def md(mu=1019,sigma=209): phi = [] for i in range(400,1600,50): phi.append(gaussian.pdf(i,mu,sigma)) stddraw.setYscale(0,1.1*max(phi)) stdstats.plotLines(phi) stddraw.show()
def main():
hurst_ex = float(sys.argv[1])
scale = 2**(2 * hurst_ex)
fill_brownian(array, i, i1, variance, scale)
stddraw.setYscale(-1, 1)
stddraw.setXscale(0, len(array))
stddraw.setPenRadius(0.0)
stdstats.plotLines(array)
stddraw.show()
def main():
sh = []
ch = []
x = -5
while x < 5:
sh.append(sinh(x))
ch.append(cosh(x))
x += 0.2
y = max(max(sh), max(ch))
print(sh)
print(ch)
print(y)
stddraw.setYscale(-y, y)
stdstats.plotPoints(sh)
stdstats.plotLines(ch)
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()
end = i
year = year[begin:end]
volume = volume[begin:end]
price = price[begin:end]
t = 0
for i in range(len(volume)):
volume[i] = eval(volume[i]) / 100000
t += volume[i]
if (i + 1) % bili == 0:
hVolume.append(t / bili)
t = 0
t = 0
for i in range(len(price)):
price[i] = eval(price[i])
t += price[i]
if (i + 1) % bili == 0:
hPrice.append(t / bili)
t = 0
print(len(hVolume))
stddraw.setCanvasSize(1000, 500)
stddraw.setYscale(0, 1.1 * max(max(hVolume), max(hPrice)))
stdstats.plotLines(hVolume)
stddraw.setPenColor(stddraw.BLUE)
stdstats.plotLines(hPrice)
stddraw.show()
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
r = [0] * 13
for i in range(n):
t = twodice()
r[t] += 1
return r
def crapS(n):
r = [0] * 13
for i in range(n):
t = SicheDice()
r[t] += 1
return r
def drawDice(a=[]):
stddraw.setYscale(0, 1.1 * max(a))
stdstats.plotBars(a)
n = 1000000
d = crap(n)
dS = crapS(n)
print(d)
print(dS)
print(((d[7] + d[11])) / sum(d))
print((d[2] + d[3] + d[12]) / sum(d))
drawDice(d)
stdstats.plotLines(dS)
stddraw.show()
def plot(self):
#stddraw.setPenColor('GRAY')
stdstats.plotPoints(self._l)
stdstats.plotLines(self._l)
# 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
