diff --git a/doc/developer/kernels.md b/doc/developer/kernels.md index 7bd65bd293..09aa23c39a 100644 --- a/doc/developer/kernels.md +++ b/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++ diff --git a/doc/developer/metrics.md b/doc/developer/metrics.md index b4cec62526..4a6ec5bc6a 100644 --- a/doc/developer/metrics.md +++ b/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) diff --git a/doc/developer/trees.md b/doc/developer/trees.md index 97c76c5418..17e9525939 100644 --- a/doc/developer/trees.md +++ b/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 diff --git a/doc/tutorials/approx_kfn.md b/doc/tutorials/approx_kfn.md index c703aefa45..33a4237d98 100644 --- a/doc/tutorials/approx_kfn.md +++ b/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. diff --git a/doc/tutorials/cf.md b/doc/tutorials/cf.md index 2d4a313ba8..9452ebfdd0 100644 --- a/doc/tutorials/cf.md +++ b/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 diff --git a/doc/tutorials/image.md b/doc/tutorials/image.md index dc08a085dd..01169e7afb 100644 --- a/doc/tutorials/image.md +++ b/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 diff --git a/doc/tutorials/range_search.md b/doc/tutorials/range_search.md index 55dd98acae..42fa5b6526 100644 --- a/doc/tutorials/range_search.md +++ b/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 diff --git a/doc/user/cv.md b/doc/user/cv.md index be392c2b0f..53067911d7 100644 --- a/doc/user/cv.md +++ b/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.) diff --git a/doc/user/hpt.md b/doc/user/hpt.md index 881d60923b..738a9c6f26 100644 --- a/doc/user/hpt.md +++ b/doc/user/hpt.md @@ -179,7 +179,7 @@ HyperParameterTuner 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.