type_garffdata.html
来自「一个由Mike Gashler完成的机器学习方面的includes neural」· HTML 代码 · 共 133 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>GArffData</b></big></big></big><br>extends <a href="type_GPointerArray.html">GPointerArray</a><br></td><td></td></tr></table><br><br><big><big><i>Statics (public)</i></big></big><br><div style="margin-left: 40px;">double <big><b>Normalize</b></big>(double dVal, double dInputMin, double dInputRange, double dOutputMin, double dOutputRange)<br><div style="margin-left: 80px;"><font color=brown> Normalize a value from the input min and range to the output min and range</font></div><br>void <big><b>Test</b></big>()<br></div><br><big><big><i>Constructors (public)</i></big></big><br><div style="margin-left: 40px;"><big><b>GArffData</b></big>(int nGrowSize)<br><div style="margin-left: 80px;"><font color=brown> nGrowSize specifies the amount of space (number of vectors) to initially allocate for data. It will dynamically resize as necessary.</font></div><br></div><br><big><big><i>Destructors</i></big></big><br><div style="margin-left: 40px;"><big><b>~GArffData</b></big>()<br></div><br><big><big><i>Public</i></big></big><br><div style="margin-left: 40px;">void <big><b>AddGaussianNoiseDimensions</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation, int nNoiseDims)<br><div style="margin-left: 80px;"><font color=brown> Adds nNoiseDims dimensions of random gaussian dimensions to the data. (Also adds corresponding attributes to pRelation).</font></div><br>void <big><b>AddVector</b></big>(double* pVector)<br><div style="margin-left: 80px;"><font color=brown> Takes ownership of pVector</font></div><br>void <big><b>ComputeCoprobabilityMatrix</b></big>(<a href="type_GMatrix.html">GMatrix</a>* pOutMatrix, <a href="type_GArffRelation.html">GArffRelation</a>* pRelation, int nAttr, double noDataValue)<br><div style="margin-left: 80px;"><font color=brown> Computes the probability of each possible value for one attribute given knowledge of a specific value for another of the attributes</font></div><br>double <big><b>ComputeCovariance</b></big>(int nAttr1, double dMean1, int nAttr2, double dMean2)<br><div style="margin-left: 80px;"><font color=brown> Computes the covariance between two attributes</font></div><br>void <big><b>ComputeCovarianceMatrix</b></big>(<a href="type_GMatrix.html">GMatrix</a>* pOutMatrix, <a href="type_GArffRelation.html">GArffRelation</a>* pRelation)<br><div style="margin-left: 80px;"><font color=brown> Computes the covariance matrix of the data</font></div><br>double <big><b>ComputeMean</b></big>(int nAttribute)<br><div style="margin-left: 80px;"><font color=brown> Computes the arithmetic mean of a single attribute</font></div><br>double <big><b>ComputeMinimumVariancePivot</b></big>(int nAttr)<br><div style="margin-left: 80px;"><font color=brown> Computes the best pivot for minimizing the sum of the variance of each half</font></div><br>void <big><b>ComputePrincipleComponent</b></big>(int nDims, double* pOutVector, int nIterations, bool bExtract)<br><div style="margin-left: 80px;"><font color=brown> This is an efficient algorithm for iteratively computing the principle component vector of the data. See "EM Algorithms for PCA and SPCA" by Sam Roweis, 1998 NIPS. if bExtract is true, it will remove the component from the data (so you can call it again to get the second principle component, etc).</font></div><br>double <big><b>ComputeVariance</b></big>(double dMean, int nAttribute)<br><div style="margin-left: 80px;"><font color=brown> Computes the average variance of a single attribute</font></div><br>void <big><b>CopyVector</b></big>(double* pVector, int nAttributeCount)<br><div style="margin-left: 80px;"><font color=brown> Adds a copy of the vector to the data set</font></div><br>void <big><b>DeleteVector</b></big>(int nIndex)<br><div style="margin-left: 80px;"><font color=brown> deletes the vector with the specified index</font></div><br>void <big><b>DiscretizeNonContinuousOutputs</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation)<br><div style="margin-left: 80px;"><font color=brown> Snaps all non-continuous output values to the nearest discreet value</font></div><br>void <big><b>DropAllVectors</b></big>()<br><div style="margin-left: 80px;"><font color=brown> Abandons (leaks) all the vectors of data</font></div><br>double <big><b>DropVector</b></big>(int* nIndex)<br><div style="margin-left: 80px;"><font color=brown> you must delete the vector this returns</font></div><br>void <big><b>GetMeans</b></big>(double* pOutMeans, int nAttributes)<br><div style="margin-left: 80px;"><font color=brown> Finds the arithmetic means of all attributes</font></div><br>void <big><b>GetMinAndRange</b></big>(int nAttribute, double* pMin, double* pRange)<br><div style="margin-left: 