6b8a9a5f0e738d98904ff7c46c79a44bfdd56686,testing/test_predict.py,TestFullCov,setUp,#TestFullCov#,49

Before Change

                              rng.randn(self.M, self.input_dim),\
                              rng.randn(self.Ntest, self.input_dim)
        k = lambda : GPflow.kernels.Matern32(self.input_dim)
        self.models = [GPflow.gpr.GPR(X, Y, kern=k()),
                  GPflow.sgpr.SGPR(X, Y, Z=Z, kern=k()),
                  GPflow.sgpr.GPRFITC(X, Y, Z=Z, kern=k()),
                  GPflow.svgp.SVGP(X, Y, Z=Z, kern=k(), likelihood=GPflow.likelihoods.Gaussian(), whiten=False, q_diag=True),
                  GPflow.svgp.SVGP(X, Y, Z=Z, kern=k(), likelihood=GPflow.likelihoods.Gaussian(), whiten=True, q_diag=False),
                  GPflow.svgp.SVGP(X, Y, Z=Z, kern=k(), likelihood=GPflow.likelihoods.Gaussian(), whiten=True, q_diag=True),
                  GPflow.svgp.SVGP(X, Y, Z=Z, kern=k(), likelihood=GPflow.likelihoods.Gaussian(), whiten=True, q_diag=False),
                  GPflow.vgp.VGP(X, Y, kern=k(), likelihood=GPflow.likelihoods.Gaussian()),
                  GPflow.vgp.VGP(X, Y, kern=k(), likelihood=GPflow.likelihoods.Gaussian()),
                  GPflow.gpmc.GPMC(X, Y, kern=k(), likelihood=GPflow.likelihoods.Gaussian()),
                  GPflow.sgpmc.SGPMC(X, Y, kern=k(), likelihood=GPflow.likelihoods.Gaussian(), Z=Z)]

    def test_cov_and_samples(self):
        for m in self.models:
            mu1, var = m.predict_f(self.Xtest)

After Change

    if a test fails, it should be clearer where the error is. 
    
    def setUp(self):
        tf.reset_default_graph()
        self.input_dim = 3
        self.output_dim = 2
        self.N = 20
        self.Ntest = 30

Instances