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Numerical.cpp
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/*
* Argus Open Source
* Software to apply Statistical Disclosure Control techniques
*
* Copyright 2014 Statistics Netherlands
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the European Union Public Licence
* (EUPL) version 1.1, as published by the European Commission.
*
* You can find the text of the EUPL v1.1 on
* https://joinup.ec.europa.eu/software/page/eupl/licence-eupl
*
* This software is distributed on an "AS IS" basis without
* warranties or conditions of any kind, either express or implied.
*/
// CatalaanCtrl.cpp : Implementation of CCatalaanCtrl
//#include "stdafx.h"
#include <math.h>
//#include "Catalaan.h"
#include "Numerical.h"
#include "MicroAggregation.h"
#include <float.h>
#include <locale.h>
#include <string>
/////////////////////////////////////////////////////////////////////////////
// CCatalaanCtrl
//void CCatalaanCtrl::FireUpdateProgress (short Perc)
//{ Fire_UpdateProgress(Perc); }
void Numerical::DoMicroAggregation(std::string inFileName, std::string outFileName,
std::string separator, long nVar,
long nMinRecPerGr, long nGroupsPerVar,
long typeSolution, long *nColumnsPerGr,
long *errorCode) {
long numberOfRecords;
double** inputData = ReadFromFile(inFileName, separator, nVar, &numberOfRecords, errorCode);
if (inputData == NULL) {
return;
}
long* var;
//The variables are already sorted in the inputfile
var = new long [nVar];
for (int i = 0; i < nVar; i++) {
var[i] = i;
}
CMicroAggregation microAggr;
long result = microAggr.Microaggregation(nVar, numberOfRecords, nMinRecPerGr,
nGroupsPerVar, nColumnsPerGr, var,
inputData, typeSolution);
if (result != MIC_OK) {
*errorCode = result;
return;
}
WriteOutput(outFileName, separator, nVar, numberOfRecords, inputData);
//Do The deletes
//NB: var was already deleted in the MicroAggregation function;
for (int i = 0; i < numberOfRecords; i++) {
delete[] inputData[i];
}
delete[] inputData;
*errorCode = 0;
}
void Numerical::SetProgressListener(IProgressListener2* progressListener) {
//TODO
;
}
void Numerical::DoRankSwap(std::string inFileName,
std::string outFileName,
std::string separator,
long numVar,
long perc,
long *errorCode) {
long numberOfLines;
double** inputdata = ReadFromFile(inFileName, separator, numVar, &numberOfLines, errorCode);
if (inputdata == NULL)
return;
CRankSwap oRankSwap;
long progress = 0;
long result = oRankSwap.rankswap(inputdata, numberOfLines, numVar, perc, &progress);
if (result != MIC_OK) {
*errorCode = result;
return;
}
WriteOutput(outFileName, separator, numVar, numberOfLines, inputdata);
for (int i = 0; i < numberOfLines; i++) {
delete inputdata[i];
}
delete inputdata;
*errorCode = 0;
}
double** Numerical::ReadFromFile(std::string inFileName,
std::string separator,
long nVar,
long* numberOfLines,
long* errorCode) {
FILE *fdin;
char str[MAXRECORDLENGTH];
std::string stempstr;
std::string stemp;
int iseppos;
double **inputdata;
double d;
setlocale(LC_NUMERIC, "C");
fdin = fopen (inFileName.c_str(), "r");
if (fdin == 0) {
*errorCode = -1;
return NULL;
}
str[0] = 0;
fgets((char *)str, MAXRECORDLENGTH, fdin);
if (str[0] == 0) {
*errorCode = -1;
return NULL;
}
rewind(fdin);
long icount = 0; // length of file
while (!feof(fdin)) {
fgets((char *) str, MAXRECORDLENGTH, fdin);
icount ++;
}
*numberOfLines = icount;
inputdata = new double*[*numberOfLines];
for (long i=0; i<*numberOfLines; i++) {
inputdata[i] = new double [nVar];
if (!inputdata[i])
{
*errorCode = MIC_OUT_MEM;
return NULL;
}
}
rewind(fdin);
long recnr = 0;
while (!feof(fdin)) {
