public float Evaluate(List <float[]> data, SVM svm)
{
var correct = 0;
for (int i = 0; i < data.Count; ++i)
{
var x = new Unit(data[i][0], 0.0f);
var y = new Unit(data[i][1], 0.0f);
var label = data[i][2];
var predictedValue = svm.Forward(x, y).value;
var predictedLabel = predictedValue > 0.0f ? 1.0f : -1.0f;
if (predictedLabel == label)
{
correct++;
}
}
return(correct / (float)data.Count);
}
private static double EvalTrainingAccuracy(List <double[]> data, List <int> labels, SVM svm)
{
var num_correct = 0;
for (var i = 0; i < data.Count; i++)
{
var x = new Unit(data[i][0], 0.0);
var y = new Unit(data[i][1], 0.0);
var true_label = labels[i];
// see if the prediction matches the provided label
var predicted_label = svm.Forward(x, y).Value > 0 ? 1 : -1;
if (predicted_label == true_label)
{
num_correct++;
}
}
double acc = (double)num_correct / data.Count;
return(acc);
}