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);
}
private void button1_Click(object sender, EventArgs e)
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader(file));
double[] Data = new double[insts.numAttributes()];
for (int i = 0; i < list.Count; i++)
{
if (list[i].GetType() == typeof(TextBox))
{
TextBox txt = (TextBox)list[i];
string value = txt.Text.Replace('.', ',');
Data[i] = Convert.ToDouble(value);
}
else
{
ComboBox combobox = (ComboBox)list[i];
Data[i] = Convert.ToDouble(combobox.SelectedIndex);
}
}
// Data[(insts.numAttributes() - 1] = 0;
insts.setClassIndex(insts.numAttributes() - 1);
Instance newInsts = new Instance(1.0, Data);
insts.add(newInsts);
string type = model.GetType().ToString();
if (type == "weka.classifiers.bayes.NaiveBayes")
{
weka.filters.Filter myDiscretize = new weka.filters.unsupervised.attribute.Discretize();
myDiscretize.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myDiscretize);
}
else if (type == "weka.classifiers.lazy.IBk")
{
weka.filters.Filter myDummy = new weka.filters.unsupervised.attribute.NominalToBinary();
myDummy.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myDummy);
weka.filters.Filter myNormalize = new weka.filters.unsupervised.instance.Normalize();
myNormalize.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myNormalize);
}
double index = model.classifyInstance(insts.lastInstance());
string result = insts.attribute(insts.numAttributes() - 1).value(Convert.ToInt16(index));
MessageBox.Show(result);
}
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 Instances CreateInstancesWithoutClass(cExtendedTable Input)
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
// Descriptors loop
for (int i = 0; i < Input.Count; i++)
{
//if (ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(Input[i].Name));
columnNo++;
}
// weka.core.FastVector attVals = new FastVector();
Instances data1 = new Instances("MyRelation", atts, 0);
for (int IdxRow = 0; IdxRow < Input[0].Count; IdxRow++)
{
double[] vals = new double[data1.numAttributes()];
for (int Col = 0; Col < columnNo; Col++)
{
// if (Glo .ListDescriptors[Col].IsActive() == false) continue;
vals[Col] = Input[Col][IdxRow];// double.Parse(dt.Rows[IdxRow][Col].ToString());
}
data1.add(new DenseInstance(1.0, vals));
}
return data1;
}
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);
}
private Instances createWhyInstances()
{
FastVector fvWhy = createWhyFastVector();
Instances whyInstances = new Instances("WhyInstances", fvWhy, listSecondaryWhyCandidates.Count);
foreach (Token candidate in listSecondaryWhyCandidates)
{
if (candidate.Value == null) continue;
Instance whyInstance = createSingleWhyInstance(fvWhy, candidate);
whyInstance.setDataset(whyInstances);
whyInstances.add(whyInstance);
}
whyInstances.setClassIndex(fvWhy.size() - 1);
return whyInstances;
}
private static Instances CreateInstanceOnFly(double[] a, double[] b)
{
FastVector atts;
Instances data;
double[] vals;
// 1. set up attributes
atts = new FastVector();
// - numeric
atts.addElement(new Attribute("att1"));
atts.addElement(new Attribute("att2"));
// 2. create Instances object
data = new Instances("MyRelation", atts, 0);
for (int i = 0; i < a.Length; ++i)
{
// 3. fill with data
// first instance
vals = new double[data.numAttributes()];
// - numeric
vals[0] = a[i];
// - nominal
vals[1] = b[i];
data.add(new weka.core.DenseInstance(1.0, vals));
}
return data;
}
public static void Test()
{
weka.core.Instances data = new weka.core.Instances(new java.io.FileReader("./data/Classification/Communication.arff"));
data.setClassIndex(data.numAttributes() - 1);
weka.classifiers.Classifier cls = new weka.classifiers.bayes.BayesNet();
//Save BayesNet results in .txt file
using (System.IO.StreamWriter file = new System.IO.StreamWriter("./data/Classification/Communication_Report.txt"))
{
file.WriteLine("Performing " + percentSplit + "% split evaluation.");
int runs = 1;
// perform cross-validation
for (int i = 0; i < runs; i++)
{
// randomize data
int seed = i + 1;
java.util.Random rand = new java.util.Random(seed);
weka.core.Instances randData = new weka.core.Instances(data);
randData.randomize(rand);
//weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData);
int trainSize = (int)Math.Round((double)data.numInstances() * percentSplit / 100);
int testSize = data.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(data, 0, 0);
weka.core.Instances test = new weka.core.Instances(data, 0, 0);
train.setClassIndex(train.numAttributes() - 1);
test.setClassIndex(test.numAttributes() - 1);
//Print classifier analytics for all the dataset
file.WriteLine("EVALUATION OF TEST DATASET.");
//int numCorrect = 0;
for (int j = 0; j < data.numInstances(); j++)
{
weka.core.Instance currentInst = randData.instance(j);
if (j < trainSize)
{
train.add(currentInst);
}
else
{
test.add(currentInst);
/*
* double predictedClass = cls.classifyInstance(currentInst);
*
* double[] prediction = cls.distributionForInstance(currentInst);
*
* for (int p = 0; p < prediction.Length; p++)
* {
* file.WriteLine("Probability of class [{0}] for [{1}] is: {2}", currentInst.classAttribute().value(p), currentInst, Math.Round(prediction[p], 4));
* }
* file.WriteLine();
*
* file.WriteLine();
* if (predictedClass == data.instance(j).classValue())
* numCorrect++;*/
}
}
// build and evaluate classifier
cls.buildClassifier(train);
// Test the model
weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(randData);
eval.evaluateModel(cls, test);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryTest = eval.toSummaryString();
file.WriteLine(strSummaryTest);
file.WriteLine();
//Print detailed class statistics
file.WriteLine(eval.toClassDetailsString());
file.WriteLine();
//Print confusion matrix
file.WriteLine(eval.toMatrixString());
file.WriteLine();
// Get the confusion matrix
double[][] cmMatrixTest = eval.confusionMatrix();
System.Console.WriteLine("Bayesian Network results saved in Communication_Report.txt file successfully.");
}
}
}
/// <summary> Method for testing this class.
