/// <summary>
/// A test case for PolyChromaticClosestPoint.FindPairByCentroids where clusters conform to a Gaussian distribution.
/// </summary>
/// <param name="nPoints">Number of points in each cluster.</param>
/// <param name="dimensions">Number of Dimensions in each point.</param>
/// <param name="numClusters">Number of clusters to create.</param>
public void FindPairByCentroidsTestCase(int nPoints, int dimensions, int numClusters)
{
var successes = 0;
var worstRatio = 1.0;
var color1 = "0";
var data = new GaussianClustering
{
ClusterCount = numClusters,
Dimensions = dimensions,
MaxCoordinate = 1000,
MinClusterSize = nPoints,
MaxClusterSize = nPoints
};
var clusters = data.MakeClusters();
PolyChromaticClosestPoint <string> pccp;
pccp = new PolyChromaticClosestPoint <string>(clusters);
for (var iColor2 = 1; iColor2 < numClusters; iColor2++)
{
var color2 = iColor2.ToString();
var exact = pccp.FindPairExhaustively(color1, color2);
var approximate = pccp.FindPairByCentroids(color1, color2);
var expectedDistance = exact.SquareDistance;
var actualDistance = approximate.SquareDistance;
if (actualDistance <= expectedDistance)
{
successes++;
}
else
{
worstRatio = Math.Max(worstRatio, actualDistance / (double)expectedDistance);
}
if (exact.SquareDistance >= approximate.SquareDistance)
{
Console.WriteLine("FindPairByCentroids CORRECT. Exact {0}. Approx {1}", exact, approximate);
}
else
{
Console.WriteLine("FindPairByCentroids INCORRECT. Exact {0}. Approx {1}. Too high by {2:N3}%",
exact, approximate, 100.0 * (approximate.SquareDistance / (double)exact.SquareDistance - 1.0));
}
}
Assert.AreEqual(numClusters - 1, successes,
string.Format("Did not succeed every time. Failed {0} of {1} times. Worst distance ratio is {2:N4}. {3} points of {4} dimensions.",
numClusters - successes - 1,
numClusters - 1,
worstRatio,
nPoints,
dimensions
)
);
}
public void AllColorPairsClosestClusterTest(int nPoints, int dimensions, int numClusters, int numCurvesToTry)
{
var rankHistogram = new int[numClusters + 1]; // We will skip the first element so as to have a one-based array.
var data = new GaussianClustering
{
ClusterCount = numClusters,
Dimensions = dimensions,
MaxCoordinate = 1000,
MinClusterSize = nPoints,
MaxClusterSize = nPoints
};
var worstDistanceRatio = 1.0;
var ratioSum = 0.0;
var ratioCount = 0;
var clusters = data.MakeClusters();
var bitsPerDimension = (1 + data.MaxCoordinate).SmallestPowerOfTwo();
var results = OptimalIndex
.Search(
clusters.Points().Select(up => HilbertPoint.CastOrConvert(up, bitsPerDimension, true)).ToList(),
5 /*outlier size */, 10 /* NoiseSkipBy */, 1 /* ReducedNoiseSkipBy */, numCurvesToTry
);
var pccp1 = new PolyChromaticClosestPoint <string>(clusters, results.Index);
var allColorPairs = pccp1.FindAllClustersApproximately();
foreach (var color1 in clusters.ClassLabels())
{
var exact = pccp1.FindClusterExhaustively(color1).Swap(color1);
var color1Pairs = allColorPairs
.Where(cp => cp.Color1.Equals(color1) || cp.Color2.Equals(color1))
.Select(cp => cp.Swap(color1))
.ToList();
var approximateColor2Distance = color1Pairs.First(cp => cp.Color2.Equals(exact.Color2)).SquareDistance;
var approximateRank = color1Pairs.Count(cp => cp.SquareDistance < approximateColor2Distance) + 1;
rankHistogram[approximateRank]++;
#pragma warning disable RECS0018 // Comparison of floating point numbers with equality operator
var ratio = exact.SquareDistance == 0.0 ? 0 : approximateColor2Distance / (double)exact.SquareDistance;
#pragma warning restore RECS0018 // Comparison of floating point numbers with equality operator
ratioSum += ratio;
ratioCount++;
worstDistanceRatio = Math.Max(worstDistanceRatio, ratio);
}
Debug.WriteLine(string.Format("Worst distance overage = {0:N3}%", (worstDistanceRatio - 1.0) * 100.0));
Debug.WriteLine(string.Format("Average distance overage = {0:N3}%", ((ratioSum / ratioCount) - 1.0) * 100.0));
for (var iRank = 1; iRank <= numClusters; iRank++)
{
if (rankHistogram[iRank] > 0 || iRank < 4)
{
Debug.WriteLine(string.Format("For {0} Clusters the closest cluster found was Ranked #{1}.", rankHistogram[iRank], iRank));
}
}
// Accept a win, place or show: the true closest cluster shows up as no worse than the 3rd ranked cluster according to the approximate measure.
