asl_recognizer.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>asl_recognizer</title><script type="text/javascript" src="/02C1CE28-1C79-264F-94AB-BEECB1A7C8A1/main.js" charset="UTF-8"></script><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://cdn.mathjax.org/mathjax/latest/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="Artificial-Intelligence-Engineer-Nanodegree---Probabilistic-Models">Artificial Intelligence Engineer Nanodegree - Probabilistic Models<a class="anchor-link" href="#Artificial-Intelligence-Engineer-Nanodegree---Probabilistic-Models">&#182;</a></h1><h2 id="Project:-Sign-Language-Recognition-System">Project: Sign Language Recognition System<a class="anchor-link" href="#Project:-Sign-Language-Recognition-System">&#182;</a></h2><ul>
<li><a href="#intro">Introduction</a></li>
<li><a href="#part1_tutorial">Part 1 Feature Selection</a><ul>
<li><a href="#part1_tutorial">Tutorial</a></li>
<li><a href="#part1_submission">Features Submission</a></li>
<li><a href="#part1_test">Features Unittest</a></li>
</ul>
</li>
<li><a href="#part2_tutorial">Part 2 Train the models</a><ul>
<li><a href="#part2_tutorial">Tutorial</a></li>
<li><a href="#part2_submission">Model Selection Score Submission</a></li>
<li><a href="#part2_test">Model Score Unittest</a></li>
</ul>
</li>
<li><a href="#part3_tutorial">Part 3 Build a Recognizer</a><ul>
<li><a href="#part3_tutorial">Tutorial</a></li>
<li><a href="#part3_submission">Recognizer Submission</a></li>
<li><a href="#part3_test">Recognizer Unittest</a></li>
</ul>
</li>
<li><a href="#part4_info">Part 4 (OPTIONAL) Improve the WER with Language Models</a></li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><a id='intro'></a></p>
<h2 id="Introduction">Introduction<a class="anchor-link" href="#Introduction">&#182;</a></h2><p>The overall goal of this project is to build a word recognizer for American Sign Language video sequences, demonstrating the power of probabalistic models.  In particular, this project employs  <a href="https://en.wikipedia.org/wiki/Hidden_Markov_model">hidden Markov models (HMM's)</a> to analyze a series of measurements taken from videos of American Sign Language (ASL) collected for research (see the <a href="http://www-i6.informatik.rwth-aachen.de/~dreuw/database-rwth-boston-104.php">RWTH-BOSTON-104 Database</a>).  In this video, the right-hand x and y locations are plotted as the speaker signs the sentence.
<a href="https://drive.google.com/open?id=0B_5qGuFe-wbhUXRuVnNZVnMtam8"><img src="http://www-i6.informatik.rwth-aachen.de/~dreuw/images/demosample.png" alt="ASLR demo"></a></p>
<p>The raw data, train, and test sets are pre-defined.  You will derive a variety of feature sets (explored in Part 1), as well as implement three different model selection criterion to determine the optimal number of hidden states for each word model (explored in Part 2). Finally, in Part 3 you will implement the recognizer and compare the effects the different combinations of feature sets and model selection criteria.</p>
<p>At the end of each Part, complete the submission cells with implementations, answer all questions, and pass the unit tests.  Then submit the completed notebook for review!</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><a id='part1_tutorial'></a></p>
<h2 id="PART-1:-Data">PART 1: Data<a class="anchor-link" href="#PART-1:-Data">&#182;</a></h2><h3 id="Features-Tutorial">Features Tutorial<a class="anchor-link" href="#Features-Tutorial">&#182;</a></h3><h5 id="Load-the-initial-database">Load the initial database<a class="anchor-link" href="#Load-the-initial-database">&#182;</a></h5><p>A data handler designed for this database is provided in the student codebase as the <code>AslDb</code> class in the <code>asl_data</code> module.  This handler creates the initial <a href="http://pandas.pydata.org/pandas-docs/stable/">pandas</a> dataframe from the corpus of data included in the <code>data</code> directory as well as dictionaries suitable for extracting data in a format friendly to the <a href="https://hmmlearn.readthedocs.io/en/latest/">hmmlearn</a> library.  We'll use those to create models in Part 2.</p>
<p>To start, let's set up the initial database and select an example set of features for the training set.  At the end of Part 1, you will create additional feature sets for experimentation.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[1]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></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">asl_data</span> <span class="k">import</span> <span class="n">AslDb</span>


<span class="n">asl</span> <span class="o">=</span> <span class="n">AslDb</span><span class="p">()</span> <span class="c1"># initializes the database</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> <span class="c1"># displays the first five rows of the asl database, indexed by video and frame</span>
</pre></div>

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

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


<div class="output_area">
<div class="prompt output_prompt">Out[1]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<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></th>
      <th>left-x</th>
      <th>left-y</th>
      <th>right-x</th>
      <th>right-y</th>
      <th>nose-x</th>
      <th>nose-y</th>
      <th>speaker</th>
    </tr>
    <tr>
      <th>video</th>
      <th>frame</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th rowspan="5" valign="top">98</th>
      <th>0</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
    </tr>
    <tr>
      <th>1</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
    </tr>
    <tr>
      <th>2</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
    </tr>
    <tr>
      <th>3</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
    </tr>
    <tr>
      <th>4</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</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="n">asl</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="mi">98</span><span class="p">,</span><span class="mi">1</span><span class="p">]</span>  <span class="c1"># look at the data available for an individual frame</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_stderr output_text">
<pre>C:\Users\Gowtham Raj\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix
  &#34;&#34;&#34;Entry point for launching an IPython kernel.
</pre>
</div>
</div>

<div class="output_area">
<div class="prompt output_prompt">Out[2]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>left-x         149
left-y         181
right-x        170
right-y        175
nose-x         161
nose-y          62
speaker    woman-1
Name: (98, 1), dtype: object</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>The frame represented by video 98, frame 1 is shown here:
<img src="http://www-i6.informatik.rwth-aachen.de/~dreuw/database/rwth-boston-104/overview/images/orig/098-start.jpg" alt="Video 98"></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">
<h5 id="Feature-selection-for-training-the-model">Feature selection for training the model<a class="anchor-link" href="#Feature-selection-for-training-the-model">&#182;</a></h5><p>The objective of feature selection when training a model is to choose the most relevant variables while keeping the model as simple as possible, thus reducing training time.  We can use the raw features already provided or derive our own and add columns to the pandas dataframe <code>asl.df</code> for selection. As an example, in the next cell a feature named <code>'grnd-ry'</code> is added. This feature is the difference between the right-hand y value and the nose y value, which serves as the "ground" right y value.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;grnd-ry&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-y&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;nose-y&#39;</span><span class="p">]</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>  <span class="c1"># the new feature &#39;grnd-ry&#39; is now in the frames dictionary</span>
</pre></div>

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

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


<div class="output_area">
<div class="prompt output_prompt">Out[3]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<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></th>
      <th>left-x</th>
      <th>left-y</th>
      <th>right-x</th>
      <th>right-y</th>
      <th>nose-x</th>
      <th>nose-y</th>
      <th>speaker</th>
      <th>grnd-ry</th>
    </tr>
    <tr>
      <th>video</th>
      <th>frame</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th rowspan="5" valign="top">98</th>
      <th>0</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
    </tr>
    <tr>
      <th>1</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
    </tr>
    <tr>
      <th>2</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
    </tr>
    <tr>
      <th>3</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
    </tr>
    <tr>
      <th>4</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</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">
<h5 id="Try-it!">Try it!<a class="anchor-link" href="#Try-it!">&#182;</a></h5>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[4]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">asl_utils</span> <span class="k">import</span> <span class="n">test_features_tryit</span>
<span class="c1"># TODO add df columns for &#39;grnd-rx&#39;, &#39;grnd-ly&#39;, &#39;grnd-lx&#39; representing differences between hand and nose locations</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;grnd-rx&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-x&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;nose-x&#39;</span><span class="p">]</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;grnd-ly&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-y&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;nose-y&#39;</span><span class="p">]</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;grnd-lx&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-x&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;nose-x&#39;</span><span class="p">]</span>
<span class="c1"># test the code</span>
<span class="n">test_features_tryit</span><span class="p">(</span><span class="n">asl</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>asl.df sample
</pre>
</div>
</div>

<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></th>
      <th>left-x</th>
      <th>left-y</th>
      <th>right-x</th>
      <th>right-y</th>
      <th>nose-x</th>
      <th>nose-y</th>
      <th>speaker</th>
      <th>grnd-ry</th>
      <th>grnd-rx</th>
      <th>grnd-ly</th>
      <th>grnd-lx</th>
    </tr>
    <tr>
      <th>video</th>
      <th>frame</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th rowspan="5" valign="top">98</th>
      <th>0</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
      <td>9</td>
      <td>119</td>
      <td>-12</td>
    </tr>
    <tr>
      <th>1</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
      <td>9</td>
      <td>119</td>
      <td>-12</td>
    </tr>
    <tr>
      <th>2</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
      <td>9</td>
      <td>119</td>
      <td>-12</td>
    </tr>
    <tr>
      <th>3</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
      <td>9</td>
      <td>119</td>
      <td>-12</td>
    </tr>
    <tr>
      <th>4</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
      <td>9</td>
      <td>119</td>
      <td>-12</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area">
<div class="prompt output_prompt">Out[4]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<font color=green>Correct!</font><br/>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[5]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># collect the features into a list</span>
<span class="n">features_ground</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;grnd-rx&#39;</span><span class="p">,</span><span class="s1">&#39;grnd-ry&#39;</span><span class="p">,</span><span class="s1">&#39;grnd-lx&#39;</span><span class="p">,</span><span class="s1">&#39;grnd-ly&#39;</span><span class="p">]</span>
 <span class="c1">#show a single set of features for a given (video, frame) tuple</span>
<span class="p">[</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="mi">98</span><span class="p">,</span><span class="mi">1</span><span class="p">][</span><span class="n">v</span><span class="p">]</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">features_ground</span><span class="p">]</span>
</pre></div>

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

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


<div class="output_area">
<div class="prompt output_prompt">Out[5]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>[9, 113, -12, 119]</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">
<h5 id="Build-the-training-set">Build the training set<a class="anchor-link" href="#Build-the-training-set">&#182;</a></h5><p>Now that we have a feature list defined, we can pass that list to the <code>build_training</code> method to collect the features for all the words in the training set.  Each word in the training set has multiple examples from various videos.  Below we can see the unique words that have been loaded into the training set:</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features_ground</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training words: </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">training</span><span class="o">.</span><span class="n">words</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 words: [&#39;JOHN&#39;, &#39;WRITE&#39;, &#39;HOMEWORK&#39;, &#39;IX-1P&#39;, &#39;SEE&#39;, &#39;YESTERDAY&#39;, &#39;IX&#39;, &#39;LOVE&#39;, &#39;MARY&#39;, &#39;CAN&#39;, &#39;GO&#39;, &#39;GO1&#39;, &#39;FUTURE&#39;, &#39;GO2&#39;, &#39;PARTY&#39;, &#39;FUTURE1&#39;, &#39;HIT&#39;, &#39;BLAME&#39;, &#39;FRED&#39;, &#39;FISH&#39;, &#39;WONT&#39;, &#39;EAT&#39;, &#39;BUT&#39;, &#39;CHICKEN&#39;, &#39;VEGETABLE&#39;, &#39;CHINA&#39;, &#39;PEOPLE&#39;, &#39;PREFER&#39;, &#39;BROCCOLI&#39;, &#39;LIKE&#39;, &#39;LEAVE&#39;, &#39;SAY&#39;, &#39;BUY&#39;, &#39;HOUSE&#39;, &#39;KNOW&#39;, &#39;CORN&#39;, &#39;CORN1&#39;, &#39;THINK&#39;, &#39;NOT&#39;, &#39;PAST&#39;, &#39;LIVE&#39;, &#39;CHICAGO&#39;, &#39;CAR&#39;, &#39;SHOULD&#39;, &#39;DECIDE&#39;, &#39;VISIT&#39;, &#39;MOVIE&#39;, &#39;WANT&#39;, &#39;SELL&#39;, &#39;TOMORROW&#39;, &#39;NEXT-WEEK&#39;, &#39;NEW-YORK&#39;, &#39;LAST-WEEK&#39;, &#39;WILL&#39;, &#39;FINISH&#39;, &#39;ANN&#39;, &#39;READ&#39;, &#39;BOOK&#39;, &#39;CHOCOLATE&#39;, &#39;FIND&#39;, &#39;SOMETHING-ONE&#39;, &#39;POSS&#39;, &#39;BROTHER&#39;, &#39;ARRIVE&#39;, &#39;HERE&#39;, &#39;GIVE&#39;, &#39;MAN&#39;, &#39;NEW&#39;, &#39;COAT&#39;, &#39;WOMAN&#39;, &#39;GIVE1&#39;, &#39;HAVE&#39;, &#39;FRANK&#39;, &#39;BREAK-DOWN&#39;, &#39;SEARCH-FOR&#39;, &#39;WHO&#39;, &#39;WHAT&#39;, &#39;LEG&#39;, &#39;FRIEND&#39;, &#39;CANDY&#39;, &#39;BLUE&#39;, &#39;SUE&#39;, &#39;BUY1&#39;, &#39;STOLEN&#39;, &#39;OLD&#39;, &#39;STUDENT&#39;, &#39;VIDEOTAPE&#39;, &#39;BORROW&#39;, &#39;MOTHER&#39;, &#39;POTATO&#39;, &#39;TELL&#39;, &#39;BILL&#39;, &#39;THROW&#39;, &#39;APPLE&#39;, &#39;NAME&#39;, &#39;SHOOT&#39;, &#39;SAY-1P&#39;, &#39;SELF&#39;, &#39;GROUP&#39;, &#39;JANA&#39;, &#39;TOY1&#39;, &#39;MANY&#39;, &#39;TOY&#39;, &#39;ALL&#39;, &#39;BOY&#39;, &#39;TEACHER&#39;, &#39;GIRL&#39;, &#39;BOX&#39;, &#39;GIVE2&#39;, &#39;GIVE3&#39;, &#39;GET&#39;, &#39;PUTASIDE&#39;]
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered">
<div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>The training data in <code>training</code> is an object of class <code>WordsData</code> defined in the <code>asl_data</code> module.  in addition to the <code>words</code> list, data can be accessed with the <code>get_all_sequences</code>, <code>get_all_Xlengths</code>, <code>get_word_sequences</code>, and <code>get_word_Xlengths</code> methods. We need the <code>get_word_Xlengths</code> method to train multiple sequences with the <code>hmmlearn</code> library.  In the following example, notice that there are two lists; the first is a concatenation of all the sequences(the X portion) and the second is a list of the sequence lengths(the Lengths portion).</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="n">training</span><span class="o">.</span><span class="n">get_word_Xlengths</span><span class="p">(</span><span class="s1">&#39;CHOCOLATE&#39;</span><span class="p">)</span>
<span class="c1">#training.get_all_Xlengths()</span>
<span class="c1">#training.get_all_sequences()</span>
</pre></div>

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

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


<div class="output_area">
<div class="prompt output_prompt">Out[7]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>(array([[-11,  48,   7, 120],
        [-11,  48,   8, 109],
        [ -8,  49,  11,  98],
        [ -7,  50,   7,  87],
        [ -4,  54,   7,  77],
        [ -4,  54,   6,  69],
        [ -4,  54,   6,  69],
        [-13,  52,   6,  69],
        [-13,  52,   6,  69],
        [ -8,  51,   6,  69],
        [ -8,  51,   6,  69],
        [ -8,  51,   6,  69],
        [ -8,  51,   6,  69],
        [ -8,  51,   6,  69],
        [-10,  59,   7,  71],
        [-15,  64,   9,  77],
        [-17,  75,  13,  81],
        [ -4,  48,  -4, 113],
        [ -2,  53,  -4, 113],
        [ -4,  55,   2,  98],
        [ -4,  58,   2,  98],
        [ -1,  59,   2,  89],
        [ -1,  59,  -1,  84],
        [ -1,  59,  -1,  84],
        [ -7,  63,  -1,  84],
        [ -7,  63,  -1,  84],
        [ -7,  63,   3,  83],
        [ -7,  63,   3,  83],
        [ -7,  63,   3,  83],
        [ -7,  63,   3,  83],
        [ -7,  63,   3,  83],
        [ -7,  63,   3,  83],
        [ -7,  63,   3,  83],
        [ -4,  70,   3,  83],
        [ -4,  70,   3,  83],
        [ -2,  73,   5,  90],
        [ -3,  79,  -4,  96],
        [-15,  98,  13, 135],
        [ -6,  93,  12, 128],
        [ -2,  89,  14, 118],
        [  5,  90,  10, 108],
        [  4,  86,   7, 105],
        [  4,  86,   7, 105],
        [  4,  86,  13, 100],
        [ -3,  82,  14,  96],
        [ -3,  82,  14,  96],
        [  6,  89,  16, 100],
        [  6,  89,  16, 100],
        [  7,  85,  17, 111]], dtype=int64), [17, 20, 12])</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">
<h6 id="More-feature-sets">More feature sets<a class="anchor-link" href="#More-feature-sets">&#182;</a></h6><p>So far we have a simple feature set that is enough to get started modeling.  However, we might get better results if we manipulate the raw values a bit more, so we will go ahead and set up some other options now for experimentation later.  For example, we could normalize each speaker's range of motion with grouped statistics using <a href="http://pandas.pydata.org/pandas-docs/stable/api.html#api-dataframe-stats">Pandas stats</a> functions and <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html">pandas groupby</a>.  Below is an example for finding the means of all speaker subgroups.</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="n">df_means</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;speaker&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">df_means</span>
</pre></div>

