public List <double> testMLPUsingWeka(string[] attributeArray, string[] classNames, double[] dataValues, string classHeader, string defaultclass, string modelName, int hiddelLayers = 7, double learningRate = 0.03, double momentum = 0.4, int decimalPlaces = 2, int trainingTime = 1000) { java.util.ArrayList classLabel = new java.util.ArrayList(); foreach (string className in classNames) { classLabel.Add(className); } weka.core.Attribute classHeaderName = new weka.core.Attribute(classHeader, classLabel); java.util.ArrayList attributeList = new java.util.ArrayList(); foreach (string attribute in attributeArray) { weka.core.Attribute newAttribute = new weka.core.Attribute(attribute); attributeList.Add(newAttribute); } attributeList.add(classHeaderName); weka.core.Instances data = new weka.core.Instances("TestInstances", attributeList, 0); data.setClassIndex(data.numAttributes() - 1); // Set instance's values for the attributes weka.core.Instance inst_co = new DenseInstance(data.numAttributes()); for (int i = 0; i < data.numAttributes() - 1; i++) { inst_co.setValue(i, dataValues.ElementAt(i)); } inst_co.setValue(classHeaderName, defaultclass); data.add(inst_co); java.io.File path = new java.io.File("/models/"); weka.classifiers.functions.MultilayerPerceptron clRead = loadModel(modelName, path); clRead.setHiddenLayers(hiddelLayers.ToString()); clRead.setLearningRate(learningRate); clRead.setMomentum(momentum); clRead.setNumDecimalPlaces(decimalPlaces); clRead.setTrainingTime(trainingTime); weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize(); myRandom.setInputFormat(data); data = weka.filters.Filter.useFilter(data, myRandom); double classValue = clRead.classifyInstance(data.get(0)); double[] predictionDistribution = clRead.distributionForInstance(data.get(0)); List <double> predictionDistributions = new List <double>(); for (int predictionDistributionIndex = 0; predictionDistributionIndex < predictionDistribution.Count(); predictionDistributionIndex++) { string classValueString1 = classLabel.get(predictionDistributionIndex).ToString(); double prob = predictionDistribution[predictionDistributionIndex] * 100; predictionDistributions.Add(prob); } List <double> prediction = new List <double>(); prediction.Add(classValue); prediction.AddRange(predictionDistributions); return(prediction); }
public string testHybridEmotionUsingWeka(string[] attributeArray, string[] classNames, double[] dataValues, string classHeader, string defaultclass, string modelName) { java.util.ArrayList classLabel = new java.util.ArrayList(); foreach (string className in classNames) { classLabel.Add(className); } weka.core.Attribute classHeaderName = new weka.core.Attribute(classHeader, classLabel); java.util.ArrayList attributeList = new java.util.ArrayList(); foreach (string attribute in attributeArray) { weka.core.Attribute newAttribute = new weka.core.Attribute(attribute); attributeList.Add(newAttribute); } attributeList.add(classHeaderName); weka.core.Instances data = new weka.core.Instances("TestInstances", attributeList, 0); data.setClassIndex(data.numAttributes() - 1); // Set instance's values for the attributes weka.core.Instance inst_co = new DenseInstance(data.numAttributes()); for (int i = 0; i < data.numAttributes() - 1; i++) { inst_co.setValue(i, dataValues.ElementAt(i)); } inst_co.setValue(classHeaderName, defaultclass); data.add(inst_co); java.io.File path = new java.io.File("/models/"); weka.classifiers.meta.Bagging clRead = loadBaggingModel(modelName, path); weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize(); myRandom.setInputFormat(data); data = weka.filters.Filter.useFilter(data, myRandom); double classValue = clRead.classifyInstance(data.get(0)); string classValueString = classLabel.get(Int32.Parse(classValue.ToString())).ToString(); return(classValueString); }