///
/// </summary>
/// <param name="argv">should contain one element: the name of an ARFF file
/// </param>
//@ requires argv != null;
//@ requires argv.length == 1;
//@ requires argv[0] != null;
public static void test(System.String[] argv)
{
Instances instances, secondInstances, train, test, empty;
//Instance instance;
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.util.Random.Random' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
System.Random random = new System.Random((System.Int32) 2);
//UPGRADE_ISSUE: Class hierarchy differences between 'java.io.Reader' and 'System.IO.StreamReader' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1186'"
System.IO.StreamReader reader;
int start, num;
//double newWeight;
FastVector testAtts, testVals;
int i, j;
try
{
if (argv.Length > 1)
{
throw (new System.Exception("Usage: Instances [<filename>]"));
}
// Creating set of instances from scratch
testVals = new FastVector(2);
testVals.addElement("first_value");
testVals.addElement("second_value");
testAtts = new FastVector(2);
testAtts.addElement(new Attribute("nominal_attribute", testVals));
testAtts.addElement(new Attribute("numeric_attribute"));
instances = new Instances("test_set", testAtts, 10);
instances.add(new Instance(instances.numAttributes()));
instances.add(new Instance(instances.numAttributes()));
instances.add(new Instance(instances.numAttributes()));
instances.ClassIndex = 0;
System.Console.Out.WriteLine("\nSet of instances created from scratch:\n");
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(instances);
if (argv.Length == 1)
{
System.String filename = argv[0];
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
reader = new System.IO.StreamReader(filename, System.Text.Encoding.Default);
// Read first five instances and print them
System.Console.Out.WriteLine("\nFirst five instances from file:\n");
instances = new Instances(reader, 1);
instances.ClassIndex = instances.numAttributes() - 1;
i = 0;
while ((i < 5) && (instances.readInstance(reader)))
{
i++;
}
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(instances);
// Read all the instances in the file
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073'"
reader = new System.IO.StreamReader(filename, System.Text.Encoding.Default);
instances = new Instances(reader);
// Make the last attribute be the class
instances.ClassIndex = instances.numAttributes() - 1;
// Print header and instances.
System.Console.Out.WriteLine("\nDataset:\n");
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(instances);
System.Console.Out.WriteLine("\nClass index: " + instances.classIndex());
}
// Test basic methods based on class index.
System.Console.Out.WriteLine("\nClass name: " + instances.classAttribute().name());
System.Console.Out.WriteLine("\nClass index: " + instances.classIndex());
System.Console.Out.WriteLine("\nClass is nominal: " + instances.classAttribute().Nominal);
System.Console.Out.WriteLine("\nClass is numeric: " + instances.classAttribute().Numeric);
System.Console.Out.WriteLine("\nClasses:\n");
for (i = 0; i < instances.numClasses(); i++)
{
System.Console.Out.WriteLine(instances.classAttribute().value_Renamed(i));
}
System.Console.Out.WriteLine("\nClass values and labels of instances:\n");
for (i = 0; i < instances.numInstances(); i++)
{
Instance inst = instances.instance(i);
System.Console.Out.Write(inst.classValue() + "\t");
System.Console.Out.Write(inst.toString(inst.classIndex()));
if (instances.instance(i).classIsMissing())
{
System.Console.Out.WriteLine("\tis missing");
}
else
{
System.Console.Out.WriteLine();
}
}
// Create random weights.
System.Console.Out.WriteLine("\nCreating random weights for instances.");
for (i = 0; i < instances.numInstances(); i++)
{
instances.instance(i).Weight = random.NextDouble();
}
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(instances.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(instances.sumOfWeights());
// Insert an attribute
secondInstances = new Instances(instances);
Attribute testAtt = new Attribute("Inserted");
secondInstances.insertAttributeAt(testAtt, 0);
System.Console.Out.WriteLine("\nSet with inserted attribute:\n");
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(secondInstances);
System.Console.Out.WriteLine("\nClass name: " + secondInstances.classAttribute().name());
// Delete the attribute
secondInstances.deleteAttributeAt(0);
System.Console.Out.WriteLine("\nSet with attribute deleted:\n");
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(secondInstances);
System.Console.Out.WriteLine("\nClass name: " + secondInstances.classAttribute().name());
// Test if headers are equal
System.Console.Out.WriteLine("\nHeaders equal: " + instances.equalHeaders(secondInstances) + "\n");
// Print data in internal format.
