def test_with_cache(self):
cache = numpy.zeros(4, dtype=numpy.int32)
w1, w2, dist = self._get_w1_w2_dist()
r = libwmdrelax.emd_relaxed(w1, w2, dist, cache)
self.assertAlmostEqual(r, 0.3945127)
r = libwmdrelax.emd_relaxed(w1, w2, dist, cache=cache)
self.assertAlmostEqual(r, 0.3945127)
def test_with_cache(self):
cache = libwmdrelax.emd_relaxed_cache_init(4)
w1, w2, dist = self._get_w1_w2_dist()
r = libwmdrelax.emd_relaxed(w1, w2, dist, cache)
self.assertAlmostEqual(r, 0.6125112)
r = libwmdrelax.emd_relaxed(w1, w2, dist, cache=cache)
self.assertAlmostEqual(r, 0.6125112)
libwmdrelax.emd_relaxed_cache_fini(cache)
def _estimate_WMD_relaxation_batch(self, words1, weights1, i2):
joint, w1, w2 = self._common_vocabulary_batch(words1, weights1, i2)
w1 /= w1.sum()
w2 /= w2.sum()
evec = self.embeddings[joint]
evec_sqr = (evec * evec).sum(axis=1)
dists = evec_sqr - 2 * evec.dot(evec.T) + evec_sqr[:, numpy.newaxis]
dists[dists < 0] = 0
dists = numpy.sqrt(dists)
return libwmdrelax.emd_relaxed(w1, w2, dists, self._relax_cache), \
w1, w2, dists
def test_no_cache(self):
w1, w2, dist = self._get_w1_w2_dist()
r = libwmdrelax.emd_relaxed(w1, w2, dist)
self.assertAlmostEqual(r, 0.6125112)
