Fix Doxygen warnings

Doxyfile

@@ -75,6 +75,7 @@ FILE_VERSION_FILTER    =
 #---------------------------------------------------------------------------
 QUIET                  = NO
 WARNINGS               = YES
+WARN_AS_ERROR          = YES
 WARN_IF_UNDOCUMENTED   = YES
 WARN_IF_DOC_ERROR      = YES
 WARN_NO_PARAMDOC       = YES

doc/guide/hpt.hpp

@@ -1,7 +1,7 @@
 namespace mlpack {
 namespace hpt {
 
-/*! @page hpt Hyper-Parameter Tuning
+/*! @page hpt_guide Hyper-Parameter Tuning
 
 @section hptintro Introduction
 
@@ -32,7 +32,7 @@ The interface of the hyper-parameter tuning module is quite similar to the
 interface of the @ref cv "cross-validation module". To construct a \c
 HyperParameterTuner object you need to specify as template parameters what
 machine learning algorithm, cross-validation strategy, performance measure, and
-optimization strategy (\ref optimization::GridSearch "GridSearch" will be used by
+optimization strategy (\c ens::GridSearch will be used by
 default) you are going to use.  Then, you must pass the same arguments as for
 the cross-validation classes: the data and labels (or responses) to use are
 given to the constructor, and the possible hyperparameter values are given to
@@ -68,7 +68,7 @@ computation time.
   std::tie(bestLambda) = hpt.Optimize(lambdas);
 @endcode
 
-In this example we have used \ref optimization::GridSearch "GridSearch" (the
+In this example we have used \c ens::GridSearch (the
 default optimizer) to find a good value for the \c lambda hyper-parameter.  For
 that we have specified what values should be tried.
 
@@ -121,7 +121,7 @@ real-valued hyperparameters, but wish to further tune those values.
 In this case, we can use a gradient-based optimizer for hyperparameter search.
 In the following example, we try to optimize the \c lambda1 and \c lambda2
 hyper-parameters for \ref regression::LARS "LARS" with the
-\ref optimization::GradientDescent "GradientDescent" optimizer.
+\c ens::GradientDescent optimizer.
 
 @code
   HyperParameterTuner<LARS, MSE, SimpleCV, GradientDescent> hpt3(validationSize,
@@ -147,7 +147,7 @@ hyper-parameters for \ref regression::LARS "LARS" with the
 
 The \c HyperParameterTuner class is very similar to the
 \ref cv::KFoldCV "KFoldCV" and \ref cv::SimpleCV "SimpleCV" classes (see the
-@ref "cross-validation tutorial" for more information on those two classes), but
+\ref cv "cross-validation tutorial" for more information on those two classes), but
 there are a few important differences.
 
 First, the \c HyperParameterTuner accepts five different hyperparameters; only
@@ -190,7 +190,7 @@ HyperParameterTuner<LinearRegression, MSE, SimpleCV> hpt(0.2, dataset,
 @endcode
 
 Next, we must set up the hyperparameters to be optimized.  If we are doing a
-grid search with the \ref optimization::GridSearch "GridSearch" optimizer (the
+grid search with the \c ens::GridSearch optimizer (the
 default), then we only need to pass a `std::vector` (for non-numeric
 hyperparameters) or an `arma::vec` (for numeric hyperparameters) containing all
 of the possible choices that we wish to search over.
@@ -222,7 +222,7 @@ Alternately, the \c Fixed() method (detailed in the @ref hptfixed
 "Fixed arguments" section) can be used to fix the values of some parameters.
 
