asl_recognizer.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>asl_recognizer</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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<style type="text/css">
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html {
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  -ms-text-size-adjust: 100%;
  -webkit-text-size-adjust: 100%;
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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]) {
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  height: 0;
}
[hidden],
template {
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}
a {
  background-color: transparent;
}
a:active,
a:hover {
  outline: 0;
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abbr[title] {
  border-bottom: 1px dotted;
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b,
strong {
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dfn {
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}
h1 {
  font-size: 2em;
  margin: 0.67em 0;
}
mark {
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}
small {
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sub,
sup {
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  position: relative;
  vertical-align: baseline;
}
sup {
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}
sub {
  bottom: -0.25em;
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img {
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svg:not(:root) {
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figure {
  margin: 1em 40px;
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hr {
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pre {
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code,
kbd,
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samp {
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input,
optgroup,
select,
textarea {
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button {
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button,
select {
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button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
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button[disabled],
html input[disabled] {
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button::-moz-focus-inner,
input::-moz-focus-inner {
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input {
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input[type="checkbox"],
input[type="radio"] {
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input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
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input[type="search"] {
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input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
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fieldset {
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legend {
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textarea {
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optgroup {
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table {
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td,
th {
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/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
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}
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}
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}
.glyphicon-btc:before {
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}
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}
.glyphicon-yen:before {
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}
.glyphicon-jpy:before {
  content: "\00a5";
}
.glyphicon-ruble:before {
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}
.glyphicon-rub:before {
  content: "\20bd";
}
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.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;
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<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>
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<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>

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<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>

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<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>
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<div>
<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>

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<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>
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<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>
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<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>

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<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>

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<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>
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      <td>113</td>
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      <th>1</th>
      <td>149</td>
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      <th>2</th>
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<h5 id="Try-it!">Try it!<a class="anchor-link" href="#Try-it!">&#182;</a></h5>
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<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>
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<pre>asl.df sample
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      <td>113</td>
      <td>9</td>
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      <td>149</td>
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      <td>113</td>
      <td>9</td>
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      <td>113</td>
      <td>9</td>
      <td>119</td>
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    </tr>
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<font color=green>Correct!</font><br/>
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<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>
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<pre>[9, 113, -12, 119]</pre>
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<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>

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<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>
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<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;]
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<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>

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<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>
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<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]]), [17, 20, 12])</pre>
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<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>

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<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>
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      <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>
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      <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>
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<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>

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<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>
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<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>
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  </thead>
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    <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>
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<h5 id="Try-it!">Try it!<a class="anchor-link" href="#Try-it!">&#182;</a></h5>
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<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="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>
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<pre>df_std
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<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>
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      <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>
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<font color=green>Correct!</font><br/>
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<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>

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<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&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span>
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<pre>23.686534848552704</pre>
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<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">right_x_mean</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_x_std</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_y_mean</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">right_y_std</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">left_x_mean</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_x_std</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_y_mean</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">left_y_std</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">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_x_mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">right_x_std</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_y_mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">right_y_std</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_x_mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">left_x_std</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_y_mean</span><span class="p">)</span> <span class="o">/</span> <span class="n">left_y_std</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>
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<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="c1"># Initially, I was attempting to use np.sqrt(var1**2 + var2**2) but found np.hypot is equivalent</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">hypot</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">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;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="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;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;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="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">hypot</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">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;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;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;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="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">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>
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<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="n">value</span><span class="o">=</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="n">value</span><span class="o">=</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="n">value</span><span class="o">=</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="n">value</span><span class="o">=</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>
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<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="c1"># TODO define a list named &#39;features_custom&#39; for building the training set</span>

<span class="c1"># Mean normalisation</span>

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

<span class="n">features_custom</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;norm-mean-rx&#39;</span><span class="p">,</span> <span class="s1">&#39;norm-mean-ry&#39;</span><span class="p">,</span> <span class="s1">&#39;norm-mean-lx&#39;</span><span class="p">,</span><span class="s1">&#39;norm-mean-ly&#39;</span><span class="p">]</span>
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<p><strong>Question 1:</strong>  What custom features did you choose for the features_custom set and why?</p>
<p><strong>Answer 1:</strong>   I chose to use mean normalisation because some learning algorithms do not work as effectively with high data variance.  Utilizing this technique helps model data more realistically and can potentially lead to faster convergence.</p>

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<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>

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<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>
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<pre>....
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<pre>&lt;unittest.runner.TextTestResult run=4 errors=0 failures=0&gt;</pre>
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<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>
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<li>Log likelihood using cross-validation folds (CV)</li>
<li>Bayesian Information Criterion (BIC)</li>
<li>Discriminative Information Criterion (DIC) </li>
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<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>

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<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>
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<pre>Number of states trained in model for BOOK is 3
logL = -2331.1138127433205
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<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>

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<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>
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<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]

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<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>

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<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">4</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>
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<pre>Number of states trained in model for CHOCOLATE is 4
hidden state #0
mean =  [   0.58333258   87.91666611   12.75000001  108.49999882]
variance =  [  39.4105671    18.74389523    9.85499958  144.41752618]

hidden state #1
mean =  [ -9.23835975  55.307463     6.92298661  71.30538347]
variance =  [ 16.30887738  45.97071639   3.76856896  15.98215464]

hidden state #2
mean =  [  -6.37712306   51.09867416    3.64009161  104.46268173]
variance =  [  10.28220014   12.44316166   27.33390004  106.91029392]

hidden state #3
mean =  [ -4.93681705  64.73171915   1.62598519  84.91459657]
variance =  [  6.1617479   28.36616959   5.47102461  13.11696032]

logL = -565.1243334331187
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<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>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">matplotlib</span> inline
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<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>
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<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>

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<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>
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<pre>Number of states trained in model for VEGETABLE is 3
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<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>

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<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>
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<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]
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<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>

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<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>

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<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>

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<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>
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<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">load_ext</span> autoreload
<span class="o">%</span><span class="k">autoreload</span> 2
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<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">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_ground</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>
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<pre>Training complete for FISH with 13 states with time 0.4253648680023616 seconds
Training complete for BOOK with 2 states with time 1.8489230920022237 seconds
Training complete for VEGETABLE with 14 states with time 0.9291535000011208 seconds
Training complete for FUTURE with 2 states with time 1.6821549189990037 seconds
Training complete for JOHN with 12 states with time 26.261220239997783 seconds
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO: Implement SelectorBIC in module my_model_selectors.py</span>
<span class="kn">import</span> <span class="nn">importlib</span>
<span class="kn">import</span> <span class="nn">my_model_selectors</span>
<span class="n">importlib</span><span class="o">.</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_ground</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>
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<pre>Training complete for FISH with 5 states with time 0.3723322360019665 seconds
Training complete for BOOK with 8 states with time 2.1757402180010104 seconds
Training complete for VEGETABLE with 15 states with time 0.7782322439961717 seconds
Training complete for FUTURE with 9 states with time 2.3811681659935857 seconds
Training complete for JOHN with 14 states with time 25.10130008499982 seconds
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<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">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_ground</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>
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<pre>Training complete for FISH with 3 states with time 0.4183853629947407 seconds
Training complete for BOOK with 15 states with time 2.875323718006257 seconds
Training complete for VEGETABLE with 15 states with time 1.0956263839980238 seconds
Training complete for FUTURE with 15 states with time 3.3576061600033427 seconds
Training complete for JOHN with 15 states with time 27.579075854999246 seconds
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<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>  If we observe the states and time it took train each word; there are clear advantages and disadvantages for the various model selectors implemented.  For instance, when we select best model based on average log Likelihood of cross-validation folds it's possible to achieve training with fewer states.  The disadvantage of the CV model is that it took longer to train on certain words.  When we select best model based on Discriminative Information Criterion (DIC), it appears the model may be overfitting due to the large number of states for each word.  However, this model trains some word at a reasonably fast speed.  Lastly, when we select the model with the lowest Bayesian Information Criterion(BIC) score, some words have a low number of hidden states and a quickly train each word.  However, some words have high number of states which can be a sign of overfitting</p>

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<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>

