DeepLearning_Introduction.slides.html


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<title>DeepLearning_Introduction slides</title>

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

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

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

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

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
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<h1 id="Deep-Learning:-Introduction"><font color="blue">Deep Learning: Introduction</font><a class="anchor-link" href="#Deep-Learning:-Introduction">&#182;</a></h1><h2 id="Sanjiv-R.-Das">Sanjiv R. Das<a class="anchor-link" href="#Sanjiv-R.-Das">&#182;</a></h2><h3 id="Professor-of-Finance-and-Data-Science">Professor of Finance and Data Science<a class="anchor-link" href="#Professor-of-Finance-and-Data-Science">&#182;</a></h3><h3 id="Santa-Clara-University">Santa Clara University<a class="anchor-link" href="#Santa-Clara-University">&#182;</a></h3><h2 id="London,-June-2018"><font color="green">London, June 2018</font><a class="anchor-link" href="#London,-June-2018">&#182;</a></h2><p>REFERENCE BOOK: <a href="http://srdas.github.io/DLBook">http://srdas.github.io/DLBook</a></p>
<p>Github: <a href="http://github.com/srdas/DLIntro">http://github.com/srdas/DLIntro</a></p>

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<p>ABSTRACT: This workshop will cover the essentials of deep learning from a mathematical and programming perspective.</p>
<ul>
<li>What is deep learning?</li>
<li>Types of deep learning</li>
<li>Mathematics of Deep Learning: universal approximation using neural nets, Backpropagation, and techniques for fitting deep learning nets.</li>
<li>Programming deep learning: tensorflow, mxnet, h2o in R and Python with applications.</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">pylab</span> inline
<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">IPython.external</span> <span class="k">import</span> <span class="n">mathjax</span>
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<pre>Populating the interactive namespace from numpy and matplotlib
</pre>
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<p><img src="images/ML.png" alt=""></p>
<p><a href="https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12">https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12</a></p>

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<p><img src="images/DL_PatternRecognition.png" alt=""></p>

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<h2 id="Net-input">Net input<a class="anchor-link" href="#Net-input">&#182;</a></h2><p>$$
a_j^{(r)} = \sum_{i=1}^{n^{(r-1)}} W_{ij} Z_i^{(r-1)} + b_j^{(r)}
$$</p>

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<h2 id="Examples-of-Different-Types-of-Neurons">Examples of Different Types of Neurons<a class="anchor-link" href="#Examples-of-Different-Types-of-Neurons">&#182;</a></h2><h3 id="Sigmoid">Sigmoid<a class="anchor-link" href="#Sigmoid">&#182;</a></h3><p>$$
Z_j^{(r)} = \frac{1}{1+\exp(-a_j^{(r)})} \in (0,1)
$$</p>

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<h2 id="ReLU-(restricted-linear-unit)">ReLU (restricted linear unit)<a class="anchor-link" href="#ReLU-(restricted-linear-unit)">&#182;</a></h2><p>$$
Z_j^{(r)} = \max[0, a_j^{(r)}] \in (0,1)
$$</p>

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<h2 id="TanH-(hyperbolic-tangent)">TanH (hyperbolic tangent)<a class="anchor-link" href="#TanH-(hyperbolic-tangent)">&#182;</a></h2><p>$$
Z_j^{(r)} = \tanh[a_j^{(r)}] \in (-1,+1)
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<h2 id="Output-Layer">Output Layer<a class="anchor-link" href="#Output-Layer">&#182;</a></h2><p><img src="images/Softmax.png" height="200" width="700"></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#The Softmax function</span>
<span class="c1">#Assume 10 output nodes with randomly generated values</span>
<span class="n">z</span> <span class="o">=</span> <span class="n">randn</span><span class="p">(</span><span class="mi">32</span><span class="p">)</span>  <span class="c1">#inputs from last hidden layer of 32 nodes to the output layer</span>
<span class="n">w</span> <span class="o">=</span> <span class="n">rand</span><span class="p">(</span><span class="mi">32</span><span class="o">*</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span><span class="mi">32</span><span class="p">))</span>  <span class="c1">#weights for the output layer</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>   <span class="c1">#bias terms at output later</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">w</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">+</span> <span class="n">b</span>  <span class="c1">#Net input at output layer</span>
<span class="n">e</span> <span class="o">=</span> <span class="n">exp</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="n">softmax_output</span> <span class="o">=</span> <span class="n">e</span><span class="o">/</span><span class="nb">sum</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">softmax_output</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;final tag =&#39;</span><span class="p">,</span><span class="n">where</span><span class="p">(</span><span class="n">softmax_output</span><span class="o">==</span><span class="n">softmax_output</span><span class="o">.</span><span class="n">max</span><span class="p">())[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
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<pre>[0.005 0.152 0.005 0.013 0.036 0.081 0.603 0.039 0.057 0.008]
final tag = 6
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<p><img src="images/Loss_function.png" alt=""></p>

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<h2 id="More-on-Cross-Entropy">More on Cross Entropy<a class="anchor-link" href="#More-on-Cross-Entropy">&#182;</a></h2><p><a href="https://rdipietro.github.io/friendly-intro-to-cross-entropy-loss/">https://rdipietro.github.io/friendly-intro-to-cross-entropy-loss/</a></p>
<p>Notation from the previous slides:</p>
<ul>
<li>$y_i$: actual probability of the correct class $i$, i.e., $1$ or $0$. </li>
<li>$a_i$ or $\hat{y_i}$: predicted probability of the correct class. </li>
<li>$n$: number of observations in the data. </li>
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<h2 id="Entropy">Entropy<a class="anchor-link" href="#Entropy">&#182;</a></h2><p>$$
E = - \frac{1}{n} \sum_i [y_i \ln y_i]
$$</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.33</span><span class="p">,</span> <span class="mf">0.33</span><span class="p">,</span> <span class="mf">0.34</span><span class="p">]</span>
<span class="n">bits</span> <span class="o">=</span> <span class="n">log2</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">bits</span><span class="p">)</span>
<span class="n">entropy</span> <span class="o">=</span> <span class="o">-</span><span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="o">*</span><span class="n">bits</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">entropy</span><span class="p">)</span>
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<pre>[-1.59946207 -1.59946207 -1.55639335]
1.58481870497303
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">]</span>
<span class="n">entropy</span> <span class="o">=</span> <span class="o">-</span><span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="o">*</span><span class="n">log2</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">entropy</span><span class="p">)</span>
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<pre>1.4854752972273344
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">log2</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
<span class="n">entropy</span> <span class="o">=</span> <span class="o">-</span><span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="o">*</span><span class="n">log2</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">entropy</span><span class="p">)</span>
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<pre>[-3.32192809 -3.32192809 -0.32192809]
0.9219280948873623
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<p>Cross-entropy:</p>
<p>$$
C = - \frac{1}{n} \sum_i [y_i \ln a_i]
$$</p>
<p>where $a_i = {\hat y_i}$.</p>
<p>Note that $C &gt; E$, always.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Correct prediction</span>
<span class="n">y</span> <span class="o">=</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">1</span><span class="p">]</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">]</span>
<span class="n">crossentropy</span> <span class="o">=</span> <span class="o">-</span><span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="o">*</span><span class="n">log2</span><span class="p">(</span><span class="n">yhat</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">crossentropy</span><span class="p">)</span>
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<pre>0.3219280948873623
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Wrong prediction</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">]</span>
<span class="n">crossentropy</span> <span class="o">=</span> <span class="o">-</span><span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="o">*</span><span class="n">log2</span><span class="p">(</span><span class="n">yhat</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">crossentropy</span><span class="p">)</span>
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<pre>1.7369655941662063
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<h3 id="Kullback-Leibler-Divergence">Kullback-Leibler Divergence<a class="anchor-link" href="#Kullback-Leibler-Divergence">&#182;</a></h3><p>$$
KL = \sum_i y_i \cdot \log \left(\frac{y_i}{\hat{y_i}} \right)
$$</p>
<p>Measures the extra bits required if the wrong selection is made.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Correct prediction</span>
<span class="n">y</span> <span class="o">=</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="mf">1.0</span><span class="p">]</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">]</span>
<span class="n">KL</span> <span class="o">=</span> <span class="o">-</span><span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">*</span><span class="n">log2</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">/</span><span class="n">yhat</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">KL</span><span class="p">)</span>

<span class="c1">#Wrong prediction</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">]</span>
<span class="n">KL</span> <span class="o">=</span> <span class="o">-</span><span class="nb">sum</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">*</span><span class="n">log2</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">/</span><span class="n">yhat</span><span class="p">[</span><span class="mi">2</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">KL</span><span class="p">)</span>
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<pre>-0.32192809488736235
-1.7369655941662063
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<p><img src="images/Delta_values.png" alt=""></p>

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<p><img src="images/Output_layer.png" alt=""></p>

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<p><img src="images/Recap.png" alt=""></p>

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<h2 id="SoftMax-Function">SoftMax Function<a class="anchor-link" href="#SoftMax-Function">&#182;</a></h2><p>$$ 
L = -\sum_j T_j \log(h_j)
$$</p>
<p>$$
h_j = \frac{e^{a_j}}{\sum_i e^{a_i}} = \frac{e^{a_j}}{D}
$$</p>
<p>$$
a_j = \sum_i w_{ij} z_i + b_j
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<h2 id="Delta-of-Softmax">Delta of Softmax<a class="anchor-link" href="#Delta-of-Softmax">&#182;</a></h2><p>$$
\frac{\partial L}{\partial a_j} = -\sum_i T_i \frac{\partial \log h_i}{\partial a_j}
$$</p>
<p>$$
\log h_j = a_j - \log D, \quad \frac{\partial D}{\partial a_j} = \sum_i e^{z_i} \delta_{ij} = e^{z_j}
$$</p>
<p>$$
\frac{\partial \log h_j}{\partial a_j} = \delta_{ji} - \frac{1}{D} \frac{\partial D}{\partial a_j} = \delta_{ji} - h_j
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<p>Noting that</p>
<p>$$
\frac{\partial \log h_j}{\partial a_j} = \frac{1}{h_j} \frac{\partial h_j}{\partial a_j}
$$</p>
<p>we get</p>
<p>$$
\frac{\partial h_j}{\partial a_j} = h_j (\delta_{ji} - h_i)
$$</p>
<p>Finally,</p>
<p>$$
\frac{\partial L}{\partial a_j} = \sum_i T_i (h_j - \delta_{ij}) 
= h_j (\sum_j T_j) - T_j = h_j - T_j
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<h2 id="Batch-Stochastic-Gradient">Batch Stochastic Gradient<a class="anchor-link" href="#Batch-Stochastic-Gradient">&#182;</a></h2><p><img src="images/batch_stochastic_gradient.png" height="150" width="700"></p>

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<h2 id="Fitting-the-NN">Fitting the NN<a class="anchor-link" href="#Fitting-the-NN">&#182;</a></h2><ol>
<li><p>Initialize all the weight and bias parameters $(w_{ij}^{(r)},b_i^{(r)})$  (this is a critical step).</p>
</li>
<li><p>For $q = 0,...,{M\over B}-1$, repeat the following steps (2a) - (2f):</p>
</li>
</ol>

