def test_muller_fit():
variance = 0.1 # given in the ques
rvs = generate_muller_rvs(variance)
number_of_bins = int(1.88 * size(rvs)**(2 / 5.0))
integrand = lambda x: stats.norm.pdf(x, scale=variance)
writer.append('testing box-muller')
gof.do_chi(rvs, number_of_bins, integrand)
def test_gamma_fit():
shape = 5 #given from hw1
rvs = generate_gamma_rvs(shape)
number_of_bins = int(1.88 * size(rvs)**(2 / 5.0))
integrand = lambda x: stats.gamma(5).pdf(x)
writer.append('testing gamma distribution')
gof.do_chi(rvs, number_of_bins, integrand)
def test_muller_fit():
variance = 0.1 # given in the ques
rvs = generate_muller_rvs(variance)
number_of_bins = int(1.88 * size(rvs) ** (2/5.0))
integrand = lambda x : stats.norm.pdf(x, scale=variance)
writer.append('testing box-muller')
gof.do_chi(rvs, number_of_bins, integrand)
def test_cauchy_fit(sample_size):
rvs = generate_cauchy_rvs(sample_size)
number_of_bins = int(1.88 * size(rvs)**(2 / 5.0))
integrand = lambda x: stats.gamma(5).pdf(
x) #cauchy was derived from gamma through the rejection method
writer.append('testing cauchy distribution with ' + str(sample_size) +
' samples')
gof.do_chi(rvs, number_of_bins, integrand)
def test_random_fit():
rvs = generate_random_rvs()
number_of_bins = int(1.88 * size(rvs)**(2 / 5.0))
integrand = lambda x: 1 #pdf for uniform rv
writer.append('testing random')
gof.do_chi(rvs, number_of_bins, integrand)
def test_cauchy_fit(sample_size):
rvs = generate_cauchy_rvs(sample_size)
number_of_bins = int(1.88 * size(rvs) ** (2/5.0))
integrand = lambda x : stats.gamma(5).pdf(x) #cauchy was derived from gamma through the rejection method
writer.append('testing cauchy distribution with '+str(sample_size)+' samples')
gof.do_chi(rvs, number_of_bins, integrand)
def test_lcg_fit():
rvs = generate_lcg_rvs()
number_of_bins = int(1.88 * size(rvs) ** (2/5.0))
integrand = lambda x : 1 #pdf for uniform rv
writer.append('testing lcg')
gof.do_chi(rvs, number_of_bins, integrand)