[shark] 01/79: updated documentation to reflect 3.0 release
Ghislain Vaillant
ghisvail-guest at moszumanska.debian.org
Thu Nov 26 15:39:05 UTC 2015
This is an automated email from the git hooks/post-receive script.
ghisvail-guest pushed a commit to branch master
in repository shark.
commit d576bf7045f8c9c4f914f87558e949dc8d7a68aa
Author: Oswin <oswin.krause at di.ku.dk>
Date: Mon Oct 26 09:06:35 2015 +0100
updated documentation to reflect 3.0 release
---
doc/sphinx_pages/index.rst | 4 -
doc/sphinx_pages/rest_sources/about_shark/news.rst | 8 +-
.../rest_sources/downloads/downloads.rst | 10 ++-
.../rest_sources/getting_started/installation.rst | 6 +-
.../tutorials/concepts/data/datasets.tut | 4 +-
.../tutorials/concepts/misc/statistics.rst | 96 +++++++++++++---------
.../tutorials/concepts/misc/statistics.tut | 2 +-
.../tutorials/first_steps/shark_layout.rst | 9 +-
.../rest_sources/tutorials/tutorials.rst | 1 -
9 files changed, 76 insertions(+), 64 deletions(-)
diff --git a/doc/sphinx_pages/index.rst b/doc/sphinx_pages/index.rst
index 7e2b19b..d79164a 100644
--- a/doc/sphinx_pages/index.rst
+++ b/doc/sphinx_pages/index.rst
@@ -114,10 +114,6 @@
Summary
=======
-.. note::
-
- This is Shark 3.0 beta. See the :doc:`news <rest_sources/about_shark/news>` for more information.
-
**SHARK is a fast, modular, feature-rich open-source C++ machine learning library**.
It provides methods for linear and nonlinear optimization, kernel-based learning
algorithms, neural networks, and various other machine learning techniques (see the
diff --git a/doc/sphinx_pages/rest_sources/about_shark/news.rst b/doc/sphinx_pages/rest_sources/about_shark/news.rst
index b16f5fe..9c7469d 100644
--- a/doc/sphinx_pages/rest_sources/about_shark/news.rst
+++ b/doc/sphinx_pages/rest_sources/about_shark/news.rst
@@ -1,8 +1,14 @@
News
====
+
+Shark 3.0.0 Released
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+We are happy to announce the release of Shark 3.0.0
+
Shark moves to GitHub
-^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^
Today, Shark moved to GitHub, please update your repositories, see the downloads page for more details.
diff --git a/doc/sphinx_pages/rest_sources/downloads/downloads.rst b/doc/sphinx_pages/rest_sources/downloads/downloads.rst
index c877d4b..c235e13 100644
--- a/doc/sphinx_pages/rest_sources/downloads/downloads.rst
+++ b/doc/sphinx_pages/rest_sources/downloads/downloads.rst
@@ -40,12 +40,14 @@ Get the latest snapshot from our svn repository!
-.. Shark sources
- -------------
+Shark sources
+-------------
- Please download the following package if you want to build Shark yourself:
+ We have two source packages available:
- `Shark source code <https://nisys.dyndns.biz/shark/job/Shark_Source_Package/lastSuccessfulBuild/artifact/libshark-3.0.0-src.tar.bz2>`_
+ `Shark-3.0.0.zip <https://github.com/Shark-ML/Shark/archive/v3.0.0.zip>`_
+
+ `Shark-3.0.0.tar.gz <https://github.com/Shark-ML/Shark/archive/v3.0.0.tar.gz>`_
See the :doc:`installation guide <../getting_started/installation>`
for details on how to compile and install the library.
diff --git a/doc/sphinx_pages/rest_sources/getting_started/installation.rst b/doc/sphinx_pages/rest_sources/getting_started/installation.rst
index 76eb436..609180d 100644
--- a/doc/sphinx_pages/rest_sources/getting_started/installation.rst
+++ b/doc/sphinx_pages/rest_sources/getting_started/installation.rst
@@ -15,16 +15,14 @@ Packages
We are supporting packages for the following platforms:
* Arch Linux
- - current development version downloadable from AUR as packages ``shark-ml-atlas-svn`` and ``shark-ml-svn``
+ - current development version downloadable from AUR as packages ``shark-ml-atlas-git`` and ``shark-ml-git``
for a shark version with and without ATLAS.
* More to come!
