def test_generic_compute_Lx_batches():
## now compare against theano version
vv = T.matrix()
gg = T.matrix()
hh = T.matrix()
aa = T.vector()
bb = T.vector()
cc = T.vector()
xxw_mat = T.matrix()
xxv_mat = T.matrix()
xxw = T.vector()
xxv = T.vector()
xxa = T.vector()
xxb = T.vector()
xxc = T.vector()
# test compute_Lx
LLx = natural.generic_compute_Lx_batches([vv, gg, hh],
[xxw_mat, xxv_mat],
[xxa, xxb, xxc],
256, 64)
f = theano.function([vv, gg, hh, xxw_mat, xxv_mat, xxa, xxb, xxc], LLx)
t1 = time.time()
rvals = f(v, g, h, xw_mat, xv_mat, xa, xb, xc)
print 'Elapsed: ', time.time() - t1
numpy.testing.assert_almost_equal(Lx_w, rvals[0], decimal=3)
numpy.testing.assert_almost_equal(Lx_v, rvals[1], decimal=3)
numpy.testing.assert_almost_equal(Lx_a, rvals[2], decimal=3)
numpy.testing.assert_almost_equal(Lx_b, rvals[3], decimal=3)
numpy.testing.assert_almost_equal(Lx_c, rvals[4], decimal=3)
def test_generic_compute_Lx_batches():
## now compare against theano version
vv = T.matrix()
gg = T.matrix()
hh = T.matrix()
aa = T.vector()
bb = T.vector()
cc = T.vector()
xxw_mat = T.matrix()
xxv_mat = T.matrix()
xxw = T.vector()
xxv = T.vector()
xxa = T.vector()
xxb = T.vector()
xxc = T.vector()
# test compute_Lx
LLx = natural.generic_compute_Lx_batches([vv, gg, hh], [xxw_mat, xxv_mat],
[xxa, xxb, xxc], 256, 64)
f = theano.function([vv, gg, hh, xxw_mat, xxv_mat, xxa, xxb, xxc], LLx)
t1 = time.time()
rvals = f(v, g, h, xw_mat, xv_mat, xa, xb, xc)
print 'Elapsed: ', time.time() - t1
numpy.testing.assert_almost_equal(Lx_w, rvals[0], decimal=3)
numpy.testing.assert_almost_equal(Lx_v, rvals[1], decimal=3)
numpy.testing.assert_almost_equal(Lx_a, rvals[2], decimal=3)
numpy.testing.assert_almost_equal(Lx_b, rvals[3], decimal=3)
numpy.testing.assert_almost_equal(Lx_c, rvals[4], decimal=3)
def test_generic_compute_Lx_batches():
## now compare against theano version
vv = T.matrix()
gg = T.matrix()
hh = T.matrix()
qq = T.matrix()
aa = T.vector()
bb = T.vector()
cc = T.vector()
dd = T.vector()
xxw_mat = T.matrix()
xxv_mat = T.matrix()
xxz_mat = T.matrix()
xxw = T.vector()
xxv = T.vector()
xxa = T.vector()
xxb = T.vector()
xxc = T.vector()
xxd = T.vector()
# test compute_Lx
LLx = natural.generic_compute_Lx_batches([vv, gg, hh, qq],
[xxw_mat, xxv_mat, xxz_mat],
[xxa, xxb, xxc, xxd], 256, 64)
f = theano.function(
[vv, gg, hh, qq, xxw_mat, xxv_mat, xxz_mat, xxa, xxb, xxc, xxd], LLx)
rvals = f(v, g, h, q, xw_mat, xv_mat, xz_mat, xa, xb, xc, xd)
numpy.testing.assert_almost_equal(Lx_w, rvals[0], decimal=3)
numpy.testing.assert_almost_equal(Lx_v, rvals[1], decimal=3)
numpy.testing.assert_almost_equal(Lx_z, rvals[2], decimal=3)
numpy.testing.assert_almost_equal(Lx_a, rvals[3], decimal=3)
numpy.testing.assert_almost_equal(Lx_b, rvals[4], decimal=3)
numpy.testing.assert_almost_equal(Lx_c, rvals[5], decimal=3)
numpy.testing.assert_almost_equal(Lx_d, rvals[6], decimal=3)
def test_generic_compute_Lx_batches():
## now compare against theano version
vv = T.matrix()
gg = T.matrix()
hh = T.matrix()
qq = T.matrix()
aa = T.vector()
bb = T.vector()
cc = T.vector()
dd = T.vector()
xxw_mat = T.matrix()
xxv_mat = T.matrix()
xxz_mat = T.matrix()
xxw = T.vector()
xxv = T.vector()
xxa = T.vector()
xxb = T.vector()
xxc = T.vector()
xxd = T.vector()
# test compute_Lx
LLx = natural.generic_compute_Lx_batches([vv, gg, hh, qq],
[xxw_mat, xxv_mat, xxz_mat],
[xxa, xxb, xxc, xxd],
256, 64)
f = theano.function([vv, gg, hh, qq, xxw_mat, xxv_mat, xxz_mat, xxa, xxb, xxc, xxd], LLx)
rvals = f(v, g, h, q, xw_mat, xv_mat, xz_mat, xa, xb, xc, xd)
numpy.testing.assert_almost_equal(Lx_w, rvals[0], decimal=3)
numpy.testing.assert_almost_equal(Lx_v, rvals[1], decimal=3)
numpy.testing.assert_almost_equal(Lx_z, rvals[2], decimal=3)
numpy.testing.assert_almost_equal(Lx_a, rvals[3], decimal=3)
numpy.testing.assert_almost_equal(Lx_b, rvals[4], decimal=3)
numpy.testing.assert_almost_equal(Lx_c, rvals[5], decimal=3)
numpy.testing.assert_almost_equal(Lx_d, rvals[6], decimal=3)