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<p>a. For the training inputs $X_i(m), qB\le m\le (q+1)B$, compute the model predictions $y(m)$ given by</p>
<p>$$
a_i^{(r)}(m) = \sum_{j=1}^{P^{r-1}} w_{ij}^{(r)}z_j^{(r-1)}(m)+b_i^{(r)} \quad \mbox{and} \quad z_i^{(r)}(m) = f(a_i^{(r)}(m)), \quad 2 \leq r \leq R, 1 \leq i \leq P^r  
$$</p>
<p>and for $r=1$,</p>
<p>$$
a_i^{(1)}(m) = \sum_{j=1}^{P^1} w_{ij}^{(1)}x_j(m)+b_i^{(1)} \quad \mbox{and} \quad z_i^{(1)}(m) = f(a_i^{(1)}(m)), \quad  1 \leq i \leq N    
$$</p>

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<p>The logits and classification probabilities are computed using</p>
<p>$$
a_i^{(R+1)}(m) = \sum_{j=1}^K w_{ij}^{(R+1)}z_j^{(R)}(m)+b_i^{(R+1)}
$$</p>
<p>and</p>
<p>$$
y_i(m) = \frac{\exp(a_i^{(R+1)}(m))}{\sum_{k=1}^K \exp(a_k^{(R+1)}(m))}, \quad  1 \leq i \leq K   
$$</p>
<p>This step constitutes the <em>forward pass</em> of the algorithm.</p>

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<p>b. Evaluate the gradients $\delta_k^{(R+1)}(m)$ for the logit layer nodes using</p>
<p>$$
\delta_k^{(R+1)}(m) = y_k(m) - t_k(m),\ \ 1\le k\le K
$$</p>
<p>This step and the following one constitute the start of the backward pass of the algorithm, in which we compute the gradients $\delta_k^{(r)}, 1 \leq k \leq K, 1\le r\le R$ for all the hidden nodes.</p>
<p>c. Back-propagate the $\delta$s using the following equation to obtain the $\delta_j^{(r)}(m), 1 \leq r \leq R, 1 \leq j \leq P^r$  for each hidden node in the network.</p>
<p>$$
\delta_j^{(r)}(m) = f'(a_j^{(r)}(m)) \sum_k w_{kj}^{(r+1)} \delta_k^{(r+1)}(m), \quad 1 \leq r \leq R
$$</p>

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<p>d. Compute the gradients of the Cross Entropy Function $\mathcal L(m)$ for the $m$-th training vector $(X{(m)}, T{(m)})$ with respect to all the weight and bias parameters using the following equation.</p>
<p>$$
\frac{\partial\mathcal L(m)}{\partial w_{ij}^{(r+1)}} = \delta_i^{(r+1)}(m) z_j^{(r)}(m)
$$</p>
<p>and</p>
<p>$$
\frac{\partial \mathcal L(m)}{\partial b_i^{(r+1)}} = \delta_i^{(r+1)}(m), \quad 0 \leq r \leq R
$$</p>

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<p>e. Change the model weights according to the formula</p>
<p>$$
w_{ij}^{(r)} \leftarrow w_{ij}^{(r)} - \frac{\eta}{B}\sum_{m=qB}^{(q+1)B} \frac{\partial\mathcal L(m)}{\partial w_{ij}^{(r)}},  
$$</p>
<p>$$
b_i^{(r)} \leftarrow b_i^{(r)} - \frac{\eta}{B}\sum_{m=qB}^{(q+1)B} \frac{\partial{\mathcal L}(m)}{\partial b_i^{(r)}},
$$</p>
<p>f. Increment $q\leftarrow ((q+1)\mod B)$, and go back to step $(a)$.</p>

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<p>Compute the Loss Function $L$ over the Validation Dataset given by</p>
<p>$$
L = -{1\over V}\sum_{m=1}^V\sum_{k=1}^K t_k{(m)} \log y_k{(m)}
$$</p>
<p>If $L$ has dropped below some threshold, then stop. Otherwise go back to Step 2.</p>

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<h2 id="Gradient-Descent-Example">Gradient Descent Example<a class="anchor-link" href="#Gradient-Descent-Example">&#182;</a></h2><p><img src="images/Gradient_Descent_Scheme.png" alt=""></p>

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<div class="prompt input_prompt">In&nbsp;[30]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
    <span class="k">return</span> <span class="mi">3</span><span class="o">*</span><span class="n">x</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span><span class="mi">5</span><span class="o">*</span><span class="n">x</span> <span class="o">+</span> <span class="mi">10</span>

<span class="n">x</span> <span class="o">=</span> <span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
<span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="n">grid</span><span class="p">()</span>
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<div class="prompt input_prompt">In&nbsp;[35]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dx</span> <span class="o">=</span> <span class="mf">0.001</span>
<span class="n">eta</span> <span class="o">=</span> <span class="mf">0.05</span>  <span class="c1">#learning rate</span>
<span class="n">x</span> <span class="o">=</span> <span class="o">-</span><span class="mi">3</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">20</span><span class="p">):</span>
    <span class="n">df_dx</span> <span class="o">=</span> <span class="p">(</span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="o">+</span><span class="n">dx</span><span class="p">)</span><span class="o">-</span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">))</span><span class="o">/</span><span class="n">dx</span>
    <span class="n">x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">-</span> <span class="n">eta</span><span class="o">*</span><span class="n">df_dx</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">f</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
</pre></div>

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<pre>-1.8501500000001698 29.519915067502733
-1.0452550000002532 18.503949045077853
-0.4818285000003115 13.105618610239208
-0.08742995000019249 10.460081738472072
0.18864903499989083 9.163520200219716
0.3819043244999847 8.528031116715445
0.5171830271499616 8.216519714966186
0.6118781190049631 8.06379390252634
0.6781646833034642 7.988898596522943
0.7245652783124417 7.95215813604575
0.7570456948186948 7.934126078037087
0.7797819863730542 7.925269906950447
0.7956973904611502 7.920916059254301
0.8068381733227685 7.918772647178622
0.8146367213259147 7.917715356568335
0.8200957049281836 7.917192371084045
0.8239169934497053 7.916932669037079
0.8265918954147882 7.9168030076222955
0.8284643267903373 7.916737788340814
0.8297750287532573 7.916704651261121
</pre>
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<h2 id="Vanishing-Gradients">Vanishing Gradients<a class="anchor-link" href="#Vanishing-Gradients">&#182;</a></h2><ul>
<li><p>In large problems, gradients simply vanish too soon before training has reached an acceptable level of accuracy.</p>
</li>
<li><p>There are several issues with gradient descent that need handling, and to solve this, there are several fixes that may be applied.</p>
</li>
<li><p>Learning rate $\eta$ may be too large or too small.</p>
</li>
</ul>

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<p><img src="images/LearningRate_Matters.png" alt=""></p>

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<h2 id="Gradients-in-Multiple-Dimensions">Gradients in Multiple Dimensions<a class="anchor-link" href="#Gradients-in-Multiple-Dimensions">&#182;</a></h2><p><img src="images/GD_MultipleDimensions.png" alt=""></p>

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<h2 id="Saddle-Points">Saddle Points<a class="anchor-link" href="#Saddle-Points">&#182;</a></h2><p><img src="images/GD_saddle.png" height="100" width="400"></p>

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<h2 id="Effect-of-the-Learning-Rate">Effect of the Learning Rate<a class="anchor-link" href="#Effect-of-the-Learning-Rate">&#182;</a></h2><p>The idea is to start with a high learning rate and then adaptively reduce it as we get closer to the minimum of the loss function.</p>
<p><img src="images/LearningRateAnnealing.png" width="500"></p>

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<h2 id="Annealing">Annealing<a class="anchor-link" href="#Annealing">&#182;</a></h2><p>Adjust the learning rate as a step function when the reduction in the loss function begins to plateau.</p>
<p><img src="images/LearningRateAnnealing2.png" width="500"></p>

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<h2 id="Learning-Rate-Algorithms">Learning Rate Algorithms<a class="anchor-link" href="#Learning-Rate-Algorithms">&#182;</a></h2><ul>
<li><p>Improve the speed of convergence (for example the Momentum, Nesterov Momentum, and Adam algorithms).</p>
</li>
<li><p>Adapt the effective Learning Rate as the training progresses (for example the ADAGRAD, RMSPROP and Adam algorithms).</p>
</li>
</ul>

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<h2 id="Momentum">Momentum<a class="anchor-link" href="#Momentum">&#182;</a></h2><p>Fixes the problem where the gradient is multidimensional and has a fast gradient on some axes and a slow one on the others.</p>
<p>At the end of the $n^{th}$ iteration of the Backprop algorithm, define a sequence $v(n)$ by</p>
<p>$$
v(n) = \rho\; v(n-1) - \eta \; g(n), \quad \quad v(0)= -\eta g(0)
$$</p>
<p>where $\rho$ is new hyper-parameter called the "momentum" parameter, and $g(n)$ is the gradient evaluated at parameters value $w(n)$.</p>

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<p>$g(n)$ is defined by</p>
<p>$$
g(n) = \frac{\partial {\mathcal L(n)}}{\partial w}
$$</p>
<p>for Stochastic Gradient Descent and</p>
<p>$$
g(n) = {1\over B}\sum_{m=nB}^{(n+1)B}\frac{\partial {\mathcal L(m)}}{\partial w}
$$</p>
<p>for Batch Stochastic Gradient Descent (note that in this case $n$ is an index into the batch number).</p>

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<p>The change in parameter values on each iteration is now defined as</p>
<p>$$
w(n+1) = w(n) + v(n)  
$$</p>
<p>It can be shown from these equations that $v(n)$ can be written as</p>
<p>$$
v(n) = - \eta\sum_{i=0}^{n} \rho^{n-i} g(i)   
$$</p>
<p>so that</p>
<p>$$
w(n+1) = w(n) - \eta\sum_{i=0}^{n} \rho^{n-i} g(i)   
$$</p>

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<h2 id="Behavior-of-Momentum-Parameter">Behavior of Momentum Parameter<a class="anchor-link" href="#Behavior-of-Momentum-Parameter">&#182;</a></h2><p>When the momentum parameter $\rho = 0$, then this equation reduces to the usual Stochastic Gradient Descent iteration. On the other hand, when $\rho &gt; 0$, then we get some interesting behaviors:</p>
<ul>
<li>If the gradients $g(i)$ are such that they change sign frequently (as in the steep side of the loss surface), then the stepsize $\sum_{i=0}^n \rho^{n-i}g(i)$ will be small. Thus the change in these parameters with the number of iterations will limited.</li>
</ul>

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<li>If the gradients $g(i)$ are such that they maintain their sign (as in the shallow portion of the loss surface), then the stepsize $\sum_{i=0}^n \rho^{n-i}g(i)$ will be large. This means that if the gradients maintain their sign then the corresponding parameters will take bigger and bigger steps as the algorithm progresses, even though the individual gradients may be small.</li>
</ul>