System.Console.Out.WriteLine("\nData (internal values):\n");
for (i = 0; i < instances.numInstances(); i++)
{
for (j = 0; j < instances.numAttributes(); j++)
{
if (instances.instance(i).isMissing(j))
{
System.Console.Out.Write("? ");
}
else
{
System.Console.Out.Write(instances.instance(i).value_Renamed(j) + " ");
}
}
System.Console.Out.WriteLine();
}
// Just print header
System.Console.Out.WriteLine("\nEmpty dataset:\n");
empty = new Instances(instances, 0);
//UPGRADE_TODO: Method 'java.io.PrintStream.println' was converted to 'System.Console.Out.WriteLine' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javaioPrintStreamprintln_javalangObject'"
System.Console.Out.WriteLine(empty);
System.Console.Out.WriteLine("\nClass name: " + empty.classAttribute().name());
// Create copy and rename an attribute and a value (if possible)
if (empty.classAttribute().Nominal)
{
Instances copy = new Instances(empty, 0);
copy.renameAttribute(copy.classAttribute(), "new_name");
copy.renameAttributeValue(copy.classAttribute(), copy.classAttribute().value_Renamed(0), "new_val_name");
System.Console.Out.WriteLine("\nDataset with names changed:\n" + copy);
System.Console.Out.WriteLine("\nOriginal dataset:\n" + empty);
}
// Create and prints subset of instances.
start = instances.numInstances() / 4;
num = instances.numInstances() / 2;
System.Console.Out.Write("\nSubset of dataset: ");
System.Console.Out.WriteLine(num + " instances from " + (start + 1) + ". instance");
secondInstances = new Instances(instances, start, num);
System.Console.Out.WriteLine("\nClass name: " + secondInstances.classAttribute().name());
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(secondInstances.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(secondInstances.sumOfWeights());
// Create and print training and test sets for 3-fold
// cross-validation.
System.Console.Out.WriteLine("\nTrain and test folds for 3-fold CV:");
if (instances.classAttribute().Nominal)
{
instances.stratify(3);
}
for (j = 0; j < 3; j++)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.util.Random.Random' may cause compilation errors. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1092'"
train = instances.trainCV(3, j, new System.Random((System.Int32) 1));
test = instances.testCV(3, j);
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nTrain: ");
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(train.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(train.sumOfWeights());
System.Console.Out.WriteLine("\nClass name: " + train.classAttribute().name());
System.Console.Out.WriteLine("\nTest: ");
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(test.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(test.sumOfWeights());
System.Console.Out.WriteLine("\nClass name: " + test.classAttribute().name());
}
// Randomize instances and print them.
System.Console.Out.WriteLine("\nRandomized dataset:");
instances.randomize(random);
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(instances.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(instances.sumOfWeights());
// Sort instances according to first attribute and
// print them.
System.Console.Out.Write("\nInstances sorted according to first attribute:\n ");
instances.sort(0);
// Print all instances and their weights (and the sum of weights).
System.Console.Out.WriteLine("\nInstances and their weights:\n");
System.Console.Out.WriteLine(instances.instancesAndWeights());
System.Console.Out.Write("\nSum of weights: ");
System.Console.Out.WriteLine(instances.sumOfWeights());
}
catch (System.Exception)
{
//.WriteStackTrace(e, Console.Error);
}
}
/// <summary> Merges two sets of Instances together. The resulting set will have
/// all the attributes of the first set plus all the attributes of the
/// second set. The number of instances in both sets must be the same.
///
/// </summary>
/// <param name="first">the first set of Instances
/// </param>
/// <param name="second">the second set of Instances
/// </param>
/// <returns> the merged set of Instances
/// </returns>
/// <exception cref="IllegalArgumentException">if the datasets are not the same size
/// </exception>
public static Instances mergeInstances(Instances first, Instances second)
{
if (first.numInstances() != second.numInstances())
{
throw new System.ArgumentException("Instance sets must be of the same size");
}
// Create the vector of merged attributes
FastVector newAttributes = new FastVector();
for (int i = 0; i < first.numAttributes(); i++)
{
newAttributes.addElement(first.attribute(i));
}
for (int i = 0; i < second.numAttributes(); i++)
{
newAttributes.addElement(second.attribute(i));
}
// Create the set of Instances
Instances merged = new Instances(first.relationName() + '_' + second.relationName(), newAttributes, first.numInstances());
// Merge each instance
for (int i = 0; i < first.numInstances(); i++)
{
merged.add(first.instance(i).mergeInstance(second.instance(i)));
}
return merged;
}
/// <summary> Copies instances from one set to the end of another
/// one.
///
/// </summary>
/// <param name="source">the source of the instances
/// </param>
/// <param name="from">the position of the first instance to be copied
/// </param>
/// <param name="dest">the destination for the instances
/// </param>
/// <param name="num">the number of instances to be copied
/// </param>
//@ requires 0 <= from && from <= numInstances() - num;
//@ requires 0 <= num;
protected internal virtual void copyInstances(int from, Instances dest, int num)
{
for (int i = 0; i < num; i++)
{
dest.add(instance(from + i));
}
}
/// <summary> Creates a new dataset of the same size using random sampling
/// with replacement according to the given weight vector. The
/// weights of the instances in the new dataset are set to one.
