static void GenerateAndExecuteExperiments(List <BreastCancerCase> data, double ratio)
{
int nChoices = 2;
List <float> weightOptions = Enumerable.Range(1, nChoices)
.Select(x => (float)x / nChoices)
.ToList();
// List<double> ratioChoices = new List<double>() {0.1, 0.2, 0.3};
int k = TumourFeatures.FeatureCount;
using (var writer = new StreamWriter("./experiments" + ratio + ".csv"))
{
Enumerable.Range(1, k * 3).ToList()
.ForEach(x => writer.Write("w" + x + ","));
writer.WriteLine("ratio,acc");
//foreach (double ratio in ratioChoices) {
var generator = GenerateWeights(k, weightOptions);
//int replications = 20;
int replications = 1;
int combinations = (int)Math.Pow(weightOptions.Count, k) * replications;
int current = 0;
List <float> weights = new List <float>()
{
1, 0.5f, 1, 1, 1, 0.5f, 1, 1, 0.5f, 0.5f
};
//foreach (List<float> weights in generator)
//{
ReplicateUntil(weights, k * 3);
//List<double> values = new List<double>();
for (int i = 0; i < replications; i++)
{
ExperimentSetup setup = new ExperimentSetup(data, ratio);
setup.MeanWeights = weights.GetRange(0, k);
setup.ErrorWeights = weights.GetRange(k, k);
setup.ExtremeWeights = weights.GetRange(k * 2, k);
double accuracy = RunExperiment(setup, true);
weights.ForEach(x => writer.Write(x + ","));
writer.WriteLine(ratio + "," + accuracy);
Console.WriteLine((current++) + "/" + combinations + " => " + accuracy);
}
//}
//}
}
}
static double RunExperiment(ExperimentSetup setup, bool print = false)
{
int n = setup.TestSet.Count;
int[,] confusionMatrix = new int[n, n]; // Implicitly filled with zeros
foreach (BreastCancerCase testCase in setup.TestSet)
{
List <BreastCancerCase> ordered = setup.CaseBase
.OrderByDescending(value => value.CompareTo(
testCase, setup.MeanWeights, setup.ErrorWeights, setup.ExtremeWeights
)).ToList();
if (print)
{
Console.WriteLine("Case under evaluation: ");
testCase.Print();
Console.WriteLine("\nMost similar cases in the database:\n");
}
bool actual = testCase.Malignant;
bool predicted = ordered[0].Malignant;
int row = predicted ? 1 : 0;
int column = actual ? 1 : 0;
confusionMatrix[row, column]++;
if (print)
{
for (int i = 0; i < 5; i++)
{
BreastCancerCase match = ordered[i];
double y = match.CompareTo(testCase, setup.MeanWeights, setup.ErrorWeights, setup.ExtremeWeights);
Console.WriteLine("Match n. #" + i);
match.Print();
bool valid = match.Malignant == actual;
Console.WriteLine("\nSimilarity: " + y);
Console.WriteLine("Valid diagnosis: " + (valid ? "YES" : "NO"));
Console.WriteLine();
}
}
}
// True Negatives + True Positives / Negatives + Positives
double accuracy = (double)(confusionMatrix[0, 0] + confusionMatrix[1, 1]) / (double)n;
if (print)
{
Console.WriteLine("Outcome:");
Console.WriteLine("\tB\tM");
Console.WriteLine("B\t" + confusionMatrix[0, 0] + "\t" + confusionMatrix[0, 1]);
Console.WriteLine("M\t" + confusionMatrix[1, 0] + "\t" + confusionMatrix[1, 1]);
Console.WriteLine("Accuracy: " + accuracy);
}
return(accuracy);
}