Merge pull request #214 from sjsrey/region-docs

Region docs

spopt/region/azp_util.py

@@ -153,11 +153,9 @@ def __init__(self, spatially_extensive_attr, threshold, decorated_strategy):
         Parameters
         ----------
         spatially_extensive_attr : numpy.ndarray
-            See corresponding argument in
-            ``region.max_p_regions.heuristics.MaxPRegionsHeu.fit_from_scipy_sparse_matrix``.
+             Values that will serve as domain for the threshold constraint
         threshold : numbers.Real or numpy.ndarray
-            See corresponding argument in
-            ``region.max_p_regions.heuristics.MaxPRegionsHeu.fit_from_scipy_sparse_matrix``
+             Value for the threshold constraint
         decorated_strategy : AllowMoveStrategy
             The ``AllowMoveStrategy`` related to the algorithms local search.
         """

spopt/region/maxp.py

@@ -6,6 +6,9 @@
 Journal of Geographical Information Science. Accepted 2020-04-12.
 """
 
+__author__ = ["Ran Wei", "Serge Rey", "Elijah Knaap"]
+__email__ = "[email protected]"
+
 from ..BaseClass import BaseSpOptHeuristicSolver
 from .base import (
     w_to_g,
@@ -727,8 +730,8 @@ class MaxPHeuristic(BaseSpOptHeuristicSolver):
         Set to ``True`` for reporting solution progress/debugging.
         Default is ``False``.
 
-    Returns
-    -------
+    Attributes
+    ----------
 
     max_p : int
         The number of regions.

spopt/region/region_k_means.py

@@ -1,3 +1,14 @@
+"""
+Region k-means
+
+
+K-means with the constraint that all clusters form a spatially connected component.
+"""
+
+__author__ = "Serge Rey"
+__email__ = "[email protected]"
+
+
 from collections import defaultdict
 import numpy
 from ..BaseClass import BaseSpOptHeuristicSolver
@@ -14,7 +25,7 @@
 
 
 def region_k_means(X, n_clusters, w):
-    """Solve the region-K-means problem, the K-means with the constraint
+    """Solve the region-K-means problem with the constraint
     that each cluster forms a spatially connected component.
 
     Parameters
@@ -119,7 +130,7 @@ def region_k_means(X, n_clusters, w):
 
 
 class RegionKMeansHeuristic(BaseSpOptHeuristicSolver):
-    """Solve the region-K-means problem, the K-means with the constraint
+    """Solve the region-K-means problem with the constraint
     that each cluster forms a spatially connected component.
 
 
@@ -138,14 +149,15 @@ class RegionKMeansHeuristic(BaseSpOptHeuristicSolver):
     Attributes
     ----------
     
-    labels_ : 
-        ...
-    
-    centroids_ : 
-        ...
+    labels_ : numpy.array 
+        Region IDs for observations
     
-    iters_ : 
-        ...
+    centroids_ : numpy.ndarray
+       Floating point array of centroids in the shape of ``(k, n_features)``
+       found at the last iteration of ``region_k_means``.
+
+    iters : int
+        The number of iterations for the reassignment phase.
 
     """