Deploying to gh-pages from @ b03bdb5 🚀

_examples/backend/index.html

@@ -2939,7 +2939,7 @@ <h3 id="transforming-multiple-parameters">Transforming Multiple Parameters<a cla
     kernel=Matern32(
       active_dims=slice(None, None, None),
       n_dims=None,
-      compute_engine=&lt;gpjax.kernels.computations.dense.DenseKernelComputation object at 0x7f88e584ca00&gt;,
+      compute_engine=&lt;gpjax.kernels.computations.dense.DenseKernelComputation object at 0x7f56b21ac970&gt;,
       lengthscale=PositiveReal(
         value=Array(1., dtype=float64, weak_type=True),
         _tag=&#39;positive&#39;
@@ -2963,7 +2963,7 @@ <h3 id="transforming-multiple-parameters">Transforming Multiple Parameters<a cla
       _tag=&#39;positive&#39;
     ),
     num_datapoints=100,
-    integrator=&lt;gpjax.integrators.AnalyticalGaussianIntegrator object at 0x7f88e5886950&gt;
+    integrator=&lt;gpjax.integrators.AnalyticalGaussianIntegrator object at 0x7f56b21ea890&gt;
   ),
   jitter=1e-06
 )
@@ -3055,7 +3055,7 @@ <h3 id="transforming-multiple-parameters">Transforming Multiple Parameters<a cla
 <div class="language-text highlight"><pre><span></span><code>ConjugatePosterior(
   jitter=1e-06,
   likelihood=Gaussian(
-    integrator=&lt;gpjax.integrators.AnalyticalGaussianIntegrator object at 0x7f88e5886950&gt;,
+    integrator=&lt;gpjax.integrators.AnalyticalGaussianIntegrator object at 0x7f56b21ea890&gt;,
     num_datapoints=100,
     obs_stddev=PositiveReal(
       _tag=&#39;positive&#39;,
@@ -3066,7 +3066,7 @@ <h3 id="transforming-multiple-parameters">Transforming Multiple Parameters<a cla
     jitter=1e-06,
     kernel=Matern32(
       active_dims=slice(None, None, None),
-      compute_engine=&lt;gpjax.kernels.computations.dense.DenseKernelComputation object at 0x7f88e584ca00&gt;,
+      compute_engine=&lt;gpjax.kernels.computations.dense.DenseKernelComputation object at 0x7f56b21ac970&gt;,
       lengthscale=PositiveReal(
         _tag=&#39;positive&#39;,
         value=Array(2., dtype=float64, weak_type=True)
@@ -3214,7 +3214,7 @@ <h2 id="nnx-modules">NNX Modules<a class="headerlink" href="#nnx-modules" title=
 </span><span id="__span-17-4"><a id="__codelineno-17-4" name="__codelineno-17-4" href="#__codelineno-17-4"></a><span class="n">meanf</span> <span class="o">=</span> <span class="n">LinearMeanFunction</span><span class="p">(</span><span class="n">intercept</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">slope</span><span class="o">=</span><span class="mf">2.0</span><span class="p">)</span>
 </span><span id="__span-17-5"><a id="__codelineno-17-5" name="__codelineno-17-5" href="#__codelineno-17-5"></a><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">meanf</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>[&lt;matplotlib.lines.Line2D at 0x7f88dcac9720&gt;]
+<div class="language-text highlight"><pre><span></span><code>[&lt;matplotlib.lines.Line2D at 0x7f56b06692d0&gt;]
 </code></pre></div>
 <p><img alt="png" src="../backend_files/backend_36_1.png" /></p>
 <p>Looks good! To conclude this section, let's now parameterise a GP with our new mean

_examples/barycentres/index.html

@@ -3088,7 +3088,7 @@ <h2 id="plotting-the-result">Plotting the result<a class="headerlink" href="#plo
 </span><span id="__span-5-33"><a id="__codelineno-5-33" name="__codelineno-5-33" href="#__codelineno-5-33"></a><span class="p">)</span>
 </span><span id="__span-5-34"><a id="__codelineno-5-34" name="__codelineno-5-34" href="#__codelineno-5-34"></a><span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.legend.Legend at 0x7fde38c47cd0&gt;
+<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.legend.Legend at 0x7f47c0a23880&gt;
 </code></pre></div>
 <p><img alt="png" src="../barycentres_files/barycentres_11_1.png" /></p>
 <h2 id="displacement-interpolation">Displacement interpolation<a class="headerlink" href="#displacement-interpolation" title="Permanent link"></a></h2>
@@ -3111,10 +3111,10 @@ <h2 id="system-configuration">System configuration<a class="headerlink" href="#s
 Python version       : 3.10.14
 IPython version      : 8.27.0
 
