[mlpack-git] master: Fix names of executables. (b7272f8)

Thu Dec 24 11:54:15 EST 2015

Repository : https://github.com/mlpack/mlpack.org

On branch  : master
Link       : https://github.com/mlpack/mlpack.org/compare/80bbbf3fef9edd106814fbda416e1e3e4e7e1220...b7272f856a52df170fef0e0ecc8eda990f061cac

>---------------------------------------------------------------

commit b7272f856a52df170fef0e0ecc8eda990f061cac
Author: Ryan Curtin <ryan at ratml.org>
Date:   Thu Dec 24 11:53:57 2015 -0500

    Fix names of executables.


>---------------------------------------------------------------

b7272f856a52df170fef0e0ecc8eda990f061cac
 docs/mlpack-git/man.html | 84 ++++++++++++++++++++++++++++--------------------
 1 file changed, 49 insertions(+), 35 deletions(-)

diff --git a/docs/mlpack-git/man.html b/docs/mlpack-git/man.html
index ce7d7b5..23eb1e1 100644
--- a/docs/mlpack-git/man.html
+++ b/docs/mlpack-git/man.html
@@ -10,7 +10,6 @@ library">
 <title>mlpack: a scalable c++ machine learning library</title>
 </head>
 <link rel="stylesheet" href="../../style.css">
-<link rel="stylesheet" href="../style-man.css">
 <link href='http://fonts.googleapis.com/css?family=Maven+Pro:500'
 rel='stylesheet' type='text/css'>
 <body>
@@ -41,68 +40,83 @@ of the algorithms it implements.  These may be used to perform many machine
 learning tasks without the overhead of writing C++, or may be used as part of a
 larger machine learning solution.</p>
 <p>Below is a list of the command-line executables <font
-class="whitebold">mlpack (git master branch)</font> provides, with links to the
-documentation for each executable.  This documentation may also be accessed with
-the <font class="code">--help</font> parameter or through the man pages provided
-with your distribution of <font class="whitebold">mlpack</font>.</p>
+class="whitebold">mlpack 2.0.0</font> provides, with links to the documentation
+for each executable.  This documentation may also be accessed with the <font
+class="code">--help</font> parameter or through the man pages provided with your
+distribution of <font class="whitebold">mlpack</font>.</p>
 <ul>
-<li><a href="man/allknn.html" class="manlink">allknn</a>: all <i>k</i>-nearest
+<li><a href="man/mlpack_adaboost.html" class="manlink">mlpack_adaboost</a>:
+train and classify with AdaBoost, an ensembling classifier</li>
+<li><a href="man/mlpack_allknn.html" class="manlink">mlpack_allknn</a>: all <i>k</i>-nearest
 neighbor search with trees</li>
-<li><a href="man/allkfn.html" class="manlink">allkfn</a>: all <i>k</i>-furthest
+<li><a href="man/mlpack_allkfn.html" class="manlink">mlpack_allkfn</a>: all <i>k</i>-furthest
 neighbor search with trees</li>
-<li><a href="man/allkrann.html" class="manlink">allkrann</a>: rank-approximate
+<li><a href="man/mlpack_allkrann.html" class="manlink">mlpack_allkrann</a>: rank-approximate
 <i>k</i>-nearest neighbor search with trees</li>
-<li><a href="man/cf.html" class="manlink">cf</a>: generate recommendations via
+<li><a href="man/mlpack_cf.html" class="manlink">mlpack_cf</a>: generate recommendations via
 collaborative filtering</li>
-<li><a href="man/decision_stump.html" class="manlink">decision_stump</a>:
+<li><a href="man/mlpack_decision_stump.html" class="manlink">mlpack_decision_stump</a>:
 classify with a decision stump</li>
-<li><a href="man/det.html" class="manlink">det</a>: density estimation
+<li><a href="man/mlpack_det.html" class="manlink">mlpack_det</a>: density estimation
 trees</li>
-<li><a href="man/emst.html" class="manlink">emst</a>: calculate a Euclidean
+<li><a href="man/mlpack_emst.html" class="manlink">mlpack_emst</a>: calculate a Euclidean
 minimum spanning tree</li>
-<li><a href="man/fastmks.html" class="manlink">fastmks</a>: perform fast
+<li><a href="man/mlpack_fastmks.html" class="manlink">mlpack_fastmks</a>: perform fast
 max-kernel search with trees</li>
-<li><a href="man/gmm.html" class="manlink">gmm</a>: train or classify with a
+<li><a href="man/mlpack_gmm_train.html" class="manlink">mlpack_gmm_train</a>: train a
 Gaussian mixture model</li>
-<li><a href="man/hmm_generate.html" class="manlink">hmm_generate</a>: generate
+<li><a href="man/mlpack_gmm_generate.html"
+class="manlink">mlpack_gmm_generate</a>: generate a random sequence from a GMM</li>
+<li><a href="man/mlpack_gmm_probability.html"
+class="manlink">mlpack_gmm_probability</a>: calculate the probability of a set
