Udacity- Artifical Intelligence for Robotic Education-Problems Solutions

 

Generalized Uniform Distribution

#  Modify your code to create probability vectors, p, of arbitrary 

#  size, n. Use n=5 to verify that your new solution matches 

#  the previous one.

p=[]

n=5

for i in range (n):

    p.append(1./n)

print p

pHit and pMiss

#Write code that outputs p after multiplying each entry 

#by pHit or pMiss at the appropriate places. Remember that

#the red cells 1 and 2 are hits and the other green cells

#are misses.

p=[0.2,0.2,0.2,0.2,0.2]

pHit = 0.6

pMiss = 0.2

   

p[0] = p[0]*pMiss

p[1] = p[1]*pHit

p[2] = p[2]*pHit

p[3] = p[3]*pMiss

p[4] = p[4]*pMiss   

print p

Sum of Probabilities

#Modify the program to find and print the sum of all 

#the entries in the list p.

p=[0.2, 0.2, 0.2, 0.2, 0.2]

pHit = 0.6

pMiss = 0.2

p[0]=p[0]*pMiss

p[1]=p[1]*pHit

p[2]=p[2]*pHit

p[3]=p[3]*pMiss

p[4]=p[4]*pMiss

# Enter your code below

print sum(p)

Sense Function

#Modify the code below so that the function sense, which 

#takes p and Z as inputs, will output the NON-normalized 

#probability distribution, q, after multiplying the entries 

#in p by pHit or pMiss according to the color in the 

#corresponding cell in world.

p=[0.2, 0.2, 0.2, 0.2, 0.2]

world=['green', 'red', 'red', 'green', 'green']

Z = 'red'

pHit = 0.6

pMiss = 0.2

def sense(p, Z):

   

    q = [ ]

    for i in range(len(p)):

        hit = (Z == world[i])

        q.append(p[i] * (hit * pHit + (1-hit) * pMiss))

    return q

print sense(p,Z)

Normalized Sense Function