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<div class=" highlight hl-ipython3"><pre><span></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>
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<pre>....
----------------------------------------------------------------------
Ran 4 tests in 27.806s

OK
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<pre>&lt;unittest.runner.TextTestResult run=4 errors=0 failures=0&gt;</pre>
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<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>

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<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="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>
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<pre>Number of word models returned = 112
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<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>
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<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>
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<pre>Number of test set items: 178
Number of test set sentences: 40
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<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>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># TODO implement the recognize method in my_recognizer</span>
<span class="kn">import</span> <span class="nn">importlib</span>
<span class="kn">import</span> <span class="nn">my_recognizer</span>
<span class="n">importlib</span><span class="o">.</span><span class="n">reload</span><span class="p">(</span><span class="n">my_recognizer</span><span class="p">)</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>
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<h3 id="SelectorBIC">SelectorBIC<a class="anchor-link" href="#SelectorBIC">&#182;</a></h3>
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<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_delta</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>
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<pre>
**** WER = 0.6348314606741573
Total correct: 65 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: JOHN *LOVE *ARRIVE                                            JOHN WRITE HOMEWORK
    7: JOHN *BOX *GIVE1 *ARRIVE                                      JOHN CAN GO CAN
   12: JOHN *BOX *LOVE CAN                                           JOHN CAN GO CAN
   21: *MARY *MARY *LOVE *MARY *CAR *ARRIVE *FUTURE *MARY            JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: JOHN *IX *MARY IX IX                                          JOHN LIKE IX IX IX
   28: JOHN *MARY *JOHN IX *SHOULD                                   JOHN LIKE IX IX IX
   30: JOHN *IX IX *JOHN IX                                          JOHN LIKE IX IX IX
   36: *JOHN *JOHN *JOHN IX *MARY *MARY                              MARY VEGETABLE KNOW IX LIKE CORN1
   40: *MARY IX *JOHN MARY *MARY                                     JOHN IX THINK MARY LOVE
   43: JOHN *IX *FINISH HOUSE                                        JOHN MUST BUY HOUSE
   50: *JOHN JOHN BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD
   54: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE
   57: *MARY *JOHN *IX *IX                                           JOHN DECIDE VISIT MARY
   67: JOHN *JOHN *MARY BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE
   71: JOHN *MARY VISIT MARY                                         JOHN WILL VISIT MARY
   74: JOHN *JOHN *IX MARY                                           JOHN NOT VISIT MARY
   77: *JOHN *CAR MARY                                               ANN BLAME MARY
   84: *JOHN *CAR *IX BOOK                                           IX-1P FIND SOMETHING-ONE BOOK
   89: *MARY *JOHN *IX *IX *JOHN *WHAT *CAN                          JOHN IX GIVE MAN IX NEW COAT
   90: JOHN *JOHN *JOHN *IX *IX *JOHN                                JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *IX *JOHN *IX WOMAN BOOK                                 JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: *JOHN NEW CAR *BOOK                                           POSS NEW CAR BREAK-DOWN
  105: JOHN *JOHN                                                    JOHN LEG
  107: JOHN *JOHN *CAR *JOHN *MARY                                   JOHN POSS FRIEND HAVE CANDY
  108: *JOHN *BOOK                                                   WOMAN ARRIVE
  113: *JOHN CAR *MARY *MARY *GIVE1                                  IX CAR BLUE SUE BUY
  119: *JOHN *GIVE1 IX CAR *MARY                                     SUE BUY IX CAR BLUE
  122: JOHN *ARRIVE *WHAT                                            JOHN READ BOOK
  139: JOHN *GIVE1 WHAT *JOHN *MARY                                  JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN BUY *FUTURE WHAT *MARY                                   JOHN BUY YESTERDAY WHAT BOOK
  158: *GIVE1 JOHN *JOHN                                             LOVE JOHN WHO
  167: JOHN IX *IX *BOOK *WHAT                                       JOHN IX SAY LOVE MARY
  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME
  174: *GIVE1 *MARY GIVE1 *MARY *FINISH                              PEOPLE GROUP GIVE1 JANA TOY
  181: JOHN ARRIVE                                                   JOHN ARRIVE
  184: *IX *JOHN *GIVE1 *WHO *MARY                                   ALL BOY GIVE TEACHER APPLE
  189: JOHN *JOHN *JOHN *ARRIVE                                      JOHN GIVE GIRL BOX
  193: JOHN *IX *WOMAN BOX                                           JOHN GIVE GIRL BOX
  199: *JOHN *WHAT *MARY                                             LIKE CHOCOLATE WHO
  201: JOHN *IX MARY *IX BUY HOUSE                                   JOHN TELL MARY IX-1P BUY HOUSE
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<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_ground</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>
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**** WER = 0.5674157303370787
Total correct: 77 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: JOHN *NEW *GIVE1                                              JOHN WRITE HOMEWORK
    7: *SOMETHING-ONE CAN GO *WHAT                                   JOHN CAN GO CAN
   12: *IX *WHAT *CAN CAN                                            JOHN CAN GO CAN
   21: JOHN *GIVE1 *JOHN *FUTURE *NEW-YORK *WHAT *CHICAGO *MARY      JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: JOHN *IX IX *WHO IX                                           JOHN LIKE IX IX IX
   28: JOHN *WHO IX *FUTURE *LOVE                                    JOHN LIKE IX IX IX
   30: JOHN *MARY *MARY *MARY *MARY                                  JOHN LIKE IX IX IX
   36: *VISIT *VISIT *GIVE *GO *MARY *IX                             MARY VEGETABLE KNOW IX LIKE CORN1
   40: *MARY *GO *GIVE MARY *MARY                                    JOHN IX THINK MARY LOVE
   43: JOHN *IX BUY HOUSE                                            JOHN MUST BUY HOUSE
   50: *JOHN JOHN BUY CAR *JOHN                                      FUTURE JOHN BUY CAR SHOULD
   54: JOHN SHOULD NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE
   57: *MARY *VISIT VISIT MARY                                       JOHN DECIDE VISIT MARY
   67: JOHN *JOHN *MARY BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE
   71: JOHN *FUTURE VISIT MARY                                       JOHN WILL VISIT MARY
   74: *IX *VISIT *MARY MARY                                         JOHN NOT VISIT MARY
   77: *JOHN BLAME *NEW-YORK                                         ANN BLAME MARY
   84: *NEW-YORK *NEW-YORK *VISIT BOOK                               IX-1P FIND SOMETHING-ONE BOOK
   89: *MARY IX *IX *IX IX *ARRIVE *BOOK                             JOHN IX GIVE MAN IX NEW COAT
   90: JOHN *TOY1 IX *TOY1 *VISIT *ARRIVE                            JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *IX IX *IX *SEARCH-FOR BOOK                              JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: *IX NEW CAR *ARRIVE                                           POSS NEW CAR BREAK-DOWN
  105: JOHN *FRANK                                                   JOHN LEG
  107: JOHN *GO *NEW-YORK *ARRIVE *JOHN                              JOHN POSS FRIEND HAVE CANDY
  108: *IX *NEW-YORK                                                 WOMAN ARRIVE