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<h2 id="Properties-of-the-Momentum-algorithm">Properties of the Momentum algorithm<a class="anchor-link" href="#Properties-of-the-Momentum-algorithm">&#182;</a></h2><ul>
<li>The Momentum algorithm thus accelerates parameter convergence for parameters whose gradients consistently point in the same direction, and slows parameter change for parameters whose gradient changes sign frequently, thus resulting in faster convergence.</li>
<li>The variable $v(n)$ is analogous to velocity in a dynamical system, while the parameter $(1-\rho)$ plays the role of the co-efficient of friction. </li>
<li>The value of $\rho$ determines the degree of momentum, with the momentum becoming stronger as $\rho$ approaches $1$. </li>
</ul>

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<p>Note that
$$
\sum_{i=0}^{n} \rho^{n-i}g(i) \le {g_{max}\over 1-\rho}
$$
$\rho$ is usually set to the neighborhood of $0.9$ and from the above equation it follows that
$\sum_{i=0}^n \rho^{n-i}g(i)\approx 10g$ assuming all the $g(i)$ are approximately equal to $g$.  Hence the effective gradient is ten times the value of the actual gradient. This results in an "overshoot" where the value of the parameter shoots past the minimum point to the other side of the bowl, and then reverses itself. This is a desirable behavior since it prevents the algorithm from getting stuck at a saddle point or a local minima, since the momentum carries it out from these areas.</p>

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<h2 id="Nesterov-Momentum">Nesterov Momentum<a class="anchor-link" href="#Nesterov-Momentum">&#182;</a></h2><ol>
<li>Recall that the Momentum parameter update equations can be written as:
$$
v(n) = \rho\; v(n-1) - \eta \; g(w(n))
$$
$$
w(n+1) = w(n) + v(n) 
$$</li>
<li>These equations can be improved by evaluation of the gradient at parameter value $w(n+1)$ instead. </li>
</ol>

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<p><strong>Circular</strong>? in order to compute $w(n+1)$ we first need to compute $g(w(n))$. 
$$
w(n+1)\approx w(n) + \rho v(n-1) 
$$</p>
<ol>
<li>Gives the velocity update equation for Nesterov Momentum
$$
v(n) = \rho\; v(n-1) - \eta \; g(w(n)+\rho v(n-1))
$$
where $g(w(n)+\rho v(n-1))$ denotes the gradient computed at parameter values $w(n) + \rho v(n-1)$. </li>
<li>Gradient Descent process speeds up considerably when compared to the plain Momentum method.</li>
</ol>

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<h2 id="The-ADAGRAD-Algorithm">The ADAGRAD Algorithm<a class="anchor-link" href="#The-ADAGRAD-Algorithm">&#182;</a></h2><ol>
<li><p>Parameter update rule:
$$
w(n+1) = w(n) - \frac{\eta}{\sqrt{\sum_{i=1}^n g(n)^2+\epsilon}}\; g(n) 
$$</p>
</li>
<li><p>Constant $\epsilon$ has been added to better condition the denominator and is usually set to a small number such $10^{-7}$.</p>
</li>
</ol>

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<li><p>Each parameter gets its own adaptive Learning Rate, such that large gradients have smaller learning rates and small gradients have larger learning rates ($\eta$ is usually defaulted to $0.01$). As a result the progress along each dimension evens out over time, which helps the training process.</p>
</li>
<li><p>The change in rates happens automatically as part of the parameter update equation.</p>
</li>
<li><p>Downside: accumulation of gradients in the denominator leads to the continuous decrease in Learning Rates which can lead to a halt of training in large networks that require a greater number of iterations.</p>
</li>
</ol>

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<h2 id="The-RMSPROP-Algorithm">The RMSPROP Algorithm<a class="anchor-link" href="#The-RMSPROP-Algorithm">&#182;</a></h2><ol>
<li>Accumulates the sum of gradients using a sliding window:
$$
E[g^2]_n = \rho E[g^2]_{n-1} + (1-\rho) g(n)^2
$$
where $\rho$ is a decay constant (usually set to $0.9$). This operation (called a Low Pass Filter) has a windowing effect, since it forgets gradients that are far back in time. </li>
<li>The quantity $RMS[g]_n$ defined by
$$
RMS[g]_n = \sqrt{E[g^2]_n + \epsilon}
$$
\begin{equation}
w(n+1) = w(n) - \frac{\eta}{RMS[g]_n}\; g(n)  
\end{equation}</li>
</ol>

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<p>Note that
$$
E[g^2]_n = (1-\rho)\sum_{i=0}^n \rho^{n-i} g(i)^2 \le \frac{g_{max}}{1-\rho}
$$
which shows that the parameter $\rho$ prevents the sum from blowing up, and a large value of $\rho$ is equivalent to using a larger window of previous gradients in computing the sum.</p>

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<h2 id="The-ADAM-Algorithm">The ADAM Algorithm<a class="anchor-link" href="#The-ADAM-Algorithm">&#182;</a></h2><ol>
<li>The Adaptive Moment Estimation (Adam) algorithm combines the best of algorithms such as Momentum that speed up the training process, with algorithms such as RMSPROP that adaptively vary the effective Learning Rate.</li>
</ol>
<p>$$
\Lambda(n) = \beta\Lambda(n-1) +(1-\beta)g(n),\ \ \ {\hat\Gamma}(n) = \frac{\Gamma(n)}{1-\beta^n}
$$</p>
<p>$$
\Delta(n) = \alpha\Delta(n-1) + (1-\alpha) g(n)^2,\ \ \ {\hat\Delta}(n) = \frac{\Delta(n)}{1-\alpha^n}
$$</p>

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<p>$$
w(n+1) = w(n) - \eta\frac{\hat\Lambda(n)}{\sqrt{\hat\Delta(n) + \epsilon}}
$$</p>
<ol>
<li>$\Delta(n)$ is identical to that of $E[g^2]_n$ in the RMSPROP, and it serves an identical purpose, i.e., rates for parameters with larger gradients are equalized with those for parameters with smaller gradients.</li>
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<li>The sequence $\Lambda(n)$ is used to provide "Momentum" to the updates, and works in a fashion similar to the velocity sequence $v(n)$ in the Momentum algorithm.</li>
</ol>
<p>$$
\Lambda(n) = (1-\rho)\sum_{i=0}^n \rho^{n-i} g(i) \le \frac{g_{max}}{1-\rho}
$$</p>
<ol>
<li>The parameters $\alpha$ and $\beta$ are usually defaulted to $10^{-8}$ and $0.999$ respectively.</li>
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<h2 id="Back-to-Activation-Functions-(Vanishing-Gradients)">Back to Activation Functions (Vanishing Gradients)<a class="anchor-link" href="#Back-to-Activation-Functions-(Vanishing-Gradients)">&#182;</a></h2><p><img src="images/sigmoid_activation.png" width="700"></p>

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<h2 id="tanh-Activation">tanh Activation<a class="anchor-link" href="#tanh-Activation">&#182;</a></h2><p><img src="images/tanh_activation.png" width="500"></p>
<ol>
<li><p>Unless the input is in the neghborhood of zero, the function enters its saturated regime.</p>
</li>
<li><p>It is superior to the sigmoid in one respect, i.e., its output is zero centered. This  speeds up the training process.</p>
</li>
<li><p>The $\tanh$ function is rarely used in modern DLNs, the exception being a type DLN called LSTM.</p>
</li>
</ol>

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<h2 id="ReLU-Activation">ReLU Activation<a class="anchor-link" href="#ReLU-Activation">&#182;</a></h2><p><img src="images/relu_activation.png" width="400"></p>
<p>$$
z = \max(0,a)
$$</p>
<ol>
<li><p>No saturation problem.</p>
</li>
<li><p>Gradients $\frac{\partial L}{\partial w}$ propagate undiminished through the network, provided all the nodes are active.</p>
</li>
</ol>

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<h2 id="Dead-ReLU-Problem">Dead ReLU Problem<a class="anchor-link" href="#Dead-ReLU-Problem">&#182;</a></h2><p><img src="images/dead_relu.png" width="400" height="800"></p>
<ol>
<li><p>The dotted line in this figure shows a case in which the weight parameters $w_i$ are such that the hyperplane $\sum w_i z_i$ does not intersect the "data cloud" of possible input activations. There does not exist any possible input values that can lead to $\sum w_i z_i &gt; 0$. The neuron's output activation will always be zero, and it will kill all gradients backpropagating down from higher layers.</p>
</li>
<li><p>Vary initialization to correct this.</p>
</li>
</ol>

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<h2 id="Leaky-ReLU">Leaky ReLU<a class="anchor-link" href="#Leaky-ReLU">&#182;</a></h2><p><img src="images/leaky_relu.png" alt=""></p>
<p>$$
z = \max(ca,a),\ \ 0\le c&lt;1
$$</p>
<ul>
<li>Fixes the dead ReLU problem. </li>
</ul>

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<h2 id="PreLU">PreLU<a class="anchor-link" href="#PreLU">&#182;</a></h2><p><img src="images/prelu.png" width="500"></p>
<p>$$
z_i = \max(\beta_i a_i,a_i), \quad 1 \le i \le S
$$</p>
<ol>
<li>Instead of deciding on the value of $c$ through experimentation, why not determine it using Backpropagation as well. This is the idea behind the Pre-ReLU or PReLU function.</li>
</ol>

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<p>Note that each neuron $i$ now has its own parameter $\beta_i, 1\le i\le S$, where $S$ is the number of nodes in the network. These parameters are iteratively estimated using Backprop.</p>
<p>$$
\frac{\partial\mathcal L}{\partial\beta_i} = \frac{\partial\mathcal L}{\partial z_i}\frac{\partial z_i}{\partial\beta_i},\ \ 1\le i\le S
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<p>Substituting the value for $\frac{\partial z_i}{\partial\beta_i}$ we obtain
$$
\frac{\partial\mathcal L}{\partial\beta_i} = a_i\frac{\partial\mathcal L}{\partial z_i}\ \ if\ a_i \le 0\ \ \mbox{and} \ \ 0 \ \ \mbox{otherwise}
$$</p>
<p>which is then used to update $\beta_i$ using $\beta_i\rightarrow\beta_i - \eta\frac{\partial\mathcal L}{\partial\beta_i}$.</p>
<p>Once training is complete, the PreLU based DLN network ends up with a different value of $\beta_i$ at each neuron, which increases the flexibility of the network at the cost of an increase in the number of parameters.</p>

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<h2 id="Maxout">Maxout<a class="anchor-link" href="#Maxout">&#182;</a></h2><p><img src="images/maxout.png" width="400"></p>
<p>Generalizes Leaky ReLU.</p>
<p>$$
z'_i = \max(c\big[\sum_j w_{ij}z_j +b_i\big],\sum_j w_{ij}z_j +b_i),
$$</p>
<p>We may allow the two hyperplanes to be independent with their own set of parameters, as shown in the Figure above.</p>

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<h2 id="Initializing-Weights">Initializing Weights<a class="anchor-link" href="#Initializing-Weights">&#182;</a></h2><p>In practice, the DLN weight parameters are initialized with random values drawn from Gaussian or Uniform distributions and the following rules are used:</p>
<ul>
<li><p>Guassian Initialization: If the weight is between layers with $n_{in}$ input neurons and $n_{out}$ output neurons, then they are initialized using a Gaussian random distribution with mean zero and standard deviation $\sqrt{2\over n_{in}+n_{out}}$.</p>
</li>
<li><p>Uniform Initialization: In the same configuration as above, the weights should be initialized using an Uniform distribution between $-r$ and $r$, where $r = \sqrt{6\over n_{in}+n_{out}}$.</p>
</li>
</ul>

