def setUp(self):
SP.random.seed(1)
self.n = 4
self.rank = 1
d = SP.rand(self.n) + 1
C0 = dlimix.CSumCF()
C0.addCovariance(dlimix.CLowRankCF(self.n, self.rank))
C0.addCovariance(dlimix.CDiagonalCF(self.n))
self.C = dlimix.CFixedDiagonalCF(C0, d)
self.name = 'CFixedDiagonalCF'
self.n_params = self.C.getNumberParams()
params = SP.randn(self.n_params)
self.C.setParams(params)
"""
def setUp(self):
SP.random.seed(1)
self.n=10
n_dim1=8
n_dim2=12
self.C=dlimix.CSumCF()
self.C.addCovariance(dlimix.CCovSqexpARD(n_dim1));
self.C.addCovariance(dlimix.CCovLinearARD(n_dim2));
self.n_dim=self.C.getNumberDimensions()
X=SP.rand(self.n,self.n_dim)
self.C.setX(X)
self.name = 'CSumCF'
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=10
n_dim2=12
K0 = SP.eye(self.n)
self.C=dlimix.CSumCF()
#sum of fixed CF and linearARD
covar1 = dlimix.CFixedCF(K0)
covar2 = dlimix.CCovLinearARD(n_dim2)
self.C.addCovariance(covar1)
self.C.addCovariance(covar2)
self.n_dim=self.C.getNumberDimensions()
self.X=SP.rand(self.n,self.n_dim)
self.C.setX(self.X)
self.name = 'CSumCF'
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)
#1. simulate
self.settings = {'K': 5, 'N': 100, 'D': 80}
self.simulation = self.simulate()
N = self.settings['N']
K = self.settings['K']
D = self.settings['D']
#2. setup GP
K0 = SP.dot(self.simulation['S'], self.simulation['S'].T)
K0[:] = 0
covar1 = dlimix.CFixedCF(K0)
covar2 = dlimix.CCovLinearISO(K)
covar = dlimix.CSumCF()
covar.addCovariance(covar1)
covar.addCovariance(covar2)
ll = dlimix.CLikNormalIso()
#create hyperparm
covar_params = SP.array([0.0, 1.0])
lik_params = SP.array([0.1])
hyperparams = dlimix.CGPHyperParams()
hyperparams['covar'] = covar_params
hyperparams['lik'] = lik_params
hyperparams['X'] = self.simulation['X0']
#cretae GP
self.gp = dlimix.CGPbase(covar, ll)
#set data
self.gp.setY(self.simulation['Y'])
self.gp.setX(self.simulation['X0'])
self.gp.setParams(hyperparams)
pass
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.CSumCF()
if trait_covar_type == 'freeform':
cov.addCovariance(dlimix.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.CFixedCF(fixed_trait_covar))
diag = sp.zeros(1)
elif trait_covar_type == 'diag':
cov.addCovariance(dlimix.CDiagonalCF(self.P))
diag = sp.ones(self.P)
elif trait_covar_type == 'lowrank':
cov.addCovariance(dlimix.CLowRankCF(self.P, rank))
diag = sp.zeros(self.P * rank)
elif trait_covar_type == 'lowrank_id':
cov.addCovariance(dlimix.CLowRankCF(self.P, rank))
cov.addCovariance(dlimix.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.CLowRankCF(self.P, rank))
cov.addCovariance(dlimix.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.CSumCF()
cov1.addCovariance(dlimix.CLowRankCF(self.P, rank))
cov1.addCovariance(dlimix.CDiagonalCF(self.P))
cov.addCovariance(dlimix.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.CFixedDiagonalCF(dlimix.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.CFixedCF(sp.ones((self.P, self.P))))
diag = sp.zeros(1)
elif trait_covar_type == 'block_id':
cov.addCovariance(dlimix.CFixedCF(sp.ones((self.P, self.P))))
cov.addCovariance(dlimix.CFixedCF(sp.eye(self.P)))
diag = sp.concatenate([sp.zeros(1), sp.ones(1)])
elif trait_covar_type == 'block_diag':
cov.addCovariance(dlimix.CFixedCF(sp.ones((self.P, self.P))))
cov.addCovariance(dlimix.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.CFixedCF(sp.eye(self.P))
_cov.setParams(sp.array([sp.sqrt(jitter)]))
_cov.setParamMask(sp.zeros(1))
cov.addCovariance(_cov)
return cov, diag