# %%
import os, matplotlib
# %%
plot([0, 1, 2], [0, 1, 4])
# %%
import comp_prob_inference
# %%
print(comp_prob_inference.flip_fair_coin())
flips = comp_prob_inference.flip_fair_coins(100)
print(flips)
# plot flips
comp_prob_inference.plot_discrete_histogram(flips, frequency=True)
# %%
n = 100000
heads_so_far = 0
fraction_of_heads = []
for i in range(n):
if comp_prob_inference.flip_fair_coin() == 'heads':
heads_so_far += 1
fraction_of_heads.append(heads_so_far / (i + 1))
import matplotlib.pyplot as plt
plt.figure(figsize=(8, 4))
plt.plot(range(1, n + 1), fraction_of_heads)
plt.xlabel('Number of flips')
plt.ylabel('Fraction of heads')
# %%
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import comp_prob_inference as cpi
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt
# The PDF document
pdf_pages = PdfPages("flips.pdf")
print(cpi.flip_fair_coin())
flips = cpi.flip_fair_coins(100)
cpi.plot_discrete_histogram(flips)
pdf_pages.savefig()
cpi.plot_discrete_histogram(flips, frequency=True)
pdf_pages.savefig()
n = 100000
heads_so_far = 0
fraction_of_heads = []
for i in range(n):
if cpi.flip_fair_coin() == 'heads':
heads_so_far += 1
fraction_of_heads.append(heads_so_far / (i + 1))
plt.figure(figsize=(8, 4))
plt.plot(range(1, n + 1), fraction_of_heads)
plt.xlabel('Number of flips')
plt.ylabel('Fraction of heads')