public static double[] clusterProportions(string clusterModelPath)
{
dataPrepClusterKmean cls = new dataPrepClusterKmean();
cls.buildModel(clusterModelPath);
int nClusters = ((Accord.MachineLearning.KMeans)cls.Model).Clusters.Count;
double[] prop = new double[nClusters];
for (int i = 0; i < nClusters; i++)
{
Accord.MachineLearning.KMeansCluster k = ((Accord.MachineLearning.KMeans)cls.Model).Clusters[i];
prop[i] = k.Proportion;
}
return(prop);
}
public static int[] sampleSizeMaxCluster(string clusterModelPath, double proportionOfMean = 0.1, double alpha = 0.05)
{
dataPrepClusterKmean cls = new dataPrepClusterKmean();
cls.buildModel(clusterModelPath);
int nClusters = ((Accord.MachineLearning.KMeans)cls.Model).Clusters.Count;
int[] maxN = new int[nClusters];
for (int i = 0; i < nClusters; i++)
{
Accord.MachineLearning.KMeansCluster k = ((Accord.MachineLearning.KMeans)cls.Model).Clusters[i];
int mx = sampleSizeMaxMean(k.Covariance, k.Mean, proportionOfMean, alpha)[0];
maxN[i] = mx;
}
return(maxN);
}
public static int[] sampleSizeMaxStrata(string strataModelPath, double proportionOfMean = 0.1, double alpha = 0.05)
{
dataPrepStrata strata = new dataPrepStrata();
strata.buildModel(strataModelPath);
int nStrata = strata.Labels.Count;
int[] maxN = new int[nStrata];
for (int i = 0; i < nStrata; i++)
{
Accord.MachineLearning.KMeansCluster k = ((Accord.MachineLearning.KMeans)strata.Model).Clusters[i];
int mx = sampleSizeMaxMean(k.Covariance, k.Mean, proportionOfMean, alpha)[0];
maxN[i] = mx;
}
return(maxN);
}
