def setUp(self):
SP.random.seed(1)
self.n = 4
self.rank = 2
self.C = dlimix_legacy.CLowRankCF(self.n, self.rank)
self.name = 'CLowRankCF'
self.n_params = self.C.getNumberParams()
params = SP.exp(SP.randn(self.n_params))
self.C.setParams(params)
def setUp(self):
SP.random.seed(1)
self.n = 4
self.rank = 1
d = SP.rand(self.n) + 1
C0 = dlimix_legacy.CSumCF()
C0.addCovariance(dlimix_legacy.CLowRankCF(self.n, self.rank))
C0.addCovariance(dlimix_legacy.CDiagonalCF(self.n))
self.C = dlimix_legacy.CFixedDiagonalCF(C0, d)
self.name = 'CFixedDiagonalCF'
self.n_params = self.C.getNumberParams()
params = SP.randn(self.n_params)
self.C.setParams(params)
"""
def __init__(self,
Y=None,
X0=None,
k=1,
standardize=False,
interaction=True):
"""
Y: data [NxG]
X0: known latent factors [Nxk0]
k: number of latent factors to infer
"""
assert Y != None, 'gpCLVM:: set Y!'
assert X0 != None, 'gpCLVM:: set X0!'
self.cache = {}
self.interaction = interaction
# read data
self._setY(Y, standardize=standardize)
self._setX0(X0)
self.k = k
# covariance for known latex factor
self.C0 = limix.CFixedCF(self.K0)
# covariance for unknow latent factor
self.C = limix.CProductCF()
self.Ca = limix.CLowRankCF(self.N, self.k)
self.C.addCovariance(self.Ca)
if self.interaction == True:
self.Cb1 = limix.CFixedCF(SP.ones((self.N, self.N)))
self.Cb1.setParamMask(SP.zeros(1))
self.Cb2 = limix.CFixedCF(self.K0)
self.Cb = limix.CSumCF()
self.Cb.addCovariance(self.Cb1)
self.Cb.addCovariance(self.Cb2)
self.C.addCovariance(self.Cb)
# total covariance
covar = limix.CSumCF()
covar.addCovariance(self.C0)
covar.addCovariance(self.C)
# likelihood
self.ll = limix.CLikNormalIso()
# init GP and hyper params
self.gp = limix.CGPbase(covar, self.ll)
self.gp.setY(self.Y)
def _buildTraitCovar(self,
trait_covar_type='lowrank_diag',
rank=1,
fixed_trait_covar=None,
jitter=1e-4,
d=None):
"""
Internal functions that builds the trait covariance matrix using the LIMIX framework
Args:
trait_covar_type: type of covaraince to use. Default 'freeform'. possible values are
rank: rank of a possible lowrank component (default 1)
fixed_trait_covar: PxP matrix for the (predefined) trait-to-trait covariance matrix if fixed type is used
jitter: diagonal contribution added to trait-to-trait covariance matrices for regularization
Returns:
LIMIX::PCovarianceFunction for Trait covariance matrix
vector labelling Cholesky parameters for different initializations
"""
cov = dlimix_legacy.CSumCF()
if trait_covar_type == 'freeform':
cov.addCovariance(dlimix_legacy.CFreeFormCF(self.P))
L = sp.eye(self.P)
diag = sp.concatenate([L[i, :(i + 1)] for i in range(self.P)])
elif trait_covar_type == 'fixed':
assert fixed_trait_covar is not None, 'VarianceDecomposition:: set fixed_trait_covar'
assert fixed_trait_covar.shape[
0] == self.N, 'VarianceDecomposition:: Incompatible shape for fixed_trait_covar'
assert fixed_trait_covar.shape[
1] == self.N, 'VarianceDecomposition:: Incompatible shape for fixed_trait_covar'
cov.addCovariance(dlimix_legacy.CFixedCF(fixed_trait_covar))
diag = sp.zeros(1)
elif trait_covar_type == 'diag':
cov.addCovariance(dlimix_legacy.CDiagonalCF(self.P))
diag = sp.ones(self.P)
elif trait_covar_type == 'lowrank':
cov.addCovariance(dlimix_legacy.CLowRankCF(self.P, rank))
diag = sp.zeros(self.P * rank)
elif trait_covar_type == 'lowrank_id':
cov.addCovariance(dlimix_legacy.CLowRankCF(self.P, rank))
cov.addCovariance(dlimix_legacy.CFixedCF(sp.eye(self.P)))
diag = sp.concatenate([sp.zeros(self.P * rank), sp.ones(1)])
elif trait_covar_type == 'lowrank_diag':
cov.addCovariance(dlimix_legacy.CLowRankCF(self.P, rank))
cov.addCovariance(dlimix_legacy.CDiagonalCF(self.P))
diag = sp.concatenate([sp.zeros(self.P * rank), sp.ones(self.P)])
elif trait_covar_type == 'lowrank_diag1':
assert d.shape[0] == self.P, 'dimension mismatch for d'
cov1 = dlimix_legacy.CSumCF()
cov1.addCovariance(dlimix_legacy.CLowRankCF(self.P, rank))
cov1.addCovariance(dlimix_legacy.CDiagonalCF(self.P))
cov.addCovariance(dlimix_legacy.CFixedDiagonalCF(cov1, d))
diag = sp.concatenate([sp.zeros(self.P * rank), sp.ones(self.P)])
elif trait_covar_type == 'freeform1':
assert d.shape[0] == self.P, 'dimension mismatch for d'
cov.addCovariance(
dlimix_legacy.CFixedDiagonalCF(
dlimix_legacy.CFreeFormCF(self.P), d))
L = sp.eye(self.P)
diag = sp.concatenate([L[i, :(i + 1)] for i in range(self.P)])
elif trait_covar_type == 'block':
cov.addCovariance(dlimix_legacy.CFixedCF(sp.ones(
(self.P, self.P))))
diag = sp.zeros(1)
elif trait_covar_type == 'block_id':
cov.addCovariance(dlimix_legacy.CFixedCF(sp.ones(
(self.P, self.P))))
cov.addCovariance(dlimix_legacy.CFixedCF(sp.eye(self.P)))
diag = sp.concatenate([sp.zeros(1), sp.ones(1)])
elif trait_covar_type == 'block_diag':
cov.addCovariance(dlimix_legacy.CFixedCF(sp.ones(
(self.P, self.P))))
cov.addCovariance(dlimix_legacy.CDiagonalCF(self.P))
diag = sp.concatenate([sp.zeros(1), sp.ones(self.P)])
else:
assert True == False, 'VarianceDecomposition:: trait_covar_type not valid'
if jitter > 0:
_cov = dlimix_legacy.CFixedCF(sp.eye(self.P))
_cov.setParams(sp.array([sp.sqrt(jitter)]))
_cov.setParamMask(sp.zeros(1))
cov.addCovariance(_cov)
return cov, diag