Assert.IsTrue(rankHistogram[1] + rankHistogram[2] + rankHistogram[3] == numClusters,
string.Format("Found the closest cluster for {0} colors", rankHistogram[1])
);
}
public void ClosestOfFiftyClusters()
{
int hilbertTries = 1000;
var correctColorCount = 0;
var correctCrosscheckCount = 0;
var correctDistanceCount = 0;
var nPoints = 100;
var dimensions = 100;
var clusterCount = 50;
var data = new GaussianClustering
{
ClusterCount = clusterCount,
Dimensions = dimensions,
MaxCoordinate = 1000,
MinClusterSize = nPoints,
MaxClusterSize = nPoints
};
var closestExact = new PolyChromaticClosestPoint <string> .ClosestPair();
var closestApproximate = new PolyChromaticClosestPoint <string> .ClosestPair();
var bitsPerDimension = (1 + data.MaxCoordinate).SmallestPowerOfTwo();
var clusters = data.MakeClusters();
Assert.AreEqual(clusterCount, clusters.NumPartitions, "Test data are grouped into fewer clusters than requested.");
PolyChromaticClosestPoint <string> pccp;
if (hilbertTries <= 1)
{
pccp = new PolyChromaticClosestPoint <string>(clusters);
}
else
{
var reducedNoiseSkipBy = 1;
var results = OptimalIndex.Search(
clusters.Points().Select(up => HilbertPoint.CastOrConvert(up, bitsPerDimension, true)).ToList(),
5 /*outlier size */, 10 /* NoiseSkipBy */, reducedNoiseSkipBy, hilbertTries
);
pccp = new PolyChromaticClosestPoint <string>(clusters, results.Index);
}
foreach (var color in pccp.Clusters.ClassLabels())
{
var exact = pccp.FindClusterExhaustively(color);
var approximate = pccp.FindClusterApproximately(color);
var crosscheck = pccp.FindClusterIteratively(color);
if (exact.SquareDistance >= approximate.SquareDistance)
{
correctDistanceCount++;
}
if (exact.Color2.Equals(approximate.Color2))
{
correctColorCount++;
}
if (exact.Color2.Equals(crosscheck.Color2))
{
correctCrosscheckCount++;
}
if (exact.SquareDistance < closestExact.SquareDistance)
{
closestExact = exact;
}
if (approximate.SquareDistance < closestApproximate.SquareDistance)
{
closestApproximate = approximate;
}
var ratio = approximate.SquareDistance / (double)exact.SquareDistance;
Console.WriteLine(string.Format("Exact {0} vs Approx. {1} vs Cross {2}. Over by {3:N3}%", exact, approximate, crosscheck, (ratio - 1.0) * 100.0));
}
if (closestExact.SquareDistance >= closestApproximate.SquareDistance)
{
Console.WriteLine("DID FIND the closest pair of points overall. Exact {0}. Approx {1}", closestExact, closestApproximate);
}
else
{
Console.WriteLine("DID NOT FIND the closest pair of points overall. Exact {0}. Approx {1}", closestExact, closestApproximate);
}
Assert.IsTrue(correctColorCount == clusterCount && correctDistanceCount == clusterCount,
string.Format("Of {0} clusters, only {1} searches found the closest cluster and {2} found the shortest distance. Crosscheck = {3}",
clusterCount,
correctColorCount,
correctDistanceCount,
correctCrosscheckCount
)
);
}
public void ClosestClusterTest(int nPoints, int dimensions, int numClusters, int numCurvesToTry, int numCurvesToKeep)
{
var correctColorCount = 0;
var correctDistanceCount = 0;
var data = new GaussianClustering
{
ClusterCount = numClusters,
Dimensions = dimensions,
MaxCoordinate = 1000,
MinClusterSize = nPoints,
MaxClusterSize = nPoints
};
var closestExact = new PolyChromaticClosestPoint <string> .ClosestPair();
var closestApproximate = new PolyChromaticClosestPoint <string> .ClosestPair();
var clusters = data.MakeClusters();
var pccps = new List <PolyChromaticClosestPoint <string> >();
var bitsPerDimension = (1 + data.MaxCoordinate).SmallestPowerOfTwo();
var bestIndices = OptimalIndex.SearchMany(
clusters.Points().Select(up => HilbertPoint.CastOrConvert(up, bitsPerDimension, true)).ToList(),
numCurvesToKeep,
5 /*outlier size */, 10 /* NoiseSkipBy */, 1 /* ReducedNoiseSkipBy */, numCurvesToTry
);
//var pointLists = bestIndices.Select(result => result.Index.SortedPoints).ToList();
//foreach (var pList in pointLists)
// pccps.Add(new PolyChromaticClosestPoint<string>(clusters, pList));
