/*
#if LANG_JP
* /// <summary>
* /// 階層型 k-means tree で利用される branching ファクタ
* /// </summary>
#else
* /// <summary>
* /// The branching factor to use for the hierarchical k-means tree
* /// </summary>
#endif
* public int Branching
* {
* get
* {
* unsafe
* {
* return *FlannInvoke.flann_KMeansIndexParams_branching(ptr);
* }
* }
* set
* {
* unsafe
* {
* FlannInvoke.flann_KMeansIndexParams_branching(ptr) = value;
* }
* }
* }
#if LANG_JP
* /// <summary>
* /// k-means tree を作成する際の，k-means クラスタリングステージでの反復数の上限．ここで -1 は，k-means クラスタリングが収束するまで続けられることを意味します
* /// </summary>
#else
* /// <summary>
* /// 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
* /// </summary>
#endif
* public int Iterations
* {
* get
* {
* unsafe
* {
* return *FlannInvoke.flann_KMeansIndexParams_iterations(ptr);
* }
* }
* set
* {
* unsafe
* {
* FlannInvoke.flann_KMeansIndexParams_iterations(ptr) = value;
* }
* }
* }
#if LANG_JP
* /// <summary>
* /// k-means クラスタリングの初期中心を選択するアルゴリズム．
* /// </summary>
#else
* /// <summary>
* /// The algorithm to use for selecting the initial centers when performing a k-means clustering step.
* /// </summary>
#endif
* public FlannCentersInit CentersInit
* {
* get
* {
* unsafe
* {
* return *FlannInvoke.flann_KMeansIndexParams_centers_init(ptr);
* }
* }
* set
* {
* unsafe
* {
* FlannInvoke.flann_KMeansIndexParams_centers_init(ptr) = value;
* }
* }
* }
#if LANG_JP
* /// <summary>
* /// このパラメータ（クラスタ境界インデックス）は，階層的 k-means tree の探索方法に影響を与えます．
* /// cb_index が0の場合，最も近い中心のクラスタが，次に探索される k-means 領域になります．0より大きい値の場合も，領域サイズが考慮されます
* /// </summary>
#else
* /// <summary>
* /// 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 choosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.
* /// </summary>
#endif
* public float CbIndex
* {
* get
* {
* unsafe
* {
* return *FlannInvoke.flann_KMeansIndexParams_cb_index(ptr);
* }
* }
* set
* {
* unsafe
* {
* FlannInvoke.flann_KMeansIndexParams_cb_index(ptr) = value;
* }
* }
* }
* //*/
#endregion
#region Init & Disposal
#if LANG_JP
/// <summary>
///
/// </summary>
/// <param name="branching">階層型 k-means tree で利用される branching ファクタ</param>
/// <param name="iterations">k-means tree を作成する際の，k-means クラスタリングステージでの反復数の上限．ここで -1 は，k-means クラスタリングが収束するまで続けられることを意味します</param>
/// <param name="centersInit">k-means クラスタリングの初期中心を選択するアルゴリズム．</param>
/// <param name="cbIndex">このパラメータ（クラスタ境界インデックス）は，階層的 k-means tree の探索方法に影響を与えます． cb_index が0の場合，最も近い中心のクラスタが，次に探索される k-means 領域になります．0より大きい値の場合も，領域サイズが考慮されます</param>
#else
/// <summary>
///
/// </summary>
/// <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. </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 choosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.</param>
#endif
public KMeansIndexParams(int branching = 32, int iterations = 11, FlannCentersInit centersInit = FlannCentersInit.Random, float cbIndex = 0.2f)
{
ptr = NativeMethods.flann_KMeansIndexParams_construct(branching, iterations, centersInit, cbIndex);
if (ptr == IntPtr.Zero)
{
throw new OpenCvSharpException("Failed to create KMeansIndexParams");
}
}
/// <summary>
///
/// </summary>
/// <param name="branching">階層型 k-means tree で利用される branching ファクタ</param>
/// <param name="iterations">k-means tree を作成する際の，k-means クラスタリングステージでの反復数の上限．ここで -1 は，k-means クラスタリングが収束するまで続けられることを意味します</param>
/// <param name="centersInit">k-means クラスタリングの初期中心を選択するアルゴリズム．</param>
/// <param name="cbIndex">このパラメータ（クラスタ境界インデックス）は，階層的 k-means tree の探索方法に影響を与えます． cb_index が0の場合，最も近い中心のクラスタが，次に探索される k-means 領域になります．0より大きい値の場合も，領域サイズが考慮されます</param>
#else
/// <summary>
///
/// </summary>
/// <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. </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 choosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.</param>
#endif
public KMeansIndexParams(int branching = 32, int iterations = 11, FlannCentersInit centersInit = FlannCentersInit.Random, float cbIndex = 0.2f)
