Skip to content

Instantly share code, notes, and snippets.

@psycharo-zz
Last active January 10, 2020 14:08
Show Gist options
  • Save psycharo-zz/6a7d94404aed0be0112bf9751876f36b to your computer and use it in GitHub Desktop.
Save psycharo-zz/6a7d94404aed0be0112bf9751876f36b to your computer and use it in GitHub Desktop.
n_masks = 100
n_channels = 100
# do not sample more masks than this
n_max_samples = 50
target_corr = 0.1
target_corr_eps = 1.0e-2
rate = target_corr
min_corr = target_corr - target_corr_eps
max_corr = target_corr + target_corr_eps
n_indices = int(n_channels * rate)
masks = np.zeros((n_masks, n_channels))
indices = np.random.choice(n_channels, n_indices, replace=False)
masks[0,indices] = 1.0
n_samples = 1
def _sample_mask():
indices = np.random.choice(n_channels, n_indices, replace=False)
mask = np.zeros((n_channels))
mask[indices] = 1.0
return mask
for i in range(1, n_masks):
# generating the next mask
prev_masks = masks[:n_samples]
for j in range(n_max_samples):
mask = _sample_mask()
mean_corr = np.mean(np.dot(prev_masks, mask) / n_indices)
if min_corr < mean_corr and mean_corr < max_corr:
break
# checking if it satisfies our constraints
masks[n_samples,:] = mask
n_samples += 1
corr = np.dot(masks, masks.T) / n_indices
np.mean(corr)
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment