public void Test_IncrementalMeanAverage()
{
// Test samples.
double[][] data = new double[10][];
// Each sample belongs to some cluster.
int[] clustering = new int[data.Length];
for (int i = 0; i < 10; i++)
{
data[i] = new double[] { i };
clustering[i] = 0; // We have a single cluster. Every sample belongs to that cluster.
}
double[][] means = new double[1][];
means[0] = new double[] { 0 };
KMeansAlgorithm.UpdateMeans(data, clustering, means);
// Mean of 1,2,3,4,5,6,7,8,9, 10 is 4.5
Assert.True(means[0][0] == 4.5);
data = new double[5][];
// Each sample belongs to some cluster.
clustering = new int[data.Length];
for (int i = 0; i < 5; i++)
{
data[i] = new double[] { i };
clustering[i] = 0; // We have a single cluster. Every sample belongs to that cluster.
}
// Mean of 1,2,3,4,5 is 2
KMeansAlgorithm.UpdateMeans(data, clustering, means, 0, new double[] { 0 });
Assert.True(means[0][0] == 2);
data = new double[5][];
// Each sample belongs to some cluster.
clustering = new int[data.Length];
for (int i = 0; i < 5; i++)
{
data[i] = new double[] { i + 5 };
clustering[i] = 0; // We have a single cluster. Every sample belongs to that cluster.
}
KMeansAlgorithm.UpdateMeans(data, clustering, means, 5, new double[] { 2 });
// M1 = mean of 1,2,3,4,5
// M2 = mean of 6,7,8,9,10
// Mean of M1 and M2 together is 4.5
// (1/q1+q2)[q1*M1+q2*M2]
// where q1 is number of elements inside of M1 and q2 number of elements inside of M2
Assert.True(means[0][0] == 4.5);
}
public void Test_IncrementalMeanAverageSet()
{
for (int numOfSamples = 100; numOfSamples < 150000; numOfSamples += 15000)
{
// Test samples.
double[][] data = new double[numOfSamples][];
// Each sample belongs to some cluster.
int[] clustering = new int[data.Length];
for (int i = 0; i < numOfSamples; i++)
{
data[i] = new double[] { i };
clustering[i] = 0; // We have a single cluster. Every sample belongs to that cluster.
}
double[][] means = new double[1][];
means[0] = new double[] { 0 };
KMeansAlgorithm.UpdateMeans(data, clustering, means);
// Mean of numOfSamples
var mean = means[0][0];
data = new double[numOfSamples / 2][];
// Each sample belongs to some cluster.
clustering = new int[data.Length];
for (int i = 0; i < numOfSamples / 2; i++)
{
data[i] = new double[] { i };
clustering[i] = 0; // We have a single cluster. Every sample belongs to that cluster.
}
// Calculate mean of numOfSamples/2
KMeansAlgorithm.UpdateMeans(data, clustering, means, 0, new double[] { 0 });
// Mean of numOfSamples/2
var mean1 = means[0][0];
data = new double[numOfSamples / 2][];
// Each sample belongs to some cluster.
clustering = new int[data.Length];
for (int i = 0; i < numOfSamples / 2; i++)
{
data[i] = new double[] { i + (numOfSamples / 2) };
clustering[i] = 0; // We have a single cluster. Every sample belongs to that cluster.
}
KMeansAlgorithm.UpdateMeans(data, clustering, means, numOfSamples / 2, new double[] { mean1 });
// Mean for numbers from numOfSamples/2 to numOfSamples
var mean2 = means[0][0];
// M1 = mean of numOfSamples/2 (minibatch 1)
// M2 = mean for numbers from numOfSamples/2 to numOfSamples (minibatch 2)
// mean is batch for numbers from 1 to numOfSamples
// (1/q1+q2)[q1*M1+q2*M2]
// where q1 is number of elements inside of M1 and q2 number of elements inside of M2
Assert.True(Math.Round(mean2, 2) == Math.Round(mean, 2));
}
}
