def _find_impostors_batch(x1, x2, t1, t2, return_dist=False, batch_size=500):
"""Find impostor pairs in chunks to avoid large memory usage
Parameters
----------
x1 : array_like
An array of transformed data samples with shape (n_samples, n_features).
x2 : array_like
An array of transformed data samples with shape (m_samples, n_features) where m_samples < n_samples.
t1 : array_like
An array of distances to the margins with shape (n_samples,).
t2 : array_like
An array of distances to the margins with shape (m_samples,).
batch_size : int (Default value = 500)
The size of each chunk of x1 to compute distances to.
return_dist : bool (Default value = False)
Whether to return the distances to the impostors.
Returns
-------
tuple: (array_like, array_like, [array_like])
imp1 : array_like
An array of sample indices with shape (n_impostors,).
imp2 : array_like
An array of sample indices that violate a margin with shape (n_impostors,).
dist : array_like, optional
An array of pairwise distances of (imp1, imp2) with shape (n_impostors,).
"""
n, m = len(t1), len(t2)
imp1, imp2, dist = [], [], []
for chunk in gen_batches(n, batch_size):
dist_out_in = euclidean_distances(x1[chunk], x2, squared=True)
i1, j1 = np.where(dist_out_in < t1[chunk, None])
i2, j2 = np.where(dist_out_in < t2[None, :])
if len(i1):
imp1.extend(i1 + chunk.start)
imp2.extend(j1)
if return_dist:
dist.extend(dist_out_in[i1, j1])
if len(i2):
imp1.extend(i2 + chunk.start)
imp2.extend(j2)
if return_dist:
dist.extend(dist_out_in[i2, j2])
if return_dist:
return imp1, imp2, dist
else:
return imp1, imp2
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