type_grealvectorsearch.html

来自「一个由Mike Gashler完成的机器学习方面的includes neural」· HTML 代码 · 共 25 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>GRealVectorSearch</b></big></big></big><br><br></td><td> This is the base class of all search algorithms that can jump to any vector in the search space and seek the vector that minimizes error. The GRealVectorCritic will keep track of the best vector yet found, so ask it when you want the results.</td></tr></table><br><br><big><big><i>Constructors (public)</i></big></big><br><div style="margin-left: 40px;"><big><b>GRealVectorSearch</b></big>(<a href="type_GRealVectorCritic.html">GRealVectorCritic</a>* pCritic)<br></div><br><big><big><i>Destructors</i></big></big><br><div style="margin-left: 40px;"><big><b>~GRealVectorSearch</b></big>()<br></div><br><big><big><i>Abstracts</i></big></big><br><div style="margin-left: 40px;">void <big><b>Iterate</b></big>()<br><div style="margin-left: 80px;"><font color=brown> Call this method in a loop to perform the search. There is no built-in stopping criteria. In other words, these search algorithms will keep searching for better vectors forever if you let them, so you need to use the critic to determine when to stop yourself.</font></div><br></div><br></body></html>

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