def main(argv):
M = int(argv[1])
N = int(argv[2])
S = int(argv[3])
# Create a RandomQueue object containing Queue objects.
servers = randomqueue.RandomQueue()
for i in range(M):
servers.enqueue(linkedlistqueue.Queue())
for j in range(N):
# Assign an item to a server.
min = servers.sample()
for k in range(1, S):
# Pick a random server, update if new min.
q = servers.sample()
if len(q) < len(min):
min = q
# min is the shortest server queue.
min.enqueue(j)
lengths = []
for q in servers:
lengths += [len(q)]
stddraw.createWindow()
stddraw.setYscale(0, 2.0*N/M)
stdstats.plotBars(lengths)
stddraw.show()
stddraw.wait()
def main(argv):
M = int(argv[1])
N = int(argv[2])
S = int(argv[3])
# Create a RandomQueue object containing Queue objects.
servers = randomqueue.RandomQueue()
for i in range(M):
servers.enqueue(linkedlistqueue.Queue())
for j in range(N):
# Assign an item to a server.
min = servers.sample()
for k in range(1, S):
# Pick a random server, update if new min.
q = servers.sample()
if len(q) < len(min):
min = q
# min is the shortest server queue.
min.enqueue(j)
lengths = []
for q in servers:
lengths += [len(q)]
stddraw.createWindow()
stddraw.setYscale(0, 2.0 * N / M)
stdstats.plotBars(lengths)
stddraw.show()
stddraw.wait()
def main():
#print(deck())
a = deck()
#b = popdeck(a,5)
b = [16,2,4,1,0]
r = pair(b) #获得对数或三条数
num = numofGroupDeck(b) #获得牌面数字
c = colorofGroupDeck(b) #获得花色
print('牌:',b)
print('牌面数字:',num)
print('花色:',c)
print('一对:',onePair(r))
print('两对:',twoPair(r))
print('三条:',threeofAkind(r))
print('满堂红:',fullHouse(r))
print('同花:',flushColor(c))
print('顺子:',straight(num))
print('同花顺:',straightFlush(c,num))
#统计各种情况出现概率
n = 100000 #试验次数
p = [0]*7 #记录各种情况结果
for i in range(n):
a = deck()
b = popdeck(a,5)
r = pair(b)
num = numofGroupDeck(b)
c = colorofGroupDeck(b)
if onePair(r):
p[0] += 1
elif twoPair(r):
p[1] += 1
elif threeofAkind(r):
p[2] += 1
elif fullHouse(r):
p[3] += 1
if straightFlush(c,num):
p[6] += 1
elif flushColor(c):
p[4] += 1
elif straight(num):
p[5] += 1
print('p:',p)
stddraw.setYscale(0,1.1*max(p))
stdstats.plotBars(p)
#使用数学公式验证,先选牌面数字，再选花色
q = [0]*7
q[0] = comb(13,1)*comb(4,2)*comb(12,3)*comb(4,1)**3/(comb(52,5))
q[1] = comb(13,2)*comb(4,2)**2*comb(11,1)*comb(4,1)/(comb(52,5))
q[2] = comb(13,1)*comb(4,3)*comb(12,2)*comb(4,1)**2/(comb(52,5))
q[3] = comb(13,1)*comb(4,3)*comb(12,1)*comb(4,2)/(comb(52,5))
flush = comb(13,5)*comb(4,1)/(comb(52,5))
Straight = 9*comb(4,1)**5/(comb(52,5))
q[6] = flush*Straight
q[4] = flush-q[6]
q[5] = Straight-q[6]
for i in range(len(q)):
print('q[i]:{:.4f}'.format(q[i]))
stddraw.show()
def main():
n = int(sys.argv[1])
p = float(sys.argv[2])
trials = int(sys.argv[3])
t = int(sys.argv[4])
q = evaluate(n, p, trials)
stdio.writeln(q)
norm = exTimes(n, p, trials, t)
phi = stdarray.create1D(n + 1, 0.0)
stddev = math.sqrt(n) / 2.0
for i in range(n + 1):
phi[i] = gaussian.pdf(i, n / 2.0, stddev)
stddraw.setCanvasSize(1000, 400)
stddraw.setYscale(0, 1.1 * max(max(norm), max(phi)))
stdstats.plotBars(norm)
stdstats.plotLines(phi)
stddraw.show()
def main(argv):
n = int(argv[1])
t = int(argv[2])
stddraw.createWindow()
stddraw.setYscale(0, 0.2)
freq = [0] * (n+1)
for t in range(t):
freq[binomial(n)] += 1
norm = [0.0] * (n+1)
for i in range(n+1):
norm[i] = float(freq[i]) / float(t)
stdstats.plotBars(norm)
stddev = math.sqrt(n) / 2.0
phi = [0.0] * (n+1)
for i in range(n+1):
phi[i] = gaussian.phi(i, n/2.0, stddev)
stdstats.plotLines(phi)
stddraw.show()
stddraw.wait()
def draw(self):
stddraw.setYscale(0, self._max)
stdstats.plotBars(self._freq)
def draw(self):
stddraw.setYscale(0, self._max)
stdstats.plotBars(self._freq)
import stdstats
import stddraw
#a = [1,1,9,3,5,6,8,10]
a = [1]
print('mean:', stdstats.mean(a))
print('var:', stdstats.var(a))
print('stddev:', stdstats.stddev(a))
print('median:', stdstats.median(a))
stddraw.setYscale(min(a) - 2, max(a) + 2)
stdstats.plotPoints(a)
stdstats.plotLines(a)
stdstats.plotBars(a)
stddraw.show()