{
IntPtr p = NativeMethods.flann_Ptr_KMeansIndexParams_new(branching, iterations, centersInit, cbIndex);
if (p == IntPtr.Zero)
{
throw new OpenCvSharpException($"Failed to create {nameof(AutotunedIndexParams)}");
}
PtrObj = new Ptr(p);
ptr = PtrObj.Get();
}
/// <summary>
///
/// </summary>
/// <param name="trees">並列な kd-tree の個数．[1..16] の範囲が適切な値です</param>
/// <param name="branching">階層型 k-means tree で利用される branching ファクタ</param>
/// <param name="iterations">k-means tree を作成する際の，k-means クラスタリングステージでの反復数の上限．ここで -1 は，k-means クラスタリングが収束するまで続けられることを意味します</param>
/// <param name="centersInit">k-means クラスタリングの初期中心を選択するアルゴリズム．</param>
/// <param name="cbIndex">このパラメータ（クラスタ境界インデックス）は，階層的 k-means tree の探索方法に影響を与えます． cb_index が0の場合，最も近い中心のクラスタが，次に探索される k-means 領域になります．0より大きい値の場合も，領域サイズが考慮されます</param>
#else
/// <summary>
///
/// </summary>
/// <param name="trees">The number of parallel kd-trees to use. Good values are in the range [1..16]</param>
/// <param name="branching">The branching factor to use for the hierarchical k-means tree</param>
/// <param name="iterations">The maximum number of iterations to use in the k-means clustering stage when building the k-means tree. A value of -1 used here means that the k-means clustering should be iterated until convergence</param>
/// <param name="centersInit">The algorithm to use for selecting the initial centers when performing a k-means clustering step. </param>
/// <param name="cbIndex">This parameter (cluster boundary index) influences the way exploration is performed in the hierarchical kmeans tree. When cb_index is zero the next kmeans domain to be explored is choosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.</param>
#endif
public CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11,
FlannCentersInit centersInit = FlannCentersInit.Random, float cbIndex = 0.2f)
: base(null)
{
NativeMethods.HandleException(
NativeMethods.flann_Ptr_CompositeIndexParams_new(trees, branching, iterations, centersInit, cbIndex, out var p));
if (p == IntPtr.Zero)
{
throw new OpenCvSharpException($"Failed to create {nameof(CompositeIndexParams)}");
}
PtrObj = new Ptr(p);
ptr = PtrObj.Get();
}
public static extern IntPtr flann_Ptr_CompositeIndexParams_new(int trees, int branching, int iterations, FlannCentersInit centersInit, float cbIndex);
public static extern IntPtr flann_Ptr_KMeansIndexParams_new(
int branching, int iterations, [MarshalAs(UnmanagedType.I4)] FlannCentersInit centersInit, float cbIndex);
/*
#if LANG_JP
/// <summary>
/// 並列な kd-tree の個数．[1..16] の範囲が適切な値です
/// </summary>
#else
/// <summary>
/// The number of parallel kd-trees to use. Good values are in the range [1..16]
/// </summary>
#endif
public int Trees
{
get
{
unsafe
{
return *FlannInvoke.flann_CompositeIndexParams_trees(ptr);
}
}
set
{
unsafe
{
*FlannInvoke.flann_CompositeIndexParams_trees(ptr) = value;
}
}
}
#if LANG_JP
/// <summary>
/// 階層型 k-means tree で利用される branching ファクタ
/// </summary>
#else
/// <summary>
/// The branching factor to use for the hierarchical k-means tree
/// </summary>
#endif
public int Branching
{
get
{
unsafe
{
return *FlannInvoke.flann_CompositeIndexParams_branching(ptr);
}
}
set
{
unsafe
{
*FlannInvoke.flann_CompositeIndexParams_branching(ptr) = value;
}
}
}
#if LANG_JP
/// <summary>
/// k-means tree を作成する際の，k-means クラスタリングステージでの反復数の上限．ここで -1 は，k-means クラスタリングが収束するまで続けられることを意味します
/// </summary>
#else
/// <summary>
/// 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
/// </summary>
#endif
public int Iterations
{
get
{
unsafe
{
return *FlannInvoke.flann_CompositeIndexParams_iterations(ptr);
}
}
set
{
unsafe
{
*FlannInvoke.flann_CompositeIndexParams_iterations(ptr) = value;
}
}
}
#if LANG_JP
/// <summary>
/// k-means クラスタリングの初期中心を選択するアルゴリズム．
/// </summary>
#else
/// <summary>
/// The algorithm to use for selecting the initial centers when performing a k-means clustering step.