  113: IX CAR *GO *MARY *TOY1                                        IX CAR BLUE SUE BUY
  119: *VISIT *ARRIVE IX *BOX *GO                                    SUE BUY IX CAR BLUE
  122: JOHN *CHICAGO BOOK                                            JOHN READ BOOK
  139: JOHN *BUY1 WHAT *NEW-YORK BOOK                                JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN *GIVE1 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: JOHN MARY BLAME                                               JOHN MARY BLAME
  174: *CAN *GIVE1 GIVE1 *YESTERDAY *WHAT                            PEOPLE GROUP GIVE1 JANA TOY
  181: JOHN ARRIVE                                                   JOHN ARRIVE
  184: *TOY1 *SEARCH-FOR *GIVE1 TEACHER *YESTERDAY                   ALL BOY GIVE TEACHER APPLE
  189: JOHN *SOMETHING-ONE *VISIT BOX                                JOHN GIVE GIRL BOX
  193: JOHN *SOMETHING-ONE *VISIT BOX                                JOHN GIVE GIRL BOX
  199: *JOHN *ARRIVE *GO                                             LIKE CHOCOLATE WHO
  201: JOHN *MARY *LOVE *LOVE BUY HOUSE                              JOHN TELL MARY IX-1P BUY HOUSE
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<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_norm</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>
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**** WER = 0.6179775280898876
Total correct: 68 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: JOHN *ARRIVE *ARRIVE                                          JOHN WRITE HOMEWORK
    7: *MARY *BUY GO *WHAT                                           JOHN CAN GO CAN
   12: JOHN *WHAT *WHAT CAN                                          JOHN CAN GO CAN
   21: *MARY *NEW *VISIT *GO *CAR *CAR *FUTURE CHICKEN               JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: *ANN LIKE *GO *LIKE IX                                        JOHN LIKE IX IX IX
   28: *ANN LIKE *GO *MARY IX                                        JOHN LIKE IX IX IX
   30: *SHOOT *IX *MARY IX IX                                        JOHN LIKE IX IX IX
   36: MARY *MARY *GIVE3 *VISIT LIKE *IX                             MARY VEGETABLE KNOW IX LIKE CORN1
   40: *MARY *JOHN *FUTURE1 *VEGETABLE *MARY                         JOHN IX THINK MARY LOVE
   43: JOHN *FUTURE BUY HOUSE                                        JOHN MUST BUY HOUSE
   50: *POSS *SEE BUY CAR *IX                                        FUTURE JOHN BUY CAR SHOULD
   54: JOHN *FUTURE *MAN *ARRIVE HOUSE                               JOHN SHOULD NOT BUY HOUSE
   57: *IX *IX *IX *VISIT                                            JOHN DECIDE VISIT MARY
   67: *MARY *IX *JOHN BUY HOUSE                                     JOHN FUTURE NOT BUY HOUSE
   71: JOHN *FUTURE VISIT MARY                                       JOHN WILL VISIT MARY
   74: *IX *BILL *MARY MARY                                          JOHN NOT VISIT MARY
   77: ANN BLAME MARY                                                ANN BLAME MARY
   84: *JOHN *WHAT *VISIT BOOK                                       IX-1P FIND SOMETHING-ONE BOOK
   89: *FUTURE IX *IX *IX IX NEW *BREAK-DOWN                         JOHN IX GIVE MAN IX NEW COAT
   90: *SELF *IX IX *IX WOMAN BOOK                                   JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *IX IX *IX *IX BOOK                                      JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: POSS NEW *HOUSE BREAK-DOWN                                    POSS NEW CAR BREAK-DOWN
  105: JOHN *POSS                                                    JOHN LEG
  107: *MARY POSS *BOX *MARY *MARY                                   JOHN POSS FRIEND HAVE CANDY
  108: *IX *JOHN                                                     WOMAN ARRIVE
  113: *SHOULD CAR *IX *JOHN *BOX                                    IX CAR BLUE SUE BUY
  119: *PREFER *BUY1 IX *JOHN *FINISH                                SUE BUY IX CAR BLUE
  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK
  139: JOHN *BUY1 *CAR *JOHN BOOK                                    JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN BUY *POSS WHAT BOOK                                      JOHN BUY YESTERDAY WHAT BOOK
  158: LOVE *IX WHO                                                  LOVE JOHN WHO
  167: JOHN IX *SAY-1P LOVE *IX                                      JOHN IX SAY LOVE MARY
  171: *MARY *JOHN BLAME                                             JOHN MARY BLAME
  174: *NEW *GIVE1 GIVE1 *YESTERDAY *CAR                             PEOPLE GROUP GIVE1 JANA TOY
  181: JOHN *BOX                                                     JOHN ARRIVE
  184: *IX *IX *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE
  189: *MARY *MARY *YESTERDAY *ARRIVE                                JOHN GIVE GIRL BOX
  193: *IX *YESTERDAY *YESTERDAY BOX                                 JOHN GIVE GIRL BOX
  199: *JOHN *ARRIVE *JOHN                                           LIKE CHOCOLATE WHO
  201: JOHN *GIVE1 *IX *WOMAN BUY HOUSE                              JOHN TELL MARY IX-1P BUY HOUSE
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<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>
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**** WER = 0.5617977528089888
Total correct: 78 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: *GO *BROTHER *GIVE1                                           JOHN WRITE HOMEWORK
    7: JOHN *BOX GO *ARRIVE                                          JOHN CAN GO CAN
   12: JOHN *WHAT *JOHN CAN                                          JOHN CAN GO CAN
   21: JOHN *ARRIVE WONT *PREFER *GIVE1 *WHAT *FUTURE *WHO           JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: JOHN LIKE 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 *JOHN BUY HOUSE                                          JOHN MUST BUY HOUSE
   50: *JOHN *SEE BUY CAR *JOHN                                      FUTURE JOHN BUY CAR SHOULD
   54: JOHN SHOULD *WHO 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 *CORN *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: JOHN *SHOULD GIVE *IX IX *ARRIVE *BOOK                        JOHN IX GIVE MAN IX NEW COAT
   90: JOHN *HAVE IX SOMETHING-ONE *GO *CHOCOLATE                    JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN GIVE 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 *IX *HAVE *GO *MARY                                      JOHN POSS FRIEND HAVE CANDY
  108: *LOVE *LOVE                                                   WOMAN ARRIVE
  113: IX CAR *IX *MARY *BUY1                                        IX CAR BLUE SUE BUY
  119: *MARY *BUY1 IX *BLAME *GO                                     SUE BUY IX CAR BLUE
  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK
  139: JOHN *BUY1 WHAT *JOHN BOOK                                    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: *GO ARRIVE                                                    JOHN ARRIVE
  184: *GO BOY *GIVE1 TEACHER *YESTERDAY                             ALL BOY GIVE TEACHER APPLE
  189: *MARY *JOHN *YESTERDAY BOX                                    JOHN GIVE GIRL BOX
  193: JOHN *HAVE *YESTERDAY BOX                                     JOHN GIVE GIRL BOX
  199: *JOHN *STUDENT *GO                                            LIKE CHOCOLATE WHO
  201: JOHN *MARY *LOVE *WOMAN BUY HOUSE                             JOHN TELL MARY IX-1P BUY HOUSE
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<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_custom</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>
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<pre>
**** WER = 0.5224719101123596
Total correct: 85 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: *PAST WRITE HOMEWORK                                          JOHN WRITE HOMEWORK
    7: JOHN *PEOPLE *JOHN CAN                                        JOHN CAN GO CAN
   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN
   21: JOHN FISH WONT *CORN1 BUT *PARTY *CHICKEN *EAT                JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: *ANN LIKE *ANN *CORN1 *ANN                                    JOHN LIKE IX IX IX
   28: *ANN LIKE *ANN *LIKE *ANN                                     JOHN LIKE IX IX IX
   30: JOHN LIKE *MARY *LIKE *MARY                                   JOHN LIKE IX IX IX
