Dear Room,
I was trying to go through a code given in
http://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm[ Forward
Backward is an algorithm of Machine Learning-I am not talking on that
I am just trying to figure out a query on its Python coding.]
I came across the following codes.
>>> states = ('Healthy', 'Fever')
>>> end_state = 'E'
>>> observations = ('normal', 'cold', 'dizzy')
>>> start_probability = {'Healthy': 0.6, 'Fever': 0.4}
>>> transition_probability = {
'Healthy' : {'Healthy': 0.69, 'Fever': 0.3, 'E': 0.01},
'Fever' : {'Healthy': 0.4, 'Fever': 0.59, 'E': 0.01},
}
>>> emission_probability = {
'Healthy' : {'normal': 0.5, 'cold': 0.4, 'dizzy': 0.1},
'Fever' : {'normal': 0.1, 'cold': 0.3, 'dizzy': 0.6},
}
def fwd_bkw(x, states, a_0, a, e, end_st):
L = len(x)
fwd = []
f_prev = {} #THE PROBLEM
# forward part of the algorithm
for i, x_i in enumerate(x):
f_curr = {}
for st in states:
if i == 0:
# base case for the forward part
prev_f_sum = a_0[st]
else:
prev_f_sum = sum(f_prev[k]*a[k][st] for k in states) ##
f_curr[st] = e[st][x_i] * prev_f_sum
fwd.append(f_curr)
f_prev = f_curr
p_fwd = sum(f_curr[k]*a[k][end_st] for k in states)
As this value was being called in prev_f_sum = sum(f_prev[k]*a[k][st] for k in
states marked ##
I wanted to know what values it is generating.
So, I had made the following experiment, after
for i, x_i in enumerate(x):
I had put print f_prev
but I am not getting how f_prev is getting the values.
Here,
x=observations,
states= states,
a_0=start_probability,
a= transition_probability,
e=emission_probability,
end_st= end_state
Am I missing any minor aspect?
Code is running fine.
If any one of the esteemed members may kindly guide me.
Regards,
Subhabrata Banerjee.
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