 For continuous optimizers like
-\ref optimization::GradientDescent "GradientDescent", a range does not need to
+\c ens::GradientDescent, a range does not need to
 be specified but instead only a single value.  See the
 \ref hptgradient "Gradient-Based Optimization" section for more details.
 

doc/policies/elemtype.hpp

@@ -1,6 +1,6 @@
 /*! @page elem The ElemType policy in mlpack
 
-@section Overview
+@section elem_overview Overview
 
 \b mlpack algorithms should be as generic as possible.  Often this means
 allowing arbitrary metrics or kernels to be used, but this also means allowing

doc/policies/kernels.hpp

@@ -19,9 +19,8 @@ A kernel (or `Mercer kernel') \f$\mathcal{K}(\cdot, \cdot)\f$ takes two objects
 as input and returns some sort of similarity value.  The specific details and
 properties of kernels are outside the scope of this documentation; for a better
 introduction to kernels and kernel methods, there are numerous better resources
-available, including \ref
-http://www.eric-kim.net/eric-kim-net/posts/1/kernel_trick.html "Eric Kim's
-tutorial".
+available, including
+<a href="http://www.eric-kim.net/eric-kim-net/posts/1/kernel_trick.html">Eric Kim's tutorial</a>
 
 mlpack implements a number of kernel methods and, accordingly, each of these
 methods allows arbitrary kernels to be used via the \c KernelType template

doc/policies/trees.hpp

@@ -47,11 +47,13 @@ are nearby should lie in similar nodes.
 We can rigorously define what a tree is, using the definition of **space tree**
 introduced in the following paper:
 
+@code
 @quote
 R.R. Curtin, W.B. March, P. Ram, D.V. Anderson, A.G. Gray, and C.L. Isbell Jr.,
 "Tree-independent dual-tree algorithms," in Proceedings of the 30th
 International Conference on Machine Learning (ICML '13), pp. 1435--1443, 2013.
 @endquote
+@endcode
 
 The definition is:
 
@@ -398,7 +400,7 @@ This section is divided into five parts:
 
 @subsection treetype_rigorous_template Template parameters
 
-\ref treetype_template_param "An earlier section" discussed the three different
+\ref treetype_template_params "An earlier section" discussed the three different
 template parameters that are required by the \c TreeType policy.
 
 The \ref metrics "MetricType policy" provides one method that will be useful for

doc/tutorials/ann/ann.txt

@@ -79,9 +79,8 @@ have a number of methods in common:
 
 @note
 To be able to optimize the network, both classes implement the OptimizerFunction
-API; see \ref optimizertutorial "Optimizer API" for more information. In short,
-the \c FNN and \c RNN class implement two methods: \c Evaluate() and \c
-Gradient().  This enables the optimization given some learner and some
+API. In short, the \c FNN and \c RNN class implement two methods: \c Evaluate()
+and \c Gradient().  This enables the optimization given some learner and some
 performance measure.
 
 Similar to the existing layer infrastructure, the \c FFN and \c RNN classes are
@@ -230,13 +229,13 @@ int main()
   arma::mat predictionTemp;
   model.Predict(testData, predictionTemp);
 
-  /* 
-    Since the predictionsTemp is of dimensions (3 x number_of_data_points) 
-    with continuous values, we first need to reduce it to a dimension of 
+  /*
+    Since the predictionsTemp is of dimensions (3 x number_of_data_points)
+    with continuous values, we first need to reduce it to a dimension of
     (1 x number_of_data_points) with scalar values, to be able to compare with
     testLabels.
 
-    The first step towards doing this is to create a matrix of zeros with the 
+    The first step towards doing this is to create a matrix of zeros with the
     desired dimensions (1 x number_of_data_points).
 
     In predictionsTemp, the 3 dimensions for each data point correspond to the
@@ -252,8 +251,8 @@ int main()
         arma::max(predictionTemp.col(i)) == predictionTemp.col(i), 1)) + 1;
   }
 
-  /* 
-    Compute the error between predictions and testLabels, 
+  /*
+    Compute the error between predictions and testLabels,
     now that we have the desired predictions.
   */
   size_t correct = arma::accu(prediction == testLabels);
@@ -266,7 +265,7 @@ int main()
 @endcode
 
 Now, the matrix prediction holds the classification of each point in the
-dataset. Subsequently, we find the classification error by comparing it 
+dataset. Subsequently, we find the classification error by comparing it
 with testLabels.
 
