public Signal()
{
pca = new PCA();
knn = new KNN();
svm = new SVM();
svmscale = new SVMScale();
}
public double buildSVMTestCorpus(string filename)
{
double total = 0, tp = 0;
string trainDataPath = filename + "SimpleTrainSVM.txt";
if (File.Exists(trainDataPath))
{
_test = ProblemHelper.ReadProblem(trainDataPath);
_test = ProblemHelper.ScaleProblem(_test);
svm_node[][] sn = _test.x;
total = sn.Length;
double[] lbls = _test.y;
for (int i = 0; i < sn.Length; i++)
{
if (_test.y[i] == svm.Predict(sn[i]))
{
tp++;
}
}
fileExistance = true;
//ProblemHelper.WriteProblem(filename+"TestSVM.txt", _test);
}
else
{
SVMScale readyData = new SVMScale();
readyData.buildSVMCorpus(filename);
readyData.scaleSVMData(filename);
buildSVMTestCorpus(filename);
}
return((tp / total) * 100);
}
public SVM()
{
fileExistance = false;
predictionDictionary = new Dictionary <int, string> {
{ 1, "Neutral" }, { 2, "Up" }, { 3, "Down" }, { 4, "Left" }, { 5, "Right" }
};
scale = new SVMScale();
svmnode = new svm_node[25];
int i = 0;
for (; i < 25; i++)
{
svmnode[i] = new svm_node();
svmnode[i].index = i + 1;
}
}
