type_gstochasticgreedysearch.html

来自「一个由Mike Gashler完成的机器学习方面的includes neural」· HTML 代码 · 共 29 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>GStochasticGreedySearch</b></big></big></big><br>extends <a href="type_GRealVectorSearch.html">GRealVectorSearch</a><br></td><td> At each iteration this algorithm moves in a random direction from the best point ever found. The size of the step is a random number raised to a constant power (called the conservativeness).</td></tr></table><br><br><big><big><i>Constructors (public)</i></big></big><br><div style="margin-left: 40px;"><big><b>GStochasticGreedySearch</b></big>(<a href="type_GRealVectorCritic.html">GRealVectorCritic</a>* pCritic, double dMin, double dRange)<br></div><br><big><big><i>Destructors</i></big></big><br><div style="margin-left: 40px;"><big><b>~GStochasticGreedySearch</b></big>()<br></div><br><big><big><i>Virtual (public)</i></big></big><br><div style="margin-left: 40px;">void <big><b>Iterate</b></big>()<br></div><br><big><big><i>Public</i></big></big><br><div style="margin-left: 40px;">void <big><b>SetConservativeness</b></big>(double d)<br><div style="margin-left: 80px;"><font color=brown> d should be greater than 1. A bigger value will cause it to usually take small steps and only rarely take big steps. A smaller value will cause it to take big steps more frequently.</font></div><br></div><br></body></html>

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