bayesian_linear_regression_util.py

import numpy as np
import matplotlib.pyplot as plt

from scipy import stats


def plot_data(x, t):
    plt.scatter(x, t, marker='o', c="k", s=20)


def plot_truth(x, y, label='Truth'):
    plt.plot(x, y, 'k--', label=label)


def plot_predictive(x, y, std, y_label='Prediction', std_label='Uncertainty', plot_xy_labels=True):
    y = y.ravel()
    std = std.ravel()

    plt.plot(x, y, label=y_label)
    plt.fill_between(x.ravel(), y + std, y - std, alpha = 0.5, label=std_label)

    if plot_xy_labels:
        plt.xlabel('x')
        plt.ylabel('y')


def plot_posterior_samples(x, ys, plot_xy_labels=True):
    plt.plot(x, ys[:, 0], 'r-', alpha=0.5, label='Post. samples')
    for i in range(1, ys.shape[1]):
        plt.plot(x, ys[:, i], 'r-', alpha=0.5)

    if plot_xy_labels:
        plt.xlabel('x')
        plt.ylabel('y')


def plot_posterior(mean, cov, w0, w1):
    resolution = 100

    grid_x = grid_y = np.linspace(-1, 1, resolution)
    grid_flat = np.dstack(np.meshgrid(grid_x, grid_y)).reshape(-1, 2)

    densities = stats.multivariate_normal.pdf(grid_flat, mean=mean.ravel(), cov=cov).reshape(resolution, resolution)
    plt.imshow(densities, origin='lower', extent=(-1, 1, -1, 1))
    plt.scatter(w0, w1, marker='x', c="r", s=20, label='Truth')

    plt.xlabel('w0')
    plt.ylabel('w1')


def print_comparison(title, a, b, a_prefix='np', b_prefix='br'):
    print(title)
    print('-' * len(title))
    print(f'{a_prefix}:', a)
    print(f'{b_prefix}:', b)
    print()