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probml · Aug 14, 2023 · 0d2b82e · 0d2b82e
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diff --git a/.doctrees/api.doctree b/.doctrees/api.doctree
diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle
diff --git a/.doctrees/notebooks/generalized_gaussian_ssm/cmgf_logistic_regression_demo.doctree b/.doctrees/notebooks/generalized_gaussian_ssm/cmgf_logistic_regression_demo.doctree
diff --git a/.doctrees/notebooks/generalized_gaussian_ssm/cmgf_mlp_classification_demo.doctree b/.doctrees/notebooks/generalized_gaussian_ssm/cmgf_mlp_classification_demo.doctree
diff --git a/.doctrees/notebooks/generalized_gaussian_ssm/cmgf_poisson_demo.doctree b/.doctrees/notebooks/generalized_gaussian_ssm/cmgf_poisson_demo.doctree
diff --git a/.doctrees/notebooks/hmm/autoregressive_hmm.doctree b/.doctrees/notebooks/hmm/autoregressive_hmm.doctree
diff --git a/.doctrees/notebooks/hmm/casino_hmm_inference.doctree b/.doctrees/notebooks/hmm/casino_hmm_inference.doctree
diff --git a/.doctrees/notebooks/hmm/casino_hmm_learning.doctree b/.doctrees/notebooks/hmm/casino_hmm_learning.doctree
diff --git a/.doctrees/notebooks/hmm/gaussian_hmm.doctree b/.doctrees/notebooks/hmm/gaussian_hmm.doctree
diff --git a/.doctrees/notebooks/linear_gaussian_ssm/kf_linreg.doctree b/.doctrees/notebooks/linear_gaussian_ssm/kf_linreg.doctree
diff --git a/.doctrees/notebooks/linear_gaussian_ssm/kf_tracking.doctree b/.doctrees/notebooks/linear_gaussian_ssm/kf_tracking.doctree
diff --git a/.doctrees/notebooks/linear_gaussian_ssm/lgssm_hmc.doctree b/.doctrees/notebooks/linear_gaussian_ssm/lgssm_hmc.doctree
diff --git a/.doctrees/notebooks/linear_gaussian_ssm/lgssm_learning.doctree b/.doctrees/notebooks/linear_gaussian_ssm/lgssm_learning.doctree
diff --git a/.doctrees/notebooks/linear_gaussian_ssm/lgssm_parallel_inference.doctree b/.doctrees/notebooks/linear_gaussian_ssm/lgssm_parallel_inference.doctree
diff --git a/.doctrees/notebooks/nonlinear_gaussian_ssm/ekf_mlp.doctree b/.doctrees/notebooks/nonlinear_gaussian_ssm/ekf_mlp.doctree
diff --git a/.doctrees/notebooks/nonlinear_gaussian_ssm/ekf_ukf_pendulum.doctree b/.doctrees/notebooks/nonlinear_gaussian_ssm/ekf_ukf_pendulum.doctree
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diff --git a/_modules/dynamax/hidden_markov_model/models/arhmm.html b/_modules/dynamax/hidden_markov_model/models/arhmm.html
@@ -324,7 +324,9 @@ <h1>Source code for dynamax.hidden_markov_model.models.arhmm</h1><div class="hig
         <span class="k">if</span> <span class="n">method</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;kmeans&quot;</span><span class="p">:</span>
             <span class="k">assert</span> <span class="n">emissions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;Need emissions to initialize the model with K-Means!&quot;</span>
             <span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
-            <span class="n">km</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_states</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">emissions</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">emission_dim</span><span class="p">))</span>
+            <span class="n">key</span><span class="p">,</span> <span class="n">subkey</span> <span class="o">=</span> <span class="n">jr</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>  <span class="c1"># Create a random seed for SKLearn.</span>
+            <span class="n">sklearn_key</span> <span class="o">=</span> <span class="n">jr</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">subkey</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(),</span> <span class="n">minval</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">maxval</span><span class="o">=</span><span class="mi">2147483647</span><span class="p">)</span>  <span class="c1"># Max int32 value.</span>
+            <span class="n">km</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_states</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">sklearn_key</span><span class="p">))</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">emissions</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">emission_dim</span><span class="p">))</span>
             <span class="n">_emission_weights</span> <span class="o">=</span> <span class="n">jnp</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">num_states</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">emission_dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">emission_dim</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_lags</span><span class="p">))</span>
             <span class="n">_emission_biases</span> <span class="o">=</span> <span class="n">jnp</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">km</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">)</span>
             <span class="n">_emission_covs</span> <span class="o">=</span> <span class="n">jnp</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">jnp</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">emission_dim</span><span class="p">)[</span><span class="kc">None</span><span class="p">,</span> <span class="p">:,</span> <span class="p">:],</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_states</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>

diff --git a/_modules/dynamax/hidden_markov_model/models/gamma_hmm.html b/_modules/dynamax/hidden_markov_model/models/gamma_hmm.html
@@ -321,7 +321,9 @@ <h1>Source code for dynamax.hidden_markov_model.models.gamma_hmm</h1><div class=
         <span class="k">if</span> <span class="n">method</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s2">&quot;kmeans&quot;</span><span class="p">:</span>
             <span class="k">assert</span> <span class="n">emissions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;Need emissions to initialize the model with K-Means!&quot;</span>
             <span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
-            <span class="n">km</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_states</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">emissions</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+            <span class="n">key</span><span class="p">,</span> <span class="n">subkey</span> <span class="o">=</span> <span class="n">jr</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>  <span class="c1"># Create a random seed for SKLearn.</span>
+            <span class="n">sklearn_key</span> <span class="o">=</span> <span class="n">jr</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">subkey</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="p">(),</span> <span class="n">minval</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">maxval</span><span class="o">=</span><span class="mi">2147483647</span><span class="p">)</span>  <span class="c1"># Max int32 value.</span>
+            <span class="n">km</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_states</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">sklearn_key</span><span class="p">))</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">emissions</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
 
             <span class="n">_emission_concentrations</span> <span class="o">=</span> <span class="n">jnp</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">num_states</span><span class="p">,))</span>
             <span class="n">_emission_rates</span> <span class="o">=</span> <span class="n">jnp</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="n">km</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">)</span>