use cyflann instead of pyflann (radius search not working)

Author: naspert

pygsp/graphs/nngraphs/nngraph.py

@@ -27,14 +27,14 @@
 
                     }
 
-def _import_pfl():
+def _import_cfl():
     try:
-        import pyflann as pfl
+        import cyflann as cfl
     except Exception:
-        raise ImportError('Cannot import pyflann. Choose another nearest '
+        raise ImportError('Cannot import cyflann. Choose another nearest '
                           'neighbors method or try to install it with '
-                          'pip (or conda) install pyflann (or pyflann3).')
-    return pfl
+                          'pip (or conda) install cyflann.')
+    return cfl
 
 
 
@@ -50,15 +50,15 @@ def _knn_flann(X, num_neighbors, dist_type, order):
     if dist_type == 'max_dist':
         raise ValueError('FLANN and max_dist is not supported')
 
-    pfl = _import_pfl()
-    pfl.set_distance_type(dist_type, order=order)
-    flann = pfl.FLANN()
-
+    cfl = _import_cfl()
+    cfl.set_distance_type(dist_type, order=order)
+    c = cfl.FLANNIndex(algorithm='kdtree')
+    c.build_index(X)
     # Default FLANN parameters (I tried changing the algorithm and
     # testing performance on huge matrices, but the default one
     # seems to work best).
-    NN, D = flann.nn(X, X, num_neighbors=(num_neighbors + 1), 
-                     algorithm='kdtree')
+    NN, D = c.nn_index(X, num_neighbors + 1)
+    c.free_index()
     if dist_type == 'euclidean': # flann returns squared distances
         return NN, np.sqrt(D)
     return NN, D
@@ -102,21 +102,20 @@ def _radius_flann(X, epsilon, dist_type, order=0):
     # do not allow it
     if dist_type == 'max_dist':
         raise ValueError('FLANN and max_dist is not supported')
-    pfl = _import_pfl()
-    
-    pfl.set_distance_type(dist_type, order=order)
-    flann = pfl.FLANN()
-    flann.build_index(X)
 
+    cfl = _import_cfl()
+    cfl.set_distance_type(dist_type, order=order)
+    c = cfl.FLANNIndex(algorithm='kdtree')
+    c.build_index(X)
     D = []
     NN = []
     for k in range(N):
-        nn, d = flann.nn_radius(X[k, :], epsilon*epsilon)
+        nn, d = c.nn_radius(X[k, :], epsilon*epsilon)
         D.append(d)
         NN.append(nn)
-    flann.delete_index()
+    c.free_index()
     if dist_type == 'euclidean': # flann returns squared distances
-        return NN, np.sqrt(D)
+        return NN, list(map(np.sqrt, D))
     return NN, D
 
 def center_input(X, N):
@@ -228,7 +227,13 @@ def __init__(self, Xin, NNtype='knn', backend='scipy-kdtree', center=True,
             Xout = rescale_input(Xout, N, d)
 
 
+        if self._nn_functions.get(NNtype) == None:
+            raise ValueError('Invalid NNtype {}'.format(self.NNtype))
 
+        if self._nn_functions[NNtype].get(backend) == None:
+            raise ValueError('Invalid backend {} for type {}'.format(backend, 
+                             self.NNtype))
+        
         if self.NNtype == 'knn':
             spi = np.zeros((N * k))
             spj = np.zeros((N * k))
@@ -262,8 +267,6 @@ def __init__(self, Xin, NNtype='knn', backend='scipy-kdtree', center=True,
                                                  float(self.sigma))
                 start = start + leng
 
-        else:
-            raise ValueError('Unknown NNtype {}'.format(self.NNtype))
 
         W = sparse.csc_matrix((spv, (spi, spj)), shape=(N, N))
 

setup.py

@@ -30,8 +30,8 @@
             # Construct patch graphs from images.
             'scikit-image',
             # Approximate nearest neighbors for kNN graphs.
-            'pyflann; python_version == "2.*"',
-            'pyflann3; python_version == "3.*"',
+            'flann',
+	    'cyflann',
             # Convex optimization on graph.
             'pyunlocbox',
             # Plot graphs, signals, and filters.