Optimizing objective function in SciPy optimize.minimize

Question

res_M = minimize(L_M, x0=x_M, args=(data, w_vector),
                 method='L-BFGS-B', bounds=[(0.001, 1), (0.001, 1), (0.001, 1)])

def L_M(x, data, w_vector):    
    sum = 0
    for i in range(len(data)):
        sum += w_vector[i]*(data[i][0]*np.log(x[0])+data[i][1]*np.log(x[1])+data[i][2]*np.log(x[2]))
    return -1*sum

As part of an Expectation-Maximization(EM) algorithm I am calling SciPy's optimize.minimize function in the M-step. x_M are three values between 0 and 1, initially all 0.5. The w_vectors are calculated in the E-Step, and consist of a NumPy 1D array of the lengths of the data set with floats in the range 0 and 1. Each line in the data set is three integer feature values between 0 and 3, for example [1 0 2].

The for loop in the objective function is slowing things down. I want to optimize it using vectorized calculations instead. I have tried the following, but it changes the result:

def L_M(x, data, w_vector):
        length = len(data)        
        a_i = data[np.arange(length)][0].sum()
        f_i = data[np.arange(length)][1].sum()
        l_i = data[np.arange(length)][2].sum()
        sum = (w_vector[np.arange(length)].sum())*(a_i*np.log(x[0])+f_i *np.log(x[1])+l_i*np.log(x[2]))
        return -1*sum

The minimize function is getting called many times and I hope to test it on some very large data sets so any ideas on how to rewrite it would be much appreciated.