def test_blend_2():
from divisi2.blending import blend, blend_svd
Uref, Sref, Vref = blend([mat_4x3, third_mat_4x3]).svd(k=2)
U, S, V = blend_svd([mat_4x3, third_mat_4x3], k=2)
rec_ref = dot(Uref * Sref, Vref.T)
rec_opt = dot(U * S, V.T)
assert np.allclose(rec_ref, rec_opt)
def make_blend(thefile):
conceptnet = divisi2.network.conceptnet_matrix('en')
thegame = divisi2.load(thefile).normalize_all()
blended_matrix = blend(conceptnet, thegame)
u,s,v = blended_matrix().svd()
similarity = divisi2.reconstruct_similarity(u,s)
pd[thefile.split('.')[0]] = similarity
return similarity
def test_cnet_blend():
from divisi2.blending import blend, blend_svd
matrix = divisi2.network.conceptnet_matrix('en')
isa = divisi2.network.filter_by_relation(matrix, 'IsA').squish().normalize_all()
atloc = divisi2.network.filter_by_relation(matrix, 'AtLocation').squish().normalize_all()
Uref, Sref, Vref = blend([isa, atloc]).svd(k=3)
U, S, V = blend_svd([isa, atloc], k=3)
rec_ref = divisi2.reconstruct(Uref, Sref, Vref)
rec_opt = divisi2.reconstruct(U, S, V)
# Check a random sampling of the items.
import random
for row in random.sample(rec_ref.row_labels, 50):
for col in random.sample(rec_ref.col_labels, 50):
assert np.allclose(rec_ref.entry_named(row, col),
rec_opt.entry_named(row, col))