AABB
AlignmentBehavior
ArriveBehavior
AStar
BFS
BoundingSphere
BVH
BVHNode
Cell
CellSpacePartitioning
CohesionBehavior
CompositeGoal
ConvexHull
Corridor
CostTable
DFS
Dijkstra
Edge
EntityManager
EvadeBehavior
EventDispatcher
Behavior
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FuzzyAND
FuzzyCompositeTerm
FuzzyFAIRLY
FuzzyModule
FuzzyOR
FuzzyRule
FuzzySet
FuzzyTerm
FuzzyVariable
FuzzyVERY
GameEntity
Goal
GoalEvaluator
Graph
GraphUtils
HalfEdge
HeuristicPolicyDijkstra
HeuristicPolicyEuclid
HeuristicPolicyEuclidSquared
HeuristicPolicyManhattan
InterposeBehavior
LeftSCurveFuzzySet
LeftShoulderFuzzySet
LineSegment
Logger
MathUtils
Matrix3
Matrix4
MemoryRecord
MemorySystem
MeshGeometry
MessageDispatcher
MovingEntity
NavEdge
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NavMeshLoader
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Node
NormalDistFuzzySet
OBB
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OffsetPursuitBehavior
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State
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TriangularFuzzySet
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Vector3
Vehicle
Version
WanderBehavior

calculate

calculate 方法用于根据欧几里得平方距离计算两个物品之间的相似度分数。

参数

  • v1: <code>Array.<number></code> - 第一个物品的特征向量。
  • v2: <code>Array.<number></code> - 第二个物品的特征向量。

返回值

  • <code>number</code> - 计算出的相似度分数。

示例

import { HeuristicPolicyEuclidSquared } from 'yuka';

const distance = HeuristicPolicyEuclidSquared.calculate( [ 2, 3 ], [ 5, 6 ] );
console.log( distance ); // 输出:18

实现原理

欧几里得平方距离公式:

$$d(v_1, v_2) = \sum_{i=1}^n (v_{1_i} - v_{2_i})^2$$

其中 $v_1$ 和 $v_2$ 分别为两个物品的特征向量,$n$ 为特征向量的维数。本方法的实现即是根据该公式计算出两个物品之间的相似度分数。

引用