static void RunFinalTests(CandidateSolution <double, StateData> candidate, out double finalScore, out string testDetails)
{
var sampleData = new Dataset();
StringBuilder sb = new StringBuilder();
sb.AppendLine("Test results at " + DateTime.Now.ToShortDateString() + " " + DateTime.Now.ToShortTimeString());
// run through our test data and see how close the answer the genetic program
// comes up with is to the training data. Lower fitness scores are better than bigger scores
double totalDifference = 0;
while (true)
{
var row = sampleData.GetRowOfTestingData();
if (row == null)
{
break;
}
// populate vars with values from row of the training data
for (int i = 0; i < Dataset.NumColumns; i++)
{
if (i != Dataset.LabelColumnIndex)
{
candidate.SetVariableValue("v" + i, row[i]);
}
}
var result = candidate.Evaluate();
// now figure the difference between the calculated value and the training data
var actualAnswer = row[Dataset.LabelColumnIndex];
var diff = result - actualAnswer;
totalDifference += diff * diff;
Console.WriteLine("Ans: " + actualAnswer.ToString(" 0") + " AI: " + result.ToString("0.00"));
sb.AppendLine(actualAnswer.ToString("0.0") + ", " + result.ToString("0.000"));
}
totalDifference = Math.Sqrt(totalDifference);
finalScore = (float)totalDifference;
sb.AppendLine("\"Final score\", " + finalScore.ToString("0.000"));
testDetails = sb.ToString();
}
static float EvaluateCandidate(CandidateSolution <double, StateData> candidate)
{
var sampleData = new Dataset();
// run through our test data and see how close the answer the genetic program
// comes up with is to the training data. Lower fitness scores are better than bigger scores
double totalDifference = 0;
while (true)
{
var row = sampleData.GetRowOfTrainingData();
if (row == null)
{
break;
}
// populate vars with values from row of the training data, and then get the calculated value
for (int i = 0; i < Dataset.NumColumns; i++)
{
if (i != Dataset.LabelColumnIndex)
{
candidate.SetVariableValue("v" + i, row[i]);
}
}
var result = candidate.Evaluate();
// now figure the difference between the calculated value and the training data
var actualAnswer = row[Dataset.LabelColumnIndex];
var diff = result - actualAnswer;
totalDifference += diff * diff;
}
// sqrt of the summed squared diffences
totalDifference = Math.Sqrt(totalDifference);
// fitness function returns a float
return((float)totalDifference);
}