steffen/self-replicating-neural-networks
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

MetaNetworks Debugged

Browse Source
This commit is contained in:
Steffen Illium
2022-01-31 10:35:11 +01:00
parent 49c0d8a621
commit 246d825bb4
8 changed files with 169 additions and 109 deletions
Download Patch File Download Diff File Expand all files Collapse all files

9
journal_basins.py
View File

@@ -26,11 +26,14 @@ def l1(tup):
def mean_invariate_manhattan_distance(x, y):
# One of these one-liners that might be smart or really dumb. Goal is to find pairwise
# distances of ascending values, ie. sum (abs(min1_X-min1_Y), abs(min2_X-min2Y) ...) / mean.
# One of these one-liners that might be smart or really dumb. Goal is to find pairwise
# distances of ascending values, ie. sum (abs(min1_X-min1_Y), abs(min2_X-min2Y) ...) / mean.
# Idea was to find weight sets that have same values but just in different positions, that would
# make this distance 0.
return np.mean(list(map(l1, zip(sorted(x.numpy()), sorted(y.numpy())))))
try:
return np.mean(list(map(l1, zip(sorted(x.detach().numpy()), sorted(y.detach().numpy())))))
except AttributeError:
return np.mean(list(map(l1, zip(sorted(x.numpy()), sorted(y.numpy())))))
def distance_matrix(nets, distance="MIM", print_it=True):