private void bgw_DoWork(object sender, DoWorkEventArgs e)
{
var data = (List <object>)e.Argument;
Metrics.metric metric = data[0] as Metrics.metric;
string choosedMetric = data[1] as string;
double precision;
int maxK = KChecker.maxK(sampleColection);
for (int i = 1; i < maxK; i++)
{
precision = OneVsRest.precision(sampleColection, i, metric, p);
kResults.Add("K: " + i + " Metric: " + choosedMetric + "\nPrecision: " + Math.Round(Convert.ToDecimal(precision), 2) + "%\n----------\n");
double procentage = (double)i / (maxK - 1) * 100;
bgw.ReportProgress((int)procentage);
}
}
public static double precision(SampleColection sampleColection, int k, Metrics.metric choosedMetric, int p)
{
SampleColection newSampleColection = sampleColection.copy();
int probes = 0;
int correct = 0;
var samples = newSampleColection.samples;
Sample tmp;
for (int i = 0; i < samples.Count; i++)
{
probes++;
tmp = newSampleColection.samples.First();
newSampleColection.removeSample(tmp);
int?result = Algorithm.chooseDecision(tmp.attributes, newSampleColection, k, choosedMetric, p);
if (result == tmp.decision)
{
correct++;
}
newSampleColection.addSample(tmp);
}
return((double)correct / probes * 100);
}
static Dictionary <int, double> countSumOfKminDistances(List <double> sampleToCheck, SampleColection data, int k, Metrics.metric choosedMetric, int p)
{
var distances = new Dictionary <int, List <double> >();
distances = countDistances(sampleToCheck, data, choosedMetric, p);
double sumOfKminDistances = 0;
var minDistances = new Dictionary <int, double>();
foreach (var item in distances)
{
item.Value.Sort();
for (int i = 0; i < k; i++)
{
sumOfKminDistances += item.Value[i];
}
minDistances.Add(item.Key, sumOfKminDistances);
sumOfKminDistances = 0;
}
return(minDistances);
}
public static int?chooseDecision(List <double> attributesToCheck, SampleColection sampleColection, int k, Metrics.metric choosedMetric, int p)
{
var normalizedAttributes = Normalization.normalizeAttributes(sampleColection, attributesToCheck);
var normalizedSampleColection = Normalization.normalizeSampleColection(sampleColection);
var minDistances = new Dictionary <int, double>();
minDistances = countSumOfKminDistances(normalizedAttributes, normalizedSampleColection, k, choosedMetric, p);
double min = minDistances.First().Value;
int decision = minDistances.First().Key;
foreach (var item in minDistances)
{
if (item.Value < min)
{
min = item.Value;
decision = item.Key;
}
}
if (isOnlyOneMin(minDistances, min))
{
return(decision);
}
return(null);
}
static Dictionary <int, List <double> > countDistances(List <double> sampleToCheck, SampleColection data, Metrics.metric choosedMetric, int p)
{
var distances = new Dictionary <int, List <double> >();
double distance;
foreach (Sample sample in data.samples)
{
distance = choosedMetric(sampleToCheck, sample.attributes, p);
if (!distances.ContainsKey(sample.decision))
{
distances.Add(sample.decision, new List <double> {
distance
});
}
else
{
distances[sample.decision].Add(distance);
}
}
return(distances);
}
