# 定义目标函数 def objective_function(x): weights = x[:11] T = x[11] composite_index = pearson_fusion(data, weight

如果您想返回均方误差，可以将目标函数修改为： ```python def objective_function(x): weights = x[:11] T = x[11] composite_index = pearson_fusion(data, weights) N = len(composite_index) dt = T/N t = np.linspace(0, T, N+1) dW = np.sqrt(dt)*np.random.randn(N) W = np.cumsum(dW) W = np.insert(W, 0, 0) weights = np.repeat(weights, N+1).reshape((11,N+1)) Z =np.dot(weights, W.flatten()) predicted_failure_time = t[np.argmax(Z > 0.9*np.max(Z))] mse = ((predicted_failure_time - actual_failure_time)**2)/len(data.iloc[:-1]) return mse ``` 在这个修改后的目标函数中，我们计算了预测故障时间和实际故障时间之间的均方误差，并将其作为目标函数的返回值。