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<p>When using the ReLU or its variants, these rules have to be modified slightly:</p>
<ul>
<li><p>Guassian Initialization: If the weight is between layers with $n_{in}$ input neurons and $n_{out}$ output neurons, then they are initialized using a Gaussian random distribution with mean zero and standard deviation $\sqrt{4\over n_{in}+n_{out}}$.</p>
</li>
<li><p>Uniform Initialization: In the same configuration as above, the weights should be initialized using an Uniform distribution between $-r$ and $r$, where $r = \sqrt{12\over n_{in}+n_{out}}$.</p>
</li>
</ul>
<p>The reasoning behind scaling down the initialization values as the number of incident weights increases is to prevent saturation of the node activations during the forward pass of the Backprop algorithm, as well as large values of the gradients during backward pass.</p>

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<h2 id="Data-Preprocessing">Data Preprocessing<a class="anchor-link" href="#Data-Preprocessing">&#182;</a></h2><p><img src="images/data_preprocessing.png" width="600"></p>
<p><strong>Centering</strong>: This is also sometimes called Mean Subtraction, and is the most common form of preprocessing. Given an input dataset consisting of $M$ vectors $X(m) = (x_1(m),...,x_N(m)), m = 1,...,M$, it consists of subtracting the mean across each individual input component $x_i, 1\leq i\leq N$ such that
$$
x_i(m) \leftarrow x_i(m) - \frac{\sum_{s=1}^{M}x_i(s)}{M},\ \ 1\leq i\leq N, 1\le m\le M
$$</p>

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<p><strong>Scaling</strong>: After the data has been centered, it can be scaled in one of two ways:</p>
<ul>
<li>By dividing by the standard deviation, once again along each dimension, so that the overall transform is</li>
</ul>
<p>$$
x_i(m) \leftarrow \frac{x_i(m) - - \frac{\sum_{s=1}^{M}x_i(s)}{M}}{\sigma_i},\ \ 1\leq i\leq N, 1\le m\le M
$$</p>
<ul>
<li><p>By Normalizing each dimension so that the min and max along each axis are -1 and +1 repectively.</p>
</li>
<li><p>In general Scaling helps optimization because it balances out the rate at which the weights connected to the input nodes learn.</p>
</li>
</ul>

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<h2 id="Zero-Centering">Zero-Centering<a class="anchor-link" href="#Zero-Centering">&#182;</a></h2><p>Recall that for a K-ary Linear Classifier, the parameter update equation is given by:</p>
<p>$$
w_{kj} \leftarrow w_{kj} - \eta x_j(y_k-t_k),\ \ 1\le k\le K,\ \ 1\le j\le N
$$</p>
<p>If the training sample is such that $t_q = 1$ and $t_k = 0, j\ne q$, then the update becomes:</p>
<p>$$
w_{qj} \leftarrow w_{qj} - \eta x_j(y_q-1)
$$</p>
<p>and</p>
<p>$$ 
w_{kj} \leftarrow w_{kj} - \eta x_j(y_k),\ \ k\ne q
$$</p>

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<p>Lets assume that the input data is not centered so that $x_j\ge 0, j=1,...,N$. Since $0\le y_k\le 1$ it follows that</p>
<p>$$
\Delta w_{kj} = -\eta x_jy_k &lt;0, k\ne q
$$</p>
<p>and</p>
<p>$$
\Delta w_{qj} = -\eta x_j(y_q - 1) &gt; 0
$$</p>
<p>i.e. the update results in all the weights moving in the same direction, except for one. This is shown graphically in the Figure above, in which the system is trying move in the direction of the blue arrow which is the quickest path to the minimum. However if the input data is not centered, then it is forced to move in a zig-zag fashion as shown in the red-curve. The zig-zag motion is caused due to the fact that all the parameters move in the same direction at each step due to the lact of zero-centering in the input data.</p>

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<h2 id="Zero-Centering-Helps">Zero Centering Helps<a class="anchor-link" href="#Zero-Centering-Helps">&#182;</a></h2><p><img src="images/zero_centering_helps.png" width="500"></p>

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<h2 id="Batch-Normalization">Batch Normalization<a class="anchor-link" href="#Batch-Normalization">&#182;</a></h2><p>Normalization applied to the hidden layers.</p>
<p><img src="images/batch_normalization.png"></p>

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<ul>
<li><p>Higher learning rates: In a non-normalized network, a large learning rate can lead to oscillations and cause the loss function increase rather than decrease.</p>
</li>
<li><p>Better Gradient Propagation through the network, enabling DLNs with more hidden layers.</p>
</li>
<li><p>Reduces strong dependencies on the parameter initialization values.</p>
</li>
<li><p>Helps to regularize the model.</p>
</li>
</ul>

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<h2 id="Under-and-Over-fitting">Under and Over-fitting<a class="anchor-link" href="#Under-and-Over-fitting">&#182;</a></h2><p><img src="images/underoverfitting.png" alt=""></p>

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<h2 id="Regularization">Regularization<a class="anchor-link" href="#Regularization">&#182;</a></h2><ul>
<li><p>Early Stopping</p>
</li>
<li><p>L1 Regularization</p>
</li>
<li><p>L2 Regularization</p>
</li>
<li><p>Dropout Regularization</p>
</li>
<li><p>Training Data Augmentation</p>
</li>
<li><p>Batch Normalization</p>
</li>
</ul>

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<h2 id="Early-Stopping">Early Stopping<a class="anchor-link" href="#Early-Stopping">&#182;</a></h2><p><img src="images/early_stopping.png" width="700"></p>

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<h2 id="L2-Regularization">L2 Regularization<a class="anchor-link" href="#L2-Regularization">&#182;</a></h2><p>L2 Regularization is a commonly used technique in ML systems is also sometimes referred to as “Weight Decay”. It works by adding a quadratic term to the Cross Entropy Loss Function $\mathcal L$, called the Regularization Term, which results in a new Loss Function $\mathcal L_R$ given by:</p>
<p>\begin{equation}
\mathcal L_R = {\mathcal L} + \frac{\lambda}{2} \sum_{r=1}^{R+1} \sum_{j=1}^{P^{r-1}} \sum_{i=1}^{P^r} (w_{ij}^{(r)})^2  
\end{equation}</p>
<p>L2 Regularization also leads to more "diffuse" weight parameters, in other words, it encourages the network to use all its inputs a little rather than some of its inputs a lot.</p>

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<h2 id="L1-Regularization">L1 Regularization<a class="anchor-link" href="#L1-Regularization">&#182;</a></h2><p>L1 Regularization uses a Regularization Function which is the sum of the absolute value of all the weights in DLN, resulting in the following loss function ($\mathcal L$ is the usual Cross Entropy loss):</p>
<p>$$
\mathcal L_R = \mathcal L + {\lambda} \sum_{r=1}^{R+1} \sum_{j=1}^{P^{r-1}} \sum_{i=1}^{P^r} |w_{ij}^{(r)}|  
$$</p>
<p>At a high level L1 Regularization is similar to L2 Regularization since it leads to smaller weights.</p>

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<li><p>Both L1 and L2 Regularizations lead to a reduction in the weights with each iteration. However the way the weights drop is different:</p>
</li>
<li><p>In L2 Regularization the weight reduction is multiplicative and proportional to the value of the weight, so it is faster for large weights and de-accelerates as the weights get smaller.</p>
</li>
<li><p>In L1 Regularization on the other hand, the weights are reduced by a fixed amount in every iteration, irrespective of the value of the weight. Hence for larger weights L2 Regularization is faster than L1, while for smaller weights the reverse is true.</p>
</li>
<li><p>As a result L1 Regularization leads to DLNs in which the weight of most of the connections tends towards zero, with a few connections with larger weights left over. This type of DLN that results after the application of L1 Regularization is said to be “sparse”.</p>
</li>
</ol>

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<h2 id="Dropout-regularization">Dropout regularization<a class="anchor-link" href="#Dropout-regularization">&#182;</a></h2><p><img src="images/dropout.png" alt=""></p>

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<p>The basic idea behind Dropout is to run each iteration of the Backprop algorithm on randomly modified versions of the original DLN. The random modifications are carried out to the topology of the DLN using the following rules:</p>
<ul>
<li>Assign probability values $p^{(r)}, 0 \leq r \leq R$, which is defined as the probability that a node is present in the model, and use these to generate $\{0,1\}$-valued Bernoulli random variables $e_j^{(r)}$:</li>
</ul>
<p>$$
e_j^{(r)} \sim Bernoulli(p^{(r)}), \quad 0 \leq r \leq R,\ \  1 \leq j \leq P^r
$$</p>

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<li>Modify the input vector as follows:</li>
</ul>
<p>\begin{equation}
\hat x_j = e_j^{(0)} x_j, \quad 1 \leq j \leq N   
\end{equation}</p>
<ul>
<li>Modify the activations $z_j^{(r)}$ of the hidden layer r as follows:</li>
</ul>
<p>\begin{equation}
\hat z_j^{(r)} = e_j^{(r)} z_j^{(r)}, \quad 1 \leq r \leq R,\ \  1 \leq j \leq P^r   
\end{equation}</p>

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<li><p>After the Backprop is complete, we have effectively trained a collection of up to $2^s$ thinned DLNs all of which share the same weights, where $s$ is the total number of hidden nodes in the DLN.</p>
</li>
<li><p>In order to test the network, strictly speaking we should be averaging the results from all these thinned models, however a simple approximate averaging method works quite well.</p>
</li>
<li><p>The main idea is to use the complete DLN as the test network.</p>
</li>
</ol>

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<h2 id="Bagging-(Ensemble-Learning)">Bagging (Ensemble Learning)<a class="anchor-link" href="#Bagging-(Ensemble-Learning)">&#182;</a></h2><p><img src="images/bagging.png" width="500"></p>

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<p><img src="images/TensorFlow_playground.png" alt=""></p>
<p><a href="http://playground.tensorflow.org/">http://playground.tensorflow.org/</a></p>