fgets((char *)str, MAXRECORDLENGTH, fdin);
stempstr = std::string(str);
iseppos = stempstr.find(separator,0);
int pos = 0;
icount = 0;
while (iseppos != -1) {
stemp=stempstr.substr(pos,iseppos);
//sVariableCode.SetAt(icount,stemp);
//Check if it is a numerical value
//inputdata[recnr][icount] = strtod(stemp,&stop);
if (!ConvertNumeric(stemp.c_str(), d) ) {
//return ISNOTNUMERIC; // is not numeric! (variabelenummer meegeven voor gebruiker)
}
inputdata[recnr][icount] = d;
icount ++;
pos = iseppos+1;
iseppos = stempstr.find(separator,pos);
}
stemp = stempstr.substr(pos);
if ((stempstr.length() == 0) || (icount < nVar-1) || (icount > nVar))
{
//return false;
}
else
{
// sVariableCode.SetAt(icount,stempstr);
if (!ConvertNumeric(stemp.c_str(), d) ) {
//return ISNOTNUMERIC; // is not numeric! (variabelenummer meegeven voor gebruiker)
}
inputdata[recnr][icount] = d;
}
recnr++;
}
fclose(fdin);
*errorCode = 0;
return inputdata;
}
bool Numerical::ConvertNumeric(const char* code, double &d)
{ char *stop;
d = strtod(code, &stop);
if (*stop != 0) {
while (*(stop) == ' ') stop++;
if (*stop != 0) {
return false;
}
}
return true;
}
void Numerical::WriteOutput(std::string outFileName, std::string seperator,
long nVar, long nRec,
double **out_Data)
{
FILE *fdout;
long i,j;
fdout = fopen(outFileName.c_str(),"w");
for (i=0; i<nRec; i++) {
for (j=0; j <nVar; j++) {
fprintf(fdout,"%f", out_Data[i][j]);
if (j <nVar-1) {
fprintf(fdout,"%s",seperator.c_str());
}
}
if (i<nRec-1) {
fprintf(fdout,"\n");
}
}
fclose (fdout);
}
//////////////////////////////////////////////
////////////////////////////////////////////
//
//long Numerical:: Microaggregation(long n_var, long n_elements,
// long elms_p_group, long group_var,
// long *how_many,long *var,
// double **out_data, int optim)
//{
// long prog;
// long max_group, utemp;
// long *beg_order=NULL, i=0, ii, j, k, h, r=0, s=0, first, remain, loop;
// double max_dist, distance, min_dist, dtemp, data_add, sqr_add;
// double *mean_var=NULL, **mean=NULL, *all_mean=NULL, *all_stdev=NULL;
// long *partition=NULL, beg_var, end_var;
// long *var_order=NULL, *used_vars;
// long res;
//
// // =========================================================================
// // Parameter control
// // =========================================================================
//
//
// if (n_elements<3)
// {
// return MIC_ERR_NEL;
// }
// if (n_var<1)
// {
// return MIC_ERR_NVAR;
// }
//
// if (elms_p_group<2)
// {
// return MIC_ERR_MEG;
// }
//
// if (group_var<1)
// {
//
// return MIC_ERR_GOV;
// }
//
// var_order = new long [n_var];
//
// if (!var_order)
// {
// return MIC_OUT_MEM;
// }
//
//
// // Read how many elements are in each group of variables
//
// if (group_var == 1) {
// how_many[0] = n_var;
// var[0] = var_order[0] = 1;
// }
//
//
// if (group_var > 1) {
// long add_hm = 0;
//
// for (i=0 ; i<group_var ; i++)
// {
//
//
// if (how_many[i]<1)
// {
// return MIC_ERR_VPG;
// }
// add_hm+= how_many[i];
// }
//
// if (add_hm!=n_var)
// {
// return MIC_ERR_VPG;
// }
//
// used_vars = new long[n_var];
//
// for (i=0 ; i<n_var ; i++)
// {
// used_vars[i] = var[i];
//
//
// for (j=0; j<i; j++)
// if (used_vars[j] == var[i])
// {
// return MIC_ERR_COL;
// }
//
// if (var[i]> n_var)
// {
// return MIC_ERR_COL;
// }
// }
//
//
// for (i=0 ; i<n_var ; i++)
// var[i] = var[i]-1;
//
// for (i=0 ; i<n_var ; i++)
// var_order[var[i]] = i;
// }
//
// // =========================================================================
// // If different groups are defined data must be sorted in order to form
// // the groups
