Couple missed issues fixed

Jordan Cheney
1 parent 7898a0ed
Showing 3 changed files with 3 additions and 2 deletions
docs/docs/api_docs/cpp_api/file/statics.md
docs/docs/tutorials.md
docs/themes/readthedocs/toc.html
@@ -199,6 +199,7 @@ Deserialize a file from a data stream.
 [QList]: http://doc.qt.io/qt-5/QList.html "QList"
 [QVariant]: http://doc.qt.io/qt-5/qvariant.html "QVariant"
 [QString]: http://doc.qt.io/qt-5/QString.html "QString"
+[QDebug]: http://doc.qt.io/qt-5/qdebug.html "QDebug"
 [QDataStream]: http://doc.qt.io/qt-5/qdatastream.html "QDataStream"
 [QRectF]: http://doc.qt.io/qt-5/qrectf.html "QRectF"
 [QPointF]: http://doc.qt.io/qt-5/qpointf.html "QPointF"
@@ -152,7 +152,7 @@ Notice the ```-train``` flag used in the algorithm. [-train](api_docs/cl_api.md#
  
 [-train](api_docs/cl_api.md#train) has an optional second argument: the name for a trained model (`EigenFaces` in the example above). The optional model file is a serialized and compressed binary file that stores the parameters learned during algorithm training.  The model file should only be considered optional when your algorithm string uses a [LoadStoreTransform](plugin_docs/core.md#loadstoretransform) (discussed in depth later in this tutorial).  Otherwise, none of the parameters learned during algorithm training will be stored!
  
-As was briefly discussed above, each [Transform](api_docs/cpp_api/transform/transform.md) in is either [trainable](api_docs/cpp_api/transform/members.md#trainable) or not (in our case only ```PCA(0.95)``` is trainable). At train time, the training data is projected through each [UntrainableTransforms](api_docs/cpp_api/untrainabletransform/untrainabletransform.md) in sequence, just as it would be at test time. When the data reaches a trainable transform, the [train](api_docs/cpp_api/transform/functions.md#train-1) method is called with the data projected through the preceding [Transform](api_docs/cpp_api/transform/transform.md)s as its input. After training, the project method is called on the newly trained transform and the data continues to propagate through the algorithm.
+As was briefly discussed above, each [Transform](api_docs/cpp_api/transform/transform.md) in is either [trainable](api_docs/cpp_api/transform/members.md#trainable) or not (in our case only ```PCA(0.95)``` is trainable). At train time, the training data is projected through each [UntrainableTransform](api_docs/cpp_api/untrainabletransform/untrainabletransform.md) in sequence, just as it would be at test time. When the data reaches a trainable transform, the [train](api_docs/cpp_api/transform/functions.md#train-1) method is called with the data projected through the preceding [Transforms](api_docs/cpp_api/transform/transform.md) as its input. After training, the project method is called on the newly trained transform and the data continues to propagate through the algorithm.
  
 After training is complete the algorithm is serialized to a model file (if you have specified one). The algorithm string is serialized first such that the algorithm can be recreated, followed by the parameters for each transform using the [store](api_docs/cpp_api/object/functions.md#store) method. Note that only trainable [Transforms](api_docs/cpp_api/transform/transform.md) need to implement [store](api_docs/cpp_api/object/functions.md#store) because [UntrainableTransforms](api_docs/cpp_api/untrainabletransform/untrainabletransform.md) can be recreated solely from their algorithm string descriptions.
  
@@ -12,7 +12,7 @@
     <li class="toctree-l1 {% if nav_item.active%}current{%endif%}">
     <a class="{% if nav_item.active%}current{%endif%}" href="{{ nav_item.url }}">{{ nav_item.title }}</a>
         {% if nav_item == current_page %}
-            <ul>
+            <ul class="subnav">
             {% for toc_item in toc %}
                 <li class="toctree-l3"><a href="{{ toc_item.url }}">{{ toc_item.title }}</a></li>
             {% endfor %}