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

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


<div class="output_area">
<div class="prompt output_prompt">Out[8]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<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>left-x</th>
      <th>left-y</th>
      <th>right-x</th>
      <th>right-y</th>
      <th>nose-x</th>
      <th>nose-y</th>
      <th>grnd-ry</th>
      <th>grnd-rx</th>
      <th>grnd-ly</th>
      <th>grnd-lx</th>
    </tr>
    <tr>
      <th>speaker</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>man-1</th>
      <td>206.248203</td>
      <td>218.679449</td>
      <td>155.464350</td>
      <td>150.371031</td>
      <td>175.031756</td>
      <td>61.642600</td>
      <td>88.728430</td>
      <td>-19.567406</td>
      <td>157.036848</td>
      <td>31.216447</td>
    </tr>
    <tr>
      <th>woman-1</th>
      <td>164.661438</td>
      <td>161.271242</td>
      <td>151.017865</td>
      <td>117.332462</td>
      <td>162.655120</td>
      <td>57.245098</td>
      <td>60.087364</td>
      <td>-11.637255</td>
      <td>104.026144</td>
      <td>2.006318</td>
    </tr>
    <tr>
      <th>woman-2</th>
      <td>183.214509</td>
      <td>176.527232</td>
      <td>156.866295</td>
      <td>119.835714</td>
      <td>170.318973</td>
      <td>58.022098</td>
      <td>61.813616</td>
      <td>-13.452679</td>
      <td>118.505134</td>
      <td>12.895536</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">
<p>To select a mean that matches by speaker, use the pandas <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.map.html">map</a> method:</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="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-x-mean&#39;</span><span class="p">]</span><span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;speaker&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">df_means</span><span class="p">[</span><span class="s1">&#39;left-x&#39;</span><span class="p">])</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">
<div class="prompt output_prompt">Out[9]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<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></th>
      <th>left-x</th>
      <th>left-y</th>
      <th>right-x</th>
      <th>right-y</th>
      <th>nose-x</th>
      <th>nose-y</th>
      <th>speaker</th>
      <th>grnd-ry</th>
      <th>grnd-rx</th>
      <th>grnd-ly</th>
      <th>grnd-lx</th>
      <th>left-x-mean</th>
    </tr>
    <tr>
      <th>video</th>
      <th>frame</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th rowspan="5" valign="top">98</th>
      <th>0</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
      <td>9</td>
      <td>119</td>
      <td>-12</td>
      <td>164.661438</td>
    </tr>
    <tr>
      <th>1</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
      <td>9</td>
      <td>119</td>
      <td>-12</td>
      <td>164.661438</td>
    </tr>
    <tr>
      <th>2</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
      <td>9</td>
      <td>119</td>
      <td>-12</td>
      <td>164.661438</td>
    </tr>
    <tr>
      <th>3</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
      <td>9</td>
      <td>119</td>
      <td>-12</td>
      <td>164.661438</td>
    </tr>
    <tr>
      <th>4</th>
      <td>149</td>
      <td>181</td>
      <td>170</td>
      <td>175</td>
      <td>161</td>
      <td>62</td>
      <td>woman-1</td>
      <td>113</td>
      <td>9</td>
      <td>119</td>
      <td>-12</td>
      <td>164.661438</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">
<h5 id="Try-it!">Try it!<a class="anchor-link" href="#Try-it!">&#182;</a></h5>
</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="kn">from</span> <span class="nn">asl_utils</span> <span class="k">import</span> <span class="n">test_std_tryit</span>
<span class="c1"># TODO Create a dataframe named `df_std` with standard deviations grouped by speaker</span>
<span class="n">df_std</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">&#39;speaker&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
<span class="n">df_std</span>
<span class="c1"># test the code</span>
<span class="n">test_std_tryit</span><span class="p">(</span><span class="n">df_std</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>df_std
</pre>
</div>
</div>

<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>left-x</th>
      <th>left-y</th>
      <th>right-x</th>
      <th>right-y</th>
      <th>nose-x</th>
      <th>nose-y</th>
      <th>grnd-ry</th>
      <th>grnd-rx</th>
      <th>grnd-ly</th>
      <th>grnd-lx</th>
      <th>left-x-mean</th>
    </tr>
    <tr>
      <th>speaker</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>man-1</th>
      <td>15.154425</td>
      <td>36.328485</td>
      <td>18.901917</td>
      <td>54.902340</td>
      <td>6.654573</td>
      <td>5.520045</td>
      <td>53.487999</td>
      <td>20.269032</td>
      <td>36.572749</td>
      <td>15.080360</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>woman-1</th>
      <td>17.573442</td>
      <td>26.594521</td>
      <td>16.459943</td>
      <td>34.667787</td>
      <td>3.549392</td>
      <td>3.538330</td>
      <td>33.972660</td>
      <td>16.764706</td>
      <td>27.117393</td>
      <td>17.328941</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>woman-2</th>
      <td>15.388711</td>
      <td>28.825025</td>
      <td>14.890288</td>
      <td>39.649111</td>
      <td>4.099760</td>
      <td>3.416167</td>
      <td>39.128572</td>
      <td>16.191324</td>
      <td>29.320655</td>
      <td>15.050938</td>
      <td>0.0</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area">
<div class="prompt output_prompt">Out[10]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<font color=green>Correct!</font><br/>
</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><a id='part1_submission'></a></p>
<h3 id="Features-Implementation-Submission">Features Implementation Submission<a class="anchor-link" href="#Features-Implementation-Submission">&#182;</a></h3><p>Implement four feature sets and answer the question that follows.</p>
<ul>
<li><p>normalized Cartesian coordinates</p>
<ul>
<li>use <em>mean</em> and <em>standard deviation</em> statistics and the <a href="https://en.wikipedia.org/wiki/Standard_score">standard score</a> equation to account for speakers with different heights and arm length</li>
</ul>
</li>
<li><p>polar coordinates</p>
<ul>
<li>calculate polar coordinates with <a href="https://en.wikipedia.org/wiki/Polar_coordinate_system#Converting_between_polar_and_Cartesian_coordinates">Cartesian to polar equations</a></li>
<li>use the <a href="https://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.arctan2.html">np.arctan2</a> function and <em>swap the x and y axes</em> to move the $0$ to $2\pi$ discontinuity to 12 o'clock instead of 3 o'clock;  in other words, the normal break in radians value from $0$ to $2\pi$ occurs directly to the left of the speaker's nose, which may be in the signing area and interfere with results.  By swapping the x and y axes, that discontinuity move to directly above the speaker's head, an area not generally used in signing.</li>
</ul>
</li>
<li><p>delta difference</p>
<ul>
<li>as described in Thad's lecture, use the difference in values between one frame and the next frames as features</li>
<li>pandas <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.diff.html">diff method</a> and <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.fillna.html">fillna method</a> will be helpful for this one</li>
</ul>
</li>
<li><p>custom features</p>
<ul>
<li>These are your own design; combine techniques used above or come up with something else entirely. We look forward to seeing what you come up with! 
Some ideas to get you started:<ul>
<li>normalize using a <a href="https://en.wikipedia.org/wiki/Feature_scaling">feature scaling equation</a></li>
<li>normalize the polar coordinates</li>
<li>adding additional deltas</li>
</ul>
</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;[11]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO add features for normalized by speaker values of left, right, x, y</span>
<span class="c1"># Name these &#39;norm-rx&#39;, &#39;norm-ry&#39;, &#39;norm-lx&#39;, and &#39;norm-ly&#39;</span>
<span class="c1"># using Z-score scaling (X-Xmean)/Xstd</span>

<span class="n">left_mean_x</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;speaker&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">df_means</span><span class="p">[</span><span class="s1">&#39;left-x&#39;</span><span class="p">])</span>
<span class="n">left_mean_y</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;speaker&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">df_means</span><span class="p">[</span><span class="s1">&#39;left-y&#39;</span><span class="p">])</span>
<span class="n">right_mean_x</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;speaker&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">df_means</span><span class="p">[</span><span class="s1">&#39;right-x&#39;</span><span class="p">])</span>
<span class="n">right_mean_y</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;speaker&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">df_means</span><span class="p">[</span><span class="s1">&#39;right-y&#39;</span><span class="p">])</span>

<span class="n">left_stddev_x</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;speaker&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">df_std</span><span class="p">[</span><span class="s1">&#39;left-x&#39;</span><span class="p">])</span>
<span class="n">left_stddev_y</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;speaker&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">df_std</span><span class="p">[</span><span class="s1">&#39;left-y&#39;</span><span class="p">])</span>
<span class="n">right_stddev_x</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;speaker&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">df_std</span><span class="p">[</span><span class="s1">&#39;right-x&#39;</span><span class="p">])</span>
<span class="n">right_stddev_y</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;speaker&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">df_std</span><span class="p">[</span><span class="s1">&#39;right-y&#39;</span><span class="p">])</span>

<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;norm-rx&#39;</span><span class="p">]</span><span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-x&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">right_mean_x</span><span class="p">)</span> <span class="o">/</span> <span class="n">right_stddev_x</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;norm-ry&#39;</span><span class="p">]</span><span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-y&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">right_mean_y</span><span class="p">)</span> <span class="o">/</span> <span class="n">right_stddev_y</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;norm-lx&#39;</span><span class="p">]</span><span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-x&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">left_mean_x</span><span class="p">)</span> <span class="o">/</span> <span class="n">left_stddev_x</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;norm-ly&#39;</span><span class="p">]</span><span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-y&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">left_mean_y</span><span class="p">)</span> <span class="o">/</span> <span class="n">left_stddev_y</span>

<span class="n">features_norm</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;norm-rx&#39;</span><span class="p">,</span> <span class="s1">&#39;norm-ry&#39;</span><span class="p">,</span> <span class="s1">&#39;norm-lx&#39;</span><span class="p">,</span><span class="s1">&#39;norm-ly&#39;</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;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO add features for polar coordinate values where the nose is the origin</span>
<span class="c1"># Name these &#39;polar-rr&#39;, &#39;polar-rtheta&#39;, &#39;polar-lr&#39;, and &#39;polar-ltheta&#39;</span>
<span class="c1"># Note that &#39;polar-rr&#39; and &#39;polar-rtheta&#39; refer to the radius and angle</span>

<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;polar-rr&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;grnd-rx&#39;</span><span class="p">])</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;grnd-ry&#39;</span><span class="p">]))</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;polar-rtheta&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arctan2</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;grnd-rx&#39;</span><span class="p">],</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;grnd-ry&#39;</span><span class="p">])</span>

<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;polar-lr&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;grnd-lx&#39;</span><span class="p">])</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;grnd-ly&#39;</span><span class="p">]))</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;polar-ltheta&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arctan2</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;grnd-lx&#39;</span><span class="p">],</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;grnd-ly&#39;</span><span class="p">])</span>

<span class="n">features_polar</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;polar-rr&#39;</span><span class="p">,</span> <span class="s1">&#39;polar-rtheta&#39;</span><span class="p">,</span> <span class="s1">&#39;polar-lr&#39;</span><span class="p">,</span> <span class="s1">&#39;polar-ltheta&#39;</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;[13]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO add features for left, right, x, y differences by one time step, i.e. the &quot;delta&quot; values discussed in the lecture</span>
<span class="c1"># Name these &#39;delta-rx&#39;, &#39;delta-ry&#39;, &#39;delta-lx&#39;, and &#39;delta-ly&#39;</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;delta-rx&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-x&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">diff</span><span class="p">()</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;delta-ry&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-y&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">diff</span><span class="p">()</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;delta-lx&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-x&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">diff</span><span class="p">()</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;delta-ly&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-y&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">diff</span><span class="p">()</span><span class="o">.</span><span class="n">fillna</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>

<span class="n">features_delta</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;delta-rx&#39;</span><span class="p">,</span> <span class="s1">&#39;delta-ry&#39;</span><span class="p">,</span> <span class="s1">&#39;delta-lx&#39;</span><span class="p">,</span> <span class="s1">&#39;delta-ly&#39;</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;[14]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO add features of your own design, which may be a combination of the above or something else</span>
<span class="c1"># Name these whatever you would like</span>

<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;cust-norm-rx&#39;</span><span class="p">]</span><span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-x&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-x&#39;</span><span class="p">]))</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-x&#39;</span><span class="p">])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-x&#39;</span><span class="p">]))</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;cust-norm-ry&#39;</span><span class="p">]</span><span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-y&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-y&#39;</span><span class="p">]))</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-y&#39;</span><span class="p">])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;right-y&#39;</span><span class="p">]))</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;cust-norm-lx&#39;</span><span class="p">]</span><span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-x&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-x&#39;</span><span class="p">]))</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-x&#39;</span><span class="p">])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-x&#39;</span><span class="p">]))</span>
<span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;cust-norm-ly&#39;</span><span class="p">]</span><span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-y&#39;</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-y&#39;</span><span class="p">]))</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-y&#39;</span><span class="p">])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;left-y&#39;</span><span class="p">]))</span>

<span class="n">features_custom</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;cust-norm-rx&#39;</span><span class="p">,</span> <span class="s1">&#39;cust-norm-ry&#39;</span><span class="p">,</span> <span class="s1">&#39;cust-norm-lx&#39;</span><span class="p">,</span><span class="s1">&#39;cust-norm-ly&#39;</span><span class="p">]</span>
<span class="c1"># TODO define a list named &#39;features_custom&#39; for building the training set</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">
<p><strong>Question 1:</strong>  What custom features did you choose for the features_custom set and why?</p>
<p><strong>Answer 1:</strong> The method rescales the range of features to scale the range [0, 1] or [−1, 1]. The method has been chosen for its simplicity and computational efficiency. It's helpful in machine learning algorithms so the gradient descent converges much faster.</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><a id='part1_test'></a></p>
<h3 id="Features-Unit-Testing">Features Unit Testing<a class="anchor-link" href="#Features-Unit-Testing">&#182;</a></h3><p>Run the following unit tests as a sanity check on the defined "ground", "norm", "polar", and 'delta"
feature sets.  The test simply looks for some valid values but is not exhaustive.  However, the project should not be submitted if these tests don't pass.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[15]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">unittest</span>
<span class="c1"># import numpy as np</span>

<span class="k">class</span> <span class="nc">TestFeatures</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>

    <span class="k">def</span> <span class="nf">test_features_ground</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">sample</span> <span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="mi">98</span><span class="p">,</span> <span class="mi">1</span><span class="p">][</span><span class="n">features_ground</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="p">[</span><span class="mi">9</span><span class="p">,</span> <span class="mi">113</span><span class="p">,</span> <span class="o">-</span><span class="mi">12</span><span class="p">,</span> <span class="mi">119</span><span class="p">])</span>

    <span class="k">def</span> <span class="nf">test_features_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">sample</span> <span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="mi">98</span><span class="p">,</span> <span class="mi">1</span><span class="p">][</span><span class="n">features_norm</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
        <span class="n">np</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="p">[</span> <span class="mf">1.153</span><span class="p">,</span>  <span class="mf">1.663</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.891</span><span class="p">,</span>  <span class="mf">0.742</span><span class="p">],</span> <span class="mi">3</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">test_features_polar</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">sample</span> <span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="mi">98</span><span class="p">,</span><span class="mi">1</span><span class="p">][</span><span class="n">features_polar</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
        <span class="n">np</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="p">[</span><span class="mf">113.3578</span><span class="p">,</span> <span class="mf">0.0794</span><span class="p">,</span> <span class="mf">119.603</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.1005</span><span class="p">],</span> <span class="mi">3</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">test_features_delta</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">sample</span> <span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="mi">98</span><span class="p">,</span> <span class="mi">0</span><span class="p">][</span><span class="n">features_delta</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
        <span class="n">sample</span> <span class="o">=</span> <span class="p">(</span><span class="n">asl</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="mi">98</span><span class="p">,</span> <span class="mi">18</span><span class="p">][</span><span class="n">features_delta</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="n">sample</span> <span class="ow">in</span> <span class="p">[[</span><span class="o">-</span><span class="mi">16</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="o">-</span><span class="mi">14</span><span class="p">,</span> <span class="o">-</span><span class="mi">9</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]],</span> <span class="s2">&quot;Sample value found was </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">sample</span><span class="p">))</span>
                         
<span class="n">suite</span> <span class="o">=</span> <span class="n">unittest</span><span class="o">.</span><span class="n">TestLoader</span><span class="p">()</span><span class="o">.</span><span class="n">loadTestsFromModule</span><span class="p">(</span><span class="n">TestFeatures</span><span class="p">())</span>
<span class="n">unittest</span><span class="o">.</span><span class="n">TextTestRunner</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">suite</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_stderr output_text">
<pre>....
----------------------------------------------------------------------
Ran 4 tests in 0.039s

OK
</pre>
</div>
</div>

<div class="output_area">
<div class="prompt output_prompt">Out[15]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;unittest.runner.TextTestResult run=4 errors=0 failures=0&gt;</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><a id='part2_tutorial'></a></p>
<h2 id="PART-2:-Model-Selection">PART 2: Model Selection<a class="anchor-link" href="#PART-2:-Model-Selection">&#182;</a></h2><h3 id="Model-Selection-Tutorial">Model Selection Tutorial<a class="anchor-link" href="#Model-Selection-Tutorial">&#182;</a></h3><p>The objective of Model Selection is to tune the number of states for each word HMM prior to testing on unseen data.  In this section you will explore three methods:</p>
<ul>
<li>Log likelihood using cross-validation folds (CV)</li>
<li>Bayesian Information Criterion (BIC)</li>
<li>Discriminative Information Criterion (DIC) </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">
<h5 id="Train-a-single-word">Train a single word<a class="anchor-link" href="#Train-a-single-word">&#182;</a></h5><p>Now that we have built a training set with sequence data, we can "train" models for each word.  As a simple starting example, we train a single word using Gaussian hidden Markov models (HMM).   By using the <code>fit</code> method during training, the <a href="https://en.wikipedia.org/wiki/Baum%E2%80%93Welch_algorithm">Baum-Welch Expectation-Maximization</a> (EM) algorithm is invoked iteratively to find the best estimate for the model <em>for the number of hidden states specified</em> from a group of sample seequences. For this example, we <em>assume</em> the correct number of hidden states is 3, but that is just a guess.  How do we know what the "best" number of states for training is?  We will need to find some model selection technique to choose the best parameter.</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="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">hmmlearn.hmm</span> <span class="k">import</span> <span class="n">GaussianHMM</span>

<span class="k">def</span> <span class="nf">train_a_word</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="n">num_hidden_states</span><span class="p">,</span> <span class="n">features</span><span class="p">):</span>
    
    <span class="n">warnings</span><span class="o">.</span><span class="n">filterwarnings</span><span class="p">(</span><span class="s2">&quot;ignore&quot;</span><span class="p">,</span> <span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">)</span>
    <span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>  
    <span class="n">X</span><span class="p">,</span> <span class="n">lengths</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_word_Xlengths</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">GaussianHMM</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">num_hidden_states</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">lengths</span><span class="p">)</span>
    <span class="n">logL</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">lengths</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">logL</span>

<span class="n">demoword</span> <span class="o">=</span> <span class="s1">&#39;BOOK&#39;</span>
<span class="n">model</span><span class="p">,</span> <span class="n">logL</span> <span class="o">=</span> <span class="n">train_a_word</span><span class="p">(</span><span class="n">demoword</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">features_ground</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of states trained in model for </span><span class="si">{}</span><span class="s2"> is </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">demoword</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">n_components</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;logL = </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">logL</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>Number of states trained in model for BOOK is 3
logL = -2331.113812743319
</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>The HMM model has been trained and information can be pulled from the model, including means and variances for each feature and hidden state.  The <a href="http://math.stackexchange.com/questions/892832/why-we-consider-log-likelihood-instead-of-likelihood-in-gaussian-distribution">log likelihood</a> for any individual sample or group of samples can also be calculated with the <code>score</code> method.</p>