// // =========================================================================
// if (group_var>1) {
// for (j=0; j<n_var; j++)
// for (k=j+1; k<n_var; k++)
// if (var_order[j] > var_order[k])
// {
// for (i=0 ; i<n_elements ; i++)
// {
// dtemp = out_data[i][j];
// out_data[i][j] = out_data[i][k];
// out_data[i][k] = dtemp;
// }
// utemp = var_order[j];
// var_order[j] = var_order[k];
// var_order[k] = utemp;
// }
// }
//
// // =========================================================================
// // If only a variable is defined, the optimal microaggregation is performed
// // using the Hansen-Mukherjee polynomial exact method
// // =========================================================================
// if ((n_var==group_var)&&(optim))
// {
// res = Do_Opt_Mic ( n_elements, n_var, elms_p_group, out_data,&(prog));
// //res = Do_Opt_Mic ( n_elements, n_var, elms_p_group, out_data);
//
// if (!res) // An error has ocurred
// return res;
//
// }
// else /* Mateo-Domingo method */
// {
//
// // =========================================================================
// // Mean and standard deviation are calculated in order to standardize the
// // the data, so distance calculation between elements will be more accurate.
// // =========================================================================
// all_mean = new double [n_var];
// if (!all_mean)
// {
// return MIC_OUT_MEM;
// }
// all_stdev = new double [n_var];
// if (!all_stdev)
// {
// return MIC_OUT_MEM;
// }
//
// for (j=0; j<n_var; j++)
// {
// for (i=0, data_add=0.0, sqr_add=0.0; i<n_elements; i++)
// {
// data_add += out_data[i][j];
// sqr_add += out_data[i][j]*out_data[i][j];
// }
// all_mean[j] = data_add/(double)n_elements;
// all_stdev[j] = (sqr_add-n_elements*all_mean[j]*all_mean[j])/((double)n_elements-1);
// all_stdev[j] = sqrt(all_stdev[j]);
// if (all_stdev[j]==0.0)
// {
// return MIC_ERR_STD;
// }
// }
//
// // =========================================================================
// // Standardized data are calculated and kept.
// // =========================================================================
// for (j=0; j<n_var; j++)
// for (i=0; i<n_elements; i++)
// out_data[i][j] = (out_data[i][j]-all_mean[j])/all_stdev[j];
//
// // =========================================================================
// // The initial sorting of the data is kept.
// // =========================================================================
// beg_order = new long [n_elements];
// if (!beg_order)
// {
// return MIC_OUT_MEM;
// }
//
// for (i=0 ; i<n_elements ; i++)
// beg_order[i] = i;
//
// // =====================================================================
// // Fixed size multivariate microaggregation is performed (data projection
// // according to distance between elements)
// // =====================================================================
//
// // The number of groups of a k-partition with k = elms_p_group
// max_group = n_elements/elms_p_group;
// partition = new long [max_group];
//
// if (!partition)
// {
//
// return MIC_OUT_MEM;
// }
//
// // Initial partition is performed in a trivial way
// for (i=0 ; i<max_group-1 ; i++)
// partition[i] = elms_p_group;
//
// partition[max_group-1] = n_elements-i*elms_p_group;
//
// // ===================================================================
// // The two most outstanding polongs are computed in the following way:
// // the 1st polong, so-called 'r', is determined as the one farthest
// // from the average of the data set polongs and the 2nd polong, 's', is
// // determined as the one farthest from the first polong.
