stats.cpp

实现决策树分类训练试验。 源自c4.5

/*************************************************************************/
/*									 */
/*  Statistical routines for C4.5					 */
/*  -----------------------------					 */
/*									 */
/*************************************************************************/
#include "stdafx.h"
#include "MyBase.h"

#ifdef _DEBUG
#define new DEBUG_NEW
#undef THIS_FILE
static char THIS_FILE[] = __FILE__;
#endif

									
/*************************************************************************/
/*									 */
/*  Compute the additional errors if the error rate increases to the	 */
/*  upper limit of the confidence level.  The coefficient is the	 */
/*  square of the number of standard deviations corresponding to the	 */
/*  selected confidence level.  (Taken from Documenta Geigy Scientific	 */
/*  Tables (Sixth Edition), p185 (with modifications).)			 */
/*									 */
/*************************************************************************/


float Val[] = {  0,  0.001, 0.005, 0.01, 0.05, 0.10, 0.20, 0.40, 1.00},
      Dev[] = {4.0,  3.09,  2.58,  2.33, 1.65, 1.28, 0.84, 0.25, 0.00};


float AddErrs(ItemCount N, ItemCount e)
{
    static float Coeff=0;
    float Val0, Pr;

    if ( ! Coeff )
    {
		/*  Compute and retain the coefficient value, interpolating from
			the values in Val and Dev  */

		int i;

		i = 0;
		while ( CF > Val[i] ) i++;

		Coeff = Dev[i-1] +
			  (Dev[i] - Dev[i-1]) * (CF - Val[i-1]) /(Val[i] - Val[i-1]);
		Coeff = Coeff * Coeff;
    }

    if ( e < 1E-6 )
    {
		return N * (1 - exp(log(CF) / N));
    }
    else
    if ( e < 0.9999 )
    {
		Val0 = N * (1 - exp(log(CF) / N));
		return Val0 + e * (AddErrs(N, 1.0) - Val0);
    }
    else if ( e + 0.5 >= N )
    {
		return 0.67 * (N - e);
    }
    else
    {
		Pr = (e + 0.5 + Coeff/2
				+ sqrt(Coeff * ((e + 0.5) * (1 - (e + 0.5)/N) + Coeff/4)) )
				 / (N + Coeff);
		return (N * Pr - e);
    }
}