I am getting runtime warnings when trying to normalize and update weights

Question

I am trying to calculate the weights of certain particles in a particle filter and then normalize those weights accordingly. My code:

def update(particles, weights, landmark, sigma):
    n = 0.0
    for i in range(len(weights)):
        distance = np.power((particles[i][0] - landmark[0]) ** 2 + (particles[i][1] - 
        landmark[1])**2, 0.5)
        likelihood = exp(-(np.power(distance, 2))/2 * sigma ** 2)
        weights[i] = weights[i] * likelihood
        n += weights[i]
        weights += 1.e-30
        if n != 0:
            weights = weights / n

However, I am getting the error: /Users/scottdayton/PycharmProjects/Uncertainty Research/particle.py:30: RuntimeWarning: overflow encountered in true_divide weights = weights / n /Users/scottdayton/PycharmProjects/Uncertainty Research/particle.py:30: RuntimeWarning: invalid value encountered in true_divide weights = weights / n

Answer 1

As said in comments, I added parenthesis to your code but there might be another thing. I feel that you are trying to multiply weights with the likelihood and then normalize the result. To do so you should cut the loop in 2:

• Correction of the weights and sum.
• Normalization to sum up to one.

I'll write it like this:

def update(particles, weights, landmark, sigma):
    n = 0.0
    # Correction of weights and computation of the sum
    for i in range(len(weights)):
        distance = np.power((particles[i][0] - landmark[0]) ** 2 + (particles[i][1] - 
        landmark[1])**2, 0.5)
        likelihood = np.exp(-(np.power(distance, 2))/(2 * sigma ** 2))
        weights[i] = weights[i] * likelihood + 1.e-30
        n += weights[i]
    # Normalization to sum up to one
    for i in range(len(weights)):
        weights[i] = weights[i] / n