dgp.py 文件源码

def init_layers(X, Z, dims, final_mean_function):
    M = Z.shape[0]
    q_mus, q_sqrts, mean_functions, Zs = [], [], [], []
    X_running, Z_running = X.copy(), Z.copy()

    for dim_in, dim_out in zip(dims[:-2], dims[1:-1]):
        if dim_in == dim_out: # identity for same dims
            W = np.eye(dim_in)
        elif dim_in > dim_out: # use PCA mf for stepping down
            _, _, V = np.linalg.svd(X_running, full_matrices=False)
            W = V[:dim_out, :].T
        elif dim_in < dim_out: # identity + pad with zeros for stepping up
            I = np.eye(dim_in)
            zeros = np.zeros((dim_in, dim_out - dim_in))
            W = np.concatenate([I, zeros], 1)

        mean_functions.append(Linear(A=W))
        Zs.append(Z_running.copy())
        q_mus.append(np.zeros((M, dim_out)))
        q_sqrts.append(np.eye(M)[:, :, None] * np.ones((1, 1, dim_out)))

        Z_running = Z_running.dot(W)
        X_running = X_running.dot(W)

    # final layer (as before but no mean function)
    mean_functions.append(final_mean_function)
    Zs.append(Z_running.copy())
    q_mus.append(np.zeros((M, dims[-1])))
    q_sqrts.append(np.eye(M)[:, :, None] * np.ones((1, 1, dims[-1])))

    return q_mus, q_sqrts, Zs, mean_functions