// // ====================================================================
// // *prog = 0; prlongf ("\n");
// prog = 0;
// FireUpdateProgress(0);
// for (ii=0, beg_var=0, end_var=0; ii<group_var; ii++)
// {
// end_var += how_many[ii];
// remain = n_elements;
// loop = 0;
// mean_var = new double[end_var-beg_var];
//
// while (remain >= 3*elms_p_group)
// {
// prog = (n_elements - remain)+ii*n_elements;
//
// FireUpdateProgress(((short)(prog)*100.0)/(group_var*n_elements));
//
// // prlongf ("\rProgress: \t%.2f percent",((double)(*prog)*100.0)/(group_var*n_elements));
// first = loop*elms_p_group;
// for(k=beg_var; k<end_var; k++)
// mean_var[k-beg_var]=0.0;
//
// // The mean for each variable is calculated, using the
// // elements not yet used
// for(k=beg_var; k<end_var; k++)
// {
// for (j=first; j<n_elements; j++ )
// mean_var[k-beg_var]+=out_data[j][k];
// mean_var[k-beg_var]=mean_var[k-beg_var]/(n_elements-first);
// }
//
// // 'r' is calculated
// for (j=first, max_dist=0.0; j<n_elements; j++)
// {
// for (k=beg_var, distance=0.0; k<end_var; k++)
// distance += (out_data[j][k]-mean_var[k-beg_var])*(out_data[j][k]-mean_var[k-beg_var]);
// if (distance > max_dist)
// {
// max_dist = distance;
// r = j;
// }
// }
//
// // 's' is calculated
// for (j=first, max_dist=0.0; j<n_elements; j++)
// {
// for (k=beg_var, distance=0.0; k<end_var; k++)
// distance += (out_data[j][k]-out_data[r][k])*(out_data[j][k]-out_data[r][k]);
// if (distance > max_dist)
// {
// max_dist = distance;
// s = j;
// }
// }
// // ============================================================
// // 'r' will be always less than 's'
// // ============================================================
// if (s < r)
// {
// utemp = r;
// r = s;
// s = utemp;
// }
//
// // ============================================================
// // The element 'r' is the first element and it is placed
// // in the first free position of the microaggregation array.
// // ============================================================
// if (r > first)
// {
// for (j=beg_var; j<end_var; j++)
// {
// dtemp = out_data[r][j];
// out_data[r][j] = out_data[first][j];
// out_data[first][j] = dtemp;
// }
// utemp = beg_order[r];
// beg_order[r] = beg_order[first];
// beg_order[first] = utemp;
// }
//
// // ========================================================
// // Element 's' is placed in the first free position
// // that will appear when 'r' group is completed.
// // ========================================================
// if (s != first+elms_p_group)
// {
// for (j=beg_var; j<end_var; j++)
// {
// dtemp = out_data[s][j];
// out_data[s][j] = out_data[first+elms_p_group][j];
// out_data[first+elms_p_group][j] = dtemp;
// }
// utemp = beg_order[s];
// beg_order[s] = beg_order[first+elms_p_group];
// beg_order[first+elms_p_group] = utemp;
// }
//
// // ===========================================================
// // Look for the other elms_p_group-1 elements closer to
// // element 'r' in order to form a group. Elements are sorted
// // so they are stored together.
// // ===========================================================
// for (i=1; i<elms_p_group; i++)
// {
// for (j=first+i, min_dist=max_dist; j<n_elements; j++)
// {
// for (k=beg_var, distance=0.0; k<end_var; k++)
// distance += (out_data[j][k]-out_data[first][k])*(out_data[j][k]-out_data[first][k]);
// if (distance < min_dist)
// {
// min_dist = distance;
// r = j;
// }
// }
// if (r > first+i)
// {
// for (k=beg_var; k<end_var; k++)
// {
// dtemp = out_data[r][k];
// out_data[r][k] = out_data[first+i][k];
// out_data[first+i][k] = dtemp;
// }
// utemp = beg_order[r];
// beg_order[r] = beg_order[first+i];
// beg_order[first+i] = utemp;
// }
// }
//
// first += elms_p_group;
// // ===========================================================
// // Look for the other elms_p_group-1 elements which are closer to
// // element 'r' in order to form a group. Elements are sorted
// // so they are stored together.