/// The length of the weight vector has to be the same as the
/// number of instances in the dataset, and all weights have to
/// be positive.
///
/// </summary>
/// <param name="random">a random number generator
/// </param>
/// <param name="weights">the weight vector
/// </param>
/// <returns> the new dataset
/// </returns>
/// <exception cref="IllegalArgumentException">if the weights array is of the wrong
/// length or contains negative weights.
/// </exception>
public virtual Instances resampleWithWeights(System.Random random, double[] weights)
{
if (weights.Length != numInstances())
{
throw new System.ArgumentException("weights.length != numInstances.");
}
Instances newData = new Instances(this, numInstances());
if (numInstances() == 0)
{
return newData;
}
double[] probabilities = new double[numInstances()];
double sumProbs = 0, sumOfWeights = Utils.sum(weights);
for (int i = 0; i < numInstances(); i++)
{
sumProbs += random.NextDouble();
probabilities[i] = sumProbs;
}
Utils.normalize(probabilities, sumProbs / sumOfWeights);
// Make sure that rounding errors don't mess things up
probabilities[numInstances() - 1] = sumOfWeights;
int k = 0; int l = 0;
sumProbs = 0;
while ((k < numInstances() && (l < numInstances())))
{
if (weights[l] < 0)
{
throw new System.ArgumentException("Weights have to be positive.");
}
sumProbs += weights[l];
while ((k < numInstances()) && (probabilities[k] <= sumProbs))
{
newData.add(instance(l));
newData.instance(k).Weight = 1;
k++;
}
l++;
}
return newData;
}
/// <summary> Creates a new dataset of the same size using random sampling
/// with replacement.
///
/// </summary>
/// <param name="random">a random number generator
/// </param>
/// <returns> the new dataset
/// </returns>
public virtual Instances resample(System.Random random)
{
Instances newData = new Instances(this, numInstances());
while (newData.numInstances() < numInstances())
{
newData.add(instance(random.Next(numInstances())));
}
return newData;
}
// Test the classification result of each map that a user played,
// with the data available as if they were playing through it
public static void classifyTest(String dataString, String playerID)
{
try {
java.io.StringReader stringReader = new java.io.StringReader(dataString);
java.io.BufferedReader buffReader = new java.io.BufferedReader(stringReader);
/* NOTE THAT FOR NAIVE BAYES ALL WEIGHTS CAN BE = 1*/
//weka.core.converters.ConverterUtils.DataSource source = new weka.core.converters.ConverterUtils.DataSource("iris.arff");
weka.core.Instances thisData = new weka.core.Instances(buffReader); //source.getDataSet();
if (thisData.classIndex() == -1)
thisData.setClassIndex(thisData.numAttributes() - 1);
weka.core.Instances thisUniqueData = new weka.core.Instances(thisData);
if (thisUniqueData.classIndex() == -1)
thisUniqueData.setClassIndex(thisUniqueData.numAttributes() - 1);
thisUniqueData.delete();
if (allUniqueData == null) {
allUniqueData = new weka.core.Instances(thisData);
if (allUniqueData.classIndex() == -1)
allUniqueData.setClassIndex(allUniqueData.numAttributes() - 1);
allUniqueData.delete();
}
weka.core.InstanceComparator com = new weka.core.InstanceComparator(false);
for (int i = 0; i < thisData.numInstances(); i++)
{
bool dup = false;
for (int j = 0; j < allUniqueData.numInstances(); j++)
{
if (com.compare(thisData.instance(i),allUniqueData.instance(j)) == 0)
{
Debug.Log("Duplicate found!");
dup = true;
break;
}
}
if (!dup)
allUniqueData.add(thisData.instance(i));
else
dupInstances++;
}
for (int i = 0; i < thisData.numInstances(); i++)
{
bool dup = false;
for (int j = 0; j < thisUniqueData.numInstances(); j++)
{
if (com.compare(thisData.instance(i),thisUniqueData.instance(j)) == 0)
{
Debug.Log("Duplicate found!");
dup = true;
break;
}
}
if (!dup)
thisUniqueData.add(thisData.instance(i));
else
dupInstancesSamePlayer++;
}
//Debug.Log("All Data Instance Count = " + thisData.numInstances());
//Debug.Log("Unique Data Instance Count = " + thisUniqueData.numInstances());
//Debug.Log("Done!");
} catch (java.lang.Exception ex)
{
Debug.LogError(ex.getMessage());
}
}
/// <summary>
/// Create an instances structure with classes for supervised methods
/// </summary>
/// <param name="NumClass"></param>
/// <returns></returns>
public Instances CreateInstancesWithClasses(cInfoClass InfoClass, int NeutralClass)
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
for (int i = 0; i < ParentScreening.ListDescriptors.Count; i++)
