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probml · Jun 20, 2023 · 5331340 · 5331340
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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/index.doctree b/.doctrees/index.doctree
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
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diff --git a/.doctrees/notebooks/hmm/gaussian_hmm.doctree b/.doctrees/notebooks/hmm/gaussian_hmm.doctree
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diff --git a/_modules/dynamax/hidden_markov_model/models/categorical_glm_hmm.html b/_modules/dynamax/hidden_markov_model/models/categorical_glm_hmm.html
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diff --git a/_modules/dynamax/hidden_markov_model/models/gmm_hmm.html b/_modules/dynamax/hidden_markov_model/models/gmm_hmm.html
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diff --git a/_modules/dynamax/linear_gaussian_ssm/models.html b/_modules/dynamax/linear_gaussian_ssm/models.html
diff --git a/_modules/dynamax/nonlinear_gaussian_ssm/inference_ekf.html b/_modules/dynamax/nonlinear_gaussian_ssm/inference_ekf.html
diff --git a/_modules/dynamax/nonlinear_gaussian_ssm/inference_ukf.html b/_modules/dynamax/nonlinear_gaussian_ssm/inference_ukf.html
diff --git a/_modules/dynamax/nonlinear_gaussian_ssm/models.html b/_modules/dynamax/nonlinear_gaussian_ssm/models.html
diff --git a/_modules/dynamax/parameters.html b/_modules/dynamax/parameters.html
diff --git a/_modules/dynamax/ssm.html b/_modules/dynamax/ssm.html
diff --git a/_modules/dynamax/utils/utils.html b/_modules/dynamax/utils/utils.html
diff --git a/_modules/index.html b/_modules/index.html
diff --git a/_sources/README.md.txt → _sources/README.md b/_sources/README.md.txt → _sources/README.md
diff --git a/_sources/api.rst.txt → _sources/api.rst b/_sources/api.rst.txt → _sources/api.rst
@@ -74,6 +74,10 @@ default to weak priors without any stickiness.
   :show-inheritance:
   :members: initialize
 
+.. autoclass:: dynamax.hidden_markov_model.GammaHMM
+  :show-inheritance:
+  :members: initialize
+
 .. autoclass:: dynamax.hidden_markov_model.GaussianHMM
   :show-inheritance:
   :members: initialize

diff --git a/_sources/index.rst.txt → _sources/index.rst b/_sources/index.rst.txt → _sources/index.rst
@@ -229,6 +229,7 @@ API documentation
    :maxdepth: 3
    :caption: API Documentation
 
+   types
    api
 
 

diff --git a/...m/cmgf_logistic_regression_demo.ipynb.txt → ...n_ssm/cmgf_logistic_regression_demo.ipynb b/...m/cmgf_logistic_regression_demo.ipynb.txt → ...n_ssm/cmgf_logistic_regression_demo.ipynb
diff --git a/...sm/cmgf_mlp_classification_demo.ipynb.txt → ...an_ssm/cmgf_mlp_classification_demo.ipynb b/...sm/cmgf_mlp_classification_demo.ipynb.txt → ...an_ssm/cmgf_mlp_classification_demo.ipynb
diff --git a/..._gaussian_ssm/cmgf_poisson_demo.ipynb.txt → ...ized_gaussian_ssm/cmgf_poisson_demo.ipynb b/..._gaussian_ssm/cmgf_poisson_demo.ipynb.txt → ...ized_gaussian_ssm/cmgf_poisson_demo.ipynb
diff --git a/...otebooks/hmm/autoregressive_hmm.ipynb.txt → ...es/notebooks/hmm/autoregressive_hmm.ipynb b/...otebooks/hmm/autoregressive_hmm.ipynb.txt → ...es/notebooks/hmm/autoregressive_hmm.ipynb
diff --git a/...ebooks/hmm/casino_hmm_inference.ipynb.txt → .../notebooks/hmm/casino_hmm_inference.ipynb b/...ebooks/hmm/casino_hmm_inference.ipynb.txt → .../notebooks/hmm/casino_hmm_inference.ipynb
@@ -628,7 +628,7 @@
    "source": [
     "## Conclusion\n",
     "\n",
-    "This notebook showed how to construct a simple categorical HMM, initialize its parameters, sample data, and use it to perform state inference (i.e. filtering, smoothing, and finding the most likely states). However, often we do not know the model paramters and instead need to estimate them from data. The next notebook shows how to do exactly that."
+    "This notebook showed how to construct a simple categorical HMM, initialize its parameters, sample data, and use it to perform state inference (i.e. filtering, smoothing, and finding the most likely states). However, often we do not know the model parameters and instead need to estimate them from data. The next notebook shows how to do exactly that."
    ]
   }
  ],

