from math import cos, atan
N = 10000 # number of replications */
HALF_PI =(2.0 * atan(1.0)) # 1.5707963... */
R = 1.0 # length of the needle */
def Uniform(a,b):
# --------------------------------------------
# * generate a Uniform random variate, use a < b
# * --------------------------------------------
# */
return (a + (b - a) * random())
################################Main Program#############################
putSeed(-1) # any negative integer will do */
seed = getSeed() # trap the value of the intial seed */
crosses = 0 # tracks number of crosses
for i in range(0,N):
u = random() #get first endpoint
theta = Uniform(-HALF_PI, HALF_PI) #get Angle
v = u + R * cos(theta) #get second endpoint
if (v > 1.0):
crosses += 1 #increase number of crosses
p = float(crosses / N) # estimate the probability */
print("\nbased on {0:1d} replications and a needle of length {1:5.2f}".format(N, R))
print("with an initial seed of {0:1d}".format(seed))
delay = 0.0 #delay times
wait = 0.0 #wait times
service = 0.0 #service times
interarrival = -1.0 #interarrival times
#################################Main Program############################
index = 0 # job index */
arrival = START # arrival time */
delay = -1 # delay in queue */
service = -1 # service time */
wait = -1 # delay + service */
departure = START # departure time */
sum = sumOf()
putSeed(123456789)
while (index < LAST):
index += 1
arrival = GetArrival()
if (arrival < departure):
delay = departure - arrival # delay in queue */
else:
delay = 0.0 # no delay */
service = GetService()
wait = delay + service
departure = arrival + wait # time of departure */
sum.delay += delay
sum.wait += wait
sum.service += service
#EndWhile
def Equilikely(a,b):
# # ------------------------------------------------
# * generate an Equilikely random variate, use a < b
# * ------------------------------------------------
# */
return (a + int((b - a + 1) * random()))
# i # replication index */
# x # sum of three dice */
count=[0 for i in range(0,19)] # histogram */
p=[0.0 for i in range(0,19)] # probability estimates */
putSeed(0)
for i in range(0,N):
x = Equilikely(1, 6) + Equilikely(1, 6) + Equilikely(1, 6)
count[x] += 1
for x in range(3,19): # estimate probabilities */
p[x] = float(count[x]) / N
print("\nbased on {0:d} replications the estimated probabilities are:\n".format(N))
for x in range(3,19):
print("p[{0:2d}] = {1:5.3f}".format(x, p[x]))
# C output:
# Enter a positive integer seed (9 digits or less) >> 123456789
test = 0
condition = True
while (condition == True): # test to see if at least */
test = (a[j] == j) # one element is in its */
j += 1 # 'natural' position */
condition = (j != SIZE) and (test == 0) # - return a 1 if so */
# - return a 0 otherwise */
if (test == 1):
return (1)
else:
return (0)
###############################Main Program##############################
putSeed(0)
arr = [None for i in range(0, SIZE)]
Initialize(arr)
for i in range(0, N): # do N Monte Carlo replications */
Shuffle(arr)
count += Check(arr)
p = float(N - count) / N # estimate the probability */
print("\nfor {0:1d} replications and an array of size {1:d}".format(N, SIZE))
print("the estimated probability is {0:5.3f}".format(p))
# c output:
# Enter a positive integer seed (9 digits or less) >> 123456789