 In the next example, we create simple noisy sine sequences, which are trained
@@ -420,7 +419,7 @@ not work; this example exists for its API, not its implementation.
 Note that layer sometimes have different properties. These properties are
 known at compile-time through the mlpack::ann::LayerTraits class, and some
 properties may imply the existence (or non-existence) of certain functions.
-Refer to the LayerTraits @ref LayerTraits for more documentation on that.
+Refer to the LayerTraits @ref layer_traits.hpp for more documentation on that.
 
 The two template parameters below must be template parameters to the layer, in
 the order given below. More template parameters are fine, but they must come
@@ -662,10 +661,10 @@ $ cat model.xml
 </boost_serialization>
 @endcode
 
-As you can see, the \c <parameter> section of \c model.xml contains the trained
+As you can see, the \c \<parameter\> section of \c model.xml contains the trained
 network weights.  We can see that this section also contains the network input
 size, which is 66 rows and 1 column. Note that in this example, we used three
-different layers, as can be seen by looking at the \c <network> section. Each
+different layers, as can be seen by looking at the \c \<network\> section. Each
 node has a unique id that is used to reconstruct the model when loading.
 
 The models can also be saved as \c .bin or \c .txt; the \c .xml format provides

doc/tutorials/approx_kfn/approx_kfn.txt

@@ -87,7 +87,7 @@ In order to solve this problem, \b mlpack provides a number of interfaces.
    approximate furthest neighbors
  - a simple \ref cpp_qdafn_akfntut "C++ class for QDAFN"
  - a simple \ref cpp_ds_akfntut "C++ class for DrusillaSelect"
- - a simple \ref cpp_kfn_akfntut "C++ class for tree-based and brute-force"
+ - a simple \ref cpp_ns_akfntut "C++ class for tree-based and brute-force"
    search
 
 @section toc_akfntut Table of Contents

doc/tutorials/cf/cf.txt

@@ -310,7 +310,7 @@ factorization with alternating least squares update rules).  These include:
 
 The amf::AMF<> class has many other possibilities than those listed here; it is
 a framework for alternating matrix factorization techniques.  See the
-\ref amf::AMF<> "class documentation" or \ref amftutorial "tutorial on AMF" for
+\ref mlpack::amf::AMF<> "class documentation" or \ref amftutorial "tutorial on AMF" for
 more information.
 
 The use of another factorizer is straightforward; the example from the previous
@@ -458,15 +458,15 @@ items and number of columns equal to the \c rank parameter, and \c H should have
 number of rows equal to the \c rank parameter, and number of columns equal to
 the number of users.
 
-The \ref mlpack::amf::AMF "amf::AMF<> class" can be used as a base for
+The \ref mlpack::amf::AMF<> "amf::AMF<> class" can be used as a base for
 factorizers that alternate between updating \c W and updating \c H.  A useful
 reference is the \ref amftutorial "AMF tutorial".
 
 @section further_doc_cftut Further documentation
 
 Further documentation for the \c CF class may be found in the \ref
-mlpack::cf::CF "complete API documentation".  In addition, more information on
-the \c AMF class of factorizers may be found in its \ref mlpack::amf::AMF
+mlpack::cf "complete API documentation".  In addition, more information on
+the \c AMF class of factorizers may be found in its \ref mlpack::amf::AMF<>
 "complete API documentation".
 
