"""Hamming metric for segmentation."""
import numpy as np
from scipy.sparse import block_diag, triu
from ruptures.metrics.sanity_check import sanity_check
from ruptures.utils import pairwise
def membership_mat(bkps):
"""Return membership matrix for the given segmentation."""
n_samples = bkps[-1]
m_mat = np.zeros((n_samples, n_samples))
for start, end in pairwise([0] + bkps):
m_mat[start:end, start:end] = 1
return m_mat
def hamming(bkps1, bkps2):
"""Modified Hamming distance for partitions.
For all pair of points (x, y), x != y, the functions computes the
number of times the two partitions disagree.
The result is scaled to be within 0 and 1.
Args:
bkps1 (list): list of the last index of each regime.
bkps2 (list): list of the last index of each regime.
Returns:
float: Hamming distance.
"""
sanity_check(bkps1, bkps2)
n_samples = max(bkps1)
disagreement = abs(membership_mat(bkps1) - membership_mat(bkps2))
disagreement = triu(disagreement, k=1).sum() * 1.
disagreement /= n_samples * n_samples / 2 # scaling
return disagreement