# draw the results to standard draw. Also draw the predicted Gaussian
# distribution function, thereby allowing easy comparison of the
# experimental results to the theoretically predicted results.
n = int(sys.argv[1])
trials = int(sys.argv[2])
freq = stdarray.create1D(n + 1, 0)
for t in range(trials):
heads = stdrandom.binomial(n, 0.5)
freq[heads] += 1
norm = stdarray.create1D(n + 1, 0.0)
for i in range(n + 1):
norm[i] = 1.0 * freq[i] / trials
phi = stdarray.create1D(n + 1, 0.0)
stddev = math.sqrt(n) / 2.0
for i in range(n + 1):
phi[i] = gaussian.pdf(i, n / 2.0, stddev)
stddraw.setCanvasSize(1000, 400)
stddraw.setYscale(0, 1.1 * max(max(norm), max(phi)))
stdstats.plotBars(norm)
stdstats.plotLines(phi)
stddraw.show()
#-----------------------------------------------------------------------
# python bernoulli.py 20 100000
def drawDice(a=[]):
stddraw.setYscale(0, 1.1 * max(a))
stdstats.plotBars(a)
# Create a RandomQueue object containing Queue objects.
servers = RandomQueue()
for i in range(serverCount):
servers.enqueue(Queue())
for j in range(itemCount):
# Assign an item to a server.
best = servers.sample()
for k in range(1, sampleSize):
# Pick a random server, update if new best.
queue = servers.sample()
if len(queue) < len(best):
best = queue
# best is the shortest server queue.
best.enqueue(j)
lengths = []
while not servers.isEmpty():
lengths += [len(servers.dequeue())]
stddraw.clear(stddraw.LIGHT_GRAY)
stddraw.setYscale(0, 2.0*itemCount/serverCount)
stdstats.plotBars(lengths)
stddraw.show()
#-----------------------------------------------------------------------
# python loadbalance.py 50 500 1
# python loadbalance.py 50 500 2
# Create a RandomQueue object containing Queue objects.
servers = RandomQueue()
for i in range(serverCount):
servers.enqueue(Queue())
for j in range(itemCount):
# Assign an item to a server.
best = servers.sample()
for k in range(1, sampleSize):
# Pick a random server, update if new best.
queue = servers.sample()
if len(queue) < len(best):
best = queue
# best is the shortest server queue.
best.enqueue(j)
lengths = []
while not servers.isEmpty():
lengths += [len(servers.dequeue())]
stddraw.clear(stddraw.LIGHT_GRAY)
stddraw.setYscale(0, 2.0 * itemCount / serverCount)
stdstats.plotBars(lengths)
stddraw.show()
# -----------------------------------------------------------------------
# python loadbalance.py 50 500 1
# python loadbalance.py 50 500 2
def draw(self):
stddraw.setYscale(0, max(self._freqCounts))
stdstats.plotBars(self._freqCounts)
# distribution function, thereby allowing easy comparison of the
# experimental results to the theoretically predicted results.
n = int(sys.argv[1])
trials = int(sys.argv[2])
freq = stdarray.create1D(n+1, 0)
for t in range(trials):
heads = stdrandom.binomial(n, 0.5)
freq[heads] += 1
norm = stdarray.create1D(n+1, 0.0)
for i in range(n+1):
norm[i] = 1.0 * freq[i] / trials
phi = stdarray.create1D(n+1, 0.0)
stddev = math.sqrt(n)/2.0
for i in range(n+1):
phi[i] = gaussian.pdf(i, n/2.0, stddev)
stddraw.setCanvasSize(1000, 400)
stddraw.setYscale(0, 1.1 * max(max(norm), max(phi)))
stdstats.plotBars(norm)
stdstats.plotLines(phi)
stddraw.show()
#-----------------------------------------------------------------------
# python bernoulli.py 20 100000
def draw(self):
stddraw.setYscale(0, max(self._freqCounts))
stdstats.plotBars(self._freqCounts)
