test_tracegraph_elbo.py 文件源码

def setUp(self):
        # normal-normal; known covariance
        self.lam0 = Variable(torch.Tensor([0.1, 0.1]))   # precision of prior
        self.mu0 = Variable(torch.Tensor([0.0, 0.5]))   # prior mean
        # known precision of observation noise
        self.lam = Variable(torch.Tensor([6.0, 4.0]))
        self.n_outer = 3
        self.n_inner = 3
        self.n_data = Variable(torch.Tensor([self.n_outer * self.n_inner]))
        self.data = []
        self.sum_data = ng_zeros(2)
        for _out in range(self.n_outer):
            data_in = []
            for _in in range(self.n_inner):
                data_in.append(Variable(torch.Tensor([-0.1, 0.3]) + torch.randn(2) / torch.sqrt(self.lam.data)))
                self.sum_data += data_in[-1]
            self.data.append(data_in)
        self.analytic_lam_n = self.lam0 + self.n_data.expand_as(self.lam) * self.lam
        self.analytic_log_sig_n = -0.5 * torch.log(self.analytic_lam_n)
        self.analytic_mu_n = self.sum_data * (self.lam / self.analytic_lam_n) +\
            self.mu0 * (self.lam0 / self.analytic_lam_n)
        self.verbose = True

    # this tests rao-blackwellization in elbo for nested list map_datas