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<h2 id="Pattern-Recognition:-Cancer">Pattern Recognition: Cancer<a class="anchor-link" href="#Pattern-Recognition:-Cancer">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Read in the data set</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/BreastCancer.csv&quot;</span><span class="p">)</span>
<span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Id</th>
      <th>Cl.thickness</th>
      <th>Cell.size</th>
      <th>Cell.shape</th>
      <th>Marg.adhesion</th>
      <th>Epith.c.size</th>
      <th>Bare.nuclei</th>
      <th>Bl.cromatin</th>
      <th>Normal.nucleoli</th>
      <th>Mitoses</th>
      <th>Class</th>
    </tr>
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    <tr>
      <th>0</th>
      <td>1000025</td>
      <td>5</td>
      <td>1</td>
      <td>1</td>
      <td>1</td>
      <td>2</td>
      <td>1</td>
      <td>3</td>
      <td>1</td>
      <td>1</td>
      <td>benign</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1002945</td>
      <td>5</td>
      <td>4</td>
      <td>4</td>
      <td>5</td>
      <td>7</td>
      <td>10</td>
      <td>3</td>
      <td>2</td>
      <td>1</td>
      <td>benign</td>
    </tr>
    <tr>
      <th>2</th>
      <td>1015425</td>
      <td>3</td>
      <td>1</td>
      <td>1</td>
      <td>1</td>
      <td>2</td>
      <td>2</td>
      <td>3</td>
      <td>1</td>
      <td>1</td>
      <td>benign</td>
    </tr>
    <tr>
      <th>3</th>
      <td>1016277</td>
      <td>6</td>
      <td>8</td>
      <td>8</td>
      <td>1</td>
      <td>3</td>
      <td>4</td>
      <td>3</td>
      <td>7</td>
      <td>1</td>
      <td>benign</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1017023</td>
      <td>4</td>
      <td>1</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>1</td>
      <td>3</td>
      <td>1</td>
      <td>1</td>
      <td>benign</td>
    </tr>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Convert the class variable into binary numeric</span>
<span class="n">ynum</span> <span class="o">=</span> <span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">x</span><span class="p">),</span><span class="mi">1</span><span class="p">))</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">y</span><span class="p">)):</span>
    <span class="k">if</span> <span class="n">y</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">==</span><span class="s2">&quot;malignant&quot;</span><span class="p">:</span>
        <span class="n">ynum</span><span class="p">[</span><span class="n">j</span><span class="p">]</span><span class="o">=</span><span class="mi">1</span>
<span class="n">ynum</span><span class="p">[:</span><span class="mi">10</span><span class="p">]</span>
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<pre>array([[0.],
       [0.],
       [0.],
       [0.],
       [0.],
       [1.],
       [0.],
       [0.],
       [0.],
       [0.]])</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Make label data have 1-shape, 1=malignant</span>
<span class="kn">from</span> <span class="nn">keras</span> <span class="k">import</span> <span class="n">utils</span>
<span class="n">y</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">ynum</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="c1">#x = x.as_matrix()</span>
<span class="nb">print</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">labels</span><span class="p">[:</span><span class="mi">10</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">labels</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">ynum</span><span class="p">))</span>
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<pre>/Users/srdas/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
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<pre>[[1. 0.]
 [1. 0.]
 [1. 0.]
 [1. 0.]
 [1. 0.]
 [0. 1.]
 [1. 0.]
 [1. 0.]
 [1. 0.]
 [1. 0.]]
(683, 9)
(683, 2)
(683, 1)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Define the neural net and compile it</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Activation</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">input_dim</span><span class="o">=</span><span class="mi">9</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span>
              <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span>
              <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Fit/train the model (x,y need to be matrices)</span>
<span class="n">model</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">ynum</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span><span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
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<pre>Epoch 1/25
 - 0s - loss: 0.6186 - acc: 0.5476
Epoch 2/25
 - 0s - loss: 0.4578 - acc: 0.8946
Epoch 3/25
 - 0s - loss: 0.3477 - acc: 0.9151
Epoch 4/25
 - 0s - loss: 0.2756 - acc: 0.9458
Epoch 5/25
 - 0s - loss: 0.2359 - acc: 0.9444
Epoch 6/25
 - 0s - loss: 0.2116 - acc: 0.9414
Epoch 7/25
 - 0s - loss: 0.1978 - acc: 0.9502
Epoch 8/25
 - 0s - loss: 0.1680 - acc: 0.9561
Epoch 9/25
 - 0s - loss: 0.1535 - acc: 0.9649
Epoch 10/25
 - 0s - loss: 0.1389 - acc: 0.9649
Epoch 11/25
 - 0s - loss: 0.1416 - acc: 0.9561
Epoch 12/25
 - 0s - loss: 0.1184 - acc: 0.9649
Epoch 13/25
 - 0s - loss: 0.1204 - acc: 0.9649
Epoch 14/25
 - 0s - loss: 0.1076 - acc: 0.9678
Epoch 15/25
 - 0s - loss: 0.0929 - acc: 0.9678
Epoch 16/25
 - 0s - loss: 0.0966 - acc: 0.9707
Epoch 17/25
 - 0s - loss: 0.0902 - acc: 0.9736
Epoch 18/25
 - 0s - loss: 0.0809 - acc: 0.9722
Epoch 19/25
 - 0s - loss: 0.0824 - acc: 0.9766
Epoch 20/25
 - 0s - loss: 0.0741 - acc: 0.9766
Epoch 21/25
 - 0s - loss: 0.0773 - acc: 0.9751
Epoch 22/25
 - 0s - loss: 0.0657 - acc: 0.9839
Epoch 23/25
 - 0s - loss: 0.0671 - acc: 0.9810
Epoch 24/25
 - 0s - loss: 0.0647 - acc: 0.9780
Epoch 25/25
 - 0s - loss: 0.0635 - acc: 0.9795
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<pre>&lt;keras.callbacks.History at 0x1134a5400&gt;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Accuracy</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict_classes</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">)</span>
<span class="n">acc</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">yhat</span><span class="o">==</span><span class="n">ynum</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy = &quot;</span><span class="p">,</span><span class="n">acc</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">ynum</span><span class="p">))</span>

<span class="c1">## Confusion matrix</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
<span class="n">confusion_matrix</span><span class="p">(</span><span class="n">yhat</span><span class="p">,</span><span class="n">ynum</span><span class="p">)</span>
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<pre>Accuracy =  0.9853587115666179
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<pre>array([[434,   0],
       [ 10, 239]])</pre>
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<h1 id="Another-Canonical-Example:--Digit-Recognition-(MNIST)">Another Canonical Example:  Digit Recognition (MNIST)<a class="anchor-link" href="#Another-Canonical-Example:--Digit-Recognition-(MNIST)">&#182;</a></h1><ul>
<li>Extensible to many finance prediction problems. </li>
<li>Information set: 784 (28 x 28) pixels for category prediction. </li>
<li>Would you run a multinomial regression on these 784 columns. </li>
</ul>
<p><img src="images/MNIST.png" width="400"></p>
<p><a href="https://www.cs.toronto.edu/~kriz/cifar.html">https://www.cs.toronto.edu/~kriz/cifar.html</a></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Read in the data set</span>
<span class="n">train</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/train.csv&quot;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;data/test.csv&quot;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">train</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">test</span><span class="p">))</span>
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<pre>(60000, 785)
(10000, 785)
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Reformat the data</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">train</span><span class="o">.</span><span class="n">as_matrix</span><span class="p">()[:,</span><span class="mi">0</span><span class="p">:</span><span class="mi">784</span><span class="p">]</span>
<span class="n">Y_train</span> <span class="o">=</span> <span class="n">train</span><span class="o">.</span><span class="n">as_matrix</span><span class="p">()[:,</span><span class="mi">784</span><span class="p">:</span><span class="mi">785</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">X_train</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">Y_train</span><span class="p">))</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">test</span><span class="o">.</span><span class="n">as_matrix</span><span class="p">()[:,</span><span class="mi">0</span><span class="p">:</span><span class="mi">784</span><span class="p">]</span>
<span class="n">Y_test</span> <span class="o">=</span> <span class="n">test</span><span class="o">.</span><span class="n">as_matrix</span><span class="p">()[:,</span><span class="mi">784</span><span class="p">:</span><span class="mi">785</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">X_test</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">Y_test</span><span class="p">))</span>
<span class="n">y</span><span class="o">.</span><span class="n">labels</span> <span class="o">=</span> <span class="n">utils</span><span class="o">.</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">labels</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">labels</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">,:])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">Y_train</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">5</span><span class="p">])</span>
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<pre>(60000, 784)
(60000, 1)
(10000, 784)
(10000, 1)
(60000, 10)
[[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
 [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]]
[[3]
 [0]
 [0]
 [2]]
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<pre>/Users/srdas/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  
/Users/srdas/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  This is separate from the ipykernel package so we can avoid doing imports until
/Users/srdas/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:6: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  
/Users/srdas/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  import sys
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">hist</span><span class="p">(</span><span class="n">Y_train</span><span class="p">);</span> <span class="n">grid</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Define the neural net and compile it</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">Dropout</span>
<span class="kn">from</span> <span class="nn">keras.optimizers</span> <span class="k">import</span> <span class="n">SGD</span>

<span class="n">data_dim</span> <span class="o">=</span> <span class="n">shape</span><span class="p">(</span><span class="n">X_train</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">Sequential</span><span class="p">([</span>
    <span class="n">Dense</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">784</span><span class="p">,)),</span>
    <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">),</span>
    <span class="n">Dense</span><span class="p">(</span><span class="mi">100</span><span class="p">),</span>
    <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">),</span>
    <span class="n">Dense</span><span class="p">(</span><span class="mi">100</span><span class="p">),</span>
    <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">),</span>
    <span class="n">Dense</span><span class="p">(</span><span class="mi">100</span><span class="p">),</span>
    <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">),</span>
    <span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
    <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;softmax&#39;</span><span class="p">),</span>
<span class="p">])</span>

<span class="c1">#model = Sequential()</span>
<span class="c1">#model.add(Dense(100, activation=&#39;sigmoid&#39;, input_dim=data_dim))</span>
<span class="c1">#model.add(Dropout(0.25))</span>
<span class="c1">#model.add(Dense(100, activation=&#39;sigmoid&#39;))</span>
<span class="c1">#model.add(Dropout(0.25))</span>
<span class="c1">#model.add(Dense(100, activation=&#39;sigmoid&#39;))</span>
<span class="c1">#model.add(Dropout(0.25))</span>
<span class="c1">#model.add(Dense(100, activation=&#39;sigmoid&#39;))</span>
<span class="c1">#model.add(Dropout(0.25))</span>
<span class="c1">#model.add(Dense(10, activation=&#39;softmax&#39;))</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span>
              <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;categorical_crossentropy&#39;</span><span class="p">,</span>
              <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Fit/train the model (x,y need to be matrices)</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span><span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
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<pre>Epoch 1/10
 - 7s - loss: 0.7339 - acc: 0.7693
Epoch 2/10
 - 7s - loss: 0.3392 - acc: 0.9016
Epoch 3/10
 - 8s - loss: 0.2837 - acc: 0.9159
Epoch 4/10
 - 9s - loss: 0.2514 - acc: 0.9253
Epoch 5/10
 - 10s - loss: 0.2290 - acc: 0.9307
Epoch 6/10
 - 9s - loss: 0.2204 - acc: 0.9336
Epoch 7/10
 - 10s - loss: 0.2124 - acc: 0.9357
Epoch 8/10
 - 9s - loss: 0.2053 - acc: 0.9384
Epoch 9/10
 - 9s - loss: 0.1949 - acc: 0.9414
Epoch 10/10
 - 9s - loss: 0.1930 - acc: 0.9415
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<pre>&lt;keras.callbacks.History at 0x1151adcf8&gt;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## In Sample</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict_classes</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">)</span>

<span class="c1">## Confusion matrix</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">yhat</span><span class="p">,</span><span class="n">Y_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">cm</span><span class="p">)</span>