{
if (ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(ParentScreening.ListDescriptors[i].GetName()));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
for (int i = 0; i < InfoClass.NumberOfClass; i++)
attVals.addElement("Class__" + (i).ToString());
atts.addElement(new weka.core.Attribute("Class__", attVals));
Instances data1 = new Instances("MyRelation", atts, 0);
int IdxWell = 0;
foreach (cWell CurrentWell in this.ListActiveWells)
{
if (CurrentWell.GetCurrentClassIdx() == NeutralClass) continue;
double[] vals = new double[data1.numAttributes()];
int IdxCol = 0;
for (int Col = 0; Col < ParentScreening.ListDescriptors.Count; Col++)
{
if (ParentScreening.ListDescriptors[Col].IsActive() == false) continue;
vals[IdxCol++] = CurrentWell.ListSignatures[Col].GetValue();
}
vals[columnNo] = InfoClass.CorrespondanceTable[CurrentWell.GetCurrentClassIdx()];
data1.add(new DenseInstance(1.0, vals));
IdxWell++;
}
data1.setClassIndex((data1.numAttributes() - 1));
return data1;
}
/// <summary> Sets distribution associated with model.</summary>
public override void resetDistribution(Instances data)
{
Instances insts = new Instances(data, data.numInstances());
for (int i = 0; i < data.numInstances(); i++)
{
if (whichSubset(data.instance(i)) > - 1)
{
insts.add(data.instance(i));
}
}
Distribution newD = new Distribution(insts, this);
newD.addInstWithUnknown(data, m_attIndex);
m_distribution = newD;
}
/// <summary>
/// Create an instances structure with classes for supervised methods
/// </summary>
/// <param name="NumClass"></param>
/// <returns></returns>
public Instances CreateInstancesWithClasses(List<bool> ListClassSelected)
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
for (int i = 0; i < ParentScreening.ListDescriptors.Count; i++)
{
if (ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(ParentScreening.ListDescriptors[i].GetName()));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
foreach (var item in cGlobalInfo.ListWellClasses)
{
attVals.addElement(item.Name);
}
atts.addElement(new weka.core.Attribute("ClassAttribute", attVals));
Instances data1 = new Instances("MyRelation", atts, 0);
int IdxWell = 0;
foreach (cWell CurrentWell in this.ListActiveWells)
{
if (!ListClassSelected[CurrentWell.GetCurrentClassIdx()]) continue;
double[] vals = new double[data1.numAttributes()];
int IdxCol = 0;
for (int Col = 0; Col < ParentScreening.ListDescriptors.Count; Col++)
{
if (ParentScreening.ListDescriptors[Col].IsActive() == false) continue;
vals[IdxCol++] = CurrentWell.ListSignatures[Col].GetValue();
}
vals[columnNo] = CurrentWell.GetCurrentClassIdx();
data1.add(new DenseInstance(1.0, vals));
IdxWell++;
}
data1.setClassIndex((data1.numAttributes() - 1));
return data1;
}
public static void BayesTest()
{
try
{
weka.core.Instances insts = new weka.core.Instances(new java.io.FileReader("iris.arff"));
insts.setClassIndex(insts.numAttributes() - 1);
weka.classifiers.Classifier cl = new weka.classifiers.bayes.BayesNet();
System.Console.WriteLine("Performing " + percentSplit + "% split evaluation.");
//randomize the order of the instances in the dataset.
weka.filters.Filter myRandom = new weka.filters.unsupervised.instance.Randomize();
myRandom.setInputFormat(insts);
insts = weka.filters.Filter.useFilter(insts, myRandom);
int trainSize = insts.numInstances() * percentSplit / 100;
int testSize = insts.numInstances() - trainSize;
weka.core.Instances train = new weka.core.Instances(insts, 0, trainSize);
weka.core.Instances test = new weka.core.Instances(insts, 0, 0);
cl.buildClassifier(train);
//print model
System.Console.WriteLine(cl);
int numCorrect = 0;
for (int i = trainSize; i < insts.numInstances(); i++)
{
weka.core.Instance currentInst = insts.instance(i);
double predictedClass = cl.classifyInstance(currentInst);
test.add(currentInst);
double[] prediction = cl.distributionForInstance(currentInst);
for (int x = 0; x < prediction.Length; x++)
{
System.Console.WriteLine("Probability of class [{0}] for [{1}] is: {2}", currentInst.classAttribute().value(x), currentInst, Math.Round(prediction[x], 4));
}
System.Console.WriteLine();
if (predictedClass == insts.instance(i).classValue())
{
numCorrect++;
}
}
System.Console.WriteLine(numCorrect + " out of " + testSize + " correct (" +
(double)((double)numCorrect / (double)testSize * 100.0) + "%)");
// Train the model
weka.classifiers.Evaluation eTrain = new weka.classifiers.Evaluation(train);
