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@frnsys frnsys/
Last active Aug 29, 2015

What would you like to do?
simple HAC implementation
import operator
from itertools import combinations
from functools import reduce
import numpy as np
def hac(vecs, sim_func, threshold):
Hierarchical Agglomerative Clustering.
def sim(pair):
a = np.array([vecs[i] for i in pair[0]])
b = np.array([vecs[i] for i in pair[1]])
return sim_func(a, b)
size = len(vecs)
labels = [0 for i in range(size)]
# Each item starts in its own cluster.
clusters = {(i,) for i in range(size)}
# Initialize the label.
j = len(clusters) + 1
while True:
pairs = combinations(clusters, 2)
# Calculate the similarity for each pair.
# (pair, similarity)
scores = [(p, sim(p)) for p in pairs]
# Get the highest similarity to determine which pair is merged.
mxm = max(scores, key=operator.itemgetter(1))
# Stop if the highest similarity is below the threshold.
if mxm[1] < threshold:
# Remove the to-be-merged pair from the set of clusters,
# then merge (flatten) them.
pair = mxm[0]
clusters -= set(pair)
flat_pair = reduce(lambda x,y: x + y, pair)
# Update the labels for the pairs' members.
for i in flat_pair:
labels[i] = j
# Add the new cluster to the clusters.
# If one cluster is left, we can't continue merging.
if len(clusters) == 1:
# Increment the label.
j += 1
return labels
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