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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>GPolynomial</b></big></big></big><br><br></td><td> This represents a multi-dimensional polynomial</td></tr></table><br><br><big><big><i>Statics (public)</i></big></big><br><div style="margin-left: 40px;"><a href="type_GPolynomial.html">GPolynomial</a>* <big><b>DivideData</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation, <a href="type_GArffData.html">GArffData</a>* pData, int nOutputAttr, int nControlPoints)<br><div style="margin-left: 80px;"><font color=brown> Generates an optimal polynomial that uses the first "n - 1" inputs to calculate threshold values for the last input such that the two halves can be fitted with a polynomial as well as possible</font></div><br><a href="type_GPolynomial.html">GPolynomial</a>* <big><b>FitData</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation, <a href="type_GArffData.html">GArffData</a>* pData, int nOutputAttr, int nControlPoints)<br><div style="margin-left: 80px;"><font color=brown> Generates a polynomial fitted to predict the provided data as best as possible</font></div><br></div><br><big><big><i>Constructors (public)</i></big></big><br><div style="margin-left: 40px;"><big><b>GPolynomial</b></big>(int nDimensions, int nControlPoints)<br></div><br><big><big><i>Destructors</i></big></big><br><div style="margin-left: 40px;"><big><b>~GPolynomial</b></big>()<br></div><br><big><big><i>Public</i></big></big><br><div style="margin-left: 40px;">double <big><b>Eval</b></big>(double* pVariables)<br><div style="margin-left: 80px;"><font color=brown> Evaluates the polynomial. pCoordinates should be an array of size m_nDimensions</font></div><br>double <big><b>GetCoefficient</b></big>(int* pDegrees)<br><div style="margin-left: 80px;"><font color=brown> Returns the coefficient at the specified degrees. pDegrees should be an array of size m_nDimensions</font></div><br>double <big><b>MeasureMeanSquareError</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation, <a href="type_GArffData.html">GArffData</a>* pData, int nOutputAttr)<br><div style="margin-left: 80px;"><font color=brown> Returns the mean square error of this polynomial's ability to predict the specified output value of the given data</font></div><br>void <big><b>SetCoefficient</b></big>(int* pDegrees, double dVal)<br><div style="margin-left: 80px;"><font color=brown> Sets the coefficient at the specified degrees. pDegrees should be an array of size m_nDimensions</font></div><br></div><br><big><big><i>Protected</i></big></big><br><div style="margin-left: 40px;">int <big><b>CalcIndex</b></big>(int* pDegrees)<br><div style="margin-left: 80px;"><font color=brown> This uses the multi-dimensional version of Newton's polynomial algorithm. pControlPoints should point to an array of size (nControlPoints * nDimensions) and pValues should point to an array of size (nControlPoints ^ nDimensions) where "^" means "to the power of" For example, to specify the points: (x1, y1, z1), (x2, y1, z1), ... (x2, y2, z2) nDimensions would be 3, nControlPoints would be 2, pControlPoints would be { x1, x2, y1, y2, z1, z2 }, and pValues would be { f(x1, y1, z1), f(x2, y1, z1), f(x1, y2, z1), f(x2, y2, z1), ... f(x2, y2, z2) } static GPolynomial* Newton(int nDimensions, int nControlPoints, double* pControlPoints, double* pValues);</font></div><br>double <big><b>DivideAndMeasureError</b></big>(<a href="type_GArffRelation.html">GArffRelation</a>* pRelation, <a href="type_GArffData.html">GArffData</a>* pData, int nOutputAttr)<br></div><br></body></html>?? 快捷鍵說(shuō)明
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