+of points coming from a given GMM</li>
+<li><a href="man/mlpack_hmm_generate.html" class="manlink">mlpack_hmm_generate</a>: generate
 observations from a hidden Markov model (HMM)</li>
-<li><a href="man/hmm_loglik.html" class="manlink">hmm_loglik</a>: calculate the
+<li><a href="man/mlpack_hmm_loglik.html" class="manlink">mlpack_hmm_loglik</a>: calculate the
 log-likelihood of some observations from an HMM</li>
-<li><a href="man/hmm_train.html" class="manlink">hmm_train</a>: train a hidden
+<li><a href="man/mlpack_hmm_train.html" class="manlink">mlpack_hmm_train</a>: train a hidden
 Markov model (HMM)</li>
-<li><a href="man/hmm_viterbi.html" class="manlink">hmm_viterbi</a>: find the
+<li><a href="man/mlpack_hmm_viterbi.html" class="manlink">mlpack_hmm_viterbi</a>: find the
 most probable hidden states in an HMM for some observations</li>
-<li><a href="man/kernel_pca.html" class="manlink">kernel_pca</a>: perform kernel
+<li><a href="man/mlpack_hoeffding_tree.html"
+class="manlink">mlpack_hoeffding_tree</a>: train and classify with Hoeffding
+trees, a streaming decision tree for very large datasets</li>
+<li><a href="man/mlpack_kernel_pca.html" class="manlink">mlpack_kernel_pca</a>: perform kernel
 principal components analysis</li>
-<li><a href="man/kmeans.html" class="manlink">kmeans</a>: perform <i>k</i>-means
+<li><a href="man/mlpack_kmeans.html" class="manlink">mlpack_kmeans</a>: perform <i>k</i>-means
 clustering</li>
-<li><a href="man/lars.html" class="manlink">lars</a>: least-angle
+<li><a href="man/mlpack_lars.html" class="manlink">mlpack_lars</a>: least-angle
 regression</li>
-<li><a href="man/linear_regression.html" class="manlink">linear_regression</a>:
+<li><a href="man/mlpack_linear_regression.html" class="manlink">mlpack_linear_regression</a>:
 simple least-squares linear regression</li>
-<li><a href="man/local_coordinate_coding.html"
-class="manlink">local_coordinate_coding</a>: local coordinate
+<li><a href="man/mlpack_local_coordinate_coding.html"
+class="manlink">mlpack_local_coordinate_coding</a>: local coordinate
 coding</li>
-<li><a href="man/logistic_regression.html"
-class="manlink">logistic_regression</a>: train or classify with logistic
+<li><a href="man/mlpack_logistic_regression.html"
+class="manlink">mlpack_logistic_regression</a>: train or classify with logistic
 regression</li>
-<li><a href="man/lsh.html" class="manlink">lsh</a>: approximate <i>k</i>-nearest
+<li><a href="man/mlpack_lsh.html" class="manlink">mlpack_lsh</a>: approximate <i>k</i>-nearest
 neighbor search with locality-sensitive hashing</li>
-<li><a href="man/nbc.html" class="manlink">nbc</a>: train or classify with the
+<li><a href="man/mlpack_mean_shift.html" class="manlink">mlpack_mean_shift</a>:
+mean shift clustering</li>
+<li><a href="man/mlpack_nbc.html" class="manlink">mlpack_nbc</a>: train or classify with the
 naive Bayes classifier</li>
-<li><a href="man/nca.html" class="manlink">nca</a>: neighborhood components
+<li><a href="man/mlpack_nca.html" class="manlink">mlpack_nca</a>: neighborhood components
 analysis</li>
-<li><a href="man/nmf.html" class="manlink">nmf</a>: non-negative matrix
+<li><a href="man/mlpack_nmf.html" class="manlink">mlpack_nmf</a>: non-negative matrix
 factorization</li>
-<li><a href="man/pca.html" class="manlink">pca</a>: principal components
+<li><a href="man/mlpack_pca.html" class="manlink">mlpack_pca</a>: principal components
 analysis</li>
-<li><a href="man/perceptron.html" class="manlink">perceptron</a>: train or
+<li><a href="man/mlpack_perceptron.html" class="manlink">mlpack_perceptron</a>: train or
 classify with a perceptron</li>
-<li><a href="man/radical.html" class="manlink">radical</a>: RADICAL (independent
+<li><a href="man/mlpack_radical.html" class="manlink">mlpack_radical</a>: RADICAL (independent
 components analysis)</li>
-<li><a href="man/range_search.html" class="manlink">range_search</a>: range
+<li><a href="man/mlpack_range_search.html" class="manlink">mlpack_range_search</a>: range
 search with trees</li>
-<li><a href="man/sparse_coding.html" class="manlink">sparse_coding</a>: sparse
+<li><a href="man/mlpack_softmax_regression.html"
+class="manlink">mlpack_softmax_regression</a>: train or classify with softmax
+regression</li>
+<li><a href="man/mlpack_sparse_coding.html" class="manlink">mlpack_sparse_coding</a>: sparse
 coding with dictionary learning</li>
 </ul>
 </div>

