/// <summary>
/// Creates binary black and white training set for the specified image labels and rgb data
/// white = 0 black = 1 </summary>
/// <param name="imageLabels"> image labels </param>
/// <param name="rgbDataMap"> map collection of rgb data </param>
/// <returns> binary black and white training set for the specified image data </returns>
//JAVA TO C# CONVERTER WARNING: Method 'throws' clauses are not available in .NET:
//ORIGINAL LINE: public static org.neuroph.core.data.DataSet createBlackAndWhiteTrainingSet(java.util.List<String> imageLabels, java.util.Map<String, FractionRgbData> rgbDataMap) throws org.neuroph.core.exceptions.VectorSizeMismatchException
public static DataSet createBlackAndWhiteTrainingSet(List <string> imageLabels, IDictionary <string, FractionRgbData> rgbDataMap)
{
// TODO: Use some binarization image filter to do this; currently it works with averaging RGB values
int inputCount = rgbDataMap.Values.GetEnumerator().next().FlattenedRgbValues.length / 3;
int outputCount = imageLabels.Count;
DataSet trainingSet = new DataSet(inputCount, outputCount);
foreach (KeyValuePair <string, FractionRgbData> entry in rgbDataMap)
{
double[] inputRGB = entry.Value.FlattenedRgbValues;
double[] inputBW = FractionRgbData.convertRgbInputToBinaryBlackAndWhite(inputRGB);
double[] response = createResponse(entry.Key, imageLabels);
trainingSet.addRow(new DataSetRow(inputBW, response));
}
// set labels for output columns
int inputSize = trainingSet.InputSize;
for (int c = 0; c < trainingSet.OutputSize; c++)
{
trainingSet.setColumnName(inputSize + c, imageLabels[c]);
}
return(trainingSet);
}
public override bool Equals(object obj)
{
if (obj == null || !(obj is FractionRgbData))
{
return(false);
}
FractionRgbData other = (FractionRgbData)obj;
return(Arrays.Equals(flattenedRgbValues, other.FlattenedRgbValues));
}
