entropyscaler.cpp

粗糙集应用软件

//-------------------------------------------------------------------
// Author........: Aleksander 豩rn/Knut Magne Risvik
// Date..........:
// Description...:
// Revisions.....:
//===================================================================

#include <stdafx.h> // Precompiled headers.
#include <copyright.h>

#include <kernel/algorithms/entropyscaler.h>

#include <kernel/structures/decisiontable.h>

#include <kernel/basic/set.h>
#include <kernel/basic/algorithm.h>
#include <kernel/basic/message.h>

#include <kernel/utilities/mathkit.h>

#include <kernel/system/math.h>

//-------------------------------------------------------------------
// Static methods (file scope).
//===================================================================

//-------------------------------------------------------------------
// Method........: StaticLog2
// Author........: Aleksander 豩rn
// Date..........:
// Description...: Computes the the base-2 logarithm.
// Comments......: Invokes an extra function call, but..
// Revisions.....:
//===================================================================

static float
StaticLog2(float x) {

	// Define a nice constant.
	const double log2 = 0.301029995664;

	return (log(x) / log2);

}

//-------------------------------------------------------------------
// Method........: StaticComputeProportion
// Author........: Aleksander 豩rn/Knut Magne Risvik
// Date..........:
// Description...: Computes the proportions of objects within the
//                 range [begin, end) that belong to the given decision
//                 classes.
//
// Comments......: Made static so as to not obfuscate the header file
//                 and make it less readable. Consider making it a
//                 member method if any classes are to be derived from
//                 EntropyScaler.
// Revisions.....:
//===================================================================

static bool
StaticComputeProportions(const Vector(OrthogonalScaler::OVPair) &objects,             // Input:  The objects (indices) and their attribute values (sorted by the latter).
                         int                                     begin,               // Input:  Start index (included).
                         int                                     end,                 // Input:  End index (excluded).
                         const DecisionTable                    &table,               // Input:  The table the objects belong to.
                         int                                     decision_attribute,  // Input:  Index of decision attribute.
                         bool                                    masked,              // Input:  Operate on a masked table?
                         const Vector(int)                      &decision_classes,    // Input:  The distinct decision classes.
												 Vector(float)                          &proportions) {       // Output: The proportions (estimated probabilities).

	// Verify input.
	if (begin >= end)
		return false;

	// How many decision classes are there?
	int no_decision_classes = decision_classes.size();

	Vector(int) counts;

	// Initialize count vector.
	counts.reserve(no_decision_classes);

	int i, j;

	for (i = 0; i < no_decision_classes; i++)
		counts.push_back(0);

	int no_total = 0;

	// Iterate over given range, and count.
	for (i = begin; i < end; i++) {

		// Find out which decision class the current object belongs to.
		int   object_no      = objects[i].first;
		int   decision_class = table.GetEntry(object_no, decision_attribute, masked);

		// A