// // ===========================================================
// for (i=1 ; i<elms_p_group ; i++)
// {
// for (j=first+i, min_dist=max_dist; j<n_elements; j++)
// {
// for (k=beg_var, distance=0.0; k<end_var; k++)
// distance += (out_data[j][k]-out_data[first][k])*(out_data[j][k]-out_data[first][k]);
// if (distance < min_dist)
// {
// min_dist = distance;
// r = j;
// }
// }
// if (r > first+i)
// {
// for (k=beg_var; k<end_var; k++)
// {
// dtemp = out_data[r][k];
// out_data[r][k] = out_data[first+i][k];
// out_data[first+i][k] = dtemp;
// }
// utemp = beg_order[r];
// beg_order[r] = beg_order[first+i];
// beg_order[first+i] = utemp;
// }
// }
//
// loop += 2;
// remain = remain-2*elms_p_group;
// }
//
// // =================================================================
// // If there are less than 2*elms_p_group unclassified elements,
// // they will form a group: the last one. If there are 2*elms_p_group
// // or more elements, another iteration is performed. In that case,
// // the two most outstanding elements are taken.
// // One of its elements is chosen (call it 'r') and is grouped
// // with the nearest 2*elms_p_group-1 elements to form a group.
// // The remaining elements will form the last group.
// // ===============================================================
// if (remain >= 2*elms_p_group)
// {
// first = loop*elms_p_group;
//
// for(k=beg_var; k<end_var; k++)
// mean_var[k-beg_var]=0.0;
//
// // The mean for each variable is calculated, using the
// // elements not yet used
// for(k=beg_var; k<end_var; k++)
// {
// for (j=first; j<n_elements; j++ )
// mean_var[k-beg_var]+=out_data[j][k];
// mean_var[k-beg_var]=mean_var[k-beg_var]/(n_elements-first);
// }
//
// // 'r' is calculated
// for (j=first, max_dist=0.0; j<n_elements; j++)
// {
// for (k=beg_var, distance=0.0; k<end_var; k++)
// distance += (out_data[j][k]-mean_var[k-beg_var])*(out_data[j][k]-mean_var[k-beg_var]);
// if (distance > max_dist)
// {
// max_dist = distance;
// r = j;
// }
// }
//
// // 's' is calculated
// for (j=first, max_dist=0.0; j<n_elements; j++)
// {
// for (k=beg_var, distance=0.0; k<end_var; k++)
// distance += (out_data[j][k]-out_data[r][k])*(out_data[j][k]-out_data[r][k]);
// if (distance > max_dist)
// {
// max_dist = distance;
// s = j;
// }
// }
//
// // ============================================================
// // 'r' will be always less than 's'
// // ============================================================
// if (s < r)
// {
// utemp = r;
// r = s;
// s = utemp;
// }
//
// if (r > first)
// {
// for (j=beg_var; j<end_var; j++)
// {
// dtemp = out_data[r][j];
// out_data[r][j] = out_data[first][j];
// out_data[first][j] = dtemp;
// }
// utemp = beg_order[r];
// beg_order[r] = beg_order[first];
// beg_order[first] = utemp;
// }
//
// // Look for the nearest element to the last element taken,
// // and sort the rest of elements following the same criterion
// for (i=1; i<elms_p_group; i++)
// {