 */

doc/tutorials/det/det.txt

@@ -344,9 +344,9 @@ For further documentation on the DTree class, consult the
 The usual regularized error \f$R_\alpha(t)\f$ of a node \f$t\f$ is given by:
 \f$R_\alpha(t) = R(t) + \alpha |\tilde{t}|\f$ where
 
-\f[
+\f{
 R(t) = -\frac{|t|^2}{N^2 V(t)}.
-\f]
+\f}
 
 \f$V(t)\f$ is the volume of the node \f$t\f$ and \f$\tilde{t}\f$ is
 the set of leaves in the subtree rooted at \f$t\f$.

doc/tutorials/image/image.txt

@@ -5,32 +5,32 @@
 
 @page imagetutorial Image Utilities tutorial
 
-@section intro_imagetut Introduction
+@section intro_imagetu Introduction
 
 Image datasets are becoming increasingly popular in deep learning.
 
 mlpack's image saving/loading functionality is based on [stb/](https://github.com/nothings/stb).
 
-@section toc_imagetut Table of Contents
+@section toc_imagetu Table of Contents
 
 This tutorial is split into the following sections:
 
- - \ref intro_imagetut
- - \ref toc_imagetut
- - \ref model_api_imagetut
- - \ref imageinfo_api_imagetut
- - \ref load_api_imagetut
- - \ref save_api_imagetut
+ - \ref intro_imagetu
+ - \ref toc_imagetu
+ - \ref model_api_imagetu
+ - \ref imageinfo_api_imagetu
+ - \ref load_api_imagetu
+ - \ref save_api_imagetu
 
-@section model_api_imagetut Model API
+@section model_api_imagetu Model API
 
 Image utilities supports loading and saving of images.
 
 It supports filetypes "jpg", "png", "tga","bmp", "psd", "gif", "hdr", "pic", "pnm" for loading and "jpg", "png", "tga", "bmp", "hdr" for saving.
 
 The datatype associated is unsigned char to support RGB values in the range 1-255. To feed data into the network typecast of `arma::Mat` may be required. Images are stored in matrix as (width * height * channels, NumberOfImages). Therefore imageMatrix.col(0) would be the first image if images are loaded in imageMatrix.
 
-@section imageinfo_api_imagetut ImageInfo
+@section imageinfo_api_imagetu ImageInfo
 
 ImageInfo class contains the metadata of the images.
 @code
@@ -48,7 +48,7 @@ ImageInfo class contains the metadata of the images.
 Other public memebers include:
   - quality Compression of the image if saved as jpg (0-100).
 
-@section load_api_imagetut Load
+@section load_api_imagetu Load
 
 
 Standalone loading of images.
@@ -115,7 +115,7 @@ Loading multiple images:
   data::load(files, matrix, info, false, true);
 @endcode
 
-@section save_api_imagetut Save
+@section save_api_imagetu Save
 
 Save images expects a matrix of type unsigned char in the form (width * height * channels, NumberOfImages).
 Just like load it can be used to save one image or multiple images. Besides image data it also expects the shape of the image as input (width, height, channels).

doc/tutorials/kmeans/kmeans.txt

@@ -140,7 +140,7 @@ last iteration when the cluster was not empty.
 $ mlpack_kmeans -c 5 -i dataset.csv -v -e -o assignments.csv -C centroids.csv
 @endcode
 
-@subsection cli_ex3_kmtut Killing empty clusters
+@subsection cli_ex3a_kmtut Killing empty clusters
 
 If you would like to kill empty clusters , instead of reinitializing
 them, simply specify the \c -E (\c --kill_empty_clusters) option.  Note that

doc/tutorials/linear_regression/linear_regression.txt

@@ -132,7 +132,7 @@ $ cat lr.xml
 @endcode
 
 As you can see, the function for this input is \f$f(y)=0+1x_1\f$.  We can see
-that the model we have trained catches this; in the \c <parameters> section of
+that the model we have trained catches this; in the \c \<parameters\> section of
 \c lr.xml, we can see that there are two elements, which are (approximately) 0
 and 1.  The first element corresponds to the intercept 0, and the second column
 corresponds to the coefficient 1 for the variable \f$x_1\f$.  Note that in this