public static object ConvertToJavaTypes(object value)
{
if (value is Boolean) return new java.lang.Boolean((bool)value);
if (value is int) return new java.lang.Integer((int)value);
if (value is Int64) return new java.lang.Long((Int64)value);
if (value is double) return new java.lang.Double((double)value);
if (value is DateTime)
{
var datetime = (DateTime) value;
return new java.util.Date(
datetime.Year, datetime.Month, datetime.Day,
datetime.Hour, datetime.Minute, datetime.Second);
}
if (value is string) return java.lang.String.valueOf(value);
if (value is IEnumerable)
{
var enumerable = value as IEnumerable;
var iterable = new java.util.ArrayList();
foreach (var resultItem in enumerable)
{
iterable.Add(resultItem);
}
return iterable;
}
return value;
}
/// <summary>
/// Converts list of integer to arraylist of java integers
/// </summary>
/// <param name="intList">the list of integer to convert</param>
/// <returns>the converted list of integers as arraylist of java integers</returns>
public static java.util.ArrayList ToJavaIntList(List <int> intList)
{
java.util.ArrayList javalist = new java.util.ArrayList();
foreach (int i in intList)
{
javalist.Add(ToJavaInt(i));
}
return(javalist);
}
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 static Instances GetInstancesForWeka(string[] domains, string[] labelsClass, Label status, ProgressBar progress)
{
java.util.ArrayList attributes = new java.util.ArrayList();
//Добавление атрибутов
InputLayerWrapperMulti wrapper = new InputLayerWrapperMulti();
for (int i = 0; i < wrapper.namesAttributes.Length; i++)
{
attributes.Add(new weka.core.Attribute(wrapper.namesAttributes[i]));
}
java.util.ArrayList classes = new java.util.ArrayList();
foreach (var item in labelsClass.ToList().Distinct())
{
classes.add(item);
}
attributes.Add(new weka.core.Attribute("class", classes)); //Метка класса
Instances instances = new Instances($"Domains {DateTime.Now}", attributes, 0);
int numAttributes = instances.numAttributes();
int countRows = domains.Length;
for (int i = 0; i < countRows; i++)
{
instances.add(GetInstanceForWeka(domains[i], labelsClass[i], numAttributes));
progress.Invoke(new Action(() =>
{
progress.Value = i;
status.Text = $"{i}/{countRows}";
}));
}
return(instances);
}
public string testEmotionUsingWeka(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)
{
List <double> predictions = testMLPUsingWeka(attributeArray, classNames, dataValues, classHeader, defaultclass, modelName, hiddelLayers, learningRate, momentum, decimalPlaces, trainingTime);
java.util.ArrayList classLabel = new java.util.ArrayList();
foreach (string className in classNames)
{
classLabel.Add(className);
}
string classValueString = classLabel.get(Int32.Parse(predictions.ElementAt(0).ToString())).ToString();
return(classValueString);
}
public static object ConvertToJavaTypes(object value)
{
if (value is Boolean)
{
return(new java.lang.Boolean((bool)value));
}
if (value is int)
{
return(new java.lang.Integer((int)value));
}
if (value is Int64)
{
return(new java.lang.Long((Int64)value));
}
if (value is double)
{
return(new java.lang.Double((double)value));
}
if (value is DateTime)
{
var datetime = (DateTime)value;
return(new java.util.Date(
datetime.Year, datetime.Month, datetime.Day,
datetime.Hour, datetime.Minute, datetime.Second));
}
if (value is string)
{
return(java.lang.String.valueOf(value));
}
if (value is IEnumerable)
{
var enumerable = value as IEnumerable;
var iterable = new java.util.ArrayList();
foreach (var resultItem in enumerable)
{
iterable.Add(resultItem);
}
return(iterable);
}
return(value);
}
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);
}
public static void CreateArffFiles()
{
java.util.ArrayList atts;
java.util.ArrayList attsRel;
java.util.ArrayList attVals;
java.util.ArrayList attValsRel;
Instances data;
Instances dataRel;
double[] vals;
double[] valsRel;
int i;
// 1. set up attributes
atts = new java.util.ArrayList();
// - numeric
atts.Add(new weka.core.Attribute("att1"));
// - nominal
attVals = new java.util.ArrayList();
for (i = 0; i < 5; i++)
{
attVals.add("val" + (i + 1));
}
weka.core.Attribute nominal = new weka.core.Attribute("att2", attVals);
atts.add(nominal);
// - string
atts.add(new weka.core.Attribute("att3", (java.util.ArrayList)null));
// - date
atts.add(new weka.core.Attribute("att4", "yyyy-MM-dd"));
// - relational
attsRel = new java.util.ArrayList();
// -- numeric
attsRel.add(new weka.core.Attribute("att5.1"));
// -- nominal
attValsRel = new java.util.ArrayList();
for (i = 0; i < 5; i++)
{
attValsRel.Add("val5." + (i + 1));
}
attsRel.add(new weka.core.Attribute("att5.2", attValsRel));
dataRel = new Instances("att5", attsRel, 0);
atts.add(new weka.core.Attribute("att5", dataRel, 0));
// 2. create Instances object
data = new Instances("MyRelation", atts, 0);
// 3. fill with data
// first instance
vals = new double[data.numAttributes()];
// - numeric
vals[0] = Math.PI;
// - nominal
vals[1] = attVals.indexOf("val3");
// - string
vals[2] = data.attribute(2).addStringValue("This is a string!");
// - date
vals[3] = data.attribute(3).parseDate("2001-11-09");
// - relational
dataRel = new Instances(data.attribute(4).relation(), 0);
// -- first instance
valsRel = new double[2];
valsRel[0] = Math.PI + 1;
valsRel[1] = attValsRel.indexOf("val5.3");
weka.core.Instance inst = new DenseInstance(2);
inst.setValue(1, valsRel[0]);
inst.setValue(1, valsRel[1]);
dataRel.add(inst);
// -- second instance
valsRel = new double[2];
valsRel[0] = Math.PI + 2;
valsRel[1] = attValsRel.indexOf("val5.2");
dataRel.add(inst);
vals[4] = data.attribute(4).addRelation(dataRel);
// add
weka.core.Instance inst2 = new DenseInstance(4);
inst2.setValue(1, vals[0]);
inst2.setValue(1, vals[1]);
inst2.setValue(1, vals[2]);
inst2.setValue(1, vals[3]);
data.add(inst2);
// second instance
vals = new double[data.numAttributes()]; // important: needs NEW array!
// - numeric
vals[0] = Math.E;
// - nominal
vals[1] = attVals.indexOf("val1");
// - string
vals[2] = data.attribute(2).addStringValue("And another one!");
// - date
vals[3] = data.attribute(3).parseDate("2000-12-01");
// - relational
dataRel = new Instances(data.attribute(4).relation(), 0);
// -- first instance
valsRel = new double[2];
valsRel[0] = Math.E + 1;
valsRel[1] = attValsRel.indexOf("val5.4");
dataRel.add(inst);
// -- second instance
valsRel = new double[2];
valsRel[0] = Math.E + 2;
valsRel[1] = attValsRel.indexOf("val5.1");
dataRel.add(inst);
vals[4] = data.attribute(4).addRelation(dataRel);
// add
data.add(inst2);
data.setClassIndex(data.numAttributes() - 1);
// 4. output data
for (int x = 0; x < data.numInstances(); x++)
{
weka.core.Instance ins = data.instance(x);
System.Console.WriteLine(ins.value(x).ToString());
}
return;
}
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);
}