</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="k">def</span> <span class="nf">show_model_stats</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of states trained in model for </span><span class="si">{}</span><span class="s2"> is </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">word</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">n_components</span><span class="p">))</span>    
    <span class="n">variance</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">covars_</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">n_components</span><span class="p">)])</span>    
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">n_components</span><span class="p">):</span>  <span class="c1"># for each hidden state</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;hidden state #</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">i</span><span class="p">))</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;mean = &quot;</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">means_</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;variance = &quot;</span><span class="p">,</span> <span class="n">variance</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
        <span class="nb">print</span><span class="p">()</span>
    
<span class="n">show_model_stats</span><span class="p">(</span><span class="n">demoword</span><span class="p">,</span> <span class="n">model</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>Number of states trained in model for BOOK is 3
hidden state #0
mean =  [ -3.46504869  50.66686933  14.02391587  52.04731066]
variance =  [ 49.12346305  43.04799144  39.35109609  47.24195772]

hidden state #1
mean =  [ -11.45300909   94.109178     19.03512475  102.2030162 ]
variance =  [  77.403668    203.35441965   26.68898447  156.12444034]

hidden state #2
mean =  [ -1.12415027  69.44164191  17.02866283  77.7231196 ]
variance =  [ 19.70434594  16.83041492  30.51552305  11.03678246]

</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">
<h5 id="Try-it!">Try it!<a class="anchor-link" href="#Try-it!">&#182;</a></h5><p>Experiment by changing the feature set, word, and/or num_hidden_states values in the next cell to see changes in values.</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="n">my_testword</span> <span class="o">=</span> <span class="s1">&#39;CHOCOLATE&#39;</span>
<span class="n">model</span><span class="p">,</span> <span class="n">logL</span> <span class="o">=</span> <span class="n">train_a_word</span><span class="p">(</span><span class="n">my_testword</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">features_norm</span><span class="p">)</span> <span class="c1"># Experiment here with different parameters</span>
<span class="n">show_model_stats</span><span class="p">(</span><span class="n">my_testword</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;logL = </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">logL</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>Number of states trained in model for CHOCOLATE is 3
hidden state #0
mean =  [ 0.53813142 -0.04704542  0.04545    -0.61902479]
variance =  [ 0.02432711  0.04933945  0.03044391  0.13395751]

hidden state #1
mean =  [ 1.0625066  -0.1740204  -1.31812255 -1.61791893]
variance =  [ 0.05296979  0.00373254  0.03820247  0.05531266]

hidden state #2
mean =  [ 0.38934967 -0.18383814 -0.34623283 -1.59201968]
variance =  [ 0.0697732   0.02960059  0.07727065  0.06911305]

logL = 22.65197722447528
</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;[19]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">my_testword</span> <span class="o">=</span> <span class="s1">&#39;CHOCOLATE&#39;</span>
<span class="n">model</span><span class="p">,</span> <span class="n">logL</span> <span class="o">=</span> <span class="n">train_a_word</span><span class="p">(</span><span class="n">my_testword</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">features_polar</span><span class="p">)</span> <span class="c1"># Experiment here with different parameters</span>
<span class="n">show_model_stats</span><span class="p">(</span><span class="n">my_testword</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;logL = </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">logL</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>Number of states trained in model for CHOCOLATE is 3
hidden state #0
mean =  [ 56.19235087  -0.16348589  71.60640024   0.0958256 ]
variance =  [  5.16348282e+01   4.22849414e-03   1.74535632e+01   1.18341913e-03]

hidden state #1
mean =  [  8.81313612e+01   8.31771967e-03   1.09304329e+02   1.18434797e-01]
variance =  [  2.06973439e+01   5.41190376e-03   1.41647879e+02   1.92144403e-03]

hidden state #2
mean =  [  6.05010053e+01  -9.31757873e-02   9.14166393e+01   2.57297227e-02]
variance =  [  6.15523307e+01   3.56540651e-03   1.30626433e+02   1.81995354e-03]

logL = -179.21522065553515
</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;[20]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">my_testword</span> <span class="o">=</span> <span class="s1">&#39;CHOCOLATE&#39;</span>
<span class="n">model</span><span class="p">,</span> <span class="n">logL</span> <span class="o">=</span> <span class="n">train_a_word</span><span class="p">(</span><span class="n">my_testword</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">features_custom</span><span class="p">)</span> <span class="c1"># Experiment here with different parameters</span>
<span class="n">show_model_stats</span><span class="p">(</span><span class="n">my_testword</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;logL = </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">logL</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>Number of states trained in model for CHOCOLATE is 3
hidden state #0
mean =  [ 0.60660804  0.41076929  0.24517462  0.36090672]
variance =  [ 0.00072196  0.00128934  0.00205851  0.00198233]

hidden state #1
mean =  [ 0.60946103  0.37000359  0.24113516  0.514514  ]
variance =  [ 0.00157305  0.00167877  0.0047263   0.00404435]

hidden state #2
mean =  [ 0.67890772  0.52219796  0.33547009  0.5092081 ]
variance =  [ 0.00204538  0.00110493  0.0011575   0.00455643]

logL = 383.27108537096603
</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;[21]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">my_testword</span> <span class="o">=</span> <span class="s1">&#39;CHOCOLATE&#39;</span>
<span class="n">model</span><span class="p">,</span> <span class="n">logL</span> <span class="o">=</span> <span class="n">train_a_word</span><span class="p">(</span><span class="n">my_testword</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">features_ground</span><span class="p">)</span> <span class="c1"># Experiment here with different parameters</span>
<span class="n">show_model_stats</span><span class="p">(</span><span class="n">my_testword</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;logL = </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">logL</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>Number of states trained in model for CHOCOLATE is 3
hidden state #0
mean =  [ -5.40587658  60.1652424    2.32479599  91.3095432 ]
variance =  [   7.95073876   64.13103127   13.68077479  129.5912395 ]

hidden state #1
mean =  [ -9.30211403  55.32333876   6.92259936  71.24057775]
variance =  [ 16.16920957  46.50917372   3.81388185  15.79446427]

hidden state #2
mean =  [   0.58333333   87.91666667   12.75        108.5       ]
variance =  [  39.41055556   18.74388889    9.855       144.4175    ]

logL = -601.3291470028635
</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">
<h5 id="Visualize-the-hidden-states">Visualize the hidden states<a class="anchor-link" href="#Visualize-the-hidden-states">&#182;</a></h5><p>We can plot the means and variances for each state and feature.  Try varying the number of states trained for the HMM model and examine the variances.  Are there some models that are "better" than others?  How can you tell?  We would like to hear what you think in the classroom online.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[22]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
</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;[23]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">math</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="p">(</span><span class="n">cm</span><span class="p">,</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span><span class="p">,</span> <span class="n">mlab</span><span class="p">)</span>

<span class="k">def</span> <span class="nf">visualize</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot; visualize the input model for a particular word &quot;&quot;&quot;</span>
    <span class="n">variance</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">covars_</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">n_components</span><span class="p">)])</span>
    <span class="n">figures</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">parm_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">means_</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
        <span class="n">xmin</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">means_</span><span class="p">[:,</span><span class="n">parm_idx</span><span class="p">])</span> <span class="o">-</span> <span class="nb">max</span><span class="p">(</span><span class="n">variance</span><span class="p">[:,</span><span class="n">parm_idx</span><span class="p">]))</span>
        <span class="n">xmax</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">means_</span><span class="p">[:,</span><span class="n">parm_idx</span><span class="p">])</span> <span class="o">+</span> <span class="nb">max</span><span class="p">(</span><span class="n">variance</span><span class="p">[:,</span><span class="n">parm_idx</span><span class="p">]))</span>
        <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">n_components</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sharey</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="n">colours</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">rainbow</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">n_components</span><span class="p">))</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">colour</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">axs</span><span class="p">,</span> <span class="n">colours</span><span class="p">)):</span>
            <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">xmin</span><span class="p">,</span> <span class="n">xmax</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
            <span class="n">mu</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">means_</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">parm_idx</span><span class="p">]</span>
            <span class="n">sigma</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">covars_</span><span class="p">[</span><span class="n">i</span><span class="p">])[</span><span class="n">parm_idx</span><span class="p">])</span>
            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mlab</span><span class="o">.</span><span class="n">normpdf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">),</span> <span class="n">c</span><span class="o">=</span><span class="n">colour</span><span class="p">)</span>
            <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> feature </span><span class="si">{}</span><span class="s2"> hidden state #</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">word</span><span class="p">,</span> <span class="n">parm_idx</span><span class="p">,</span> <span class="n">i</span><span class="p">))</span>

            <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
        <span class="n">figures</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">plt</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">figures</span><span class="p">:</span>
        <span class="n">p</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
        