/// <summary> /// Build the learning model for classification /// </summary> /// <param name="InstancesList">list of instances </param> /// <param name="NumberofClusters">Number of Clusters</param> /// <param name="TextBoxForFeedback">Text box for the results (can be NULL)</param> /// <param name="PanelForVisualFeedback">Panel to display visual results if avalaible (can be NULL)</param> public Classifier PerformTraining(FormForClassificationInfo WindowForClassificationParam, Instances InstancesList, /*int NumberofClusters,*/ RichTextBox TextBoxForFeedback, Panel PanelForVisualFeedback, out weka.classifiers.Evaluation ModelEvaluation, bool IsCellular) { // weka.classifiers.Evaluation ModelEvaluation = null; // FormForClassificationInfo WindowForClassificationParam = new FormForClassificationInfo(GlobalInfo); ModelEvaluation = null; // if (WindowForClassificationParam.ShowDialog() != System.Windows.Forms.DialogResult.OK) return null; // weka.classifiers.Evaluation ModelEvaluation = new Evaluation( cParamAlgo ClassifAlgoParams = WindowForClassificationParam.GetSelectedAlgoAndParameters(); if (ClassifAlgoParams == null) return null; //this.Cursor = Cursors.WaitCursor; // cParamAlgo ClassificationAlgo = WindowForClassificationParam.GetSelectedAlgoAndParameters(); cListValuesParam Parameters = ClassifAlgoParams.GetListValuesParam(); //Classifier this.CurrentClassifier = null; // -------------------------- Classification ------------------------------- // create the instances // InstancesList = this.ListInstances; this.attValsWithoutClasses = new FastVector(); if (IsCellular) for (int i = 0; i < cGlobalInfo.ListCellularPhenotypes.Count; i++) this.attValsWithoutClasses.addElement(cGlobalInfo.ListCellularPhenotypes[i].Name); else for (int i = 0; i < cGlobalInfo.ListWellClasses.Count; i++) this.attValsWithoutClasses.addElement(cGlobalInfo.ListWellClasses[i].Name); InstancesList.insertAttributeAt(new weka.core.Attribute("Class", this.attValsWithoutClasses), InstancesList.numAttributes()); //int A = Classes.Count; for (int i = 0; i < Classes.Count; i++) InstancesList.get(i).setValue(InstancesList.numAttributes() - 1, Classes[i]); InstancesList.setClassIndex(InstancesList.numAttributes() - 1); weka.core.Instances train = new weka.core.Instances(InstancesList, 0, InstancesList.numInstances()); if (PanelForVisualFeedback != null) PanelForVisualFeedback.Controls.Clear(); #region List classifiers #region J48 if (ClassifAlgoParams.Name == "J48") { this.CurrentClassifier = new weka.classifiers.trees.J48(); ((J48)this.CurrentClassifier).setMinNumObj((int)Parameters.ListDoubleValues.Get("numericUpDownMinInstLeaf").Value); ((J48)this.CurrentClassifier).setConfidenceFactor((float)Parameters.ListDoubleValues.Get("numericUpDownConfFactor").Value); ((J48)this.CurrentClassifier).setNumFolds((int)Parameters.ListDoubleValues.Get("numericUpDownNumFolds").Value); ((J48)this.CurrentClassifier).setUnpruned((bool)Parameters.ListCheckValues.Get("checkBoxUnPruned").Value); ((J48)this.CurrentClassifier).setUseLaplace((bool)Parameters.ListCheckValues.Get("checkBoxLaplacianSmoothing").Value); ((J48)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value); ((J48)this.CurrentClassifier).setSubtreeRaising((bool)Parameters.ListCheckValues.Get("checkBoxSubTreeRaising").Value); // CurrentClassif.SetJ48Tree((J48)this.CurrentClassifier, Classes.Length); this.CurrentClassifier.buildClassifier(train); // display results training // display tree if (PanelForVisualFeedback != null) { GViewer GraphView = DisplayTree(GlobalInfo, ((J48)this.CurrentClassifier), IsCellular).gViewerForTreeClassif; GraphView.Size = new System.Drawing.Size(PanelForVisualFeedback.Width, PanelForVisualFeedback.Height); GraphView.Anchor = (AnchorStyles.Bottom | AnchorStyles.Top | AnchorStyles.Left | AnchorStyles.Right); PanelForVisualFeedback.Controls.Clear(); PanelForVisualFeedback.Controls.Add(GraphView); } } #endregion #region Random Tree else if (ClassifAlgoParams.Name == "RandomTree") { this.CurrentClassifier = new weka.classifiers.trees.RandomTree(); if ((bool)Parameters.ListCheckValues.Get("checkBoxMaxDepthUnlimited").Value) ((RandomTree)this.CurrentClassifier).setMaxDepth(0); else ((RandomTree)this.CurrentClassifier).setMaxDepth((int)Parameters.ListDoubleValues.Get("numericUpDownMaxDepth").Value); ((RandomTree)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value); ((RandomTree)this.CurrentClassifier).setMinNum((double)Parameters.ListDoubleValues.Get("numericUpDownMinWeight").Value); if ((bool)Parameters.ListCheckValues.Get("checkBoxIsBackfitting").Value) { ((RandomTree)this.CurrentClassifier).setNumFolds((int)Parameters.ListDoubleValues.Get("numericUpDownBackFittingFolds").Value); } else { ((RandomTree)this.CurrentClassifier).setNumFolds(0); } this.CurrentClassifier.buildClassifier(train); //string StringForTree = ((RandomTree)this.CurrentClassifier).graph().Remove(0, ((RandomTree)this.CurrentClassifier).graph().IndexOf("{") + 2); //Microsoft.Msagl.GraphViewerGdi.GViewer GraphView = new GViewer(); //GraphView.Graph = GlobalInfo.WindowHCSAnalyzer.ComputeAndDisplayGraph(StringForTree);//.Remove(StringForTree.Length - 3, 3)); //GraphView.Size = new System.Drawing.Size(panelForGraphicalResults.Width, panelForGraphicalResults.Height); //GraphView.Anchor = (AnchorStyles.Bottom | AnchorStyles.Top | AnchorStyles.Left | AnchorStyles.Right); //this.panelForGraphicalResults.Controls.Clear(); //this.panelForGraphicalResults.Controls.Add(GraphView); } #endregion #region Random Forest else if (ClassifAlgoParams.Name == "RandomForest") { this.CurrentClassifier = new weka.classifiers.trees.RandomForest(); if ((bool)Parameters.ListCheckValues.Get("checkBoxMaxDepthUnlimited").Value) ((RandomForest)this.CurrentClassifier).setMaxDepth(0); else ((RandomForest)this.CurrentClassifier).setMaxDepth((int)Parameters.ListDoubleValues.Get("numericUpDownMaxDepth").Value); ((RandomForest)this.CurrentClassifier).setNumTrees((int)Parameters.ListDoubleValues.Get("numericUpDownNumTrees").Value); ((RandomForest)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value); this.CurrentClassifier.buildClassifier(train); } #endregion #region KStar else if (ClassifAlgoParams.Name == "KStar") { this.CurrentClassifier = new weka.classifiers.lazy.KStar(); ((KStar)this.CurrentClassifier).setGlobalBlend((int)Parameters.ListDoubleValues.Get("numericUpDownGlobalBlend").Value); ((KStar)this.CurrentClassifier).setEntropicAutoBlend((bool)Parameters.ListCheckValues.Get("checkBoxBlendAuto").Value); this.CurrentClassifier.buildClassifier(train); } #endregion #region SVM else if (ClassifAlgoParams.Name == "SVM") { this.CurrentClassifier = new weka.classifiers.functions.SMO(); ((SMO)this.CurrentClassifier).setC((double)Parameters.ListDoubleValues.Get("numericUpDownC").Value); ((SMO)this.CurrentClassifier).setKernel(WindowForClassificationParam.GeneratedKernel); ((SMO)this.CurrentClassifier).setRandomSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value); this.CurrentClassifier.buildClassifier(train); } #endregion #region KNN else if (ClassifAlgoParams.Name == "KNN") { this.CurrentClassifier = new weka.classifiers.lazy.IBk(); string OptionDistance = " -K " + (int)Parameters.ListDoubleValues.Get("numericUpDownKNN").Value + " -W 0 "; string WeightType = (string)Parameters.ListTextValues.Get("comboBoxDistanceWeight").Value; switch (WeightType) { case "No Weighting": OptionDistance += ""; break; case "1/Distance": OptionDistance += "-I"; break; case "1-Distance": OptionDistance += "-F"; break; default: break; } OptionDistance += " -A \"weka.core.neighboursearch.LinearNNSearch -A \\\"weka.core."; string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value; // OptionDistance += " -A \"weka.core."; switch (DistanceType) { case "Euclidean": OptionDistance += "EuclideanDistance"; break; case "Manhattan": OptionDistance += "ManhattanDistance"; break; case "Chebyshev": OptionDistance += "ChebyshevDistance"; break; default: break; } if (!