def setColCovars(self, rank, Cn):
"""
set column covariances
"""
self.rank = rank
# col covars
self.Cr = covariance.lowrank(self.P, self.rank)
self.Cr.setParams(1e-3 * SP.randn(self.P * self.rank))
self.Cn = Cn
def setColCovars(self,rank,Cn):
"""
set column covariances
"""
self.rank=rank
# col covars
self.Cr = covariance.lowrank(self.P,self.rank)
self.Cr.setParams(1e-3*SP.randn(self.P*self.rank))
self.Cn = Cn
def fitSingleTraitModel(Y,XX=None,S_XX=None,U_XX=None,verbose=False):
""" fit single trait model """
N,P = Y.shape
RV = {}
Cg = covariance.lowrank(1)
Cn = covariance.lowrank(1)
gp = gp2kronSum(mean(Y[:,0:1]),Cg,Cn,XX=XX,S_XX=S_XX,U_XX=U_XX)
params0 = {'Cg':SP.sqrt(0.5)*SP.ones(1),'Cn':SP.sqrt(0.5)*SP.ones(1)}
var = SP.zeros((P,2))
conv1 = SP.zeros(P,dtype=bool)
for p in range(P):
if verbose:
print '.. fitting variance trait %d'%p
gp.setY(Y[:,p:p+1])
conv1[p],info = OPT.opt_hyper(gp,params0,factr=1e3)
var[p,0] = Cg.K()[0,0]
var[p,1] = Cn.K()[0,0]
RV['conv1'] = conv1
RV['varST'] = var
return RV
def fitSingleTraitModel(Y, XX=None, S_XX=None, U_XX=None, verbose=False):
""" fit single trait model """
N, P = Y.shape
RV = {}
Cg = covariance.lowrank(1)
Cn = covariance.lowrank(1)
gp = gp2kronSum(mean(Y[:, 0:1]), Cg, Cn, XX=XX, S_XX=S_XX, U_XX=U_XX)
params0 = {
'Cg': SP.sqrt(0.5) * SP.ones(1),
'Cn': SP.sqrt(0.5) * SP.ones(1)
}
var = SP.zeros((P, 2))
conv1 = SP.zeros(P, dtype=bool)
for p in range(P):
if verbose:
print '.. fitting variance trait %d' % p
gp.setY(Y[:, p:p + 1])
conv1[p], info = OPT.opt_hyper(gp, params0, factr=1e3)
var[p, 0] = Cg.K()[0, 0]
var[p, 1] = Cn.K()[0, 0]
RV['conv1'] = conv1
RV['varST'] = var
return RV