   36: MARY *JANA *GIVE1 *VISIT LIKE *MARY                           MARY VEGETABLE KNOW IX LIKE CORN1
   40: JOHN *VISIT *CORN *VEGETABLE *MARY                            JOHN IX THINK MARY LOVE
   43: JOHN *FUTURE BUY HOUSE                                        JOHN MUST BUY HOUSE
   50: *JOHN *FRANK *WRITE *WHAT SHOULD                              FUTURE JOHN BUY CAR SHOULD
   54: JOHN SHOULD *FUTURE *CHOCOLATE HOUSE                          JOHN SHOULD NOT BUY HOUSE
   57: JOHN *VEGETABLE VISIT *GIVE2                                  JOHN DECIDE VISIT MARY
   67: JOHN FUTURE *FUTURE *ARRIVE HOUSE                             JOHN FUTURE NOT BUY HOUSE
   71: JOHN WILL *BLAME MARY                                         JOHN WILL VISIT MARY
   74: JOHN *WHO *MARY MARY                                          JOHN NOT VISIT MARY
   77: ANN BLAME *ANN                                                ANN BLAME MARY
   84: *JOHN *STUDENT *FUTURE BOOK                                   IX-1P FIND SOMETHING-ONE BOOK
   89: *MARY *POSS *SOMETHING-ONE *SOMETHING-ONE *SOMETHING-ONE *NEW-YORK COAT  JOHN IX GIVE MAN IX NEW COAT
   90: *WHO *IX IX *IX WOMAN BOOK                                    JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *SOMETHING-ONE IX *IX 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: *MARY POSS FRIEND *MARY *JOHN                                 JOHN POSS FRIEND HAVE CANDY
  108: *THINK *LAST-WEEK                                             WOMAN ARRIVE
  113: *SHOOT CAR BLUE *JOHN *BUY1                                   IX CAR BLUE SUE BUY
  119: *WHO *BUY1 IX *TOY *POSS                                      SUE BUY IX CAR BLUE
  122: JOHN READ BOOK                                                JOHN READ BOOK
  139: JOHN *BUY1 *TOY YESTERDAY BOOK                                JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN BUY YESTERDAY WHAT BOOK                                  JOHN BUY YESTERDAY WHAT BOOK
  158: LOVE *MARY WHO                                                LOVE JOHN WHO
  167: JOHN *JOHN *MARY LOVE *LOVE                                   JOHN IX SAY LOVE MARY
  171: *MARY *JOHN BLAME                                             JOHN MARY BLAME
  174: *GROUP GROUP GIVE1 *WHO TOY                                   PEOPLE GROUP GIVE1 JANA TOY
  181: *SUE *FRIEND                                                  JOHN ARRIVE
  184: ALL BOY *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE
  189: *MARY *SELF *CORN *TOY                                        JOHN GIVE GIRL BOX
  193: JOHN *SELF *CORN BOX                                          JOHN GIVE GIRL BOX
  199: *FRANK CHOCOLATE *TELL                                        LIKE CHOCOLATE WHO
  201: JOHN *MARY *WOMAN *WOMAN BUY HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE
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<h3 id="SelectorDIC">SelectorDIC<a class="anchor-link" href="#SelectorDIC">&#182;</a></h3>
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<div class="prompt input_prompt">In&nbsp;[75]:</div>
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<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_delta</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>
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<pre>
**** WER = 0.6235955056179775
Total correct: 67 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: JOHN *GIVE1 *ARRIVE                                           JOHN WRITE HOMEWORK
    7: JOHN *BOX *GIVE1 *ARRIVE                                      JOHN CAN GO CAN
   12: JOHN *BOX *JOHN CAN                                           JOHN CAN GO CAN
   21: JOHN *MARY *LOVE *MARY *BUY *FUTURE *FUTURE *MARY             JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: JOHN *IX *JOHN IX IX                                          JOHN LIKE IX IX IX
   28: JOHN *MARY *JOHN IX *SHOULD                                   JOHN LIKE IX IX IX
   30: JOHN *IX *SHOULD *JOHN IX                                     JOHN LIKE IX IX IX
   36: *JOHN *JOHN *JOHN IX *MARY *MARY                              MARY VEGETABLE KNOW IX LIKE CORN1
   40: *MARY IX *JOHN MARY *MARY                                     JOHN IX THINK MARY LOVE
   43: JOHN *IX BUY HOUSE                                            JOHN MUST BUY HOUSE
   50: *JOHN JOHN BUY CAR *MARY                                      FUTURE JOHN BUY CAR SHOULD
   54: JOHN *JOHN *JOHN BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE
   57: *MARY *JOHN *IX *IX                                           JOHN DECIDE VISIT MARY
   67: JOHN *JOHN *MARY BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE
   71: JOHN *MARY VISIT MARY                                         JOHN WILL VISIT MARY
   74: JOHN *JOHN *IX MARY                                           JOHN NOT VISIT MARY
   77: *JOHN *ARRIVE MARY                                            ANN BLAME MARY
   84: *GO *CAR *IX BOOK                                             IX-1P FIND SOMETHING-ONE BOOK
   89: *MARY *JOHN *IX *IX *JOHN *WHAT *ARRIVE                       JOHN IX GIVE MAN IX NEW COAT
   90: JOHN *JOHN *JOHN *IX WOMAN *MARY                              JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *IX *JOHN *IX *IX BOOK                                   JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: *JOHN NEW CAR *BOOK                                           POSS NEW CAR BREAK-DOWN
  105: JOHN *JOHN                                                    JOHN LEG
  107: JOHN *JOHN *CAR *MARY *JOHN                                   JOHN POSS FRIEND HAVE CANDY
  108: *JOHN *BOOK                                                   WOMAN ARRIVE
  113: *JOHN CAR *MARY *IX *GIVE1                                    IX CAR BLUE SUE BUY
  119: *JOHN *BUY1 IX CAR *MARY                                      SUE BUY IX CAR BLUE
  122: JOHN *ARRIVE *WHAT                                            JOHN READ BOOK
  139: JOHN *GIVE1 WHAT *JOHN *WHAT                                  JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN BUY *FUTURE WHAT *WHAT                                   JOHN BUY YESTERDAY WHAT BOOK
  158: *ARRIVE JOHN *JOHN                                            LOVE JOHN WHO
  167: JOHN IX *IX *BOOK MARY                                        JOHN IX SAY LOVE MARY
  171: JOHN *JOHN BLAME                                              JOHN MARY BLAME
  174: *GIVE1 *MARY GIVE1 *JOHN *CAR                                 PEOPLE GROUP GIVE1 JANA TOY
  181: JOHN ARRIVE                                                   JOHN ARRIVE
  184: *IX *JOHN *GIVE1 TEACHER *MARY                                ALL BOY GIVE TEACHER APPLE
  189: JOHN *JOHN *JOHN *ARRIVE                                      JOHN GIVE GIRL BOX
  193: JOHN *IX *IX BOX                                              JOHN GIVE GIRL BOX
  199: *JOHN *WHAT *MARY                                             LIKE CHOCOLATE WHO
  201: JOHN *IX *IX *JOHN BUY HOUSE                                  JOHN TELL MARY IX-1P BUY HOUSE
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<div class="prompt input_prompt">In&nbsp;[76]:</div>
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<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_ground</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>
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**** WER = 0.5730337078651685
Total correct: 76 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: JOHN *NEW *GIVE1                                              JOHN WRITE HOMEWORK
    7: *SOMETHING-ONE *CAR *ARRIVE *WHAT                             JOHN CAN GO CAN
   12: *IX *WHAT *WHAT *CAR                                          JOHN CAN GO CAN
   21: JOHN *GIVE1 *JOHN *FUTURE *CAR *CAR *FUTURE *MARY             JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: JOHN *IX IX *WHO IX                                           JOHN LIKE IX IX IX
   28: JOHN *WHO IX *FUTURE *LOVE                                    JOHN LIKE IX IX IX
   30: JOHN *MARY *MARY *MARY *MARY                                  JOHN LIKE IX IX IX
   36: *VISIT *VISIT *IX *GO *MARY *IX                               MARY VEGETABLE KNOW IX LIKE CORN1
   40: *MARY *GO *GIVE MARY *MARY                                    JOHN IX THINK MARY LOVE
