def kneighbors(self, X, n_neighbors=None, return_distance=True):
"""Finds the K-neighbors of a point.
Returns indices of and distances to the neighbors of each
point.
Parameters
----------
X: numpy.array 1D
Array of strings containing the words whose neighbors should
be determined.
n_neighbors: int
Number of neighbors to get (default is the value passed to
the constructor).
return_distance: boolean (default=True)
If False, distances will not be returned.
Returns
-------
neighbors: numpy.array
Indices of the nearest points in the population matrix.
distances: numpy.array (optional)
Array representing the lengths to points, only present if
`return_distance=True`.
"""
n_neighbors = n_neighbors or self.topn
n_neighbors = min(n_neighbors, self.syn0_.shape[0] - 1)
neighbors, similarities = [], []
for x in X:
xvec = self._get_vector_from_word(x)
similarity = np.dot(self.syn0_, xvec.T)
# Throw away the smallest index, since it is the initial
# word itself.
# pylint: disable=invalid-unary-operand-type
neighbor_indices = fast_argsort(-similarity, n_neighbors + 1)[1:]
neighbors.append(neighbor_indices)
similarities.append(similarity[neighbor_indices])
neighbors = np.vstack(neighbors)
if not return_distance:
return neighbors
# normalize distances to [0, 1]
distances = (np.vstack(similarities) - 1) / -2.
return neighbors, distances
def test_compare_fast_argsort_with_argsort_only_n_smallest(
self, vec, n, fast_argsort):
slow = np.argsort(vec)[:n]
fast = fast_argsort(vec, n)
assert (slow == fast).all()
def test_compare_fast_argsort_with_argsort_all(self, vec, fast_argsort):
slow = np.argsort(vec)
fast = fast_argsort(vec, len(vec))
assert (slow == fast).all()