Installation
---------------------------------------------
-To install Shark, get the sources::
-
- git clone https://github.com/Shark-ML/Shark/
+To install Shark, get the sources from our * :doc:`Downloads page <downloads>`
Then build the library::
diff --git a/doc/sphinx_pages/rest_sources/tutorials/concepts/data/datasets.tut b/doc/sphinx_pages/rest_sources/tutorials/concepts/data/datasets.tut
index b341ee9..d1804ae 100644
--- a/doc/sphinx_pages/rest_sources/tutorials/concepts/data/datasets.tut
+++ b/doc/sphinx_pages/rest_sources/tutorials/concepts/data/datasets.tut
@@ -258,7 +258,7 @@ batches since it needs to find the batch the element is located in.
.. warning::
Element-wise random access to a ``Data`` a object is a linear time
operation! It is not to be confused with constant-time element
- access in arrays (and ``std::vector``). Thus aside from only very
+ access in arrays. Thus aside from only very
small data sets or performance uncritical code you should never use
element-wise random-access to a data container.
@@ -377,7 +377,7 @@ It is applied to the data set by calling: ::
..sharkcode<Data/Datasets.tpp,transform-4>
.. note::
- Never ever forget the definition of the ``result_type``!
+ Never never forget the definition of the ``result_type``!
It is needed by ``transform`` to be smart, i.e., to deduce
the corresponding batch type.
If you happen to get nasty template error messages with
diff --git a/doc/sphinx_pages/rest_sources/tutorials/concepts/misc/statistics.rst b/doc/sphinx_pages/rest_sources/tutorials/concepts/misc/statistics.rst
index d812c66..37db2a6 100644
--- a/doc/sphinx_pages/rest_sources/tutorials/concepts/misc/statistics.rst
+++ b/doc/sphinx_pages/rest_sources/tutorials/concepts/misc/statistics.rst
@@ -3,63 +3,79 @@ Iterative Calculation of Statistics
The Shark machine learning library includes a component for
iteratively calculating simple descriptive statistics of a
-sequence of values. The class :doxy:`Statistics` is a thin
-wrapper around the boost::accumulators component. This tutorial
-illustrates its usage.
+sequence of points for experimental evaluation. The class :doxy:`ResultTable`
+includes a simple data aggregation tool that for a set of experiments
+with different parameters aggregates results over a set of trials. It
+supports missing values to reflect failed trials as well.
+The class :doxy:`Statistics` takes these results to cpmpute a set of statistics.
+The class offers the possibility to label the dimensions of the points and statistics
+to automatically generate human readable output, for example in a csv table.
-We need the following header files: ::
+For this simple application, we are going to generate some points from
+a gaussian distribution and then mark some points as missing.
+For this experiment, we need the following header files: ::
#include <shark/Statistics/Statistics.h>
#include <shark/Rng/GlobalRng.h>
-We start out by creating an object of class :doxy:`Statistics`: ::
+We start out by creating an object of class :doxy:`ResultTable`.
+We give the table a name and also label the inputs as to generate
+a more readable output later on::
- Statistics stats;
+ statistics::ResultTable<double> table(2,"VarianceOfGaussian");//set a name for the results
+ table.setDimensionName(0,"input1");
+ table.setDimensionName(1,"input2");
-Now we feed numbers into this object. For demonstration purposes we
-sample 100,000 i.i.d. standard normally distributed values from the
-random number generator. ::
- // Sample 10000 standard normally distributed random numbers
- // and update statistics for these numbers iteratively.
- for (std::size_t i = 0; i < 100000; i++)
- stats( Rng::gauss() );
+Now we feed numbers into this object. For demonstration purposes we
+sample 10,000 i.i.d. standard normally distributed values with varying
+variance. To simulate a failed experiment, we make a coin toss for variable two
+and mark this input as missing. Finally, we insert the values into the table::
+
+
+ // Fill the table with randomly generated numbers for different variances and mean and also add missing values
+ for(std::size_t k = 1; k != 10; ++k){
+ double var= 10.0*k;
+ for (std::size_t i = 0; i < 10000; i++){
+ double value1=Rng::gauss(0,var);
+ double value2=Rng::gauss(0,var);
+ if(Rng::coinToss() == 1)
+ value2=statistics::missingValue();
+ table.update(var,value1,value2 );
+ }
+ }
-We can output a summary to the console: ::
+Next, we generate a :doxy:`Statistics` object and add the statistics, here
+we use Mean, Variance and Percentage of Missing values::
- // Output results to the console.
