def vote(n, p, w):
n1 = stdrandom.binomial(n, 1 - w) #n次投票，不错误的次数为n1
na = stdrandom.binomial(n1, p) + stdrandom.binomial(
n - n1, 1 - p) #正常的N1次p概率投给A；错误的N-N1次，票数记反，相当于A，B概率对调。返回na为A获得的票数
print('A获得：', na)
if na > n / 2:
return 'A获胜'
else:
return 'A失败'
def main():
n = int(sys.argv[1]) # number of biased coin flips per trial
p = float(sys.argv[2]) # heads with probability p
trialCount = int(sys.argv[3]) # number of trials
histogram = Histogram(n + 1)
for trial in range(trialCount):
heads = stdrandom.binomial(n, p)
histogram.addDataPoint(heads)
stddraw.setCanvasSize(500, 200)
stddraw.clear(stddraw.LIGHT_GRAY)
histogram.draw()
stddraw.show()
def main():
n = int(sys.argv[1]) # number of biased coin flips per trial
p = float(sys.argv[2]) # heads with probability p
trialCount = int(sys.argv[3]) # number of trials
histogram = Histogram(n + 1)
for trial in range(trialCount):
heads = stdrandom.binomial(n, p)
histogram.addDataPoint(heads)
stddraw.setCanvasSize(500, 200)
stddraw.clear(stddraw.LIGHT_GRAY)
histogram.draw()
stddraw.show()
#-----------------------------------------------------------------------
# Accept integers n and trials as command-line arguments.
# Perform trials experiments, each of which counts the number
# of heads found when a fair coin is flipped n times. Then
# 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)
import stdrandom t = 10000 s = 0 for i in range(t): if stdrandom.binomial(12,0.05) > 0: s += 1 print(s/t)
#-----------------------------------------------------------------------
# Accept integers n and trials as command-line arguments.
# Perform trials experiments, each of which counts the number
# of heads found when a fair coin is flipped n times. Then
# 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)
