35 lines
966 B
Python
35 lines
966 B
Python
import torch
|
|
import numpy as np
|
|
import networkx as nx
|
|
from dtaidistance import dtw
|
|
"""
|
|
Utils from: https://github.com/Oxfordblue7/GCFL
|
|
"""
|
|
|
|
|
|
def norm(w):
|
|
return torch.norm(torch.cat([v.flatten() for v in w.values()])).item()
|
|
|
|
|
|
def compute_pairwise_distances(seqs, standardize=False):
|
|
""" computes DTW distances for gcfl+"""
|
|
if standardize:
|
|
# standardize to only focus on the trends
|
|
seqs = np.array(seqs)
|
|
seqs = seqs / seqs.std(axis=1).reshape(-1, 1)
|
|
distances = dtw.distance_matrix(seqs)
|
|
else:
|
|
distances = dtw.distance_matrix(seqs)
|
|
return distances
|
|
|
|
|
|
def min_cut(similarity, cluster):
|
|
g = nx.Graph()
|
|
for i in range(len(similarity)):
|
|
for j in range(len(similarity)):
|
|
g.add_edge(i, j, weight=similarity[i][j])
|
|
cut, partition = nx.stoer_wagner(g)
|
|
c1 = np.array([cluster[x] for x in partition[0]])
|
|
c2 = np.array([cluster[x] for x in partition[1]])
|
|
return c1, c2
|