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value) OptionDistance += " -D"; OptionDistance += " -R "; OptionDistance += "first-last\\\"\""; ((IBk)this.CurrentClassifier).setOptions(weka.core.Utils.splitOptions(OptionDistance)); //((IBk)this.CurrentClassifier).setKNN((int)Parameters.ListDoubleValues.Get("numericUpDownKNN").Value); this.CurrentClassifier.buildClassifier(train); } #endregion #region Multilayer Perceptron else if (ClassifAlgoParams.Name == "Perceptron") { this.CurrentClassifier = new weka.classifiers.functions.MultilayerPerceptron(); ((MultilayerPerceptron)this.CurrentClassifier).setMomentum((double)Parameters.ListDoubleValues.Get("numericUpDownMomentum").Value); ((MultilayerPerceptron)this.CurrentClassifier).setLearningRate((double)Parameters.ListDoubleValues.Get("numericUpDownLearningRate").Value); ((MultilayerPerceptron)this.CurrentClassifier).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeed").Value); ((MultilayerPerceptron)this.CurrentClassifier).setTrainingTime((int)Parameters.ListDoubleValues.Get("numericUpDownTrainingTime").Value); ((MultilayerPerceptron)this.CurrentClassifier).setNormalizeAttributes((bool)Parameters.ListCheckValues.Get("checkBoxNormAttribute").Value); ((MultilayerPerceptron)this.CurrentClassifier).setNormalizeNumericClass((bool)Parameters.ListCheckValues.Get("checkBoxNormNumericClasses").Value); this.CurrentClassifier.buildClassifier(train); } #endregion #region ZeroR else if (ClassifAlgoParams.Name == "ZeroR") { this.CurrentClassifier = new weka.classifiers.rules.OneR(); this.CurrentClassifier.buildClassifier(train); } #endregion #region OneR else if (ClassifAlgoParams.Name == "OneR") { this.CurrentClassifier = new weka.classifiers.rules.OneR(); ((OneR)this.CurrentClassifier).setMinBucketSize((int)Parameters.ListDoubleValues.Get("numericUpDownMinBucketSize").Value); this.CurrentClassifier.buildClassifier(train); } #endregion #region Naive Bayes else if (ClassifAlgoParams.Name == "NaiveBayes") { this.CurrentClassifier = new weka.classifiers.bayes.NaiveBayes(); ((NaiveBayes)this.CurrentClassifier).setUseKernelEstimator((bool)Parameters.ListCheckValues.Get("checkBoxKernelEstimator").Value); this.CurrentClassifier.buildClassifier(train); } #endregion #region Logistic else if (ClassifAlgoParams.Name == "Logistic") { this.CurrentClassifier = new weka.classifiers.functions.Logistic(); ((Logistic)this.CurrentClassifier).setUseConjugateGradientDescent((bool)Parameters.ListCheckValues.Get("checkBoxUseConjugateGradientDescent").Value); ((Logistic)this.CurrentClassifier).setRidge((double)Parameters.ListDoubleValues.Get("numericUpDownRidge").Value); this.CurrentClassifier.buildClassifier(train); } #endregion //weka.classifiers.functions.SMO //BayesNet #endregion if (TextBoxForFeedback != null) { TextBoxForFeedback.Clear(); TextBoxForFeedback.AppendText(this.CurrentClassifier.ToString()); } TextBoxForFeedback.AppendText("\n" + (InstancesList.numAttributes() - 1) + " attributes:\n\n"); for (int IdxAttributes = 0; IdxAttributes < InstancesList.numAttributes() - 1; IdxAttributes++) { TextBoxForFeedback.AppendText(IdxAttributes + "\t: " + InstancesList.attribute(IdxAttributes).name() + "\n"); } #region evaluation of the model and results display if ((WindowForClassificationParam.numericUpDownFoldNumber.Enabled) && (TextBoxForFeedback != null)) { TextBoxForFeedback.AppendText("\n-----------------------------\nModel validation\n-----------------------------\n"); ModelEvaluation = new weka.classifiers.Evaluation(InstancesList); ModelEvaluation.crossValidateModel(this.CurrentClassifier, InstancesList, (int)WindowForClassificationParam.numericUpDownFoldNumber.Value, new java.util.Random(1)); TextBoxForFeedback.AppendText(ModelEvaluation.toSummaryString()); TextBoxForFeedback.AppendText("\n-----------------------------\nConfusion