6a455db8f33ceefa0f8c544dc427ba6402f85588,gpflow/conditionals/util.py,,independent_interdomain_conditional,#Any#Any#Any#Any#,173

Before Change

        - mean: [N, P]
        - variance: [N, P], [N, P, P], [P, N, N], [N, P, N, P]
    
    M, L, N, P = [Kmn.shape[i] for i in range(Kmn.shape.ndims)]

    Lm = tf.linalg.cholesky(Kmm)  // [L, M, M]

After Change

        - mean: [N, P]
        - variance: [N, P], [N, P, P], [P, N, N], [N, P, N, P]
    
    M, L, N, P = tf.unstack(tf.shape(Kmn), num=Kmn.shape.ndims, axis=0)

    Lm = tf.linalg.cholesky(Kmm)  // [L, M, M]

    // Compute the projection matrix A

Instances