public void PropogateInput(Example example)
{
List <BestExample> best = new List <BestExample>();
double maxBest = 0.0;
foreach (Example e in savedExamples)
{
double d = distance_sq(e, example);
if (best.Count < k)
{
best.Add(new BestExample(e, d));
if (d > maxBest)
{
maxBest = d;
}
}
else if (d < maxBest)
{
// at this point, the Best list contains k elements
best.Sort();
maxBest = best[k - 1].distance;
best[k - 1] = new BestExample(e, d);
}
}
if (best.Count < 1)
{
throw new LearnerNotReady();
}
// perform a weighted average
List <Double> result = new List <Double>();
for (int i = 0; i < best[0].example.Labels.Count; i++)
{
result.Add(0.0);
}
double sumWeight = 0.0;
foreach (BestExample be in best)
{
double thisWeight = 1 / (1 + be.distance);
for (int i = 0; i < result.Count; i++)
{
result[i] += be.example.Labels[i] * thisWeight;
}
sumWeight += thisWeight;
}
for (int i = 0; i < result.Count; i++)
{
result[i] /= sumWeight;
}
example.Predictions = result;
}
public void PropogateInput(Example example)
{
List<BestExample> best = new List<BestExample>();
double maxBest = 0.0;
foreach (Example e in savedExamples)
{
double d = distance_sq(e, example);
if (best.Count < k)
{
best.Add(new BestExample(e, d));
if (d > maxBest) { maxBest = d; }
}
else if (d < maxBest)
{
// at this point, the Best list contains k elements
best.Sort();
maxBest = best[k - 1].distance;
best[k - 1] = new BestExample(e, d);
}
}
if (best.Count < 1)
throw new LearnerNotReady();
// perform a weighted average
List<Double> result = new List<Double>();
for (int i = 0; i < best[0].example.Labels.Count; i++)
{
result.Add(0.0);
}
double sumWeight = 0.0;
foreach (BestExample be in best)
{
double thisWeight = 1 / (1 + be.distance);
for (int i = 0; i < result.Count; i++)
{
result[i] += be.example.Labels[i] * thisWeight;
}
sumWeight += thisWeight;
}
for (int i = 0; i < result.Count; i++)
{
result[i] /= sumWeight;
}
example.Predictions = result;
}
