853a8c984cac59a56fcf66dcbfe4358246344f55,testing/test_method_equivalence.py,TestEquivalence,setUp,#TestEquivalence#,20

Before Change

        m1 = GPflow.gpr.GPR(X, Y, kern=GPflow.kernels.RBF(1))
        m2 = GPflow.vgp.VGP(X, Y, GPflow.kernels.RBF(1), likelihood=GPflow.likelihoods.Gaussian())
        m3 = GPflow.svgp.SVGP(X, Y, GPflow.kernels.RBF(1)+GPflow.kernels.White(1),
                              likelihood=GPflow.likelihoods.Gaussian(),
                              Z=X.copy(), q_diag=False)
        m3.Z.fixed = True
        m4 = GPflow.svgp.SVGP(X, Y, GPflow.kernels.RBF(1)+GPflow.kernels.White(1),
                              likelihood=GPflow.likelihoods.Gaussian(),
                              Z=X.copy(), q_diag=False, whiten=True)
        m4.Z.fixed=True
        m5 = GPflow.sgpr.SGPR(X, Y, GPflow.kernels.RBF(1)+GPflow.kernels.White(1), Z=X.copy())
        m5.Z.fixed = True
        self.models = [m1, m2, m3, m4, m5]
        for m in self.models:

After Change

        m1 = GPflow.gpr.GPR(X, Y, kern=GPflow.kernels.RBF(1))
        m2 = GPflow.vgp.VGP(X, Y, GPflow.kernels.RBF(1), likelihood=GPflow.likelihoods.Gaussian())
        m3 = GPflow.svgp.SVGP(X, Y, GPflow.kernels.RBF(1),
                              likelihood=GPflow.likelihoods.Gaussian(),
                              Z=X.copy(), q_diag=False)
        m3.Z.fixed = True
        m4 = GPflow.svgp.SVGP(X, Y, GPflow.kernels.RBF(1),
                              likelihood=GPflow.likelihoods.Gaussian(),
                              Z=X.copy(), q_diag=False, whiten=True)
        m4.Z.fixed=True
        m5 = GPflow.sgpr.SGPR(X, Y, GPflow.kernels.RBF(1),
                              Z=X.copy())
        m5.Z.fixed = True
        self.models = [m1, m2, m3, m4, m5]

Instances