public void DifferenceOperatorForNulls()
{
DivikResult r1 = null;
DivikResult r2 = null;
Assert.False(condition: r1 != r2, message: "Nulls indicated as unequal");
}
/// <summary>
/// Performs DiviK clustering on the specified data.
/// </summary>
/// <param name="dataset">Input dataset.</param>
/// <param name="options">Configuration.</param>
/// <returns>Segmentation result.</returns>
/// <exception cref="System.ObjectDisposedException">thrown if this object has been disposed.</exception>
public DivikResult Divik(IDataset dataset, DivikOptions options)
{
// This is needed to not to make MCR go wild
const int numberOfOutputArgs = 2;
var coordinates = dataset.GetRawSpacialCoordinates(is2D: true);
var coords = new double[coordinates.GetLength(dimension: 0), coordinates.GetLength(dimension: 1)];
for (var i = 0; i < coordinates.GetLength(dimension: 0); ++i)
{
for (var j = 0; j < coordinates.GetLength(dimension: 1); ++j)
{
coords[i, j] = coordinates[i, j];
}
}
var varargin = options.ToVarargin();
var matlabDivikResult = _segmentation.divik(
numberOfOutputArgs,
dataset.GetRawIntensities(),
coordinates,
varargin);
// matlabResult[0] is equal to the "partition" field in matlabResult[1], that's why we only use matlabResult[1]
// Besides it helps to create recursive single constructor for DivikResult
var result = new DivikResult(matlabResult: matlabDivikResult[Segmentation.DivikStructureLocation]);
return(result);
}
public void EqualityOperatorForNulls()
{
DivikResult r1 = null;
DivikResult r2 = null;
Assert.True(condition: r1 == r2, message: "Nulls not indicated as equal");
}
/// <summary>
/// Initializes a new instance of the <see cref="DivikResultSummary"/> class.
/// </summary>
/// <param name="tree">The result tree.</param>
public DivikResultSummary(DivikResult tree)
{
Depth = tree.Depth();
var counts = Partition.GetClusterSizes(tree.Merged).Values;
NumberOfClusters = (uint)counts.Count;
var numberOfObservations = tree.Merged.Length;
ClusterSizeMean = (double)numberOfObservations / NumberOfClusters;
ClusterSizeVariance = counts
.Select(count => Math.Pow(count - ClusterSizeMean, 2))
.Sum() / NumberOfClusters;
SizeReduction = 1.0 - (double)NumberOfClusters / numberOfObservations;
}
public void SetUpFixture()
{
var dataset = new BasicTextDataset(_testFilePath);
var options = DivikOptions.ForLevels(levels: 1);
options.MaxK = 2;
options.Caching = false;
options.PlottingPartitions = false;
options.PlottingDecomposition = false;
options.PlottingDecompositionRecursively = false;
options.PlottingRecursively = false;
options.UsingAmplitudeFiltration = false;
_segmentation = new Segmentation();
_result = _segmentation.Divik(dataset, options);
}
public void InequalityOperatorAgainstNull()
{
DivikResult r1 = null;
Assert.True(condition: r1 != _result, message: "null not indicated unequal to instance");
}
public void EqualityOperatorAgainstNull()
{
DivikResult r1 = null;
Assert.False(condition: r1 == _result, message: "null indicated equal to instance");
}
