coloring_matrix = cholesky(real_cov)
n_samples_range = np.arange(6, 31, 1)
repeat = 100
lw_mse = np.zeros((n_samples_range.size, repeat))
oa_mse = np.zeros((n_samples_range.size, repeat))
lw_shrinkage = np.zeros((n_samples_range.size, repeat))
oa_shrinkage = np.zeros((n_samples_range.size, repeat))
for i, n_samples in enumerate(n_samples_range):
for j in range(repeat):
X = np.dot(np.random.normal(size=(n_samples, n_features)),
coloring_matrix.T)
lw = LedoitWolf(store_precision=False)
lw.fit(X, assume_centered=True)
lw_mse[i, j] = lw.error_norm(real_cov, scaling=False)
lw_shrinkage[i, j] = lw.shrinkage_
oa = OAS(store_precision=False)
oa.fit(X, assume_centered=True)
oa_mse[i, j] = oa.error_norm(real_cov, scaling=False)
oa_shrinkage[i, j] = oa.shrinkage_
# plot MSE
pl.subplot(2, 1, 1)
pl.errorbar(n_samples_range,
lw_mse.mean(1),
yerr=lw_mse.std(1),
label='Ledoit-Wolf',
color='g')
pl.errorbar(n_samples_range,