<span class="n">visualize</span><span class="p">(</span><span class="n">my_testword</span><span class="p">,</span> <span class="n">model</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,iVBORw0KGgoAAAANSUhEUgAAAYQAAAEICAYAAABfz4NwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXl8VcXZ+L9PFkIWthANe4KCIi4oqwoqVqrgRqtI3cCl
FWnV2qqvS7U/0Le26luta93aqriCWnfqTkQUKouIAgJhkwBBwh4ChCTP7485kcP13pub5N7cm+T5
5nM+OefMMzPPzD1nntnOjKgqhmEYhpEUbwUMwzCMxMAMgmEYhgGYQTAMwzA8zCAYhmEYgBkEwzAM
w8MMgmEYhgGYQTAaGSKSKyLTRWSHiNwbb31iiYhMFJHnwrgvFJGhIdyGikhRGL9Pi8ifoqCm0YQw
gxBHRORCEZkjIqUisl5E/iMiQzy3oIWBiKiI9PBd9xaRN0Vkm1dIThOR4wP8tPDCWyYiO0VklYj8
S0TyfTJnisgXnvsmEXleRLr43C8VkRk1pOdpEakQkY6+e4956SsVkXIR2eu7/o+I5HtpKg04fhEi
mnFACdBaVa8Pm8E1EI9C0UvvNBEpE5FvRWRYXcNS1cNVtSCK6sWUwGc3AvkCEflVlOKeIiKnikia
iBQHuKV578N2ESkWkeuiEWdjxAxCnPAeuvuBPwO5QDfgEeDsWoRxMPAZ8DXQHegEvAa8LyLH+URf
8cK9EGgD9AHmAKd44YwCXvD0yQEOB/YAM0SkXYS6ZALnAtuAi6vvq+p4Vc1S1SwvrZOrr1V1hC+I
tr77Wao6OURUecAiTYAvKkUkpQ7eXgS+BNoDtwKviMgBUVXMCEY/3DN/FPBNgNtEoCfu2ToZuFFE
hjeodomCqtrRwAeuUC4FzgsjMxF4Lsh9BXp4588CU4PIPApM986HAbuAriHiEWA1cGPA/STci3OH
d30pMCOMvmOBNcC1wDeRpgnI99KUEkG+PQ3sBcq9/Bvm6XkzsBzYBEwBsn1+XgaKcYZqOnC4d39c
QFhvBeavL84/eedDgSLgJi/MZ737ZwLzga3A58BRIfQ/BGdoW/nuTQfGh8mvKcAkYAewEOjvc18F
DPPO0z1dtwCLgP8BinyyxwDzvHAmAy9Vp6umNHjx3AAs8PJxMtAyhM49gE88uRJcBaA6nQrs9PL7
F0A74G1go6f320AXT/5OoBLY7ck/7N3vBXwAbAaWAKMjeG7aASu8818D9wS4rwNO9V3fAbwUr/Ih
nkfcFWiOBzAcqCBMIUhkBqEYuCyIzMney5QO3AV8EiaeXl6Y3YO43Q7M9M4vJbxB+Ai4B9faqQD6
RZImamEQPPmnAwqya4FZQBcgDXgceNHnfjnQynO7H5gfKqzA/A2UwRmECuBuL7x0XEH7PTAISAYu
wRWgaUF0/zmwOODeQ8BDYZ6B3cDpXth/AWb53FexzyDcBXwKZANdcca8yHNrgTP6vwdSgVE4Y1id
rrBp8M6/wLVAs4HFhDZiL+JaPklAS2BImLxtj2tVZni/0cvA6z73AuBXvutMXKXjMiDF07sE6B1C
l1NwBq4UZ/i3ev93eucn4YyFArk+f+cCX8erfIjnYV1G8aE9UKKqFTXIjRaRrf4jwD0HWB/E33rc
C5ntxRVMxh8GIWTW+9xDIiLdcEboBVXdgDMOY2vyF0BJQFoPi9DfeOBWVS1S1T24QnRUdXeOqv5L
VXf43PqISJta6uanCpigqntUdReupfG4qv5XVStV9RlcK+DYIH6zcDVnP9txhWEoZqjqVFWtxLUI
+4SQGw3cqaqbVXUN8KDP7VicIbhfVfeq6ivAbJ97JGl4UFXXqepm4C3g6BB67MV1vXRS1d2qGnLc
SVU3qeqrqlqmqjtwrYKTQsnjWjGrVPUpVa1Q1S+BV4HzQoT/kaq2BV73ZDrjjFuOqrZV1U9wvwns
/7vU9Js0WcwgxIdNQE4EfdBTvAf3hyPAvQToGMRfR1zBtcWLK5iMPwxCyHT0uYdjDK7mO9+7fh64
UERSI/BbTU5AWhdH6C8PeM1nMBfjWke5IpIsIneJyHIR2Y4rDCACIxeGjaq6OyD+6wOMdldcbTqQ
UqB1wL02uG6cUPgHQMuAliGem0642nM1qwPc1qpX/Q3iHkkaAvXIIjg34rohv/BmQV0eQg4RyRCR
x0Vktff7TAfaikhyCC95wKAAPS8COoQIv8iTuQB4BtcKygPWi8h9nlip99//u9T0mzRZzCDEh5m4
GtjP6hnOhwSvHY3GdfWUeTID/TOGAliC6xffLxwRScI1nT+KQI+xwEHeDI1i4D5coXt6RKmoH2uA
EQHGpKWqrsUNoo/EjTW0wXVPgSuwwHUVBFKG68KoJrCwCfSzBlcz98efoaovBgl7IS6f/LXPPt79
+rIeV4hX0y3ArbOISAj32qQhLKparKpXqGon4Erg72FmFl0PHAoMUtXWwIne/VC/zxpc96dfzyxV
/XUIXbrgumc/9CpTTwBXef6u82S24PLH3/KK1m/S6DCDEAdUdRvw/4BHRORnXk0pVURGiMg9tQjq
duB4EblTRLJFpJWIXIMroG/y4voQNwj3moj0E5EUT268iFzu1RpvAG4TNw22pYh0AP6BqzX9zRef
eO7+4zjgYGAgrhvhaOAI3Kyl2nYb1YXHgDtFJM9T8AARGem5tcIZ3k24Qv7PAX43AAcF3JuPa90k
ezNNwnVhADwJjBeRQeLIFJEzAgp9AFR1qRf+BC/vzgGOxHV71JcpwC0i0s4z/tf43Gbixj5+6z1n
5+B+r1qnoSZE5Dxf5WMLrlCv8q4D87sVbsLDVhHJBiYEBBco/zZwiIiM8dKRKiIDauhe7IcbTAfo
i5tpFMgk3PPfzgvrCtzYUbPDDEKcUNV7geuA23CzLNYAV+P6OyMNYxkwBFejWYWr6ZwLnKaqn/lE
RwFTcbNDtuEGHPvjWg+om+I5BjfouAk3SyUdGKyqm3zhHI97gf3HL4E3VPVrr3ZYrKrFwAPAmd6L
HglbZf/vECKdC/4A8CZuqu0O3ADzIM9tEq5rZK2XplkBfv8J9Pa6H6rz/VrgLNyg40XU8Huo6hxc
AfIwrgAsxA3Ah+J8XN5vwQ0Sj1LVjTWmsmZux6V1JfA+bryhWsdy4BxPr824GT7/rkcawjEA+K+I
lOJ+l2tVdYXnNhF4xsvv0bhB/nRct+Qs4N2AsB7AjQdtEZEHvXGGU3F5uA7XjVU9wB+KfsA8r3XU
i+A1/wm4WWqrcQPZ96hqoC7NAtm/W9EwDMNorlgLwTAMwwDMIBiGYRgeZhAMwzAMwAyCYRiG4VGX
xbniRk5Ojubn58dbjYjYuXMnmZmZ8VYj4bB8CY7lS3AsX4JT23yZO3duiarWuIhiozII+fn5zJkT
bBpx4lFQUMDQoUPjrUbCYfkSHMuX4Fi+BKe2+SIiq2uWamQGwTASgQXPwc6NkNYaWraFniMgNaNm
f4aR6JhBMIxasPx9eG3M/vd6ngEXvh0ffQwjmtigsmFESGU5vHstZPeAGzbA71bDiX+EZe/AikhW
fDKMBMcMgmFEyH8fgpJv4bT7IfNAaNMNTvgDtMmD96+Hqsp4a2gY9cMMgmFEQGkxfHI79DwdDjlj
3/2UlnDKX2DDV7Dg2dD+DaMxYAbBMCLgw5uhco9rHQRyxPnQeSB8fCvsLWt43QwjWphBMIwa2LIC
vnoGBv0O2vf8sbsInHov7FgHs4IYDMNoLJhBMIwaWPiy+z/gN6Flug2B7j+B+U+BLSBsNFbMIBhG
DSya4rqE2uaFl+s9GjYXwvdfN4xehhFtzCAYRhg2L4f181xhXxO9fgaSBIteib1ehhELzCAYRhgW
ed1FvUfVLJuVC3knmkEwGi9mEAwjDItehs6Dau4uquawc6FkMWxcFFu9DCMWmEEwjBD80F10XuR+
DjvH/V/0amx0MoxYYgbBMEJQm+6ialp1gq6DYbF1GxmNEDMIhhGChVNq111UTe9RsGEBbFoaG70M
I1bEzCCIyHARWSIihSJycxD3XiIyU0T2iMgNsdLDMOrClhVQ/GXtuouqsW4jo7ESE4MgIsnAI8AI
oDdwgYj0DhDbDPwW+GssdDCM+rDsP+7/oWfX3m+bbtBpACx5Pbo6GUasiVULYSBQqKorVLUceAkY
6RdQ1e9VdTawN0Y6GEadWf4utDvILXVdF3qeDmtnQ9mm6OplGLEkVhvkdAbW+K6LgEF1CUhExgHj
AHJzcykoKKi3cg1BaWlpo9G1IWkM+VJVLhR+OIQOpxXzySfL6hTG9gNbg/bl7QcWcuBPNtYo3xjy
JR5YvgQnVvmS8DumqeoTwBMA/fv318ayv6rtBRucxpAvKz+GT3fDib/szKFDO9cpjKoTYPFtkLbm
cCJJbmPIl3hg+RKcWOVLrLqM1gJdfdddvHuGkfAUvgtJqdD95LqHkZQMB/8UCt+zxe6MxkOsDMJs
oKeIdBeRFsD5wJsxisswokrhu5B3ArTIql84Bw+H0vW22J3ReIiJQVDVCuBq4D1gMTBFVReKyHgR
GQ8gIh1EpAi4DrhNRIpEpHUs9DGMSNm+1hXgBw+vf1g9TnP/C9+tf1iG0RDEbAxBVacCUwPuPeY7
L8Z1JRlGwrD8Pfe/RxQMQqtOkHuUMwiDb6x/eIYRa+xLZcPwUfiuK8gPPCI64R18Gnw3A8pLoxOe
YcQSMwiG4VFVASs+cN1FItEJs8dwqNoLK6dFJzzDiCVmEAzDY+0XsHvrvr7/aNB1MKRm2jiC0Tgw
g2AYHkvfAUmGg0+NXpgpaW6v5cKpNv3USHzMIBiGx7J3oNtgaNk2uuH2PAO2rnIb5xhGImMGwTCA
7UWw4StXeEebQ7wwl74d/bANI5qYQTAMYJk3QfqQM6Mfdusu0OFo1wIxjETGDIJh4GrvbfMh57DY
hN/zDPjuM9i1OTbhG0Y0MINgNHsqdsPKj1yhHa3ppoEcciZopVvbyDASFTMIRrNnVQHsLYvN+EE1
nQZAxgGwzMYRjATGDILR7Fn6DqSkQ/7Q2MWRlOw2zSl8130AZxiJiBkEo1mj6gZ7DzoFUtNjG1fP
M9wYQtGs2MZjGHXFDILRrCn5FraujG13UTUHnwpJKTb91EhczCAYzZpFLwMSm+mmgbRsA3knwpI3
7KtlIzExg2A0W1RhwXNu7KB1Ay3E3vs81yop/rJh4jOM2mAGwWi2rP0CNi+Do8Y0XJyHj4bkFvDV
sw0Xp2FEihkEo9my4DlIaQmHndNwcaZnwyFnwTcvQOXehovXMCLBDILRLKncCwtfgkPPdn37DUmf
sbDze1j+fsPGaxg1YQbBaJYsfw/KSuDIixs+7h7DISMHFkxq+LgNIxxmEIxmyYLnIL19dPZOri3J
LeCIC+DbN9yGPIaRKJhBMJode7a7qZ9HnA/JqfHRoc9YqNwDC1+OT/yGEQwzCEaz4+sX3IJ2R14U
Px069oOcXvDVM/HTwTACMYNgNCsq9sCnf4bOg6DLsfHTQwT6XgFrPoPVn8ZPD8PwYwbBaFZ8+U/Y
vgZO/t/YLXUdKf3HQ1YH+PhW+3LZSAzMIBjNhord8Omd0G0IHDQs3tpAagac+Ef47lPYMrtdvNUx
DDMIRvNh7hOwYx0MvSP+rYNq+v7K7dS28p8HWSvBiDtmEIxmwd4ymPEXt25R95Pjrc0+klvASROh
dGkrvn0t3toYzR0zCEazYPqfoLQYht4eb01+zFEXQ0a3nXx8K+zdFW9tjOaMGQSjybPoFdc6OPpy
t/x0opGUDAf9Zjkl38Kbl9sAsxE/zCAYTZoNC+D1S9wU0zP+Hm9tQtN+0GZO+Qt88xJ8cke8tTGa
KynxVsAwYkVZCbw0Elq2hdH/hpS0eGsUnsE3Qcli+GSi+2jtiF/EWyOjuWEtBKNJsqoAnugPO9bD
L16DVh3jrVHNiMCZT7hpsa+PhRl3QVVFvLUymhMxMwgiMlxElohIoYjcHMRdRORBz32BiPSNlS5G
82FvGbx3HTxzslun6JJp0HlgvLWKnJQ0+MXrbkvPj26Bfx4H338Tb62M5kJMuoxEJBl4BPgpUATM
FpE3VXWRT2wE0NM7BgGPev8No1bs3QUrP4aFk+Hb16F8Bwy4CobdDS0y461d7cloD+e94gbDp14F
j/Vxg+GHjYLDfg6tOsVbQ6OpEqsxhIFAoaquABCRl4CRgN8gjAQmqaoCs0SkrYh0VNX10VZm3RzY
XBjtUMPz/aID+aa4YeNsDPjzZb/ZNL5zVXetVe6oqoDKcneU73RLRu/eCtu/g5IlsO07J9+yrduz
+JjLodvgBkxUDBCBw89z30zMegAWvwr/udod6e2hfU/I7uHO01q7I6Wl+64hKdXNXEJAkgI+wpP9
40h07D3aR95Jse/6jJVB6Ays8V0X8ePafzCZzsB+BkFExgHjAHJzcykoKKi1Msvu78m6NzrX2l/9
6M3iBo6xcVD/fElKqyQlq4IW2eVkHFxG3slltDpkB+36byEpVVmxF1YUREPXhqO0tDTks510Chx+
CuxclcHmL7LZtSaD0rXpbPwgnYrSFCrLkkEbQeleJ+w9qubIuxeQPXAzEP55qQ8JP8tIVZ8AngDo
37+/Dh06tNZh9O8Fu++KsmI18MUXXzBwYCPqvG4gfpQvIWqskuQOxI0FJKW6/5tLN3DBxefx5Zdf
Mu6scdx7770NpnssKSgoIPDZnjhxIoWFhTz33HP7bl667/Twww/nkZce4aQTh1Je6lZyrdrrWlKf
fl7AldddzNfTi36Q97fIrr75UjrlduEPv/tTTNITLew92kfrLkfRIsudB3teooKqRv0AjgPe813f
AtwSIPM4cIHvegnQMVy4/fr108bCtGnTapR5/vnntV+/fpqZmakdOnTQ4cOH66effqqqqhMmTNCL
LrroR34AXbZs2Q/XCxcu1LPOOktbt26tWVlZOnToUP3ss8/287Nnzx6dMGGC9ujRQzMyMjQvL08v
u+wyXbly5Q8yb731lg4YMEAzMjI0OztbL7zwQl2zZs0P7k899ZQOHjw4bHouueQSTU5O1nXr1v1w
78orr9TMzEzNzMzU1NRUTU5O/uF6+PDhunLlSgV+uFd9vPTSS0HjuOOOO/TnP/+5VlVVhdUlEi65
5BK99dZb6x1ObVi5cqUOHTpU09PT9dBDD9UPPvhAVYM/L6GegUiYNm2adu7cOaR7PNIe+OzWxEkn
naQ33HBDVOI+77zz9L333tPdu3drbm7ufm6TJ0/W4447TtPT0/Wkk06KSnyxJpLyxQ8wRyMou0Vj
8FmkiKQAS4FTgLXAbOBCVV3okzkDuBo4Hded9KCqhq0KiMhGYHXUFY4NOUBJGPdcoAMuPdtxveit
gVa47rNOQBqwMsBfP+AbYI/nfhiwESj2wsjBdb0tBXZ6fnoAqV5cZUAykO3JlwDtgHzPfYvn3sXT
ZRFQCbT3wl4SIj1JQB8vzPXAhiAynbw0fuu71wI4EpgbItxA8oC9wLoI5cORD5RHKaxI6QWU4t6L
Np4O3wBt+fHzEuoZiIRWQHdgQQj3fBo+7f5nNxIO9WRXRSHuI4DFuPzsgns/qmmF6y1piXs+Qz3j
iURN5Usgeap6QI1SkViNuhy4gn4psBy41bs3HhjvnQtuJtJy4Gugf6x0icdBGIuMKwhKgfPCyEwE
ngtyX4Ee3vmzwNQgMo8C073zYcAuoGuIeARnCG4MuJ+Ee3nv8K4vBWaE0XcsbkzoWuCbMGnaFHAv
30tTSgR5+jTOGJR7+TfM0/Nm7znaBEwBsn1+XsYZy23AdOBw7/64gLDeCsxfX5x/8s6H4oz1TV6Y
z3r3zwTmA1uBz4GjQuh/CK6Aa+W7N917L370vHj5NQWYBOwAFvrfE1xBOcw7T/d03YIz4v8DFPlk
jwHmeeFMBl6qTldNafDiuQFnXLZ5/luGSGMP4BNPrgSY7Eun4ioppcAvcBWRt3EVmi3eeRdP/k5c
RaTKk3/Yu98L+ADYjCu4R0fw3LQDVnjnvwbuCSH3K6CgIcqH+h7BnpeohBvvhDXVI9wPBgwHKghT
CBKZQSgGLgsic7L3MqUDdwGfhImnlxdm9yButwMzvfNLCW8QPgLuwbV8KoB+IdJUZ4PgyT8dUJBd
C8zC1frScF2RL/rcL8fVANOA+4H5ocIKzN9AGZxBqADu9sJLxxW03+NaucnAJbgCNC2I7j8HFgfc
e8g7QhmE3bjKVTLwF2CWz30V+wzCXcCnuJZfV5wxL/LcWuCM/u9xLcVROGNYna6wafDOv8C1WLJx
Ne3xIX6fF4FbcYa6JTAkTN62B84FMrzf6GXgdZ97AbDKd52Jq3RchqvRH4MzOr1D6HIKzsCV4gz/
Vu//Tu/8pAD5Zm8Q7Evl+NAeKFHVmr5DHS0iW/1HgHsOAbOyPNbjXshsL65wU3lzfH6ChZMT5P5+
iEg3nBF6QVU34IzD2Jr8BVASkNbDIvQ3HtcCLVLVPbhCdJTXbYmq/ktVd/jc+ohIm1rq5qcKmKCq
e1R1F66l8biq/ldVK1X1GVwrINgGnVm4mrOf7bjCMBQzVHWqqlbiWoR9QsiNBu5U1c2qugZ40Od2
LM4Q3K+qe1X1FVw3bjWRpOFBVV2nqpuBt4CjQ+ixF9et10lVd6vqjFAJU9VNqvqqqpap6g5cq+Ck
UPK4VswqVX1KVStU9UvgVeC8EOF/pKptgdc9mc4445ajqm1V9ZMwcTVLzCDEjifCuG0CcqoLrTBM
8R7cH44A9xIg2MzkjriCa4sXV7jZy9X9kKHCiaSfcgyu5jvfu34euFBEUoPILgsRRk5AWiOdbZgH
vOYzmItxraNcEUkWkbtEZLmIbGdfX3SNRi4MG1V1d0D81wcY7a642nQgpbg+aj9tcN04oZ4X/yz8
MqBliOemE/tP414d4LZWvaplEPdI0hCoR1YIfW/EdUN+ISILReTyEHKISIaIPC4iq73fZzrQ1vuw
tZrpAXoOCtDzItxYXLDwizyZC4BncK2gPGC9iNwXSq9GQrjypc6YQYgR6qbLhmImrgb2s3pG8yHB
a0ejcV09ZZ7MQBHpEiKMJbh+8f3CEZEkXHP+owj0GAscJCLFIlIM3IcrdE8PIhvtTwTXACMCjElL
VV0LXIj7AHIY+wZwYd9k12AzKspwXRjVBBY2gX7W4Grm/vgzVPXFIGEvxOWTv0XQB1hYw/MSCetx
hXg13QLcOovs9yma3702aQiLqhar6hWq2gm4Evi7iPQIIX49buB4kKq2BqoXJ/f/Pn6DsAbX/enX
M0tVfx1Cly647tkPvcrUE8BVnr/rapu2RCIKz0tQzCDEAVXdBvw/4BER+ZlXU0oVkREick8tgrod
OF5E7hSRbBFpJSLX4Arom7y4PsQNwr0mIv1EJMWTGy8il3u1xhuA20TkQhFpKSIdgH/garN/88Un
nrv/OA44GPd1+tHecQTwArXvNqoLjwF3ikiep+ABIjLSc2uFM7ybcIX8nwP8bgAOCrg3H9e6SRaR
4YTvwgB4EhgvIoO89bkyReSMgEIfAFVd6oU/wcu7c3AzrF6NOLWhmQLcIiLtPON/jc9tJm7s47fe
c3YO7veqdRpqQkTO81U+tuAK9SrvOjC/W+EmPGwVkWxgQkBwgfJvA4eIyBgvHakiMqCG7sV+uMF0
gL7AnCA6J4tIS9y4RJL32wRr3TZ5zCDECVW9F7gOuA03y2INbhru67UIYxkwBFfLXIWrCZ4LnKaq
n/lERwFTcbNDtuEGHPvjWg+o6mRct8/vcYXnItyA6WBV3eQL53jcC+w/fgm8oapfe7XDYlUtBh4A
zvRe9EjYKiKlviPSGtwDwJvA+yKyAzfAXP1V/CRc18haL02zAvz+E+jtdT9U5/u1wFm4QceLqOH3
UNU5wBXAw7gCsJD9Ph/7Eefj8n4LbpB4lKpurDGVNXM7Lq0rgfdx4w3VOpYD53h6bcbN8Pl3PdIQ
jgHAf0WkFPe7XKveEja4MZxnvPwejRvkT8d1S84C3g0I6wHceNAWEXnQG2c4FZeH63DdWNUD/KHo
B8zzWke9cK20QMbgnuVHgRO88ydrleomQky+QzAMwzAaH9ZCMAzDMAAzCIZhGIaHGQTDMAwDqKdB
kJp3ReslIjNFZI+I3FAbv4ZhGEbDUudBZe/jkaX4dkXDrV66yCdzIO5DkJ8BW1T1r5H6DUZOTo7m
5+fXSd+GZufOnWRmNsLtumKM5UtwLF+CY/kSnNrmy9y5c0s0gsXt6rMfQo27oqnq98D34lY2rZXf
YOTn5zNnzo+mESckMVuvvJFj+RIcy5fgWL4Ep7b5IiIRrRJdny6jUDuexdqvYUSFSqqYzxpeYR4L
KKKCynirZBhxJeF3TJMobKEZD2K1xV1jJxHypUqU9Z0qKOq6lz0tleQKWJSynrf3fkXHdal0W51K
clXDbkmZCPmSiFi+BCcRt9Bcy/5rp3Tx7kXVr0ZhC814YE3d4CRCvrzN1yznO7rQjhPoQY+UA1jJ
JuakrubbvGLa5B3AzzkaoeGMQiLkSyJi+RKcWOVLfQzCbKCniHTHFebn4xYTi7Vfw6gzX1HEPL7j
eA7mFA79odA/iBwOIodPKWQaS+hIG4770TJHhtG0qbNBUNUKEbkaeA+3qca/VHWhiIz33B/zFkmb
g1skrUpEfofbzGJ7ML/1TYxhhGMD23mHr8kjm59wSNAWwBAOpphtfMhicmnNQfVaKdswGhf1GkNQ
1am4RdP89x7znRfjuoMi8msYsWIPe3mZebQklXM5hqQQ8ykE4Wz6UEIprzKPcZxAG9IbWFvDiA/2
pbLRLChgGVvYybkcQxYtw8qmkcJo+rOXSqY1iv3WDSM6mEEwmjzb2cUcVtOHLuTRPiI/7clkAPks
YC0b2RFjDQ0jMTCDYDR5ZlCIopxAz1r5G8zBtCCFApbGSDPDSCzMIBhNmq2UMY81HENX2u23M2bN
ZNCCY+nOYopZx9YYaWgYiYMZBKNJ8ymFCMIJhNrWNzzH0Z10UplmrQSjGWAGwWiybGYn8ymiH91o
XceZQmmkMpiDWc5GVrM5yhoaRmJhBsFosnzOcpIRhnBwvcIZQD4ZtGAWK2oWNoxGjBkEo0myi3IW
sJYj6VytWANVAAAgAElEQVTjNNOaSCWZvnRlKRvYQlmUNDSMxMMMgtEkmU8RFVQxgPyohNefPECY
Q0SrCBtGo8QMgtHkqEKZzSq6kU0HWkclzNakcxgd+JI17LVlso0mihkEo8mxjO/Zyi4GRql1UM0A
8tnNXr6OeFFfw2hcmEEwmhyzWUUrWnIouVENtxvt6EBrvmAVSt22njWMRMYMgtGkKKGUFZTQj24k
R/nxFoSB5PM9O2wKqtEkMYNgNCnmsJpkkuhHt5iEfzidSCfVBpeNJokZBKPJsJdKvqKIw+hAJmkx
iSOVZPrQhW8pppQ9MYnDMOKFGQSjybCI9eyhgr4xah1U05duVKF8RVFM4zGMhsYMgtFkmMtq2pNJ
HtkxjSeHLPLIZh7f2eCy0aQwg2A0CTawnSK20pduQbfGjDZ96cYWyljJppjHZRgNhRkEo0kwj+9I
Jok+wXdsjTqH0YF0Uplrg8tGE8IMgtHo2UslC1hLbzqQQYsGiTPFG1xewgZK2d0gcRpGrDGDYDR6
FrKuQQaTA6keXJ5vg8tGE6FeBkFEhovIEhEpFJGbg7iLiDzouS8Qkb4+t1Ui8rWIzBeROfXRw2je
zOU7csikW4wHkwPJIYt82jOX76iywWWjCVBngyAiycAjwAigN3CBiPQOEBsB9PSOccCjAe4nq+rR
qtq/rnoYzZt1bGMtW+lPXoMMJgfSnzy2sYtCvm/wuA0j2tSnhTAQKFTVFapaDrwEjAyQGQlMUscs
oK2IdKxHnIaxH3NYRSrJHNVAg8mBHEouWaQx2waXjSZASj38dgbW+K6LgEERyHQG1gMKfCgilcDj
qvpEsEhEZByudUFubi4FBQX1ULnhKC0tbTS6NiTRzJe9KcqC48rI3ZDCrKWfRSXMupCdX8Xy/I38