/// </summary>
#endif
public FlannCentersInit CentersInit
{
get
{
unsafe
{
return *FlannInvoke.flann_CompositeIndexParams_centers_init(ptr);
}
}
set
{
unsafe
{
*FlannInvoke.flann_CompositeIndexParams_centers_init(ptr) = value;
}
}
}
#if LANG_JP
/// <summary>
/// このパラメータ（クラスタ境界インデックス）は，階層的 k-means tree の探索方法に影響を与えます．
/// cb_index が0の場合，最も近い中心のクラスタが，次に探索される k-means 領域になります．0より大きい値の場合も，領域サイズが考慮されます
/// </summary>
#else
/// <summary>
/// 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 choosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.
/// </summary>
#endif
public float CbIndex
{
get
{
unsafe
{
return *FlannInvoke.flann_CompositeIndexParams_cb_index(ptr);
}
}
set
{
unsafe
{
*FlannInvoke.flann_CompositeIndexParams_cb_index(ptr) = value;
}
}
}
//*/
#endregion
#region Init & Disposal
#if LANG_JP
/// <summary>
///
/// </summary>
/// <param name="trees">並列な kd-tree の個数．[1..16] の範囲が適切な値です</param>
/// <param name="branching">階層型 k-means tree で利用される branching ファクタ</param>
/// <param name="iterations">k-means tree を作成する際の，k-means クラスタリングステージでの反復数の上限．ここで -1 は，k-means クラスタリングが収束するまで続けられることを意味します</param>
/// <param name="centersInit">k-means クラスタリングの初期中心を選択するアルゴリズム．</param>
/// <param name="cbIndex">このパラメータ（クラスタ境界インデックス）は，階層的 k-means tree の探索方法に影響を与えます． cb_index が0の場合，最も近い中心のクラスタが，次に探索される k-means 領域になります．0より大きい値の場合も，領域サイズが考慮されます</param>
#else
/// <summary>
///
/// </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. </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 choosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.</param>
#endif
public CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11, FlannCentersInit centersInit = FlannCentersInit.Random, float cbIndex = 0.2f)
{
ptr = NativeMethods.flann_CompositeIndexParams_new(trees, branching, iterations, centersInit, cbIndex);
if (ptr == IntPtr.Zero)
throw new OpenCvSharpException("Failed to create CompositeIndexParams");
}
/*
#if LANG_JP
/// <summary>
/// 階層型 k-means tree で利用される branching ファクタ
/// </summary>
#else
/// <summary>
/// The branching factor to use for the hierarchical k-means tree
/// </summary>
#endif
public int Branching
{
get
{
unsafe
{
return *FlannInvoke.flann_KMeansIndexParams_branching(ptr);
}
}
set
{
unsafe
{
*FlannInvoke.flann_KMeansIndexParams_branching(ptr) = value;
}
}
}
#if LANG_JP
/// <summary>
/// k-means tree を作成する際の，k-means クラスタリングステージでの反復数の上限．ここで -1 は，k-means クラスタリングが収束するまで続けられることを意味します
/// </summary>
#else
/// <summary>
/// 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
/// </summary>
#endif
public int Iterations
{
get
{
unsafe
{
return *FlannInvoke.flann_KMeansIndexParams_iterations(ptr);
}
}
set
{
unsafe
{
*FlannInvoke.flann_KMeansIndexParams_iterations(ptr) = value;
}
}
}
#if LANG_JP
/// <summary>
/// k-means クラスタリングの初期中心を選択するアルゴリズム．
/// </summary>
#else
/// <summary>
/// The algorithm to use for selecting the initial centers when performing a k-means clustering step.