var indices = bestIndices.Select(result => result.Index).ToList();
foreach (var index in indices)
{
pccps.Add(new PolyChromaticClosestPoint <string>(clusters, index));
}
var pccp1 = pccps[0];
foreach (var color in pccp1.Clusters.ClassLabels())
{
var exact = pccp1.FindClusterExhaustively(color);
var approximate = pccps.Select(pccp => pccp.FindClusterApproximately(color)).OrderBy(cp => cp).First();
if (exact.SquareDistance >= approximate.SquareDistance)
{
correctDistanceCount++;
}
if (exact.Color2.Equals(approximate.Color2))
{
correctColorCount++;
}
if (exact.SquareDistance < closestExact.SquareDistance)
{
closestExact = exact;
}
if (approximate.SquareDistance < closestApproximate.SquareDistance)
{
closestApproximate = approximate;
}
var ratio = approximate.SquareDistance / (double)exact.SquareDistance;
Console.WriteLine(string.Format("Exact {0} vs Approx. {1}. Over by {2:N3}%", exact, approximate, (ratio - 1.0) * 100.0));
}
if (closestExact.SquareDistance >= closestApproximate.SquareDistance)
{
Console.WriteLine("DID FIND the closest pair of points overall. Exact {0}. Approx {1}", closestExact, closestApproximate);
}
else
{
Console.WriteLine("DID NOT FIND the closest pair of points overall. Exact {0}. Approx {1}", closestExact, closestApproximate);
}
Assert.IsTrue(correctColorCount == numClusters && correctDistanceCount == numClusters,
string.Format("Of {0} clusters, only {1} searches found the closest cluster and {2} found the shortest distance.",
numClusters,
correctColorCount,
correctDistanceCount
)
);
}
/// <summary>
/// A test case for PolyChromaticClosestPoint.FindPairApproximately where clusters conform to a Gaussian distribution.
/// </summary>
/// <param name="nPoints">Number of points in each cluster.</param>
/// <param name="dimensions">Number of Dimensions in each point.</param>
/// <param name="numClusters">Number of clusters to create.</param>
/// <param name="hilbertsToTry">Number of randomly generated Hilbert curves to try.</param>
public void GaussianPolyChromaticPairTestCase(int nPoints, int dimensions, int numClusters, int hilbertsToTry = 1)
{
var successes = 0;
var worstRatio = 1.0;
var color1 = "0";
var data = new GaussianClustering
{
ClusterCount = numClusters,
Dimensions = dimensions,
MaxCoordinate = 1000,
MinClusterSize = nPoints,
MaxClusterSize = nPoints
};
var clusters = data.MakeClusters();
PolyChromaticClosestPoint <string> pccp;
if (hilbertsToTry <= 1)
{
pccp = new PolyChromaticClosestPoint <string>(clusters);
}
else
{
var bitsPerDimension = (1 + data.MaxCoordinate).SmallestPowerOfTwo();
var results = OptimalIndex.Search(
clusters.Points().Select(up => HilbertPoint.CastOrConvert(up, bitsPerDimension, true)).ToList(),
5 /*outlier size */, 10 /* NoiseSkipBy */, 1 /* ReducedNoiseSkipBy */, hilbertsToTry
);
pccp = new PolyChromaticClosestPoint <string>(clusters, results.Index);
}
for (var iColor2 = 1; iColor2 < numClusters; iColor2++)
{
var color2 = iColor2.ToString();
var exact = pccp.FindPairExhaustively(color1, color2);
var approximate = pccp.FindPairApproximately(color1, color2);
var expectedDistance = exact.SquareDistance;
var actualDistance = approximate.SquareDistance;
if (actualDistance <= expectedDistance)
{
successes++;
}
else
{
worstRatio = Math.Max(worstRatio, actualDistance / (double)expectedDistance);
}
if (exact.SquareDistance >= approximate.SquareDistance)
{
Console.WriteLine("FindPairApproximately CORRECT. Exact {0}. Approx {1}", exact, approximate);
}
else
{
Console.WriteLine("FindPairApproximately INCORRECT. Exact {0}. Approx {1}. Too high by {2:N3}%",
exact, approximate, 100.0 * (approximate.SquareDistance / (double)exact.SquareDistance - 1.0));
}
}
Assert.AreEqual(numClusters - 1, successes,
string.Format("Did not succeed every time. Failed {0} of {1} times. Worst distance ratio is {2:N4}. {3} points of {4} dimensions.",
numClusters - successes - 1,
numClusters - 1,
worstRatio,
nPoints,
dimensions
)
);
}