Z9bHpO+uWx3LnpfgWL4EJ1b5Uh+DUF+GqOpaETkQ+EBEvlXV6YFCnqF4AqB///46dOjQBlazbhQU
FNBYdG1IopkvM1lBFYs5u9Nx5HaKzr4HdWE7u3mAj0k5tiNDOaxOYdjzEhzLl+DEKl/q02W0Fujq
u+7i3YtIRlWr/38PvIbrgjKMiFCUOaymK+3IjdImOHWlNS3pRS7zbfMco5FTH4MwG+gpIt1FpAVw
PvBmgMybwFhvttGxwDZVXS8imSLSCkBEMoFTgW/qoYvRzChkI1soi9oWmfWlP3nsYi8LWRdvVQyj
ztS5y0hVK0TkauA9IBn4l6ouFJHxnvtjwFTgdKAQKAMu87znAq+JSLUOL6jqu3VOhdHsmM0qMknj
MDrEWxUA8mnPAWTxX1bRhy5xmfFkGPWlXmMIqjoVV+j77z3mO1fgqiD+VgB96hO30XzZwHYK2chQ
Don6Jjh1RRCO4yDeZAHLKaEHB8RbJcOoNYnxNhlGLfiM5bQgOWG6i6o5ks60oiWfURhvVQyjTphB
MBoVWyhjIevoSzfSSY23OvuRTBLH0p3VbGYtW+OtjmHUGjMIRqNiJitIIonjOCjeqgSlL91oSQqf
sTzeqhhGrTGDYDQaStnDfNZwlNc1k4ikkUJ/8viWYkoojbc6hlErzCAYjYb/spIKqjieg+OtSlgG
0p0UkvjcWglGI8MMgtEo2MFuvmAVvelIezLjrU5YskijH3l8RREb2B5vdQwjYswgGI2CaSyhkip+
wqHxViUiTqQHaaTyAYtt32Wj0WAGwUh41rGN+RQxiO5kJ3jroJp0WnAiPVlBCYVsjLc6hhERZhCM
hEZR3mcRGbTgBHrEW51aMYA8ssngAxZTRVW81TGMGjGDYCQ0iynmOzZzMofSMsG+O6iJZJIYxmGU
UMq8/VaBN4zExAyCkbDsZi/vs4hcWnHMfovmNh4OJZc8svmIb9lKWbzVMYywmEEwEhJFeYsFlLKH
MziSpEa6WJwgnE0fFPg3863ryEhozCAYCclcvmMxxfyEQ+lCu3irUy/akcEZHEERW5hu6xwZCYwZ
BCPhKGY777GIHhyQsEtU1JYj6cxRdOZTlrGazfFWxzCCYgbBSChK2cMrzCOdVEbSp0ntKzCCI2hH
Bq8yj022rIWRgJhBMBKGHexmEjPZwW5G0ZdM0uKtUlRJI4Vf0B9FmcQsNrEz3ioZxn6YQTASgu3s
ZhKz2MZuLmQg3ciOt0ox4QBaMYZjqfSMwmYzCkYCYQbBiDtFbOEZZrKDPVzEQPKaqDGo5kBaMYZB
VFLFU8ykkO/jrZJhAGYQjDhSQSUf8S1P8TmVVHFxE24ZBJJLay7hWDJpwQvMZlnPPZRTEW+1jGZO
vfZUNoy6UEkVi1jPDArZSClH04VT6d3ovkSuLwfQil8xmI9ZwqxOK3mM6RzPwfShC6kkx1s9oxlS
rxaCiAwXkSUiUigiNwdxFxF50HNfICJ9I/VrNC0UZQPbWZ1XzgN8zGvMpwrlfPpzNn2anTGoJoVk
TqU3R33VkgxaMJVvuJ+P+JhvWctWqmylVKMBqXMLQUSSgUeAnwJFwGwReVNVF/nERgA9vWMQ8Cgw
KEK/RiNEUcqpYAd72EQpJexkA9tZySZ2sge6w8EcwFnk04MDmtS00vrQdmsyIxnMd2xmJiuZwXJm
sJx0UsmnPbm05gCyaE8WrUijJamWd0bUqU+X0UCgUFVXAIjIS8BIwF+ojwQmqaoCs0SkrYh0BPIj
8Bs1vmYtq9kUi6BDsu6QPZSyoEHjrAvh6p/qW8lfvb8qoIoqqlAqqaKCKsqpZC8V7KGCnZRTGbA8
QyvS6E57DiKH4pnLGH7cwFglp1EjCHm0J4/27GQPKylhOSWsZhOLKd5PNgkhkzTSSKEFybQghWSS
vENIQhDffxc+YY1IIpqXxvIeNQQD6c6BtIppHPUxCJ1hvyUci3CtgJpkOkfoFwARGQeMA8jNzaWg
oKDWiq7sXs6GDg07YKfZVWzeU9SgccYC2WcRXIGi1YeQVAVSBclVQnIlpFcKrcuTaVGeQupeIaNM
SC9LIqVSgG1sZRsVpWV1+g2bOqWlpUHzpQ1wFElUJmVQllHFrgylvIWyt4VSnlpBZfJediVDabKi
SVAloEmKCu6AH0p6TcQSvwaaynsUDSoWb6TtVje2FOp5qTeqWqcDGAX8w3c9Bng4QOZtYIjv+iOg
fyR+gx39+vXTxsK0adPirUJCUt98KS4u1hNOOEGzsrL0uuuui45SCUCwfJkwYYJedNFFIf307t07
ZH5OmzZNO3fuHNLvJZdcorfeemtt1Wxw7D0KTm3zBZijEZTr9RlUXgv7rUncxbsXiUwkfps8L7zw
Av379ycrK4uOHTsyYsQIZsyYAcDEiRO5+OKLf+RHRCgs3LdA2qJFizj77LNp06YNrVq14uSTT+bz
zz/fz095eTkTJ06kZ8+eZGZmkp+fz+WXX86qVat+kHn77bcZOHAgmZmZtG/fnosuuoiion01s6ef
fpohQ4aETc+ll15KSkoK69ev/+He+PHjycrKIisrixYtWjBs2LAfrkeMGMGqVasQkR/uVR+TJ08O
GscTTzxBTk4O27dv59577w2rT01ceuml3HbbbfUKo7asWrWKk08+mYyMDHr16sWHH35Y57AWLlzI
0KFDo6dcjAl8dmti6NChvPPOO1GJe/To0bz//vvs2bOHDh067Od2ww030LNnT1q1akWvXr2YNGlS
VOJsjIgzHnXwKJICLAVOwRXms4ELVXWhT+YM4GrgdFyX0IOqOjASvyHi3AisrpPCDU8OUBLGPRfo
gEvPdlzrvjXQCteF1glIA1YG+OsHfAPs8dwPAzYCxV4YObguuaXww2ewPYBUL64yIBnI9uRLgHa4
cZ3VwBbPvYunyyKgEmjvhb0kRHqSgD5emOuBDUFkOnlp/NZ3rwVwJDA3RLiB5AF7gXURyocjHyiP
UliR0gsoxT33bTwdvgHa8uPnJdQzEAmtgO4QsgM+n4ZPu//ZjYRDPdlVUYj7CGAxLj+74N6PajoB
m4HdQCZuEswySOjPyGsqXwLJU9UDapSKpBkR6sAV9EuB5cCt3r3xwHjvXHCziZYDXwP9w/ltSgdh
mmi4gqAUOC+MzETguSD3FejhnT8LTA0i8ygw3TsfBuwCuoaIR3CG4MaA+0m4l/cO7/pSYEYYfcfi
xoWuBb4Jk6ZNAffyvTSlRJCnT+OMQbmXf8M8PW/2nqNNwBQg2+fnZZyx3AZMBw737o8LCOutwPz1
xfkn73wozljf5IX5rHf/TGA+sBX4HDgqhP6H4Aq4Vr5707135kfPi5dfU4BJwA5gYcA7tAoY5p2n
e7puwRnx/wGKfLLHAPO8cCYDL1Wnq6Y0ePHcgDMu2zz/LUOksQfwiSdXAkz2pVNxhWwp8AtcReRt
XIVmi3fexZO/E1cRqfLkH/bu9wI+wBXgS4DRETw37YAV3vmvgXtqkH8TuD6W5UN9j2DPS1TCjXfC
muoR7gcDhgMVhCkEicwgFAOXBZE52XuZ0oG7gE/CxNPLC7N7ELfbgZne+aWENwgfAffgWj4VQL8Q
aaqzQfDknw4oyK4FZuFqfWnA48CLPvfLcbXlNOB+YH6osALzN1AGZxAqgLu98NJxBe33uBZwMnAJ
rgBNC6L7z4HFAfce8o5QBmE3rvKUDPwFmOVzX8U+g3AX8Cmu5dcVZ8yLPLcWOKP/e1xLcRTOGFan
K2wavPMvcDXpbFxNe3yI3+dF4FacoW7J/mOIgXnbHjgXyPB+o5eB133uBcAq33UmrtJxGW5CzDE4
o9M7hC6n4AxcKc7wb/X+7/TOTwriJx3Xwh0eq7IhGkew5yUahy1dER/aAyWqWtPUp9EistV/BLjn
4B7eQNbjXshsL65gMv4wCCGz3uceEhHphjNCL6jqBpxxGFuTvwBKAtJ6WIT+xuNamEWqugdXiI7y
uiVR1X+p6g6fWx8RaVNL3fxUARNUdY+q7sK1NB5X1f+qaqWqPoNrBRwbxG8WrubsZzuEnUs4Q1Wn
qmolrkXYJ4TcaOBOVd2sqmuAB31ux+IMwf2quldVX8F101YTSRoeVNV1qroZeAs4OoQee3Hdep1U
dbeqzgiVMFXdpKqvqmqZqu7AtQpOCiWPa8WsUtWnVLVCVb8EXgXOCxH+R6raFnjdk+mMM245qtpW
VT8J4u0x4CvgvTB6NFnMIMSOJ8K4bQJyqgutMEzxHtwfjgD3EqBjEH8dcQXXFi+uYDL+MAgh05HI
+inH4Gq+873r54ELRSTY58fLQoSRE5DWxRHEC67wec1nMBfjWke5IpIsIneJyHIR2c6+vugajVwY
Nqrq7oD4rw8w2l1xtelASnFjKH7a4LpxQj0v/g8QyoCWIZ6bTuw/lXt1gNta9aqWQdwjSUOgHlkh
9L0R1w35hYgsFJHLQ8ghIhki8riIrPZ+n+lAW+/D1WqmB+g5KEDPi3BjccHCL/JkLgCewbWC8oD1
InJfEPn/w401jA7Iq0QkXPlSZ8wgxAhVDfeDzcTVwH5Wz2g+JHjtaDSuq6fMkxkoIl1ChLEE1y++
XzgikoRrzn8UgR5jgYNEpFhEioH7cIXu6UFko72H5BpgRIAxaamqa4ELcR88DmPfAC7s+wYr2Etf
huvCqCawsAn0swZXM/fHn6GqLwYJeyEun/wtgj7Awhqel0hYz/4z97oFuHUWEQnhXps0hEVVi1X1
ClXtBFwJ/F1EeoQQvx43cDxIVVsDJ3r3/b+P3yCswXV/+vXMUtVfh9ClC6579kOvMvUEcJXn7zq/
rIjcjltZ4VRV3V7bdDc0UXhegmIGIQ6o6jbg/wGPiMjPvJpSqoiMEJF7ahHU7cDxInKniGSLSCsR
uQZXQN/kxfUhbhDuNRHpJyIpntx4EbncqwndANwmIheKSEsR6QD8A1eb/ZsvPvHc/cdxwMG4L9eP
9o4jgBeofbdRXXgMuFNE8jwFDxCRkZ5bK5zh3YQr5P8c4HcD/GiPzvm41k2yiAwnfBcGwJPAeBEZ
5K3dlSkiZwQU+gCo6lIv/Ale3p2Dm2H1asSpDc0U4BYRaecZ/2t8bjNxYx+/9Z6zc3C/V63TUBMi
cp6v8rEFV6hXf7oemN+tcBMetopINjAhILhA+beBQ0RkjJeOVBEZUEP3Yj/cYDpAX2BOEJ1vwVUe
hqlqwy5pkGCYQYgTqnovcB1wG26WxRrcFN3XaxHGMmAIrpa5ClcTPBc4TVU/84mOAqbiZodsww04
9se1HlDVybhun9/jCs9FuMG1wQEvyPG4F9h//BJ4Q1W/9mqHxapaDDwAnOm96JGwVURKfcd1NXsB
L543gfdFZAdugLn6q/dJuK6RtV6aZgX4/SfQ2+t+qM73a4GzcIOOF1HD76Gqc4ArgIdxBWAhbgA+
FOfj8n4LbpB4lKpurDGVNXM7Lq0rgfdx4w3VOpYD53h6bcbN8Pl3PdIQjgHAf0WkFPe7XKveEjW4
MZxnvPwejRvkT8d1S84C3g0I6wHceNAWEXnQG2c4FZeH63DdWNUD/KHoB8zzWke9cK20QP6MazEV
+p6/P9Q24U2BOn+HYBiGYTQtrIVgGIZhAGYQDMMwDA8zCIZhGAZgBsEwDMPwaFR7Kufk5Gh+fn68
1YiInTt3kpmZGW81Eg7Ll+BYvgTH8iU4tc2XuXPnlmgEi9s1KoOQn5/PnDk/mkackBQUFDSqpYkb
CsuX4Fi+BMfyJTi1zRcRiWiV6EZlEAyjSVFZCbNmwdSp9JsyBb77DrKzIScH8vPhiivgzDMhyXp2
jYbBDIJhxIMvv4Rf/QrmzYPkZCqPOAKuuQa2bYONG2HuXBg5Enr0gN//Hq68EpKTaw7XMOqBVT0M
oyEpK4ObboIBA2DtWnjqKSgpYf7998Nf/wpPPgmvvw4rV8JLL0H79nDVVTB8OJTUZj8Uw6g9ZhAM
o6HYtAlOOgnuuQcuuwwWL4ZLL4W2gYvYAikp8ItfuC6lf/wDPv0U+vaF2bN/LGsYUcIMgmE0BMXF
MHQofP01vPGGawm0axeZ31/+Ej77zI0lDBkC//lPTFU1mi9mEAwj1qxZ41oGK1fC1Klw9tm1D6Nf
PzeucPjhcO65zkAYRpQxg2AYsWTrVvjpT10L4f334Sc/qXtY7dvDu+9C165wxhnw1VfR09MwMINg
GLGjshIuuACWL4e33oLjj69/mAceCB98AK1awamnulaHYUQJMwiGEStuusnV6B95BE48sWb5SOnW
zRmF8nIYNQp2767Zj2FEgBkEw4gFkybBvfe6KaPjxkU//F69XBzz5sHvfhf98I1mSUQGQUSGi8gS
ESkUkZuDuIuIPOi5LxCRvj63VSLytYjMF5E5vvvZIvKBiCzz/kc45cIwEpxvv4Xx492sor/9rUbx
OnPWWXDjjfD44/D887GLx2g21GgQRCQZeAS3AXVv4AIR6R0gNgLo6R3jgEcD3E9W1aNVtb/v3s3A
R6raE7eR+48MjWE0OsrL4aKLICPDFdKpqbGN7847XXfUuHHuuwbDqAeRtBAGAoWqusLbm/UlYGSA
zEhgkjpmAW1FpGMN4Y4EnvHOnwF+Vgu9DSMxmTDBdeM8+SR06hT7+FJS3BfNGRkwdixUVMQ+TqPJ
EslaRp1xG8BXU8S+TczDyXTGbfquwIciUgk8rqpPeDK5qrreOy8GcoNFLiLjcK0OcnNzKSgoiEDl
+FNaWtpodG1ImnK+tJk/n6Pvvpvi009nSbt2UIt01jdfDrj6ag6fOJGVV17J6jFj6hxOotGUn5f6
ELN8UdWwBzAK+IfvegzwcIDM28AQ3/VHQH/vvLP3/0DgK+BE73prQBhbatKlX79+2liYNm1avFVI
SJpsvuzYoZqXp9qjhzuvJVHJl/PPV01NVZ0/v/5hJQhN9nmpJ7XNF2CO1lC+qmpEXUZrga6+6y7e
vYhkVLX6//fAa7guKIAN1d1K3v/vI9DFMBKTP/zBLV/99NOQlRUfHR5+2H28NnasG8swjFoSiUGY
DfQUke4i0gI4H3gzQOZNYKw32+hYYJuqrheRTBFpBSAimcCpwDc+P5d455cAb9QzLYYRHz77zBXG
V18NgwfHT4/27eGJJ2DBAvjzn+Onh9FoqdEgqGoFcDXwHrAYmKKqC0VkvIiM98SmAiuAQuBJ4Dfe
/Vxghoh8BXwBvKOq73pudwE/FZFlwDDv2jAaF7t3u30NunZNjEL4rLPcLKc//9lmHRm1JqINclR1
Kq7Q9997zHeuwFVB/K0A+oQIcxNwSm2UNYyE48473XcH774bv66iQO67z62IesUVMH267bhmRIw9
KYZRV775Bu66y/XZn3ZavLXZx4EHOqPw2Wdu+qthRIgZBMOoC1VVblvLNm3cEhWJxtixbmXVG2+E
devirY3RSDCDYBh14ckn4fPPnTHIyYm3Nj9GxC1pUV4Ov/1tvLUxGglmEAyjthQXu5VMhw51NfFE
pUcP+OMf4dVX4Z134q2N0Qgwg2AYteX3v4ddu+Cxx1xNPJG54Qbo3duturpzZ7y1MRIcMwiGURve
fdetHXTLLXDoofHWpmZatHBdR6tXw+23x1sbI8Exg2AYkbJzJ/z6184Q3HJLvLWJnCFD3LcS991n
224aYTGDYBiRcscdsGqVq3GnpcVbm9px992Qne2Wya6sjLc2RoJiBsEwIuGrr9yMossvh5NOirc2
tSc7G+6/H774wm3paRhBMINgGDVRWelq1tnZ8H//F29t6s4FF8Dw4fsW4jOMAMwgGEZNPPSQq1nf
d58zCo0VEXj0UVCF3/zG/TcMH2YQDCMcy5e7GvUZZ7hF4xo7+fnwpz+57xKmTIm3NkaCYQbBMEJR
VeUWiEtNbRzfHETKb38LAwa45bq/t21IjH2YQTCMUDz5JEybBn/9K3TpEm9tokdyMjz1FGzfbl1H
xn5EZBBEZLiILBGRQhG5OYi7iMiDnvsCEenr3e8qItNEZJGILBSRa31+JorIWhGZ7x2nRy9ZhlFP
vvsO/ud/4JRT3Bz+psbhh7tptK++CpMnx1sbI0Go0SCISDLwCDAC6A1cICK9A8RGAD29YxzwqHe/
ArheVXsDxwJXBfj9m6oe7R377bdgGHGjshKqN6p/8smm01UUyA03wLHHumUtiovjrY2RAETSQhgI
FKrqClUtB14CRgbIjAQmefs5zwLaikhHVV2vqvMAVHUHbse1zlHU3zCiz1//6jaWeegh6N493trE
juRktwd0WZlrBVnXUbMnkh3TOgNrfNdFwKAIZDoD66tviEg+cAzwX5/cNSIyFpiDa0lsCYxcRMbh
Wh3k5uZSUFAQgcrxp7S0tNHo2pAker5kLV1K39tuo+Skk1jUrRs0kK7xzJfOV1xBz4ceYtk117B2
1Ki46BCKRH9e4kXM8kVVwx7AKOAfvusxwMMBMm8DQ3zXHwH9fddZwFzgHN+9XCAZ10q5E/hXTbr0
69dPGwvTpk2LtwoJSULny86dqr16qXbqpLppU4NGHdd8qapSPess1dRU1Tlz4qdHEBL6eYkjtc0X
YI7WUL6qakRdRmuBrr7rLt69iGREJBV4FXheVf/tM0QbVLVSVauAJ3FdU4YRH1TdDmhLlsAzzzTu
D9Bqi4ibdXTggXD++bBjR7w1MuJEJAZhNtBTRLqLSAvgfODNAJk3gbHebKNjgW2qul5EBPgnsFhV
7/N7EJGOvsufA9/UORWGUV8efRSee84tET1sWLy1aXjat4cXXoAVK2D8eBtPaKbUaBBUtQK4GngP
Nyg8RVUXish4ERnviU0FVgCFuNr+b7z7g3FdTD8JMr30HhH5WkQWACcDv49aqgyjNsycCb/7nfsa
+dZb461N/DjxRDcV9YUX3MC60eyIZFAZdVNCpwbce8x3rsBVQfzNAILO2VPVMbXS1DBiQXExjBoF
XbvCs89CUjP/VvMPf4AFC9wWoYcdBmeeGW+NjAakmT/9RrNmxw7XKti6Ff79b2jXLt4axZ/q8YRj
joELL4SFC+OtkdGAmEEwmifl5a5l8NVX8Mor0KdPvDVKHDIy4I03IDPTtRDWBs4hMZoqZhCM5oeq
+xDr/ffdl8gjRsRbo8SjSxd46y3YtMkNstsieM0CMwhG86Kqyq3y+eyz8L//C5ddFm+NEpf+/eHt
t2H1ajjtNNjyo+9GjSaGGQSj+VBR4bbA/Pvf4cYbm/eMokg58UR47TU3ljB8uGsxGE0WMwhG82Dv
XrfBzTPPuKmVd93VdBetizannQYvv+zGWwYPhlWr4q2RESPMIBhNn++/h1NPdTuE/fWv8Mc/mjGo
LSNHwgcfuLw87jj48st4a2TEADMIRtNm9mzo1w9mzXLjBtdfH2+NGi8nnACffQYtWrjzp5+2L5qb
GGYQjKZJVRU8/LAruJKTXUF28cXx1qrxc9hhzrgOGOAG5C++2O28ZjQJzCAYTY/Fi50huOYa+MlP
YM4c6Ns33lo1HTp2hA8/dLO0Jk+Go4+Gd96Jt1ZGFDCDYDQdNm+GW25xBdS338KkSa6gysmJt2ZN
j+RkuO02+OQT14V05pnuq++lS+OtmVEPzCAYjZ+tW2HCBMjPh7vvhvPOc62EMWNs8DjWDB7s1j66
91749FO3V/Mll8DXX8dbM6MOmEEwGieqMGOGK3w6dXJTSU891U2NfO45t7a/0TC0aAHXXedaB7/5
jVsK5Kij3Bfgr7wCu3bFW0MjQswgGI2H8nLXd/2730GPHm6c4LXXYOxYmD/fFT5HHhlvLZsvHTrA
Aw/Ad9+58YX5811rLTfXGe4pU6CkJN5aGmGIaPlrERkOPIDb8vIfqnpXgLt47qcDZcClqjovnF8R
yQYmA/nAKmC0BtlT2Wim7N7tapyLF7tB4ZkzYe5cdz8tDU45xX1pPHo0ZGXFW1vDT/v2bnzhlltg
2jR48UV49VU3pgNuIcFjj3Urqh5zDPTqBa1bx1dnA4jAIIhIMvAI8FOgCJgtIm+q6iKf2Aigp3cM
Ah4FBtXg92bgI1W9S0Ru9q5vil7SjLijCpWVrma/axeUlZG+Zo0r2HfscNMVN21ytcaSEreq5po1
7kcRIT8AAAYmSURBVFi92k0dBdcl0a8f/PrXMHSoMwaZmXFNmhEBycluYbxhw+Dxx93v/tFH8PHH
bnbS44/vkz3gADj4YLcvRYcObiZTTg4HrFvnnqHWrd1vnpUF6emuUtCyJaSm2jhRFImkhTAQKFTV
FQAi8hIwEvAbhJHAJG+jnFki0tbbIjM/jN+RwFDP/zNAAbEyCBMnwvPPxyToUAzctcs9uPGkth8N
Bcqr7rsXeF5Vte9eVZV7aauPigq3VMTevT+KYlCouFu0cGMBXbu62uOYMW7Oe69e7n9aWu3SYiQW
KSkwaJA7/vAH99ysXu2+eF66FJYvd8eCBW4V2m3bADg80rBTU93/5OR9R1KSO0SCH7C/MQl1HoqG
NkT//KdbWyqGRGIQOgNrfNdF/Pi9DibTuQa/uaq63jsvBnKDRS4i44BxALm5uRQUFESg8v502LWL
dnl5tfZXHyoqKtiRElGPXGyp5UOrweR9L4/6z73dxTQ5+YdrTU5210lJVKWmUpWaiiYnU5WWRlVa
GmVVVaRkZ1OZkUFFRgYVbdqwt3VrKtPTg+u6davrLmrilJaW1unZbvS0a7fPUPhI2rOH1O3bKd+w
gdaVlSSX/f/27uW1jjIO4/j3adC6EFGpophAusgmXqASqtBNQPFaWlcSRfG2KbRQoVBI+w8UBHUh
IkGFgoIUVBpE0Xg5y3qJWkutxoK0UNqqGxXbJjnJz8W8acd0mpzGTOYk83wgnMw7c5J3HobzzsyZ
+c0ZOs6epePcOTrGx9HkJKsmJlg1OYmaTTQ1deFnehrldljO/w5o9o5NAbWyI1XBHdrHx8b4Jx01
l7W9tMEnVvYITkmFCUfEEDAE0NfXF/39/Zf/Dxbynv+p0WiwoL6ucM6lmHMp1mg0WOdcgP/uMZe1
vbRyldEJoCs33ZnaWllmrveeTqeVSK9+AoeZWYVaOUL4GuiRtJbsw3wAeHzWMsPAtvQdwV3AnxFx
UtLvc7x3GHgK2JNe98/XkdHR0T8kHWuhz+1gDeBr7C7mXIo5l2LOpdjl5tLSOfN5B4SIaEraBnxM
dunomxFxWNKWNP814EOyS06Pkl12+sxc701/eg+wT9JzwDHg0Rb6ckMrK9UOJH0TEX1V96PdOJdi
zqWYcylWVi4Kl68thTfkYs6lmHMp5lyKlZWL71Q2MzPAA0KZhqruQJtyLsWcSzHnUqyUXHzKyMzM
AB8hmJlZ4gHBzMwADwilkbRDUkhak2sblHRU0s+S7q+yf0tN0guSfpL0g6T3JV2bm1fbXCCrCJzW
/Wgq9Fg7krokfSHpR0mHJW1P7ddLGpH0S3q9ruq+VkFSh6TvJH2QpkvJxQNCCSR1AfcBx3NtvWQ3
5t0KPAC8mqrB1sUIcFtE3AGMAYPgXHIVgR8EeoHHUiZ10wR2REQvcDewNeUwUxW5B/gsTdfRduBI
brqUXDwglOMlYCeQ/8Z+M/BORIxHxK9kN/Gtr6JzVYiITyKimSYPkJUxgZrnQq6acERMADMVgWsl
Ik7OPEMlIv4m+/C7hSyLvWmxvcAj1fSwOpI6gYeB13PNpeTiAWGRSdoMnIiIg7NmXaoibB09C3yU
fq97LnVf/4tI6gbWAV/SYlXkFe5lsh3M6VxbKbm0RbXT5UbSp8BNBbN2A7vIThfVzly5RMT+tMxu
stMDS/uAClsWJF0NvAs8HxF/KVcSfa6qyCuVpI3AbxExKqm/aJnFzMUDwgJExL1F7ZJuB9YCB9OG
3Al8K2k9rVWNXdYulcsMSU8DG4F74sINMCs+l3nUff3Pk3QF2WDwdkS8l5pPS7o5FcusY1XkDcAm
SQ8BVwHXSHqLknLxKaNFFBGHIuLGiOiOiG6yw/87I+IUWXXXAUmrU/XXHuCrCru7pNKztXcCmyLi
TG5WrXMhV01Y0pVkX7APV9ynJZeey/4GcCQiXszNmqmKDC1WRV5JImIwIjrT58kA8HlEPEFJufgI
YYmkCrH7yB4f2gS2RsRUxd1aSq8Aq4GRdPR0ICK21D2XeSoC18kG4EngkKTvU9suFlAVuSZKycWl
K8zMDPApIzMzSzwgmJkZ4AHBzMwSDwhmZgZ4QDAzs8QDgpmZAR4QzMws+Rd6iD5ypioT0QAAAABJ
RU5ErkJggg==
"
>
</div>

