def Y(self, value):
""" set phenotype """
self._N = value.shape[0]
self._P = value.shape[1]
self._Y = value
# missing data
self._Iok = ~sp.isnan(value)
self._veIok = vec(self._Iok)[:,0]
self._miss = (~self._Iok).any()
# notify and clear_cached
self.clear_cache('pheno')
self._notify()
self._notify('pheno')
def _initGP(self):
"""
Internal method for initialization of the GP inference objetct
"""
if self._inference=='GP2KronSum':
signalPos = sp.where(sp.arange(self.n_randEffs)!=self.noisPos)[0][0]
gp = GP2KronSum(Y=self.Y, F=self.sample_designs, A=self.trait_designs,
Cg=self.trait_covars[signalPos], Cn=self.trait_covars[self.noisPos],
R=self.sample_covars[signalPos])
else:
mean = MeanKronSum(self.Y, self.sample_designs, self.trait_designs)
Iok = vec(~sp.isnan(mean.Y))[:,0]
if Iok.all(): Iok = None
covar = SumCov(*[KronCov(self.trait_covars[i], self.sample_covars[i], Iok=Iok) for i in range(self.n_randEffs)])
gp = GP(covar = covar, mean = mean)
self.gp = gp
def _initGP(self):
"""
Internal method for initialization of the GP inference objetct
"""
if self._inference == 'GP2KronSum':
signalPos = sp.where(
sp.arange(self.n_randEffs) != self.noisPos)[0][0]
gp = GP2KronSum(Y=self.Y,
F=self.sample_designs,
A=self.trait_designs,
Cg=self.trait_covars[signalPos],
Cn=self.trait_covars[self.noisPos],
R=self.sample_covars[signalPos])
else:
mean = MeanKronSum(self.Y, self.sample_designs, self.trait_designs)
Iok = vec(~sp.isnan(mean.Y))[:, 0]
if Iok.all(): Iok = None
covar = SumCov(*[
KronCov(self.trait_covars[i], self.sample_covars[i], Iok=Iok)
for i in range(self.n_randEffs)
])
gp = GP(covar=covar, mean=mean)
self.gp = gp
def WKiy(self):
R = vec(self.FYLcALc())
# the following could be further optimized but do not care for now
R -= sp.dot(self.WLW().T, vec(self.DWrYLcWc()))
return R
def WKiy(self):
R = vec(self.FYLcALc())
# the following could be further optimized but do not care for now
R -= sp.dot(self.WLW().T, vec(self.DWrYLcWc()))
return R
def ve_WrDLrYLcWc(self):
return vec(sp.dot(self.covar.Wr().T, sp.dot(self.DLrYLc(), self.covar.Wc())))
def y(self):
r = vec(self.Y)
if self._miss:
r = r[self._veIok]
return r
def yres(self):
r = vec(self.Yres())
if self._miss:
r = r[~self._veIok]
return r
def ve_WrDLrYLcWc(self):
return vec(
sp.dot(self.covar.Wr().T, sp.dot(self.DLrYLc(), self.covar.Wc())))
