public Signal()
{
pca = new PCA();
knn = new KNN();
svm = new SVM();
svmscale = new SVMScale();
}
public Tuple <double[], double[], double[], double[]> getDistance(KNN obj, double[] test1, double[] test2)
{
double[] distancedown = obj.EuclideanDistance(downT7, downT8, test1, test2);
double[] distanceup = obj.EuclideanDistance(upT7, upT8, test1, test2);
double[] distanceleft = obj.EuclideanDistance(leftT7, leftT8, test1, test2);
double[] distanceright = obj.EuclideanDistance(rightT7, rightT8, test1, test2);
return(new Tuple <double[], double[], double[], double[]>(distancedown, distanceup, distanceleft, distanceright));
}
public void buildCorpus(string subName = "karrar")
{
KNN P = new KNN();
string channelName1 = "IED_T8";
downT8 = P.readCSV(subName + "Down", channelName1);
upT8 = P.readCSV(subName + "Up", channelName1);
rightT8 = P.readCSV(subName + "Right", channelName1);
leftT8 = P.readCSV(subName + "Left", channelName1);
string channelName2 = "IED_T7";
downT7 = P.readCSV(subName + "Down", channelName2);
upT7 = P.readCSV(subName + "Up", channelName2);
rightT7 = P.readCSV(subName + "Right", channelName2);
leftT7 = P.readCSV(subName + "Left", channelName2);
}
public double calculateKNNAccuracy()
{
if (fileExistance)
{
KNN knn = new KNN();
if (rightTestData != null)
{
knn.setRight(rightTrainData);
}
if (leftTestData != null)
{
knn.setLeft(leftTrainData);
}
if (upTestData != null)
{
knn.setUp(upTrainData);
}
if (downTestData != null)
{
knn.setDown(downTrainData);
}
if (neutralTestData != null)
{
knn.setNeutral(neutralTrainData);
}
double tp = 0, fp = 0;
if (rightTestData != null)
{
for (int i = 0; i < rightTestData.Length; i++)
{
if (knn.knnClassifier(rightTestData[i]) == "Right")
{
tp++;
}
else
{
fp++;
}
}
}
if (leftTestData != null)
{
for (int i = 0; i < leftTestData.Length; i++)
{
if (knn.knnClassifier(leftTestData[i]) == "Left")
{
tp++;
}
else
{
fp++;
}
}
}
if (upTestData != null)
{
for (int i = 0; i < upTestData.Length; i++)
{
if (knn.knnClassifier(upTestData[i]) == "Up")
{
tp++;
}
else
{
fp++;
}
}
}
if (downTestData != null)
{
for (int i = 0; i < downTestData.Length; i++)
{
if (knn.knnClassifier(downTestData[i]) == "Down")
{
tp++;
}
else
{
fp++;
}
}
}
if (neutralTestData != null)
{
for (int i = 0; i < neutralTestData.Length; i++)
{
if (knn.knnClassifier(neutralTestData[i]) == "Neutral")
{
tp++;
}
else
{
fp++;
}
}
}
double accuracy = (tp / (tp + fp)) * 100;
return(accuracy);
}
else
{
return(0);
}
}