![]() ![]() append ( l_to_bel ( l )) data_str = print ( f 'observations: )') # TODO: orientation start_x = clip_x (( car_x - car_L_h ) / res ) end_x = clip_x (( car_x + car_L_h ) / res ) x_indices = range ( start_x, end_x ) start_y = clip_y (( car_y - car_W_h ) / res ) end_y = clip_y (( car_y + car_W_h ) / res + 1 ) y_indices = range ( start_y, end_y ) if start_x != end_x and start_y != end_y : yv, xv = np. exp ( l )) bels = l = l_0 for obs in data : if obs = 0 : p = 0.3 else : p = 0.8 l = l - l_0 + np. ![]() uniform ( 0, 1, npoints ): if e <= probs : obs = 0 else : obs = 1 data. Import numpy as np import matplotlib.pyplot as plt labels = bel_0 = assert ( bel_0 + bel_0 = 1 ) colors = gt_state = 1 # occupied # p(z=0 | gt_state = 1) = 0.3 # p(z=1 | gt_state = 1) = 0.7 probs = data = npoints = 10 for e in np. # Binary Bayes Filters with Recursive Estimation % matplotlib inline
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