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():
n = int(sys.argv[1])
stddraw.setYscale(-4, 4)
stddraw.setYscale(0, 5)
stddraw.setPenRadius(0.1)
x = [0.0] * (n+1)
y = [0.0] * (n+1)
for i in range(n+1):
x[i] = -0.4 + 0.8 * i/n
y[i] = gaussian.pdf(x[i])
for i in range(n):
stddraw.line(x[i], y[i], x[i+1], y[i+1])
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()
# 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
# 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
import sys
import math
import stddraw
import stdrandom
import stdstats
import gaussian
n = int(sys.argv[1])
trials = int(sys.argv[2])
freq = (n + 1) * [0]
for t in range(trials):
heads = stdrandom.binomial(n, 0.5)
#n-ta seke partab kon, beshmor chanta shir miyad
freq[heads] += 1
norm = [0.0] * (n + 1)
for i in range(n + 1):
norm[i] = 1.0 * freq[i] / trials
#rasm tozi normal
phi = [0.0] * (n + 1)
stddev = math.sqrt(n) / 2.0
for i in range(n + 1):
phi[i] = gaussian.pdf(n, n / 2, stddev)
stddraw.setCanvasSize(1000, 400)
stddraw.setYscale(0, 1.0, max(max(norm), max(phi)))
stdstats.plotBars(norm)
stdstats.plotLines(phi)
stddraw.show()