public void TestStablePoint()
{
var f = StableBloomFilter.NewDefaultStableBloomFilter(10000, 0.1);
for (int i = 0; i < 1000000; i++)
{
f.TestAndAdd(Encoding.ASCII.GetBytes(i.ToString()));
}
var zeros = 0;
for (uint i = 0; i < f.M; i++)
{
if (f.cells.Get(i) == 0)
{
zeros++;
}
}
var actual = Math.Round((double)((double)zeros / (double)f.M), 1, MidpointRounding.AwayFromZero);
var expected = Math.Round(f.StablePoint(), 1, MidpointRounding.AwayFromZero);
Assert.AreEqual(expected, actual);
// A classic Bloom filter is a special case of SBF where P is 0 and max is 1.
// It doesn't have a stable point.
var bf = StableBloomFilter.NewUnstableBloomFilter(1000, 0.1);
var stablePoint = bf.StablePoint();
if (stablePoint != 0)
{
Assert.Fail(string.Format("Expected stable point 0, got {0}", stablePoint));
}
}
public void Testinitialize()
{
n = 100000;
f = StableBloomFilter.NewUnstableBloomFilter(n, 0.1);
data = new byte[n][];
for (int i = 0; i < n; i++)
{
data[i] = Encoding.ASCII.GetBytes(i.ToString());
}
}
public void TestNewUnstableBloomFilter()
{
var f = StableBloomFilter.NewUnstableBloomFilter(100, 0.1);
var k = ProbabilisticDataStructures.Utils.OptimalK(0.1);
Assert.AreEqual(k, f.K());
Assert.AreEqual(100u, f.M);
Assert.AreEqual(0u, f.P());
Assert.AreEqual(1u, f.Max);
}
public void TestStableFalsePositiveRate()
{
var f = StableBloomFilter.NewDefaultStableBloomFilter(1000, 0.01);
var fps = Math.Round(f.FalsePositiveRate(), 2, MidpointRounding.AwayFromZero);
Assert.IsFalse(fps > 0.01);
// Classic Bloom filters have an unbound rate of false positives. Once they
// become full, every query returns a false positive.
var bf = StableBloomFilter.NewUnstableBloomFilter(1000, 0.01);
fps = bf.FalsePositiveRate();
Assert.AreEqual(1.0, fps);
}
