public void TestClassifierCreateViewTypeIsStdAndClassifierTypeIsSmartView()
{
ClassifierName = ClassifierName + "Sync" + RandomNumber;
ClassifierManager.CreateNewClassifier("Standard", "SmartView", "", false);
ClassifierManager.SaveClassifier(ClassifierName);
ClassifierManager.VerifyClassifierTitleAndStatusBar(ClassifierName, "Standard");
}
public void TestClassifierCreateViewTypeIsMarkReaderAndClassifierTypeIsSmartView()
{
ClassifierName = ClassifierName + "Sync" + RandomNumber;
ClassifierManager.CreateNewClassifier("Mark reader", "non", "", false);
ClassifierManager.SaveClassifier(ClassifierName);
ClassifierManager.VerifyClassifierTitleAndStatusBar(ClassifierName, "Mark reader");
}
private void AlgoritmAccurancy(weka.core.Instances insts, Classifier classifier, string algoritmName, bool?isNominal = null)
{
ClassifierManager manager = new ClassifierManager(insts);
manager.EliminateTargetAttribute();
if (isNominal == true)
{
manager.Discreatization(manager.Instance);
}
else if (isNominal == false)
{
manager.NominalToBinary(manager.Instance);
manager.Normalization(manager.Instance);
}
manager.Randomize(manager.Instance);
TrainModel model = manager.Train(manager.Instance, classifier);
SuccessfulAlgorithm.Add(new AlgorithmModel()
{
SuccessRatio = manager.FindAccurancy(),
AlgorithName = algoritmName,
TrainModel = model
});;
}
//MATLAB REFERENCE. You must add the matlab reference via the project menu before this will work
//MLApp.MLApp matlab;
public AdaptiveProvider(RandomizedQueue <StudyTestPair> stp, IArrayView <string> presentation, IArrayView <string> class1, IArrayView <string> class2,
AdaptiveSettings settings, IEEGDataSource dataSource, IArray <ClassificationScheme> classifiers)
{
this.pres = stp;
this.settings = settings;
this.dataSource = dataSource;
this.presentation = presentation;
this.class1 = class1;
this.class2 = class2;
this.classifiers = classifiers;
//NOTE: It will always take the first classification scheme chosen
//TODO: Make it so that only one classification scheme can be specified
classifier = new ClassifierManager(classifiers[0]);
//MATLAB REFERENCE
//matlab = new MLApp.MLApp();
blocks = new RandomizedQueue <string> [settings.NumBlocks * 2];
int limit = 0;
for (int i = 0; i < settings.NumBlocks * 2; i += 2)
{
blocks[i] = new RandomizedQueue <string>();
blocks[i + 1] = new RandomizedQueue <string>();
for (int j = 0 + limit * settings.BlockSize; j < (limit + 1) * settings.BlockSize; j++)
{
blocks[i].Add(this.class1[j]);
blocks[i + 1].Add(this.class2[j]);
}
limit++;
}
}
private void Btn_Click(object sender, EventArgs e)
{
bool isValid = true;
for (int j = 0; j < staticInsts.instance(0).numValues() - 1; j++)
{
if (!isValid)
{
break;
}
for (int i = 0; i < testValues.Count; i++)
{
if (testValues[i].GetType() == typeof(TextBox))
{
double res = 0;
if (!string.IsNullOrEmpty(((TextBox)testValues[i]).Text) &&
!double.TryParse(((TextBox)testValues[i]).Text, out res))
{
MessageBox.Show("Enter a valid value for " + labelNames[i]);
isValid = false;
break;
}
else if (string.IsNullOrEmpty(((TextBox)testValues[i]).Text))
{
MessageBox.Show("Enter a Value for " + labelNames[i]);
isValid = false;
break;
}
else
{
staticInsts.instance(0).setValue(i, Convert.ToDouble(((TextBox)testValues[i]).Text));
isValid = true;
}
}
else if (testValues[i].GetType() == typeof(ComboBox))
{
staticInsts.instance(0).setValue(i, ((ComboBox)testValues[i]).SelectedItem.ToString());
}
}
}
if (isValid)
{
ClassifierManager manager = new ClassifierManager(staticInsts);
if (algoritmName == "Naive Bayes")
{
manager.Discreatization(manager.Instance);
}
else if (algoritmName == "KNN with k = 3")
{
manager.NominalToBinary(manager.Instance);
manager.Normalization(manager.Instance);
}
double a = predictor.classifyInstance(manager.Instance.firstInstance());
string result = classes[(int)a];
MessageBox.Show("Result : " + result);
}
}
static void PropogateDataAndModels()
{
SetUp start = new SetUp();
start.RunSetup(out List <string> modelPaths, out Dictionary <string, bool> visualizationStatuses);
GetClassifiersFromPaths(modelPaths,
out List <AbstractClassifier> models);
GetVisualizers(visualizationStatuses, out Visualizers);
string configPath = null;
XmlDocument config;
bool outputStandardModels;
if (start.FindConfigPath(ref configPath))
{
config = new XmlDocument();
config.Load(configPath);
outputStandardModels = Convert.ToBoolean(config.DocumentElement.SelectSingleNode("settings").SelectSingleNode("outputStandardisedModels").FirstChild.Value);
}
else
{
throw new Exception("Config path not found.");
}
if (outputStandardModels)
{
OutputStandardisedModels(config, models);
}
if (start.GetDataPath(config, out string dataPath) == true)
{
warehouse = new DataWarehouse(dataPath);
}
else
{
throw new Exception("No data found.");
}
if (start.GetTranslatorPath(config, out string translatorPath) == true)
{
Console.ForegroundColor = ConsoleColor.Black;
Console.BackgroundColor = ConsoleColor.Green;
Console.WriteLine("Translator file found: " + translatorPath);
Console.ResetColor();
translator = DataTranslator.LoadTranslatorFromFile(translatorPath);
}
else
{
translator = new DataTranslator();
}
start.GetVisualizationDirectory(config, out VisualizerOutputDir);
cm = new ClassifierManager(models.ToArray());
}
public void TestClassifierCreateViewTypeIsSynchAndClassifierTypeIsSmartView()
{
ClassifierName = ClassifierName + "" + RandomNumber;
ClassifierManager.CreateNewClassifier("Synchronized", "SmartView", "", false);
ClassifierManager.SaveClassifier(ClassifierName);
ClassList.RecordedMethod1(ClassifierName);
ClassifierManager.VerifyClassifierTitleAndStatusBar(ClassifierName, "Synchronized");
// ClassifierManager.LoadOpenClassifierWindw(ClassifierName);
// ClassifierManager.VerifyClassifierListedInClassifierListOnOpenClassifierWindow(ClassifierName);
// ClassifierManager.ClickOpenButtonInOpenClassifierWindow();
}
public JsonResult Get(int id)
{
SecurityManager sm = new SecurityManager();
CoreLogic cr = new CoreLogic();
ClassifierManager clsm = new ClassifierManager();
//var clases5 = clsm.GetClassifier_rel(100);
var clases = clsm.GetClassifiers();
Classifier cls = clsm.GetClassifier(101);
return(Json(cls));
}
public void TestCloseClassiferManager()
{
ClassifierManager.CloseClassifierManager(ClassifierName);
}
public void TestLaunchClassiferManager()
{
ClassifierManager.LaunchClassifierManager();
}
