/// <summary>
/// Segment a signal using the Maximum Likelihood Estimation of Radhakrishnan, et al, 1991. Attempts to identify regions of the signal that are
/// considered "consistent." Assumes a signal that has a state which changes only at segment boundaries. The state change can be random and the
/// noise on top of the signal is modeled as Gaussian, but not necessarily stationary.
/// </summary>
/// <param name="signal">Input signal to be segmented.</param>
/// <param name="threshold">Segmentation threshold.</param>
/// <param name="jumpSequenceWindowSize">Length of the moving average window sized used for smoothing the input well log to arrive at an initial estimate of the jump sequence variance.</param>
/// <param name="noiseVarianceWindowSize">Length of the moving average window used for smoothing the noise variances.</param>
/// <param name="noiseVarianceEstimateMethod">
/// Noise variance estimate option.
// rmode = 0 ==> Point estimate of noise variance.
// rmode = 1 ==> Noise estimates smoothed within segments.
/// </param>
/// <param name="maxSMLRIterations">Upper bound on the number of Single Most Likelihood Replacement iterations.</param>
/// <returns>A SegmentationResults instance which contains the algorithm output of binary events, segmented log, filtered log, et cetera.</returns>
public static SegmentationResults Segment(List<double> signal, double threshold, int jumpSequenceWindowSize, int noiseVarianceWindowSize, NoiseVarianceEstimateMethod noiseVarianceEstimateMethod, int maxSMLRIterations)
{
// Scalars which are need for output from the call to the algorithm.
int numberOfBinaryEvents = 0;
double jumpSequenceVariance = 0;
double segmentDensity = 0;
int iterations = 0;
int error = 0;
// We seem to need to create the array in the function immediately prior to calling the unmanaged code, otherwise the garbage collector
// gets cute and moves things around, which the unmanaged code takes offense to.
double[] signalToPass = new double[signal.Count];
signal.CopyTo(signalToPass, 0);
// Create the output arrays. After being populated by the call to the algorithm they are grouped, stored, and returned in the SegmenationResults data structure.
double[] Q = new double[signal.Count];
double[] FLTLOG = new double[signal.Count];
double[] SEGLOG = new double[signal.Count];
double[] R = new double[signal.Count];
int size = signalToPass.Length;
int estimateMethod = (int)noiseVarianceEstimateMethod;
// Function call to the C DLL.
SegmentSignalWrapper(signalToPass, size, threshold, jumpSequenceWindowSize, noiseVarianceWindowSize, estimateMethod, maxSMLRIterations, Q, out numberOfBinaryEvents, FLTLOG, SEGLOG, R, out jumpSequenceVariance, out segmentDensity, out iterations, out error);
return new SegmentationResults(Q, numberOfBinaryEvents, FLTLOG, SEGLOG, R, jumpSequenceVariance, segmentDensity, iterations, error);
}
/// <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;
}
/// <summary>
/// Segment a signal using the Maximum Likelihood Estimation of Radhakrishnan, et al, 1991. Attempts to identify regions of the signal that are
/// considered "consistent." Assumes a signal that has a state which changes only at segment boundaries. The state change can be random and the
/// noise on top of the signal is modeled as Gaussian, but not necessarily stationary.
///
/// Assumes an upper bound of 300 on the number of Single Most Likelihood Replacement iterations.
/// </summary>
/// <param name="signal">Input signal to be segmented.</param>
/// <param name="threshold">Segmentation threshold.</param>
/// <param name="jumpSequenceWindowSize">Length of the moving average window sized used for smoothing the input well log to arrive at an initial estimate of the jump sequence variance.</param>
/// <param name="noiseVarianceWindowSize">Length of the moving average window used for smoothing the noise variances.</param>
/// <param name="noiseVarianceEstimateMethod">
/// Noise variance estimate option.
// rmode = 0 ==> Point estimate of noise variance.
// rmode = 1 ==> Noise estimates smoothed within segments.
/// </param>
/// <returns>A SegmentationResults instance which contains the algorithm output of binary events, segmented log, filtered log, et cetera.</returns>
public static SegmentationResults Segment(List<double> signal, double threshold, int jumpSequenceWindowSize, int noiseVarianceWindowSize, NoiseVarianceEstimateMethod noiseVarianceEstimateMethod)
{
return Segment(signal, threshold, jumpSequenceWindowSize, noiseVarianceWindowSize, noiseVarianceEstimateMethod, 300);
}