eTrain.evaluateModel(cl, train);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryTrain = eTrain.toSummaryString();
System.Console.WriteLine(strSummaryTrain);
//Print detailed class statistics
System.Console.WriteLine(eTrain.toClassDetailsString());
//Print confusion matrix
System.Console.WriteLine(eTrain.toMatrixString());
// Get the confusion matrix
double[][] cmMatrixTrain = eTrain.confusionMatrix();
// Test the model
weka.classifiers.Evaluation eTest = new weka.classifiers.Evaluation(test);
eTest.evaluateModel(cl, test);
// Print the results as in Weka explorer:
//Print statistics
String strSummaryTest = eTest.toSummaryString();
System.Console.WriteLine(strSummaryTest);
//Print detailed class statistics
System.Console.WriteLine(eTest.toClassDetailsString());
//Print confusion matrix
System.Console.WriteLine(eTest.toMatrixString());
// Get the confusion matrix
double[][] cmMatrixTest = eTest.confusionMatrix();
}
catch (java.lang.Exception ex)
{
ex.printStackTrace();
}
}
/// <summary>
/// Create an instances structure with classes for supervised methods
/// </summary>
/// <param name="NumClass"></param>
/// <returns></returns>
public Instances CreateInstancesWithClassesWithPlateBasedDescriptor(int NumberOfClass)
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
for (int i = 0; i < ParentScreening.ListPlateBaseddescriptorNames.Count; i++)
{
atts.addElement(new weka.core.Attribute(ParentScreening.ListPlateBaseddescriptorNames[i]));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
for (int i = 0; i < NumberOfClass; i++)
attVals.addElement("Class" + (i).ToString());
atts.addElement(new weka.core.Attribute("Class", attVals));
Instances data1 = new Instances("MyRelation", atts, 0);
int IdxWell = 0;
foreach (cWell CurrentWell in this.ListActiveWells)
{
if (CurrentWell.GetCurrentClassIdx() == -1) continue;
double[] vals = new double[data1.numAttributes()];
int IdxCol = 0;
for (int Col = 0; Col < ParentScreening.ListPlateBaseddescriptorNames.Count; Col++)
{
vals[IdxCol++] = CurrentWell.ListPlateBasedDescriptors[Col].GetValue();
}
vals[columnNo] = CurrentWell.GetCurrentClassIdx();
data1.add(new DenseInstance(1.0, vals));
IdxWell++;
}
data1.setClassIndex((data1.numAttributes() - 1));
return data1;
}
public void TrainandTest(CandidateClassifier classifierInfo, CandidateParameter cp)
{
//string dealType = classifierInfo.DealType;
Classifier cls = null;
if (TestParameters.UseTrain)
{
string modelFileName = GetModelFileName(classifierInfo.Name);
if (TestParameters.SaveModel)
{
cls = WekaUtils.TryLoadClassifier(modelFileName);
}
Instances trainInstancesNew, trainInstances;
trainInstances = m_trainInstances;
trainInstancesNew = m_trainInstancesNew;
if (cls == null)
{
if (classifierInfo.Classifier == null)
{
classifierInfo.Classifier = WekaUtils.CreateClassifier(cp.ClassifierType, m_currentTp, m_currentSl);
}
cls = classifierInfo.Classifier;
}
else
{
if (TestParameters.EnableDetailLog)
{
System.Console.WriteLine("Model is loaded.");
}
}
if (m_enableTrainSplitTest)
{
Instances trainTrainInst, trainTestInst;
DateTime splitTrainTimeEnd;
if (m_trainSplitTestNums != -1)
{
int trainTrainSize = trainInstancesNew.numInstances() - m_trainSplitTestNums;
int trainTestSize = m_trainSplitTestNums;
trainTrainInst = new Instances(trainInstancesNew, 0, trainTrainSize);
trainTestInst = new Instances(trainInstancesNew, trainTrainSize, trainTestSize);
splitTrainTimeEnd = WekaUtils.GetDateValueFromInstances(trainInstances, 0, trainTrainSize);
}
else if (m_trainSplitPercent != -1)
{
if (m_trainSplitPercent == 100.0)
{
int trainTrainSize = trainInstancesNew.numInstances();
trainTrainInst = new Instances(trainInstancesNew, 0, trainTrainSize);
trainTestInst = new Instances(trainInstancesNew, 0, trainTrainSize);
splitTrainTimeEnd = WekaUtils.GetDateValueFromInstances(trainInstances, 0, trainTrainSize);
}
else
{
int trainTrainSize = (int)Math.Round(trainInstancesNew.numInstances() * m_trainSplitPercent / 100);
int trainTestSize = trainInstancesNew.numInstances() - trainTrainSize;
trainTrainInst = new Instances(trainInstancesNew, 0, trainTrainSize);
trainTestInst = new Instances(trainInstancesNew, trainTrainSize, trainTestSize);
splitTrainTimeEnd = WekaUtils.GetDateValueFromInstances(trainInstances, 0, trainTrainSize);
}
}
else
{
trainTrainInst = new Instances(trainInstancesNew, 0);
trainTestInst = new Instances(trainInstancesNew, 0);
DateTime dt = WekaUtils.GetDateValueFromInstances(trainInstances, 0, trainInstances.numInstances() - 1);
