def test_compare():
from vsm import corpus
n = 4
c = corpus.random_corpus(1e3, 20, 1, 10, tok_name='sentences')
c = c.to_maskedcorpus()
c.mask_terms(['0'])
em = be.BeagleEnvironment()
em.train(c, n_columns=n)
env_matrix = em.matrix
psi = rand_pt_unit_sphere(n)
rand_perm = two_rand_perm(n)
print 'Training single processor model'
sm = BeagleOrderSingle()
sm.train(c, psi=psi, env_matrix=env_matrix, rand_perm=rand_perm)
print 'Training multiprocessor model'
mm = BeagleOrderMulti()
mm.train(c, psi=psi, env_matrix=env_matrix, rand_perm=rand_perm)
assert np.allclose(sm.matrix, mm.matrix, atol=1e-07)
def test_compare():
from vsm import corpus
n = 4
c = corpus.random_corpus(1e3, 20, 1, 10, tok_name='sentences')
em = be.BeagleEnvironment()
em.train(c, n_columns=n)
env_matrix = em.matrix
print 'Training single processor model'
sm = BeagleContextSingle()
sm.train(c, env_matrix=env_matrix)
print 'Training multiprocessor model'
mm = BeagleContextMulti()
mm.train(c, env_matrix=env_matrix)
assert np.allclose(sm.matrix, mm.matrix, atol=1e-07), (sm.matrix[:2], mm.matrix[:2])
def test_BeagleComposite():
from vsm import corpus
n = 256
c = corpus.random_corpus(1e5, 1e2, 1, 10, tok_name='sentences')
m = BeagleComposite()
m.train(c, n_columns=n)
return m
def test_BeagleOrderSingle():
from vsm import corpus
n = 5
c = corpus.random_corpus(1e2, 10, 1, 10, tok_name='sentences')
c = c.to_maskedcorpus()
c.mask_terms(['0'])
m = BeagleOrderSingle()
m.train(c, n_columns=n)
return c, m.matrix
def test_BeagleContextSingle():
from vsm import corpus
n = 5
c = corpus.random_corpus(1e5, 1e4, 1, 20, tok_name='sentences')
c = c.to_maskedcorpus()
c.mask_terms(['0'])
m = BeagleContextSingle()
m.train(c, n_columns=n)
return m.matrix
def test_BeagleOrderMulti():
from vsm import corpus
n = 5
print 'Generating corpus'
c = corpus.random_corpus(1e2, 10, 1, 10, tok_name='sentences')
c = c.to_maskedcorpus()
c.mask_terms(['0'])
m = BeagleOrderMulti()
m.train(c, n_columns=n)
return m.matrix