type_gmath.html

来自「一个由Mike Gashler完成的机器学习方面的includes neural」· HTML 代码 · 共 42 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>GMath</b></big></big></big><br><br></td><td></td></tr></table><br><br><big><big><i>Statics (public)</i></big></big><br><div style="margin-left: 40px;">int <big><b>analogToDigital</b></big>(double dVal, int nValues)<br><div style="margin-left: 80px;"><font color=brown> Converts an analog value in the range 0-1 to a digital value</font></div><br>double <big><b>digitalToAnalog</b></big>(double nVal, int nValues)<br><div style="margin-left: 80px;"><font color=brown> Converts a digital value to analog.  Typically the digital value will be discreet, but for applications of interpolation it may be non-discreet, so it's a double instead of an int.</font></div><br>double <big><b>gamma</b></big>(double x)<br><div style="margin-left: 80px;"><font color=brown> The gamma function</font></div><br>double <big><b>gaussian</b></big>(double x)<br><div style="margin-left: 80px;"><font color=brown> The gaussian function</font></div><br>double <big><b>Integrate</b></big>(9(GuEpzJ pFunc, double dStart, double dEnd, int nSteps)<br><div style="margin-left: 80px;"><font color=brown> Integrates the specified function from dStart to dEnd</font></div><br>void <big><b>NewtonPolynomial</b></big>(const double* pTValues, double* pFuncValues, int nPoints)<br><div style="margin-left: 80px;"><font color=brown> This implements Newton's method for determining a polynomial f(t) that goes through all the control points pFuncValues at pTValues.  (You could then convert to a Bezier curve to get a Bezier curve that goes through those points.)  The polynomial coefficients are put in pFuncValues in the form c0 + c1*t + c2*t*t + c3*t*t*t + ...</font></div><br>double <big><b>sigmoid</b></big>(double x, double steepness)<br><div style="margin-left: 80px;"><font color=brown> The sigmoid function.  It goes through the points (-inf, 0) and (inf, 1) with a slope of 0 and through (0, .5) with a slope related to steepness</font></div><br>double <big><b>sigmoidDerivative</b></big>(double x, double steepness)<br><div style="margin-left: 80px;"><font color=brown> This evaluates the derivative of the sigmoid function</font></div><br>double <big><b>smoothedIdentity</b></big>(double x, double steepness)<br><div style="margin-left: 80px;"><font color=brown> Calculates a function that always goes through (0, 0) and (1, 1), and goes through (0.5, 0.5) with a slope of "steepness". If steepness is > 1, then it will have a slope of 0 at (0, 0) and (1, 1). If steepness is < 1, it will have a slope of infinity at those points. If steepness is exactly 1, it will have a slope of 1 at those points.</font></div><br>void <big><b>Test</b></big>()<br></div><br></body></html>

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