NNGraph: clean and doc (PR #21)

Author: mdeff

README.rst

@@ -48,7 +48,7 @@ The documentation is available on
 `Read the Docs <https://pygsp.readthedocs.io>`_
 and development takes place on
 `GitHub <https://github.com/epfl-lts2/pygsp>`_.
-(A (mostly unmaintained) `Matlab version <https://epfl-lts2.github.io/gspbox-html>`_ exists.)
+A (mostly unmaintained) `Matlab version <https://epfl-lts2.github.io/gspbox-html>`_ exists.
 
 
 The PyGSP facilitates a wide variety of operations on graphs, like computing

pygsp/graphs/nngraphs/bunny.py

@@ -34,7 +34,7 @@ def __init__(self, **kwargs):
             'distance': 8,
         }
 
-        super(Bunny, self).__init__(Xin=data['bunny'],
-                                    epsilon=0.02, NNtype='radius',
+        super(Bunny, self).__init__(data['bunny'],
                                     center=False, rescale=False,
+                                    kind='radius', radius=0.02,
                                     plotting=plotting, **kwargs)

pygsp/graphs/nngraphs/cube.py

@@ -89,7 +89,7 @@ def __init__(self,
             'distance': 9,
         }
 
-        super(Cube, self).__init__(Xin=pts, k=10,
+        super(Cube, self).__init__(pts, k=10,
                                    center=False, rescale=False,
                                    plotting=plotting, **kwargs)
 

pygsp/graphs/nngraphs/imgpatches.py

@@ -10,7 +10,8 @@ class ImgPatches(NNGraph):
 
     Extract a feature vector in the form of a patch for every pixel of an
     image, then construct a nearest-neighbor graph between these feature
-    vectors. The feature matrix, i.e. the patches, can be found in :attr:`Xin`.
+    vectors. The feature matrix, i.e., the patches, can be found in
+    :attr:`features`.
 
     Parameters
     ----------
@@ -39,9 +40,10 @@ class ImgPatches(NNGraph):
     >>> from skimage import data, img_as_float
     >>> img = img_as_float(data.camera()[::64, ::64])
     >>> G = graphs.ImgPatches(img, patch_shape=(3, 3))
-    >>> print('{} nodes ({} x {} pixels)'.format(G.Xin.shape[0], *img.shape))
+    >>> N, d = G.features.shape
+    >>> print('{} nodes ({} x {} pixels)'.format(N, *img.shape))
     64 nodes (8 x 8 pixels)
-    >>> print('{} features per node'.format(G.Xin.shape[1]))
+    >>> print('{} features per node'.format(d))
     9 features per node
     >>> G.set_coordinates(kind='spring', seed=42)
     >>> fig, axes = plt.subplots(1, 2)