Python: TypeError: 'float' object has no attribute 'getitem'

Question

I am trying to implement particle filter algorithm in python. I am getting this error:

x_P_update[i] = 0.5*x_P[i] + 25*x_P[i]/(1 + x_P[i]**2) + 8*math.cos(1.2*(t-1)) +     math.sqrt(x_N)*np.random.randn()
TypeError: 'float' object has no attribute '__getitem__'

My code:

import math
import numpy as np
import matplotlib.pyplot as plt

x = 0.1 #initial value
x_N = 1 #process noise covariance in state update
x_R = 1 #noise covariance in measurement
T = 75 #number of iterations
N = 10 #number of particles

V = 2
x_P = [None]*(N)

for i in xrange(0, N):
    x_P[i] = x + math.sqrt(V)*np.random.randn()

z_out = np.array([x**2 / 20 + math.sqrt(x_R) * np.random.randn()])  #the actual output vector for measurement values.
x_out = np.array([x])  #the actual output vector for measurement values.
x_est = np.array([x]); # time by time output of the particle filters estimate
x_est_out = np.array([x_est]) # the vector of particle filter estimates.

x_P_update = [None]*N
z_update = [None]*N
P_w = [None]*N

for t in xrange(1, T+1):
    x = 0.5*x + 25*x/(1 + x**2) + 8*math.cos(1.2*(t-1)) +  math.sqrt(x_N)*np.random.randn()
    z = x**2/20 + math.sqrt(x_R)*np.random.randn()
    for i in xrange(0, N):
        #each particle is updated with process eq
        x_P_update[i] = 0.5*x_P[i] + 25*x_P[i]/(1 + x_P[i]**2) + 8*math.cos(1.2*(t-1)) + math.sqrt(x_N)*np.random.randn()
        #observations are updated for each particle
        z_update[i] = x_P_update[i]**2/20
        #generate weights
        P_w[i] = (1/math.sqrt(2*math.pi*x_R)) * math.exp(-(z - z_update[i])**2/(2*x_R))

    P_w[:] = [ k / sum(P_w) for k in P_w]
#    print(np.where(np.cumsum(P_w, axis=0) >= np.random.rand()))

  #  print(index_tuple[0][1])
#    P_w_array = np.array(list(P_w))
#    indices = [i for i in range(len(P_w)) if np.cumsum(P_w_array) >= np.random.rand()]
    for i in xrange(0, N):
        index_tuple = np.where(np.random.rand() <= np.cumsum(P_w, axis=0))
        m = index_tuple[0][1]
        x_P = x_P_update[m]

    x_est = np.array([np.mean(x_P)])
    x_out = np.array([x_out, x])
    z_out = np.array([z_out, z])
    x_est_out = np.array([x_est_out, x_est])

I am using matlab code from here to learn how to implement this algorithm in python using scipy. http://studentdavestutorials.weebly.com/particle-filter-with-matlab-code.html

I just started learning python and can't get out of this problem, kindly help.

simonzack · Accepted Answer · 2014-10-23 11:34:06Z

2

I'm not going to go through the video tutorial and fix your algorithm, but I can show you why you're getting this error.

In this line:

x_P = x_P_update[m]

You are assigning an array with a float value, which you then attempt to access as an array in the outer loop. Updating it instead will get rid of your error:

x_P[m] = x_P_update[m]

answered Oct 23, 2014 at 11:34

simonzack

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4 Comments

Praveer N Over a year ago

thanks, but now I am getting some other error. m = index_tuple[0][1] IndexError: index 1 is out of bounds for axis 0 with size 1

simonzack Over a year ago

@stoned_blasphemer that is a problem with your algorithm.

wwii Over a year ago

@stoned_blasphemer you should correct your code as shown above, post your new problem as a new question and except this answer since it answered your question.

Praveer N Over a year ago

I fixed it by changing m to index_tuple[0][0] now it works as expected! :)

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