- std::cout << stats << std::endl;
+ statistics::Statistics<double> stats(&table);
+ stats.addStatistic(statistics::Mean());//adds a statistic "Mean" for each variable
+ stats.addStatistic("Variance", statistics::Variance());//explicit name
+ stats.addStatistic("Missing", statistics::FractionMissing());
-The results looks similar to: ::
-
- Sample size: 100000
- Min: -4.09568
- Max: 4.42802
- Mean: -0.000584566
- Variance: 0.992282
- Median: 0.00121767
- Lower Quantile: -0.673034
- Upper Quantile: 0.670621
-
-Alternatively it is possible to access the individual values. The
-round bracket operator is used for this purpose: ::
-
-
- std::cout <<
- stats( Statistics::NumSamples() ) << " " <<
- stats( Statistics::Min() ) << " " <<
- stats( Statistics::Max() ) << " " <<
- stats( Statistics::Mean() ) << " " <<
- stats( Statistics::Variance() ) << " " <<
- stats( Statistics::Median() ) << " " <<
- stats( Statistics::LowerQuartile() ) << " " <<
- stats( Statistics::UpperQuartile() ) << std::endl;
+We can output a summary as csv file to the console: ::
+
+
+ printCSV(stats);
+
+The results looks similar to::
+
+ # VarianceOfGausian Mean-input1 Mean-input2 Variance-input1 Variance-input2 Missing-input1 Missing-input2
+ 10 0.00500042 -0.073452 9.77016 10.1016 0 0.5061
+ 20 0.0359687 -0.0400334 20.1318 20.2767 0 0.5038
+ 30 0.0216264 -0.120275 30.3096 29.0293 0 0.5044
+ 40 -0.0301033 0.0995221 40.3523 40.4839 0 0.4961
+ 50 0.00692523 0.118349 48.9781 50.5156 0 0.4936
+ 60 -0.0133728 -0.0109795 57.4287 59.8386 0 0.4903
+ 70 -0.190326 0.0259554 67.0553 70.0034 0 0.4987
+ 80 -0.0198076 -0.0493343 83.1629 78.0985 0 0.4917
+ 90 -0.103546 -0.263991 92.152 89.3462 0 0.4992
diff --git a/doc/sphinx_pages/rest_sources/tutorials/concepts/misc/statistics.tut b/doc/sphinx_pages/rest_sources/tutorials/concepts/misc/statistics.tut
index 499b274..2e817a6 100644
--- a/doc/sphinx_pages/rest_sources/tutorials/concepts/misc/statistics.tut
+++ b/doc/sphinx_pages/rest_sources/tutorials/concepts/misc/statistics.tut
@@ -35,7 +35,7 @@ and mark this input as missing. Finally, we insert the values into the table::
Next, we generate a :doxy:`Statistics` object and add the statistics, here
we use Mean, Variance and Percentage of Missing values::
-..sharkcode<Statistics/Statistics.tpp,statisticse>
+..sharkcode<Statistics/Statistics.tpp,statistics>
We can output a summary as csv file to the console: ::
diff --git a/doc/sphinx_pages/rest_sources/tutorials/first_steps/shark_layout.rst b/doc/sphinx_pages/rest_sources/tutorials/first_steps/shark_layout.rst
index bc88145..8ccbdc7 100644
--- a/doc/sphinx_pages/rest_sources/tutorials/first_steps/shark_layout.rst
+++ b/doc/sphinx_pages/rest_sources/tutorials/first_steps/shark_layout.rst
@@ -26,9 +26,8 @@ choices:
and also sets up a :doxy:`Data` class especially suited for
machine learning tasks: subsets (e.g., for cross-validation) are
lazy copies of the original set.
-* The folders ``Fuzzy/``, ``Network/``, ``Rng/``, ``Statistics/`` all
- implement specialized functionality pertaining to Fuzzy Logic, HTTP
- protocols (for RESTful APIs), random number generation, and various
+* The folders `Rng/`` and ``Statistics/``
+ implement specialized functionality for random number generation, and various
statistical tests or distributions, respectively.
* Currently, the only algorithms implemented in the folder ``Unsupervised/``
@@ -38,7 +37,3 @@ choices:
``"Model"``-``"ObjectiveFunction"``-``"Optimizer"`` trias. This can be seen as roughly
corresponding to the three remaining folders ``Models/``, ``ObjectiveFunctions/``,
and ``Algorithms/``
-
-.. todo::
- This mindmap will be extended in scope and provided as an interactive,
- browsable applet in the official release of Shark.
diff --git a/doc/sphinx_pages/rest_sources/tutorials/tutorials.rst b/doc/sphinx_pages/rest_sources/tutorials/tutorials.rst
index 5d5f5cf..04c23d1 100644
--- a/doc/sphinx_pages/rest_sources/tutorials/tutorials.rst
+++ b/doc/sphinx_pages/rest_sources/tutorials/tutorials.rst
@@ -163,7 +163,6 @@ Sharks comes with its own solver for Quadratic Programs:
.. _label_for_linalg_tutorials:
We give an introduction to Shark's usage of the
-`Boost uBLAS <http://www.boost.org/doc/libs/release/libs/numeric>`_
library for "all things linear algebra":
* :doc:`concepts/lin_alg/vector_matrix`
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