def test_prune_discrete(self):
"""
Testing prune_discrete function with beta/lp specified.
"""
self.setup()
ngl_edges = ngl.prune(self.X, self.edges, steps=100)
edge_set = create_edge_set(ngl_edges)
self.assertEqual(len(self.gold_strict ^ edge_set), 0, '')
def test_prune(self):
"""
Testing prune function
"""
self.setup()
ngl_edges = ngl.prune(self.X, self.edges)
edge_set = create_edge_set(ngl_edges)
self.assertEqual(len(self.gold_strict ^ edge_set), 0, '')
def populate_chunk(self, start_index):
end_index = min(start_index + self.query_size, self.X.shape[0])
count = end_index - start_index
working_set = np.array(range(start_index, end_index))
distances, edges = self.nn_index.search(working_set,
self.max_neighbors)
indices = working_set
# We will need the locations of these additional points since
# we need to know if they fall into the empty region of any
# edges above
additional_indices = np.setdiff1d(edges.ravel(), working_set)
if additional_indices.shape[0] > 0:
# It is possible that we cannot store the entirety of X
# and edges on the GPU, so figure out the subset of Xs and
# carefully replace the edges values
indices = np.hstack((working_set, additional_indices))
if not self.relaxed:
neighbor_edges = self.nn_index.search(additional_indices,
self.max_neighbors,
False)
# We don't care about whether any of the edges of these
# extra rows are valid yet, but the algorithm will need
# them to prune correctly
edges = np.vstack((edges, neighbor_edges))
# Since we will be using the edges above for queries, we
# need to make sure we have the locations of everything
# they touch
neighbor_indices = np.setdiff1d(neighbor_edges.ravel(),
indices)
if neighbor_indices.shape[0] > 0:
indices = np.hstack((indices, neighbor_indices))
indices = indices.astype(i32)
X = self.X[indices, :]
edges = ngl.prune(X,
edges,
indices=indices,
relaxed=self.relaxed,
steps=self.discrete_steps,
beta=self.beta,
lp=self.p,
count=count)
# We will cache these for later use
self.edges[start_index:end_index, :] = edges[:count]
self.distances[start_index:end_index, :] = distances[:count]
# Since, we are taking a lot of time to generate these, then we
# should give the user something to process in the meantime, so
# don't remove these lines and make sure to return in the main
# populate before the same process is done again.
self.push_edges(edges[:count], distances[:count], indices[:count])
def test_prune_discrete_template(self):
"""
Testing prune_discrete fucntion with template specified
"""
self.setup()
template = np.array(ngl.create_template(1, 2, 100), dtype=f32)
ngl_edges = ngl.prune(self.X, self.edges, template=template)
edge_set = create_edge_set(ngl_edges)
self.assertEqual(len(self.gold_strict ^ edge_set), 0, '')
def test_get_edge_list(self):
"""
Testing get_edge_list function
"""
self.setup()
ngl_edges = ngl.prune(self.X, self.edges)
edge_list = ngl.get_edge_list(ngl_edges, np.zeros(ngl_edges.shape))
edge_set = set()
for (p, q, d) in edge_list:
lo = min(p, q)
hi = max(p, q)
edge_set.add((lo, hi))
self.assertEqual(len(self.gold_strict ^ edge_set), 0, '')
def populate_whole(self):
count = self.X.shape[0]
working_set = np.array(range(count))
distances, edges = self.nn_index.search(working_set,
self.max_neighbors)
edges = ngl.prune(self.X,
edges,
relaxed=self.relaxed,
steps=self.discrete_steps,
beta=self.beta,
lp=self.p)
self.edges = edges
self.distances = distances