mklmm.py 文件源码

def getPosteriorMeanAndVar(self, diagKTestTest, KtrainTest, post, intercept=0):
        L = post['L']
        if (np.size(L) == 0): raise Exception('L is an empty array') #possible to compute it here
        Lchol = np.all((np.all(np.tril(L, -1)==0, axis=0) & (np.diag(L)>0)) & np.isreal(np.diag(L)))
        ns = diagKTestTest.shape[0]
        nperbatch = 5000
        nact = 0

        #allocate mem
        fmu = np.zeros(ns)  #column vector (of length ns) of predictive latent means
        fs2 = np.zeros(ns)  #column vector (of length ns) of predictive latent variances
        while (nact<(ns-1)):
            id = np.arange(nact, np.minimum(nact+nperbatch, ns))
            kss = diagKTestTest[id]     
            Ks = KtrainTest[:, id]
            if (len(post['alpha'].shape) == 1):
                try: Fmu = intercept[id] + Ks.T.dot(post['alpha'])
                except: Fmu = intercept + Ks.T.dot(post['alpha'])
                fmu[id] = Fmu
            else:
                try: Fmu = intercept[id][:, np.newaxis] + Ks.T.dot(post['alpha'])
                except: Fmu = intercept + Ks.T.dot(post['alpha'])
                fmu[id] = Fmu.mean(axis=1)
            if Lchol:
                V = la.solve_triangular(L, Ks*np.tile(post['sW'], (id.shape[0], 1)).T, trans=1, check_finite=False, overwrite_b=True)
                fs2[id] = kss - np.sum(V**2, axis=0)                       #predictive variances                        
            else:
                fs2[id] = kss + np.sum(Ks * (L.dot(Ks)), axis=0)           #predictive variances
            fs2[id] = np.maximum(fs2[id],0)  #remove numerical noise i.e. negative variances        
            nact = id[-1]    #set counter to index of last processed data point

        return fmu, fs2