fix ci

ReactiveBayes · Jun 27, 2023 · 239e392 · 239e392
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diff --git a/examples/.meta.jl b/examples/.meta.jl
@@ -57,7 +57,7 @@ return [
     ),
     (
         path  = "Hidden Markov Model.ipynb",
-        title = "Ensemble Learning of a Hidden Markov Model",
+        title = "How to train your Hidden Markov Model",
         description = "An example of structured variational Bayesian inference in Hidden Markov Model with unknown transition and observational matrices.",
         hidden = false
     ),

diff --git a/examples/Hidden Markov Model.ipynb b/examples/Hidden Markov Model.ipynb
@@ -808,59 +808,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Neat! Now you know how to track a Roomba if you ever need to. You also learned how to fit a Hidden Markov Model using `RxInfer` in the process. Let's end by running some benchmarks to check how fast `RxInfer` really is.\n"
+    "Neat! Now you know how to track a Roomba if you ever need to. You also learned how to fit a Hidden Markov Model using `RxInfer` in the process."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Benchmark timings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BenchmarkTools.Trial: 123 samples with 1 evaluation.\n",
-       " Range \u001b[90m(\u001b[39m\u001b[36m\u001b[1mmin\u001b[22m\u001b[39m … \u001b[35mmax\u001b[39m\u001b[90m):  \u001b[39m\u001b[36m\u001b[1m27.799 ms\u001b[22m\u001b[39m … \u001b[35m98.382 ms\u001b[39m  \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmin … max\u001b[90m): \u001b[39m 0.00% … 50.86%\n",
-       " Time  \u001b[90m(\u001b[39m\u001b[34m\u001b[1mmedian\u001b[22m\u001b[39m\u001b[90m):     \u001b[39m\u001b[34m\u001b[1m35.176 ms              \u001b[22m\u001b[39m\u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmedian\u001b[90m):    \u001b[39m 0.00%\n",
-       " Time  \u001b[90m(\u001b[39m\u001b[32m\u001b[1mmean\u001b[22m\u001b[39m ± \u001b[32mσ\u001b[39m\u001b[90m):   \u001b[39m\u001b[32m\u001b[1m40.781 ms\u001b[22m\u001b[39m ± \u001b[32m14.464 ms\u001b[39m  \u001b[90m┊\u001b[39m GC \u001b[90m(\u001b[39mmean ± σ\u001b[90m):  \u001b[39m10.01% ± 15.15%\n",
-       "\n",
-       "  \u001b[39m█\u001b[39m▂\u001b[39m \u001b[39m▂\u001b[39m \u001b[39m \u001b[39m \u001b[34m \u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[32m \u001b[39m\u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \u001b[39m \n",
-       "  \u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m█\u001b[39m▆\u001b[39m▅\u001b[39m▃\u001b[34m▅\u001b[39m\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m█\u001b[39m▃\u001b[32m▃\u001b[39m\u001b[39m▁\u001b[39m▃\u001b[39m▃\u001b[39m▅\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▄\u001b[39m▃\u001b[39m▃\u001b[39m▅\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m▃\u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m▄\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▁\u001b[39m▃\u001b[39m \u001b[39m▃\n",
-       "  27.8 ms\u001b[90m         Histogram: frequency by time\u001b[39m        88.2 ms \u001b[0m\u001b[1m<\u001b[22m\n",
-       "\n",
-       " Memory estimate\u001b[90m: \u001b[39m\u001b[33m20.33 MiB\u001b[39m, allocs estimate\u001b[90m: \u001b[39m\u001b[33m373611\u001b[39m."
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "@benchmark inference(\n",
-    "    model         = $imodel, \n",
-    "    data          = $idata,\n",
-    "    constraints   = hidden_markov_model_constraints(),\n",
-    "    initmarginals = $imarginals, \n",
-    "    returnvars    = $ireturnvars, \n",
-    "    iterations    = 20, \n",
-    "    free_energy   = true\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {