Hi,
Concerning the problem of too many nested elements, if so many levels are
really needed, you can try to use the enumitem package to increase the
number of allowed nested levels :
\usepackage{enumitem}
\setlistdepth{10}
Alexandre.
On Mon, Jun 4, 2012 at 12:52 AM, <[email protected]> wrote:
> Hi Andy,
>
> I build it on Ubuntu 12.04 (precise). If you are interested in packages
> version
> (texlive,...), http://packages.ubuntu.com should list them
> Other then that, .diff file provided shows what did I change
> in user_guide.tex file for it to compile to PDF:
>
>
> 1. As you noted in your previous message, I also got too deeply nested
> elements. For that problem, I just commented elements that seemed right to me
> - reduce "branches" one level by commenting one description or quote element,
> while letting contents to render. So when you get "too deeply nested" error
> with line number just comment some parent element near that line.
>
> 2. I also commented two gather elements holding equations,
> which could not render. BTW, both equations render wrongly in online HTML
> documentation too, because or problematic use of under-scope, see equation in
> Attributes table:
> http://scikit-learn.sourceforge.net/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html
>
> That's the same I commented in two places, which does not seem like too
> important
>
>
>
> Just right now, I tried to do the same on Windows with MiKTeX 2.9 and it
> turned out that I needed to do the same thing as described above, afterwhich
> I got same PDF output
>
>
> Problems quickly noticeable:
>
> 1. {longtable} rows aren't wrapped:
>
>
>
>
> 2. {tabular} tables with {OriginalVerbatim} it's wrapped also, where
> needed :
>
>
>
>
> 3. Pictures that render white in Example gallery (could be just local
> problem, I didn't have time to look in scripts that should produce this
> correctly):
>
> document_classification_20newsgroups.png
> document_clustering.png
> face_recognition.png
> feature_selection_pipeline.png
> gp_diabetes_dataset.png
> grid_search_digits.png
> grid_search_text_feature_extraction.png
> lasso_dense_vs_sparse_data.png
> mlcomp_sparse_document_classification.png
> plot_digits_classification_exercise.png
> svm_gui.png
> topics_extraction_with_nmf.png
> wikipedia_principal_eigenvector.png
>
>
> I hope this should cover my building process
>
> Regards
> Matt
>
>
> 03.06.2012 at 6:31 PM, Andreas Mueller wrote:
> >
> >Hi Matt.
> >Thanks for sharing your build.
> >Could you please describe how you got the tex to build?
> >I have major difficulties.
>
>
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