splitTrainTimeEnd = m_trainTimeEnd.AddMinutes(-TestParameters.BatchTestMinutes);
while (splitTrainTimeEnd > dt)
{
splitTrainTimeEnd = splitTrainTimeEnd.AddMinutes(-TestParameters.BatchTestMinutes);
}
for (int i = 0; i < trainInstances.numInstances(); ++i)
{
dt = WekaUtils.GetDateValueFromInstances(trainInstances, 0, i);
if (dt <= splitTrainTimeEnd)
{
var ins = new DenseInstance(trainInstancesNew.instance(i));
trainTrainInst.add(ins);
}
else
{
var ins = new DenseInstance(trainInstancesNew.instance(i));
trainTestInst.add(ins);
}
}
}
cls = WekaUtils.TrainInstances(trainTrainInst, TestParameters.SaveModel ? modelFileName : null, cls);
//m_classifierQueue[dealType].Enqueue(new ClassifierInfo(cls, splitTrainTimeEnd));
//foreach (var i in m_classifierQueue[dealType])
//{
// var e = WekaUtils.TestInstances(trainTestInst, i.Cls);
// i.TotalCost = i.TotalCost * m_classifierQueueFactor + e.totalCost();
// i.TotalNum = (int)(i.TotalNum * m_classifierQueueFactor) + (int)e.numInstances();
//}
//WriteEvalSummary(eval1, string.Format("Test Data from {0} to {1}", m_testTimeStart.ToString(Parameters.DateTimeFormat), m_testTimeEnd.ToString(Parameters.DateTimeFormat)));
}
else
{
cls = WekaUtils.TrainInstances(trainInstancesNew, TestParameters.SaveModel ? modelFileName : null, cls);
//m_classifierQueue[dealType].Enqueue(new ClassifierInfo(cls, m_trainTimeEnd));
//foreach (var i in m_classifierQueue[dealType])
//{
// var e = WekaUtils.TestInstances(trainInstancesNew, i.Cls);
// i.TotalCost = i.TotalCost * m_classifierQueueFactor + e.totalCost();
// i.TotalNum = (int)(i.TotalNum * m_classifierQueueFactor) + (int)e.numInstances();
//}
}
if (TestParameters.EnableDetailLog)
{
System.Console.WriteLine("Model is trained.");
}
classifierInfo.Classifier = cls;
//if (classifierInfo.CurrentTrainInstances1 != null)
//{
// classifierInfo.CurrentTrainInstances1.clear();
//}
//if (classifierInfo.CurrentTrainInstancesNew1 != null)
//{
// classifierInfo.CurrentTrainInstancesNew1.clear();
//}
//classifierInfo.CurrentTrainInstances = new Instances(trainInstances, 0, trainInstances.numInstances());
//classifierInfo.CurrentTrainInstancesNew = new Instances(trainInstancesNew, 0, trainInstancesNew.numInstances());
if (classifierInfo.MoneyManagement == null)
{
classifierInfo.MoneyManagement = WekaUtils.CreateMoneyManagement(cp.MoneyManagementType, m_currentTp, m_currentSl);
}
IMoneyManagement mm = WekaUtils.TrainInstances4MM(trainInstancesNew, TestParameters.SaveModel ? modelFileName : null, classifierInfo.MoneyManagement);
classifierInfo.MoneyManagement = mm;
}
else
{
if (classifierInfo.Classifier == null)
{
classifierInfo.Classifier = WekaUtils.CreateClassifier(cp.ClassifierType, m_currentTp, m_currentSl);
}
cls = classifierInfo.Classifier;
if (classifierInfo.MoneyManagement == null)
{
classifierInfo.MoneyManagement = WekaUtils.CreateMoneyManagement(cp.MoneyManagementType, m_currentTp, m_currentSl);
}
}
if (m_enableTest)
{
Instances testInstancesNew, testInstances;
testInstances = m_testInstances;
testInstancesNew = m_testInstancesNew;
double[] cv = WekaUtils.ClassifyInstances(testInstancesNew, cls);
if (TestParameters.EnableExcludeClassifier)
{
bool hasPositive = false;
for (int i = 0; i < cv.Length; ++i)
{
if (cv[i] == 2)
{
hasPositive = true;
break;
}
}
if (hasPositive)
{
// Exclude
if (classifierInfo.ExcludeClassifier == null)
{
string modelFileName4Exclude = GetExcludeModelFileName(classifierInfo.Name);
classifierInfo.ExcludeClassifier = WekaUtils.TryLoadClassifier(modelFileName4Exclude);
}
if (classifierInfo.ExcludeClassifier != null)
{
double[] cv2 = WekaUtils.ClassifyInstances(testInstancesNew, classifierInfo.ExcludeClassifier);
// cv2 == 0 -> is class, otherwise = double.NaN;
for (int i = 0; i < cv.Length; ++i)
{
cv[i] = cv[i] == 2 && cv2[i] == 2 ? 2 : 0;
}
}
}
}
classifierInfo.CurrentTestRet = cv;
classifierInfo.CurrentClassValue = new double[testInstances.numInstances()];
for (int i = 0; i < testInstances.numInstances(); ++i)
{
classifierInfo.CurrentClassValue[i] = testInstances.instance(i).classValue();
}
for (int i = 0; i < testInstances.numInstances(); i++)
{
if (cv[i] == 2)
{
double openPrice = testInstances.instance(i).value(testInstances.attribute("mainClose"));
DateTime openTime = WekaUtils.GetDateValueFromInstances(testInstances, 0, i);
if (testInstances.instance(i).classValue() == 2 || testInstances.instance(i).classValue() == 0)
{
