def test_uniform(rnd, loc=0, scale=1): # uniform random variate generator uniform_dist = RVGs.Uniform(loc, scale) # obtain samples samples = get_samples(uniform_dist, rnd) # report mean and variance print_test_results('Uniform', samples, expectation=(2 * loc + scale) / 2.0, variance=scale**2 / 12.0)
print("Fitting NegativeBinomial:", dictResults) # 13 Normal dist = RVGs.Normal(0, 1) dat_norm = np.array(get_samples(dist, np.random)) # mean, sigma dictResults = Fit.fit_normal(dat_norm, 'Data') # fit print("Fitting Normal:", dictResults) # 14 Triangular dist = RVGs.Triangular(0.5, loc=1, scale=2) dat_tri = np.array(get_samples(dist, np.random)) dictResults = Fit.fit_triang(dat_tri, 'Data', fixed_location=1) # fit print("Fitting Triangular:", dictResults) # 15 Uniform dist = RVGs.Uniform(0, 1) dat_unif = np.array(get_samples(dist, np.random)) # mean, sigma dictResults = Fit.fit_uniform(dat_unif, 'Data') # fit print("Fitting Uniform:", dictResults) # 16 UniformDiscrete dist = RVGs.UniformDiscrete(0, 100) dat_unifDis = np.array(get_samples(dist, np.random)) dictResults = Fit.fit_uniformDiscrete(dat_unifDis, 'Data') # fit print("Fitting UniformDiscrete:", dictResults) # 17 fitting a Weibull distribution dist = RVGs.Weibull(5, 1, 2) dat_weibull = np.array(get_samples(dist, np.random)) # generate data dictResults = Fit.fit_weibull(dat_weibull, 'Data', fixed_location=1) # fit print("Fitting Weibull:", dictResults)