def generate_sample(draw=False):
w = pd.Series([.2, .5, .3])
n = 500
bootstrapper = Bootstrapper()
dists = bootstrapper.sample_discrete(w, n)
dists = pd.Series(dists)
cov = [[1,0],[0,1]]
mu0, n0 = [0,0], dists[dists == 0].count()
mu1, n1 = [3,0], dists[dists == 1].count()
mu2, n2 = [0,3], dists[dists == 2].count()
sample = []
sample.extend(np.random.multivariate_normal(mean=mu0, cov=cov, size=n0))
sample.extend(np.random.multivariate_normal(mean=mu1, cov=cov, size=n1))
sample.extend(np.random.multivariate_normal(mean=mu2, cov=cov, size=n2))
sample = pd.DataFrame(sample)
if draw:
draw_sample(sample)
return sample
def part1():
dist = pd.Series([0.1, 0.2, 0.3, 0.4], index=[1,2,3,4])
n_values = [100, 200, 300, 400, 500]
bootstrapper = Bootstrapper()
samples = {n: bootstrapper.sample_discrete(dist, n) for n in n_values}
visualizer.plot_params_hist(samples)