<span class="c1">##</span>
<span class="n">acc</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">diag</span><span class="p">(</span><span class="n">cm</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">Y_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy = &quot;</span><span class="p">,</span><span class="n">acc</span><span class="p">)</span>
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[[5804    0   64   27    9   67   57   13   30   21]
 [   4 6577   21   19   17    8    8   36   77   26]
 [  11   42 5593   97   24   11   13   58   39   11]
 [   9   18   37 5539    0   82    0   32   79   43]
 [  11    6   54    3 5520   12   34   23   19  140]
 [  18   30   12  234    9 5063   56   14   96   49]
 [  26    6   72   15   44   68 5722    1   34    1]
 [   0   12   33   44   10   12    0 5995    7   99]
 [  34   48   62  119   22   74   28   14 5389   53]
 [   6    3   10   34  187   24    0   79   81 5506]]
Accuracy =  0.9451333333333334
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Out of Sample</span>
<span class="n">yhat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict_classes</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">)</span>

<span class="c1">## Confusion matrix</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="k">import</span> <span class="n">confusion_matrix</span>
<span class="n">cm</span> <span class="o">=</span> <span class="n">confusion_matrix</span><span class="p">(</span><span class="n">yhat</span><span class="p">,</span><span class="n">Y_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">cm</span><span class="p">)</span>

<span class="c1">##</span>
<span class="n">acc</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">diag</span><span class="p">(</span><span class="n">cm</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">Y_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Accuracy = &quot;</span><span class="p">,</span><span class="n">acc</span><span class="p">)</span>
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[[ 963    0   16    2    1   12   13    2    9    6]
 [   0 1114    2    0    3    3    1   14    8    5]
 [   1    5  959   15    5    2    5   14    4    0]
 [   1    2    9  922    0   15    0    9    9    8]
 [   0    0    8    0  929    2    4    2    6   27]
 [   3    3    2   37    1  830   14    1   16    8]
 [   7    4   15    2   13    8  914    0    9    0]
 [   1    0    6    9    2    0    0  962    3   11]
 [   4    7   13   21    2   16    7    3  897   14]
 [   0    0    2    2   26    4    0   21   13  930]]
Accuracy =  0.942
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<h1 id="Learning-the-Black-Scholes-Equation">Learning the Black-Scholes Equation<a class="anchor-link" href="#Learning-the-Black-Scholes-Equation">&#182;</a></h1><p>See : Hutchinson, Lo, Poggio (1994)</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="k">import</span> <span class="n">norm</span>
<span class="k">def</span> <span class="nf">BSM</span><span class="p">(</span><span class="n">S</span><span class="p">,</span><span class="n">K</span><span class="p">,</span><span class="n">T</span><span class="p">,</span><span class="n">sig</span><span class="p">,</span><span class="n">rf</span><span class="p">,</span><span class="n">dv</span><span class="p">,</span><span class="n">cp</span><span class="p">):</span>  <span class="c1">#cp = {+1.0 (calls), -1.0 (puts)}</span>
    <span class="n">d1</span> <span class="o">=</span> <span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">S</span><span class="o">/</span><span class="n">K</span><span class="p">)</span><span class="o">+</span><span class="p">(</span><span class="n">rf</span><span class="o">-</span><span class="n">dv</span><span class="o">+</span><span class="mf">0.5</span><span class="o">*</span><span class="n">sig</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">*</span><span class="n">T</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">sig</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">T</span><span class="p">))</span>
    <span class="n">d2</span> <span class="o">=</span> <span class="n">d1</span> <span class="o">-</span> <span class="n">sig</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">T</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">cp</span><span class="o">*</span><span class="n">S</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">dv</span><span class="o">*</span><span class="n">T</span><span class="p">)</span><span class="o">*</span><span class="n">norm</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">d1</span><span class="o">*</span><span class="n">cp</span><span class="p">)</span> <span class="o">-</span> <span class="n">cp</span><span class="o">*</span><span class="n">K</span><span class="o">*</span><span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">rf</span><span class="o">*</span><span class="n">T</span><span class="p">)</span><span class="o">*</span><span class="n">norm</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">d2</span><span class="o">*</span><span class="n">cp</span><span class="p">)</span>

<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;/Users/srdas/GoogleDrive/Papers/DeepLearning/DLinFinance/BlackScholesNN/training.csv&#39;</span><span class="p">)</span>
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<h1 id="Normalizing-spot-and-call-prices">Normalizing spot and call prices<a class="anchor-link" href="#Normalizing-spot-and-call-prices">&#182;</a></h1><p>$C$ is homogeneous degree one, so
$$ aC(S,K) = C(aS,aK) $$
This means we can normalize spot and call prices and remove  a variable by dividing by $K$.
$$ \frac{C(S,K)}{K} = C(S/K,1) $$</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Stock Price&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Stock Price&#39;</span><span class="p">]</span><span class="o">/</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Strike Price&#39;</span><span class="p">]</span>
<span class="n">df</span><span class="p">[</span><span class="s1">&#39;Call Price&#39;</span><span class="p">]</span>  <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;Call Price&#39;</span><span class="p">]</span> <span class="o">/</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;Strike Price&#39;</span><span class="p">]</span>
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<h1 id="Data,-libraries,-activation-functions">Data, libraries, activation functions<a class="anchor-link" href="#Data,-libraries,-activation-functions">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">300000</span>
<span class="n">n_train</span> <span class="o">=</span>  <span class="p">(</span><span class="nb">int</span><span class="p">)(</span><span class="mf">0.8</span> <span class="o">*</span> <span class="n">n</span><span class="p">)</span>
<span class="n">train</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n_train</span><span class="p">]</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[[</span><span class="s1">&#39;Stock Price&#39;</span><span class="p">,</span> <span class="s1">&#39;Maturity&#39;</span><span class="p">,</span> <span class="s1">&#39;Dividends&#39;</span><span class="p">,</span> <span class="s1">&#39;Volatility&#39;</span><span class="p">,</span> <span class="s1">&#39;Risk-free&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">values</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;Call Price&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">n_train</span><span class="o">+</span><span class="mi">1</span><span class="p">:</span><span class="n">n</span><span class="p">]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">test</span><span class="p">[[</span><span class="s1">&#39;Stock Price&#39;</span><span class="p">,</span> <span class="s1">&#39;Maturity&#39;</span><span class="p">,</span> <span class="s1">&#39;Dividends&#39;</span><span class="p">,</span> <span class="s1">&#39;Volatility&#39;</span><span class="p">,</span> <span class="s1">&#39;Risk-free&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">values</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">test</span><span class="p">[</span><span class="s1">&#39;Call Price&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Import libraries</span>
<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">LeakyReLU</span>
<span class="kn">from</span> <span class="nn">keras</span> <span class="k">import</span> <span class="n">backend</span>

<span class="k">def</span> <span class="nf">custom_activation</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">backend</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
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<pre>/Users/srdas/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
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<h1 id="Set-up,-compile-and-fit-the-model">Set up, compile and fit the model<a class="anchor-link" href="#Set-up,-compile-and-fit-the-model">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">nodes</span> <span class="o">=</span> <span class="mi">120</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>

<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">nodes</span><span class="p">,</span> <span class="n">input_dim</span><span class="o">=</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
<span class="c1">#model.add(&quot;relu&quot;)</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>

<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">nodes</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;elu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>

<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">nodes</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>

<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">nodes</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;elu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>

<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</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">add</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="n">custom_activation</span><span class="p">))</span>
          
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;mse&#39;</span><span class="p">,</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">)</span>

<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">validation_split</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
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<pre>Train on 216000 samples, validate on 24000 samples
Epoch 1/10
 - 11s - loss: 0.0056 - val_loss: 2.9596e-04
Epoch 2/10
 - 11s - loss: 0.0015 - val_loss: 4.5161e-04
Epoch 3/10
 - 10s - loss: 0.0011 - val_loss: 1.7058e-04
Epoch 4/10
 - 10s - loss: 9.3346e-04 - val_loss: 5.5905e-04
Epoch 5/10
 - 10s - loss: 8.1423e-04 - val_loss: 4.7955e-04
Epoch 6/10
 - 10s - loss: 7.3808e-04 - val_loss: 2.6507e-04
Epoch 7/10
 - 10s - loss: 6.8510e-04 - val_loss: 2.4959e-04
Epoch 8/10
 - 10s - loss: 6.5137e-04 - val_loss: 1.3397e-04
Epoch 9/10
 - 10s - loss: 6.3357e-04 - val_loss: 9.6116e-05
Epoch 10/10
 - 10s - loss: 6.0952e-04 - val_loss: 2.4954e-04
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<pre>&lt;keras.callbacks.History at 0x1a31a39e48&gt;</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">CheckAccuracy</span><span class="p">(</span><span class="n">y</span><span class="p">,</span><span class="n">y_hat</span><span class="p">):</span>
    <span class="n">stats</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
    
    <span class="n">stats</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">y</span> <span class="o">-</span> <span class="n">y_hat</span>
    
    <span class="n">stats</span><span class="p">[</span><span class="s1">&#39;mse&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mean</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">]</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean Squared Error:      &quot;</span><span class="p">,</span> <span class="n">stats</span><span class="p">[</span><span class="s1">&#39;mse&#39;</span><span class="p">])</span>
    
    <span class="n">stats</span><span class="p">[</span><span class="s1">&#39;rmse&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="s1">&#39;mse&#39;</span><span class="p">])</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Root Mean Squared Error: &quot;</span><span class="p">,</span> <span class="n">stats</span><span class="p">[</span><span class="s1">&#39;rmse&#39;</span><span class="p">])</span>
    
    <span class="n">stats</span><span class="p">[</span><span class="s1">&#39;mae&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mean</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">]))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean Absolute Error:     &quot;</span><span class="p">,</span> <span class="n">stats</span><span class="p">[</span><span class="s1">&#39;mae&#39;</span><span class="p">])</span>
    
    <span class="n">stats</span><span class="p">[</span><span class="s1">&#39;mpe&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="s1">&#39;mse&#39;</span><span class="p">])</span><span class="o">/</span><span class="n">mean</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean Percent Error:      &quot;</span><span class="p">,</span> <span class="n">stats</span><span class="p">[</span><span class="s1">&#39;mpe&#39;</span><span class="p">])</span>
    
    <span class="c1">#plots</span>
    <span class="n">mpl</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s1">&#39;agg.path.chunksize&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100000</span>
    <span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_hat</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Actual Price&#39;</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span><span class="n">fontname</span><span class="o">=</span><span class="s1">&#39;Times New Roman&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Predicted Price&#39;</span><span class="p">,</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span><span class="n">fontname</span><span class="o">=</span><span class="s1">&#39;Times New Roman&#39;</span><span class="p">)</span> 
    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
    
    <span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;white&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Diff&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Density&#39;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
    
