internal static extern IntPtr CvFlannIndexCreateComposite(IntPtr features, int numberOfKDTrees, int branching, int iterations, Flann.CenterInitType centersInitType, float cbIndex);
internal static extern IntPtr CvFlannIndexCreateKMeans(IntPtr features, int branching, int iterations, Flann.CenterInitType centersInitType, float cbIndex);
internal extern static IntPtr cveHierarchicalClusteringIndexParamsCreate(ref IntPtr ip, int branching, Flann.CenterInitType centersInit, int trees, int leafSize);
internal extern static IntPtr cveCompositeIndexParamsCreate(ref IntPtr ip, int trees, int branching, int iterations, Flann.CenterInitType centersInit, float cbIndex);
/// <summary>
/// Initializes a new instance of the <see cref="HierarchicalClusteringIndexParams"/>.
/// </summary>
public HierarchicalClusteringIndexParams(int branching = 32, Flann.CenterInitType centersInit = CenterInitType.Random, int trees = 4, int leafSize = 100)
{
_ptr = CvInvoke.cveHierarchicalClusteringIndexParamsCreate(ref _indexParamPtr, branching, centersInit, trees, leafSize);
}
/// <summary>
/// Initializes a new instance of the <see cref="CompositeIndexParamses"/> class.
/// </summary>
/// <param name="trees">The number of parallel kd-trees to use. Good values are in the range [1..16]</param>
/// <param name="branching">The branching factor to use for the hierarchical k-means tree</param>
/// <param name="iterations"> The maximum number of iterations to use in the k-means clustering stage when building the k-means tree. A value of -1 used here means that the k-means clustering should be iterated until convergence</param>
/// <param name="centersInit">The algorithm to use for selecting the initial centers when performing a k-means clustering step. The possible values are CENTERS_RANDOM (picks the initial cluster centers randomly), CENTERS_GONZALES (picks the initial centers using Gonzales’ algorithm) and CENTERS_KMEANSPP (picks the initial centers using the algorithm suggested in arthur_kmeanspp_2007 )</param>
/// <param name="cbIndex">This parameter (cluster boundary index) influences the way exploration is performed in the hierarchical kmeans tree. When cb_index is zero the next kmeans domain to be explored is chosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.</param>
public CompositeIndexParamses(int trees = 4, int branching = 32, int iterations = 11, Flann.CenterInitType centersInit = CenterInitType.Random, float cbIndex = 0.2f)
{
_ptr = CvInvoke.cveCompositeIndexParamsCreate(ref _indexParamPtr, trees, branching, iterations, centersInit, cbIndex);
}
