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51 changes: 28 additions & 23 deletions _modules/dynamax/generalized_gaussian_ssm/inference.html
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Expand Down Expand Up @@ -326,7 +328,6 @@ <h1>Source code for dynamax.generalized_gaussian_ssm.inference</h1><div class="h
<span class="n">_jacfwd_2d</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">f</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">jnp</span><span class="o">.</span><span class="n">atleast_2d</span><span class="p">(</span><span class="n">jacfwd</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="k">class</span> <span class="nc">EKFIntegrals</span><span class="p">(</span><span class="n">NamedTuple</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot; Lightweight container for EKF Gaussian integrals.&quot;&quot;&quot;</span>
<span class="n">gaussian_expectation</span><span class="p">:</span> <span class="n">Callable</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">f</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">P</span><span class="p">:</span> <span class="n">jnp</span><span class="o">.</span><span class="n">atleast_1d</span><span class="p">(</span><span class="n">f</span><span class="p">(</span><span class="n">m</span><span class="p">))</span>
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<span class="k">def</span> <span class="nf">_predict</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span> <span class="n">f</span><span class="p">,</span> <span class="n">Q</span><span class="p">,</span> <span class="n">u</span><span class="p">,</span> <span class="n">g_ev</span><span class="p">,</span> <span class="n">g_cov</span><span class="p">):</span>
<span class="w"> </span><span class="sd">&quot;&quot;&quot;Predict next mean and covariance under an additive-noise Gaussian filter</span>
<span class="sd"> </span>

<span class="sd"> p(x_{t+1}) = N(x_{t+1} | mu_pred, Sigma_pred)</span>
<span class="sd"> where</span>
<span class="sd"> mu_pred = gev(f, m, P)</span>
Expand Down Expand Up @@ -653,13 +654,17 @@ <h1>Source code for dynamax.generalized_gaussian_ssm.inference</h1><div class="h
<span class="k">return</span> <span class="p">(</span><span class="n">smoothed_mean</span><span class="p">,</span> <span class="n">smoothed_cov</span><span class="p">),</span> <span class="p">(</span><span class="n">smoothed_mean</span><span class="p">,</span> <span class="n">smoothed_cov</span><span class="p">)</span>

<span class="c1"># Run the smoother</span>
<span class="n">init_carry</span> <span class="o">=</span> <span class="p">(</span><span class="n">filtered_means</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">filtered_covs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">args</span> <span class="o">=</span> <span class="p">(</span><span class="n">jnp</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_timesteps</span> <span class="o">-</span> <span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">filtered_means</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">filtered_covs</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">smoothed_means</span><span class="p">,</span> <span class="n">smoothed_covs</span><span class="p">)</span> <span class="o">=</span> <span class="n">lax</span><span class="o">.</span><span class="n">scan</span><span class="p">(</span><span class="n">_step</span><span class="p">,</span> <span class="n">init_carry</span><span class="p">,</span> <span class="n">args</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">smoothed_means</span><span class="p">,</span> <span class="n">smoothed_covs</span><span class="p">)</span> <span class="o">=</span> <span class="n">lax</span><span class="o">.</span><span class="n">scan</span><span class="p">(</span>
<span class="n">_step</span><span class="p">,</span>
<span class="p">(</span><span class="n">filtered_means</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">filtered_covs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
<span class="p">(</span><span class="n">jnp</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_timesteps</span> <span class="o">-</span> <span class="mi">1</span><span class="p">),</span> <span class="n">filtered_means</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">filtered_covs</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
<span class="n">reverse</span><span class="o">=</span><span class="kc">True</span>
<span class="p">)</span>

<span class="c1"># Concatenate the last smoothed mean and covariance</span>
<span class="n">smoothed_means</span> <span class="o">=</span> <span class="n">jnp</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">smoothed_means</span><span class="p">,</span> <span class="n">filtered_means</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">]))</span>
<span class="n">smoothed_covs</span> <span class="o">=</span> <span class="n">jnp</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">smoothed_covs</span><span class="p">,</span> <span class="n">filtered_covs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">]))</span>

<span class="c1"># Reverse the arrays and return</span>
<span class="n">smoothed_means</span> <span class="o">=</span> <span class="n">jnp</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">smoothed_means</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">filtered_means</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">]))</span>
<span class="n">smoothed_covs</span> <span class="o">=</span> <span class="n">jnp</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">smoothed_covs</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">filtered_covs</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="kc">None</span><span class="p">,</span> <span class="o">...</span><span class="p">]))</span>
<span class="k">return</span> <span class="n">PosteriorGSSMSmoothed</span><span class="p">(</span>
<span class="n">marginal_loglik</span><span class="o">=</span><span class="n">ll</span><span class="p">,</span>
<span class="n">filtered_means</span><span class="o">=</span><span class="n">filtered_means</span><span class="p">,</span>
Expand Down Expand Up @@ -763,8 +768,8 @@ <h1>Source code for dynamax.generalized_gaussian_ssm.inference</h1><div class="h
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