def display_model(training_data, alpha, beta, gamma, delta):
"""
Displays a plot of the data under the maximum a posterior distribution, along side the maximum likelihood for
the data and the prior.
:param training_data: The training data to fit the distribution to.
:type training_data: list[float]
:param alpha: The alpha hyperparameter of the prior
:type alpha: float
:param beta: The beta hyperparameter of the prior
:type beta: float
:param gamma: The gamma hyperparameter of the prior
:type gamma: float
:param delta: The delta hyperparameter of the prior
:type delta: float
"""
prior_mean, prior_variance = generate_parameters([], alpha, beta, gamma, delta)
ml_mean, ml_variance = generate_maximum_likelihood_parameters(training_data)
mean, variance = generate_parameters(training_data, alpha, beta, gamma, delta)
prior_sigma = prior_variance ** 0.5
ml_sigma = ml_variance ** 0.5
sigma = variance ** 0.5
map_color = sns.color_palette()[0]
data_color = sns.color_palette()[1]
prior_color = sns.color_palette()[2]
ml_color = sns.color_palette()[3]
display.draw_normal(prior_mean, prior_sigma, color=prior_color)
display.draw_normal(ml_mean, ml_sigma, color=ml_color)
display.draw_data_under_normal(training_data, mean, sigma, data_color=data_color, normal_color=map_color)
plt.show()
def test_draw_normal(self, mock_plt):
mean = 1
sigma = 1
plotting_space = np.linspace(scipy.stats.norm.ppf(0.01, mean, sigma),
scipy.stats.norm.ppf(0.99, mean, sigma),
constant.plot_samples)
distribution = scipy.stats.norm.pdf(plotting_space, mean, sigma)
draw_normal(mean, sigma)
assert np.array_equal((plotting_space, distribution), mock_plt.plot.call_args[0])
def test_draw_normal_can_be_passed_a_color_for_the_normal(self, mock_norm, mock_linspace, mock_plt):
draw_normal(0, 0, color='r')
assert mock_plt.plot.call_args[1]['color'] == 'r'
