/*
File name: pac.c
Created by: Ljubomir Buturovic
Created: 07/29/2004
Purpose: parametric classifiers menus (linear and quadratic).
*/
/*
Copyright 2004 Ljubomir J. Buturovic
Permission is hereby granted, free of charge, to any person
obtaining a copy of this software and associated documentation files
(the "Software"), to deal in the Software without restriction,
including without limitation the rights to use, copy, modify, merge,
publish, distribute, sublicense, and/or sell copies of the Software,
and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
*/
static char rcsid[] = "$Id: pac.c,v 1.25 2005/05/05 05:00:46 ljubomir Exp $";
#include <stdlib.h>
#include <errno.h>
#include <unistd.h>
#include <string.h>
#include <math.h>
#include "pcp.h"
#include "bagging.h"
#include "pau.h"
#include "lau.h"
#include "lmat.h"
#include "parametric.h"
#include "lerr.h"
/*
Collect input parameters from the user, call the parametric linear
classifier function lin_learn() on TDS, save the classifier and
display results of TDS classification.
In case of error, return error code and, in case of file error,
store the offending file name in 'xname'.
*/
int p_lin_learn(char **xname)
{
int errc;
char *fname;
float **wmx;
errc = 0;
if (tds)
{
clear_screen();
cursor_on();
fname = input_filename(CLASSIFIER_MSG, PCP_PLC, stdout);
if (fname)
{
wmx = lin_learn(WEIGHTED_COV, tds, &errc);
if (wmx)
{
errc = fmx_save(wmx, tds->c, tds->d+1, fname, 0);
if (errc == -1)
{
errc = errno;
*xname = strdup(fname);
}
else
{
/*
Run predict on tds, and display results.
*/
errc = dataset_lin_predict(tds, wmx);
if (!errc)
{
predict_disp(tds, 0, PALG_PLC);
pwait();
}
}
}
free(fname);
}
}
else
errc = PERR_UNDEFINED_TDS;
return errc;
}
/*
Collect input parameters from the user, call the parametric
quadratic classifier learning function pqc_learn() on TDS, save the
classifier and display results of TDS classification.
In case of error, return error code and, in case of file error,
store the offending file name in 'xname'.
*/
int p_pqc_learn(char **xname)
{
int errc;
char *fname;
struct qmodel *qcx;
errc = 0;
if (tds)
{
clear_screen();
cursor_on();
fname = input_filename(CLASSIFIER_MSG, PCP_PQC, stdout);
if (fname)
{
qcx = pqc_learn(tds, &errc);
if (qcx)
{
errc = pqc_save(qcx, tds->d, tds->c, fname);
if (errc == -1)
{
*xname = strdup(fname);
errc = errno;
}
else
{
/*
Run prediction on tds, and display results.
*/
errc = dataset_pqc_predict(tds, qcx->wmx, qcx->sigma);
if (!errc)
{
predict_disp(tds, 0, PALG_PQC);
pwait();
}
}
}
free(fname);
}
}
else
errc = PERR_UNDEFINED_TDS;
return errc;
}
/*
Classify test data set using parametric quadratic classifier stored
in a file. The function obtains input parameters from the user,
calls dataset_pqc_predict(), and displays classification results.
In case of success, return 0. In case of failure, return error
code. In case of file access error, return the relevant file name in
'xname'.
*/
int p_pqc_predict(char **xname)
{
int i;
int j;
int errc;
int verbose;
int min_range;
int max_range;
int predicted_class;
int icx;
int nmodels;
int type;
int rows;
int columns;
float *predictions;
char *fname;
char *output_fname;
char *tname;
char *name;
float *weights;
struct qmodel *model;
struct qmodel **models;
FILE *outdev;
FILE *fptr;
FILE *output_fptr;
clear_screen();
outdev = stdout;
cursor_on();
fname = input_filename(PCP_UMSG_PAC_FNAME, PCP_PQC, outdev);
fptr = fopen(fname, "r");
if (fptr)
{
fclose(fptr);
output_fname = strdup(PCP_RCL);
output_fptr = fopen(output_fname, "w");
if (output_fptr)
{
models = pqc_load_models(fname, &type, &nmodels, &weights, &rows, &columns, &errc);
if (models)
{
/*
Check consistency between the models and test data set.
*/
if (((teds->c > 1) && (teds->c != rows)) || (teds->d+1 != columns))
errc = LERR_INCONSISTENT_MODEL;
else
{
predictions = calloc(teds->c, sizeof(float));
teds->prediction = malloc(teds->nv*sizeof(int));
for (i = 0; i < teds->nv; i++)
{
fvec_set(predictions, teds->c, 0.0);
for (j = 0; j < nmodels; j++)
{
model = models[j];
predicted_class = pqc_predict(model, teds->c, teds->d+1, teds->x[i]);
predictions[predicted_class] += weights[j];
}
predicted_class = fvec_argmax(predictions, teds->c);
teds->prediction[i] = predicted_class;
name = bname(teds->fnames[predicted_class]);
icx = dataset_class(i, teds->c, teds->nd);
tname = bname(teds->fnames[icx]);
fprintf(output_fptr, "%d\t%s\t%s\n", i+1, tname, name);
}
free(predictions);
free(models);
fclose(output_fptr);
min_range = 0;
max_range = 1;
verbose = input_integer(stdin, outdev, OUTPUT_MSG, PCP_QLEN,
&min_range, &min_range, &max_range);
/*
Display results.
*/
predict_disp(teds, verbose, PALG_PQC);
pwait();
}
}
else
{
/*
This is an interesting case: pqc_load_models() could
not load the models, and yet there was no error (for
example, file not found, or malloc() error)
detected. This can happen if the file is empty, for
example. We set LERR_INCONSISTENT_MODEL error.
*/
if (!errc)
errc = LERR_INCONSISTENT_MODEL;
*xname = fname;
fclose(output_fptr);
unlink(output_fname);
}
}
else
{
errc = errno;
*xname = output_fname;
}
}
else
{
errc = errno;
*xname = fname;
}
reset_video();
return errc;
}
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