BF: Fixing some of the broken links to api documentation

PyMVPA · May 28, 2008 · f44663e · f44663e
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Showing 7 changed files with 18 additions and 17 deletions.
diff --git a/doc/classifiers.txt b/doc/classifiers.txt
@@ -221,7 +221,7 @@ and some Dataset_ `data`.
   ...                                      NFoldSplitter(cvtype=1))
   >>> error = cvterr(data)
 
-.. _Dataset: api/mvpa.datasets.dataset.Dataset-class.html
+.. _Dataset: api/mvpa.datasets.base.Dataset-class.html
 .. _Splitter: api/mvpa.datasets.splitter.Splitter-class.html
 .. _CrossValidatedTransferError: api/mvpa.algorithms.cvtranserror.CrossValidatedTransferError-class.html
 
@@ -323,19 +323,20 @@ of a C extension for the performance critical pieces of the code.
 Support Vector Machines
 =======================
 
-Support vector machines [#]_ are a popular classifier that can deal with very high dimensional
-problems (`Wikipedia entry about SVM`_).
+Support vector machines [#]_ are a popular classifier that can deal with very
+high dimensional problems (`Wikipedia entry about SVM`_).
 
-The support vector machine classes provide a family of classifers by wrapping
-the libsvm_ library.  While the SVMBase_ class provides a complete interface,
-the other child classes make it easy to run standard classifiers, such as
-linear SVM, with a default set of parameters (see LinearCSVMC_, LinearNuSVMC_,
-RbfNuSVMC_ and RbfCSVMC_).
+The support vector machine classes provide a family of classifiers by wrapping
+libsvm_ and Shogun_ libraries.  While the SVMBase_ class provides a complete
+interface, the other child classes make it easy to run standard classifiers,
+such as linear SVM, with a default set of parameters (see LinearCSVMC_,
+LinearNuSVMC_, RbfNuSVMC_ and RbfCSVMC_).
 
 .. [#] Vapnik, V. (1995). *The Nature of Statistical Learning Theory*.
        Springer, New York.
 
 .. _libsvm: http://www.csie.ntu.edu.tw/~cjlin/libsvm/
+.. _Shogun: http://www.shogun-toolbox.org
 .. _Wikipedia entry about SVM: http://en.wikipedia.org/wiki/Support_Vector_Machine
 .. _LinearCSVMC: api/mvpa.clfs.svm.LinearCSVMC-class.html
 .. _LinearNuSVMC: api/mvpa.clfs.svm.LinearNuSVMC-class.html

diff --git a/doc/datasets.txt b/doc/datasets.txt
@@ -21,7 +21,7 @@ and dataset attributes.  Sample attributes assign a value to each data
 sample and dataset attributes are additional information or
 functionality that applies to the whole dataset.
 
-.. _Dataset: api/mvpa.datasets.dataset.Dataset-class.html
+.. _Dataset: api/mvpa.datasets.base.Dataset-class.html
 
 .. <gjd> i had a hard time making sense of the above
    paragraph on first reading. add an example to get

diff --git a/doc/devguide.txt b/doc/devguide.txt
@@ -116,7 +116,7 @@ dataset vs data
   Such convention should be enforced in all ``*train``,
   ``*predict`` functions of classifiers.
 
-.. _Dataset: api/mvpa.datasets.dataset.Dataset-class.html
+.. _Dataset: api/mvpa.datasets.base.Dataset-class.html
 .. _NumPy: http://numpy.scipy.org/
 
 
@@ -302,7 +302,7 @@ Outstanding Questions:
   * Shouldn't there be a `sensitivities` state?
 
 
-.. _Absolute: api/mvpa.misc.Absolute-def.html
+.. _Absolute: api/mvpa.misc.transformers-module.html#Absolute
 .. _DatasetMeasure: api/mvpa.measures.base.DatasetMeasure-class.html
 .. _FeaturewiseDatasetMeasure: api/mvpa.measures.base.FeaturewiseDatasetMeasure-class.html
 .. _Sensitivity: api/mvpa.measures.base.Sensitivity-class.html

diff --git a/doc/featsel.txt b/doc/featsel.txt
@@ -25,7 +25,7 @@ RFE_
 
 (to be written)
 
-.. _RFE: api/mvpa.algorithms.rfe.RFE-class.html
+.. _RFE: api/mvpa.featsel.rfe.RFE-class.html
 
 
 .. index:: incremental feature search, IFS
@@ -38,7 +38,7 @@ IFS_
 
 (to be written)
 
-.. _IFS: api/mvpa.algorithms.ifs.IFS-class.html
+.. _IFS: api/mvpa.featsel.ifs.IFS-class.html
 
 .. What are the practical differences (besides speed) between RFE and IFS?
 
diff --git a/doc/measures.txt b/doc/measures.txt
@@ -72,7 +72,7 @@ analyzers directly compute a score from a Dataset. Meta sensitivity analyzers
 on the other hand utilize another sensitivity analyzer to compute their
 sensitivity maps.
 
-.. _Dataset: api/mvpa.datasets.dataset.Dataset-class.html
+.. _Dataset: api/mvpa.datasets.base.Dataset-class.html
 .. _Sensitivity: api/mvpa.measures.base.Sensitivity-class.html
 
 
@@ -94,7 +94,7 @@ Higher F-scores indicate higher sensitivities, as with all other sensitivity
 analyzers.
 
 
-.. _OneWayAnova: api/mvpa.algorithms.anova.OneWayAnova-class.html
+.. _OneWayAnova: api/mvpa.measures.anova.OneWayAnova-class.html
 
 
 .. index:: classifier weights, weights, SVM, measure

diff --git a/doc/misc.txt b/doc/misc.txt
@@ -169,7 +169,7 @@ bugs in the internal logic.
 .. _optVerbose: api/mvpa.misc.cmdline-module.html#optVerbose
 .. _optDebug: api/mvpa.misc.cmdline-module.html#optDebug
 .. _misc module sources: api/mvpa.misc-pysrc.html
-.. _Recursive Feature Elimination call: api/mvpa.algorithms.rfe.RFE-class.html#__call__
+.. _Recursive Feature Elimination call: api/mvpa.featsel.rfe.RFE-class.html#__call__
 
 
 Additional Little Helpers

diff --git a/doc/scenarios.txt b/doc/scenarios.txt
@@ -63,7 +63,7 @@ searchlight map (`sl_map`) can be mapped back into the original dataspace
 and viewed as a brain overlay. The :ref:`example section <example_searchlight>`
 contains a typical application of this algorithm.
 
-.. _Searchlight: api/mvpa.algorithms.searchlight.Searchlight-class.html
+.. _Searchlight: api/mvpa.measures.searchlight.Searchlight-class.html
 
 .. Mention the fact that it also is a special `SensitivityAnalyzer`