-gpjax                 : 0.9.1
-tensorflow_probability: 0.24.0
 jax                   : 0.4.27
 matplotlib            : 3.9.2
+tensorflow_probability: 0.24.0
+gpjax                 : 0.9.1
 
 Watermark: 2.4.3
 </code></pre></div>

_examples/bayesian_optimisation/index.html

@@ -3635,11 +3635,11 @@ <h2 id="system-configuration">System Configuration<a class="headerlink" href="#s
 Python version       : 3.10.14
 IPython version      : 8.27.0
 
-matplotlib            : 3.9.2
 optax                 : 0.2.3
-jax                   : 0.4.27
 gpjax                 : 0.9.1
 tensorflow_probability: 0.24.0
+matplotlib            : 3.9.2
+jax                   : 0.4.27
 
 Watermark: 2.4.3
 </code></pre></div>

_examples/classification/index.html

@@ -2763,7 +2763,7 @@ <h2 id="dataset">Dataset<a class="headerlink" href="#dataset" title="Permanent l
 </span><span id="__span-1-9"><a id="__codelineno-1-9" name="__codelineno-1-9" href="#__codelineno-1-9"></a><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
 </span><span id="__span-1-10"><a id="__codelineno-1-10" name="__codelineno-1-10" href="#__codelineno-1-10"></a><span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.collections.PathCollection at 0x7fd110d96f80&gt;
+<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.collections.PathCollection at 0x7f25b059b0d0&gt;
 </code></pre></div>
 <p><img alt="png" src="../classification_files/classification_3_1.png" /></p>
 <h2 id="map-inference">MAP inference<a class="headerlink" href="#map-inference" title="Permanent link"></a></h2>
@@ -2840,7 +2840,7 @@ <h2 id="map-inference">MAP inference<a class="headerlink" href="#map-inference"
 </span><span id="__span-5-32"><a id="__codelineno-5-32" name="__codelineno-5-32" href="#__codelineno-5-32"></a>
 </span><span id="__span-5-33"><a id="__codelineno-5-33" name="__codelineno-5-33" href="#__codelineno-5-33"></a><span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.legend.Legend at 0x7fd1104aa770&gt;
+<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.legend.Legend at 0x7f25a9aa2b30&gt;
 </code></pre></div>
 <p><img alt="png" src="../classification_files/classification_12_1.png" /></p>
 <p>Here we projected the map estimates <span class="arithmatex">\(\hat{\boldsymbol{f}}\)</span> for the function values
@@ -2966,7 +2966,7 @@ <h2 id="laplace-approximation">Laplace approximation<a class="headerlink" href="
 </span><span id="__span-8-31"><a id="__codelineno-8-31" name="__codelineno-8-31" href="#__codelineno-8-31"></a><span class="p">)</span>
 </span><span id="__span-8-32"><a id="__codelineno-8-32" name="__codelineno-8-32" href="#__codelineno-8-32"></a><span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.legend.Legend at 0x7fd1104abf70&gt;
+<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.legend.Legend at 0x7f25b02f2950&gt;
 </code></pre></div>
 <p><img alt="png" src="../classification_files/classification_20_1.png" /></p>
 <h2 id="system-configuration">System configuration<a class="headerlink" href="#system-configuration" title="Permanent link"></a></h2>
@@ -2981,13 +2981,13 @@ <h2 id="system-configuration">System configuration<a class="headerlink" href="#s
 Python version       : 3.10.14
 IPython version      : 8.27.0
 
-cola                  : 0.0.5
+jax                   : 0.4.27
 tensorflow_probability: 0.24.0
-optax                 : 0.2.3
+cola                  : 0.0.5
 matplotlib            : 3.9.2
-gpjax                 : 0.9.1
-jax                   : 0.4.27
 flax                  : 0.9.0
+optax                 : 0.2.3
+gpjax                 : 0.9.1
 
