def test_fitting_binomial():
print("\nTesting Binomial with n=100, p=0.3, loc=1:")
dist = RVGs.Binomial(n=100, p=0.3, loc=1)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
# method of moment
dict_mm_results = RVGs.Binomial.fit_mm(mean=np.mean(data),
st_dev=np.std(data),
fixed_location=1)
# maximum likelihood
dict_ml_results = RVGs.Binomial.fit_ml(data=data, fixed_location=1)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_binomial_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_binomial_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
def test_fitting_beta():
print("\nTesting Beta with a=2, b=3, loc=1, scale=2:")
dist = RVGs.Beta(a=2, b=3, loc=1, scale=2)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
# method of moment
dict_mm_results = RVGs.Beta.fit_mm(mean=np.mean(data),
st_dev=np.std(data),
minimum=1,
maximum=3)
# maximum likelihood
dict_ml_results = RVGs.Beta.fit_ml(data=data, minimum=1, maximum=3)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_beta_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_beta_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
def test_fitting_johnson_sb():
print("\nTesting Johnson Sb with a=10, b=5, loc=10, scale=100")
dist = RVGs.JohnsonSb(a=10, b=5, loc=10, scale=100)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_ml_results = RVGs.JohnsonSb.fit_ml(data=data, fixed_location=10)
print(" Fit:")
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_johnson_sb_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
def test_fitting_triangular():
print("\nTesting triangular with c=0.2, loc=6, scale=7")
dist = RVGs.Triangular(c=0.2, loc=6, scale=7)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_ml_results = RVGs.Triangular.fit_ml(data=data, fixed_location=6)
print(" Fit:")
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_triangular_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
def test_fitting_negbinomial():
print("\nTesting NegBinomial with n=10, p=0.2, loc=1")
dist = RVGs.NegativeBinomial(n=10, p=0.2, loc=1)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_mm_results = RVGs.NegativeBinomial.fit_mm(mean=np.average(data),
st_dev=np.std(data),
fixed_location=1)
dict_ml_results = RVGs.NegativeBinomial.fit_ml(data=data, fixed_location=1)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_negbinomial_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
def test_fitting_geometric():
print("\nTesting Geometric with p=0.3, loc=1")
dist = RVGs.Geometric(p=0.3, loc=1)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_mm_results = RVGs.Geometric.fit_mm(mean=np.average(data),
fixed_location=1)
dict_ml_results = RVGs.Geometric.fit_ml(data=data, fixed_location=1)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_geometric_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_geometric_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
def test_fitting_uniform_discrete():
print("\nTesting uniform discrete with l=10, u=18")
dist = RVGs.UniformDiscrete(l=10, u=18)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_mm_results = RVGs.UniformDiscrete.fit_mm(mean=np.average(data),
st_dev=np.std(data))
dict_ml_results = RVGs.UniformDiscrete.fit_ml(data=data)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_uniform_discrete_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_uniform_discrete_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
def test_fitting_poisson():
print("\nTesting Poisson with mean=100 and loc = 10")
dist = RVGs.Poisson(mu=100, loc=10)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_mm_results = RVGs.Poisson.fit_mm(mean=np.average(data),
fixed_location=10)
dict_ml_results = RVGs.Poisson.fit_ml(data=data, fixed_location=10)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_poisson_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_poisson_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
def test_fitting_normal():
print("\nTesting Normal with loc=10, scale=2")
dist = RVGs.Normal(loc=10, scale=2)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_mm_results = RVGs.Normal.fit_mm(mean=np.average(data),
st_dev=np.std(data))
dict_ml_results = RVGs.Normal.fit_ml(data=data)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_normal_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_normal_fit(data=data,
fit_results=dict_mm_results,
title='Maximum Likelihood')
def test_fitting_gamma_poisson():
print("\nTesting Gamma Poisson with a=2, gamma_scale=4, loc=2")
dist = RVGs.GammaPoisson(a=2, gamma_scale=4, loc=2)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
dict_mm_results = RVGs.GammaPoisson.fit_mm(mean=np.average(data),
st_dev=np.std(data),
fixed_location=2)
dict_ml_results = RVGs.GammaPoisson.fit_ml(data=data, fixed_location=2)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_gamma_poisson_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_gamma_poisson_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
def test_fitting_exponential():
print("\nTesting Exponential with scale=0.5, loc=2")
dist = RVGs.Exponential(scale=0.5, loc=2)
print(' percentile interval: ', dist.get_percentile_interval(alpha=0.05))
data = np.array(get_samples(dist, np.random))
# method of moment
dict_mm_results = RVGs.Exponential.fit_mm(mean=np.average(data),
fixed_location=2)
# maximum likelihood
dict_ml_results = RVGs.Exponential.fit_ml(data=data, fixed_location=2)
print(" Fit:")
print(" MM:", dict_mm_results)
print(" ML:", dict_ml_results)
# plot the fitted distributions
Plot.plot_exponential_fit(data=data,
fit_results=dict_mm_results,
title='Method of Moment')
Plot.plot_exponential_fit(data=data,
fit_results=dict_ml_results,
title='Maximum Likelihood')
# make a histogram
Hist.plot_histogram(data=cols[0], title='Weekly Number of Drinks', bin_width=1)
# mean and standard deviation
stat = Stat.SummaryStat(name='Weekly number of drinks', data=cols[0])
print('Mean = ', stat.get_mean())
print('StDev = ', stat.get_stdev())
# fit a Poisson distribution
fit_results = RVGs.Poisson.fit_ml(data=cols[0])
print('Fitting a Poisson distribution:', fit_results)
# plot the fitted Poisson distribution
Plot.plot_poisson_fit(data=cols[0],
fit_results=fit_results,
x_label='Weekly number of drinks',
x_range=(0, 40),
bin_width=1)
# fit a gamma-Poisson distribution
fit_results = RVGs.GammaPoisson.fit_ml(data=cols[0])
print('Fitting a gamma-Poisson distribution:', fit_results)
# plot the fitted gamma-Poisson distribution
Plot.plot_gamma_poisson_fit(data=cols[0],
fit_results=fit_results,
x_label='Weekly number of drinks',
x_range=(0, 40),
bin_width=1)
# fit a beta-binomial distribution