private void binaryReport(Forms.RunningProcess.frmRunningProcessDialog rd) { KMeansClusterCollection gCol = (KMeansClusterCollection)clusterCollection; for (int i = 0; i < gCol.Count; i++) { KMeansCluster gClust = gCol[i]; double[] mns = gClust.Mean; rd.addMessage("\n\nCluster " + Labels[i] + ":\nMeans: " + String.Join(", ", (from double d in mns select d.ToString()).ToArray())); } }
private void kmeansReport(Forms.RunningProcess.frmRunningProcessDialog rd) { KMeansClusterCollection gCol = kmeans.Clusters; for (int i = 0; i < gCol.Count; i++) { KMeansCluster gClust = gCol[i]; double[] mns = gClust.Mean; double[,] cov = gClust.Covariance; double[,] corr = getCorr(cov); rd.addMessage("\n\nStratum " + Labels[i] + ":\nMeans: " + String.Join(", ", (from double d in mns select d.ToString()).ToArray()) + "\nCovariance:"); for (int j = 0; j < VariableFieldNames.Length; j++) { string[] covStrArr = new string[VariableFieldNames.Length]; for (int l = 0; l < covStrArr.Length; l++) { covStrArr[l] = cov[l, j].ToString(); } rd.addMessage("\n" + String.Join(",", covStrArr)); } rd.addMessage("\nCorr:"); for (int j = 0; j < VariableFieldNames.Length; j++) { string[] corrStrArr = new string[VariableFieldNames.Length]; for (int l = 0; l < corrStrArr.Length; l++) { corrStrArr[l] = corr[l, j].ToString(); } rd.addMessage("\n" + String.Join(",", corrStrArr)); } } }
private static void fillStrataReport(string modelPath, Forms.RunningProcess.frmRunningProcessDialog rp, double proportion, double alpha) { dataPrepStrata strata = new dataPrepStrata(); strata.buildModel(modelPath); List<string> lbl = strata.Labels; rp.addMessage("Samples by strata (Stratum; number of samples)"); rp.addMessage("-".PadRight(45, '-')); int[] samples = sampleSizeMaxStrata(modelPath, proportion, alpha); for (int i = 0; i < samples.Length; i++) { rp.addMessage("\t" + lbl[i] + "; " + samples[i].ToString()); } rp.addMessage("-".PadRight(45, '-')); rp.addMessage("Total number of samples = " + samples.Sum().ToString()); }
private static void fillPcaReport(string modelPath, Forms.RunningProcess.frmRunningProcessDialog rp, double proportion = 0.1, double alpha = 0.05) { dataPrepPrincipleComponents pca = new dataPrepPrincipleComponents(); pca.buildModel(modelPath); double[] meanVector = pca.MeanVector; double[] std = pca.StdVector; int numSamp = sampleSizeMaxMean(meanVector, std, proportion, alpha); rp.addMessage("\nTotal Number of Samples = " + numSamp.ToString()); }
private static void fillCovCorr(string modelPath, Forms.RunningProcess.frmRunningProcessDialog rp, double proportion = 0.1, double alpha = 0.05) { dataPrepVarCovCorr covCorr = new dataPrepVarCovCorr(); covCorr.buildModel(modelPath); double[,] covCorrArr = covCorr.CovarianceMatrix; double[] means = covCorr.MeanVector; string[] varFieldNames = covCorr.VariableFieldNames; int[] nAndIndex = sampleSizeMaxMean(covCorrArr,means, proportion, alpha); string vName = varFieldNames[nAndIndex[1]]; double mean = means[nAndIndex[1]]; double std = covCorr.StdVector[nAndIndex[1]]; rp.addMessage("\nTotal Number of Samples = " + nAndIndex[0].ToString() + "\n\nMax sample from variable " + vName + "\n\tMean = " + mean.ToString() + "\n\tSTD = " +std.ToString()); }
private static void fillCluserReport(string modelPath, Forms.RunningProcess.frmRunningProcessDialog rp, double proportion = 0.1, double alpha = 0.05) { dataPrepClusterKmean clus = new dataPrepClusterKmean(); clus.buildModel(modelPath); List<string> lbl = clus.Labels; rp.addMessage("Samples by class (Cluster; number of samples)"); rp.addMessage("-".PadRight(45, '-')); int[] samples = sampleSizeMaxCluster(modelPath, proportion, alpha); for (int i = 0; i < samples.Length; i++) { rp.addMessage("\t"+lbl[i] + "; " + samples[i].ToString()); } rp.addMessage("-".PadRight(45, '-')); rp.addMessage("Total number of samples = " + samples.Sum().ToString()); }
private static void fillAaReport(string modelPath,Forms.RunningProcess.frmRunningProcessDialog rp,double proportion=0.1, double alpha=0.05) { rp.addMessage("\nTotal Number of Samples = " + sampleSizeKappa(modelPath, proportion,alpha).ToString()); }