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)