class cross references

artlib/elementary/ART2.py

@@ -45,8 +45,9 @@ class ART2A(BaseART):
     .. # Neural Networks, 4, 493 – 504. doi:10.1016/0893-6080(91) 90045-7.
 
 
-    ART2-A is similar to :class:`ART1` but designed for analog data. This method is
-    implemented for historical purposes and is not recommended for use.
+    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:`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:`BayesianART` but differs in that the hyper-ellipsoid always have
-    their principal axes square to the coordinate frame. It is also faster than
-    Bayesian ART.
+    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`.
 
     """
 

artlib/hierarchical/DeepARTMAP.py

@@ -16,13 +16,14 @@
 class DeepARTMAP(BaseEstimator, ClassifierMixin, ClusterMixin):
     """DeepARTMAP for Hierachical Supervised and Unsupervised Learning.
 
-    This module implements DeepARTMAP, a generalization of the :class:`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:`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.
+    This module implements DeepARTMAP, a generalization of the
+    :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`
+    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.
 
     .. # Carpenter, G. A., Grossberg, S., & Reynolds, J. H. (1991a).
     .. # ARTMAP: Supervised real-time learning and classification of nonstationary data

artlib/hierarchical/SMART.py

@@ -24,10 +24,11 @@ class SMART(DeepARTMAP):
     .. # (pp. 940–944). volume 2.
     .. # doi:10.1109/ICNN.1994.374307.
 
-    SMART accepts an uninstantiated ART 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:`DeepARTMAP`, which
-    forms the backbone of this class, where all channels receive the same data.
+    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.
 
     """
 

artlib/reinforcement/FALCON.py

@@ -27,9 +27,9 @@ 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:`FusionART` backbone but only accepts 3 channels:
-    State, Action, and Reward. Specific functions are implemented for getting optimal
-    reward and action predictions.
+    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.
 
     """
 

artlib/supervised/ARTMAP.py

@@ -23,10 +23,11 @@ class ARTMAP(SimpleARTMAP):
     ARTMAP accepts two :class:`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:`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:`FusionART` provides
-    substantially better fit for regression problems which are not monotonic.
+    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
+    regression problems which are not monotonic.
 
     """
 

artlib/supervised/SimpleARTMAP.py

@@ -22,12 +22,13 @@ class SimpleARTMAP(BaseARTMAP):
     .. # Adaptive Resonance Theory Microchips: Circuit Design Techniques.
     .. # Norwell, MA, USA: Kluwer Academic Publishers.
 
-    SimpleARTMAP is a special case of :class:`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:`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.
+    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
+    violated. This enables SimpleARTMAP to identify discrete clusters belonging to
+    each category label.
 
     """
 

artlib/topological/DualVigilanceART.py

@@ -20,12 +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:`BaseART` module to cluster with both an upper
-    and lower vigilance value. The upper-vigilance value allows the base ART 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 base ART module is :class:`FuzzyART`, a Dual Vigilance Fuzzy ART
+    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,
+    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
     clustering result would look  like a series of hyper-boxes forming an arbitrary
     geometry.
 

artlib/topological/TopoART.py

@@ -29,11 +29,11 @@ class TopoART(BaseART):
     .. # Berlin, Heidelberg: Springer Berlin Heidelberg.
     .. # doi:10.1007/978-3-642-15825-4_21.
 
-    Topo ART clusters accepts an instatiated :class:`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 a distributed
-    learning model.
+    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
+    a distributed learning model.
 
     """
 

artlib/topological/__init__.py

@@ -5,9 +5,10 @@
 geometry of the data. Topological clustering techniques, such as hierarchical clustering
 and Mapper, are often used in fields like data analysis and computational topology.
 
-The two modules herein provide contrasting advantages. :class:`TopoART` allows for the
-creation of an adjacency matrix which can be useful when clusters overlap or are in
-close proximity. :class:`DualVigilanceART` allows for the abstract merging of many
+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.