/// </summary>
#endif
public FlannCentersInit CentersInit
{
get
{
unsafe
{
return *FlannInvoke.flann_KMeansIndexParams_centers_init(ptr);
}
}
set
{
unsafe
{
*FlannInvoke.flann_KMeansIndexParams_centers_init(ptr) = value;
}
}
}
#if LANG_JP
/// <summary>
/// このパラメータ（クラスタ境界インデックス）は，階層的 k-means tree の探索方法に影響を与えます．
/// cb_index が0の場合，最も近い中心のクラスタが，次に探索される k-means 領域になります．0より大きい値の場合も，領域サイズが考慮されます
/// </summary>
#else
/// <summary>
/// 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 choosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.
/// </summary>
#endif
public float CbIndex
{
get
{
unsafe
{
return *FlannInvoke.flann_KMeansIndexParams_cb_index(ptr);
}
}
set
{
unsafe
{
*FlannInvoke.flann_KMeansIndexParams_cb_index(ptr) = value;
}
}
}
//*/
#endregion
#region Init & Disposal
#if LANG_JP
/// <summary>
///
/// </summary>
/// <param name="branching">階層型 k-means tree で利用される branching ファクタ</param>
/// <param name="iterations">k-means tree を作成する際の，k-means クラスタリングステージでの反復数の上限．ここで -1 は，k-means クラスタリングが収束するまで続けられることを意味します</param>
/// <param name="centersInit">k-means クラスタリングの初期中心を選択するアルゴリズム．</param>
/// <param name="cbIndex">このパラメータ（クラスタ境界インデックス）は，階層的 k-means tree の探索方法に影響を与えます． cb_index が0の場合，最も近い中心のクラスタが，次に探索される k-means 領域になります．0より大きい値の場合も，領域サイズが考慮されます</param>
#else
/// <summary>
///
/// </summary>
/// <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. </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 choosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.</param>
#endif
public KMeansIndexParams(int branching = 32, int iterations = 11, FlannCentersInit centersInit = FlannCentersInit.Random, float cbIndex = 0.2f)
{
ptr = NativeMethods.flann_KMeansIndexParams_construct(branching, iterations, centersInit, cbIndex);
if (ptr == IntPtr.Zero)
throw new OpenCvSharpException("Failed to create KMeansIndexParams");
}
/*
#if LANG_JP
* /// <summary>
* /// 並列な kd-tree の個数．[1..16] の範囲が適切な値です
* /// </summary>
#else
* /// <summary>
* /// The number of parallel kd-trees to use. Good values are in the range [1..16]
* /// </summary>
#endif
* public int Trees
* {
* get
* {
* unsafe
* {
* return *FlannInvoke.flann_CompositeIndexParams_trees(ptr);
* }
* }
* set
* {
* unsafe
* {
* FlannInvoke.flann_CompositeIndexParams_trees(ptr) = value;
* }
* }
* }
#if LANG_JP
* /// <summary>
* /// 階層型 k-means tree で利用される branching ファクタ
* /// </summary>
#else
* /// <summary>
* /// The branching factor to use for the hierarchical k-means tree
* /// </summary>
#endif
* public int Branching
* {
* get
* {
* unsafe
* {
* return *FlannInvoke.flann_CompositeIndexParams_branching(ptr);
* }
* }
* set
* {
* unsafe
* {
* FlannInvoke.flann_CompositeIndexParams_branching(ptr) = value;
* }
* }
* }
#if LANG_JP
* /// <summary>
* /// k-means tree を作成する際の，k-means クラスタリングステージでの反復数の上限．ここで -1 は，k-means クラスタリングが収束するまで続けられることを意味します
* /// </summary>
#else
* /// <summary>
* /// 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
* /// </summary>
#endif
* public int Iterations
* {
* get
* {
* unsafe
* {
* return *FlannInvoke.flann_CompositeIndexParams_iterations(ptr);
* }
* }
* set
* {
* unsafe
* {
* FlannInvoke.flann_CompositeIndexParams_iterations(ptr) = value;
* }
* }
* }
#if LANG_JP
* /// <summary>
* /// k-means クラスタリングの初期中心を選択するアルゴリズム．
* /// </summary>
#else
* /// <summary>
* /// The algorithm to use for selecting the initial centers when performing a k-means clustering step.