Matrix:\n-----------------------------\n"); double[][] ConfusionMatrix = ModelEvaluation.confusionMatrix(); string NewLine = ""; for (int i = 0; i < ConfusionMatrix[0].Length; i++) { NewLine += "c" + i + "\t"; } TextBoxForFeedback.AppendText(NewLine + "\n\n"); for (int j = 0; j < ConfusionMatrix.Length; j++) { NewLine = ""; for (int i = 0; i < ConfusionMatrix[0].Length; i++) { NewLine += ConfusionMatrix[j][i] + "\t"; } // if TextBoxForFeedback.AppendText(NewLine + "| c" + j + " <=> " + cGlobalInfo.ListCellularPhenotypes[j].Name + "\n"); } } #endregion return this.CurrentClassifier; }
public List <double> testSMOUsingWeka(string[] attributeArray, string[] classNames, double[] dataValues, string classHeader, string defaultclass, string modelName, int hiddelLayers = 7, double learningRate = 0.03, double momentum = 0.4, int decimalPlaces = 2, int trainingTime = 1000) { java.util.ArrayList classLabel = new java.util.ArrayList(); foreach (string className in classNames) { classLabel.Add(className); } weka.core.Attribute classHeaderName = new weka.core.Attribute(classHeader, classLabel); java.util.ArrayList attributeList = new java.util.ArrayList(); foreach (string attribute in attributeArray) { weka.core.Attribute newAttribute = new weka.core.Attribute(attribute); attributeList.Add(newAttribute); } attributeList.add(classHeaderName); weka.core.Instances data = new weka.core.Instances("TestInstances", attributeList, 0); data.setClassIndex(data.numAttributes() - 1); // Set instance's values for the attributes weka.core.Instance inst_co = new DenseInstance(data.numAttributes()); for (int i = 0; i < data.numAttributes() - 1; i++) { inst_co.setValue(i, Math.Round(dataValues.ElementAt(i), 5)); } inst_co.setValue(classHeaderName, defaultclass); data.add(inst_co); weka.core.Instance currentInst = data.get(0); int j = 0; //foreach (float value in dataValues) //{ // // double roundedValue = Math.Round(value); // //var rounded = Math.Floor(value * 100) / 100; // if (array.ElementAt(j) != value) // { // System.Console.WriteLine("Masla occur"); // } // j++; //} //double predictedClass = cl.classifyInstance(data.get(0)); weka.classifiers.functions.SMO clRead = new weka.classifiers.functions.SMO(); try { java.io.File path = new java.io.File("/models/"); clRead = loadSMOModel(modelName, path); } catch (Exception e) { //string p1 = Assembly.GetExecutingAssembly().Location; string ClassifierName = Path.GetFileName(Path.GetFileName(modelName)); string Path1 = HostingEnvironment.MapPath(@"~//libs//models//" + ClassifierName); //string Path1 = HostingEnvironment.MapPath(@"~//libs//models//FusionCustomized.model"); clRead = (weka.classifiers.functions.SMO)weka.core.SerializationHelper.read(modelName); } // weka.classifiers.functions.SMO clRead = loadSMOModel(modelName, path); clRead.setBatchSize("100"); clRead.setCalibrator(new weka.classifiers.functions.Logistic()); clRead.setKernel(new weka.classifiers.functions.supportVector.PolyKernel()); clRead.setEpsilon(1.02E-12); clRead.setC(1.0); clRead.setDebug(false); clRead.setChecksTurnedOff(false); clRead.setFilterType(new SelectedTag(weka.classifiers.functions.SMO.FILTER_NORMALIZE, weka.classifiers.functions.SMO.TAGS_FILTER)); double classValue = clRead.classifyInstance(data.get(0)); double[] predictionDistribution = clRead.distributionForInstance(data.get(0)); //for (int predictionDistributionIndex = 0; // predictionDistributionIndex < predictionDistribution.Count(); // predictionDistributionIndex++) //{ // string classValueString1 = classLabel.get(predictionDistributionIndex).ToString(); // double prob= predictionDistribution[predictionDistributionIndex]*100; // System.Console.WriteLine(classValueString1 + ":" + prob); //} List <double> prediction = new List <double>(); prediction.Add(classValue); //prediction.AddRange(predictionDistribution); return(prediction); }