   43: JOHN *IX BUY HOUSE                                            JOHN MUST BUY HOUSE
   50: *JOHN JOHN *GIVE1 CAR *JOHN                                   FUTURE JOHN BUY CAR SHOULD
   54: JOHN SHOULD NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE
   57: *MARY *VISIT VISIT MARY                                       JOHN DECIDE VISIT MARY
   67: JOHN *JOHN *MARY BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE
   71: JOHN *FUTURE VISIT MARY                                       JOHN WILL VISIT MARY
   74: *IX *GO *MARY MARY                                            JOHN NOT VISIT MARY
   77: *JOHN BLAME MARY                                              ANN BLAME MARY
   84: *JOHN *GIVE1 *VISIT BOOK                                      IX-1P FIND SOMETHING-ONE BOOK
   89: *MARY IX *IX *IX IX *ARRIVE *BOOK                             JOHN IX GIVE MAN IX NEW COAT
   90: JOHN *SOMETHING-ONE IX *IX *VISIT *ARRIVE                     JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *IX IX *IX *IX BOOK                                      JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: *IX NEW CAR *ARRIVE                                           POSS NEW CAR BREAK-DOWN
  105: JOHN *FRANK                                                   JOHN LEG
  107: JOHN *IX *HAVE *ARRIVE *JOHN                                  JOHN POSS FRIEND HAVE CANDY
  108: *IX ARRIVE                                                    WOMAN ARRIVE
  113: IX CAR *IX *MARY *BOX                                         IX CAR BLUE SUE BUY
  119: *VISIT *BUY1 IX *BOX *IX                                      SUE BUY IX CAR BLUE
  122: JOHN *BUY BOOK                                                JOHN READ BOOK
  139: JOHN *BUY1 WHAT *MARY BOOK                                    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 *GO LOVE MARY                                      JOHN IX SAY LOVE MARY
  171: JOHN MARY BLAME                                               JOHN MARY BLAME
  174: *CAR *GIVE1 GIVE1 *YESTERDAY *WHAT                            PEOPLE GROUP GIVE1 JANA TOY
  181: JOHN ARRIVE                                                   JOHN ARRIVE
  184: *IX BOY *GIVE1 TEACHER *YESTERDAY                             ALL BOY GIVE TEACHER APPLE
  189: JOHN *SOMETHING-ONE *VISIT BOX                                JOHN GIVE GIRL BOX
  193: JOHN *SOMETHING-ONE *VISIT BOX                                JOHN GIVE GIRL BOX
  199: *JOHN *ARRIVE *GO                                             LIKE CHOCOLATE WHO
  201: JOHN *MARY *LOVE *LOVE *GIVE1 HOUSE                           JOHN TELL MARY IX-1P BUY HOUSE
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<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_norm</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>
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**** WER = 0.6235955056179775
Total correct: 67 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: JOHN WRITE *ARRIVE                                            JOHN WRITE HOMEWORK
    7: *MARY *CAR GO *WHAT                                           JOHN CAN GO CAN
   12: JOHN *CAR *WHAT CAN                                           JOHN CAN GO CAN
   21: *MARY *BOX *JOHN *BLAME *CAR *CAR *FUTURE CHICKEN             JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: JOHN LIKE IX *MARY IX                                         JOHN LIKE IX IX IX
   28: *ANN *ANN *ANN *ANN *ANN                                      JOHN LIKE IX IX IX
   30: *IX-1P *CHOCOLATE *MARY *LOVE *LOVE                           JOHN LIKE IX IX IX
   36: MARY *MARY *GIVE3 *WOMAN LIKE *IX                             MARY VEGETABLE KNOW IX LIKE CORN1
   40: *MARY *JOHN *SEE MARY *MARY                                   JOHN IX THINK MARY LOVE
   43: JOHN *FUTURE BUY HOUSE                                        JOHN MUST BUY HOUSE
   50: *POSS *SEE *BOX CAR *IX                                       FUTURE JOHN BUY CAR SHOULD
   54: JOHN *FUTURE *MAN *ARRIVE HOUSE                               JOHN SHOULD NOT BUY HOUSE
   57: *LOVE *IX *JOHN *VISIT                                        JOHN DECIDE VISIT MARY
   67: *MARY *IX *JOHN *ARRIVE HOUSE                                 JOHN FUTURE NOT BUY HOUSE
   71: JOHN *FUTURE VISIT MARY                                       JOHN WILL VISIT MARY
   74: *GO *VISIT VISIT MARY                                         JOHN NOT VISIT MARY
   77: *JOHN BLAME MARY                                              ANN BLAME MARY
   84: *JOHN *BOX *VISIT BOOK                                        IX-1P FIND SOMETHING-ONE BOOK
   89: *MARY *POSS *IX *IX IX *ARRIVE *BREAK-DOWN                    JOHN IX GIVE MAN IX NEW COAT
   90: *SELF *IX IX *IX WOMAN BOOK                                   JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *IX IX *IX *LOVE BOOK                                    JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN
  105: JOHN *POSS                                                    JOHN LEG
  107: *MARY POSS *BOX *MARY *TOY1                                   JOHN POSS FRIEND HAVE CANDY
  108: *LOVE *JOHN                                                   WOMAN ARRIVE
  113: *SHOULD CAR *IX *JOHN *BOX                                    IX CAR BLUE SUE BUY
  119: *PREFER *BUY1 IX *JOHN *GO                                    SUE BUY IX CAR BLUE
  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK
  139: JOHN *BUY1 *CAR *JOHN BOOK                                    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 IX *SAY-1P LOVE *IX                                      JOHN IX SAY LOVE MARY
  171: *LIKE *JOHN BLAME                                             JOHN MARY BLAME
  174: *CAR *GIVE1 GIVE1 *YESTERDAY *CAR                             PEOPLE GROUP GIVE1 JANA TOY
  181: JOHN *BOX                                                     JOHN ARRIVE
  184: *IX *IX *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE
  189: *MARY *MARY *YESTERDAY BOX                                    JOHN GIVE GIRL BOX
  193: *IX *YESTERDAY *YESTERDAY BOX                                 JOHN GIVE GIRL BOX
  199: *JOHN *BOX *JOHN                                              LIKE CHOCOLATE WHO
  201: JOHN *GIVE1 *IX *WOMAN *ARRIVE HOUSE                          JOHN TELL MARY IX-1P BUY HOUSE
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<div class="prompt input_prompt">In&nbsp;[78]:</div>
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    <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>
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**** 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
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<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_custom</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>
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**** WER = 0.5168539325842697
Total correct: 86 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: *PAST WRITE HOMEWORK                                          JOHN WRITE HOMEWORK
    7: JOHN *TOY *JOHN CAN                                           JOHN CAN GO CAN
   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN
   21: JOHN FISH WONT *CORN1 BUT *PARTY *CHICKEN *EAT                JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: *ANN LIKE *ANN *CORN1 *ANN                                    JOHN LIKE IX IX IX
   28: *ANN LIKE *ANN *LIKE *ANN                                     JOHN LIKE IX IX IX
   30: JOHN LIKE *MARY *LIKE *MARY                                   JOHN LIKE IX IX IX
   36: MARY *JANA *GIVE1 *VISIT LIKE *MARY                           MARY VEGETABLE KNOW IX LIKE CORN1
   40: JOHN *VISIT *CORN *VEGETABLE *MARY                            JOHN IX THINK MARY LOVE
   43: JOHN *FUTURE BUY HOUSE                                        JOHN MUST BUY HOUSE
   50: *JOHN *FRANK *WRITE *WHAT SHOULD                              FUTURE JOHN BUY CAR SHOULD
   54: JOHN SHOULD *FUTURE BUY HOUSE                                 JOHN SHOULD NOT BUY HOUSE
   57: JOHN *VEGETABLE VISIT *GIVE2                                  JOHN DECIDE VISIT MARY
   67: JOHN FUTURE *FUTURE *ARRIVE HOUSE                             JOHN FUTURE NOT BUY HOUSE
   71: JOHN WILL *BLAME MARY                                         JOHN WILL VISIT MARY
   74: JOHN *WHO *MARY MARY                                          JOHN NOT VISIT MARY
