Bugfix of cutpoint calculations in ordinal regression notebook (#564)

* Draft update of BNN notebook

* Pre-commit fixes

* Address reviewer comments

* Fixed minmax scaling in constainedUniform

* Cleanup and fixes

Author: fonnesbeck

examples/generalized_linear_models/GLM-ordinal-regression.ipynb

@@ -375,8 +375,8 @@
     "        pt.concatenate(\n",
     "            [\n",
     "                np.ones(1) * min,\n",
-    "                pt.extra_ops.cumsum(pm.Dirichlet(\"cuts_unknown\", a=np.ones(N - 2)))\n",
-    "                * (min + (max - min)),\n",
+    "                pt.extra_ops.cumsum(pm.Dirichlet(\"cuts_unknown\", a=np.ones(N - 2))) * (max - min)\n",
+    "                + min,\n",
     "            ]\n",
     "        ),\n",
     "    )"
@@ -387,7 +387,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The above function, (brainchild of Dr Ben Vincen and Adrian Seyboldt), looks a little indimidating, but it's just a convenience function to specify a prior over the cutpoints in our $Y_{latent}$. The Dirichlet distribution is special in that draws from the distribution must sum to one. The above function ensures that each draw from the prior distribution is a cumulative share of the maximum category greater than the minimum of our ordinal categorisation. "
+    "The above function, (brainchild of Dr Ben Vincent and Adrian Seyboldt), looks a little indimidating, but it's just a convenience function to specify a prior over the cutpoints in our $Y_{latent}$. The Dirichlet distribution is special in that draws from the distribution must sum to one. The above function ensures that each draw from the prior distribution is a cumulative share of the maximum category greater than the minimum of our ordinal categorisation. "
    ]
   },
   {
@@ -3146,7 +3146,8 @@
     "            [\n",
     "                np.ones(1) * min,\n",
     "                pt.extra_ops.cumsum(pm.Dirichlet(f\"cuts_unknown_{group}\", a=np.ones(N - 2)))\n",
-    "                * (min + (max - min)),\n",
+    "                * (max - min)\n",
+    "                + min,\n",
     "            ]\n",
     "        ),\n",
     "    )"

examples/generalized_linear_models/GLM-ordinal-regression.myst.md

@@ -197,14 +197,14 @@ def constrainedUniform(N, min=0, max=1):
         pt.concatenate(
             [
                 np.ones(1) * min,
-                pt.extra_ops.cumsum(pm.Dirichlet("cuts_unknown", a=np.ones(N - 2)))
-                * (min + (max - min)),
+                pt.extra_ops.cumsum(pm.Dirichlet("cuts_unknown", a=np.ones(N - 2))) * (max - min)
+                + min,
             ]
         ),
     )
 ```
 
-The above function, (brainchild of Dr Ben Vincen and Adrian Seyboldt), looks a little indimidating, but it's just a convenience function to specify a prior over the cutpoints in our $Y_{latent}$. The Dirichlet distribution is special in that draws from the distribution must sum to one. The above function ensures that each draw from the prior distribution is a cumulative share of the maximum category greater than the minimum of our ordinal categorisation. 
+The above function, (brainchild of Dr Ben Vincent and Adrian Seyboldt), looks a little indimidating, but it's just a convenience function to specify a prior over the cutpoints in our $Y_{latent}$. The Dirichlet distribution is special in that draws from the distribution must sum to one. The above function ensures that each draw from the prior distribution is a cumulative share of the maximum category greater than the minimum of our ordinal categorisation. 
 
 ```{code-cell} ipython3
 :tags: [hide-output]
@@ -554,7 +554,8 @@ def constrainedUniform(N, group, min=0, max=1):
             [
                 np.ones(1) * min,
                 pt.extra_ops.cumsum(pm.Dirichlet(f"cuts_unknown_{group}", a=np.ones(N - 2)))
-                * (min + (max - min)),
+                * (max - min)
+                + min,
             ]
         ),
     )