public void CalculateHammingDistanceCorrect()
{
byte[] first = new byte[] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 };
byte[] second = new byte[] { 1, 2, 3, 8, 5, 9, 7, 8, 11, 13 };
var result = similarityUtility.CalculateHammingDistance(first, second);
Assert.AreEqual(4, result);
}
public void ShouldBeAbleToControlReturnedCandidatesWithThresholdParameter()
{
int l = 25, k = 4, width = 128, height = 72;
var hashingConfig = new DefaultHashingConfig
{
Width = width, Height = height, NumberOfLSHTables = l, NumberOfMinHashesPerTable = k
};
var lsh = LocalitySensitiveHashingAlgorithm.Instance;
double[] howSimilarly = { 0.3, 0.5, 0.6, 0.7, 0.75, 0.8, 0.85, 0.9 };
int[] expectedThresholds = { 0, 0, 0, 2, 3, 5, 7, 11 };
const int simulations = 10000;
Parallel.For(0, howSimilarly.Length, r =>
{
var random = new Random((r + 1) * 100);
double howSimilar = howSimilarly[r];
int topWavelets = (int)(0.035 * width * height);
var agreeOn = new List <int>();
var hammingDistances = new List <int>();
for (int i = 0; i < simulations; ++i)
{
var fingerprints = TestUtilities.GenerateSimilarFingerprints(random, howSimilar, topWavelets, width * height * 2);
int hammingDistance = similarity.CalculateHammingDistance(fingerprints.Item1.ToBools(), fingerprints.Item2.ToBools());
hammingDistances.Add(hammingDistance);
var hashed1 = lsh.HashImage(new Fingerprint(fingerprints.Item1, 0, 0, Array.Empty <byte>()), hashingConfig);
var hashed2 = lsh.HashImage(new Fingerprint(fingerprints.Item2, 0, 0, Array.Empty <byte>()), hashingConfig);
int agreeCount = AgreeOn(hashed1.HashBins, hashed2.HashBins);
agreeOn.Add(agreeCount);
}
int requested = (int)((1 - howSimilar) * topWavelets * 2);
Assert.AreEqual(requested, hammingDistances.Average(), 1);
Assert.AreEqual(expectedThresholds[r], Math.Floor(agreeOn.Average()));
Console.WriteLine($"Similarity: {howSimilar: 0.00}, Avg. Table Matches {agreeOn.Average(): 0.000}");
});
}
