type_gactionpathstate.html

来自「一个由Mike Gashler完成的机器学习方面的includes neural」· HTML 代码 · 共 31 行

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<html><head><title>Generated Documentation</title></head><body>	<image src="headerimage.png">	<br><br><table><tr><td><big><big><big style="font-family: arial;"><b>GActionPathState</b></big></big></big><br><br></td><td></td></tr></table><br><br><big><big><i>Constructors (public)</i></big></big><br><div style="margin-left: 40px;"><big><b>GActionPathState</b></big>()<br></div><br><big><big><i>Destructors</i></big></big><br><div style="margin-left: 40px;"><big><b>~GActionPathState</b></big>()<br></div><br><big><big><i>Abstracts</i></big></big><br><div style="margin-left: 40px;"><a href="type_GActionPathState.html">GActionPathState</a>* <big><b>Copy</b></big>()<br><div style="margin-left: 80px;"><font color=brown> Creates a deep copy of this state object</font></div><br>double <big><b>CritiquePath</b></big>(int* nPathLen, GAction* pLastAction)<br><div style="margin-left: 80px;"><font color=brown> Evaluate the error of the given path. Many search algorithms (like GAStarSearch) rely heavily on the heuristic to make the search effective. For example, if you don't penalize redundant paths to the same state, the search space becomes exponential and therefore impossible to search. So a good critic must keep track of which states have already been visited, severely penalize longer paths to a state that has already been visited by a shorter path, and will carefully balance between path length and distance from the goal in producing the error value.</font></div><br>void <big><b>PerformAction</b></big>(int nAction)<br><div style="margin-left: 80px;"><font color=brown> Performs the specified action on the state. (so pState holds both input and output data.) This method is protected because you should call GActionPath::DoAction, and it will call this method.</font></div><br></div><br></body></html>

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