diff --git a/...tebooks/hmm/casino_hmm_learning.ipynb.txt → ...s/notebooks/hmm/casino_hmm_learning.ipynb b/...tebooks/hmm/casino_hmm_learning.ipynb.txt → ...s/notebooks/hmm/casino_hmm_learning.ipynb
@@ -11,7 +11,7 @@
     "This notebook continues the \"occasionally dishonest casino\" example from the preceding notebook.\n",
     "There, we assumed we knew the parameters of the model: the probability of switching between fair and loaded dice and the probabilities of the different outcomes (1,...,6) for each die. \n",
     "\n",
-    "Here, our goal is **learn these parameters from data**.  We will sample data from the model as before, but now we will estimate the parameters using eith stochastic gradient descent (SGD) or expectation-maximization (EM).\n",
+    "Here, our goal is **learn these parameters from data**.  We will sample data from the model as before, but now we will estimate the parameters using either stochastic gradient descent (SGD) or expectation-maximization (EM).\n",
     "\n",
     "The figure below shows the _graphical model_, complete with the parameter nodes.\n",
     "<p align=\"center\">\n",
@@ -31,7 +31,7 @@
     "\\end{align*}\n",
     "The hyperparameters can be specified in the [`CategoricalHMM`](https://probml.github.io/dynamax/api.html#dynamax.hidden_markov_model.CategoricalHMM) constructor..\n",
     "\n",
-    "The **learning objective** is to find parameters tham maximize the marginal probability,\n",
+    "The **learning objective** is to find parameters that maximize the marginal probability,\n",
     "\\begin{align*}\n",
     "\\theta^\\star &= \\text{arg max}_{\\theta} \\; p(\\theta \\mid y_{1:T}) \\\\\n",
     "&= \\text{arg max}_{\\theta} \\; p(\\theta, y_{1:T})\n",
@@ -397,7 +397,7 @@
     }
    ],
    "source": [
-    "# Print the paramters after learning\n",
+    "# Print the parameters after learning\n",
     "print(\"Full batch gradient descent params:\")\n",
     "print_params(fbgd_params)\n",
     "print(\"\")\n",
@@ -560,29 +560,29 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Not only does EM converge much faster on this example (here, in only a handful of iterations), it also converges to a better estimate of the parameters. Indeed, it essentially matches the loss obtained by the parameters that truly generated the data. We see that its parameter estimates are nearly the same as the true parameters."
+    "Not only does EM converge much faster on this example (here, in only a handful of iterations), it also converges to a better estimate of the parameters. Indeed, it essentially matches the loss obtained by the parameters that truly generated the data. We see that its parameter estimates are nearly the same as the true parameters, up to label switching. \n",
+    "\n",
+    "(Label switching refers to the fact that the generated parameters assume state 1 corresponds to the loaded die, whereas the learned parameters assume this is state 0; since these solutions have the same likelihood, and since the prior is also symmetrical, there are two equally good posterior modes, and EM will just find one of them. When you compare inferred  parameters or states between models, you may need to use our [find_permutation](https://probml.github.io/dynamax/api.html#dynamax.utils.utils.find_permutation) function to find the best correspondence between discrete latent labels.)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "initial probs:\n",
-      "[0.628 0.372]\n",
-      "transition matrix:\n",
-      "[[0.909 0.091]\n",
-      " [0.053 0.947]]\n",
-      "emission probs:\n",
-      "[[0.110 0.106 0.101 0.110 0.105 0.468]\n",
-      " [0.171 0.173 0.171 0.164 0.164 0.157]]\n"
+     "ename": "NameError",
+     "evalue": "name 'print_params' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m/Users/kpmurphy/github/dynamax/docs/notebooks/hmm/casino_hmm_learning.ipynb Cell 27\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/kpmurphy/github/dynamax/docs/notebooks/hmm/casino_hmm_learning.ipynb#X35sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m print_params(em_params)\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'print_params' is not defined"
      ]
     }
    ],
@@ -613,7 +613,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3.9.6",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -631,7 +631,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "92401d49bd83f70620e540b668b7091047c2ca041ed5ff6cc3954130de9aa1fc"
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
    }
   },
   "widgets": {

diff --git a/...rces/notebooks/hmm/gaussian_hmm.ipynb.txt → _sources/notebooks/hmm/gaussian_hmm.ipynb b/...rces/notebooks/hmm/gaussian_hmm.ipynb.txt → _sources/notebooks/hmm/gaussian_hmm.ipynb
@@ -160,7 +160,9 @@
     "\n",
     "# Specify parameters of the HMM\n",
     "initial_probs = jnp.ones(true_num_states) / true_num_states\n",
-    "transition_matrix = 0.8 * jnp.eye(true_num_states) + 0.2 * jnp.roll(jnp.eye(true_num_states), 1, axis=1)\n",
+    "transition_matrix = 0.80 * jnp.eye(true_num_states) \\\n",
+    "    + 0.15 * jnp.roll(jnp.eye(true_num_states), 1, axis=1) \\\n",
+    "    + 0.05 / true_num_states\n",
     "emission_means = jnp.column_stack([\n",
     "    jnp.cos(jnp.linspace(0, 2 * jnp.pi, true_num_states + 1))[:-1],\n",
     "    jnp.sin(jnp.linspace(0, 2 * jnp.pi, true_num_states + 1))[:-1],\n",

