Merge pull request #3392 from AdarshSantoria/doc

Some similar typos in doc

doc/developer/kernels.md

@@ -16,7 +16,7 @@ including
 
 mlpack implements a number of kernel methods and, accordingly, each of these
 methods allows arbitrary kernels to be used via the `KernelType` template
-parameter.  Like the [MetricType policy](metrictype.md), the requirements are
+parameter.  Like the [MetricType policy](metrics.md), the requirements are
 quite simple: a class implementing the `KernelType` policy must have
 
  - an `Evaluate()` function
@@ -42,7 +42,7 @@ Note that for kernels that do not hold any state, the `Evaluate()` method can be
 marked as `static`.
 
 Overall, the `KernelType` template policy is quite simple (much like the
-[MetricType policy](metrictype.md)).  Below is an example kernel class, which
+[MetricType policy](metrics.md)).  Below is an example kernel class, which
 outputs `1` if the vectors are close and `0` otherwise.
 
 ```c++

doc/developer/metrics.md

@@ -35,7 +35,7 @@ Note that for metrics that do not hold any state, the `Evaluate()` method can
 be marked as `static`.
 
 Overall, the `MetricType` template policy is quite simple (much like the
-[KernelType policy](kerneltype.md)).  Below is an example metric class, which
+[KernelType policy](kernels.md)).  Below is an example metric class, which
 implements the L2 distance:
 
 ```c++
@@ -105,4 +105,4 @@ policy:
  - `ChebyshevDistance`
  - `MahalanobisDistance`
  - `LMetric` (for arbitrary L-metrics)
- - `IPMetric` (requires a [KernelType](kerneltype.md) parameter)
+ - `IPMetric` (requires a [KernelType](kernels.md) parameter)

doc/developer/trees.md

@@ -86,11 +86,11 @@ restatement of the fourth part of the definition).
 
 Most everything in mlpack is decomposed into a series of configurable template
 parameters, and trees are no exception.  In order to ease usage of high-level
-mlpack algorithms, each \c TreeType itself must be a template class taking three
+mlpack algorithms, each `TreeType` itself must be a template class taking three
 parameters:
 
  - `MetricType` -- the underlying metric that the tree will be built on (see
-[the MetricType policy documentation](metrictype.md))
+[the MetricType policy documentation](metrics.md))
  - `StatisticType` -- holds any auxiliary information that individual
 algorithms may need
  - `MatType` -- the type of the matrix used to represent the data
@@ -424,7 +424,7 @@ This constructor should be called with `(*this)` after the node is constructed
 The last template parameter is the `MatType` parameter.  This is generally
 `arma::mat` or `arma::sp_mat`, but could be any Armadillo type, including
 matrices that hold data points of different precisions (such as `float` or even
-`int`).  It generally suffices to write \c MatType assuming that `arma::mat`
+`int`).  It generally suffices to write `MatType` assuming that `arma::mat`
 will be used, since the vast majority of the time this will be what is used.
 
 ### Constructors and destructors

doc/tutorials/approx_kfn.md

@@ -114,8 +114,8 @@ search:
 
 These two programs allow a large number of algorithms to be used to find
 approximate furthest neighbors.  Note that the `mlpack_kfn` program is also
-documented in the [KNN tutorial](knn.md) page, as it shares options with the
-`mlpack_knn` program.
+documented in the [KNN tutorial](neighbor_search.md) page, as it shares options 
+with the `mlpack_knn` program.
 
 Below are several examples of how the `mlpack_approx_kfn` and `mlpack_kfn`
 programs might be used.  The first examples focus on the `mlpack_approx_kfn`
@@ -682,7 +682,7 @@ std::cout << ds.CandidateSet().col(4).t();
 
 It is possible to retrain a `DrusillaSelect` model with new parameters or with a
 new reference set.  This is functionally equivalent to creating a new model.
-The example code below creates a first \c DrusillaSelect model using 3 tables
+The example code below creates a first `DrusillaSelect` model using 3 tables
 and 10 projections, and then retrains this with the same reference set using 10
 tables and 3 projections.
 
@@ -869,7 +869,7 @@ qdafn.Search(querySet, 3, neighbors, distances);
 The extensive `NeighborSearch` class also provides a way to search for
 approximate furthest neighbors using a different, tree-based technique.  For
 full documentation on this class, see the [NeighborSearch
-tutorial](nstutorial.md).  The `KFN` class is a convenient typedef of the
+tutorial](neighbor_search.md).  The `KFN` class is a convenient typedef of the
 `NeighborSearch` class that can be used to perform the furthest neighbors task
 with `kd`-trees.
 
@@ -982,6 +982,6 @@ kfn.Search(querySet, 2, neighbors, distances);
 ## Further documentation
 
 For further documentation on the approximate furthest neighbor facilities
-offered by mlpack, see also [the NeighborSearch tutorial](nstutorial.md).  Also,
+offered by mlpack, see also [the NeighborSearch tutorial](neighbor_search.md).  Also,
 each class (`QDAFN`, `DrusillaSelect`, `NeighborSelect`) are well-documented,
 and more details can be found in the source code documentation.

doc/tutorials/cf.md

@@ -389,8 +389,8 @@ number of rows equal to the number of items and the number of columns equal to
 the number of users, and each nonzero element in the matrix corresponds to a
 non-missing rating.
 
-The method that the factorizer implements is specified via the \c
-FactorizerTraits class, which is a template metaprogramming traits class:
+The method that the factorizer implements is specified via the
+`FactorizerTraits` class, which is a template metaprogramming traits class:
 
 ```c++
 template<typename FactorizerType>

doc/tutorials/image.md

@@ -2,7 +2,7 @@
 
 Image datasets are becoming increasingly popular in deep learning.
 
-mlpack's image saving/loading functionality is based on [stb/](https://github.com/nothings/stb).
+mlpack's image saving/loading functionality is based on [stb](https://github.com/nothings/stb).
 
 ## Model API
 

doc/tutorials/range_search.md

@@ -3,8 +3,8 @@
 Range search is a simple machine learning task which aims to find all the
 neighbors of a point that fall into a certain range of distances.  In this
 setting, we have a *query* and a *reference* dataset.  Given a certain range,
-for each point in the *query* dataset, we wish to know all points in the \b
-reference dataset which have distances within that given range to the given
+for each point in the *query* dataset, we wish to know all points in the
+*reference* dataset which have distances within that given range to the given
 query point.
 
 Alternately, if the query and reference datasets are the same, the problem can

doc/user/cv.md

@@ -70,7 +70,7 @@ SoftmaxRegression(const arma::mat& data,
 ```
 
 which has the parameter `lambda` after three conventional arguments (`data`,
-\c labels and \c numClasses). We can skip passing `fitIntercept` and
+`labels` and `numClasses`). We can skip passing `fitIntercept` and
 `optimizer` since there are the default values.  (Technically, we don't even
 need to pass `lambda` since there is a default value.)
 

doc/user/hpt.md

@@ -179,7 +179,7 @@ HyperParameterTuner<LinearRegression, MSE, SimpleCV> hpt(0.2, dataset,
 ```
 
 Next, we must set up the hyperparameters to be optimized.  If we are doing a
-grid search with the \c ens::GridSearch optimizer (the
+grid search with the `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.