// for (j=first+i, min_dist=max_dist; j<n_elements; j++)
// {
// for (k=beg_var, distance=0.0; k<end_var; k++)
// distance += (out_data[j][k]-out_data[first][k])*(out_data[j][k]-out_data[first][k]);
// if (distance < min_dist)
// {
// min_dist = distance;
// r = j;
// }
// }
// if (r > first+i)
// {
// for (k=beg_var; k<end_var; k++)
// {
// dtemp = out_data[r][k];
// out_data[r][k] = out_data[first+i][k];
// out_data[first+i][k] = dtemp;
// }
// utemp = beg_order[r];
// beg_order[r] = beg_order[first+i];
// beg_order[first+i] = utemp;
// }
// }
// }
//
// delete [] mean_var;
//
// // The mean of each group is calculated and stored
// mean = new double* [max_group];
// if (!mean)
// {
//
// return MIC_OUT_MEM;
// }
//
// for (i=0; i<max_group; i++)
// {
// mean[i] = new double [how_many[ii]];
// if (!mean[i])
// {
//
// return MIC_OUT_MEM;
// }
// }
// for (k=0; k<max_group; k++)
// for (j=beg_var; j<end_var; j++)
// {
// for (h=0, data_add=0.0, i=k*elms_p_group; h<partition[k]; h++,i++)
// data_add += out_data[i][j];
// mean[k][j-beg_var] = data_add/(double)partition[k];
// }
//
// for (k=0, i=0; k<max_group; k++)
// for (h=0; h<partition[k]; h++)
// {
// for (j=beg_var; j<end_var; j++)
// out_data[i][j] = mean[k][j-beg_var];
// i++;
// }
//
// for (i=0; i<max_group; i++)
// delete [] mean[i];
// delete [] mean;
//
//
// // ==============================================================
// // Data are 'destandardized' and stored
// // ==============================================================
// for (j=beg_var; j<end_var; j++)
// for (i=0; i<n_elements; i++)
// out_data[i][j] = out_data[i][j]*all_stdev[j]+all_mean[j];
//
//
// // ===============================================================
// // Modified data are re-sorted so as to restore the
// // order of original data
// // ===============================================================
// for (i=0 ; i<n_elements ; i++)
// for (k=i+1; k<n_elements; k++)
// if (beg_order[i] > beg_order[k])
// {
// for (j=beg_var; j<end_var; j++)
// {
// dtemp = out_data[i][j];
// out_data[i][j] = out_data[k][j];
// out_data[k][j] = dtemp;
// }
// utemp = beg_order[i];
// beg_order[i] = beg_order[k];
// beg_order[k] = utemp;
// }
// beg_var = end_var;
// }
//
// // ===================================================================
// // If groups were defined, variables are re-sorted so as to restore the
// // initial order of variables
// // ===================================================================
// if (group_var>1) {
// for (j=0; j<n_var; j++)
// for (k=j+1; k<n_var; k++)
// if (var[j] > var[k])
// {
// for (i=0; i<n_elements; i++)
// {
// dtemp = out_data[i][j];
// out_data[i][j] = out_data[i][k];
// out_data[i][k] = dtemp;
// }
// utemp = var[j];
// var[j] = var[k];
// var[k] = utemp;
// }
// }
// delete [] partition;
// }
//
// delete [] beg_order;
// delete [] all_stdev;
// delete [] all_mean;
//
//
//
// delete [] var_order;
//// delete [] var;
//
// return MIC_OK; // Everything is OK!
