/// <summary>
/// Find the outliers in a dataset given the internal model
/// (call after CreateModel)
/// </summary>
/// <param name="data">Data to find outliers in</param>
/// <returns>indexing of where outliers were found in the list</returns>
public List <OneClassFV> PredictOutliers(List <OneClassFV> data)
{
if (_model == null)
{
//Remember to call CreateModel before PredictData
return(null);
}
List <int> results = new List <int>();
for (int i = 0; i < data.Count; i++)
{
double result = _model.Predict(data[i].Features);
if (result == -1)
{
results.Add(i);
}
}
List <OneClassFV> resultList = new List <OneClassFV>();
foreach (int result in results)
{
resultList.Add(data[result]);
}
return(resultList);
}
static void Main(string[] args)
{
// Load the datasets: In this example I use the same datasets for training and testing which is not suggested
SVMProblem trainingSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\ADLfall_train.txt");
// SVMProblem testSet = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\ADLfall_test.txt");
SVMProblem testSet1 = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\ADLfall_test1.txt");
// SVMProblem testSet1 = SVMProblemHelper.Load(@"C:\Users\temp\Desktop\result.txt");
// Normalize the datasets if you want: L2 Norm => x / ||x||
trainingSet = trainingSet.Normalize(SVMNormType.L2);
// testSet = testSet.Normalize(SVMNormType.L2);
testSet1 = testSet1.Normalize(SVMNormType.L2);
// Select the parameter set
SVMParameter parameter = new SVMParameter();
parameter.Type = SVMType.C_SVC;
parameter.Kernel = SVMKernelType.RBF;
parameter.C = 32768.0;
parameter.Gamma = 8.0;
// Do cross validation to check this parameter set is correct for the dataset or not
double[] crossValidationResults; // output labels
int nFold = 5;
// trainingSet1.CrossValidation(parameter, nFold, out crossValidationResults);
// Evaluate the cross validation result
// If it is not good enough, select the parameter set again
// double crossValidationAccuracy = trainingSet.EvaluateClassificationProblem(crossValidationResults);
// Train the model, If your parameter set gives good result on cross validation
// SVMModel model = trainingSet.Train(parameter);
// Save the model
// SVM.SaveModel(model, @"Model\activity_recognition.txt");
SVMModel model = SVM.LoadModel(@"Model\activity_recognition.txt");
int p, q, w, e, r, ok = 0;
double sum;
q = 0;
w = 0;
e = 0;
r = 0;
// Predict the instances in the test set
double[] testResults = testSet1.Predict(model);
while (ok < testSet1.Length)
{
var resut = model.Predict(testSet1.X[ok]);
// Console.WriteLine("resut111:" + resut);
p = Convert.ToInt16(resut);
switch (p)
{
case 1:
q++;
break;
case 2:
w++;
break;
case 3:
e++;
break;
case 4:
r++;
break;
}
ok++;
}
sum = q + w + e + r;
Console.WriteLine("result:" + Math.Round(q / sum, 2) + "," + Math.Round(w / sum, 2) + "," + Math.Round(e / sum, 2) + "," + Math.Round(r / sum, 2));
// Evaluate the test results
int[,] confusionMatrix;
double testAccuracy = testSet1.EvaluateClassificationProblem(testResults, model.Labels, out confusionMatrix);
// Print the resutls
// Console.WriteLine("\n\nCross validation accuracy: " + crossValidationAccuracy);
Console.WriteLine("\nTest accuracy: " + testAccuracy);
Console.WriteLine("\nConfusion matrix:\n");
// Print formatted confusion matrix
Console.Write(String.Format("{0,6}", ""));
for (int i = 0; i < model.Labels.Length; i++)
{
Console.Write(String.Format("{0,5}", "(" + model.Labels[i] + ")"));
}
Console.WriteLine();
for (int i = 0; i < confusionMatrix.GetLength(0); i++)
{
Console.Write(String.Format("{0,5}", "(" + model.Labels[i] + ")"));
for (int j = 0; j < confusionMatrix.GetLength(1); j++)
{
Console.Write(String.Format("{0,5}", confusionMatrix[i, j]));
}
Console.WriteLine();
}
Console.WriteLine("\n\nPress any key to quit...");
Console.ReadLine();
}
