def spanning_tree_length(X):
"""Compute the length of the euclidean MST of X.
Parameters
----------
X: ndarray, shape=[n_samples, n_features]
"""
if X.shape[0] < 2:
return 0
return minimum_spanning_tree(euclidean_distances(X)).sum()
def spanning_tree_length(X):
"""Compute the length of the euclidean MST of X.
Parameters
----------
X: ndarray, shape=[n_samples, n_features]
"""
if X.shape[0] < 2:
return 0
return minimum_spanning_tree(euclidean_distances(X)).sum()
def euclidean_mst(X, neighbors_estimator, verbose=2):
n_neighbors = min(2, X.shape[0])
while True:
# make sure we have a connected minimum spanning tree.
# otherwise we need to consider more neighbors
n_neighbors = 2 * n_neighbors
if verbose > 1:
print("Trying to build mst with %d neighbors." % n_neighbors)
distances = neighbors_estimator.kneighbors_graph(
X, n_neighbors=n_neighbors, mode='distance')
n_components, component_indicators =\
sparse.cs_graph_components(distances + distances.T)
if len(np.unique(component_indicators)) > 1:
continue
distances.sort_indices()
forest = minimum_spanning_tree(distances)
_, inds = sparse.cs_graph_components(forest + forest.T)
assert (len(np.unique(inds)) == 1)
break
return forest
def euclidean_mst(X, neighbors_estimator, verbose=2):
n_neighbors = min(2, X.shape[0])
while True:
# make sure we have a connected minimum spanning tree.
# otherwise we need to consider more neighbors
n_neighbors = 2 * n_neighbors
if verbose > 1:
print("Trying to build mst with %d neighbors." % n_neighbors)
distances = neighbors_estimator.kneighbors_graph(
X, n_neighbors=n_neighbors, mode='distance')
n_components, component_indicators =\
sparse.cs_graph_components(distances + distances.T)
if len(np.unique(component_indicators)) > 1:
continue
distances.sort_indices()
forest = minimum_spanning_tree(distances)
_, inds = sparse.cs_graph_components(forest + forest.T)
assert(len(np.unique(inds)) == 1)
break
return forest
