public static ConfusionMatrix Test(DataTable t)
{
ConfusionMatrix cmat = new ConfusionMatrix(classValues);
int numDims = t.Columns.Count - 1; //remove 1 for the class labels
foreach (DataRow row in t.Rows) {
//convert the datarow into an expression
Sample sample = new Sample(row, classIndex);
//test the sample and update results
string prediction = TestInstance(sample);
cmat.matrix[prediction][sample.classLabel]++;
if (sample.classLabel.Equals(prediction)) {
cmat.numCorrect++;
} else {
cmat.numWrong++;
}
}
cmat.accuracy = ((double)cmat.numCorrect) / t.Rows.Count;
return cmat;
}
//update the data cloud for the network to draw upon
public void UpdateDataLayer(Sample s)
{
for (int i = 0; i < s.data.Count; i++) { dataLayer[i].value = Double.Parse(s.data[i]); }
for (int i = 0; i < brain[brain.Count - 1].Count; i++) {
brain[brain.Count - 1][i].target = 0;
}
brain[brain.Count - 1][int.Parse(s.classLabel) - 1].target = 1;
}
public static string TestInstance(Sample sample)
{
neural.UpdateDataLayer(sample);
neural.FeedForward();
return (neural.GetMaxOutputIndex() + 1).ToString();
}
