For the Localization program I've submitted the code below

When I run the code I get the following result

[[0.011059807427972012, 0.02464041578496803, 0.06799662806785915, 0.04472487045812158, 0.02465153121665372], [0.0071532041833209815, 0.010171326481705892, 0.08696596002664689, 0.07988429965998084, 0.009350668508437186], [0.007397366886111671, 0.008943730670452702, 0.11272964670259776, 0.3535072295521272, 0.040655492078276775], [0.009106505805646497, 0.0071532041833209815, 0.014349221618346574, 0.04313329135844895, 0.036425599329004736]]

I would like to know why is hat been marked 'incorrect'

Thx

(Here is the code copied from the Udacity-Window)

    colors = [['red', 'green', 'green', 'red' , 'red'],
          ['red', 'red', 'green', 'red', 'red'],
          ['red', 'red', 'green', 'green', 'red'],
          ['red', 'red', 'red', 'red', 'red']]

measurements = ['green', 'green', 'green' ,'green', 'green']

motions = [[0,0],[0,1],[1,0],[1,0],[0,1]]

sensor_right = 0.7

p_move = 0.8

def show(p):
    for i in range(len(p)):
        print p[i]

#DO NOT USE IMPORT
#ENTER CODE BELOW HERE
#ANY CODE ABOVE WILL CAUSE
#HOMEWORK TO BE GRADED
#INCORRECT
print('Unit1: Assignment')

#Global variables
worldColor = [[ 'Green', 'Green', 'Green' ],
              [ 'Green', 'Red', 'Red' ],
              [ 'Green', 'Green', 'Green' ]]
worldColorSize = 3, 3

uniformedProbabilityDistribution = [[ .2, .2, .2, .2, .2],
                                    [ .2, .2, .2, .2, .2],
                                    [ .2, .2, .2, .2, .2],
                                    [ .2, .2, .2, .2, .2]]
botMovement = ['Idle', 'Left', 'Right', 'Up', 'Down']
botMotions = [ [ 0, 0], [ 0, -1], [ 0, 1], [ -1, 0], [ 1, 0] ]

sensorAccuracy = 1.0
botMvmtAccuracy = 0.5

def GetTransposedList( aList):
    tmp = []
    for i in range( len( aList ) ):
        tmp.append([ row[ i ] for row in aList ] )
    return tmp

def GetZeroList( aList):
    tmp = []
    for i in range( len( aList ) ):
        tmp.append([ 0 for i in aList[i] ] )
    return tmp

def Sum2DList( aList ):
    sum = 0
    for m in range( len(aList ) ):
        for n in range( len(aList[m]) ):
            sum += aList[m][n]
    return sum

def normedSense( aDistribution, measurementValue ):
    senseProb = GetZeroList( aDistribution )

    for m in range( len(worldColor) ):
        for n in range( len(worldColor[m]) ):
            hitFlag = ( measurementValue == worldColor[ m ][ n ] )
            senseProb[m][n] =  aDistribution[ m ][ n ] * ( ( hitFlag * sensorAccuracy ) + ( 1 - hitFlag )*(1.0 - sensorAccuracy) )

    localSum = Sum2DList( senseProb )    
    normedSenseProb = GetZeroList(aDistribution)
    for m in range( len(normedSenseProb) ):
        for n in range( len(normedSenseProb[m]) ):
            normedSenseProb[m][n] = senseProb[m][n] / localSum

    return normedSenseProb

def MoveWithMotion( aDistribution, aMotion ):
    distr = GetZeroList( aDistribution )

    for m in range( len(worldColor) ):
        for n in range( len(worldColor[m]) ):
            rowMove = ( m  - aMotion[ 0 ] ) % len( worldColor )
            columnMove = ( n  - aMotion[ 1 ] ) % len( worldColor[ m ] )
            distr[m][n] =  aDistribution[ rowMove ][ columnMove ] * botMvmtAccuracy + aDistribution[ m ][ n ] * ( 1.0 - botMvmtAccuracy )
    return distr

#Run code
worldColor = colors
sensorAccuracy = sensor_right
botMvmtAccuracy = p_move
botSense = measurements 
botMoves = motions
aDistr = uniformedProbabilityDistribution

for i in range( len(botSense ) ):
    aDistr = normedSense( MoveWithMotion( aDistr, botMoves[ i ] ), botSense[ i ] )

print(aDistr )
#Your probability array must be printed 
#with the following code.

#show(p)