</div>

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYoAAAEICAYAAABBBrPDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

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


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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">
<h5 id="ModelSelector-class">ModelSelector class<a class="anchor-link" href="#ModelSelector-class">&#182;</a></h5><p>Review the <code>ModelSelector</code> class from the codebase found in the <code>my_model_selectors.py</code> module.  It is designed to be a strategy pattern for choosing different model selectors.  For the project submission in this section, subclass <code>SelectorModel</code> to implement the following model selectors.  In other words, you will write your own classes/functions in the <code>my_model_selectors.py</code> module and run them from this notebook:</p>
<ul>
<li><code>SelectorCV</code>:  Log likelihood with CV</li>
<li><code>SelectorBIC</code>: BIC </li>
<li><code>SelectorDIC</code>: DIC</li>
</ul>
<p>You will train each word in the training set with a range of values for the number of hidden states, and then score these alternatives with the model selector, choosing the "best" according to each strategy. The simple case of training with a constant value for <code>n_components</code> can be called using the provided <code>SelectorConstant</code> subclass as follow:</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[24]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_model_selectors</span> <span class="k">import</span> <span class="n">SelectorConstant</span>

<span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features_norm</span><span class="p">)</span>  <span class="c1"># Experiment here with different feature sets defined in part 1</span>
<span class="n">word</span> <span class="o">=</span> <span class="s1">&#39;VEGETABLE&#39;</span> <span class="c1"># Experiment here with different words</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">SelectorConstant</span><span class="p">(</span><span class="n">training</span><span class="o">.</span><span class="n">get_all_sequences</span><span class="p">(),</span> <span class="n">training</span><span class="o">.</span><span class="n">get_all_Xlengths</span><span class="p">(),</span> <span class="n">word</span><span class="p">,</span> <span class="n">n_constant</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of states trained in model for </span><span class="si">{}</span><span class="s2"> is </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">word</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">n_components</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>Number of states trained in model for VEGETABLE is 3
</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;[25]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">my_model_selectors</span> <span class="k">import</span> <span class="n">SelectorConstant</span>

<span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features_polar</span><span class="p">)</span>  <span class="c1"># Experiment here with different feature sets defined in part 1</span>
<span class="n">word</span> <span class="o">=</span> <span class="s1">&#39;VEGETABLE&#39;</span> <span class="c1"># Experiment here with different words</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">SelectorConstant</span><span class="p">(</span><span class="n">training</span><span class="o">.</span><span class="n">get_all_sequences</span><span class="p">(),</span> <span class="n">training</span><span class="o">.</span><span class="n">get_all_Xlengths</span><span class="p">(),</span> <span class="n">word</span><span class="p">,</span> <span class="n">n_constant</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of states trained in model for </span><span class="si">{}</span><span class="s2"> is </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">word</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">n_components</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>Number of states trained in model for VEGETABLE is 3
</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="kn">from</span> <span class="nn">my_model_selectors</span> <span class="k">import</span> <span class="n">SelectorConstant</span>

<span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features_custom</span><span class="p">)</span>  <span class="c1"># Experiment here with different feature sets defined in part 1</span>
<span class="n">word</span> <span class="o">=</span> <span class="s1">&#39;VEGETABLE&#39;</span> <span class="c1"># Experiment here with different words</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">SelectorConstant</span><span class="p">(</span><span class="n">training</span><span class="o">.</span><span class="n">get_all_sequences</span><span class="p">(),</span> <span class="n">training</span><span class="o">.</span><span class="n">get_all_Xlengths</span><span class="p">(),</span> <span class="n">word</span><span class="p">,</span> <span class="n">n_constant</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of states trained in model for </span><span class="si">{}</span><span class="s2"> is </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">word</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">n_components</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>Number of states trained in model for VEGETABLE is 3
</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="kn">from</span> <span class="nn">my_model_selectors</span> <span class="k">import</span> <span class="n">SelectorConstant</span>

<span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features_ground</span><span class="p">)</span>  <span class="c1"># Experiment here with different feature sets defined in part 1</span>
<span class="n">word</span> <span class="o">=</span> <span class="s1">&#39;VEGETABLE&#39;</span> <span class="c1"># Experiment here with different words</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">SelectorConstant</span><span class="p">(</span><span class="n">training</span><span class="o">.</span><span class="n">get_all_sequences</span><span class="p">(),</span> <span class="n">training</span><span class="o">.</span><span class="n">get_all_Xlengths</span><span class="p">(),</span> <span class="n">word</span><span class="p">,</span> <span class="n">n_constant</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of states trained in model for </span><span class="si">{}</span><span class="s2"> is </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">word</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">n_components</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>Number of states trained in model for VEGETABLE is 3
</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">
<h5 id="Cross-validation-folds">Cross-validation folds<a class="anchor-link" href="#Cross-validation-folds">&#182;</a></h5><p>If we simply score the model with the Log Likelihood calculated from the feature sequences it has been trained on, we should expect that more complex models will have higher likelihoods. However, that doesn't tell us which would have a better likelihood score on unseen data.  The model will likely be overfit as complexity is added.  To estimate which topology model is better using only the training data, we can compare scores using cross-validation.  One technique for cross-validation is to break the training set into "folds" and rotate which fold is left out of training.  The "left out" fold scored.  This gives us a proxy method of finding the best model to use on "unseen data". In the following example, a set of word sequences is broken into three folds using the <a href="http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html">scikit-learn Kfold</a> class object. When you implement <code>SelectorCV</code>, you will use this technique.</p>

</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="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">KFold</span>

<span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features_norm</span><span class="p">)</span> <span class="c1"># Experiment here with different feature sets</span>
<span class="n">word</span> <span class="o">=</span> <span class="s1">&#39;VEGETABLE&#39;</span> <span class="c1"># Experiment here with different words</span>
<span class="n">word_sequences</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_word_sequences</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
<span class="n">split_method</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">()</span>
<span class="k">for</span> <span class="n">cv_train_idx</span><span class="p">,</span> <span class="n">cv_test_idx</span> <span class="ow">in</span> <span class="n">split_method</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">word_sequences</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Train fold indices:</span><span class="si">{}</span><span class="s2"> Test fold indices:</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">cv_train_idx</span><span class="p">,</span> <span class="n">cv_test_idx</span><span class="p">))</span>  <span class="c1"># view indices of the folds</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>Train fold indices:[2 3 4 5] Test fold indices:[0 1]
Train fold indices:[0 1 4 5] Test fold indices:[2 3]
Train fold indices:[0 1 2 3] Test fold indices:[4 5]
</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="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">KFold</span>

<span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features_polar</span><span class="p">)</span> <span class="c1"># Experiment here with different feature sets</span>
<span class="n">word</span> <span class="o">=</span> <span class="s1">&#39;VEGETABLE&#39;</span> <span class="c1"># Experiment here with different words</span>
<span class="n">word_sequences</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_word_sequences</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
<span class="n">split_method</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">()</span>
<span class="k">for</span> <span class="n">cv_train_idx</span><span class="p">,</span> <span class="n">cv_test_idx</span> <span class="ow">in</span> <span class="n">split_method</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">word_sequences</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Train fold indices:</span><span class="si">{}</span><span class="s2"> Test fold indices:</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">cv_train_idx</span><span class="p">,</span> <span class="n">cv_test_idx</span><span class="p">))</span>  <span class="c1"># view indices of the folds</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>Train fold indices:[2 3 4 5] Test fold indices:[0 1]
Train fold indices:[0 1 4 5] Test fold indices:[2 3]
Train fold indices:[0 1 2 3] Test fold indices:[4 5]
</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;[30]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">KFold</span>

<span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features_custom</span><span class="p">)</span> <span class="c1"># Experiment here with different feature sets</span>
<span class="n">word</span> <span class="o">=</span> <span class="s1">&#39;VEGETABLE&#39;</span> <span class="c1"># Experiment here with different words</span>
<span class="n">word_sequences</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_word_sequences</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
<span class="n">split_method</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">()</span>
<span class="k">for</span> <span class="n">cv_train_idx</span><span class="p">,</span> <span class="n">cv_test_idx</span> <span class="ow">in</span> <span class="n">split_method</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">word_sequences</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Train fold indices:</span><span class="si">{}</span><span class="s2"> Test fold indices:</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">cv_train_idx</span><span class="p">,</span> <span class="n">cv_test_idx</span><span class="p">))</span>  <span class="c1"># view indices of the folds</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>Train fold indices:[2 3 4 5] Test fold indices:[0 1]
Train fold indices:[0 1 4 5] Test fold indices:[2 3]
Train fold indices:[0 1 2 3] Test fold indices:[4 5]
</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;[31]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">KFold</span>

<span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features_ground</span><span class="p">)</span> <span class="c1"># Experiment here with different feature sets</span>
<span class="n">word</span> <span class="o">=</span> <span class="s1">&#39;VEGETABLE&#39;</span> <span class="c1"># Experiment here with different words</span>
<span class="n">word_sequences</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_word_sequences</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
<span class="n">split_method</span> <span class="o">=</span> <span class="n">KFold</span><span class="p">()</span>
<span class="k">for</span> <span class="n">cv_train_idx</span><span class="p">,</span> <span class="n">cv_test_idx</span> <span class="ow">in</span> <span class="n">split_method</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">word_sequences</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Train fold indices:</span><span class="si">{}</span><span class="s2"> Test fold indices:</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">cv_train_idx</span><span class="p">,</span> <span class="n">cv_test_idx</span><span class="p">))</span>  <span class="c1"># view indices of the folds</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>Train fold indices:[2 3 4 5] Test fold indices:[0 1]
Train fold indices:[0 1 4 5] Test fold indices:[2 3]
Train fold indices:[0 1 2 3] Test fold indices:[4 5]
</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>Tip:</strong> In order to run <code>hmmlearn</code> training using the X,lengths tuples on the new folds, subsets must be combined based on the indices given for the folds.  A helper utility has been provided in the <code>asl_utils</code> module named <code>combine_sequences</code> for this purpose.</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">
<h5 id="Scoring-models-with-other-criterion">Scoring models with other criterion<a class="anchor-link" href="#Scoring-models-with-other-criterion">&#182;</a></h5><p>Scoring model topologies with <strong>BIC</strong> balances fit and complexity within the training set for each word.  In the BIC equation, a penalty term penalizes complexity to avoid overfitting, so that it is not necessary to also use cross-validation in the selection process.  There are a number of references on the internet for this criterion.  These <a href="http://www2.imm.dtu.dk/courses/02433/doc/ch6_slides.pdf">slides</a> include a formula you may find helpful for your implementation.</p>
<p>The advantages of scoring model topologies with <strong>DIC</strong> over BIC are presented by Alain Biem in this <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.6208&amp;rep=rep1&amp;type=pdf">reference</a> (also found <a href="https://pdfs.semanticscholar.org/ed3d/7c4a5f607201f3848d4c02dd9ba17c791fc2.pdf">here</a>).  DIC scores the discriminant ability of a training set for one word against competing words.  Instead of a penalty term for complexity, it provides a penalty if model liklihoods for non-matching words are too similar to model likelihoods for the correct word in the word set.</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><a id='part2_submission'></a></p>
<h3 id="Model-Selection-Implementation-Submission">Model Selection Implementation Submission<a class="anchor-link" href="#Model-Selection-Implementation-Submission">&#182;</a></h3><p>Implement <code>SelectorCV</code>, <code>SelectorBIC</code>, and <code>SelectorDIC</code> classes in the <code>my_model_selectors.py</code> module.  Run the selectors on the following five words. Then answer the questions about your results.</p>
<p><strong>Tip:</strong> The <code>hmmlearn</code> library may not be able to train or score all models.  Implement try/except contructs as necessary to eliminate non-viable models from consideration.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[32]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">words_to_train</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;FISH&#39;</span><span class="p">,</span> <span class="s1">&#39;BOOK&#39;</span><span class="p">,</span> <span class="s1">&#39;VEGETABLE&#39;</span><span class="p">,</span> <span class="s1">&#39;FUTURE&#39;</span><span class="p">,</span> <span class="s1">&#39;JOHN&#39;</span><span class="p">]</span>
<span class="kn">import</span> <span class="nn">timeit</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;[33]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Implement SelectorCV in my_model_selector.py</span>
<span class="kn">from</span> <span class="nn">importlib</span> <span class="k">import</span> <span class="n">reload</span>
<span class="kn">import</span> <span class="nn">my_model_selectors</span>
<span class="n">reload</span><span class="p">(</span><span class="n">my_model_selectors</span><span class="p">)</span>

