| import os | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from scipy.stats import binom,gamma | |
| def generate_binomial_probs(size = 5,n = 5, p= 0.5,plot = True): | |
| """ | |
| To print the binomial distribtion vector and plot a discreate binomial distribution | |
| """ | |
| plt.rcParams.update({'font.family':'times new roman'}) | |
| rv = binom(n, p) | |
| x = np.arange(size) | |
| probs = rv.pmf(x) | |
| diff = 1 - sum(probs) | |
| diff = diff/len(probs) | |
| probs = probs + diff | |
| print('------binomial------') | |
| print(x) | |
| print(probs) | |
| if plot: | |
| plt.vlines(x, 0, probs, colors='k', linestyles='-', lw=1) | |
| plt.legend(loc='best', frameon=False) | |
| plt.savefig(os.path.join('results', 'binomial_dist_2.pdf')) | |
| plt.show() | |
| def plot_ep_evol(folder = 'binomial_3',seed = 0,model_names = ['VI','VIMC','PPO','SP','PSO'],x_type = 'reward',y_label = 'Cost'): | |
| plt.rcParams.update({'font.family':'times new roman'}) | |
| fig, axs = plt.subplots(len(model_names),figsize=(20,10)) | |
| i = 0 | |
| for model in model_names: | |
| x = np.abs( | |
| np.load( | |
| os.path.join( | |
| 'results', | |
| 'binomial', | |
| f'{model}_{folder}_{x_type}_test_{seed}.npy' | |
| ) | |
| ) | |
| ) | |
| axs[i].plot(x[:100],label = model) | |
| axs[i].set_title(model) | |
| i += 1 | |
| for ax in axs.flat: | |
| ax.set(xlabel='Time steps', ylabel=y_label) | |
| for ax in axs.flat: | |
| ax.label_outer() | |
| plt.savefig( | |
| os.path.join( | |
| 'results', | |
| f'evol_reward_{folder}_{seed}.pdf' | |
| ), | |
| bbox_inches='tight' | |
| ) | |