class cross references

artlib/elementary/ART2.py

@@ -45,7 +45,7 @@ class ART2A(BaseART):
     .. # Neural Networks, 4, 493 – 504. doi:10.1016/0893-6080(91) 90045-7.
 
 
-    ART2-A is similar to :class:`artlib.elementary.ART1.ART1` but designed for analog
+    ART2-A is similar to :class:`~artlib.elementary.ART1.ART1` but designed for analog
     data. This method is implemented for historical purposes and is not recommended
     for use.
 

artlib/elementary/BayesianART.py

@@ -24,8 +24,8 @@ class BayesianART(BaseART):
     .. # Networks, 18, 1628–1644. doi:10.1109/TNN.2007.900234.
 
     Bayesian ART clusters data in Bayesian Distributions (Hyper-ellipsoids) and is
-    similar to :class:`artlib.elementary.GaussianART.GaussianART` but differs in that it
-    allows arbitrary rotation of the hyper-ellipsoid.
+    similar to :class:`~artlib.elementary.GaussianART.GaussianART` but differs in that
+    it allows arbitrary rotation of the hyper-ellipsoid.
 
     """
 

artlib/elementary/GaussianART.py

@@ -23,9 +23,9 @@ class GaussianART(BaseART):
     .. # Neural Networks, 9, 881 – 897. doi:10.1016/0893-6080(95)00115-8.
 
     Guassian ART clusters data in Gaussian Distributions (Hyper-ellipsoids) and is
-    similar to :class:`artlib.elementary.BayesianART.BayesianART` but differs in that
+    similar to :class:`~artlib.elementary.BayesianART.BayesianART` but differs in that
     the hyper-ellipsoid always have their principal axes square to the coordinate
-    frame. It is also faster than :class:`artlib.elementary.BayesianART.BayesianART`.
+    frame. It is also faster than :class:`~artlib.elementary.BayesianART.BayesianART`.
 
     """
 

artlib/hierarchical/DeepARTMAP.py

@@ -17,10 +17,10 @@ class DeepARTMAP(BaseEstimator, ClassifierMixin, ClusterMixin):
     """DeepARTMAP for Hierachical Supervised and Unsupervised Learning.
 
     This module implements DeepARTMAP, a generalization of the
-    :class:`artlib.supervised.ARTMAP.ARTMAP` class :cite:`carpenter1991artmap` that
+    :class:`~artlib.supervised.ARTMAP.ARTMAP` class :cite:`carpenter1991artmap` that
     allows an arbitrary number of data channels to be divisively clustered. DeepARTMAP
     support both supervised and unsupervised modes. If only two ART modules are
-    provided, DeepARTMAP reverts to standard :class:`artlib.supervised.ARTMAP.ARTMAP`
+    provided, DeepARTMAP reverts to standard :class:`~artlib.supervised.ARTMAP.ARTMAP`
     where the first module is the A-module and the second module is the B-module.
     DeepARTMAP does not currently have a direct citation and is an original creation
     of this library.

artlib/hierarchical/SMART.py

@@ -24,11 +24,11 @@ class SMART(DeepARTMAP):
     .. # (pp. 940–944). volume 2.
     .. # doi:10.1109/ICNN.1994.374307.
 
-    SMART accepts an uninstantiated :class:`artlib.common.BaseART.BaseART` class and
+    SMART accepts an uninstantiated :class:`~artlib.common.BaseART.BaseART` class and
     hierarchically clusters data in a divisive fashion by using a set of vigilance
     values that monotonically increase in their restrictiveness. SMART is a special
-    case of :class:`artlib.hierarchical.DeepARTMAP.DeepARTMAP`, which forms the backbone
-    of this class, where all channels receive the same data.
+    case of :class:`~artlib.hierarchical.DeepARTMAP.DeepARTMAP`, which forms the
+    backbone of this class, where all channels receive the same data.
 
     """
 

artlib/reinforcement/FALCON.py

@@ -27,7 +27,7 @@ class FALCON:
     .. # In Proc. IEEE International Joint Conference on Neural Networks (IJCNN)
     .. # (pp. 3297–3302). volume 4. doi:10.1109/ IJCNN.2004.1381208.
 
-    FALCON is based on a :class:`artlib.fusion.FusionART.FusionART` backbone but only
+    FALCON is based on a :class:`~artlib.fusion.FusionART.FusionART` backbone but only
     accepts 3 channels: State, Action, and Reward. Specific functions are implemented
     for getting optimal reward and action predictions.
 
@@ -302,7 +302,7 @@ class TD_FALCON(FALCON):
 
     TD-FALCON is based on a :class:`FALCON` backbone but includes specific function for
     temporal-difference learning. Currently, only SARSA is implemented and only
-    :class:`FuzzyART` base modules are supported.
+    :class:`~artlib.elementary.FuzzyART.FuzzyART` base modules are supported.
 
     """
 

artlib/supervised/ARTMAP.py

@@ -20,13 +20,14 @@ class ARTMAP(SimpleARTMAP):
     .. # by a self-organizing neural network.
     .. # Neural Networks, 4, 565 – 588. doi:10.1016/0893-6080(91)90012-T.
 
