Ci/notebook smoke

_lamd/_lamd.yml

@@ -9,8 +9,8 @@ author:
 layout: lecture
 venue: Gaussian Process Summer School
 talktheme: white
-talkcss: mlatcl.css
-talkfontsize: smaller
+talkcss: gpss.css
+talkfontsize: xs
 bibdir: $HOME/lawrennd/bibliography/
 people: ../_people/people.yml
 postsdir: ../_lectures/
@@ -21,7 +21,7 @@ practicalsdir: ../_practicals/
 snippetsdir: $HOME/lawrennd/snippets/
 writediagramsdir: .
 scriptsdir: ./scripts/
-diagramsdir: ../slides/diagrams/
+diagramsdir: $HOME/lawrennd/slides/diagrams/
 macrosdir: $HOME/lawrennd/lamd/lamd/macros
 slidesheader: slides-header.html
 posts: True
@@ -34,7 +34,7 @@ assignment: False
 ipynb: True
 reveal: True
 transition: None
-potx: custom-reference.potx 
+potx: gpss-simple-reference.potx 
 dotx: custom-reference.dotx
 baseurl: "gpss/" # the subpath of your site, e.g. /blog/
 url: "https://mlatcl.github.io/" # the base hostname & protocol for your site

_lamd/makefile

@@ -1,4 +1,4 @@
-BASE=unsupervised-learning-with-gps
+BASE=emulation
 LAMDDIR=/Users/neil/lawrennd/lamd/lamd
 MAKEFILESDIR=/Users/neil/lawrennd/lamd/lamd/makefiles
 INCLUDESDIR=/Users/neil/lawrennd/lamd/lamd/includes

_lectures/09-latent-force-models.html

@@ -6,19 +6,18 @@
 week: 9
 reveal: 09-latent-force-models.slides.html
 ipynb: 09-latent-force-models.ipynb
-pptx: 09-latent-force-models.pptx
 layout: lecture
 categories:
 - notes
 ---
 
 
 
-<!-- CIP-0012 Track A: no-op outside HTML (overridden in talk-macros-html.gpp). --><!-- Do not edit this file locally. --><!----><!-- CIP-0012 Track A: content script load. Once-guard with alphanumeric
+<!-- CIP-0012 Track A: no-op outside HTML (overridden in talk-macros-html.gpp). --><!-- Class-test timer: no-op outside slides HTML (overridden in talk-macros-slides-html.gpp). --><!-- CIP-0016: mdpp expands \diagramcredit before gpp; leftover calls are empty. --><!-- Do not edit this file locally. --><!----><!-- CIP-0012 Track A: content script load. Once-guard with alphanumeric
      names stays in the surrounding ifndef wrapper (GPP ifndef limitation). --><!-- Do not edit this file locally. --><!-- Do not edit this file locally. --><!-- The last names to be defined. Should be defined entirely in terms of macros from above--><!--
 
