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gmm_creation
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@ -55,15 +55,12 @@ class GmmBayesianGPLVM(SparseGP_MPI):
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# Need to define what the model is initialised like
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pi = np.ones(n_component) / float(n_component) # p(k)
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variational_pi = pi.copy()
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# px_mu = np.zeros(n_component)
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# px_var = np.ones(n_component)
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px_mu = [[]] * n_component
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px_var = [[]] * n_component
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for i in range(n_component):
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px_mu[i] = np.zeros(X_variance.shape)
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px_var[i] = np.ones(X_variance.shape)
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# print("Should print")
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# print(pi)
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# print(px_mu)
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