<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Posts on Writeups</title><link>https://blog.atavius.org/posts/</link><description>Recent content in Posts on Writeups</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://blog.atavius.org/posts/index.xml" rel="self" type="application/rss+xml"/><item><title>Hello World</title><link>https://blog.atavius.org/posts/hello-world/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://blog.atavius.org/posts/hello-world/</guid><description>&lt;h1 id="linear-regression-mvp">Linear Regression MVP&lt;/h1>
&lt;p>Here is a quick example of a math formula and a Python snippet working together.&lt;/p>
&lt;h2 id="objective-function">Objective Function&lt;/h2>
&lt;p>We define our mean squared error cost function as:&lt;/p>
$$J(\theta) = \frac{1}{2m} \sum_{i=1}^{m} (h_\theta(x^{(i)}) - y^{(i)})^2$$&lt;h2 id="python-code">Python Code&lt;/h2>
&lt;div class="highlight">&lt;pre tabindex="0" style="color:#e6edf3;background-color:#0d1117;-moz-tab-size:4;-o-tab-size:4;tab-size:4;">&lt;code class="language-python" data-lang="python">&lt;span style="display:flex;">&lt;span>&lt;span style="color:#ff7b72">import&lt;/span> &lt;span style="color:#ff7b72">numpy&lt;/span> &lt;span style="color:#ff7b72">as&lt;/span> &lt;span style="color:#ff7b72">np&lt;/span>
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>&lt;span style="color:#ff7b72">def&lt;/span> &lt;span style="color:#d2a8ff;font-weight:bold">calculate_mse&lt;/span>(y_true: np&lt;span style="color:#ff7b72;font-weight:bold">.&lt;/span>ndarray, y_pred: np&lt;span style="color:#ff7b72;font-weight:bold">.&lt;/span>ndarray) &lt;span style="color:#ff7b72;font-weight:bold">-&amp;gt;&lt;/span> float:
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span> &lt;span style="color:#a5d6ff">&amp;#34;&amp;#34;&amp;#34;Computes Mean Squared Error.&amp;#34;&amp;#34;&amp;#34;&lt;/span>
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span> &lt;span style="color:#ff7b72">return&lt;/span> np&lt;span style="color:#ff7b72;font-weight:bold">.&lt;/span>mean((y_true &lt;span style="color:#ff7b72;font-weight:bold">-&lt;/span> y_pred) &lt;span style="color:#ff7b72;font-weight:bold">**&lt;/span> &lt;span style="color:#a5d6ff">2&lt;/span>)
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>&lt;span style="color:#8b949e;font-style:italic"># Quick Test&lt;/span>
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>y_true &lt;span style="color:#ff7b72;font-weight:bold">=&lt;/span> np&lt;span style="color:#ff7b72;font-weight:bold">.&lt;/span>array([&lt;span style="color:#a5d6ff">1.0&lt;/span>, &lt;span style="color:#a5d6ff">2.0&lt;/span>, &lt;span style="color:#a5d6ff">3.0&lt;/span>])
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>y_pred &lt;span style="color:#ff7b72;font-weight:bold">=&lt;/span> np&lt;span style="color:#ff7b72;font-weight:bold">.&lt;/span>array([&lt;span style="color:#a5d6ff">1.1&lt;/span>, &lt;span style="color:#a5d6ff">1.9&lt;/span>, &lt;span style="color:#a5d6ff">3.2&lt;/span>])
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>print(&lt;span style="color:#79c0ff">f&lt;/span>&lt;span style="color:#a5d6ff">&amp;#34;MSE: &lt;/span>&lt;span style="color:#a5d6ff">{&lt;/span>calculate_mse(y_true, y_pred)&lt;span style="color:#a5d6ff">:&lt;/span>&lt;span style="color:#a5d6ff">.4f&lt;/span>&lt;span style="color:#a5d6ff">}&lt;/span>&lt;span style="color:#a5d6ff">&amp;#34;&lt;/span>)
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/div></description></item></channel></rss>