public void MaxWeightHasherEvenDistributionTest()
{
Pseudorandom pseudo = new Pseudorandom();
int trials = 10000000;
MaxWeightHashing<string> hasher = CreateMaxWeightHasher();
double expectedFraction = 1d / ((double) _nodeNames.Length);
Dictionary<string, Int64> counts = new Dictionary<string, Int64>();
foreach (string number in _nodeNames)
counts[number] = 0;
for (int i = 0; i < trials; i++)
{
string rand = pseudo.GetString(13);
string word = hasher.FindMemberResponsible(rand);
counts[word]++;
}
double greatestAdjustedError = 0;
foreach (string word in _nodeNames)
{
double freq = ((double)counts[word]) / (double)trials;
double error = freq - expectedFraction;
double adjustedError = error / expectedFraction;
if (adjustedError > greatestAdjustedError)
greatestAdjustedError = adjustedError;
Assert.True(Math.Abs(adjustedError) < 0.005);
}
Console.Out.WriteLine("Highest adjusted error: {0}", greatestAdjustedError);
}
public void UniversalHashTestBias()
{
Pseudorandom pseudo = new Pseudorandom();
UniversalHashFunction f = new UniversalHashFunction("Louis Tully as played by Rick Moranis!");
ulong trials = 100000000;
ulong[] bitCounts = new ulong[64];
for (ulong trial = 0; trial < trials; trial++)
{
string randomString = pseudo.GetString(8);
UInt64 supposedlyUnbiasedBits = f.Hash(randomString, UniversalHashFunction.MaximumNumberOfResultBitsAllowing32BiasedBits);
for (int bit=0; bit < bitCounts.Length; bit++)
{
if ((supposedlyUnbiasedBits & (0x8000000000000000ul >> bit)) != 0ul)
bitCounts[bit]++;
}
}
double[] biases = bitCounts.Select(count => ( (0.5d - (((double)count) / ((double)trials)))) / 0.5d ).ToArray();
/// The first 32 bits should be unbiased
for (int bit = 0; bit < 32; bit++)
{
double bias = biases[bit];
double biasAbs = Math.Abs(bias);
Assert.True(biasAbs < 0.0005d);
}
}
public void HashRingEvenDistributionTest()
{
Pseudorandom pseudo = new Pseudorandom();
int trials = 10000000;
ConsistentHashRing<string> ring = CreateConsistentHashRingForTenValues();
Dictionary<string,double> ringCoverage = ring.FractionalCoverage;
double expectedFraction = 1d / 10d;
foreach (string word in _oneToTenAsWords)
{
double share = ringCoverage[word];
double bias = (expectedFraction - share) / expectedFraction;
double absBias = Math.Abs(bias);
Assert.True(bias < .15d);
Assert.True(bias < .15d);
}
Dictionary<string, Int64> counts = new Dictionary<string, Int64>();
Dictionary<string, double> frequencies = new Dictionary<string, double>();
foreach (string number in _oneToTenAsWords)
counts[number] = 0;
for (int i = 0; i < trials; i++)
{
string rand = pseudo.GetString(13);
string word = ring.FindMemberResponsible(rand);
counts[word]++;
}
foreach (string word in _oneToTenAsWords)
{
double freq = frequencies[word] = ((double)counts[word]) / (double)trials;
double error = freq - ringCoverage[word];
Assert.True(Math.Abs(error) < 0.01);
}
}
