20c65469fe1e090f4d51322178655c7253b85e93,test/examples/spectral_mixture_gp_regression_test.py,SpectralMixtureGPModel,__init__,#SpectralMixtureGPModel#,24

Before Change

        super(SpectralMixtureGPModel, self).__init__(GaussianLikelihood())
        self.mean_module = ConstantMean()
        self.covar_module = SpectralMixtureKernel()
        self.params = MLEParameterGroup(
            log_noise=Parameter(torch.Tensor([-2])),
            log_mixture_weights=Parameter(torch.zeros(3)),
            log_mixture_means=Parameter(torch.zeros(3)),
            log_mixture_scales=Parameter(torch.zeros(3))
        )

    def forward(self, x):
        mean_x = self.mean_module(x, constant=Variable(torch.Tensor([0])))

After Change

        super(SpectralMixtureGPModel, self).__init__(GaussianLikelihood())
        self.mean_module = ConstantMean()
        self.covar_module = SpectralMixtureKernel()
        self.params = MLEParameterGroup(
            log_noise=BoundedParameter(torch.Tensor([-2]),-15,15),
            log_mixture_weights=BoundedParameter(torch.zeros(3),-15,15),
            log_mixture_means=BoundedParameter(torch.zeros(3),-15,15),
            log_mixture_scales=BoundedParameter(torch.zeros(3),-15,15)
        )

    def forward(self, x):
        mean_x = self.mean_module(x, constant=Variable(torch.Tensor([0])))

Instances