   77: ANN BLAME *ANN                                                ANN BLAME MARY
   84: *JOHN *STUDENT *FUTURE BOOK                                   IX-1P FIND SOMETHING-ONE BOOK
   89: *MARY *POSS *SOMETHING-ONE *SOMETHING-ONE *SOMETHING-ONE *NEW-YORK COAT  JOHN IX GIVE MAN IX NEW COAT
   90: *WHO *IX IX *IX WOMAN BOOK                                    JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *SOMETHING-ONE IX *IX 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: *MARY POSS FRIEND *MARY *JOHN                                 JOHN POSS FRIEND HAVE CANDY
  108: *THINK *LAST-WEEK                                             WOMAN ARRIVE
  113: *SHOOT CAR BLUE *JOHN *BUY1                                   IX CAR BLUE SUE BUY
  119: *WHO *BUY1 IX *BLAME *POSS                                    SUE BUY IX CAR BLUE
  122: JOHN READ BOOK                                                JOHN READ BOOK
  139: JOHN *BUY1 *TOY YESTERDAY BOOK                                JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN *STUDENT YESTERDAY WHAT BOOK                             JOHN BUY YESTERDAY WHAT BOOK
  158: LOVE *MARY WHO                                                LOVE JOHN WHO
  167: JOHN *JOHN *MARY LOVE MARY                                    JOHN IX SAY LOVE MARY
  171: *MARY *JOHN BLAME                                             JOHN MARY BLAME
  174: *GROUP GROUP GIVE1 *WHO TOY                                   PEOPLE GROUP GIVE1 JANA TOY
  181: *SUE *HAVE                                                    JOHN ARRIVE
  184: ALL BOY *GIVE1 TEACHER APPLE                                  ALL BOY GIVE TEACHER APPLE
  189: *MARY *SELF *CORN *TOY                                        JOHN GIVE GIRL BOX
  193: JOHN *SELF *CORN BOX                                          JOHN GIVE GIRL BOX
  199: *FRANK CHOCOLATE *TELL                                        LIKE CHOCOLATE WHO
  201: JOHN *MARY *WOMAN *WOMAN BUY HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE
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<h3 id="SelectorCV">SelectorCV<a class="anchor-link" href="#SelectorCV">&#182;</a></h3>
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<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_delta</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>
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**** WER = 0.651685393258427
Total correct: 62 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: JOHN *WHAT *GO1                                               JOHN WRITE HOMEWORK
    7: JOHN *HAVE *GIVE1 *GO                                         JOHN CAN GO CAN
   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN
   21: JOHN *MARY *GO1 *MARY *BUY *GO *FUTURE *MARY                  JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: JOHN *IX *LOVE *GIVE IX                                       JOHN LIKE IX IX IX
   28: JOHN *MARY IX *WHAT *WOMAN                                    JOHN LIKE IX IX IX
   30: JOHN *IX *LOVE *JOHN *WOMAN                                   JOHN LIKE IX IX IX
   36: *IX *IX *GIVE IX *MARY *IX                                    MARY VEGETABLE KNOW IX LIKE CORN1
   40: *MARY IX *JOHN *IX *IX                                        JOHN IX THINK MARY LOVE
   43: JOHN *WOMAN BUY HOUSE                                         JOHN MUST BUY HOUSE
   50: *JOHN *WHAT BUY CAR *WHAT                                     FUTURE JOHN BUY CAR SHOULD
   54: JOHN *MARY *JOHN BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE
   57: JOHN *LOVE *IX *IX                                            JOHN DECIDE VISIT MARY
   67: JOHN *JOHN *LOVE BUY *BOOK                                    JOHN FUTURE NOT BUY HOUSE
   71: JOHN *MARY VISIT *BOOK                                        JOHN WILL VISIT MARY
   74: JOHN *IX *IX MARY                                             JOHN NOT VISIT MARY
   77: *JOHN *CAR MARY                                               ANN BLAME MARY
   84: *JOHN *GO *WHAT *WHAT                                         IX-1P FIND SOMETHING-ONE BOOK
   89: JOHN *GIVE GIVE *IX *VISIT *CAR COAT                          JOHN IX GIVE MAN IX NEW COAT
   90: *MARY *JOHN *JOHN *IX WOMAN *WHAT                             JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *IX IX *IX WOMAN BOOK                                    JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: *JOHN NEW CAR *CAN                                            POSS NEW CAR BREAK-DOWN
  105: JOHN *IX                                                      JOHN LEG
  107: JOHN *JOHN *LOVE *GIVE1 *MARY                                 JOHN POSS FRIEND HAVE CANDY
  108: *JOHN *BOOK                                                   WOMAN ARRIVE
  113: *JOHN *GIVE1 *MARY *IX *BUY1                                  IX CAR BLUE SUE BUY
  119: *JOHN *BUY1 *GIVE1 *GIVE1 *WHAT                               SUE BUY IX CAR BLUE
  122: JOHN *HAVE BOOK                                               JOHN READ BOOK
  139: JOHN *NEW WHAT *JOHN BOOK                                     JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN BUY *WHAT WHAT *VIDEOTAPE                                JOHN BUY YESTERDAY WHAT BOOK
  158: *NEW *MARY *MARY                                              LOVE JOHN WHO
  167: JOHN IX *IX *BOOK *BOOK                                       JOHN IX SAY LOVE MARY
  171: *IX *JOHN BLAME                                               JOHN MARY BLAME
  174: *GIVE1 *WHAT GIVE1 *JOHN *BOOK                                PEOPLE GROUP GIVE1 JANA TOY
  181: JOHN *NEW                                                     JOHN ARRIVE
  184: *GIVE *JOHN *GIVE1 TEACHER *WHAT                              ALL BOY GIVE TEACHER APPLE
  189: JOHN *IX *JOHN *BOOK                                          JOHN GIVE GIRL BOX
  193: JOHN *JOHN *WOMAN BOX                                         JOHN GIVE GIRL BOX
  199: *JOHN *BOOK *MARY                                             LIKE CHOCOLATE WHO
  201: JOHN *IX MARY *WOMAN BUY HOUSE                                JOHN TELL MARY IX-1P BUY HOUSE
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<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_ground</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>
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**** WER = 0.6123595505617978
Total correct: 69 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: JOHN *BOOK *BLAME                                             JOHN WRITE HOMEWORK
    7: JOHN CAN GO CAN                                               JOHN CAN GO CAN
   12: JOHN *CAR *CAN CAN                                            JOHN CAN GO CAN
   21: JOHN *VIDEOTAPE *FUTURE *JOHN *BLAME *CAR *VISIT *WHO         JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: JOHN *IX *LOVE IX IX                                          JOHN LIKE IX IX IX
   28: JOHN *IX IX IX *LOVE                                          JOHN LIKE IX IX IX
   30: JOHN LIKE IX IX *GO                                           JOHN LIKE IX IX IX
   36: MARY VEGETABLE *GIVE *GO *IX *IX                              MARY VEGETABLE KNOW IX LIKE CORN1
   40: JOHN *GIVE *JOHN *JOHN *GO                                    JOHN IX THINK MARY LOVE
   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE
   50: *JOHN JOHN BUY CAR *SOMETHING-ONE                             FUTURE JOHN BUY CAR SHOULD
   54: JOHN SHOULD NOT BUY HOUSE                                     JOHN SHOULD NOT BUY HOUSE
   57: *IX *VISIT VISIT *IX                                          JOHN DECIDE VISIT MARY
   67: JOHN *POSS *WHO *ARRIVE HOUSE                                 JOHN FUTURE NOT BUY HOUSE
   71: JOHN *FINISH *GO MARY                                         JOHN WILL VISIT MARY
   74: *IX *VISIT *MARY *GO                                          JOHN NOT VISIT MARY
   77: *JOHN BLAME MARY                                              ANN BLAME MARY
   84: *LOVE *ARRIVE *GO BOOK                                        IX-1P FIND SOMETHING-ONE BOOK
   89: *MARY *POSS GIVE *SOMETHING-ONE *SOMETHING-ONE *BUY *BOOK     JOHN IX GIVE MAN IX NEW COAT
   90: JOHN *SOMETHING-ONE *SOMETHING-ONE SOMETHING-ONE WOMAN *ARRIVE  JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN GIVE *SOMETHING-ONE *WOMAN *GIVE1 BOOK                   JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: POSS NEW CAR *BOOK                                            POSS NEW CAR BREAK-DOWN