classifierInfo.Deals.AddDeal(openTime,
openPrice,
classifierInfo.DealType,
classifierInfo.MoneyManagement.GetVolume(testInstances.instance(i)),
testInstances.instance(i).classValue() == 2 ? -classifierInfo.Tp : classifierInfo.Sl,
WekaUtils.GetDateValueFromInstances(testInstances, 1, i));
}
else if (testInstances.instance(i).classValue() == 1)
{
double closePriceTp, closePriceSl;
if (classifierInfo.DealType == 'B')
{
closePriceTp = openPrice + classifierInfo.Tp * DealsInfo.GetPoint(0);
closePriceSl = openPrice - classifierInfo.Sl * DealsInfo.GetPoint(0);
}
else
{
closePriceTp = openPrice - classifierInfo.Tp * DealsInfo.GetPoint(0);
closePriceSl = openPrice + classifierInfo.Sl * DealsInfo.GetPoint(0);
}
classifierInfo.Deals.AddDeal(openTime,
openPrice,
classifierInfo.DealType,
classifierInfo.MoneyManagement.GetVolume(testInstances.instance(i)),
closePriceTp, closePriceSl);
}
else
{
throw new AssertException("classValue should be 0,1,2.");
}
}
}
}
}
/// <summary>
/// Create an instances structure without classes for unsupervised methods
/// </summary>
/// <returns>a weka Instances object</returns>
public Instances CreateInstancesWithoutClass()
{
weka.core.FastVector atts = new FastVector();
int columnNo = 0;
// Descriptors loop
for (int i = 0; i < ParentScreening.ListDescriptors.Count; i++)
{
if (ParentScreening.ListDescriptors[i].IsActive() == false) continue;
atts.addElement(new weka.core.Attribute(ParentScreening.ListDescriptors[i].GetName()));
columnNo++;
}
weka.core.FastVector attVals = new FastVector();
Instances data1 = new Instances("MyRelation", atts, 0);
foreach (cWell CurrentWell in this.ListActiveWells)
{
double[] vals = new double[data1.numAttributes()];
int IdxRealCol = 0;
for (int Col = 0; Col < ParentScreening.ListDescriptors.Count; Col++)
{
if (ParentScreening.ListDescriptors[Col].IsActive() == false) continue;
vals[IdxRealCol++] = CurrentWell.ListSignatures[Col].GetValue();
}
data1.add(new DenseInstance(1.0, vals));
}
return data1;
}
/// <summary>
/// Create a single instance for WEKA
/// </summary>
/// <param name="NClasses">Number of classes</param>
/// <returns>the weka instances</returns>
public Instances CreateInstanceForNClasses(cInfoClass InfoClass)
{
List<double> AverageList = new List<double>();
for (int i = 0; i < Parent.ListDescriptors.Count; i++)
if (Parent.ListDescriptors[i].IsActive()) AverageList.Add(GetAverageValuesList()[i]);
weka.core.FastVector atts = new FastVector();
List<string> NameList = Parent.ListDescriptors.GetListNameActives();
for (int i = 0; i < NameList.Count; i++)
atts.addElement(new weka.core.Attribute(NameList[i]));
weka.core.FastVector attVals = new FastVector();
for (int i = 0; i < InfoClass.NumberOfClass; i++)
attVals.addElement("Class" + i);
atts.addElement(new weka.core.Attribute("Class__", attVals));
Instances data1 = new Instances("SingleInstance", atts, 0);
double[] newTable = new double[AverageList.Count + 1];
Array.Copy(AverageList.ToArray(), 0, newTable, 0, AverageList.Count);
//newTable[AverageList.Count] = 1;
data1.add(new DenseInstance(1.0, newTable));
data1.setClassIndex((data1.numAttributes() - 1));
return data1;
}
/// <summary> Select only instances with weights that contribute to
/// the specified quantile of the weight distribution
///
/// </summary>
/// <param name="data">the input instances
/// </param>
/// <param name="quantile">the specified quantile eg 0.9 to select
/// 90% of the weight mass
/// </param>
/// <returns> the selected instances
/// </returns>
protected internal virtual Instances selectWeightQuantile(Instances data, double quantile)
{
int numInstances = data.numInstances();
Instances trainData = new Instances(data, numInstances);
double[] weights = new double[numInstances];
double sumOfWeights = 0;
for (int i = 0; i < numInstances; i++)
{
weights[i] = data.instance(i).weight();
sumOfWeights += weights[i];
}
double weightMassToSelect = sumOfWeights * quantile;
int[] sortedIndices = Utils.sort(weights);
// Select the instances
sumOfWeights = 0;
for (int i = numInstances - 1; i >= 0; i--)
{
Instance instance = (Instance) data.instance(sortedIndices[i]).copy();
trainData.add(instance);
sumOfWeights += weights[sortedIndices[i]];
if ((sumOfWeights > weightMassToSelect) && (i > 0) && (weights[sortedIndices[i]] != weights[sortedIndices[i - 1]]))
{
break;
}
}
if (m_Debug)
{
System.Console.Error.WriteLine("Selected " + trainData.numInstances() + " out of " + numInstances);
}
return trainData;
}
