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now returning the ax for plot_latent in BGPLVM
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@ -95,3 +95,5 @@ class Bayesian_GPLVM(sparse_GP, GPLVM):
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input_1, input_2 = which_indices
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input_1, input_2 = which_indices
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ax = GPLVM.plot_latent(self, which_indices=[input_1, input_2],*args, **kwargs)
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ax = GPLVM.plot_latent(self, which_indices=[input_1, input_2],*args, **kwargs)
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ax.plot(self.Z[:, input_1], self.Z[:, input_2], '^w')
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ax.plot(self.Z[:, input_1], self.Z[:, input_2], '^w')
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return ax
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