def predictPhenos(self,use_fixed=None,use_random=None):
        """
        predict the conditional mean (BLUP)

        Args:
            use_fixed:        list of fixed effect indeces to use for predictions
            use_random:        list of random effect indeces to use for predictions
        Returns:
            predictions (BLUP)
        """
        assert self.noisPos is not None,      'No noise element'
        assert self.init,               'GP not initialised'
        assert self.Ntest is not None,        'VarianceDecomposition:: specify Ntest for predictions (method VarianceDecomposition::setTestSampleSize)'

        use_fixed  = list(range(self.n_fixedEffs))
        use_random = list(range(self.n_randEffs))

        KiY = self.gp.agetKEffInvYCache()

        if self.fast==False:
            KiY = KiY.reshape(self.P,self.N).T

        Ypred = sp.zeros((self.Ntest,self.P))

        # predicting from random effects
        for term_i in use_random:
            if term_i!=self.noisPos:
                Kstar = self.Kstar[term_i]
                if Kstar is None:
                    warnings.warn('warning: random effect term %d not used for predictions as it has None cross covariance'%term_i)
                    continue
                term  = sp.dot(Kstar.T,KiY)
                if self.P>1:
                    C    = self.getTraitCovar(term_i)
                    term = sp.dot(term,C)
                else:
                    term *= self.getVarianceComps()[0,term_i]
                Ypred += term

        # predicting from fixed effects
        weights = self.getWeights()
        w_i = 0
        for term_i in use_fixed:
            Fstar = self.Fstar[term_i]
            if Fstar is None:
                warnings.warn('warning: fixed effect term %d not used for predictions as it has None test sample design'%term_i)
                continue
            if self.P==1:    A = sp.eye(1)
            else:            A = self.vd.getDesign(term_i)
            Fstar = self.Fstar[term_i]
            W = weights[w_i:w_i+A.shape[0],0:1].T
            term = sp.dot(Fstar,sp.dot(W,A))
            w_i += A.shape[0]
            Ypred += term

        return Ypred