Update documentation

Update documentation. Add original reference for the example network
used in the MuTE paper and make clear that the Sydney PID estimator
estimates the BROJA measure. Add documentation for AdjacencyMatrix class.

idtxl/data.py

@@ -34,7 +34,7 @@ class Data():
         >>> data_2.set_data(data_new, 's')
 
     Note:
-        Realisations are stored as attribute 'data'. This can only be set via 
+        Realisations are stored as attribute 'data'. This can only be set via
         the 'set_data()' method.
 
     Args:
@@ -828,16 +828,26 @@ def generate_mute_data(self, n_samples=1000, n_replications=10):
 
         Generate example data and overwrite the instance's current data. The
         network is used as an example the paper on the MuTE toolbox (Montalto,
-        PLOS ONE, 2014, eq. 14). The network consists of five autoregressive
-        (AR) processes with model orders 2 and the following (non-linear)
-        couplings:
+        PLOS ONE, 2014, eq. 14) and was orginially proposed by Baccala &
+        Sameshima (2001). The network consists of five autoregressive (AR)
+        processes with model orders 2 and the following (non-linear) couplings:
 
         0 -> 1, u = 2 (non-linear)
         0 -> 2, u = 3
         0 -> 3, u = 2 (non-linear)
         3 -> 4, u = 1
         4 -> 3, u = 1
 
+        References:
+
+        - Montalto, A., Faes, L., & Marinazzo, D. (2014) MuTE: A MATLAB toolbox
+          to compare established and novel estimators of the multivariate
+          transfer entropy. PLoS ONE 9(10): e109462.
+          https://doi.org/10.1371/journal.pone.0109462
+        - Baccala, L.A. & Sameshima, K. (2001). Partial directed coherence: a
+          new concept in neural structure determination. Biol Cybern 84:
+          463–474. https://doi.org/10.1007/PL00007990
+
         Args:
             n_samples : int
                 number of samples simulated for each process and replication

idtxl/estimators_pid.py

@@ -5,7 +5,6 @@
 
 Bertschinger, N., Rauh, J., Olbrich, E., Jost, J., & Ay, N. (2014). Quantifying
 Unique Information. Entropy, 16(4), 2161–2183. http://doi.org/10.3390/e16042161
-
 """
 import numpy as np
 from . import synergy_tartu
@@ -17,9 +16,9 @@
 class SydneyPID(Estimator):
     """Estimate partial information decomposition of discrete variables.
 
-    Fast implementation of the partial information decomposition (PID)
-    estimator for discrete data. The estimator does not require JAVA or GPU
-    modules to run.
+    Fast implementation of the BROJA partial information decomposition (PID)
+    estimator for discrete data (Bertschinger, 2014). The estimator does not
+    require JAVA or GPU modules to run.
 
     The estimator finds shared information, unique information and
     synergistic information between the two inputs s1 and s2 with respect to
@@ -34,6 +33,12 @@ class SydneyPID(Estimator):
     CMI decreases; and an outer loop which decreases the size of the
     probability mass increment the virtualised swapping utilises.
 
+    References
+
+    - Bertschinger, N., Rauh, J., Olbrich, E., Jost, J., & Ay, N. (2014).
+      Quantifying unique information. Entropy, 16(4), 2161–2183.
+      http://doi.org/10.3390/e16042161
+
     Args:
         settings : dict
             estimation parameters

idtxl/results.py

@@ -55,14 +55,7 @@ def __setstate__(self, state):
 
 
 class AdjacencyMatrix():
-    """Adjacency matrix representing inferred networks.
-
-    Attributes:  TODO
-        is_edge : bool
-            matrix of edges in the network
-        weights : int | float | bool
-            weight for each edge
-    """
+    """Adjacency matrix representing inferred networks."""
     def __init__(self, n_nodes, weight_type):
         self._edge_matrix = np.zeros((n_nodes, n_nodes), dtype=bool)
         self._weight_matrix = np.zeros((n_nodes, n_nodes), dtype=weight_type)