From 24bc61829c370832e355884ce67a2898f4b44d76 Mon Sep 17 00:00:00 2001
From: Vedrana KRIVOKUCA <vkrivokuca@italix32.idiap.ch>
Date: Wed, 18 Oct 2017 18:15:51 +0200
Subject: [PATCH] Make HammingDistance class plus add test
---
bob/bio/vein/algorithm/HammingDistance.py | 28 ++++++++++++++++++-----
bob/bio/vein/tests/test.py | 23 +++++++++++++++++++
2 files changed, 45 insertions(+), 6 deletions(-)
diff --git a/bob/bio/vein/algorithm/HammingDistance.py b/bob/bio/vein/algorithm/HammingDistance.py
index 6497cb8..3ce7729 100644
--- a/bob/bio/vein/algorithm/HammingDistance.py
+++ b/bob/bio/vein/algorithm/HammingDistance.py
@@ -1,8 +1,24 @@
-# Calculates the Hamming distance (proportion of mismatching corresponding bits) between two binary vectors
+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
-import bob.bio.base
+
+from bob.bio.base.algorithm import Distance
import scipy.spatial.distance
-algorithm = bob.bio.base.algorithm.Distance(
- distance_function = scipy.spatial.distance.hamming,
- is_distance_function = False # setting this to False ensures that Hamming distances are returned as positive values rather than negative
-)
\ No newline at end of file
+
+
+class HammingDistance (Distance):
+ """Finger vein matching: Hamming Distance between binary fingervein feature vectors
+ """
+
+ def __init__(
+ self,
+ distance_function = scipy.spatial.distance.hamming,
+ is_distance_function = False # setting this to False ensures that Hamming distances are returned as positive values rather than negative
+ ):
+
+ # Call base class constructor
+ Distance.__init__(
+ self,
+ distance_function = distance_function,
+ is_distance_function = is_distance_function
+ )
\ No newline at end of file
diff --git a/bob/bio/vein/tests/test.py b/bob/bio/vein/tests/test.py
index 8a594b2..fb4c4e1 100644
--- a/bob/bio/vein/tests/test.py
+++ b/bob/bio/vein/tests/test.py
@@ -685,3 +685,26 @@ def test_correlation():
total = time.clock() - start
print('scipy+correlate2d, %d iterations - %.2e per iteration' % (N, total/N))
'''
+
+
+def test_hamming_distance():
+
+ from ..algorithm.HammingDistance import HammingDistance
+ HD = HammingDistance()
+
+ # Tests on simple binary arrays:
+ # 1.) Maximum HD (1.0):
+ model_1 = numpy.array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0])
+ probe_1 = numpy.array([[1, 0, 0, 0, 1, 0], [0, 1, 1, 1, 0, 1]])
+ score_max = HD.score(model_1, probe_1)
+ assert score_max == 1.0
+ # 2.) Minimum HD (0.0):
+ model_2 = numpy.array([0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1])
+ probe_2 = numpy.array([[0, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 1]])
+ score_min = HD.score(model_2, probe_2)
+ assert score_min == 0.0
+ # 3.) HD of exactly half (0.5)
+ model_3 = numpy.array([0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0])
+ probe_3 = numpy.array([[0, 1, 1, 1, 0, 1], [0, 1, 1, 1, 0, 1]])
+ score_half = HD.score(model_3, probe_3)
+ assert score_half == 0.5
\ No newline at end of file
--
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