/// <summary>
/// Write results to a file. Useful for bringing into Excel and plotting.
/// </summary>
/// <param name="input">Input to the function and the known solution.</param>
/// <param name="results">Results of the segmentation.</param>
void WriteResultsFile(double[][] input, SegmentationResults results)
{
// Write a file that can be used for plotting, if desired.
// First open the file and check that it was opened.
StreamWriter writer = new StreamWriter(Path.Combine(_workingDirectory, "C Sharp Output.txt"), false);
// Loop over data points and write to file. Writing as floats seems to work best.
for (int i = 0; i < _numberOfDataPoints; i++)
{
writer.WriteLine("{0:N2} {1:N5} {2:N0} {3:N5}", (float)input[(int)InputType.Log][i], (float)results.SegmentedLog[i], (float)results.BinaryEventSequence[i], (float)results.FilteredSignal[i]);
}
// Close our file.
writer.Close();
}
/// <summary>
/// Separate function for making the call to SegmentSignal.Segment to test repeated passing of large(r) data sets.
/// </summary>
/// <param name="data">Data set.</param>
private SegmentationResults[] DoLogSeparateForLargeDataSet(List<double> data, double threshold, int jumpSequenceWindowSize, int noiseVarienceWindowSize, NoiseVarianceEstimateMethod noiseVarianceEstimateMethod, int maxIterations)
{
SegmentationResults[] results = new SegmentationResults[2];
results[0] = SegmentSignal.Segment(data.ToArray(), threshold, jumpSequenceWindowSize, noiseVarienceWindowSize, noiseVarianceEstimateMethod, maxIterations);
results[1] = SegmentSignal.Segment(data, threshold, jumpSequenceWindowSize, noiseVarienceWindowSize, noiseVarianceEstimateMethod, maxIterations);
return results;
}
