class Test(unittest.TestCase):
def setUp(self):
def op(x):
return x
self.cache = Cacher(op, 1)
def test_pickling(self):
self.assertRaises(PickleError, self.cache.__getstate__)
self.assertRaises(PickleError, self.cache.__setstate__)
def test_copy(self):
tmp = self.cache.__deepcopy__()
assert (tmp.operation is self.cache.operation)
self.assertEqual(tmp.limit, self.cache.limit)
def test_reset(self):
self.cache.reset()
self.assertDictEqual(
self.cache.cached_input_ids,
{},
)
self.assertDictEqual(
self.cache.cached_outputs,
{},
)
self.assertDictEqual(
self.cache.inputs_changed,
{},
)
def test_name(self):
assert (self.cache.__name__ == self.cache.operation.__name__)
class Test(unittest.TestCase):
def setUp(self):
def op(x):
return x
self.cache = Cacher(op, 1)
def test_pickling(self):
self.assertRaises(PickleError, self.cache.__getstate__)
self.assertRaises(PickleError, self.cache.__setstate__)
def test_copy(self):
tmp = self.cache.__deepcopy__()
assert(tmp.operation is self.cache.operation)
self.assertEqual(tmp.limit, self.cache.limit)
def test_reset(self):
self.cache.reset()
self.assertDictEqual(self.cache.cached_input_ids, {}, )
self.assertDictEqual(self.cache.cached_outputs, {}, )
self.assertDictEqual(self.cache.inputs_changed, {}, )
def test_name(self):
assert(self.cache.__name__ == self.cache.operation.__name__)
}
"""
weave.inline(code,
support_code=support_code,
arg_names=[
'psi1', 'psi2n', 'N', 'M', 'Q', 'variance', 'l2', 'Z',
'mu', 'S', 'gamma', 'log_denom1', 'log_denom2',
'log_gamma', 'log_gamma1'
],
type_converters=weave.converters.blitz)
psi2 = psi2n.sum(axis=0)
return psi0, psi1, psi2, psi2n
from GPy.util.caching import Cacher
psicomputations = Cacher(_psicomputations, limit=1)
def psiDerivativecomputations(dL_dpsi0, dL_dpsi1, dL_dpsi2, variance,
lengthscale, Z, variational_posterior):
ARD = (len(lengthscale) != 1)
_, psi1, _, psi2n = psicomputations(variance, lengthscale, Z,
variational_posterior)
mu = variational_posterior.mean
S = variational_posterior.variance
gamma = variational_posterior.binary_prob
N, M, Q = mu.shape[0], Z.shape[0], mu.shape[1]
l2 = np.square(lengthscale)
log_denom1 = np.log(S / l2 + 1)
log_denom2 = np.log(2 * S / l2 + 1)
def setUp(self):
def op(x):
return x
self.cache = Cacher(op, 1)
def setUp(self):
def op(x):
return x
self.cache = Cacher(op, 1)
lengthscale2 = np.square(lengthscale)
denom = 1. / (2 * S + lengthscale2)
denom2 = np.square(denom)
_psi2 = _psi2computations(variance, lengthscale, Z, mu, S) # NxMxM
Lpsi2 = dL_dpsi2 * _psi2 # dL_dpsi2 is MxM, using broadcast to multiply N out
Lpsi2sum = np.einsum('nmo->n', Lpsi2) #N
Lpsi2Z = np.einsum('nmo,oq->nq', Lpsi2, Z) #NxQ
Lpsi2Z2 = np.einsum('nmo,oq,oq->nq', Lpsi2, Z, Z) #NxQ
Lpsi2Z2p = np.einsum('nmo,mq,oq->nq', Lpsi2, Z, Z) #NxQ
Lpsi2Zhat = Lpsi2Z
Lpsi2Zhat2 = (Lpsi2Z2 + Lpsi2Z2p) / 2
_dL_dvar = Lpsi2sum.sum() * 2 / variance
_dL_dmu = (-2 * denom) * (mu * Lpsi2sum[:, None] - Lpsi2Zhat)
_dL_dS = (2 * np.square(denom)) * (np.square(mu) * Lpsi2sum[:, None] -
2 * mu * Lpsi2Zhat +
Lpsi2Zhat2) - denom * Lpsi2sum[:, None]
_dL_dZ = -np.einsum('nmo,oq->oq',Lpsi2,Z)/lengthscale2+np.einsum('nmo,oq->mq',Lpsi2,Z)/lengthscale2+ \
2*np.einsum('nmo,nq,nq->mq',Lpsi2,mu,denom) - np.einsum('nmo,nq,mq->mq',Lpsi2,denom,Z) - np.einsum('nmo,oq,nq->mq',Lpsi2,Z,denom)
_dL_dl = 2 * lengthscale * (
(S / lengthscale2 * denom + np.square(mu * denom)) * Lpsi2sum[:, None]
+ (Lpsi2Z2 - Lpsi2Z2p) / (2 * np.square(lengthscale2)) -
(2 * mu * denom2) * Lpsi2Zhat + denom2 * Lpsi2Zhat2).sum(axis=0)
return _dL_dvar, _dL_dl, _dL_dZ, _dL_dmu, _dL_dS
_psi1computations = Cacher(__psi1computations, limit=5)
_psi2computations = Cacher(__psi2computations, limit=5)