Merge pull request #45 from CSU-TDA/master

Transition of DARPA SD2 Implementation of PersistenceImages() into persim module

Author: sauln

docs/conf.py

@@ -21,3 +21,9 @@
 
 html_short_title = project
 htmlhelp_basename = 'Persimdoc'
+
+autodoc_default_options = {
+    'members': True
+}
+
+autodoc_member_order = 'groupwise'

docs/index.rst

@@ -33,7 +33,7 @@ To contribute please fork the project make your changes and submit a pull reques
     :hidden:
     :caption: User Guide
 
-    notebooks/Persistence Images
+    notebooks/Persistence images
     notebooks/distances
     reference/index
 
@@ -44,7 +44,8 @@ To contribute please fork the project make your changes and submit a pull reques
     :caption: Tutorials
 
     notebooks/Classification with persistence images
-
+    notebooks/Persistence barcode measure
+    
 
 .. |Downloads| image:: https://pypip.in/download/persim/badge.svg
     :target: https://pypi.python.org/pypi/persim/

docs/notebooks/Classification with persistence images.ipynb

@@ -21,8 +21,10 @@ Persistence Images
 
 .. autosummary::
     :toctree: stubs
+    :recursive:
     :nosignatures:
 
+    persim.PersistenceImager
     persim.PersImage
 
 

docs/notebooks/Persistence Images.ipynb

@@ -122,12 +122,12 @@ def gromov_hausdorff(
 
     Parameters
     -----------
-    AG: np.array (|V(G)|×|V(G)|)
-        (Sparse) adjacency matrix of graph G, or an iterable of
+    AG: (N,N) np.array
+        (Sparse) adjacency matrix of graph G with N vertices, or an iterable of
         adjacency matrices if AH=None.
-    AH: np.array (|V(H)|×|V(H)|)
-        (Sparse) adjacency matrix of graph H, or None.
-    mapping_sample_size_order: np.array (2)
+    AH: (M,M) np.array
+        (Sparse) adjacency matrix of graph H with M vertices, or None.
+    mapping_sample_size_order: (2,) np.array 
         Parameter that regulates the number of mappings to sample when
         tightening upper bound of the mGH distance.
 
@@ -184,12 +184,12 @@ def make_distance_matrix_from_adjacency_matrix(AG):
 
     Parameters
     -----------
-    AG: np.array (|V(G)|×|V(G)|)
-        (Sparse) adjacency matrix of simple unweighted graph G.
+    AG: (N,N) np.array
+        (Sparse) adjacency matrix of simple unweighted graph G with N vertices.
 
     Returns
     --------
-    DG: np.array (|V(G)|×|V(G)|)
+    DG: (N,N)  np.array
         (Dense) distance matrix of the compact metric space
         representation of G based on its shortest path lengths.
     """