 Watermark: 2.4.3
 </code></pre></div>

_examples/collapsed_vi/index.html

@@ -2880,12 +2880,12 @@ <h2 id="runtime-comparison">Runtime comparison<a class="headerlink" href="#runti
 </span><span id="__span-8-4"><a id="__codelineno-8-4" name="__codelineno-8-4" href="#__codelineno-8-4"></a><span class="n">nmll</span> <span class="o">=</span> <span class="n">jit</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="o">-</span><span class="n">gpx</span><span class="o">.</span><span class="n">objectives</span><span class="o">.</span><span class="n">conjugate_mll</span><span class="p">(</span><span class="n">full_rank_model</span><span class="p">,</span> <span class="n">D</span><span class="p">))</span>
 </span><span id="__span-8-5"><a id="__codelineno-8-5" name="__codelineno-8-5" href="#__codelineno-8-5"></a><span class="o">%</span><span class="n">timeit</span> <span class="n">nmll</span><span class="p">()</span><span class="o">.</span><span class="n">block_until_ready</span><span class="p">()</span>
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>652 ms ± 5.51 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
+<div class="language-text highlight"><pre><span></span><code>652 ms ± 4.17 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
 </code></pre></div>
 <div class="language-python highlight"><pre><span></span><code><span id="__span-9-1"><a id="__codelineno-9-1" name="__codelineno-9-1" href="#__codelineno-9-1"></a><span class="n">nelbo</span> <span class="o">=</span> <span class="n">jit</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span> <span class="o">-</span><span class="n">gpx</span><span class="o">.</span><span class="n">objectives</span><span class="o">.</span><span class="n">collapsed_elbo</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">D</span><span class="p">))</span>
 </span><span id="__span-9-2"><a id="__codelineno-9-2" name="__codelineno-9-2" href="#__codelineno-9-2"></a><span class="o">%</span><span class="n">timeit</span> <span class="n">nelbo</span><span class="p">()</span><span class="o">.</span><span class="n">block_until_ready</span><span class="p">()</span>
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>2.38 ms ± 23 μs per loop (mean ± std. dev. of 7 runs, 1 loop each)
+<div class="language-text highlight"><pre><span></span><code>2.42 ms ± 78.9 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)
 </code></pre></div>
 <p>As we can see, the sparse approximation given here is much faster when
 compared against a full-rank model.</p>
@@ -2901,10 +2901,10 @@ <h2 id="system-configuration">System configuration<a class="headerlink" href="#s
 Python version       : 3.10.14
 IPython version      : 8.27.0
 
-jax       : 0.4.27
 matplotlib: 3.9.2
-gpjax     : 0.9.1
 optax     : 0.2.3
+gpjax     : 0.9.1
+jax       : 0.4.27
 
 Watermark: 2.4.3
 </code></pre></div>

_examples/constructing_new_kernels/index.html

@@ -2922,7 +2922,7 @@ <h2 id="kernel-combinations">Kernel combinations<a class="headerlink" href="#ker
 </span><span id="__span-5-11"><a id="__codelineno-5-11" name="__codelineno-5-11" href="#__codelineno-5-11"></a><span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">im1</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
 </span><span id="__span-5-12"><a id="__codelineno-5-12" name="__codelineno-5-12" href="#__codelineno-5-12"></a><span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">im2</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.colorbar.Colorbar at 0x7fae15d77df0&gt;
+<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.colorbar.Colorbar at 0x7f838df4e350&gt;
 </code></pre></div>
 <p><img alt="png" src="../constructing_new_kernels_files/constructing_new_kernels_11_1.png" /></p>
 <p>Similarly, products of kernels can be created through the <code>ProductKernel</code> class.</p>
@@ -2941,7 +2941,7 @@ <h2 id="kernel-combinations">Kernel combinations<a class="headerlink" href="#ker
 </span><span id="__span-6-13"><a id="__codelineno-6-13" name="__codelineno-6-13" href="#__codelineno-6-13"></a><span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">im2</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
 </span><span id="__span-6-14"><a id="__codelineno-6-14" name="__codelineno-6-14" href="#__codelineno-6-14"></a><span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">im3</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">fraction</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.colorbar.Colorbar at 0x7fae15720f40&gt;
+<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.colorbar.Colorbar at 0x7f838c7db3d0&gt;
 </code></pre></div>
 <p><img alt="png" src="../constructing_new_kernels_files/constructing_new_kernels_13_1.png" /></p>
 <h2 id="custom-kernel">Custom kernel<a class="headerlink" href="#custom-kernel" title="Permanent link"></a></h2>
@@ -3083,7 +3083,7 @@ <h3 id="prediction">Prediction<a class="headerlink" href="#prediction" title="Pe
 </span><span id="__span-10-24"><a id="__codelineno-10-24" name="__codelineno-10-24" href="#__codelineno-10-24"></a><span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">D</span><span class="o">.</span><span class="n">X</span><span class="p">,</span> <span class="n">D</span><span class="o">.</span><span class="n">y</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Observations&quot;</span><span class="p">)</span>
 </span><span id="__span-10-25"><a id="__codelineno-10-25" name="__codelineno-10-25" href="#__codelineno-10-25"></a><span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.legend.Legend at 0x7fae0ce6d600&gt;
+<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.legend.Legend at 0x7f838c0f4d30&gt;
 </code></pre></div>
 <p><img alt="png" src="../constructing_new_kernels_files/constructing_new_kernels_21_1.png" /></p>
 <h2 id="system-configuration">System configuration<a class="headerlink" href="#system-configuration" title="Permanent link"></a></h2>

