def test_multinomial(rnd, n, pvals): # multinomial random variate generator multinomial_dist = RVGs.Binomial(n, pvals) # obtain samples samples = get_samples(multinomial_dist, rnd) # report mean and variance print_test_results('Multinomial', samples, expectation=n * pvals, variance=n * pvals * (1 - pvals))
def test_binomial(rnd, n, p, loc=0): # bimonial random variate generator binomial_dist = RVGs.Binomial(n, p, loc) # obtain samples samples = get_samples(binomial_dist, rnd) # report mean and variance print_test_results('Binomial', samples, expectation=n * p + loc, variance=n * p * (1 - p))
dist = RVGs.Beta(2, 3, loc=1, scale=2) dat_beta = np.array(get_samples(dist, np.random)) # generate data dictResults = Fit.fit_beta(dat_beta, 'Data', minimum=1, maximum=3) # fit print("Fitting Beta:", dictResults) # 3 fitting a beta-binomial distribution dist = RVGs.BetaBinomial(100, 2, 3, loc=1, scale=2) # n, a, b dat_betabin = np.array(get_samples(dist, np.random)) dictResults = Fit.fit_beta_binomial(dat_betabin, 'Data', fixed_location=1, fixed_scale=2) # fit print("Fitting BetaBinomial:", dictResults) # 4 Binomial dist = RVGs.Binomial(100, 0.3, 1) dat_bin = np.array(get_samples(dist, np.random)) dictResults = Fit.fit_binomial(dat_bin, 'Data', fixed_location=1) # fit print("Fitting Binomial:", dictResults) # 5 Empirical (for int data) dat_em = np.random.poisson(30, 1000) dictResults = Fit.fit_empirical(dat_em, 'Data', bin_size=2.5) # fit print("Fitting Empirical:", dictResults) # 6 fitting a gamma distribution dist = RVGs.Gamma(10, 1, 2) dat_gamma = np.array(get_samples(dist, np.random)) # generate data dictResults = Fit.fit_gamma(dat_gamma, 'Data', fixed_location=1) # fit print("Fitting Gamma:", dictResults)