def setUp(self):
sp.random.seed(1)
self.n = 4
self.rank = 2
self.C = LowRankCov(self.n, self.rank)
self.name = 'lowrank'
self.C.setRandomParams()
def rank(self, value):
self._rank = value
if value is None:
self.cat_cov = FreeFormCov(self.n_cats, jitter=self.jitter)
else:
assert value <= self.n_cats, 'rank cant be higher than number of categories'
self.cat_cov = LowRankCov(self.n_cats, value)
self.clear_all()
self._notify()
def setColCovars(self, Cn = None, rank = 1):
assert Cn is not None, 'Cn has to be specified.'
self._rank_c = rank
self._dim_c = Cn.dim
self._Cr = LowRankCov(self._dim_c, rank)
self._Cn = Cn
# register
self._Cr.register(self.col_covs_have_changed)
self._Cn.register(self.col_covs_have_changed)
self.col_covs_have_changed()
def setColCovars(self, Cr=None, Cn=None, rank=1):
assert Cn is not None, 'Cn has to be specified.'
# set noise
self._Cn = Cn
self._dim_c = Cn.dim
# set region
if Cr is None: Cr = LowRankCov(self._dim_c, rank)
self._Cr = Cr
self._rank_c = self.Cr.X.shape[1]
# register
self._Cr.register(self.col_covs_have_changed)
self._Cn.register(self.col_covs_have_changed)
self.col_covs_have_changed()
def define_gp(Y, Xr, mean, Ie, type):
P = 2
if type == 'null':
_Cr = FixedCov(sp.ones([2, 2]))
_Cr.scale = 1e-9
_Cr.act_scale = False
covar = CategoricalLR(_Cr, sp.ones((Xr.shape[0], 1)), Ie)
else:
if type == 'block': _Cr = FixedCov(sp.ones((P, P)))
elif type == 'rank1': _Cr = LowRankCov(P, 1)
elif type == 'full': _Cr = FreeFormCov(P)
else: print('poppo')
covar = CategoricalLR(_Cr, Xr, Ie)
_gp = GP(covar=covar, mean=mean)
return _gp
def setColCovars(self, Cg=None, Cn=None, rank=1, Cr=None):
assert Cg is not None, 'Cov2KronSum: Specify Cg!'
assert Cn is not None, 'Cov2KronSum: Specify Cn!'
assert Cg.dim == Cn.dim, 'Cov2KronSum: Cg and Cn must have same dimensions!'
if Cr is None:
Cr = LowRankCov(Cg.dim, rank)
Cr.setRandomParams()
self._rank_c = rank
else:
self._rank_c = Cr.X.shape[1]
self._dim_c = Cg.dim
self._Cr = Cr
self._Cg = Cg
self._Cn = Cn
self._Cg.register(self.col_covs_have_changed)
self._Cn.register(self.col_covs_have_changed)
self.col_covs_have_changed()
def setColCovars(self, Cg=None, Cn=None, rank=1, Cr=None):
assert Cg is not None, 'Cov2KronSum: Specify Cg!'
assert Cn is not None, 'Cov2KronSum: Specify Cn!'
assert Cg.dim==Cn.dim, 'Cov2KronSum: Cg and Cn must have same dimensions!'
assert Cr is None, '%s: more general covariance matrices are not supported at the moment' % self.__class__.__name__
if Cr is None:
Cr = LowRankCov(Cg.dim, rank)
Cr.setRandomParams()
self._rank_c = rank
else:
self._rank_c = self.Cr.dim()
self._dim_c = Cg.dim
self._Cr = Cr
self._Cg = Cg
self._Cn = Cn
self._Cg.register(self.col_covs_have_changed)
self._Cn.register(self.col_covs_have_changed)
self.col_covs_have_changed()