  105: JOHN *JOHN                                                    JOHN LEG
  107: JOHN *MARY *CAN *GO *WHO                                      JOHN POSS FRIEND HAVE CANDY
  108: *MARY *LOVE                                                   WOMAN ARRIVE
  113: *MARY *CAN *GIVE *MARY *BLUE                                  IX CAR BLUE SUE BUY
  119: *VEGETABLE *LOVE *BLUE *CAN *MARY                             SUE BUY IX CAR BLUE
  122: JOHN *HOUSE BOOK                                              JOHN READ BOOK
  139: JOHN *LOVE *CAN YESTERDAY *ARRIVE                             JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN *NEW *FUTURE *CAR BOOK                                   JOHN BUY YESTERDAY WHAT BOOK
  158: LOVE *MARY WHO                                                LOVE JOHN WHO
  167: JOHN *VISIT *JANA *WOMAN *GO                                  JOHN IX SAY LOVE MARY
  171: *MARY *VISIT *MARY                                            JOHN MARY BLAME
  174: *CAN *NEW GIVE1 *GO *CAN                                      PEOPLE GROUP GIVE1 JANA TOY
  181: *BLUE *SOMETHING-ONE                                          JOHN ARRIVE
  184: *SOMETHING-ONE *GO *HOUSE TEACHER *GO                         ALL BOY GIVE TEACHER APPLE
  189: JOHN *VISIT *VISIT *CAN                                       JOHN GIVE GIRL BOX
  193: *MARY *GIVE1 *VISIT BOX                                       JOHN GIVE GIRL BOX
  199: *LOVE *NEW WHO                                                LIKE CHOCOLATE WHO
  201: JOHN *SHOULD *GIVE *JOHN BUY HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE
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<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_norm</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>
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**** WER = 0.6685393258426966
Total correct: 59 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: JOHN *BUY *ARRIVE                                             JOHN WRITE HOMEWORK
    7: JOHN *NEW GO *ARRIVE                                          JOHN CAN GO CAN
   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN
   21: *LIKE *NEW *HAVE *GO *BLAME *CAR *FUTURE *HAVE                JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: *GO LIKE *LIKE *LIKE *GO                                      JOHN LIKE IX IX IX
   28: JOHN LIKE *LIKE *LIKE *GO                                     JOHN LIKE IX IX IX
   30: *IX-1P LIKE *LOVE *LIKE *MARY                                 JOHN LIKE IX IX IX
   36: *VISIT *SHOULD *YESTERDAY *VISIT *LEAVE *LIKE                 MARY VEGETABLE KNOW IX LIKE CORN1
   40: JOHN *VISIT *SELF *SHOULD LOVE                                JOHN IX THINK MARY LOVE
   43: JOHN *JOHN BUY HOUSE                                          JOHN MUST BUY HOUSE
   50: *POSS *SEE *NEW CAR *HAVE                                     FUTURE JOHN BUY CAR SHOULD
   54: JOHN *TELL NOT *HAVE HOUSE                                    JOHN SHOULD NOT BUY HOUSE
   57: JOHN *WHO *MARY MARY                                          JOHN DECIDE VISIT MARY
   67: JOHN *SOMETHING-ONE *JOHN *ARRIVE *CAN                        JOHN FUTURE NOT BUY HOUSE
   71: JOHN *FUTURE VISIT MARY                                       JOHN WILL VISIT MARY
   74: JOHN *VISIT VISIT *VISIT                                      JOHN NOT VISIT MARY
   77: *JOHN BLAME MARY                                              ANN BLAME MARY
   84: *JOHN *ARRIVE *POSS *NEW                                      IX-1P FIND SOMETHING-ONE BOOK
   89: *TELL *HAVE *SOMETHING-ONE *SOMETHING-ONE *SOMETHING-ONE NEW COAT  JOHN IX GIVE MAN IX NEW COAT
   90: *SELF *GIVE1 *SOMETHING-ONE *GIVE1 WOMAN *VIDEOTAPE           JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *GIVE1 *WOMAN *WOMAN WOMAN BOOK                          JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: POSS NEW *HOUSE BREAK-DOWN                                    POSS NEW CAR BREAK-DOWN
  105: JOHN *POSS                                                    JOHN LEG
  107: JOHN *IX *GIVE1 *LIKE *FUTURE                                 JOHN POSS FRIEND HAVE CANDY
  108: *LOVE *JOHN                                                   WOMAN ARRIVE
  113: IX *CAN *IX SUE *HAVE                                         IX CAR BLUE SUE BUY
  119: *WHO *BUY1 *HAVE *ARRIVE *FUTURE                              SUE BUY IX CAR BLUE
  122: JOHN *GIVE1 BOOK                                              JOHN READ BOOK
  139: JOHN *BUY1 *CAR YESTERDAY *NEW                                JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN *ARRIVE *GO WHAT *COAT                                   JOHN BUY YESTERDAY WHAT BOOK
  158: *GIVE1 *MARY *CORN                                            LOVE JOHN WHO
  167: JOHN *VISIT *VISIT *MARY *LOVE                                JOHN IX SAY LOVE MARY
  171: *JANA *JOHN BLAME                                             JOHN MARY BLAME
  174: *NEW *GIVE1 GIVE1 *WHO *VISIT                                 PEOPLE GROUP GIVE1 JANA TOY
  181: *SOMETHING-ONE *GIVE1                                         JOHN ARRIVE
  184: *IX BOY *GIVE1 TEACHER *WHO                                   ALL BOY GIVE TEACHER APPLE
  189: *JANA *GIVE1 *FINISH *ARRIVE                                  JOHN GIVE GIRL BOX
  193: JOHN *YESTERDAY *FINISH BOX                                   JOHN GIVE GIRL BOX
  199: *JOHN CHOCOLATE *TELL                                         LIKE CHOCOLATE WHO
  201: JOHN *GIVE1 *WOMAN *WOMAN BUY HOUSE                           JOHN TELL MARY IX-1P BUY HOUSE
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<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>
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**** WER = 0.6404494382022472
Total correct: 64 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: *POSS *BROTHER *GO                                            JOHN WRITE HOMEWORK
    7: JOHN CAN GO CAN                                               JOHN CAN GO CAN
   12: *IX *CAR *GO1 CAN                                             JOHN CAN GO CAN
   21: JOHN *JOHN *GIVE1 *JOHN *CAR *CAR *FUTURE *WHO                JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: JOHN *IX *LOVE *JOHN IX                                       JOHN LIKE IX IX IX
   28: JOHN *JOHN *LOVE IX *LOVE                                     JOHN LIKE IX IX IX
   30: JOHN LIKE *GO *LIKE *GO                                       JOHN LIKE IX IX IX
   36: *VISIT *VISIT *GIVE *GIVE *MARY *IX                           MARY VEGETABLE KNOW IX LIKE CORN1
   40: JOHN *VISIT *VISIT *JOHN *MARY                                JOHN IX THINK MARY LOVE
   43: JOHN *POSS BUY HOUSE                                          JOHN MUST BUY HOUSE
   50: *JOHN *SEE BUY CAR SHOULD                                     FUTURE JOHN BUY CAR SHOULD
   54: JOHN SHOULD *WHO BUY HOUSE                                    JOHN SHOULD NOT BUY HOUSE
   57: *IX *VISIT VISIT MARY                                         JOHN DECIDE VISIT MARY
   67: JOHN FUTURE *WHO BUY HOUSE                                    JOHN FUTURE NOT BUY HOUSE
   71: JOHN *FINISH *GO *BLAME                                       JOHN WILL VISIT MARY
   74: *GO *VISIT *MARY *GO                                          JOHN NOT VISIT MARY
   77: *JOHN BLAME *LOVE                                             ANN BLAME MARY
   84: *LOVE *BLAME *POSS BOOK                                       IX-1P FIND SOMETHING-ONE BOOK
   89: *GIVE1 *GIVE *SOMETHING-ONE *SOMETHING-ONE *SOMETHING-ONE *ARRIVE COAT  JOHN IX GIVE MAN IX NEW COAT
   90: JOHN *HAVE *SOMETHING-ONE SOMETHING-ONE *VISIT *ARRIVE        JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN GIVE *SOMETHING-ONE SOMETHING-ONE *SOMETHING-ONE BOOK    JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: POSS NEW CAR BREAK-DOWN                                       POSS NEW CAR BREAK-DOWN
  105: JOHN *JOHN                                                    JOHN LEG
  107: JOHN *GIVE *HAVE *GO *GO                                      JOHN POSS FRIEND HAVE CANDY
  108: *MARY *LOVE                                                   WOMAN ARRIVE