-    ARTMAP accepts two :class:`BaseART` modules A and B which cluster the dependent
+    ARTMAP accepts two :class:`~artlib.common.BaseART.BaseART` modules A and B which
+    cluster the dependent
     channel (samples) and the independent channel (labels) respectively while linking
     them with a many-to-one mapping. If your labels are integers,
-    use :class:`artlib.supervised.SimpleARTMAP.SimpleARTMAP` for a faster and more
+    use :class:`~artlib.supervised.SimpleARTMAP.SimpleARTMAP` for a faster and more
     direct implementation. ARTMAP also provides the ability to fit a regression model to
     data and specific functions have been implemented to allow this. However,
-    :class:`artlib.fusion.FusionART.FusionART` provides substantially better fit for
+    :class:`~artlib.fusion.FusionART.FusionART` provides substantially better fit for
     regression problems which are not monotonic.
 
     """

artlib/supervised/SimpleARTMAP.py

@@ -22,11 +22,11 @@ class SimpleARTMAP(BaseARTMAP):
     .. # Adaptive Resonance Theory Microchips: Circuit Design Techniques.
     .. # Norwell, MA, USA: Kluwer Academic Publishers.
 
-    SimpleARTMAP is a special case of :class:`artlib.supervised.ARTMAP.ARTMAP`
+    SimpleARTMAP is a special case of :class:`~artlib.supervised.ARTMAP.ARTMAP`
     specifically for classification. It allows the clustering of data samples while
     enforcing a many-to-one mapping from sample clusters to labels. It accepts an
-    instantiated :class:`artlib.common.BaseART.BaseART` module and dynamically adapts
-    the vigilance function to preventresonance when the many-to-one mapping is
+    instantiated :class:`~artlib.common.BaseART.BaseART` module and dynamically adapts
+    the vigilance function to prevent resonance when the many-to-one mapping is
     violated. This enables SimpleARTMAP to identify discrete clusters belonging to
     each category label.
 

artlib/topological/DualVigilanceART.py

@@ -20,14 +20,14 @@ class DualVigilanceART(BaseART):
     .. # Dual vigilance fuzzy adaptive resonance theory.
     .. # Neural Networks, 109, 1–5. doi:10.1016/j.neunet.2018.09.015.
 
-    Dual Vigilance ART allows a :class:`artlib.common.BaseART.BaseART` module to
+    Dual Vigilance ART allows a :class:`~artlib.common.BaseART.BaseART` module to
     cluster with both an upper and lower vigilance value. The upper-vigilance value
-    allows the :class:`artlib.common.BaseART.BaseART` module to cluster normally,
+    allows the :class:`~artlib.common.BaseART.BaseART` module to cluster normally,
     however, data is simultaneously clustered using the lower vigilance level to
     combine multiple base ART categories into a single abstracted category. This
     permits clusters to be combined to form arbitrary shapes. For example if the
-    :class:`artlib.common.BaseART.BaseART` module is
-    :class:`artlib.elementary.FuzzyART.FuzzyART`, a Dual Vigilance Fuzzy ART
+    :class:`~artlib.common.BaseART.BaseART` module is
+    :class:`~artlib.elementary.FuzzyART.FuzzyART`, a Dual Vigilance Fuzzy ART
     clustering result would look  like a series of hyper-boxes forming an arbitrary
     geometry.
 

artlib/topological/TopoART.py

@@ -29,7 +29,7 @@ class TopoART(BaseART):
     .. # Berlin, Heidelberg: Springer Berlin Heidelberg.
     .. # doi:10.1007/978-3-642-15825-4_21.
 
-    Topo ART clusters accepts an instantiated :class:`artlib.common.BaseART.BaseART`
+    Topo ART clusters accepts an instantiated :class:`~artlib.common.BaseART.BaseART`
     module and generates a topological clustering by recording the first and second
     resonant cluster relationships in an adjacency matrix. Further, it updates the
     second resonant cluster with a lower learning rate than the first, providing for

artlib/topological/__init__.py

@@ -6,11 +6,11 @@
 and Mapper, are often used in fields like data analysis and computational topology.
 
 The two modules herein provide contrasting advantages.
-:class:`artlib.topological.TopoART` allows for the creation of an adjacency matrix
-which can be useful when clusters overlap or are in close proximity.
-:class:`artlib.topological.DualVigilanceART` allows for the abstract merging of many
-smaller clusters and is well suited to problems where the clusters take-on complex
-geometries where other clustering approaches would fail.
+:class:`~artlib.topological.TopoART.TopoART` allows for the creation of an adjacency
+matrix which can be useful when clusters overlap or are in close proximity.
+:class:`~artlib.topological.DualVigilanceART.DualVigilanceART` allows for the abstract
+merging of many smaller clusters and is well suited to problems where the clusters
+take-on complex geometries where other clustering approaches would fail.
 
 `Topological clustering <https://en.wikipedia.org/wiki/Topological_data_analysis>`_