80px;"><font color=brown> Finds the min and the range of the values of the specified attribute</font></div><br>void <big><b>GetVariance</b></big>(double* pOutVariance, double* pMeans, int nAttributes)<br><div style="margin-left: 80px;"><font color=brown> Finds the average variance of all the attributes</font></div><br>double <big><b>GetVector</b></big>(int* nIndex)<br><div style="margin-left: 80px;"><font color=brown> Returns a pointer to the vector</font></div><br>bool <big><b>IsOutputHomogenous</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation)<br><div style="margin-left: 80px;"><font color=brown> Returns true if all output values in the data set are the same</font></div><br>double <big><b>MakeSetOfMostCommonOutputs</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation)<br><div style="margin-left: 80px;"><font color=brown> Produce a vector in which each attribute holds the most common value for that attribute</font></div><br>double <big><b>MeasureEntropy</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation, int nColumn)<br><div style="margin-left: 80px;"><font color=brown> Measures the entropy of this set relative to the specified attribute</font></div><br>void <big><b>Merge</b></big>(<a href="type_GArffData.html">GArffData</a>* pData)<br><div style="margin-left: 80px;"><font color=brown> Steals all the vectors from pData and adds them to this set. (You still have to delete pData)</font></div><br>void <big><b>Normalize</b></big>(int nAttribute, double dInputMin, double dInputRange, double dOutputMin, double dOutputRange)<br><div style="margin-left: 80px;"><font color=brown> Normalizes the specified attribute values</font></div><br>bool <big><b>PickPivotToReduceInfo</b></big>(double* pOutPivot, double* pOutputInfo, <a href="type_GArffRelation.html">GArffRelation</a>* pRelation, int nAttr)<br><div style="margin-left: 80px;"><font color=brown> Computes the best pivot for minimizing the sum output info</font></div><br>void <big><b>Print</b></big>(int nAttributes)<br><div style="margin-left: 80px;"><font color=brown> Dump a representation of the data to stdout</font></div><br>void <big><b>RandomlyReplaceMissingData</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation)<br><div style="margin-left: 80px;"><font color=brown> Replaces missing data with random values</font></div><br>int <big><b>RemoveOutlyers</b></big>(double dStandardDeviations, int nAttributes)<br><div style="margin-left: 80px;"><font color=brown> Throws out all of the vectors in which any of the first "nAttributes" attributes has a value that is more than "dStandardDeviations" deviations away from the mean of that attribute. Note that a better technique would be to compute Euclidian distance using all the attributes together, but I was feeling too lazy when I wrote this.</font></div><br>void <big><b>ReplaceMissingAttributeWithMostCommonValue</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation, int nAttribute)<br><div style="margin-left: 80px;"><font color=brown> Replaces all missing data with the most common value for the attribute</font></div><br>void <big><b>Shuffle</b></big>()<br><div style="margin-left: 80px;"><font color=brown> Randomizes the order</font></div><br>void <big><b>Sort</b></big>(int nDimension)<br><div style="margin-left: 80px;"><font color=brown> Sorts the data from smallest to largest in the specified dimension</font></div><br><a href="type_GArffData.html">GArffData</a>* <big><b>SplitByAttribute</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>** pRelation, int nAttribute)<br><div style="margin-left: 80px;"><font color=brown> Splits this set of data into a unique set for each possible enumeration value of the attribute. You are responsible to delete each set of data as well as the array of pointers that this returns</font></div><br><a href="type_GArffData.html">GArffData</a>* <big><b>SplitByPivot</b></big>(int* nColumn, double dPivot)<br><div style="margin-left: 80px;"><font color=brown> Splits this set of data into two sets such that this set contains all vectors where the value in element "nColumn" is greater than dPivot and the set returned contains those less-than-or-equal-to dPivot.</font></div><br><a href="type_GArffData.html">GArffData</a>* <big><b>SplitBySize</b></big>(int* nRows)<br><div style="margin-left: 80px;"><font color=brown> Splits this set of data into two sets such that this set contains "nRows" vectors and the returned set contains the rest</font></div><br>double <big><b>SwapVector</b></big>(int* nIndex, double* pVector)<br><div style="margin-left: 80px;"><font color=brown> Swaps pVector with the vector at nIndex. You're responsible to delete the vector this returns</font></div><br></div><br></body></html>⌨️ 快捷键说明
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