Skip to content

change convergence to be before point reassignment, change point reas… #29

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Apr 13, 2018
+15 −5
Merged

change convergence to be before point reassignment, change point reas… #29

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
20 changes: 15 additions & 5 deletions TICC_solver.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -186,21 +186,30 @@ def solve(window_size=10, number_of_clusters=5, lambda_parameter=11e-2,
##Update cluster points - using NEW smoothening
clustered_points = updateClusters(LLE_all_points_clusters,switch_penalty = switch_penalty)

# recalculate lengths
new_train_clusters = collections.defaultdict(list) # {cluster: [point indices]}
for point, cluster in enumerate(clustered_points):
new_train_clusters[cluster].append(point)

len_new_train_clusters = {k: len(new_train_clusters[k]) for k in range(num_clusters)}

before_empty_cluster_assign = clustered_points.copy()

if iters != 0:
cluster_norms = [(np.linalg.norm(old_computed_covariance[num_clusters,i]), i) for i in range(num_clusters)]
norms_sorted = sorted(cluster_norms,reverse = True)
# clusters that are not 0 as sorted by norm
valid_clusters = [cp[1] for cp in norms_sorted if len_train_clusters[cp[1]] != 0]
valid_clusters = [cp[1] for cp in norms_sorted if len_new_train_clusters[cp[1]] != 0]

# Add a point to the empty clusters
# assuming more non empty clusters than empty ones
counter = 0
for cluster in range(num_clusters):
if len_train_clusters[cluster] == 0:
if len_new_train_clusters[cluster] == 0:
cluster_selected = valid_clusters[counter] # a cluster that is not len 0
counter = (counter+1) % len(valid_clusters)
print("cluster that is zero is:", cluster, "selected cluster instead is:", cluster_selected)
start_point = np.random.choice(train_clusters[cluster_selected]) # random point number from that cluster
start_point = np.random.choice(new_train_clusters[cluster_selected]) # random point number from that cluster
for i in range(0, cluster_reassignment):
# put cluster_reassignment points from point_num in this cluster
point_to_move = start_point + i
Expand Down Expand Up @@ -260,10 +269,11 @@ def solve(window_size=10, number_of_clusters=5, lambda_parameter=11e-2,

print("\n\n\n")

if np.array_equal(old_clustered_points,clustered_points):
# check points without reassignment
if np.array_equal(old_clustered_points,before_empty_cluster_assign):
print("\n\n\n\nCONVERGED!!! BREAKING EARLY!!!")
break
old_clustered_points = clustered_points
old_clustered_points = before_empty_cluster_assign
# end of training

if pool is not None:
Expand Down