  113: IX *HAVE *MARY *JOHN *BUY1                                    IX CAR BLUE SUE BUY
  119: *VISIT *BUY1 *GO *VISIT *VISIT                                SUE BUY IX CAR BLUE
  122: JOHN *BOOK BOOK                                               JOHN READ BOOK
  139: JOHN *BUY1 *DECIDE YESTERDAY *ARRIVE                          JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN *NEW *GO *CAR BOOK                                       JOHN BUY YESTERDAY WHAT BOOK
  158: LOVE *MARY WHO                                                LOVE JOHN WHO
  167: JOHN *MARY *VISIT LOVE *GO                                    JOHN IX SAY LOVE MARY
  171: *IX *VISIT *SOMETHING-ONE                                     JOHN MARY BLAME
  174: *CAN *VISIT GIVE1 *GO *VISIT                                  PEOPLE GROUP GIVE1 JANA TOY
  181: *YESTERDAY *SOMETHING-ONE                                     JOHN ARRIVE
  184: *GIVE *GIVE *GO TEACHER *GIRL                                 ALL BOY GIVE TEACHER APPLE
  189: JOHN *VISIT *VISIT *CAN                                       JOHN GIVE GIRL BOX
  193: JOHN *HAVE *VISIT BOX                                         JOHN GIVE GIRL BOX
  199: *LOVE *NEW *JOHN                                              LIKE CHOCOLATE WHO
  201: JOHN *SHOULD *WOMAN *JOHN BUY HOUSE                           JOHN TELL MARY IX-1P BUY HOUSE
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<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_custom</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>
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**** WER = 0.6179775280898876
Total correct: 68 out of 178
Video  Recognized                                                    Correct
=====================================================================================================
    2: *IX-1P *NEW-YORK *WHO                                         JOHN WRITE HOMEWORK
    7: JOHN *HAVE GO CAN                                             JOHN CAN GO CAN
   12: JOHN CAN *GO1 CAN                                             JOHN CAN GO CAN
   21: JOHN FISH WONT *CORN1 BUT *CAR *CHICKEN *EAT                  JOHN FISH WONT EAT BUT CAN EAT CHICKEN
   25: *MARY LIKE *LOVE *LIKE *MARY                                  JOHN LIKE IX IX IX
   28: *MARY LIKE *LOVE *LIKE *MARY                                  JOHN LIKE IX IX IX
   30: *IX *IX IX IX IX                                              JOHN LIKE IX IX IX
   36: MARY *JOHN *GIVE1 *VISIT *MARY *JOHN                          MARY VEGETABLE KNOW IX LIKE CORN1
   40: JOHN *MARY *CORN *JANA *IX                                    JOHN IX THINK MARY LOVE
   43: JOHN *SHOULD BUY HOUSE                                        JOHN MUST BUY HOUSE
   50: *JOHN *FRANK BUY CAR SHOULD                                   FUTURE JOHN BUY CAR SHOULD
   54: JOHN SHOULD *GIVE1 *CHOCOLATE *COAT                           JOHN SHOULD NOT BUY HOUSE
   57: *IX *WHO VISIT MARY                                           JOHN DECIDE VISIT MARY
   67: JOHN FUTURE *SHOULD BUY HOUSE                                 JOHN FUTURE NOT BUY HOUSE
   71: JOHN *GIVE1 VISIT MARY                                        JOHN WILL VISIT MARY
   74: *IX *WHO *MARY MARY                                           JOHN NOT VISIT MARY
   77: *MARY BLAME MARY                                              ANN BLAME MARY
   84: *JOHN *ARRIVE *FUTURE *NEW-YORK                               IX-1P FIND SOMETHING-ONE BOOK
   89: *GIVE1 *POSS *WILL MAN *SOMETHING-ONE NEW *BREAK-DOWN         JOHN IX GIVE MAN IX NEW COAT
   90: *JANA *GIVE1 *BOY *GIVE1 WOMAN *VIDEOTAPE                     JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
   92: JOHN *GIVE1 *SOMETHING-ONE SOMETHING-ONE WOMAN BOOK           JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
  100: POSS *NEW-YORK *HOUSE BREAK-DOWN                              POSS NEW CAR BREAK-DOWN
  105: JOHN *SEE                                                     JOHN LEG
  107: *MARY *BLUE *HAVE *JOHN *JOHN                                 JOHN POSS FRIEND HAVE CANDY
  108: *LOVE *LOVE                                                   WOMAN ARRIVE
  113: IX *HAVE BLUE *JOHN *BUY1                                     IX CAR BLUE SUE BUY
  119: *WHO *BUY1 *BLUE *CAN *SELF                                   SUE BUY IX CAR BLUE
  122: JOHN READ BOOK                                                JOHN READ BOOK
  139: JOHN *BUY1 *CAN YESTERDAY *VIDEOTAPE                          JOHN BUY WHAT YESTERDAY BOOK
  142: JOHN *ARRIVE *GO *CAR *NEW-YORK                               JOHN BUY YESTERDAY WHAT BOOK
  158: *STOLEN *MARY WHO                                             LOVE JOHN WHO
  167: JOHN *MARY *MARY LOVE *LOVE                                   JOHN IX SAY LOVE MARY
  171: JOHN *JOHN *DECIDE                                            JOHN MARY BLAME
  174: *DECIDE *GIVE1 GIVE1 *CORN *DECIDE                            PEOPLE GROUP GIVE1 JANA TOY
  181: *GIRL *HAVE                                                   JOHN ARRIVE
  184: *GIVE1 BOY *TEACHER TEACHER *CORN                             ALL BOY GIVE TEACHER APPLE
  189: *JANA *JOHN *CORN *BUY1                                       JOHN GIVE GIRL BOX
  193: JOHN *GIVE1 *CORN BOX                                         JOHN GIVE GIRL BOX
  199: *JOHN CHOCOLATE *TELL                                         LIKE CHOCOLATE WHO
  201: JOHN *GIVE1 *WOMAN *LOVE BUY HOUSE                            JOHN TELL MARY IX-1P BUY HOUSE
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<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></p>
<h5 id="WER-Table">WER Table<a class="anchor-link" href="#WER-Table">&#182;</a></h5><table>
<thead><tr>
<th>Features</th>
<th style="text-align:center">BIC Selector</th>
<th style="text-align:center">DIC Selector</th>
<th style="text-align:right">CV Selector</th>
</tr>
</thead>
<tbody>
<tr>
<td>Delta</td>
<td style="text-align:center">.63</td>
<td style="text-align:center">.62</td>
<td style="text-align:right">.65</td>
</tr>
<tr>
<td>Ground</td>
<td style="text-align:center">.56</td>
<td style="text-align:center">.57</td>
<td style="text-align:right">.61</td>
</tr>
<tr>
<td>Norm</td>
<td style="text-align:center">.61</td>
<td style="text-align:center">.62</td>
<td style="text-align:right">.66</td>
</tr>
<tr>
<td>Polar</td>
<td style="text-align:center">.56</td>
<td style="text-align:center">.55</td>
<td style="text-align:right">.64</td>
</tr>
<tr>
<td>Custom</td>
<td style="text-align:center">.52</td>
<td style="text-align:center">.51</td>
<td style="text-align:right">.61</td>
</tr>
</tbody>
</table>
<p>After reviewing the table, it's clear to see that BIC Selector and DIC Selector are very close in WER.  This may be true due to the fact that both of these selectors leverage all samples when training models.  Additionally, it appears that my custom feature outperforms all other individual features -- These examples display the power of mean normalisation.  CV Selector appears to have scored worst for WER; this may be due to the fact of generalization and that CV does not use the entire training set to train models.  Leveraging CV Selector will often avoid overfitting during training.  DIC Selector achieved the lowest WER at .51 when using custom features -- predicting 86 out of 178 correctly.  DIC outperformed BIC when using custom features because DIC uses more comparisons and works greats for classification problems.  To improve WER, I'd recommend leveraging N-Grams and transition probabilities.  With sufficient training data it's possible to improve the WER by eliminating improbable word transitions.</p>

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<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>

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<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>
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<pre>..
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Ran 2 tests in 30.095s

OK
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<pre>&lt;unittest.runner.TextTestResult run=2 errors=0 failures=0&gt;</pre>
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<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>

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<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>
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