def dependent_events(gen=method_2_(), n=1000, pa=0.8, pb=0.5, pb_a=0.3):
x1 = [next(gen) for _ in xrange(n)]
x2 = [next(gen) for _ in xrange(n)]
a, b, a_and_b, a_and_not_b, not_a_and_b, not_a_and_not_b = 0, 0, 0, 0, 0, 0
for i in range(n):
if x1[i] <= pa:
if x2[i] <= pb_a:
a_and_b += 1
b += 1
else:
a_and_not_b += 1
a += 1
else:
if x2[i] <= (pb - pb_a * pa) / (1 - pa):
not_a_and_b += 1
b += 1
else:
not_a_and_not_b += 1
print "3. dependent_events"
print " A: {}, {}".format(pa, a)
print " B: {}, {}".format(pb, b)
print " B_A: {}".format(pb_a)
print " A and B: {}, {}%".format(a_and_b, float(a_and_b) / n)
print " !A and !B: {}, {}%".format(not_a_and_not_b,
float(not_a_and_not_b) / n)
print " !A and B: {}, {}%".format(not_a_and_b, float(not_a_and_b) / n)
print " A and !B: {}, {}%\n".format(a_and_not_b, float(a_and_not_b) / n)
def independent_events(gen=method_2_(), n=1000, pa=0.8, pb=0.5):
xa = [next(gen) for _ in xrange(n)]
xb = [next(gen) for _ in xrange(n)]
a, b, a_b, a_not_b, not_a_b, not_a_not_b = 0, 0, 0, 0, 0, 0
for i in xrange(n):
if xa[i] <= pa:
if xb[i] <= pb:
a_b += 1
b += 1
else:
a_not_b += 1
a += 1
else:
if xb[i] <= pb:
not_a_b += 1
b += 1
else:
not_a_not_b += 1
print "2. independent_events"
print " A: {}, {}%".format(a, float(a) / n)
print " B: {}, {}%".format(b, float(b) / n)
print " A and B: {}, {}%".format(a_b, float(a_b) / n)
print " !A and !B: {}, {}%".format(not_a_not_b, float(not_a_not_b) / n)
print " !A and B: {}, {}%".format(not_a_b, float(not_a_b) / n)
print " A and !B: {}, {}%\n".format(a_not_b, float(a_not_b) / n)
def exponential_distribution(self, lmbd):
"""
y = 1 - exp(-lambda * x)
x = -(1 / lmbd) * ln(1 - y)
"""
gen = method_2_()
while True:
x = gen.next()
yield -(1.0 / lmbd) * numpy.log(1 - x)
def uniform_distribution(self):
"""
x = y * (b - a) + a
y = (x - a) / (b - a)
"""
gen = method_2_()
while True:
x = gen.next()
yield x * (self.b - self.a) + self.a
def generate_contineous_law(self, i, n):
pyplot.subplot(1, 2, i)
gen = method_2_()
xn = [next(gen) for _ in xrange(n)]
yn = [-1.0 * (1.0 / self.l) * math.log(1 - xn[i]) for i in xrange(n)]
pyplot.hist(yn, normed=1)
M = self.calculate_M(yn)
D = self.calculete_D(yn, M)
pyplot.title('n = {}, M = {}. D = {}'.format(n, M, D))
pyplot.plot(self.x, self.y)
return yn
def simple_event(gen=method_2_(), n=1000, p=0.3):
x = [next(gen) for _ in xrange(n)]
result = [1 if x[i] <= p else 0 for i in xrange(len(x))]
M = 1.0 / len(result) * sum(result)
D = 1.0 / len(result) * sum([v**2 for v in result]) - M**2
count = sum(result)
print "1. simple_event."
print " A: {}, {}% ".format(count, float(count) / n)
print " !A : {}, {}%".format(n - count, float(n - count) / n)
print " M : {}, D : {}\n".format(M, D)
def full_group_events(
gen=method_2_(), p=(0.05, 0.1, 0.1, 0.3, 0.2, 0.25), n=1000):
y = [next(gen) for _ in xrange(n)]
pn = list(accumulate(p))
result = [num(i, pn) for i in y]
counts = Counter(result)
print "4. full_group_events"
print "p: {}, n: {}".format(p, n)
for k in counts:
print " {}: {}, {}%".format(k, counts[k], counts[k] / float(len(y)))
def generate_empirical_matrix(self, f_x, f_y):
empirical_matrix_distribution = np.zeros((self.m, self.n))
N, k, l = 10000, 0, 0
gen = method_2_()
z = [next(gen) for _ in xrange(N * 2)]
for i in xrange(0, len(z), 2):
for j in xrange(self.n + 1):
if z[i] <= f_x[j]:
k = j - 1
break
for j in xrange(self.m + 1):
if z[i + 1] <= f_y[j, k]:
l = j - 1
break
empirical_matrix_distribution[l, k] += 1
for i in xrange(self.m):
for j in xrange(self.n):
empirical_matrix_distribution[i, j] /= N
return empirical_matrix_distribution