    <span class="k">return</span> <span class="n">stats</span>
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<h1 id="Predict-and-check-accuracy-(in-sample)">Predict and check accuracy (in-sample)<a class="anchor-link" href="#Predict-and-check-accuracy-(in-sample)">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y_train_hat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="c1">#reduce dim (240000,1) -&gt; (240000,) to match y_train&#39;s dim</span>
<span class="n">y_train_hat</span> <span class="o">=</span> <span class="n">squeeze</span><span class="p">(</span><span class="n">y_train_hat</span><span class="p">)</span>
<span class="n">CheckAccuracy</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_train_hat</span><span class="p">)</span>
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<pre>Mean Squared Error:       0.0002501892926358584
Root Mean Squared Error:  0.015817373126908854
Mean Absolute Error:      0.011584855780651939
Mean Percent Error:       0.05912880061083275
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<pre>{&#39;diff&#39;: array([-0.00627303, -0.01567328, -0.00412639, ..., -0.00694811,
        -0.01056686, -0.00361252]),
 &#39;mse&#39;: 0.0002501892926358584,
 &#39;rmse&#39;: 0.015817373126908854,
 &#39;mae&#39;: 0.011584855780651939,
 &#39;mpe&#39;: 0.05912880061083275}</pre>
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<h1 id="Predict-and-check-accuracy-(validation-sample)">Predict and check accuracy (validation-sample)<a class="anchor-link" href="#Predict-and-check-accuracy-(validation-sample)">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">y_test_hat</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="n">y_test_hat</span> <span class="o">=</span> <span class="n">squeeze</span><span class="p">(</span><span class="n">y_test_hat</span><span class="p">)</span>
<span class="n">test_stats</span> <span class="o">=</span> <span class="n">CheckAccuracy</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_hat</span><span class="p">)</span>
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<pre>Mean Squared Error:       0.0002495010473928844
Root Mean Squared Error:  0.01579560215353895
Mean Absolute Error:      0.011570688743185188
Mean Percent Error:       0.05913218242518501
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<h1 id="Random-Forest-of-decision-trees">Random Forest of decision trees<a class="anchor-link" href="#Random-Forest-of-decision-trees">&#182;</a></h1><p>A Random Forest uses several decision trees to make hypotheses about regions within subsamples of the data, then makes predictions based on the majority vote of these trees. This safeguards against overfitting/memorization of the training data.</p>

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<h1 id="Prepare-Data">Prepare Data<a class="anchor-link" href="#Prepare-Data">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">300000</span>
<span class="n">n_train</span> <span class="o">=</span>  <span class="p">(</span><span class="nb">int</span><span class="p">)(</span><span class="mf">0.8</span> <span class="o">*</span> <span class="n">n</span><span class="p">)</span>
<span class="n">train</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n_train</span><span class="p">]</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[[</span><span class="s1">&#39;Stock Price&#39;</span><span class="p">,</span> <span class="s1">&#39;Maturity&#39;</span><span class="p">,</span> <span class="s1">&#39;Dividends&#39;</span><span class="p">,</span> <span class="s1">&#39;Volatility&#39;</span><span class="p">,</span> <span class="s1">&#39;Risk-free&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">values</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;Call Price&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">test</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">n_train</span><span class="o">+</span><span class="mi">1</span><span class="p">:</span><span class="n">n</span><span class="p">]</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">test</span><span class="p">[[</span><span class="s1">&#39;Stock Price&#39;</span><span class="p">,</span> <span class="s1">&#39;Maturity&#39;</span><span class="p">,</span> <span class="s1">&#39;Dividends&#39;</span><span class="p">,</span> <span class="s1">&#39;Volatility&#39;</span><span class="p">,</span> <span class="s1">&#39;Risk-free&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">values</span>
<span class="n">y_test</span> <span class="o">=</span> <span class="n">test</span><span class="p">[</span><span class="s1">&#39;Call Price&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
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<h1 id="Fit-Random-Forest">Fit Random Forest<a class="anchor-link" href="#Fit-Random-Forest">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="k">import</span> <span class="n">RandomForestRegressor</span>

<span class="n">forest</span> <span class="o">=</span> <span class="n">RandomForestRegressor</span><span class="p">()</span>
<span class="n">forest</span> <span class="o">=</span> <span class="n">forest</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">y_test_hat</span> <span class="o">=</span> <span class="n">forest</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">stats</span> <span class="o">=</span> <span class="n">CheckAccuracy</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_hat</span><span class="p">)</span>
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<pre>Mean Squared Error:       5.1224296628746105e-05
Root Mean Squared Error:  0.007157115105176534
Mean Absolute Error:      0.0052494561647393806
Mean Percent Error:       0.026793270172516082
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<h2 id="Stock-Market-Efficiency">Stock Market Efficiency<a class="anchor-link" href="#Stock-Market-Efficiency">&#182;</a></h2><p><img src="images/Numerai.png" width="1000"></p>

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<h1 id="Predicting-the-Direction-of-the-S&amp;P500-Index">Predicting the Direction of the S&amp;P500 Index<a class="anchor-link" href="#Predicting-the-Direction-of-the-S&amp;P500-Index">&#182;</a></h1><p>Using daily data from 1963</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Predicting the Direction of the S&amp;P500 Index</span>

<span class="c1">#Using daily data from 1963</span>

<span class="c1">## Read in the data into a dataframe</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;/Users/srdas/GoogleDrive/Papers/DeepLearning/DLinFinance/SP_Data_shared/mer_df_percentile.csv&quot;</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[[</span><span class="mi">0</span><span class="p">]],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">df</span><span class="p">))</span>
<span class="c1">#print(df.columns)</span>
<span class="c1">#df.head()</span>

<span class="c1">## Add a column for the sign of the SPX return: 1 is positive, 0 is negative</span>
<span class="n">df</span><span class="p">[</span><span class="s2">&quot;Sign&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">maximum</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">sign</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;SPX&quot;</span><span class="p">]))</span>
<span class="c1">#df.head(20)</span>
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<pre>(13532, 21)
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<h1 id="S&amp;P-500-Returns">S&amp;P 500 Returns<a class="anchor-link" href="#S&amp;P-500-Returns">&#182;</a></h1><p><img src="images/SP_returns_hist.png" width="1000"></p>

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<h1 id="Data,-lookback,-lookforward">Data, lookback, lookforward<a class="anchor-link" href="#Data,-lookback,-lookforward">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">n</span> <span class="o">=</span> <span class="mi">30</span>               <span class="c1">#Lookback period</span>
<span class="n">nfwd</span> <span class="o">=</span> <span class="mi">30</span>            <span class="c1">#Look ahead forecast period for validation</span>
<span class="n">train_size</span> <span class="o">=</span> <span class="mi">10000</span>   <span class="c1">#Training size</span>

<span class="n">m</span> <span class="o">=</span> <span class="n">shape</span><span class="p">(</span><span class="n">df</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>  
<span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="c1">#Number of rows</span>

<span class="n">df2</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">n</span><span class="p">:</span><span class="n">m</span><span class="p">][</span><span class="n">df</span><span class="o">.</span><span class="n">columns</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="mi">21</span><span class="p">]]]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">df2</span><span class="p">))</span>
<span class="n">df2</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre>13532
(13502, 3)
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>date</th>
      <th>SPX</th>
      <th>Sign</th>
    </tr>
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    <tr>
      <th>30</th>
      <td>1963-05-14</td>
      <td>-0.003831</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>31</th>
      <td>1963-05-15</td>
      <td>0.003133</td>
      <td>1.0</td>
    </tr>
    <tr>
      <th>32</th>
      <td>1963-05-16</td>
      <td>-0.002556</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>33</th>
      <td>1963-05-17</td>
      <td>0.000569</td>
      <td>1.0</td>
    </tr>
    <tr>
      <th>34</th>
      <td>1963-05-20</td>
      <td>-0.004695</td>
      <td>0.0</td>
    </tr>
  </tbody>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#Special processing</span>
<span class="c1">## Reset the index so than pandas does not merge on index and just cbinds</span>
<span class="n">df2</span> <span class="o">=</span> <span class="n">df2</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1">#df2.head()</span>

<span class="c1">## Add n lagged columns of data, ncols = 3 + 19*n</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="p">(</span><span class="n">arange</span><span class="p">(</span><span class="n">n</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
    <span class="n">tempdf</span> <span class="o">=</span> <span class="n">df</span><span class="p">[(</span><span class="n">n</span><span class="o">-</span><span class="n">j</span><span class="p">):(</span><span class="n">m</span><span class="o">-</span><span class="n">j</span><span class="p">)][</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">arange</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">21</span><span class="p">)]]</span>
    <span class="n">tempdf</span> <span class="o">=</span> <span class="n">tempdf</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    <span class="n">df2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df2</span><span class="p">,</span><span class="n">tempdf</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">df2</span><span class="p">))</span>
<span class="c1">#df2.head()</span>

