public IList <TPredictionResult> Predict(IDataFrame queryDataFrame, IPredictionModel model, int dependentFeatureIndex)
{
ValidateModel(model);
var knnModel = model as IKnnPredictionModel <TPredictionResult>;
var results = new ConcurrentBag <RowIndexDistanceDto <TPredictionResult> >();
var normalizedData = NormalizeData(queryDataFrame, knnModel, dependentFeatureIndex);
var normalizedTrainingData = normalizedData.Item1;
var queryMatrix = normalizedData.Item2;
Parallel.For(0, queryDataFrame.RowCount, queryRowIdx =>
{
var rowVector = queryMatrix.Row(queryRowIdx);
var distances = new ConcurrentBag <RowIndexDistanceDto <TPredictionResult> >();
for (int trainingRowIdx = 0; trainingRowIdx < normalizedTrainingData.RowCount; trainingRowIdx++)
{
var trainingRow = normalizedTrainingData.Row(trainingRowIdx);
TPredictionResult dependentFeatureValue = knnModel.ExpectedTrainingOutcomes[trainingRowIdx];
double distance = DistanceMeasure.Distance(rowVector, trainingRow);
var distanceDto = new RowIndexDistanceDto <TPredictionResult>(trainingRowIdx, distance, dependentFeatureValue);
distances.Add(distanceDto);
}
var sortedDistances = distances.OrderBy(distDto => distDto.Distance).Take(knnModel.KNeighbors);
var result = new RowIndexDistanceDto <TPredictionResult>(queryRowIdx, 0, _resultHandler(sortedDistances, WeightingFunction));
results.Add(result);
});
return(results.OrderBy(res => res.RowIndex).Select(res => res.DependentFeatureValue).ToList());
}