 --><h2 id="gaussian-process-summer-school-notebook-setup">Gaussian Process Summer School notebook setup</h2><div style="text-align:right"><span class="editsection-bracket" style="">[</span><span class="editsection" style=""><a href="https://github.com/lawrennd/snippets/edit/main/_gpss/includes/gpss-notebook-setup.md" target="_blank" onclick="ga('send', 'event', 'Edit Page', 'Edit', 'https://github.com/lawrennd/snippets/edit/main/_gpss/includes/gpss-notebook-setup.md', 13);">edit</a></span><span class="editsection-bracket" style="">]</span></div><p>We install packages used across the notebooks for data loading, plotting helpers, and Gaussian process modelling.</p><div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> importlib.util</span></code></pre></div><div class="sourceCode" id="cb2"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>cmd <span class="op">=</span> install_command(<span class="st">&#39;pods&#39;</span>)</span></code></pre></div><div class="sourceCode" id="cb3"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>system {cmd}</span></code></pre></div><div class="sourceCode" id="cb4"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>cmd <span class="op">=</span> install_command(<span class="st">&#39;notutils&#39;</span>)</span></code></pre></div><div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>system {cmd}</span></code></pre></div><div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>cmd <span class="op">=</span> install_command(<span class="st">&#39;mlai&#39;</span>)</span></code></pre></div><div class="sourceCode" id="cb7"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>system {cmd}</span></code></pre></div><!--setupplotcode{import seaborn as sns
 sns.set_style('darkgrid')
 sns.set_context('paper')
-sns.set_palette('colorblind')}--><div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>cmd <span class="op">=</span> install_command(<span class="st">&#39;GPy&#39;</span>)</span></code></pre></div><div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>system {cmd}</span></code></pre></div><h2 id="gpy-a-gaussian-process-framework-in-python">GPy: A Gaussian Process Framework in Python</h2><div style="text-align:right"><span class="editsection-bracket" style="">[</span><span class="editsection" style=""><a href="https://github.com/lawrennd/snippets/edit/main/_software/includes/gpy-software.md" target="_blank" onclick="ga('send', 'event', 'Edit Page', 'Edit', 'https://github.com/lawrennd/snippets/edit/main/_software/includes/gpy-software.md', 13);">edit</a></span><span class="editsection-bracket" style="">]</span></div><p>Gaussian processes are a flexible tool for non-parametric analysis with uncertainty. The GPy software was started in Sheffield to provide a easy to use interface to GPs. One which allowed the user to focus on the modelling rather than the mathematics.</p><div class="figure"><div id="gpy-software-figure" class="figure-frame"><div class="centered" style=""><img class="" src="https://mlatcl.github.io/gpss/slides/diagrams/gp/gpy.png" width="70%" height="auto" align="center" style="background:none; border:none; box-shadow:none; display:block; margin-left:auto; margin-right:auto;vertical-align:middle"></div></div><div id="gpy-software-magnify" class="magnify" onclick="magnifyFigure(&#39;gpy-software&#39;)"><button type="button" class="img-button" aria-label="Enlarge figure"><img src="{{ '/assets/images/Magnify_Large.svg' | relative_url }}" style="width:1.5em; min-width:44px; min-height:44px; padding:0.5em"></button></div><div id="gpy-software-caption" class="caption-frame"><p>Figure: GPy is a BSD licensed software code base for implementing Gaussian process models in Python. It is designed for teaching and modelling. We welcome contributions which can be made through the GitHub repository <a href="https://github.com/SheffieldML/GPy" class="uri">https://github.com/SheffieldML/GPy</a></p></div></div><p>GPy is a BSD licensed software code base for implementing Gaussian process models in python. This allows GPs to be combined with a wide variety of software libraries.</p><p>The software itself is available on <a href="https://github.com/SheffieldML/GPy">GitHub</a> and the team welcomes contributions.</p><p>The aim for GPy is to be a probabilistic-style programming language, i.e., you specify the model rather than the algorithm. As well as a large range of covariance functions the software allows for non-Gaussian likelihoods, multivariate outputs, dimensionality reduction and approximations for larger data sets.</p><p>The documentation for GPy can be found <a href="https://gpy.readthedocs.io/en/latest/">here</a>.</p><h2 id="thanks">Thanks!</h2><p>For more information on these subjects and more you might want to check the following resources.</p><ul><li>company: <a href="https://trent.ai">Trent AI</a></li><li>book: <a href="https://www.penguin.co.uk/books/455130/the-atomic-human-by-lawrence-neil-d/9780241625248">The Atomic Human</a></li><li>twitter: <a href="https://twitter.com/lawrennd">@lawrennd</a></li><li>podcast: <a href="http://thetalkingmachines.com">The Talking Machines</a></li><li>newspaper: <a href="http://www.theguardian.com/profile/neil-lawrence">Guardian Profile Page</a></li><li>blog: <a href="http://inverseprobability.com/blog.html">http://inverseprobability.com</a></li></ul><h1 id="references">References</h1>
+sns.set_palette('colorblind')}--><div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>cmd <span class="op">=</span> install_command(<span class="st">&#39;GPy&#39;</span>)</span></code></pre></div><div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="op">%</span>system {cmd}</span></code></pre></div><h2 id="gpy-a-gaussian-process-framework-in-python">GPy: A Gaussian Process Framework in Python</h2><div style="text-align:right"><span class="editsection-bracket" style="">[</span><span class="editsection" style=""><a href="https://github.com/lawrennd/snippets/edit/main/_software/includes/gpy-software.md" target="_blank" onclick="ga('send', 'event', 'Edit Page', 'Edit', 'https://github.com/lawrennd/snippets/edit/main/_software/includes/gpy-software.md', 13);">edit</a></span><span class="editsection-bracket" style="">]</span></div><p>Gaussian processes are a flexible tool for non-parametric analysis with uncertainty. The GPy software was started in Sheffield to provide a easy to use interface to GPs. One which allowed the user to focus on the modelling rather than the mathematics.</p><div class="figure"><div id="gpy-software-figure" class="figure-frame"><div class="centered" style=""><img class="" src="https://mlatcl.github.io/gpss/diagrams/gp/gpy.png" width="70%" height="auto" align="center" style="background:none; border:none; box-shadow:none; display:block; margin-left:auto; margin-right:auto;vertical-align:middle"></div></div><div id="gpy-software-magnify" class="magnify" onclick="magnifyFigure(&#39;gpy-software&#39;)"><button type="button" class="img-button" aria-label="Enlarge figure"><img src="{{ '/assets/images/Magnify_Large.svg' | relative_url }}" style="width:1.5em; min-width:44px; min-height:44px; padding:0.5em"></button></div><div id="gpy-software-caption" class="caption-frame"><p>Figure: GPy is a BSD licensed software code base for implementing Gaussian process models in Python. It is designed for teaching and modelling. We welcome contributions which can be made through the GitHub repository <a href="https://github.com/SheffieldML/GPy" class="uri">https://github.com/SheffieldML/GPy</a></p></div></div><p>GPy is a BSD licensed software code base for implementing Gaussian process models in python. This allows GPs to be combined with a wide variety of software libraries.</p><p>The software itself is available on <a href="https://github.com/SheffieldML/GPy">GitHub</a> and the team welcomes contributions.</p><p>The aim for GPy is to be a probabilistic-style programming language, i.e., you specify the model rather than the algorithm. As well as a large range of covariance functions the software allows for non-Gaussian likelihoods, multivariate outputs, dimensionality reduction and approximations for larger data sets.</p><p>The documentation for GPy can be found <a href="https://gpy.readthedocs.io/en/latest/">here</a>.</p><h2 id="thanks">Thanks!</h2><p>For more information on these subjects and more you might want to check the following resources.</p><ul><li>company: <a href="https://trent.ai">Trent AI</a></li><li>book: <a href="https://www.penguin.co.uk/books/455130/the-atomic-human-by-lawrence-neil-d/9780241625248">The Atomic Human</a></li><li>twitter: <a href="https://twitter.com/lawrennd">@lawrennd</a></li><li>podcast: <a href="http://thetalkingmachines.com">The Talking Machines</a></li><li>newspaper: <a href="http://www.theguardian.com/profile/neil-lawrence">Guardian Profile Page</a></li><li>blog: <a href="http://inverseprobability.com/blog.html">http://inverseprobability.com</a></li></ul><h1 id="references">References</h1>