIiIpowRQRFLFzOaY2bdmtiTxt8tG1NfdzNL229kisplTAigi
aXSiu2+b+Mv+JZeiyfFbzSIi1U4JoIgIYGadzWysmf1/e/cTokUdx3H8/UlrTaEVRVjcwFOePESw
x0AQFLUlL0GFCB3C27IHI+gSxN6FDl3sEChEwsKquB0UJLxFh6JDXtwVHghqgxaXRbA/3w4zC+Py
oLiPdHDer9M8M78fv9/cPs93ZviuJvkpyeHOtQ+S/JJkLclSkrPt+V3At8D+bjUxyVdJ5jrzH6kS
tlXIj9s2WetJtrfz5pOsJFlOMvP/3b2kvjEASuq9JJM0vZ3naFranQPmk+xrh/wOvAW8QtOS7XyS
N6pqnab12q9bqCa+B5wEdtP0Jr1G075tEjgCzCY59kxuUJI2MQBK6qOFttK3mmQBOA0sVtViVf1b
VTeAH4ATAFV1varuVuM7mhZhb464h8+ralBVD4ApYF9VfVZVD6tqCbgAvDviGpI0lO+eSOqjU1V1
c+NHki+Ad5JMd8a8CNxqrx8HPgUO0vxx3gn8POIeBp3jAzSPkVc757YBt0dcQ5KGMgBKUhPGLlbV
h5svJBkD5oEzwJWq+qutGqYdMqyd0jpNSNwwMWRMd94AWK6q17ayeUl6Wj4CliS4BEwnOZZkW5Id
7YcbrwIvAWPACvB3Ww082pn7G7A3yXjn3I/AiSR7kkwAs09Y/3vgfvthyMvtHg4lmXpmdyhJHQZA
Sb1XVQPgbeATmqA3AD4CXqiqNWAGuAz8CbwPXO3MvQN8DSy17xTuBy7SfNBxj+Z9wW+esP4/wDTw
OrAM/AF8CYw/bp4kbVWqhj29kCRJ0vPKCqAkSVLPGAAlSZJ6xgAoSZLUMwZASZKknjEASpIk9YwB
UJIkqWcMgJIkST1jAJQkSeqZ/wAKdp/83RnW2wAAAABJRU5ErkJggg==
"
>
</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></p>
<p>4 of my guess was right, but rank was wrong.</p>
<p>Above graph surprisly give low rank for capital-gain and capital-loss. Yes, from different perspective it makes sense that higher degree education indimates more economic stable.</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;[18]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><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="k">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="nb">print</span><span class="p">(</span><span class="s2">&quot;Final Model trained on full data</span><span class="se">\n</span><span class="s2">------&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy on testing data: </span><span class="si">{:.4f}</span><span class="s2">&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="nb">print</span><span class="p">(</span><span class="s2">&quot;F-score on testing data: </span><span class="si">{:.4f}</span><span class="s2">&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="nb">print</span><span class="p">(</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="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy on testing data: </span><span class="si">{:.4f}</span><span class="s2">&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="nb">print</span><span class="p">(</span><span class="s2">&quot;F-score on testing data: </span><span class="si">{:.4f}</span><span class="s2">&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>      Iter       Train Loss   Remaining Time 
         1           1.0455            3.02s
         2           0.9876            3.41s
         3           0.9415            3.04s
         4           0.9040            2.83s
         5           0.8715            2.72s
         6           0.8439            2.63s
         7           0.8203            2.58s
         8           0.7997            2.54s
         9           0.7826            2.49s
        10           0.7666            2.46s
        20           0.6833            2.24s
        30           0.6519            2.00s
        40           0.6344            1.79s
        50           0.6232            1.62s
        60           0.6159            1.49s
        70           0.6089            1.30s
        80           0.6044            1.13s
        90           0.6013            0.98s
       100           0.5989            0.80s
Final Model trained on full data
------
Accuracy on testing data: 0.8690
F-score on testing data: 0.7495

Final Model trained on reduced data
------
Accuracy on testing data: 0.8584
F-score on testing data: 0.7237
</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>Wow.. There is only marginal difference but it is very fast. Yes, considering the time final one performs better. </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>