Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Documentation Generation #50

Merged
merged 6 commits into from
May 21, 2021
Merged

Documentation Generation #50

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
c83f9dd
Initial version of documentation generation
bajo1207 May 18, 2021
ba63fb2
Working documentation
bajo1207 May 18, 2021
dae6c14
Html upload and ignore file
bajo1207 May 18, 2021
c04a6d7
Remove changes to prep_hdro
bajo1207 May 19, 2021
12eb7ee
Generate and link to notebooks
bajo1207 May 21, 2021
915291b
Add nbconvert to requirements
bajo1207 May 21, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 4 additions & 0 deletions .docignore
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,4 @@
WaterSecurity.data
WaterSecurity.documentation
WaterSecurity.run
WaterSecurity.generate_documentation
Empty file added __init__.py
View file
Open in desktop
Empty file.
165 changes: 165 additions & 0 deletions documentation/WaterSecurity/classification/classifier.html
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,165 @@
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.9.2" />
<title>WaterSecurity.classification.classifier API documentation</title>
<meta name="description" content="" />
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
<link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
<link rel="stylesheet preload" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/styles/github.min.css" crossorigin>
<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/10.1.1/highlight.min.js" integrity="sha256-Uv3H6lx7dJmRfRvH8TH6kJD1TSK1aFcwgx+mdg3epi8=" crossorigin></script>
<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>WaterSecurity.classification.classifier</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from sklearn.base import BaseEstimator, RegressorMixin
import numpy as np


class Classifier(BaseEstimator, RegressorMixin):
# @TODO implement
def fit(self, x_data, y_data):
&#34;&#34;&#34;
x_data: the nxm features
y_data: the n labels, with values 0,1,2 or 3
&#34;&#34;&#34;
return self

def predict(self, x_data):
&#34;&#34;&#34;
x_data: the nxm features
Returns n predictions, which have values 0 to 3, they can be floats
&#34;&#34;&#34;
return np.zeros(x_data.shape[0])</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="WaterSecurity.classification.classifier.Classifier"><code class="flex name class">
<span>class <span class="ident">Classifier</span></span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all estimators in scikit-learn.</p>
<h2 id="notes">Notes</h2>
<p>All estimators should specify all the parameters that can be set
at the class level in their <code>__init__</code> as explicit keyword
arguments (no <code>*args</code> or <code>**kwargs</code>).</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Classifier(BaseEstimator, RegressorMixin):
# @TODO implement
def fit(self, x_data, y_data):
&#34;&#34;&#34;
x_data: the nxm features
y_data: the n labels, with values 0,1,2 or 3
&#34;&#34;&#34;
return self

def predict(self, x_data):
&#34;&#34;&#34;
x_data: the nxm features
Returns n predictions, which have values 0 to 3, they can be floats
&#34;&#34;&#34;
return np.zeros(x_data.shape[0])</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>sklearn.base.BaseEstimator</li>
<li>sklearn.base.RegressorMixin</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="WaterSecurity.classification.classifier.Classifier.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, x_data, y_data)</span>
</code></dt>
<dd>
<div class="desc"><p>x_data: the nxm features
y_data: the n labels, with values 0,1,2 or 3</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, x_data, y_data):
&#34;&#34;&#34;
x_data: the nxm features
y_data: the n labels, with values 0,1,2 or 3
&#34;&#34;&#34;
return self</code></pre>
</details>
</dd>
<dt id="WaterSecurity.classification.classifier.Classifier.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, x_data)</span>
</code></dt>
<dd>
<div class="desc"><p>x_data: the nxm features
Returns n predictions, which have values 0 to 3, they can be floats</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, x_data):
&#34;&#34;&#34;
x_data: the nxm features
Returns n predictions, which have values 0 to 3, they can be floats
&#34;&#34;&#34;
return np.zeros(x_data.shape[0])</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="WaterSecurity.classification" href="index.html">WaterSecurity.classification</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="WaterSecurity.classification.classifier.Classifier" href="#WaterSecurity.classification.classifier.Classifier">Classifier</a></code></h4>
<ul class="">
<li><code><a title="WaterSecurity.classification.classifier.Classifier.fit" href="#WaterSecurity.classification.classifier.Classifier.fit">fit</a></code></li>
<li><code><a title="WaterSecurity.classification.classifier.Classifier.predict" href="#WaterSecurity.classification.classifier.Classifier.predict">predict</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.9.2</a>.</p>
</footer>
</body>
</html>
Loading