<span class="kn">from</span> <span class="nn">my_model_selectors</span> <span class="k">import</span> <span class="n">SelectorCV</span>

<span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features_norm</span><span class="p">)</span>  <span class="c1"># Experiment here with different feature sets defined in part 1</span>
<span class="n">sequences</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_all_sequences</span><span class="p">()</span>
<span class="n">Xlengths</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_all_Xlengths</span><span class="p">()</span>
<span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">words_to_train</span><span class="p">:</span>
    <span class="n">start</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">SelectorCV</span><span class="p">(</span><span class="n">sequences</span><span class="p">,</span> <span class="n">Xlengths</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> 
                    <span class="n">min_n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">max_n_components</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">14</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">()</span>
    <span class="n">end</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span><span class="o">-</span><span class="n">start</span>
    <span class="k">if</span> <span class="n">model</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training complete for </span><span class="si">{}</span><span class="s2"> with </span><span class="si">{}</span><span class="s2"> states with time </span><span class="si">{}</span><span class="s2"> seconds&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">n_components</span><span class="p">,</span> <span class="n">end</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training failed for </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">word</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 complete for FISH with 15 states with time 1.2547799425877821 seconds
Training complete for BOOK with 15 states with time 36.71532525625578 seconds
Training complete for VEGETABLE with 15 states with time 9.015129160423115 seconds
Training complete for FUTURE with 15 states with time 39.50121898562647 seconds
Training complete for JOHN with 15 states with time 371.50343765039986 seconds
</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;[34]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Training with features_polar using model SelectorCV</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;[35]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Training with features_custom using model SelectorCV</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;[36]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Training with features_ground using model SelectorCV</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;[37]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Implement SelectorBIC in module my_model_selectors.py</span>
<span class="kn">from</span> <span class="nn">importlib</span> <span class="k">import</span> <span class="n">reload</span>
<span class="kn">import</span> <span class="nn">my_model_selectors</span>
<span class="n">reload</span><span class="p">(</span><span class="n">my_model_selectors</span><span class="p">)</span>

<span class="kn">from</span> <span class="nn">my_model_selectors</span> <span class="k">import</span> <span class="n">SelectorBIC</span>

<span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features_norm</span><span class="p">)</span>  <span class="c1"># Experiment here with different feature sets defined in part 1</span>
<span class="n">sequences</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_all_sequences</span><span class="p">()</span>
<span class="n">Xlengths</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_all_Xlengths</span><span class="p">()</span>
<span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">words_to_train</span><span class="p">:</span>
    <span class="n">start</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">SelectorBIC</span><span class="p">(</span><span class="n">sequences</span><span class="p">,</span> <span class="n">Xlengths</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> 
                    <span class="n">min_n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">max_n_components</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">14</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">()</span>
    <span class="n">end</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span><span class="o">-</span><span class="n">start</span>
    <span class="k">if</span> <span class="n">model</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training complete for </span><span class="si">{}</span><span class="s2"> with </span><span class="si">{}</span><span class="s2"> states with time </span><span class="si">{}</span><span class="s2"> seconds&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">n_components</span><span class="p">,</span> <span class="n">end</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training failed for </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">word</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 complete for FISH with 7 states with time 0.42127025384934313 seconds
Training complete for BOOK with 15 states with time 1.9558818509808589 seconds
Training complete for VEGETABLE with 13 states with time 0.8263476681833026 seconds
Training complete for FUTURE with 15 states with time 2.2684682509070626 seconds
Training complete for JOHN with 15 states with time 17.697479938150423 seconds
</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;[38]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Training with features_polar using model SelectorBIC</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;[39]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Training with features_custom using model SelectorBIC</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;[40]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Training with features_ground using model SelectorBIC</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;[41]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Implement SelectorDIC in module my_model_selectors.py</span>
<span class="kn">from</span> <span class="nn">importlib</span> <span class="k">import</span> <span class="n">reload</span>
<span class="kn">import</span> <span class="nn">my_model_selectors</span>
<span class="n">reload</span><span class="p">(</span><span class="n">my_model_selectors</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">my_model_selectors</span> <span class="k">import</span> <span class="n">SelectorDIC</span>

<span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features_norm</span><span class="p">)</span>  <span class="c1"># Experiment here with different feature sets defined in part 1</span>
<span class="n">sequences</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_all_sequences</span><span class="p">()</span>
<span class="n">Xlengths</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_all_Xlengths</span><span class="p">()</span>
<span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">words_to_train</span><span class="p">:</span>
    <span class="n">start</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">SelectorDIC</span><span class="p">(</span><span class="n">sequences</span><span class="p">,</span> <span class="n">Xlengths</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> 
                    <span class="n">min_n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">max_n_components</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">14</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">()</span>
    <span class="n">end</span> <span class="o">=</span> <span class="n">timeit</span><span class="o">.</span><span class="n">default_timer</span><span class="p">()</span><span class="o">-</span><span class="n">start</span>
    <span class="k">if</span> <span class="n">model</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training complete for </span><span class="si">{}</span><span class="s2"> with </span><span class="si">{}</span><span class="s2"> states with time </span><span class="si">{}</span><span class="s2"> seconds&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">n_components</span><span class="p">,</span> <span class="n">end</span><span class="p">))</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training failed for </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">word</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 complete for FISH with 2 states with time 2.5495129177489844 seconds
Training complete for BOOK with 15 states with time 4.526663230580766 seconds
Training complete for VEGETABLE with 5 states with time 3.024565312370896 seconds
Training complete for FUTURE with 15 states with time 4.234422839959166 seconds
Training complete for JOHN with 15 states with time 19.319166447644648 seconds
</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;[42]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Training with features_polar using model SelectorDIC</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;[43]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Training with features_custom using model SelectorDIC</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;[44]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Training with features_ground using model SelectorDIC</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">
<p><strong>Question 2:</strong>  Compare and contrast the possible advantages and disadvantages of the various model selectors implemented.</p>
<p><strong>Answer 2:</strong></p>
<p>**Cross Validation:</p>
<p>The advantage of CV is all observations are used for both training and testing, and each observation is used for testing exactly once. This is advantageous when we have less data. But the disadvantage is it's not computationally efficient.</p>
<p>**BIC:</p>
<p>The main advantage of BIC is it penalizes the complexity of the model. Here complexity is defined as number of parameters. The BIC is preferred when there is a low dimensional model.</p>
<p>**DIC:</p>
<p>As discussed in the paper, the DIC outperforms BIC in terms of performance. But the DIC exhibts higher number of parameters.</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><a id='part2_test'></a></p>
<h3 id="Model-Selector-Unit-Testing">Model Selector Unit Testing<a class="anchor-link" href="#Model-Selector-Unit-Testing">&#182;</a></h3><p>Run the following unit tests as a sanity check on the implemented model selectors.  The test simply looks for valid interfaces  but is not exhaustive. However, the project should not be submitted if these tests don't pass.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[45]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">importlib</span> <span class="k">import</span> <span class="n">reload</span>
<span class="kn">import</span> <span class="nn">my_model_selectors</span>
<span class="n">reload</span><span class="p">(</span><span class="n">my_model_selectors</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">asl_test_model_selectors</span> <span class="k">import</span> <span class="n">TestSelectors</span>
<span class="n">suite</span> <span class="o">=</span> <span class="n">unittest</span><span class="o">.</span><span class="n">TestLoader</span><span class="p">()</span><span class="o">.</span><span class="n">loadTestsFromModule</span><span class="p">(</span><span class="n">TestSelectors</span><span class="p">())</span>
<span class="n">unittest</span><span class="o">.</span><span class="n">TextTestRunner</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">suite</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_stderr output_text">
<pre>....
----------------------------------------------------------------------
Ran 4 tests in 470.480s

OK
</pre>
</div>
</div>

<div class="output_area">
<div class="prompt output_prompt">Out[45]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;unittest.runner.TextTestResult run=4 errors=0 failures=0&gt;</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><a id='part3_tutorial'></a></p>
<h2 id="PART-3:-Recognizer">PART 3: Recognizer<a class="anchor-link" href="#PART-3:-Recognizer">&#182;</a></h2><p>The objective of this section is to "put it all together".  Using the four feature sets created and the three model selectors, you will experiment with the models and present your results.  Instead of training only five specific words as in the previous section, train the entire set with a feature set and model selector strategy.</p>
<h3 id="Recognizer-Tutorial">Recognizer Tutorial<a class="anchor-link" href="#Recognizer-Tutorial">&#182;</a></h3><h5 id="Train-the-full-training-set">Train the full training set<a class="anchor-link" href="#Train-the-full-training-set">&#182;</a></h5><p>The following example trains the entire set with the example <code>features_ground</code> and <code>SelectorConstant</code> features and model selector.  Use this pattern for you experimentation and final submission cells.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[46]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># autoreload for automatically reloading changes made in my_model_selectors and my_recognizer</span>
<span class="o">%</span><span class="k">reload_ext</span> autoreload
<span class="o">%</span><span class="k">autoreload</span> 2

<span class="kn">from</span> <span class="nn">my_model_selectors</span> <span class="k">import</span> <span class="n">SelectorConstant</span>

<span class="k">def</span> <span class="nf">train_all_words</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">model_selector</span><span class="p">):</span>
    <span class="n">training</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_training</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>  <span class="c1"># Experiment here with different feature sets defined in part 1</span>
    <span class="n">sequences</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_all_sequences</span><span class="p">()</span>
    <span class="n">Xlengths</span> <span class="o">=</span> <span class="n">training</span><span class="o">.</span><span class="n">get_all_Xlengths</span><span class="p">()</span>
    <span class="n">model_dict</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">training</span><span class="o">.</span><span class="n">words</span><span class="p">:</span>
        <span class="n">model</span> <span class="o">=</span> <span class="n">model_selector</span><span class="p">(</span><span class="n">sequences</span><span class="p">,</span> <span class="n">Xlengths</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> 
                        <span class="n">n_constant</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">()</span>
        <span class="n">model_dict</span><span class="p">[</span><span class="n">word</span><span class="p">]</span><span class="o">=</span><span class="n">model</span>
    <span class="k">return</span> <span class="n">model_dict</span>

<span class="n">models</span> <span class="o">=</span> <span class="n">train_all_words</span><span class="p">(</span><span class="n">features_ground</span><span class="p">,</span> <span class="n">SelectorConstant</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of word models returned = </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="nb">len</span><span class="p">(</span><span class="n">models</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>Number of word models returned = 112
</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">
<h5 id="Load-the-test-set">Load the test set<a class="anchor-link" href="#Load-the-test-set">&#182;</a></h5><p>The <code>build_test</code> method in <code>ASLdb</code> is similar to the <code>build_training</code> method already presented, but there are a few differences:</p>
<ul>
<li>the object is type <code>SinglesData</code> </li>
<li>the internal dictionary keys are the index of the test word rather than the word itself</li>
<li>the getter methods are <code>get_all_sequences</code>, <code>get_all_Xlengths</code>, <code>get_item_sequences</code> and <code>get_item_Xlengths</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;[47]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">test_set</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_test</span><span class="p">(</span><span class="n">features_ground</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of test set items: </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">test_set</span><span class="o">.</span><span class="n">num_items</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of test set sentences: </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="nb">len</span><span class="p">(</span><span class="n">test_set</span><span class="o">.</span><span class="n">sentences_index</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>Number of test set items: 178
Number of test set sentences: 40
</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><a id='part3_submission'></a></p>
<h3 id="Recognizer-Implementation-Submission">Recognizer Implementation Submission<a class="anchor-link" href="#Recognizer-Implementation-Submission">&#182;</a></h3><p>For the final project submission, students must implement a recognizer following guidance in the <code>my_recognizer.py</code> module.  Experiment with the four feature sets and the three model selection methods (that's 12 possible combinations). You can add and remove cells for experimentation or run the recognizers locally in some other way during your experiments, but retain the results for your discussion.  For submission, you will provide code cells of <strong>only three</strong> interesting combinations for your discussion (see questions below). At least one of these should produce a word error rate of less than 60%, i.e. WER &lt; 0.60 .</p>
<p><strong>Tip:</strong> The hmmlearn library may not be able to train or score all models.  Implement try/except contructs as necessary to eliminate non-viable models from consideration.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[48]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO implement the recognize method in my_recognizer</span>
<span class="kn">from</span> <span class="nn">my_recognizer</span> <span class="k">import</span> <span class="n">recognize</span>
<span class="kn">from</span> <span class="nn">asl_utils</span> <span class="k">import</span> <span class="n">show_errors</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;[49]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO Choose a feature set and model selector</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">features_polar</span> <span class="c1"># change as needed</span>
<span class="n">model_selector</span> <span class="o">=</span> <span class="n">SelectorDIC</span> <span class="c1"># change as needed</span>

<span class="c1"># TODO Recognize the test set and display the result with the show_errors method</span>
<span class="n">models</span> <span class="o">=</span> <span class="n">train_all_words</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">model_selector</span><span class="p">)</span>
<span class="n">test_set</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_test</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
<span class="n">probabilities</span><span class="p">,</span> <span class="n">guesses</span> <span class="o">=</span> <span class="n">recognize</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">test_set</span><span class="p">)</span>
<span class="n">show_errors</span><span class="p">(</span><span class="n">guesses</span><span class="p">,</span> <span class="n">test_set</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>
**** WER = 0.5449438202247191
Total correct: 81 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: JOHN *NEW *GIVE1                                              JOHN WRITE HOMEWORK
    7: JOHN CAN GO CAN                                               JOHN CAN GO CAN
   12: JOHN *WHAT *JOHN CAN                                          JOHN CAN GO CAN
   21: JOHN *NEW *JOHN *PREFER *GIVE1 *WHAT *FUTURE *WHO             JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: JOHN *IX IX *WHO IX                                           JOHN LIKE IX IX IX
   28: JOHN *FUTURE IX *FUTURE *LOVE                                 JOHN LIKE IX IX IX
   30: JOHN LIKE *MARY *MARY *MARY                                   JOHN LIKE IX IX IX
   36: *IX *VISIT *GIVE *GIVE *MARY *MARY                            MARY VEGETABLE KNOW IX LIKE CORN1
   40: JOHN *GO *GIVE *JOHN *MARY                                    JOHN IX THINK MARY LOVE
   43: JOHN *IX BUY HOUSE                                            JOHN MUST BUY HOUSE
   50: *JOHN *SEE BUY CAR *JOHN                                      FUTURE JOHN BUY CAR SHOULD
   54: JOHN SHOULD NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE
   57: *MARY *GO *GO MARY                                            JOHN DECIDE VISIT MARY
   67: *SHOULD FUTURE *MARY BUY HOUSE                                JOHN FUTURE NOT BUY HOUSE
   71: JOHN *FUTURE *GIVE1 MARY                                      JOHN WILL VISIT MARY
   74: *IX *GO *GO *VISIT                                            JOHN NOT VISIT MARY
   77: *JOHN *GIVE1 MARY                                             ANN BLAME MARY
   84: *HOMEWORK *GIVE1 *GIVE1 *COAT                                 IX-1P FIND SOMETHING-ONE BOOK
   89: *GIVE *GIVE *WOMAN *WOMAN IX *ARRIVE *BOOK                    JOHN IX GIVE MAN IX NEW COAT
   90: JOHN GIVE IX SOMETHING-ONE WOMAN *ARRIVE                      JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *WOMAN IX *WOMAN WOMAN BOOK                              JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN
  105: JOHN *SEE                                                     JOHN LEG
  107: JOHN POSS *HAVE HAVE *MARY                                    JOHN POSS FRIEND HAVE CANDY
  108: *LOVE *LOVE                                                   WOMAN ARRIVE
  113: IX CAR *IX *MARY *JOHN                                        IX CAR BLUE SUE BUY
  119: *MARY *BUY1 IX *BLAME *IX                                     SUE BUY IX CAR BLUE
  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK
  139: JOHN *ARRIVE WHAT *MARY *ARRIVE                               JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK
  158: LOVE JOHN WHO                                                 LOVE JOHN WHO
  167: JOHN *MARY *VISIT LOVE MARY                                   JOHN IX SAY LOVE MARY
  171: *IX MARY BLAME                                                JOHN MARY BLAME
  174: *JOHN *JOHN GIVE1 *YESTERDAY *JOHN                            PEOPLE GROUP GIVE1 JANA TOY
  181: *EAT ARRIVE                                                   JOHN ARRIVE
  184: *GO BOY *GIVE1 TEACHER *YESTERDAY                             ALL BOY GIVE TEACHER APPLE
  189: *MARY *GO *YESTERDAY BOX                                      JOHN GIVE GIRL BOX
  193: JOHN *GO *YESTERDAY BOX                                       JOHN GIVE GIRL BOX
  199: *JOHN *STUDENT *GO                                            LIKE CHOCOLATE WHO
  201: JOHN *MAN *LOVE *JOHN BUY HOUSE                               JOHN TELL MARY IX-1P BUY HOUSE
</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;[50]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO Choose a feature set and model selector</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">features_polar</span> <span class="c1"># change as needed</span>
<span class="n">model_selector</span> <span class="o">=</span> <span class="n">SelectorBIC</span> <span class="c1"># change as needed</span>