diff --git a/...s/linear_gaussian_ssm/kf_linreg.ipynb.txt → ...books/linear_gaussian_ssm/kf_linreg.ipynb b/...s/linear_gaussian_ssm/kf_linreg.ipynb.txt → ...books/linear_gaussian_ssm/kf_linreg.ipynb
diff --git a/...linear_gaussian_ssm/kf_tracking.ipynb.txt → ...oks/linear_gaussian_ssm/kf_tracking.ipynb b/...linear_gaussian_ssm/kf_tracking.ipynb.txt → ...oks/linear_gaussian_ssm/kf_tracking.ipynb
@@ -13,6 +13,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "17072519",
    "metadata": {},
@@ -64,7 +65,7 @@
     "0 & 0 & 1 & 0\n",
     "    \\end{pmatrix}\n",
     "    }_{H}\n",
-    "    \n",
+    "    \\;\n",
     "\\underbrace{\\begin{pmatrix} u_t\\\\ \\dot{u}_t \\\\ v_t \\\\ \\dot{v}_t \\end{pmatrix}}_{z_t}    \n",
     " + r_t\n",
     "\\end{align*}\n",
@@ -586,7 +587,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3.9.6 ('dynamax')",
+   "display_name": "Python 3.8.10 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -600,11 +601,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.8.10"
   },
   "vscode": {
    "interpreter": {
-    "hash": "92401d49bd83f70620e540b668b7091047c2ca041ed5ff6cc3954130de9aa1fc"
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
    }
   }
  },

diff --git a/...s/linear_gaussian_ssm/lgssm_hmc.ipynb.txt → ...books/linear_gaussian_ssm/lgssm_hmc.ipynb b/...s/linear_gaussian_ssm/lgssm_hmc.ipynb.txt → ...books/linear_gaussian_ssm/lgssm_hmc.ipynb
@@ -109,8 +109,8 @@
                 "                    label=\"smoothed\" if i == 0 else None)[0]\n",
                 "        plt.fill_between(\n",
                 "            jnp.arange(num_timesteps),\n",
-                "            spc * i + smoothed_emissions[:, i] - 2 * jnp.sqrt(smoothed_emissions_std[:, i]),\n",
-                "            spc * i + smoothed_emissions[:, i] + 2 * jnp.sqrt(smoothed_emissions_std[:, i]),\n",
+                "            spc * i + smoothed_emissions[:, i] - 2 * smoothed_emissions_std[:, i],\n",
+                "            spc * i + smoothed_emissions[:, i] + 2 * smoothed_emissions_std[:, i],\n",
                 "            color=ln.get_color(),\n",
                 "            alpha=0.25,\n",
                 "        )\n",
@@ -758,7 +758,7 @@
     ],
     "metadata": {
         "kernelspec": {
-            "display_name": "Python 3.9.6 ('dynamax')",
+            "display_name": "base",
             "language": "python",
             "name": "python3"
         },
@@ -772,12 +772,12 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.9.6"
+            "version": "3.8.5 (default, Sep  4 2020, 02:22:02) \n[Clang 10.0.0 ]"
         },
         "orig_nbformat": 4,
         "vscode": {
             "interpreter": {
-                "hash": "92401d49bd83f70620e540b668b7091047c2ca041ed5ff6cc3954130de9aa1fc"
+                "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
             }
         }
     },

diff --git a/...ear_gaussian_ssm/lgssm_learning.ipynb.txt → .../linear_gaussian_ssm/lgssm_learning.ipynb b/...ear_gaussian_ssm/lgssm_learning.ipynb.txt → .../linear_gaussian_ssm/lgssm_learning.ipynb
@@ -157,8 +157,8 @@
     "                        label=\"smoothed\" if i == 0 else None)[0]\n",
     "        plt.fill_between(\n",
     "            jnp.arange(num_timesteps),\n",
-    "            spc * i + smoothed_emissions[:, i] - 2 * jnp.sqrt(smoothed_emissions_std[i]),\n",
-    "            spc * i + smoothed_emissions[:, i] + 2 * jnp.sqrt(smoothed_emissions_std[i]),\n",
+    "            spc * i + smoothed_emissions[:, i] - 2 * smoothed_emissions_std[i],\n",
+    "            spc * i + smoothed_emissions[:, i] + 2 * smoothed_emissions_std[i],\n",
     "            color=ln.get_color(),\n",
     "            alpha=0.25,\n",
     "        )\n",
@@ -350,7 +350,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3.9.6 ('dynamax')",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -364,11 +364,11 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.9.6 (default, Jun 29 2021, 05:25:02) \n[Clang 12.0.5 (clang-1205.0.22.9)]"
   },
   "vscode": {
    "interpreter": {
-    "hash": "92401d49bd83f70620e540b668b7091047c2ca041ed5ff6cc3954130de9aa1fc"
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
    }
   }
  },

diff --git a/...an_ssm/lgssm_parallel_inference.ipynb.txt → ...ussian_ssm/lgssm_parallel_inference.ipynb b/...an_ssm/lgssm_parallel_inference.ipynb.txt → ...ussian_ssm/lgssm_parallel_inference.ipynb