* /// </summary>
#endif
* public FlannCentersInit CentersInit
* {
* get
* {
* unsafe
* {
* return *FlannInvoke.flann_CompositeIndexParams_centers_init(ptr);
* }
* }
* set
* {
* unsafe
* {
* FlannInvoke.flann_CompositeIndexParams_centers_init(ptr) = value;
* }
* }
* }
#if LANG_JP
* /// <summary>
* /// このパラメータ（クラスタ境界インデックス）は，階層的 k-means tree の探索方法に影響を与えます．
* /// cb_index が0の場合，最も近い中心のクラスタが，次に探索される k-means 領域になります．0より大きい値の場合も，領域サイズが考慮されます
* /// </summary>
#else
* /// <summary>
* /// 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 choosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.
* /// </summary>
#endif
* public float CbIndex
* {
* get
* {
* unsafe
* {
* return *FlannInvoke.flann_CompositeIndexParams_cb_index(ptr);
* }
* }
* set
* {
* unsafe
* {
* FlannInvoke.flann_CompositeIndexParams_cb_index(ptr) = value;
* }
* }
* }
* //*/
#endregion
#region Init & Disposal
#if LANG_JP
/// <summary>
///
/// </summary>
/// <param name="trees">並列な kd-tree の個数．[1..16] の範囲が適切な値です</param>
/// <param name="branching">階層型 k-means tree で利用される branching ファクタ</param>
/// <param name="iterations">k-means tree を作成する際の，k-means クラスタリングステージでの反復数の上限．ここで -1 は，k-means クラスタリングが収束するまで続けられることを意味します</param>
/// <param name="centersInit">k-means クラスタリングの初期中心を選択するアルゴリズム．</param>
/// <param name="cbIndex">このパラメータ（クラスタ境界インデックス）は，階層的 k-means tree の探索方法に影響を与えます． cb_index が0の場合，最も近い中心のクラスタが，次に探索される k-means 領域になります．0より大きい値の場合も，領域サイズが考慮されます</param>
#else
/// <summary>
///
/// </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. </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 choosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.</param>
#endif
public CompositeIndexParams(int trees = 4, int branching = 32, int iterations = 11, FlannCentersInit centersInit = FlannCentersInit.Random, float cbIndex = 0.2f)
{
ptr = NativeMethods.flann_CompositeIndexParams_new(trees, branching, iterations, centersInit, cbIndex);
if (ptr == IntPtr.Zero)
{
throw new OpenCvSharpException("Failed to create CompositeIndexParams");
}
}
public static extern IntPtr flann_CompositeIndexParams_new(int trees, int branching, int iterations, FlannCentersInit centers_init, float cb_index);
public static extern IntPtr flann_CompositeIndexParams_construct(int trees, int branching, int iterations, FlannCentersInit centers_init, float cb_index);
public static extern ExceptionStatus flann_Ptr_CompositeIndexParams_new(
int trees, int branching, int iterations, FlannCentersInit centersInit, float cbIndex, out IntPtr returnValue);
public static extern ExceptionStatus flann_Ptr_KMeansIndexParams_new(
int branching, int iterations, [MarshalAs(UnmanagedType.I4)] FlannCentersInit centersInit, float cbIndex, out IntPtr returnValue);
