Python代码报错TypeError:无法解包不可迭代NoneType对象求解决

Hey there, let's break down what's causing this error and fix it step by step.

The Root Cause #

Your function predict_using_gain_guess calculates the estimates and predictions lists correctly, but it doesn't return them! In Python, any function without an explicit return statement automatically returns None. When you try to unpack None into two variables (iestimates, predictions = ...), Python throws that TypeError because None isn't something you can split into multiple values.

The Fix #

Add a return statement at the end of your function to pass the calculated lists back to the caller. Here's the corrected code:

import matplotlib.pyplot as plt

# Weights (lb)
Measured = [158, 164.2, 160.3, 159.9, 162.1, 164.6, 169.6, 167.4, 166.4, 171, 171.2, 172.6]
# time step of 1 day
time_step = 1
# kalman Gain
Kg = 4/10

def predict_using_gain_guess(updated_estimate, gain_rate):
    estimates, predictions = [updated_estimate], []
    for z in Measured:
        # predict new position based on hypothesis that we are gaining 1lb every day
        prediction_hp = updated_estimate + gain_rate*time_step
        # update filter
        updated_estimate = prediction_hp + Kg * (z - prediction_hp)
        # save
        estimates.append(updated_estimate)
        predictions.append(prediction_hp)
    # Add this line to return the calculated lists
    return estimates, predictions

initial_estimate = 160
iestimates, predictions = predict_using_gain_guess(updated_estimate=initial_estimate, gain_rate=1)

# Optional: Visualize the results
plt.figure(figsize=(10,6))
plt.plot(Measured, label='Measured Weight', marker='o')
plt.plot(iestimates, label='Kalman Estimates', marker='x')
plt.plot(predictions, label='Predictions', linestyle='--')
plt.xlabel('Day')
plt.ylabel('Weight (lb)')
plt.legend()
plt.show()

What Changed? #

I added return estimates, predictions at the end of the predict_using_gain_guess function. Now when you call the function, it returns the two lists you need, and you can safely unpack them into iestimates and predictions without errors.

Run this corrected code, and it should execute without the TypeError. The optional matplotlib code will also let you visualize how your Kalman filter estimates and predictions compare to the measured weights.

内容的提问来源于stack exchange，提问作者Leila