December 2016
Intermediate to advanced
440 pages
9h 44m
Japanese
Content preview from アルゴリズムクイックリファレンス 第2版
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338
■
10
章 空間木構造
10.6.1
入出力
入力は
d
次元空間の
n
個の点集合
P
および
範囲クエリ
を規定する
d
次元の超立体で
ある。
範囲クエリ
は入力集合の次元ごとに、
d
個の個別範囲で指定されるので、座
標軸ごとに並んだ
d
次元データセットとして与えられる。
d
＝
2
の場合、これは
範囲
クエリ
が、
x
座標の範囲と
y
座標の範囲の両方を与えることを意味する。
範囲クエリ
は、範囲にある全点集合を生成する。点の表示順序は特に指定されて
いない。
10.6.2
文脈
k-d
木では、大きな次元を扱いづらいので、アルゴリズムとこの方式全体は、低
次元データに使用を限るべきだ。
2
次元データでは、
k-d
木は最近傍と
範囲クエリ
の
両方の問題に対して優れた性能を示す。
10.6.3
解
例10-3に示した
Java
の実装は、クラス
DimensionalNode
のメソッドになってお
り、
KDTree
にある
range(IHypercube)
メソッドから委譲（
delegate
）される。このア
ルゴリズムは、
DimensionalNode
の領域が
範囲クエリ
に完全に含まれているときに
一番効率が良くなる。その状況では、ある節点の子孫は先祖の節点の範囲の中に完
全に含まれるという
k-d
木の特性により、
DimensionalNode
のすべての子孫節点が
結果集合に追加できる。
例
10-3
範囲クエリの実装
public void search (IHypercube space, ArrayList<IMultiPoint> results) {
// ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.