_examples/decision_making/index.html

@@ -3093,10 +3093,10 @@ <h2 id="system-configuration">System Configuration<a class="headerlink" href="#s
 Python version       : 3.10.14
 IPython version      : 8.27.0
 
-matplotlib: 3.9.2
-jax       : 0.4.27
-gpjax     : 0.9.1
 optax     : 0.2.3
+gpjax     : 0.9.1
+jax       : 0.4.27
+matplotlib: 3.9.2
 
 Watermark: 2.4.3
 </code></pre></div>

_examples/deep_kernels/index.html

@@ -2888,7 +2888,7 @@ <h2 id="dataset">Dataset<a class="headerlink" href="#dataset" title="Permanent l
 </span><span id="__span-1-17"><a id="__codelineno-1-17" name="__codelineno-1-17" href="#__codelineno-1-17"></a><span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">xtest</span><span class="p">,</span> <span class="n">ytest</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;True function&quot;</span><span class="p">)</span>
 </span><span id="__span-1-18"><a id="__codelineno-1-18" name="__codelineno-1-18" href="#__codelineno-1-18"></a><span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;best&quot;</span><span class="p">)</span>
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.legend.Legend at 0x7f6344ccc3a0&gt;
+<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.legend.Legend at 0x7fbb64834550&gt;
 </code></pre></div>
 <p><img alt="png" src="../deep_kernels_files/deep_kernels_3_1.png" /></p>
 <h2 id="deep-kernels">Deep kernels<a class="headerlink" href="#deep-kernels" title="Permanent link"></a></h2>
@@ -3040,7 +3040,7 @@ <h2 id="prediction">Prediction<a class="headerlink" href="#prediction" title="Pe
 </span><span id="__span-6-31"><a id="__codelineno-6-31" name="__codelineno-6-31" href="#__codelineno-6-31"></a><span class="p">)</span>
 </span><span id="__span-6-32"><a id="__codelineno-6-32" name="__codelineno-6-32" href="#__codelineno-6-32"></a><span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.legend.Legend at 0x7f633ccec070&gt;
+<div class="language-text highlight"><pre><span></span><code>&lt;matplotlib.legend.Legend at 0x7fbb5ca0c6a0&gt;
 </code></pre></div>
 <p><img alt="png" src="../deep_kernels_files/deep_kernels_13_1.png" /></p>
 <h2 id="system-configuration">System configuration<a class="headerlink" href="#system-configuration" title="Permanent link"></a></h2>
@@ -3055,11 +3055,11 @@ <h2 id="system-configuration">System configuration<a class="headerlink" href="#s
 Python version       : 3.10.14
 IPython version      : 8.27.0
 
+optax     : 0.2.3
 matplotlib: 3.9.2
+jax       : 0.4.27
 gpjax     : 0.9.1
-optax     : 0.2.3
 flax      : 0.9.0
-jax       : 0.4.27
 
 Watermark: 2.4.3
 </code></pre></div>

_examples/graph_kernels/index.html

@@ -2965,9 +2965,9 @@ <h2 id="system-configuration">System configuration<a class="headerlink" href="#s
 Python version       : 3.10.14
 IPython version      : 8.27.0
 
-jax       : 0.4.27
-matplotlib: 3.9.2
 networkx  : 3.3
+matplotlib: 3.9.2
+jax       : 0.4.27
 gpjax     : 0.9.1
 
 Watermark: 2.4.3