def nn_multi_base_context(self, targets, n=5):
"""
Find and return the n nearest base neighbours to a set of target vectors
from the context matrix.
:rtype : 2-tuple of ndarrays
:param target: int
:param n: int
"""
d = np.ones(self.size, dtype=np.float)
new_target = self.base_vectors[targets].sum(axis=0)
for i, vector in enumerate(self.context_vectors):
compare = True
for index in targets:
if i == index:
compare = False
if compare:
d[i] = VectorMath.cosine(new_target, vector)
args = np.argsort(d)[1:n + 1]
vals = d[args]
return args, vals
def nn_base_base(self, target, n=5):
"""
Find and return the n nearest base neighbours to a target vector from
the base matrix.
:rtype : 2-tuple of ndarrays
:param target: int
:param n: int
"""
d = np.zeros(self.size, dtype=np.float)
for i, vector in enumerate(self.base_vectors):
d[i] = VectorMath.cosine(self.base_vectors[target], vector)
args = np.argsort(d)[1:n + 1]
vals = d[args]
return args, vals
