type_gmanifoldsculpting.html
来自「一个由Mike Gashler完成的机器学习方面的includes neural」· HTML 代码 · 共 76 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>GManifoldSculpting</b></big></big></big><br><br></td><td> Manifold Sculpting</td></tr></table><br><br><big><big><i>Statics (public)</i></big></big><br><div style="margin-left: 40px;"><a href="type_GArffData.html">GArffData</a>* <big><b>DoManifoldSculpting</b></big>(PreProcessType* ePreProcess, <a href="type_GArffRelation.html">GArffRelation</a>* pRelation, <a href="type_GArffData.html">GArffData</a>* pData, int nTargetDimensions, int nNeighbors, int nPreludeIterations, int nIterationsSinceBest)<br><div style="margin-left: 80px;"><font color=brown> This is an all-in-one function for using ManifoldSculpting. It constructs a "ManifoldSculpting" object for the number of data points in "pData". Then it calls "SetData" to set all the data points in the collection and "SquishBegin" to initialize the manifold learner. Next it performs the pre-processing step with the specified dimensionality reduction algorithm and re-sets the data. Now it's ready to begin the squishing iterations. It calls "SquishPass" "nPreludeIterations" times, then it continues calling "SquishPass" until the error hasn't improved for "nIterationsSinceBest" iterations. It will return a new data collection in which all of the input data will be found in the first "nTargetDimensions" input dimensions. The remaining input dimensions will contain all zeros. Any output data is just copied straight over.</font></div><br></div><br><big><big><i>Constructors (public)</i></big></big><br><div style="margin-left: 40px;"><big><b>GManifoldSculpting</b></big>(int nDataPoints, int nDimensions, int nNeighbors)<br></div><br><big><big><i>Destructors</i></big></big><br><div style="margin-left: 40px;"><big><b>~GManifoldSculpting</b></big>()<br></div><br><big><big><i>Public</i></big></big><br><div style="margin-left: 40px;">int <big><b>CountShortcuts</b></big>(int nThreshold)<br><div style="margin-left: 80px;"><font color=brown> Counts the number of times that a point has a neighbor with an index that is >= nThreshold away from this points index. (If the manifold is sampled in order such that points are expected to find neighbors with indexes close to their own, this can serve to identify when parts of the manifold are too close to other parts for so many neighbors to be used.)</font></div><br>int <big><b>DataPointSortCompare</b></big>(GManifoldSculptingNeighbor* pA, GManifoldSculptingNeighbor* pB)<br><div style="margin-left: 80px;"><font color=brown> for internal use only</font></div><br>double <big><b>GetAveNeighborDist</b></big>()<br><div style="margin-left: 80px;"><font color=brown> Returns the average distance between neighbors</font></div><br>int <big><b>GetDataPointCount</b></big>()<br><div style="margin-left: 80px;"><font color=brown> Returns the number of data points</font></div><br>double <big><b>GetLearningRate</b></big>()<br><div style="margin-left: 80px;"><font color=brown> Returns the current learning rate</font></div><br>double <big><b>GetVector</b></big>(int* n)<br><div style="margin-left: 80px;"><font color=brown> Get a single (multi-dimensional) data point</font></div><br>void <big><b>SetData</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation, <a href="type_GArffData.html">GArffData</a>* pData)<br><div style="margin-left: 80px;"><font color=brown> Sets the data points from a collection</font></div><br>void <big><b>SetSmoothingAdvantage</b></big>(int n)<br><div style="margin-left: 80px;"><font color=brown> Points that have already been adjusted in this pass will typically have more weight on the error heuristic than points that have not yet been adjusted in this pass. (This causes much faster convergence.) This method sets the weight ratio. For example, a value of 10 means points that have already been adjusted this pass will have 10 times the weight in the error heuristic.</font></div><br>void <big><b>SetSquishingRate</b></big>(double d)<br><div style="margin-left: 80px;"><font color=brown> Set the rate of squishing. (.99 is a good value)</font></div><br>void <big><b>SetVector</b></big>(int n, double* pValues, bool bAdjustable)<br><div style="margin-left: 80px;"><font color=brown> Set a single data point. For unsupervised manifold learning, bAdjustable should always be true. For semi-supervised manifold learning, bAdjustable should only be false if this is one of the supervised (fixed) points.</font></div><br>void <big><b>SquishBegin</b></big>(int nTargetDimensions)<br><div style="margin-left: 80px;"><font color=brown> This method initializes the squisher in preparation for iterative squishing</font></div><br>double <big><b>SquishPass</b></big>(int nSeedDataPoint)<br><div style="margin-left: 80px;"><font color=brown> Perform one iteration of squishing</font></div><br></div><br><big><big><i>Protected</i></big></big><br><div style="margin-left: 40px;">int <big><b>AdjustDataPoint</b></big>(int nPoint, int nTargetDimensions, double* pError)<br>double <big><b>CalculateDataPointError</b></big>(int nPoint)<br>double <big><b>CalculateDistance</b></big>(int nPoint1, int nPoint2)<br>void <big><b>CalculateMetadata</b></big>(int nTargetDimensions)<br>double <big><b>CalculateVectorCorrelation</b></big>(int a, int vertex, int b)<br>int <big><b>FindMostDistantNeighbor</b></big>(GManifoldSculptingNeighbor* pNeighbors)<br>GManifoldSculptingMetaData* <big><b>GetMetaData</b></big>(GManifoldSculptingNeighbor* pNeighbors)<br>GManifoldSculptingMetaData* <big><b>GetMetaData</b></big>(int* n)<br>GManifoldSculptingNeighbor* <big><b>GetRecord</b></big>(int* n)<br></div><br></body></html>⌨️ 快捷键说明
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