<span class="n">df2</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
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<pre>(13502, 573)
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>SPX</th>
      <th>Sign</th>
      <th>percentile_1</th>
      <th>percentile_2</th>
      <th>percentile_3</th>
      <th>percentile_5</th>
      <th>percentile_10</th>
      <th>percentile_15</th>
      <th>percentile_20</th>
      <th>percentile_30</th>
      <th>...</th>
      <th>percentile_50</th>
      <th>percentile_60</th>
      <th>percentile_70</th>
      <th>percentile_80</th>
      <th>percentile_85</th>
      <th>percentile_90</th>
      <th>percentile_95</th>
      <th>percentile_97</th>
      <th>percentile_98</th>
      <th>percentile_99</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>...</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
      <td>13502.000000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>0.000308</td>
      <td>0.527033</td>
      <td>0.002375</td>
      <td>0.003322</td>
      <td>0.000916</td>
      <td>0.000440</td>
      <td>0.002928</td>
      <td>0.002413</td>
      <td>0.002867</td>
      <td>0.001472</td>
      <td>...</td>
      <td>0.001736</td>
      <td>0.002185</td>
      <td>0.002363</td>
      <td>0.002648</td>
      <td>0.001851</td>
      <td>0.002156</td>
      <td>0.001007</td>
      <td>0.002069</td>
      <td>0.001233</td>
      <td>0.000561</td>
    </tr>
    <tr>
      <th>std</th>
      <td>0.010157</td>
      <td>0.499287</td>
      <td>0.034434</td>
      <td>0.037810</td>
      <td>0.039635</td>
      <td>0.044151</td>
      <td>0.048008</td>
      <td>0.038260</td>
      <td>0.039222</td>
      <td>0.037326</td>
      <td>...</td>
      <td>0.049407</td>
      <td>0.050472</td>
      <td>0.045221</td>
      <td>0.051415</td>
      <td>0.061166</td>
      <td>0.036148</td>
      <td>0.033513</td>
      <td>0.052842</td>
      <td>0.031063</td>
      <td>0.031789</td>
    </tr>
    <tr>
      <th>min</th>
      <td>-0.204669</td>
      <td>0.000000</td>
      <td>-0.193878</td>
      <td>-0.126913</td>
      <td>-0.233936</td>
      <td>-0.182815</td>
      <td>-0.181581</td>
      <td>-0.200000</td>
      <td>-0.134432</td>
      <td>-0.111463</td>
      <td>...</td>
      <td>-0.186989</td>
      <td>-0.162278</td>
      <td>-0.159382</td>
      <td>-0.228833</td>
      <td>-0.274992</td>
      <td>-0.140986</td>
      <td>-0.124532</td>
      <td>-0.289610</td>
      <td>-0.185077</td>
      <td>-0.155372</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>-0.004395</td>
      <td>0.000000</td>
      <td>-0.017857</td>
      <td>-0.022085</td>
      <td>-0.025000</td>
      <td>-0.026667</td>
      <td>-0.024889</td>
      <td>-0.020525</td>
      <td>-0.021739</td>
      <td>-0.020655</td>
      <td>...</td>
      <td>-0.025000</td>
      <td>-0.027027</td>
      <td>-0.024390</td>
      <td>-0.029703</td>
      <td>-0.035928</td>
      <td>-0.020377</td>
      <td>-0.020322</td>
      <td>-0.024908</td>
      <td>-0.017685</td>
      <td>-0.017765</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>0.000416</td>
      <td>1.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>...</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000154</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000283</td>
      <td>0.000132</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>0.005108</td>
      <td>1.000000</td>
      <td>0.019655</td>
      <td>0.024072</td>
      <td>0.023563</td>
      <td>0.025028</td>
      <td>0.027521</td>
      <td>0.021975</td>
      <td>0.024221</td>
      <td>0.021739</td>
      <td>...</td>
      <td>0.026447</td>
      <td>0.029586</td>
      <td>0.027258</td>
      <td>0.031440</td>
      <td>0.035713</td>
      <td>0.022703</td>
      <td>0.021154</td>
      <td>0.027531</td>
      <td>0.019135</td>
      <td>0.018117</td>
    </tr>
    <tr>
      <th>max</th>
      <td>0.115800</td>
      <td>1.000000</td>
      <td>0.259489</td>
      <td>0.166667</td>
      <td>0.319481</td>
      <td>0.218611</td>
      <td>0.285065</td>
      <td>0.245259</td>
      <td>0.212152</td>
      <td>0.200000</td>
      <td>...</td>
      <td>0.222222</td>
      <td>0.221754</td>
      <td>0.177227</td>
      <td>0.228967</td>
      <td>0.333333</td>
      <td>0.175493</td>
      <td>0.166847</td>
      <td>0.365459</td>
      <td>0.172042</td>
      <td>0.174653</td>
    </tr>
  </tbody>
</table>
<p>8 rows × 572 columns</p>
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<h1 id="Select-a-subsample-period-at-random-and-prepare-data">Select a subsample period at random and prepare data<a class="anchor-link" href="#Select-a-subsample-period-at-random-and-prepare-data">&#182;</a></h1><p>We select a date, and then train the model on the date for a consecutive period of days given by <em>train_size</em>. Testing is done on the period of days immediately following the end of the training period for <em>nfwd</em> days.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">m</span><span class="o">-</span><span class="n">train_size</span><span class="o">-</span><span class="n">nfwd</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>   <span class="c1">#Print the start observation</span>
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<pre>2995
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<h1 id="Organize-Data-and-Fit-Model">Organize Data and Fit Model<a class="anchor-link" href="#Organize-Data-and-Fit-Model">&#182;</a></h1>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## Fit the model to the first 10,000 rows of data</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">df2</span><span class="p">[</span><span class="n">x</span><span class="p">:</span><span class="n">x</span><span class="o">+</span><span class="n">train_size</span><span class="p">]</span><span class="o">.</span><span class="n">as_matrix</span><span class="p">()</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[:,</span><span class="mi">3</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">df2</span><span class="o">.</span><span class="n">columns</span><span class="p">)]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">X_train</span><span class="p">))</span>
<span class="n">Y_train</span> <span class="o">=</span> <span class="n">df2</span><span class="p">[</span><span class="n">x</span><span class="p">:</span><span class="n">x</span><span class="o">+</span><span class="n">train_size</span><span class="p">]</span><span class="o">.</span><span class="n">as_matrix</span><span class="p">()</span>
<span class="n">Y_train</span> <span class="o">=</span> <span class="n">Y_train</span><span class="p">[:,</span><span class="mi">2</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">int32</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">Y_train</span><span class="p">))</span>

<span class="n">data_dim</span> <span class="o">=</span> <span class="n">shape</span><span class="p">(</span><span class="n">X_train</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">data_dim</span><span class="p">)</span>

<span class="n">X_test</span> <span class="o">=</span> <span class="n">df2</span><span class="p">[</span><span class="n">x</span><span class="o">+</span><span class="n">train_size</span><span class="p">:(</span><span class="n">x</span><span class="o">+</span><span class="n">train_size</span><span class="o">+</span><span class="n">nfwd</span><span class="p">)]</span><span class="o">.</span><span class="n">as_matrix</span><span class="p">()</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">X_test</span><span class="p">[:,</span><span class="mi">3</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">df2</span><span class="o">.</span><span class="n">columns</span><span class="p">)]</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">X_test</span><span class="p">))</span>
<span class="n">Y_test</span> <span class="o">=</span> <span class="n">df2</span><span class="p">[</span><span class="n">x</span><span class="o">+</span><span class="n">train_size</span><span class="p">:(</span><span class="n">x</span><span class="o">+</span><span class="n">train_size</span><span class="o">+</span><span class="n">nfwd</span><span class="p">)]</span><span class="o">.</span><span class="n">as_matrix</span><span class="p">()</span>
<span class="n">Y_test</span> <span class="o">=</span> <span class="n">Y_test</span><span class="p">[:,</span><span class="mi">2</span><span class="p">:</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">int32</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">shape</span><span class="p">(</span><span class="n">Y_test</span><span class="p">))</span>
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<pre>/Users/srdas/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  
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<pre>(10000, 570)
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<pre>/Users/srdas/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  &#34;&#34;&#34;
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<pre>(10000, 1)
570
(30, 570)
(30, 1)
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<pre>/Users/srdas/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:12: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  if sys.path[0] == &#39;&#39;:
/Users/srdas/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:15: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.
  from ipykernel import kernelapp as app
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span>
<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">Dropout</span>
<span class="kn">from</span> <span class="nn">keras.layers.advanced_activations</span> <span class="k">import</span> <span class="n">LeakyReLU</span>
<span class="kn">from</span> <span class="nn">keras.utils</span> <span class="k">import</span> <span class="n">to_categorical</span>

<span class="n">Y_train2</span> <span class="o">=</span> <span class="n">to_categorical</span><span class="p">(</span><span class="n">Y_train</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span>
<span class="n">n_units</span> <span class="o">=</span> <span class="mi">64</span> <span class="c1">#200</span>

<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">n_units</span><span class="p">,</span> <span class="n">input_dim</span><span class="o">=</span><span class="n">data_dim</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>

<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">n_units</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>

<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="n">n_units</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.25</span><span class="p">))</span>

<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;sigmoid&#39;</span><span class="p">))</span>

<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;rmsprop&#39;</span><span class="p">,</span>
              <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;binary_crossentropy&#39;</span><span class="p">,</span>
              <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;accuracy&#39;</span><span class="p">])</span>
<span class="n">bsize</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">bsize</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">25</span><span class="p">,</span> <span class="n">validation_split</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
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<pre>Train on 9000 samples, validate on 1000 samples
Epoch 1/25
 - 4s - loss: 0.6930 - acc: 0.5180 - val_loss: 0.6877 - val_acc: 0.5580
Epoch 2/25
 - 3s - loss: 0.6926 - acc: 0.5244 - val_loss: 0.6885 - val_acc: 0.5580
Epoch 3/25
 - 3s - loss: 0.6919 - acc: 0.5217 - val_loss: 0.6889 - val_acc: 0.5585
Epoch 4/25
 - 3s - loss: 0.6924 - acc: 0.5257 - val_loss: 0.6896 - val_acc: 0.5590
Epoch 5/25
 - 3s - loss: 0.6923 - acc: 0.5257 - val_loss: 0.6883 - val_acc: 0.5580
Epoch 6/25
 - 3s - loss: 0.6920 - acc: 0.5259 - val_loss: 0.6884 - val_acc: 0.5580
Epoch 7/25
 - 3s - loss: 0.6920 - acc: 0.5285 - val_loss: 0.6884 - val_acc: 0.5455
Epoch 8/25
 - 3s - loss: 0.6914 - acc: 0.5249 - val_loss: 0.6880 - val_acc: 0.5580
Epoch 9/25
 - 3s - loss: 0.6917 - acc: 0.5265 - val_loss: 0.6887 - val_acc: 0.5430
Epoch 10/25
 - 3s - loss: 0.6912 - acc: 0.5275 - val_loss: 0.6905 - val_acc: 0.5425
Epoch 11/25
 - 3s - loss: 0.6920 - acc: 0.5287 - val_loss: 0.6904 - val_acc: 0.5460
Epoch 12/25
 - 3s - loss: 0.6914 - acc: 0.5276 - val_loss: 0.6913 - val_acc: 0.5360
Epoch 13/25
 - 4s - loss: 0.6913 - acc: 0.5277 - val_loss: 0.6900 - val_acc: 0.5580
Epoch 14/25
 - 3s - loss: 0.6911 - acc: 0.5264 - val_loss: 0.6893 - val_acc: 0.5485
Epoch 15/25
 - 3s - loss: 0.6907 - acc: 0.5305 - val_loss: 0.6926 - val_acc: 0.5380
Epoch 16/25
 - 3s - loss: 0.6905 - acc: 0.5328 - val_loss: 0.6883 - val_acc: 0.5485
Epoch 17/25
 - 3s - loss: 0.6910 - acc: 0.5309 - val_loss: 0.6936 - val_acc: 0.5010
Epoch 18/25
 - 3s - loss: 0.6904 - acc: 0.5320 - val_loss: 0.6880 - val_acc: 0.5585
Epoch 19/25
 - 3s - loss: 0.6897 - acc: 0.5328 - val_loss: 0.6924 - val_acc: 0.5265
Epoch 20/25
 - 3s - loss: 0.6895 - acc: 0.5343 - val_loss: 0.6914 - val_acc: 0.5360
Epoch 21/25
 - 3s - loss: 0.6888 - acc: 0.5419 - val_loss: 0.6941 - val_acc: 0.5140
Epoch 22/25
 - 3s - loss: 0.6880 - acc: 0.5413 - val_loss: 0.6914 - val_acc: 0.5495
Epoch 23/25
 - 3s - loss: 0.6888 - acc: 0.5403 - val_loss: 0.6891 - val_acc: 0.5420
Epoch 24/25
 - 3s - loss: 0.6873 - acc: 0.5465 - val_loss: 0.6926 - val_acc: 0.5455
Epoch 25/25
 - 3s - loss: 0.6872 - acc: 0.5421 - val_loss: 0.6891 - val_acc: 0.5500
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<pre>&lt;keras.callbacks.History at 0x1a7d03aeb8&gt;</pre>
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<h1 id="Model-Accuracy">Model Accuracy<a class="anchor-link" href="#Model-Accuracy">&#182;</a></h1><p>$parameters = 570 \times 65+64 \times 65+64 \times 65+65 = 45435$</p>

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<div class="prompt input_prompt">In&nbsp;[35]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">Y_train2</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<pre>[0.6846257612228394, 0.5495]</pre>
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<h2 id="THE-END">THE END<a class="anchor-link" href="#THE-END">&#182;</a></h2>
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