<span class="c1"># TODO Recognize the test set and display the result with the show_errors method</span>
<span class="n">models</span> <span class="o">=</span> <span class="n">train_all_words</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">model_selector</span><span class="p">)</span>
<span class="n">test_set</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_test</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
<span class="n">probabilities</span><span class="p">,</span> <span class="n">guesses</span> <span class="o">=</span> <span class="n">recognize</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">test_set</span><span class="p">)</span>
<span class="n">show_errors</span><span class="p">(</span><span class="n">guesses</span><span class="p">,</span> <span class="n">test_set</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>
**** WER = 0.550561797752809
Total correct: 80 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: JOHN *NEW *GIVE1                                              JOHN WRITE HOMEWORK
    7: JOHN CAN GO CAN                                               JOHN CAN GO CAN
   12: JOHN *WHAT *JOHN CAN                                          JOHN CAN GO CAN
   21: JOHN *NEW *JOHN *PREFER *CAR *WHAT *FUTURE *WHO               JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: JOHN *IX IX *WHO IX                                           JOHN LIKE IX IX IX
   28: JOHN *FUTURE IX *FUTURE *LOVE                                 JOHN LIKE IX IX IX
   30: JOHN LIKE *MARY *MARY *MARY                                   JOHN LIKE IX IX IX
   36: *IX *VISIT *GIVE *GIVE *MARY *MARY                            MARY VEGETABLE KNOW IX LIKE CORN1
   40: JOHN *GO *GIVE *JOHN *MARY                                    JOHN IX THINK MARY LOVE
   43: JOHN *IX BUY HOUSE                                            JOHN MUST BUY HOUSE
   50: *JOHN *SEE BUY CAR *JOHN                                      FUTURE JOHN BUY CAR SHOULD
   54: JOHN SHOULD NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE
   57: *MARY *GO *GO MARY                                            JOHN DECIDE VISIT MARY
   67: *SHOULD FUTURE *MARY BUY HOUSE                                JOHN FUTURE NOT BUY HOUSE
   71: JOHN *FUTURE *GIVE1 MARY                                      JOHN WILL VISIT MARY
   74: *IX *GO *GO *VISIT                                            JOHN NOT VISIT MARY
   77: *JOHN *GIVE1 MARY                                             ANN BLAME MARY
   84: *HOMEWORK *GIVE1 *POSS *COAT                                  IX-1P FIND SOMETHING-ONE BOOK
   89: *GIVE *GIVE *IX *IX IX *ARRIVE *BOOK                          JOHN IX GIVE MAN IX NEW COAT
   90: JOHN GIVE IX SOMETHING-ONE WOMAN *ARRIVE                      JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *IX IX *IX *IX BOOK                                      JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN
  105: JOHN *SEE                                                     JOHN LEG
  107: JOHN POSS *HAVE HAVE *MARY                                    JOHN POSS FRIEND HAVE CANDY
  108: *LOVE *LOVE                                                   WOMAN ARRIVE
  113: IX CAR *IX *MARY *JOHN                                        IX CAR BLUE SUE BUY
  119: *MARY *BUY1 IX *BLAME *IX                                     SUE BUY IX CAR BLUE
  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK
  139: JOHN *ARRIVE WHAT *MARY *ARRIVE                               JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK
  158: LOVE JOHN WHO                                                 LOVE JOHN WHO
  167: JOHN *MARY *VISIT LOVE MARY                                   JOHN IX SAY LOVE MARY
  171: *IX MARY BLAME                                                JOHN MARY BLAME
  174: *JOHN *JOHN GIVE1 *YESTERDAY *JOHN                            PEOPLE GROUP GIVE1 JANA TOY
  181: *EAT ARRIVE                                                   JOHN ARRIVE
  184: *GO BOY *GIVE1 TEACHER *YESTERDAY                             ALL BOY GIVE TEACHER APPLE
  189: *MARY *GO *YESTERDAY BOX                                      JOHN GIVE GIRL BOX
  193: JOHN *GO *YESTERDAY BOX                                       JOHN GIVE GIRL BOX
  199: *JOHN *STUDENT *GO                                            LIKE CHOCOLATE WHO
  201: JOHN *GIVE *LOVE *JOHN BUY HOUSE                              JOHN TELL MARY IX-1P BUY HOUSE
</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;[51]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO Choose a feature set and model selector</span>
<span class="n">features</span> <span class="o">=</span> <span class="n">features_polar</span> <span class="c1"># change as needed</span>
<span class="n">model_selector</span> <span class="o">=</span> <span class="n">SelectorCV</span> <span class="c1"># change as needed</span>

<span class="c1"># TODO Recognize the test set and display the result with the show_errors method</span>
<span class="n">models</span> <span class="o">=</span> <span class="n">train_all_words</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">model_selector</span><span class="p">)</span>
<span class="n">test_set</span> <span class="o">=</span> <span class="n">asl</span><span class="o">.</span><span class="n">build_test</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
<span class="n">probabilities</span><span class="p">,</span> <span class="n">guesses</span> <span class="o">=</span> <span class="n">recognize</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">test_set</span><span class="p">)</span>
<span class="n">show_errors</span><span class="p">(</span><span class="n">guesses</span><span class="p">,</span> <span class="n">test_set</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>
**** WER = 0.550561797752809
Total correct: 80 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: JOHN *NEW *GIVE1                                              JOHN WRITE HOMEWORK
    7: JOHN CAN GO CAN                                               JOHN CAN GO CAN
   12: JOHN *WHAT *JOHN CAN                                          JOHN CAN GO CAN
   21: JOHN *NEW *JOHN *PREFER *CAR *WHAT *FUTURE *WHO               JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: JOHN *IX IX *WHO IX                                           JOHN LIKE IX IX IX
   28: JOHN *FUTURE IX *FUTURE *LOVE                                 JOHN LIKE IX IX IX
   30: JOHN LIKE *MARY *MARY *MARY                                   JOHN LIKE IX IX IX
   36: *IX *VISIT *GIVE *GIVE *MARY *MARY                            MARY VEGETABLE KNOW IX LIKE CORN1
   40: JOHN *GO *GIVE *JOHN *MARY                                    JOHN IX THINK MARY LOVE
   43: JOHN *IX BUY HOUSE                                            JOHN MUST BUY HOUSE
   50: *JOHN *SEE BUY CAR *JOHN                                      FUTURE JOHN BUY CAR SHOULD
   54: JOHN SHOULD NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE
   57: *MARY *GO *GO MARY                                            JOHN DECIDE VISIT MARY
   67: JOHN FUTURE *MARY BUY HOUSE                                   JOHN FUTURE NOT BUY HOUSE
   71: JOHN *FUTURE *GIVE1 MARY                                      JOHN WILL VISIT MARY
   74: *IX *GO *GO *VISIT                                            JOHN NOT VISIT MARY
   77: *JOHN *GIVE1 MARY                                             ANN BLAME MARY
   84: *JOHN *GIVE1 *POSS *COAT                                      IX-1P FIND SOMETHING-ONE BOOK
   89: *GIVE *GIVE *WOMAN *IX IX *ARRIVE *BOOK                       JOHN IX GIVE MAN IX NEW COAT
   90: JOHN GIVE IX SOMETHING-ONE WOMAN *ARRIVE                      JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *WOMAN IX *IX *IX BOOK                                   JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN
  105: JOHN *SEE                                                     JOHN LEG
  107: JOHN POSS *HAVE HAVE *MARY                                    JOHN POSS FRIEND HAVE CANDY
  108: *LOVE *LOVE                                                   WOMAN ARRIVE
  113: IX CAR *IX *MARY *JOHN                                        IX CAR BLUE SUE BUY
  119: *MARY *BUY1 IX *BLAME *IX                                     SUE BUY IX CAR BLUE
  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK
  139: JOHN *ARRIVE WHAT *MARY *ARRIVE                               JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK
  158: LOVE JOHN WHO                                                 LOVE JOHN WHO
  167: JOHN *MARY *VISIT LOVE MARY                                   JOHN IX SAY LOVE MARY
  171: *IX MARY BLAME                                                JOHN MARY BLAME
  174: *JOHN *JOHN GIVE1 *YESTERDAY *JOHN                            PEOPLE GROUP GIVE1 JANA TOY
  181: *YESTERDAY ARRIVE                                             JOHN ARRIVE
  184: *GO *IX *GIVE1 TEACHER *YESTERDAY                             ALL BOY GIVE TEACHER APPLE
  189: *MARY *GO *YESTERDAY BOX                                      JOHN GIVE GIRL BOX
  193: JOHN *GO *YESTERDAY BOX                                       JOHN GIVE GIRL BOX
  199: *JOHN *STUDENT *GO                                            LIKE CHOCOLATE WHO
  201: JOHN *GIVE *WOMAN *JOHN BUY HOUSE                             JOHN TELL MARY IX-1P BUY HOUSE
</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>Question 3:</strong>  Summarize the error results from three combinations of features and model selectors.  What was the "best" combination and why?  What additional information might we use to improve our WER?  For more insight on improving WER, take a look at the introduction to Part 4.</p>
<p><strong>Answer 3:</strong> The SelectorDIC with polar features provided the best results compared with SelectorCV and SelectorBIC. This could be due to less dimentionality in polar features. As discussed previously, the DIC can recognize better than the BIC but with the cost of time complexity. The DIC is taking more time compared to other models.</p>
<p>Word Error Rate can be improved by using language models(LM) like N-gram model. The basic idea behind these LM is to compute the probability of a sentence or compute the probability of a next word by assigning the probability to the whole sequence P(w1,w2,...wn). And the model will estimate the relative likelihood of different phrases used in the language.</p>
<p>**Results:</p>
<p><strong>SelectorCV</strong></p>
<p>features_ground: 0.601123595505618 (Total correct: 71 out of 178)
features_custom: 0.601123595505618 (Total correct: 71 out of 178)
features_polar: 0.550561797752809 (Total correct: 80 out of 178)
features_norm: 0.6629213483146067 (Total correct: 60 out of 178)
features_delta: 0.7078651685393258 (Total correct: 52 out of 178)</p>
<p><strong>SelectorBIC</strong></p>
<p>features_ground: 0.6741573033707865 (Total correct: 58 out of 178)
features_custom: 0.6629213483146067 (Total correct: 60 out of 178)
features_polar: 0.550561797752809 (Total correct: 80 out of 178)
features_norm: 0.6235955056179775 (Total correct: 67 out of 178)
features_delta: 0.6404494382022472 (Total correct: 64 out of 178)</p>
<p><strong>SelectorDIC</strong></p>
<p>features_ground: 0.5674157303370787 (Total correct: 77 out of 178)
features_custom: 0.5561797752808989 (Total correct: 79 out of 178)
features_polar: 0.5449438202247191 (Total correct: 81 out of 178)
features_norm: 0.6235955056179775 (Total correct: 67 out of 178)
features_delta: 0.6404494382022472 (Total correct: 64 out of 178)</p>
<p>[1]    Biem, Alain. "A model selection criterion for classification: Application to hmm topology optimization."
    Document Analysis and Recognition, 2003. Proceedings. Seventh International Conference on. IEEE, 2003.
    <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.6208&amp;rep=rep1&amp;type=pdf">http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.58.6208&amp;rep=rep1&amp;type=pdf</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">
<p><a id='part3_test'></a></p>
<h3 id="Recognizer-Unit-Tests">Recognizer Unit Tests<a class="anchor-link" href="#Recognizer-Unit-Tests">&#182;</a></h3><p>Run the following unit tests as a sanity check on the defined recognizer.  The test simply looks for some valid values but is not exhaustive. However, the project should not be submitted if these tests don't pass.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[52]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">asl_test_recognizer</span> <span class="k">import</span> <span class="n">TestRecognize</span>
<span class="n">suite</span> <span class="o">=</span> <span class="n">unittest</span><span class="o">.</span><span class="n">TestLoader</span><span class="p">()</span><span class="o">.</span><span class="n">loadTestsFromModule</span><span class="p">(</span><span class="n">TestRecognize</span><span class="p">())</span>
<span class="n">unittest</span><span class="o">.</span><span class="n">TextTestRunner</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">suite</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_stderr output_text">
<pre>..
----------------------------------------------------------------------
Ran 2 tests in 119.633s

OK
</pre>
</div>
</div>

<div class="output_area">
<div class="prompt output_prompt">Out[52]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;unittest.runner.TextTestResult run=2 errors=0 failures=0&gt;</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><a id='part4_info'></a></p>
<h2 id="PART-4:-(OPTIONAL)--Improve-the-WER-with-Language-Models">PART 4: (OPTIONAL)  Improve the WER with Language Models<a class="anchor-link" href="#PART-4:-(OPTIONAL)--Improve-the-WER-with-Language-Models">&#182;</a></h2><p>We've squeezed just about as much as we can out of the model and still only get about 50% of the words right! Surely we can do better than that.  Probability to the rescue again in the form of <a href="https://en.wikipedia.org/wiki/Language_model">statistical language models (SLM)</a>.  The basic idea is that each word has some probability of occurrence within the set, and some probability that it is adjacent to specific other words. We can use that additional information to make better choices.</p>
<h5 id="Additional-reading-and-resources">Additional reading and resources<a class="anchor-link" href="#Additional-reading-and-resources">&#182;</a></h5><ul>
<li><a href="https://web.stanford.edu/class/cs124/lec/languagemodeling.pdf">Introduction to N-grams (Stanford Jurafsky slides)</a></li>
<li><a href="https://www-i6.informatik.rwth-aachen.de/publications/download/154/Dreuw--2007.pdf">Speech Recognition Techniques for a Sign Language Recognition System, Philippe Dreuw et al</a> see the improved results of applying LM on <em>this</em> data!</li>
<li><a href="ftp://wasserstoff.informatik.rwth-aachen.de/pub/rwth-boston-104/lm/">SLM data for <em>this</em> ASL dataset</a></li>
</ul>
<h5 id="Optional-challenge">Optional challenge<a class="anchor-link" href="#Optional-challenge">&#182;</a></h5><p>The recognizer you implemented in Part 3 is equivalent to a "0-gram" SLM.  Improve the WER with the SLM data provided with the data set in the link above using "1-gram", "2-gram", and/or "3-gram" statistics. The <code>probabilities</code> data you've already calculated will be useful and can be turned into a pandas DataFrame if desired (see next cell).<br>
Good luck!  Share your results with the class!</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[53]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># create a DataFrame of log likelihoods for the test word items</span>
<span class="n">df_probs</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">probabilities</span><span class="p">)</span>
<span class="n">df_probs</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

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

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


<div class="output_area">
<div class="prompt output_prompt">Out[53]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<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>ALL</th>
      <th>ANN</th>
      <th>APPLE</th>
      <th>ARRIVE</th>
      <th>BILL</th>
      <th>BLAME</th>
      <th>BLUE</th>
      <th>BOOK</th>
      <th>BORROW</th>
      <th>BOX</th>
      <th>...</th>
      <th>VIDEOTAPE</th>
      <th>VISIT</th>
      <th>WANT</th>
      <th>WHAT</th>
      <th>WHO</th>
      <th>WILL</th>
      <th>WOMAN</th>
      <th>WONT</th>
      <th>WRITE</th>
      <th>YESTERDAY</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-105.157213</td>
      <td>-105.157213</td>
      <td>-2873.999359</td>
      <td>-330.513567</td>
      <td>-362.170816</td>
      <td>-361.877026</td>
      <td>-105.157213</td>
      <td>-1038.955685</td>
      <td>-105.157213</td>
      <td>-654.508016</td>
      <td>...</td>
      <td>-1570.828321</td>
      <td>-166.193663</td>
      <td>-2.308451e+05</td>
      <td>-278.983071</td>
      <td>-211.611880</td>
      <td>-105.157213</td>
      <td>-657.289382</td>
      <td>-754.633694</td>
      <td>-105.157213</td>
      <td>-300.522945</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-73.891498</td>
      <td>-134.905838</td>
      <td>-9899.007748</td>
      <td>-118.958118</td>
      <td>-418.206652</td>
      <td>-275.277465</td>
      <td>-73.891498</td>
      <td>-113.149819</td>
      <td>-73.891498</td>
      <td>-553.379536</td>
      <td>...</td>
      <td>-135.572030</td>
      <td>-444.150906</td>
      <td>-4.321202e+04</td>
      <td>-341.874211</td>
      <td>-111.098536</td>
      <td>-134.905838</td>
      <td>-415.254112</td>
      <td>-1494.193684</td>
      <td>-192.761645</td>
      <td>-596.329517</td>
    </tr>
    <tr>
      <th>2</th>
      <td>-283.566963</td>
      <td>-350.748507</td>
      <td>-16163.432731</td>
      <td>-318.573229</td>
      <td>-651.551326</td>
      <td>-659.456482</td>
      <td>-283.566963</td>
      <td>-879.130768</td>
      <td>-283.566963</td>
      <td>-1191.365385</td>
      <td>...</td>
      <td>-888.788897</td>
      <td>-551.056907</td>
      <td>-3.411741e+05</td>
      <td>-760.664399</td>
      <td>-483.978522</td>
      <td>-350.748507</td>
      <td>-1412.968856</td>
      <td>-1869.519352</td>
      <td>-350.748507</td>
      <td>-962.369245</td>
    </tr>
    <tr>
      <th>3</th>
      <td>-68.356478</td>
      <td>-68.356478</td>
      <td>-1814.923873</td>
      <td>-279.642514</td>
      <td>-363.007013</td>
      <td>-255.377577</td>
      <td>-68.356478</td>
      <td>-1171.290246</td>
      <td>-68.356478</td>
      <td>-690.245418</td>
      <td>...</td>
      <td>-3949.507724</td>
      <td>-541.122128</td>
      <td>-1.143249e+06</td>
      <td>-704.807704</td>
      <td>-565.158810</td>
      <td>-68.356478</td>
      <td>-267.456250</td>
      <td>-508.134793</td>
      <td>-68.356478</td>
      <td>-521.070155</td>
    </tr>
    <tr>
      <th>4</th>
      <td>-44.517900</td>
      <td>-44.517900</td>
      <td>-3113.233817</td>
      <td>-99.786725</td>
      <td>-222.377390</td>
      <td>-57.323454</td>
      <td>-44.517900</td>
      <td>-164.498017</td>
      <td>-44.517900</td>
      <td>-49.611306</td>
      <td>...</td>
      <td>-231.796774</td>
      <td>-64.713586</td>
      <td>-4.565087e+05</td>
      <td>-50.739092</td>
      <td>-208.252232</td>
      <td>-44.517900</td>
      <td>-144.034438</td>
      <td>-812.568059</td>
      <td>-50.340589</td>
      <td>-486.300055</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 112 columns</p>
</div>
</div>

</div>

</div>
</div>

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

 
</html>