//}
//
//// =========================================================================
//// Procedures used for the optimal microaggregation
//// =========================================================================
//// =========================================================================
//// Takes the data in 'data' and performs an optimal microaggregation
//// =========================================================================
////long Do_Opt_Mic ( long n_el, long n_var, long k, double **out_data, long *prog ) {
//long Numerical::Do_Opt_Mic ( long n_el, long n_var, long k, double **out_data, long *prog) {
// double *values;
// long *beg_order;
// t_graph g;
// long i, v;
// long res;
//
// values = new double[n_el];
// if (!values)
// {
// return MIC_OUT_MEM;
// }
//
// beg_order = new long[n_el];
//
// if (!beg_order)
// {
// return MIC_OUT_MEM;
// }
//
// // Performing optimal microaggregation for each variable
// for (v=0; v<n_var; v++) {
// // Keep the original sorting of data
// for ( i=0; i<n_el; i++)
// {
// beg_order[i] = i;
// values[i] =out_data[i][v];
// }
//
// // Construct the graph
// res = Graph ( k, n_el, values, beg_order, &g);
// if (!res) // An error has ocurred
// return res;
//
// // Make the optimal microaggregation and store
// // the results in the output file
// //res = Opt_Mic ( g, k, beg_order, values, out_data, v, n_var, prog);
// res = Opt_Mic ( g, k, beg_order, values, out_data, v, n_var,prog);
// delete [] g.nodes;
//
// if (!res) // An error has ocurred
// return res;
// }
// delete [] values;
// delete [] beg_order;
// return 1;
//}
//
//// =========================================================================
//// Graph
//// =========================================================================
//long CCatalaanCtrl::Graph ( long k, long n, double *v, long *bo, t_graph *g) {
// long i, j;
// double sse;
//
// // Graph creation
// g->n_nodes = n+1; // A node '0' is added
//
// g->nodes = new t_node[n+1];
// if (!g->nodes)
// {
//
// return MIC_OUT_MEM;
// }
//
// for ( i=0; i<n+1; i++ )
// {
// g->nodes[i].costs = new double[n+1];
// if (!g->nodes[i].costs)
// {
//
// return MIC_OUT_MEM;
// }
// for ( j=0; j< n+1; j++ )
// g->nodes[i].costs[j]=DBL_MAX;
// g->nodes[i].link = -1;
// g->nodes[i].cost = DBL_MAX;
// }
//
// // Sort the values in the vector
// Quick_Sort(v, bo, 0, n-1);
//
// // Fill the costs
// for (i=0; i<= n-k; i++)
// {
// if (i==1) i = k;
// for ( j=i+k; (j <= n) && (j < i+2*k); j++ )
// {
// sse = Sum_Quad_Err ( v, i, j-i);
// /* Set the costs */
// g->nodes[i].costs[j-(i+k)] = sse;
// }
// }
// return 1;
//}
//
//// =========================================================================
//// Optimal Microaggregation
//// =========================================================================
///*long Opt_Mic ( t_graph g, long k, long *bo, double *v, double **out_data,
// long var, long vars, long *prog) {
// */
//long CCatalaanCtrl:: Opt_Mic ( t_graph g, long k, long *bo, double *v, double **out_data,
// long var, long vars, long *prog) {
// long cp, l, i, j, n = g.n_nodes-1;
// double cost = 0.0;
// double *means, mean;
// long progoptmic;
//
// // Find the shortest path
// g.nodes[0].cost = 0.0;
// g.nodes[0].link = 0;
//
// // prog=n*(n-k)*var;
// progoptmic = n*(n-k)*var;
// for ( i=0; i<= n-k; i++ )
// {
//
// if (i == 1) i = k;
// FireUpdateProgress((short)((i*100.0)/(n-k))); //AHNL 11 04 2005
// for ( j=i+k, cp = 0; cp != n; ++j, ++cp )
// { cost = g.nodes[i].cost + g.nodes[i].costs[cp];
// progoptmic = progoptmic + 1;
//
//// FireUpdateProgress(((short)(progoptmic)*100.0)/(n*(n-k)*vars)); AHNL 11 04 2005
//
// prog = &(progoptmic);
//// prlongf ("\rProgress %.2f percent", ((double)(*prog)*100.0)/(n*(n-k)*vars));
// if ( cost < g.nodes[j].cost )
// {
// g.nodes[j].cost = cost;
// g.nodes[j].link = i;
// }
// }
// }
//
// for ( i=0; i<n+1; i++ )
// delete [] g.nodes[i].costs;
//
// // Means are used to show if an element belongs to a class
// means = new double[n];
//
// if (!means)
// {
//
// return MIC_OUT_MEM;
// }
//